Annotated Bibliography, Introduction, and Summary Paragraph: Seeking the Truth
Default Mode Functional Connectivity Is Associated With Social Functioning in Schizophrenia Jaclyn M. Fox Northwestern University Samantha V. Abram University of Minnesota, Minneapolis James L. ReillyNorthwestern University Shaun Eack University of Pittsburgh Morris B. Goldman, John G. Csernansky, Lei Wang, and Matthew J. Smith Northwestern University Individuals with schizophrenia display notable deficits in social functioning. Research indicates that neural connectivity within the default mode network (DMN) is related to social cognition and social functioning in healthy and clinical populations. However, the association between DMN connectivity, social cognition, and social functioning has not been studied in schizophrenia. For the present study, the authors used resting-state neuroimaging data to evaluate connectivity between the main DMN hubs (i.e., the medial prefrontal cortex [mPFC] and the posterior cingulate cortex-anterior precuneus [PPC]) in individuals with schizophrenia (n 28) and controls (n 32). The authors also examined whether DMN connectivity was associated with social functioning via social attainment (measured by the Specific Levels of Functioning Scale) and social competence (measured by the Social Skills Performance Assessment), and if social cognition mediates the association between DMN connectivity and these measures of social functioning. Results revealed that DMN connectivity did not differ between individ- uals with schizophrenia and controls. However, connectivity between the mPFC and PCC hubs was significantly associated with social competence and social attainment in individuals with schizophrenia but not in controls as reflected by a significant group-by-connectivity interaction. Social cognition did not mediate the association between DMN connectivity and social functioning in individuals with schizo- phrenia. The findings suggest that fronto-parietal DMN connectivity in particular may be differentially associated with social functioning in schizophrenia and controls. As a result, DMN connectivity may be used as a neuroimaging marker to monitor treatment response or as a potential target for interventions that aim to enhance social functioning in schizophrenia. General Scientific Summary This study suggests that individuals with schizophrenia and healthy controls do not differ in default mode network (DMN) connectivity. However, DMN connectivity is differentially associated with social functioning in individuals with schizophrenia and healthy controls. Social cognition may not underlie the relationship between DMN connectivity and social functioning in individuals with schizophrenia. Keywords:default mode network connectivity, resting-state fMRI, social competence, social attainment, schizophrenia Supplemental materials:http://dx.doi.org/10.1037/abn0000253.supp This article was published Online First March 30, 2017. Jaclyn M. Fox, Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University; Samantha V. Abram, Department of Psychology, University of Minnesota, Minne- apolis; James L. Reilly, Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University; Shaun Eack, School of Social Work, and Western Psychiatric Institute and Clinic, School of Medicine, University of Pittsburgh; Morris B. Goldman, John G. Csernansky, Lei Wang, and Matthew J. Smith,Department of Psychiatry and Behavioral Sciences, Feinberg School of Medicine, Northwestern University. National Institute of Mental Health, Grant: R01 MH056584, Recipient: John G. Csernansky. Correspondence concerning this article should be addressed to Matthew J. Smith, who is now at the School of Social Work, University of Michigan, Ann Arbor, 1080 South University Avenue, Ann Arbor, MI 48109-1106. E-mail:[email protected] This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. Journal of Abnormal Psychology© 2017 American Psychological Association 2017, Vol. 126, No. 4, 392– 4050021-843X/17/$12.00http://dx.doi.org/10.1037/abn0000253 392 Social functioning is characterized by the way we engage with family, friends, coworkers, and service providers in conversation, shared decision-making, compromise, and social activities (Kalin et al., 2015). Social functioning is notably impaired in schizophre- nia and is considered by many to be a fundamental characteristic of the disorder (Häfner, Nowotny, Loffler, & an der Heiden, 1995). Restoring an individual with schizophrenia’s social functioning via theirsocial attainment(i.e., one’s social relationships and partic- ipation in community activities) andsocial competence(i.e., one’s capacity to effectively socialize with others) is a cornerstone of functional recovery. To date, research has evaluated several inter- ventions aimed at improving social functioning (Horan et al., 2009, 2011;Kurtz & Mueser, 2008;Liberman, Mueser, & Wallace, 1986;Penn, Roberts, Combs, & Sterne, 2007). However, the effects have been modest, and as such, interventions targeting the social dysfunction associated with schizophrenia could benefit from additional optimization (Kurtz & Mueser, 2008;Kurtz & Richardson, 2012). One approach to optimize these interventions is to identify the mechanisms that underlie social functioning impairments, such as the neural systems that support key social behaviors in schizophrenia. Therefore, research is needed to un- derstand the associations between neural networks and social functioning among individuals with schizophrenia. The dysconnectivity hypothesis is a promising theory from which to investigate the neural basis of social functioning. This hypothesis suggests that abnormal communication between neural networks is responsible for the cognitive and clinical symptoms of schizophrenia, including social functioning deficits (Bullmore, Fr- angou, & Murray, 1997;Friston, 1994;Friston & Frith, 1995; Stephan, Baldeweg, & Friston, 2006). For instance, several studies observed aberrant connectivity within the default mode network (DMN) among individuals with schizophrenia (Alonso-Solis et al., 2012;Bluhm et al., 2007;Camchong, MacDonald, Bell, Mueller, & Lim, 2011;Chai et al., 2011;Liemburg et al., 2012;Liu et al., 2012;Ongur et al., 2010;Whitfield-Gabrieli et al., 2009;Zhou et al., 2007). Moreover, this aberrant DMN connectivity has been associated with deficits in social cognition (e.g., social perception, mentalizing) among individuals with schizophrenia (Brunet, Sar- fati, Hardy-Bayle, & Decety, 2003;Delaveau et al., 2010;Fett et al., 2011;Mars et al., 2012;Mitchell, Banaji, & Macrae, 2005; Pelletier-Baldelli, Bernard, & Mittal, 2015;Shi et al., 2015;Spreng & Grady, 2010;Uddin, Iacoboni, Lange, & Keenan, 2007;Walter et al., 2009). Specifically,social perceptionis the ability to per- ceive social cues such as facial affect, tone, or gestures, whereas mentalizingis the ability to ascertain others’ emotions, beliefs, and intentions (Green, Horan, & Lee, 2015). In turn, a meta-analysis suggests that social cognition is the most proximal factor to social functioning among individuals with schizophrenia (Fett et al., 2011). Thus, social cognition may represent an underlying mech- anism that could explain the association between DMN connec- tivity and social functioning. Alternatively, prior studies suggest that functional connectivity of the DMN is directly related to social functioning in various clinical and healthy populations (Che et al., 2014;Dodell-Feder, DeLisi, & Hooker, 2014;Jung et al., 2014;Schreiner et al., 2014; Washington & VanMeter, 2015;Yerys et al., 2015). Specifically, increased functional connectivity within the DMN has been asso- ciated with better social functioning in healthy controls (Che et al., 2014;Jung et al., 2014;Washington & VanMeter, 2015), anddecreased DMN functional connectivity has been associated with social impairment in individuals with autism and 22q11.2 deletion syndrome (Jung et al., 2014;Schreiner et al., 2014;Yerys et al., 2015). Most recently, Dodell-Feder and colleagues found that reduced resting-state DMN connectivity was associated with poorer social functioning in healthy controls and first-degree rel- atives of individuals with schizophrenia (Dodell-Feder et al., 2014). Thus, the DMN’s aberrant connectivity in individuals with schizophrenia, its relation to social cognition, and its relation to social functioning in various populations suggest that the DMN may be directly associated with social functioning in individuals with schizophrenia. To our knowledge, prior research has not investigated the associations between DMN functional connectiv- ity and social functioning or the underlying mechanisms for such associations in schizophrenia. Thus, a direct investigation could elucidate whether DMN connectivity is a potential treatment target for functional recovery. The DMN consists of a frontal hub (i.e., highly connected area within the brain) located in the medial prefrontal cortex (mPFC) and a posterior hub located in the posterior cingulate cortex and precuneus that are highly connected with each other and other areas of the brain (Andrews-Hanna, 2012;Laird et al., 2011). In the current article, we defineDMN functional connectivityas internetwork connectivity (i.e., correlations between the timeseries of data-derived neural networks) between the mPFC and posterior cingulate cortex and anterior precuneus (PPC) hubs. Prior research suggests internetwork connectivity of the mPFC and PPC hubs is related to social functioning among healthy individuals and neu- ropsychiatric populations (Che et al., 2014;Dodell-Feder et al., 2014;Jung et al., 2014;Schreiner et al., 2014;Washington & VanMeter, 2015;Yerys et al., 2015). Specifically, internetwork connectivity between the mPFC and PPC hubs activates during self-reflection (Amodio & Frith, 2006;Andrews-Hanna, 2012; Ochsner et al., 2004;Schmitz & Johnson, 2007) and when a person is presented with information about people who are important to them (e.g., friends, relatives, etc.;Andrews-Hanna, 2012;Bartels & Zeki, 2004;Ochsner et al., 2005). This internetwork connectiv- ity also activates when a person is anticipating a social reward or threat, such as positive or negative affect (Andrews-Hanna, 2012; Kober et al., 2008;Maddock, 1999). In addition, DMN connectivity can be observed during both resting and task-activated neural states. Meta-analytic results sug- gest that the spatial topographies of intrinsic connectivity networks at rest are similar to networks derived during task (Fox & Raichle, 2007;Laird et al., 2011;Smith et al., 2009). However, researchers can use resting-state methods to overcome certain limitations that are associated with task-based approaches (e.g., individual differ- ences in attention, effort, or comprehension;Callicott et al., 2000; Callicott et al., 2003 ;Hooker, Bruce, Lincoln, Fisher, & Vinogra- dov, 2011). In addition, resting-state methods may be useful for capturing brain activity related to broader constructs that include multiple processes (e.g., social functioning) as this method does not depend on specific task demands (Abram et al., 2015). Thus, resting-state methods may be particularly useful for examining associations between connectivity and social functioning. A common approach for evaluating patterns of resting-state con- nectivity is probabilistic independent component analysis (pICA; McKeown etal., 1998), which is a data-driven procedure that separates multivariate signals into statistically independent sources This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 393 CONNECTIVIY ASSOCIATED WITH SOCIAL FUNCTIONING or group-level spatial maps. More specifically, the group-level spatial maps reflect temporally synchronized fluctuations in fMRI signals (Beckmann & Smith, 2004). As a data-driven procedure, pICA gen- erates components that represent functionally integrated brain regions as opposed to structurally defined brain regions. In comparison to other connectivity approach (e.g., seed-based methods), pICA reduces bias by parsing variance related to artifacts (i.e., head motion, cardiac function) from neural activity of interest (Beckmann, DeLuca, Devlin, & Smith, 2005). We therefore used pICA to derive group-level spatial maps for the current study. pICA can also help establish whether individuals with schizo- phrenia are characterized by either hypo- or hyper-DMN connec- tivity when compared to healthy controls as current findings are mixed (Alonso-Solís et al., 2015;Chang et al., 2014;Jafri, Pearl- son, Stevens, & Calhoun, 2008;Li et al., 2015;Liang et al., 2006; Liemburg et al., 2012;Mingoia et al., 2012;Ongur et al., 2010; Schilbach et al., 2016;Whitfield-Gabrieli & Ford, 2012;Zhou et al., 2007). The inconsistent findings may be attributed to method- ological differences, such as seed versus pICA analyses, or be- cause of differences in the specific networks that were examined (Jafri et al., 2008;Whitfield-Gabrieli & Ford, 2012;Zhou et al., 2007). Many studies that evaluated resting-state DMN connectiv- ity in schizophrenia using pICA reported on the connectivity between the main DMN hubs and other brain networks (Alonso- Solís et al., 2015;Jafri et al., 2008;Liang et al., 2006;Ongur et al., 2010;Zhou et al., 2007). Of the studies examining DMN connec- tivity (Alonso-Solís et al., 2015;Chang et al., 2014;Jafri et al., 2008;Li et al., 2015;Liang et al., 2006;Liemburg et al., 2012; Mingoia et al., 2012;Ongur et al., 2010;Schilbach et al., 2016; Zhou et al., 2007), only two studies directly examined connectivity between the DMN hubs (Chang et al., 2014;Liemburg et al., 2012). One of these studies found that individuals with schizo- phrenia exhibited decreased DMN internetwork connectivity as compared to controls (Liemburg et al., 2012), and one study did not observe group differences in DMN internetwork connectivity (Chang et al., 2014). Because of conflicting findings in the liter- ature, further research comparing DMN internetwork connectivity of individuals with schizophrenia to controls is needed. The aims of the current study were threefold. First, we com- pared resting-state DMN internetwork connectivity between indi- viduals with schizophrenia and controls. We hypothesized that individuals with schizophrenia, as compared to controls, would either show decreased or similar connectivity between the DMN hubs. Second, we assessed between-groups differences in DMN connectivity, and weassessed the relationship between DMN con- nectivity and social functioning via measures of social attainment and social competence. We hypothesized that DMN connectivity would be positively associated with social attainment and social competence in healthy controls and individuals with schizophrenia. Third, we investigated whether social cognition (i.e., social perception and men- talizing) mediated the association between DMN connectivity and social functioning among individuals with schizophrenia. Method Participants and Procedure Individuals with schizophrenia (n 28) and controls (n 32) ages 18 – 45 were group-matched for age, gender, and race. Par-ticipants completed a clinical interview, neuropsychological bat- tery, and resting-state functional MRI scan. Participants were recruited from the Northwestern University Schizophrenia Re- search Group. Details on recruitment can be found inSmith et al. (2015). 1Individuals with schizophrenia were included in the study if they (a) met theDSM–IVcriteria for a diagnosis of schizophre- nia, (b) were clinically stable (i.e., their symptoms remained un- changed for at least 2 weeks;Rastogi-Cruz & Csernansky, 1997), and (c) were currently on antipsychotic medication. Individuals were excluded from the study if they (a) metDSM–IVcriteria for intellectual disability, (b) met substance abuse or dependence DSM–IVcriteria in the past 6 months, (c) had a documented neurological injury or disorder, or (d) had a severe medical disor- der. Controls were also excluded from the study if they (a) had a lifetime history of aDSM–IVAxis I psychiatric disorder or (b) had a first-degree relative with a psychotic disorder. In addition, we excluded nine participants from the analysis (five individuals with schizophrenia and four controls), for excessive in-scanner motion (mean absolute displacement above 1.5 mm, or any absolute displacement [translations or rotations] above 3 mm/degrees). The institutional review board at Northwestern University Feinberg School of Medicine approved the study procedures, and all par- ticipants provided informed consent (IRB approval number: STU00013034). Measures Demographic and clinical measures.DSM–IVdiagnosis was determined through the Structured Clinical Interview for DSM Disorders (SCID-IV;First, Spitzer, Gibbon, & Williams). The SCID-IV was conducted by masters- or PhD-level research staff and validated through a semistructured interview by a study psy- chiatrist. All antipsychotic medication dosages were converted to chlorpromazine equivalents (CPZeq;Andreasen, Pressler, Nopou- los, Miller, & Ho, 2010). Clinical symptoms of schizophrenia were assessed using the Scale for the Assessment of Positive Symptoms (SAPS;Andreasen, 1983) and the Scale for the Assessment of Negative Symptoms (SANS;Andreasen, 1983). Global ratings for hallucinations, delusions, bizarre behavior, positive formal thought disorder, affective flattening, alogia, avolition, and anhedonia were provided by masters- or PhD-level research staff. We generated global scores for positive, negative, and disorganized symptoms by calculating the mean of positive Scale for the Assessment of Positive Symptoms (SAPS) items, negative SANS items, and disorganized SAPS and SANS items. Mean parental socioeco- nomic status (SES) was assessed using the Barrett Simplified Measure of Social Status (Barratt, 2005). Neurocognitive assessments.Participants completed a bat- tery of tests used to generate four neurocognitive domains (Nuech- terlein et al., 2004), including (a) crystallized IQ from scores on the Vocabulary subtest of the Wechsler Adult Intelligence Scale— Third Edition (WAIS-III;Wechsler, 1997a); (b) Working Memory from scores on the Continuous Performance Task (Barch et al., 2004) and the Digit-Span, Spatial-Span, and Letter-Number Se- quencing subtests of the Wechsler Memory Scale-Third Edition (WMS-III;Wechsler, 1997b); (c) Episodic Memory from scores 1One of our prior publications (Abram et al., 2016) uses the same sample that is presented in the current article. This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 394 FOX ET AL. on the Logical Memory I and Family Pictures I subtests of the WMS-III and the free recall score from the first five trials of the California Verbal Learning Test (Delis, Kramer, Kaplan, & Ober, 1983); (d) Executive Functioning from scores on the Wisconsin Card Sort Task (Heaton, 2003), Letter and Animal Fluency (Ben- ton, Hamsher, & Sivan, 1994), the Trail Making Test Part B (Reitan & Wolfson, 1985), and the Matrix Reasoning subtest of the WAIS-III. Domain scores were calculated by first standardizing individual subtests (using control group data) and then averaging across these standardized scores within each domain (Smith et al., 2012). A global neurocognitive score was computed using the average of these four domains. Social Cognition Social perception.We assessed social perception using a facial affect perception task and a social perception task. The facial affect perception task presented participants with 30 faces display- ing happiness, sadness, fear, disgust, anger, or neutrality, and participants selected the correct emotional expression from two emotion labels (Smith et al., 2014). We used the half-profile of nonverbal sensitivity to measure social perception. Specifically, the task presented participants with 110 video scenes containing facial expressions, voice intonations, and/or gestures. Following the scene, participants were presented with two labels and chose which label best described the social cue (Ambady, Hallahan, & Rosenthal, 1995;Rosenthal et al., 1979). We mean-centered ac- curacy rates to the control group, and averaged transformed scores from both tasks to obtain a social perception domain score. Mentalizing.We assessed mentalizing using The Awareness of Social Inference Test (TASIT) Part 3 (McDonald, 2002) and an emotional perspective-taking task (Smith et al., 2014). The TASIT-III presented participants with 16 video vignettes of com- mon social interactions. Each vignettecontained an untrue comment presented as either a lie or sarcasm. Participants answered yes/no questions about the thoughts, intentions, beliefs, and feelings of the people in the vignette. The emotional perspective-taking task pre- sented participants with 60 pictures of two person interactions depict- ing happiness, sadness, fear, disgust, anger, or neutrality. In each picture, one person’s face was masked and participants chose which of two faces depicted the emotion of the masked actor. We mean- centered accuracy rates to the control group, and then averaged the transformed accuracy scores to obtain the mentalizing domain score. Social Functioning Social attainment.We assessed social attainment using the interview version of the Specific Levels of Functioning Scale (SLOF), which is a 30 item interview-based measure that assesses the following domains: interpersonal relationships, social accept- ability, activities of daily living, and work skills (Schneider & Struening, 1983). Higher scores on the SLOF indicate better social attainment (range 84 –150). Variance in SLOF scores did not significantly differ between groups (F 27,31 1.38,p .39). The SLOF is considered the gold standard for assessing current func- tioning based on findings from the VALERO Study (Harvey et al., 2011;Leifker, Patterson, Heaton, & Harvey, 2011). Social competence.We assessed social competence using the Social Skills Performance Assessment (SSPA;Patterson, Moscona,McKibbin, Davidson, & Jeste, 2001), which is comprised of two role-play scenes between a trained actor and the participant that are video-recorded. The scenes involve meeting a new neighbor and making a maintenance request to a landlord. Two trained research assistants rated the performance on a 5-point scale across eight criteria for the first scene and nine criteria for the second scene. Additional details on the use of this measure can be found here in Smith et al. (2014). We calculated a final score by averaging the scores for each scene (intraclass correlation .97 for two blinded raters on 25% of the videos). Higher scores on the SSPA indicate better social competence (range 1.66 –5.00). Variance in SSPA scores did not significantly differ between groups (F 22,26 1.89, p .12). The SSPA is considered the gold standard for assessing social competence in schizophrenia (Harvey, Velligan, & Bellack, 2007;Kalin et al., 2015). fMRI data acquisition and pICA.Resting-state scans were acquired on a 3T TIM Trio system (Siemens Medical Systems, Malvern, PA) scanner at Northwestern University Center for Translational Imaging for the study sample. The scanning param- eters included: gradient-echo echo-planar imaging of 164 volumes; repetition time (TR) 2.5 s; echo time (TE) 20 ms; flip angle 80°; voxel size 1.7 1.7 3 mm. The group-level spatial maps were derived from resting-state scans of an independent commu- nity sample of 218 volunteers collected at the University of Min- nesota (mean age 26 years [range 20 to 39], 49% male;Abram et al., 2015). Data were preprocessed using the following prepro- cessing steps in the MELODIC (Multivariate Exploratory Linear Optimized Decomposition into Independent Components) toolkit in FSL (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/MELODIC): brain ex- traction, motion correction, and high-pass temporal filtering (threshold of 0.1 Hz;Wisner, Patzelt, Lim, & MacDonald, 2013). As a final step, the data underwent motion regression outside of FSL using participant-specific movement parameters generated during registration (Moodie, Wisner, & MacDonald, 2014;Wis- ner, Atluri, Lim, & MacDonald, 2013). As noted previously, the group-level spatial maps were gener- ated from a larger dataset of 218 community volunteers using a metamelodic spatial pICA pipeline (Abram et al., 2015). More specifically, the MELODIC function in FSL was used to run 25 temporal concatenation (model-free and multisubject) group-level pICAs. Each ICA included a random order of 80 participants as inputs to decrease the likelihood of overfitting, with a dimension- ality constraint of 60 based on reliability research (Poppe et al., 2013) and findings that large-scale networks, such as the DMN, fractionate at higher dimensionalities (Ray et al., 2013;Wisner, Atluri, et al., 2013;Wisner, Patzelt, et al., 2013). The 60 compo- nents from each ICA were merged into one file which was used as the input to a metalevel MELODIC (meta-ICA). The meta-ICA generated the 60 most consistent group-level components. Visual inspection was done to identify artifactual components, which included components likely to show homeostatic fluctuations, white matter tracts, or movement (Kelly et al., 2010). Twenty- seven nonartifactual components were identified following these procedures. As recommended by prior research, we used maps from this external sample because maps generated from larger data sets are likely to be more robust and these maps were unbiased to the schizophrenia or control groups used in the present study (Griffanti et al., 2016). We also used ICA maps from this larger dataset rather This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 395 CONNECTIVIY ASSOCIATED WITH SOCIAL FUNCTIONING than mPFC and PCC atlas masks because we did not want to make assumptions regarding exactly which voxels may contribute to a network (Esposito et al., 2005;Ramnani, Behrens, Penny, & Matthews, 2004). By using maps from an external sample, we were able to assess functional connectivity within and between functionally derived networks as opposed to structurally defined networks. We applied the group-level spatial maps from the ex- ternal dataset to our current dataset through FSL’s dual-regression function. This enabled us to derive individual time series and corresponding spatial maps for the current participants. We used the participant-specific time series and spatial maps to compute connectivity metrics for each participant. Internetwork connectivity metric computations.We used dual regression to create connectivity maps and time courses for each subject based on the group-level spatial maps from the meta-ICA (Filippini et al., 2009;Wisner, Patzelt, et al., 2013;Zuo et al., 2010). We calculated internetwork connectivity metrics between all group-level spatial maps using the participant-level timeseries data from the dual regression (Wisner, Patzelt, et al., 2013). These values were Pearson correlations between all pairs of participant-level timeseries (i.e., for all combinations of the 60 networks), and reflect the temporal association between network pairs. Group-level means were calculated using Fischer-Z values and were transformed back tor-values for reporting. In the current study, we were interested in connectivity between the mPFC and the PPC (see Supplemental Figure S1). Of the 60 group-level spatial maps (Abram et al., 2015), three nonartifactual components included mPFC and 12 nonartifactual components included PPC (see Supplemental Table S1). We examined the four components that included an mPFC or PPC overlap of at least 750 voxels in our analyses to ensure appropriate mPFC or PPC con- tribution. These components included the following compositions: the PPC, the mPFC, the precuneus (P), and the orbitofrontal cortex (OFC). Other areas such as the OFC and angular gyrus (included in the PPC component) likely emerged through this analysis be- cause they arehighly functionally connected to the main DMN hubs and are related to social functioning (Andrews, Wang, Csernansky, Gado, & Barch, 2006;Andrews-Hanna, 2012). The internetwork connectivity metrics were then as follows: (a) PPC-to-P metric, (b) mPFC-to-PPC metric, (c) OFC-to-PPC metric, (d) mPFC-to-P metric, (e) OFC-to-P metric, and (f) mPFC-to-OFC metric (seeTable 1for a full description of these metrics). Potential movement confounds.Movement was calculated as the root mean square (RMS) absolute and incremental move- ment for each group (Power, Barnes, Snyder, Schlaggar, & Pe- tersen, 2012). We did not detect group differences in absolute (x absCON 0.36,x absSCZ 0.31,t 45 0.90,p .37) or incremental (x incCON 0.05,x incSCZ 0.05,t 45 0.73,p .47) movement. RMS incremental movement was significantly correlated with the PPC-to-P metric,r .31,p .05. Neither RMS absolute nor incremental movement was correlated with the other internetwork connectivity metrics, social attainment, or so- cial competence (allp .10). Therefore, we only included RMS incremental movement as a covariate in models that contained the PPC-to-P metric. Statistical Analyses Demographic and behavioral analyses.Group differences for demographic variables, neurocognitive performance, social perception, mentalizing, social attainment, and social competence were evaluated usingttests for continuous variables and chi- square ( 2) tests for categorical variables. Connectivity analyses.We used multivariate analysis of vari- ance (MANOVA) to evaluate whether individuals with schizophrenia differed from controls with respect to the DMN connectivity metrics; in this model the PPC-to-P, mPFC-to-PPC, OFC-to-PPC, mPFC-to-P, OFC-to-P, and mPFC-to-OFC metrics served as the dependent vari- ables. Connectivity and social functioning analyses.We used ro- bust linear regression to assess the associations between our DMN connectivity variables and social attainment and social compe- tence. More specifically, we created two models with social at- tainment as the dependent variable in the first model, social com- petence as the dependent variable in the second model, and the PPC-to-P, mPFC-to-PPC, OFC-to-PPC, mPFC-to-P, OFC-to-P, and mPFC-to-OFC metrics and group status as the independent variables in both models. We used the modeling package ‘robust’ inR(Wang et al., 2008) to adjust for the presence of multivariate outliers (Field, 2009). To rule out RMS incremental movement as confounds, we reassessed the association between DMN connec- tivity and social attainment and social competence while including the aforementioned variable as a covariate. Follow-up interaction models.We used robust linear regres- sion to examine between-groups differences in the associations between DMN connectivity and social attainment and social com- petence. To conserve power, we only included DMN connectivity metrics that were significantly associated with social attainment or social competence in the main effects models. We again created two models with social attainment as the dependent variable in the first model, social competence as the dependent variable in the second model, and the mPFC-to-PPC metric, group status, and a Table 1 Internetwork Connectivity Metrics HubsInternetwork connectivity metric Posterior cingulate cortex/anterior precuneus with additional precuneus areas PPC-to-P metric Medial prefrontal cortex with posterior cingulate cortex/anterior precuneus mPFC-to-PPC metric Orbitofrontal cortex with posterior cingulate cortex/anterior precuneus OFC-to-PPC metric Medial prefrontal cortex with precuneus mPFC-to-P metric Orbitofrontal cortex with precuneus OFC-to-P metric Medial prefrontal cortex with orbitofrontal cortex mPFC-to-OFC metric Note. mPFC-to-PPC metric internetwork connectivity between the me- dial prefrontal cortex and the posterior cingulate cortex-anterior precuneus; OFC-to-PPC metric internetwork connectivity between the orbital frontal cortex and the posterior cingulate cortex-anterior precuneus; mPFC-to-P metric internetwork connectivity between the medial prefrontal cortex and the precuneus; OFC-to-P metric internetwork connectivity between the orbital frontal cortex and the precuneus; mPFC-to-OFC metric internet- work connectivity between the medial prefrontal cortex and the orbital frontal cortex. This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 396 FOX ET AL. group-by-connectivity interaction term as the independent vari- ables in both models. Based on prior research suggesting that social status may be associated with neural activity in the main DMN hubs (Muscatell et al., 2012), we included parental SES as a covariate in both models. To account for the association between neurocognitive and social deficits observed in schizophrenia (Fett et al., 2011;Ventura et al., 2015), we also included mean global neurocognition as a covariate in both models. Finally, we included gender and age as covariates in both models given research that shows gender and age-related resting-state connectivity differ- ences (Damoiseaux et al., 2008;Satterthwaite et al., 2015;Tian, Wang, Yan, & He, 2011). Mediation analyses.For a variable to be a mediator, it must be related to both the independent and dependent variables (Baron & Kenny, 1986). Therefore, to determine if mediation analyses were appropriate for our proposed mediators (i.e., social percep- tion and mentalizing), we first examined Pearson correlations to see whether the proposed mediators were significantly correlated with the social functioning variables and DMN connectivity in individuals with schizophrenia. For any significant associations, we performed mediation analyses using the Baron and Kenny method to see if social perception or mentalizing mediated these associations. Connectivity and activities of daily living analysis.As a final step, we assessed the specificity of any DMN connectivity and social functioning associations. Specifically, we tested for associations between DMN connectivity and a performance-based daily living skills measure University of California-San Diego Performance-based Skills Assessment (UPSA-B). Results Participant Characteristics As shown inTable 2, individuals with schizophrenia and con- trols did not differ according to age, gender, or race. However, the groups differed with regard to parental SES (p .01), global neurocognition (p .001), social perception (p .01), and men- talizing (p .001). Individuals with schizophrenia also scored lower than controls on the social attainment and social competence measures (bothp .001). Descriptive data for duration of illness, CPZeq, and clinical symptoms are also presented inTable 2. Between-Group Connectivity Analysis Table 3reports the between-groups connectivity results. MANOVA revealed that individuals with schizophrenia and con- trols did not differ with respect to any of the connectivity vari- ables: PPC-to-P metric, mPFC-to-PPC metric, OFC-to-PPC met- ric, mPFC-to-P metric, OFC-to-P metric, or the mPFC-to-OFC metric (F 6,53 0.49,p .81). Between-Group Robust Linear Regression Analyses Main effects models.The overall model evaluating associa- tions between all internetwork connectivity metrics and social attainment was significant (F 7,52 7.56,p .001; seeTable 4), with main effects of the mPFC-to-PPC metric and group status (bothp .001). None of the other connectivity metrics were Table 2 Study Sample Characteristics Demographics CON (n 32) SCZ (n 28) 2/t Statistics Age,M(SD) 31.46 (8.06) 33.17 (6.63) .90 Gender (% male) 53.13 64.29 .38 Parental SES,M(SD) a 30.08 (9.21) 23.15 (9.87) 2.75 Duration of illness, mean years (SD) — 14.57 (6.34) — CPZeq,M(SD) — 329.79 (207.31) — Race % Caucasian 50.00 42.90 .31 % African American 34.40 39.30 % Other 15.63 17.86 Neurocognitive function Global neurocognition,M(SD) .00 (.64) .90 (.58) 5.64 Clinical symptoms b Positive symptoms,M(SD) — 2.57 (1.86) — Negative symptoms,M(SD) — 2.86 (1.08) — Disorganized symptoms,M(SD) — 1.80 (1.29) — Social cognitive measures Social perception,M(SD) .00 (.86) .77 (1.05) 3.08 Mentalizing,M(SD) .00 (.87) 1.34 (1.09) 5.18 Functioning measures Social attainment,M(SD) 141.34 (12.72) 125.43 (14.93) 4.41 Social competence,M(SD) c 4.49 (.63) 3.19 (.87) 5.96 Note. SCZ individuals with schizophrenia; CON controls; SES socioeconomic status; CPZeq chlorpromazine equivalent; SAPS Scale for the Assessment of Positive Symptoms; SANS the Scale for the Assessment of Negative Symptoms. aCompleted byn 31 CON andn 27 SCZ. bBased off of SAPS and SANS Global Ratings. cCompleted byn 27 CON andn 23 SCZ. p .01. p .001. This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 397 CONNECTIVIY ASSOCIATED WITH SOCIAL FUNCTIONING associated with social attainment (allp .10) and, therefore, were not used in subsequent analyses. The exploratory covariate (i.e., RMS incremental movement) was nonsignificant within the model (p .10). Thus, we did not include it in the final model to optimize statistical power.The overall model evaluating associations between all internet- work connectivity metrics and social competence was also signif- icant (F 7,42 9.32,p .001; seeTable 4), with main effects of the mPFC-to-PPC metric and group status (bothp .001). Al- though the OFC-to-P metric had a trend level association with social competence (p .07), it was not used in subsequent analyses. None of the other connectivity metrics were significantly associated with social attainment (allp .10) and, were also discarded from further analysis. The exploratory covariate (i.e., RMS incremental movement) was nonsignificant within the model (p .10). Thus, we did not include it in the final model to optimize statistical power. Follow-up interaction models.The overall model evaluating associations between connectivity and social attainment was sig- nificant (F 7,50 11.68,p .001). There were significant main effects of the mPFC-to-PPC metric (p .001), group status (p .001), parental SES (p .05), and global neurocognition (p .05; seeTable 5). We observed an interaction between group and DMN connectivity (p .01). A plot of the Group DMN connectivity interaction is presented inFigure 1. The other a priori covariates (i.e., age and gender) were not significantly associated with social attainment (bothp .10). Follow-up within group analysis indi- cated that DMN connectivity was positively correlated with social attainment in individuals with schizophrenia (partialr .44,p .05) but was not associated in controls (partialr 0.08,p .10). See Supplemental Table S2 for zero-order correlations. The overall model evaluating associations between connectivity and social competence was significant (F 7,42 14.07,p .001). There were significant main effects of the mPFC-to-PPC metric (p .001), group status (p .001), and global neurocognition (p .05; seeTable 4). There was a significant interaction between group and DMN connectivity (p .01). A plot of the group-by- DMN connectivity interaction is presented inFigure 2. The other a priori covariates (i.e., age, gender, and parental SES) were not significantly associated with social competence (allp .10). Follow-up within group analysis indicated that DMN connectivity was positively correlated with social competence in individuals with schizophrenia (partialr .45,p .05) but not correlated in controls (partialr 0.03,p .10). See Supplemental Table S2 for zero-order correlations. Table 3 Between-Group Comparisons of Internetwork Connectivity Metrics CON (n 32) SCZ (n 28)Fstatisticpvalue Mean PPC-to-P metric (SD) .69 (.0.21) .66 (.22) Mean mPFC-to-PPC metric (SD) .26 (.27) .25 (.30) Mean OFC-to-PPC metric (SD) .03 (.22) .05 (.20) .49 .81 Mean mPFC-to-P metric (SD) .31 (.21) .24 (.26) Mean OFC-to-P metric (SD) .06 (.17) .11 (.21) Mean mPFC-to-OFC metric (SD) .39 (.20) .38 (.24) Note.SCZ individuals with schizophrenia; CON controls; PPC-to-P metric internetwork connectivity between the posterior cingulate cortex-anterior precuneus and the precuneus; mPFC-to-PPC metric internet- work connectivity between the medial prefrontal cortex and the posterior cingulate cortex-anterior precuneus; OFC-to-PPC metric internetwork connectivity between the orbital frontal cortex and the posterior cingulate cortex-anterior precuneus; mPFC-to-P metric internetwork connectivity between the medial prefrontal cortex and the precuneus; OFC-to-P metric internetwork connectivity between the orbital frontal cortex and the precuneus; mPFC-to-OFC metric internetwork connectivity between the medial prefrontal cortex and the orbital frontal cortex. Table 4 Internetwork Connectivity Metrics Associated With Social Attainment and Social Competence (Between-Group Model) Independent variables (SE)t-statisticpvalue Social attainment robust linear regression model Internetwork connectivity PPC-to-P metric .07 (.10) .71 .48 mPFC-to-PPC metric .50 (.16) 3.19 .001 OFC-to-PPC metric .23 (.14) 1.66 .10 mPFC-to-P metric .09 (.14) .60 .55 OFC-to-P metric .03 (.12) .29 .77 mPFC-to-OFC metric .10 (.09) 1.13 .26 Group 1.11 (.17) 6.43 .001 Social competence robust linear regression model a Internetwork connectivity PPC-to-P metric .01 (.12) .86 .40 mPFC-to-PPC metric .44 (.17) 2.53 .02 OFC-to-PPC metric .03 (.15) .17 .87 mPFC-to-P metric .18 (.17) 1.12 .27 OFC-to-P metric .04 (.14) .26 .79 mPFC-to-OFC metric .21 (.11) 1.85 .07 Group 1.39 (.19) 7.16 .001 Note. PPC-to-P metric internetwork connectivity between the poste- rior cingulate cortex-anterior precuneus and the precuneus; mPFC-to-PPC metric internetwork connectivity between the medial prefrontal cortex and the posterior cingulate cortex-anterior precuneus; OFC-to-PPC met- ric internetwork connectivity between the orbital frontal cortex and the posterior cingulate cortex-anterior precuneus; mPFC-to-P metric inter- network connectivity between the medial prefrontal cortex and the precu- neus; OFC-to-P metric internetwork connectivity between the orbital frontal cortex and the precuneus; mPFC-to-OFC metric internetwork connectivity between the medial prefrontal cortex and the orbital frontal cortex. an 27 controls andn 23 individuals with schizophrenia. p .10. p .05. p .01. p .001. This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 398 FOX ET AL. Mediation Analyses Social attainment.Pearson correlations indicated that social perception and mentalizing were not significantly correlated with social attainment (allp .10). Because neither of our possible mediators were significantly correlated with both social attainment and DMN connectivity, we did not conduct a mediation analysis.Social competence.Pearson correlations indicated that men- talizing was significantly correlated with social competence,r .45,p .05 in individuals with schizophrenia, but social percep- tion was not significantly correlated with social competence (p .10). Mentalizing was not significantly correlated with the mPFC- to-PCC metric in individuals with schizophrenia,r .09,p .10. Because neither of our possible mediators were significantly cor- related with both social competence and DMN connectivity, we did not run a mediation analysis. Within-Group Specificity Analysis Follow-up analyses indicated specificity for the associations between mPFC-to-PPC internetwork connectivity with social at- tainment and social competence. In particular, this metric was not correlated with the UPSA-B in individuals with schizophrenia,r .02,p .10. In addition, the correlation between social attainment and the mPFC-to-PPC metric was significantly stronger than the correlation between UPSA-B performance and the mPFC-to-PPC metric (Meng’sz 2.06,p .04). The correlation between social competence and the mPFC-to-PPC metric was stronger than the correlation between UPSA-B performance and the mPFC-to-PPC metric at trend level (Meng’sz 1.66,p .09). Discussion In the current study, we were interested in the association between DMN connectivity and social functioning in controls and individuals with schizophrenia and whether this association was mediated by social cognition. Specifically, we evaluated whether connectivity between DMN components differed between individ- uals with schizophrenia and controls, and whether specific DMN connectivity metrics were associated with two measures of social functioning (i.e., social attainment or social competence). We controlled for global neurocognition, age, gender, and parental SES. Our results suggested that the groups did not differ with respect to average DMN connectivity magnitude. However, con- nectivity between the mPFC and PPC hubs was differentially Table 5 Internetwork Connectivity Associated With Social Attainment and Social Competence (Between-Group Model) Independent variables (SE)t-statisticpvalue Social attainment robust linear regression model a Internetwork connectivity mPFC-to-PPC metric .46 (.10) 4.38 .001 Group .92 (.19) 4.77 .001 Age .08 (.08) .98 .33 Gender .01 (.08) .05 .96 Parental SES .20 (.09) 2.24 .03 Global neurocognition .23 (.11) 2.15 .04 Interaction term mPFC-to-PCC Group .52 (.15) 3.45 .001 Social competence robust linear regression model b Internetwork connectivity mPFC-to-PPC metric .46 (.11) 4.16 .001 Group 1.01 (.21) 4.87 .001 Age .02 (.08) .26 .80 Gender .12 (.09) 1.34 .19 Parental SES .07 (.10) .71 .48 Global neurocognition .23 (.11) 2.07 .05 Interaction term mPFC-to-PCC Group .50 (.17) 2.97 .01 Note. mPFC-to-PPC metric internetwork connectivity between the me- dial prefrontal cortex and the posterior cingulate cortex-anterior precuneus; SES socioeconomic status. an 31 controls andn 27 individuals with schizophrenia. bn 26 controls andn 22 individuals with schizophrenia. p .05. p .01. p .001. Figure 1.Functional connectivity is associated with social attainment in schizophrenia. Social attainment scores on the Specific Levels of Func- tioning Scale with higher scores indicating better social attainment; mPFC- to-PPC Metric internetwork connectivity of medial prefrontal cortex and posterior cingulate cortex-anterior precuneus with higher scores indicating more connectivity between networks. p .05. See the online article for the color version of this figure. Figure 2.Functional connectivity is associated with social competence in schizophrenia. Social competence scores on the Social Skills Perfor- mance Assessment with higher scores indicating better social competence; mPFC-to-PPC Metric internetwork connectivity of medial prefrontal cortex and posterior cingulate cortex-anterior precuneus with higher scores indicating more connectivity between networks. p .05. See the online article for the color version of this figure. This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 399 CONNECTIVIY ASSOCIATED WITH SOCIAL FUNCTIONING related to social attainment and social competence in individuals with schizophrenia and controls as indicated by a significant group-by-connectivity interaction. Follow-up tests revealed that DMN internetwork connectivity was positively associated with measures of social attainment and social competence among indi- viduals with schizophrenia but was not associated with measures of social attainment or social competence in controls. Given non- significant associations between connectivity metrics with social perception or mentalizing, we did not conduct subsequent media- tion analyses. These findings build on our prior research from this dataset that examined associations between social cognition and social func- tioning (Abram et al., 2014;Karpouzian, Alden, Reilly, & Smith, 2016;Smith et al., 2014;Smith et al., 2012), and associations between neural activation during a mentalizing task and social functioning (Smith et al., 2015). In particular, the current findings indicate that resting state DMN connectivity is associated social functioning in individuals with schizophrenia, and that social cog- nition does not mediate this particular association. In addition, these findings add to the emerging literature examining the behav- ioral correlates of DMN connectivity (Andrews-Hanna, Reidler, Sepulcre, Poulin, & Buckner, 2010;Brunet et al., 2003;Das, Lagopoulos, Coulston, Henderson, & Malhi, 2012;Dodell-Feder et al., 2014;Laird et al., 2009;Mitchell, Neil Macrae, & Banaji, 2005;Spreng & Grady, 2010;Walter et al., 2009;Welborn & Lieberman, 2015). Furthermore, to our knowledge, our study is the first to examine the associations between DMN internetwork con- nectivity and social attainment and social competence among individuals with schizophrenia. Connectivity Predictor of Social Attainment and Social Competence We observed that stronger temporal synchrony between the mPFC and PPC was associated with better social attainment and social competence in individuals with schizophrenia, but this con- nectivity was not associated with social functioning in controls. Moreover, these findings remained significant after controlling for age, gender, global neurocognition, and parental SES. Therefore, these neural-behavior associations were not simply due to neuro- cognitive impairment or demographic characteristics, and neural data may provide additional information on social attainment and social competence beyond what is captured with background char- acteristics and neuropsychological tests (MacDonald, 2013; Sprong, Schothorst, Vos, Hox, & van Engeland, 2007). Our findings are consistent with a recent study that found stronger DMN subsystem connectivity was related to better social functioning in first-degree relatives of individuals with schizophre- nia (Dodell-Feder et al., 2014). However, the null correlations between our measure of DMN connectivity and the two measures of social cognition were not consistent with prior studies showing stronger connectivity between the mPFC and PPC was related to better social cognition (Andrews-Hanna et al., 2010;Mitchell et al., 2005;Spreng & Grady, 2010;Welborn & Lieberman, 2015). Our inability to identify a mechanism underlying the association between DMN connectivity and social functioning is not unex- pected given that current understanding of mechanisms underlying the association between neural connectivity and functioning is limited.One possible explanation for the null correlation findings be- tween DMN connectivity and social cognition is that there may be an indirect association between DMN connectivity and social functioning through social– cognitive functions other than mental- izing and social perception. For example, stronger connectivity between the mPFC and PPC may facilitate enhanced self- reflection (Andrews-Hanna et al., 2010), and self-reflection has been linked with social functioning (Brune, Dimaggiob, & Ly- saker, 2011). Therefore, self-reflection may mediate the associa- tion between DMN connectivity and social functioning in schizo- phrenia. Cognitive insight is another possible mediator for the association between DMN connectivity and social functioning in schizophrenia. Research suggests that cognitive insight may be positively associated with DMN connectivity in individuals with schizophrenia (Liemburg et al., 2012), and a treatment study demonstrated that improvements in cognitive insight through metacognitive training resulted in increased social functioning in individuals in the early stages of schizophrenia (Ussorio et al., 2016). Alternatively, there may be no underlying mechanism for the association between DMN connectivity and social functioning, because DMN connectivity may be a direct neural signature for social functioning. Connectivity Alterations Between Groups The lack of a difference in connectivity between the main DMN hubs in individuals with schizophrenia compared to controls is consistent with work byChang et al. (2014)but differs from one study that reported connectivity differences between individuals with schizophrenia and controls (Liemburg et al., 2012). There are various explanations for the inconsistent findings. All three of these studies (including the current study) had relatively small sample sizes (individuals with schizophrenia,n 35), so power issues may contribute to the differences in findings. In addition, Liemburg and colleagues suggest that cognitive insight may affect DMN connectivity such that patients with poorer insight exhibit decreased DMN connectivity compared to patients with higher insight (Liemburg et al., 2012). However, only one study assessed insight so future studies may benefit from evaluating insight in this context. Based on these results, future research using similar methods (e.g., pICA) with larger sample sizes is needed to verify DMN connectivity patterns in schizophrenia. Finally, although individuals with schizophrenia and controls did not differ with respect to DMN connectivity, individuals with schizophrenia had distinctly poorer social attainment and social competence than controls. Given that DMN connectivity was only associated with social functioning for individuals with schizophre- nia, it appears that similar communication between neural hubs may have differential outcomes across controls and individuals with schizophrenia. Future research could evaluate whether strengthening DMN connectivity beyond the level of connectivity observed in controls yields improvement in social attainment and social competence in individuals with schizophrenia. Study Implications Our results suggest that DMN connectivity was associated with social attainment and social competence in schizophrenia. There- fore, DMN connectivity could be assessed as a treatment target for This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 400 FOX ET AL. interventions focused on improving social attainment and social competence in schizophrenia. Such interventions are emerging and are much needed given the poor quality of life experienced by individuals with schizophrenia (Bradshaw, 2000;Gibson et al., 2014;Granholm et al., 2005;Ntoutsia, Katsamagkos, & Econo- mou, 2013). Based on our results, interventions that strengthen connectivity between the mPFC and PPC may be especially effi- cacious. To support the feasibility of testing such a hypothesis, cognitive remediation and certain types of pharmacotherapy have been shown to alter DMN connectivity in individuals with schizo- phrenia (Bor et al., 2011;Eack, Newhill, & Keshavan, 2016; Penades et al., 2014;Sambataro et al., 2010). In addition, repetitive transcranial magnetic stimulation and electroencephalogram neu- rofeedback methods have altered DMN connectivity among healthy controls and among individuals with major depressive disorder (Kluetsch et al., 2014;Liston et al., 2014;Ros et al., 2013; van der Werf, Sanz-Arigita, Menning, & van den Heuvel, 2010). Thus, future research should examine whether these and other interventions targeting social attainment and social competence are strengthening DMN connectivity. In addition, DMN connectivity metrics could be used as neuroimaging markers to monitor the success of treatments that target social functioning. For example, the observation of early changes in DMN resting-state connectivity could predict the effectiveness of cognitive remediation or social skills training targeting schizophrenia (Subramaniam & Vinogra- dov, 2013). Limitations The current study had several limitations. First, cross-sectional studies cannot infer causality. Thus, longitudinal research is needed to determine whether DMN connectivity is a stable neu- roimaging marker of social attainment and social competence. Second the sample size was relatively small. Thus, replication and furtherexploration with a larger sample is needed. Third, our schizo- phrenia sample consisted of chronically ill individuals. Therefore, future research on the association between DMN connectivity and social attainment and social competence among first episode patients could help address the generalizability of the findings. Lastly, we did not evaluate occupational functioning as an outcome, and future studies could examine the potential association between DMN con- nectivity and occupational functioning. Conclusions In conclusion, we observed that greater connectivity between structures of the DMN was associated with better social attainment and social competence in individuals with schizophrenia, despite the lack of group differences in average DMN connectivity. In addition, we found no evidence to support social cognition as a mediator for the association between DMN connectivity and social functioning in individuals with schizophrenia. Our findings sup- port the general hypothesis that DMN connectivity could poten- tially be a novel treatment target and a neuroimaging marker for monitoring treatments aimed to enhance social attainment and social competence in schizophrenia. References Abram, S. V., Karpouzian, T. M., Reilly, J. L., Derntl, B., Habel, U., & Smith, M. J. (2014). 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Annotated Bibliography, Introduction, and Summary Paragraph: Seeking the Truth
Altered Experiential, But Not Hypothetical, Delay Discounting in Schizophrenia William P. Horan Department of Veterans Affairs VISN 22 Mental Illness Research, Education, and Clinical Center, Los Angeles, California, and David Geffen School of Medicine Matthew W. Johnson Johns Hopkins University School of Medicine Michael F. Green Department of Veterans Affairs VISN 22 Mental Illness Research, Education, and Clinical Center, Los Angeles, California, and David Geffen School of Medicine Delay discounting (DD) is a future-oriented decision-making process that refers to whether one is willing to forego a smaller, sooner reward for the sake of a larger, later reward. It can be assessed using hypothetical tasks, which involve choices between hypothetical rewards of varying amounts over delay periods of days to years, or experiential tasks, which involve receiving actual rewards in real time over delay periods of seconds to minutes. Initial studies in schizophrenia have only used hypothetical tasks and have been mixed in finding either elevated or normal levels of DD. One hundred thirty-one outpatients with schizophrenia and 70 healthy controls completed hypothetical and experiential DD tasks involving monetary rewards, and the schizophrenia group was retested after 4 weeks. Although both groups showed qualitatively similar hyperbolic discounting functions on both tasks, they showed a quantitative DD difference. The schizophrenia showed higher DD than controls on the experiential task but normal DD on the hypothetical task. This pattern was not attributable to a range of potential confounds, including smoking status, substance use disorder status, or neurocognition. It was also not attributable to differ- ences in the test–retest reliability, which was good for both tasks. The schizophrenia group’s robust pattern of altered experiential but normal hypothetical task performance points to key factors that may contribute to impaired DD in this disorder. These may include increased valuation of small (but not large) monetary rewards, or a hypersensitivity to costs associated with waiting inactively for those rewards. General Scientific Summary Delay discounting (DD) refers to whether one is willing to forego a smaller, sooner reward for the sake of a larger, later reward. This study found that people with schizophrenia showed a greater preference for smaller, sooner rewards than healthy comparison participants on a DD task that involved making choices about actual monetary rewards provided in real time. In contrast, both groups showed comparable performance on a DD task that involved making choices about hypo- thetical rewards provided in the more distant future. These findings point to key alterations in how people with schizophrenia value different types of rewards. Keywords:cost-benefit decision making, prospection, psychosis, reward valuation, reliability Supplemental materials:http://dx.doi.org/10.1037/abn0000249.supp Adaptive reward processing is critical for successful goal attain- ment and functioning across most domains of life. Emerging research has begun to investigate different aspects of rewardprocessing that may be impaired in schizophrenia and contribute to the diminished motivation and goal-directed behavior that often accompany this disorder (Reddy, Horan, & Green, 2015). In ad- This article was published Online First February 6, 2017. William P. Horan, Department of Veterans Affairs VISN 22 Mental Illness Research, Education, and Clinical Center, Los Angeles, California, and University of California Los Angeles Semel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine; Matthew W. Johnson, Department of Psychiatry and Behavioral Sciences, Johns Hop- kins University School of Medicine; Michael F. Green, Department of Veterans Affairs VISN 22 Mental Illness Research, Education, and Clinical Center, Los Angeles, California, and University of California Los AngelesSemel Institute for Neuroscience and Human Behavior, David Geffen School of Medicine. This project was funded by a VA Merit Award (to William P. Horan). Support of Matthew W. Johnson’s time was provided by Grants R01DA035277 and R01DA035277. Correspondence concerning this article should be addressed to William P. Horan, Veterans Affairs Greater Los Angeles Healthcare System, MI- RECC 210A, Building 210, 11301 Wilshire Boulevard, Los Angeles, CA 90073. E-mail:[email protected] Journal of Abnormal PsychologyIn the public domain 2017, Vol. 126, No. 3, 301–311http://dx.doi.org/10.1037/abn0000249 301 dition to studies of reward anticipation and learning (Barch, Pa- gliaccio, & Luking, 2016;Strauss, Waltz, & Gold, 2014), inves- tigators have examined reward-related decision making processes, such as effort-based decision making (M. F. Green & Horan, 2015). Another decision-making process that has received in- creased attention is DD, which refers to whether one is willing to forego a smaller, sooner reward for the sake of a larger, later reward. DD is well suited for cross-species translational research, as a number of animal models of DD have been developed (Bickel, Johnson, Koffarnus, MacKillop, & Murphy, 2014;M. W. Johnson, 2012;Vanderveldt, Oliveira, & Green, 2016). Neurobiological studies in animals demonstrate central roles for the nucleus ac- cumbens core and the orbitofrontal cortex (Dalley, Mar, Econo- midou, & Robbins, 2008;Weafer, Mitchell, & de Wit, 2014)in DD. In line with these findings, human fMRI studies of DD implicate a limbic circuit (including the ventral striatum, ven- tromedial prefrontal cortex, and posterior cingulate cortex) showing activity during selection of smaller sooner rewards, prefrontal areas associated with cognitive control (principally dorsolateral prefrontal cortex) showing activity during selection of larger but later rewards, and relative activity across these regions associated with behavioral preference (Bickel, Pitcock, Yi, & Angtuaco, 2009;McClure, Ericson, Laibson, Loewen- stein, & Cohen, 2007;McClure, Laibson, Loewenstein, & Co- hen, 2004). The vast majority of human DD studies useconventional deci- sion making paradigmsin which subjects make a series of forced choices between smaller, sooner (e.g., $5 today) or a larger, later ($1,000 in 6 months) monetary rewards. In these paradigms, par- ticipants either receive no actual rewards, referred to as “hypothet- icalDD tasks,” or are paid out for only one or a few randomly selected trials, referred to as “potentially real reward DD tasks” (M. W. Johnson, 2012). Numerous studies show that, all “else” being equal, the more a reward is delayed the less subjective value it has. People typically display a monotonically decreasing func- tion such that reward value progressively diminishes as the delay to a reward grows longer. There are, however, substantial individ- ual differences in the degree to which reward values are discounted as delays grow longer. For example, individuals with relatively greater discounting show a steeper reward/delay curve, such that smaller/sooner rewards are more readily chosen than larger/later rewards. Such individuals are more susceptible to proximal re- wards and have been described as “temporally myopic” or “im- pulsive” (Hamilton et al., 2015;Kirby, Petry, & Bickel, 1999; Sellitto, Ciaramelli, & di Pellegrino, 2010). Consistent with this description, steeper discounting curves are associated with impulse control difficulties, including nicotine use, substance use disorders, and unhealthy behaviors (MacKillop et al., 2011;Reynolds, 2006b). In contrast to conventional paradigms, a more recent develop- ment in human research is the use ofexperiential DD paradigms (M. W. Johnson, 2012;Reynolds & Schiffbauer, 2004) in which subjects make a series of choices between smaller, sooner versus later, larger monetary rewards and actually receive these rewards in real time on a trial-by-trial basis. This format much more closely parallels DD tasks used in animal research (Jimura, Chushak, & Braver, 2013;P. S. Johnson, Herrmann, & Johnson, 2015). Like hypothetical tasks, experiential tasks show good sensitivity indifferentiating between users and nonusers of nicotine and other substances (M. W. Johnson, 2012;Reynolds, 2006a). Further, they may be more sensitive to treatment-related changes than hypothet- ical tasks (e.g.,Krishnan-Sarin et al., 2007;Reynolds, Richards, & de Wit, 2006), making them potentially attractive paradigms for endpoints in clinical trials. Despite the fact that both hypothetical and experiential tasks assess DD, they often show only small intercorrelations (M. W. Johnson, 2012;Melanko, Leraas, Collins, Fields, & Reynolds, 2009). The handful of DD studies in schizophrenia has only used hypothetical tasks. Findings have been mixed with several report- ing greater DD (i.e., steeper reward/delay curves) in schizophrenia than controls (Ahn et al., 2011;Heerey, Matveeva, & Gold, 2011; Heerey, Robinson, McMahon, & Gold, 2007 ;Weller et al., 2014), but others reporting normal DD (MacKillop & Tidey, 2011;Wing, Moss, Rabin, & George, 2012). Findings regarding associations between DD and certain clinical symptoms and neurocognition have also been mixed. Although it has been proposed that negative symptoms may partly reflect DD disturbances (Strauss et al., 2014), support has been inconsistent (Ahn et al., 2011;Heerey et al., 2011;Heerey et al., 2007;Weller et al., 2014;Wing et al., 2012). Similarly, inconsistent findings have been reported for associations with neurocognition (Ahn et al., 2011;Heerey et al., 2011;Heerey et al., 2007;MacKillop & Tidey, 2011;Weller et al., 2014). On the contrary, studies consistently indicate that DD is not significantly related to positive or mood-related symptoms or to antipsychotic medications. Overall, it is difficult to integrate find- ings across studies and most of the studies have been underpow- ered (5/6 studies included 42 participants with schizophrenia). Further, all studies have been cross-sectional, raising concerns that inconsistencies may reflect problems with the reliability of the paradigms used in these studies. The current study evaluated DD using a hypothetical task and, for the first time, an experiential task, in a relatively large sample of stabilized outpatients with schizophrenia. We had four primary goals. First, to address concerns about the validity of DD data in schizophrenia (Weller et al., 2014), we examined the orderliness (i.e., whether subjects generate data showing the value of delayed rewards to increase/decrease across delays in a systematic fashion) of the DD data. In addition, we selected paradigms that enabled us to map the shape of the discounting curves to determine if indi- viduals with schizophrenia show the typical hyperbolic shape (L. Green, Fristoe, & Myerson, 1994). Second, we compared discount rates between the schizophrenia and control groups. Although prior studies using hypothetical tasks are mixed and did not sup- port strong directional hypotheses, they led us to predict that the schizophrenia group would show higher DD rates (i.e., steeper reward/delay curves) than controls on both tasks. Third, we eval- uated whether DD was related to clinical symptoms and neuro- cognition. We were particularly interested in whether greater dis- counting would relate to higher negative symptoms. Further, we determined whether the use of nicotine and other substances was associated with discounting rates in light of some evidence for steeper discounting rates among individuals with schizophrenia who are smokers (MacKillop & Tidey, 2011;Wing et al., 2012). Fourth, in the schizophrenia group, we evaluated the 1-month test–retest stability of the two tasks. 302 HORAN, JOHNSON, AND GREEN Method Participants The sample included 131 individuals with schizophrenia and 70 demographically matched healthy controls. Individuals with schizophrenia were recruited from outpatient clinics at the Uni- versity of California, Los Angeles, the Veterans Affairs Greater Los Angeles Healthcare System, and from local clinics and board and care facilities. Selection criteria for included (a)Diagnostic and Statistical Manual of Mental Disorders(4th ed.;DSM–IV; American Psychiatric Association, 1994) diagnosis of schizophre- nia determined with the Structured Clinical Interview forDSM–IV (SCID-I/P;First, Spitzer, Gibbon, & Williams, 1997); (b) age 18 – 60 years; (c) no clinically significant neurological disease; (d) no history of serious head injury; (e) no evidence of current alcohol, cannabis, or other substance dependence disorder (in the past 6 months) or current substance abuse disorder (in past month); lifetime histories of these disorders were acceptable, and nicotine- related disorders were not formally assessed; (f) no history of mental retardation or developmental disability; and (g) clinically stable (i.e., no inpatient hospitalizations for 3 months prior to enrollment, no changes in antipsychotic medication type in the 4 weeks prior to enrollment). Diagnostic assessments were con- ducted by interviewers trained according to established procedures (Ventura, Liberman, Green, Shaner, & Mintz, 1998). Eighty-five percent of the participants with schizophrenia were taking a second-generation antipsychotic, 8% a first-generation antipsy- chotic, 3% were taking both, and 4% were not taking an antipsy- chotic. The mean chlorpromazine (CPZ) equivalent units (Andrea- sen, Pressler, Nopoulos, Miller, & Ho, 2010) was 375.95 (SD 29.57). Control participants were recruited through advertisements posted on websites. Selection criteria for healthy controls included (a) no psychiatric history involving schizophrenia spectrum disor- der (including avoidant, paranoid, schizotypal, or schizoid person- ality disorders), or other psychotic or recurrent mood Axis I disorder according to the SCID-I and SCID-II; (b) no family history of a psychotic disorder among first-degree relatives based on participant report and (c) no evidence of current or lifetime history of alcohol, cannabis, or other substance dependence disor- der, and no evidence of current (in the past month) substance abuse disorder (history of abuse disorder was permitted); nicotine-related disorders were not formally assessed. Criteria concerning age, neurological disease, and head trauma were the same as listed above for the schizophrenia group. Procedures Written informed consent was obtained prior to participation in accordance with approval from the local institutional review board. The DD task data was collected as part of a larger grant-funded project on reward processing and negative symptoms in schizo- phrenia (Horan et al., 2015;Reddy, Waltz, Green, Wynn, & Horan, 2016) but has not been published elsewhere. An aim of the project was to examine associations between reward processing measures and negative symptoms among individuals with schizophrenia, and a larger clinical than healthy comparison sample was included to evaluate these within-group relationships. The hypothetical DDtask was administered earlier in the assessment battery than the hypothetical DD task. The schizophrenia group was administered both DD tasks twice (baseline, 4-week retest); controls only re- ceived the tasks at baseline. Both groups completed a neurocog- nitive battery at baseline. Hypothetical Discounting In the $1,000 delay-discounting task (M. W. Johnson & Bickel, 2002), participants made a series of choices between receiving a $1,000 delayed hypothetical reward and an adjusting smaller im- mediate reward. The magnitude of the smaller immediate option was adjusted across trials according to a previously described algorithm (Richards, Zhang, Mitchell, & de Wit, 1999) until an indifference point was determined. Once an indifference point was determined, the larger later option was delayed further and the adjustment procedure was repeated with that new delay. Seven delays were assessed: 1 day, 1 week, 1 month, 6 months, 1 year, 5 years, and 25 years. Indifference points were expressed as a proportion of the larger later reward (possible range: 0 –1). Unlike the monetary choice questionnaire (Kirby et al., 1999) used in several prior studies of schizophrenia, the present task determined an indifference point for each delay (rather than determining rate based on several choices at various delays and the assumption of a presumed hyperbolic shape), which enabled us to assess the orderliness of indifference points across delays and the shape of the discounting function. Experiential Discounting The quick discounting operant task (QDOT;M. W. Johnson, 2012; programmed in ZBasic) used a coin dispenser for money reward delivery. A visual depiction of the task is shown in sup- plemental Figure 1. Before beginning the task, participants were instructed to sit at the desk with eyes open during any waiting periods in the task, and were forbidden from engaging in other behaviors such as reading. The task consisted of 20 discrete choices between a smaller immediate reward presented in a box on the left side of the screen (e.g., “get 40 cents right now”) and an 80¢ delay reward presented in a box on the right side of the screen (e.g., “wait 5 seconds to get 80 cents”). A response button that could register a mouse click was underneath each of the two boxes. At the top center of the screen was a box displaying total earnings on the task. On any trial, if the smaller sooner reward was selected with a single mouse click, the response options disappeared and a button appeared that stated “Click here to bank your amount.” Upon a single mouse click on this button, that amount was dis- pensed from the coin dispenser, and the total earnings box was updated. If the delayed 80¢ was selected, the response options disappeared and a number in the middle of screen counted down the number of seconds to wait (i.e., counter decreased by 1 every second). When the delay elapsed, a button appeared that required the participant to click to “bank” the 80 cents, at which point the coins (e.g., quarters, dimes, nickels) were delivered and the total earnings were updated. When money was delivered, participants removed the coins from the dispensing tray and dropped them into a glass jar. There were five blocks of four trials each, with each block associated with a different delay for the 80-cent reward. The delays 303 DELAY DISCOUNTING IN SCHIZOPHRENIA were 5, 10, 20, 40, and 80 s, and followed an increasing order across blocks. On the first trial of each block, the immediate reward size was 40¢ (i.e., 50% of 80¢). The smaller reward was then adjusted within the block using a “decreasing adjustment” algorithm, which has been used in previous human studies involving hypothetical rewards (Du, Green, & Myerson, 2002; Kowal, Yi, Erisman, & Bickel, 2007). Specifically, the smaller sooner reward was adjusted by 20, 10, and 5¢ on Trials 2, 3, and 4 of the block, respectively, in the direction that would move choice toward indifference (e.g., the smaller reward on the second trial of the block would be either 20¢ if the immediate 40¢ had been selected on the first trial, or 60¢ if the delayed 80¢ had been selected). The indifference point was defined as the value that would have been presented on a 5th trial (although there was not a 5th trial) had the algorithm continued (i.e., an adjustment of 2.5¢). Indifference points therefore varied by increments of 2.5¢, and were divided by 80¢ to be expressed as the proportion of the larger reinforcer. Indifference points were expressed as a proportion of the larger later reward (possible range: 0 –1). A waiting period was imposed after the final trial to prevent participants from choosing the smaller immediate reward to end the task or session sooner (which potentially confounds monetary reinforcement with the reinforcing or punishing qualities of the experimental context). Participants were told before beginning the task that the total duration of the task would be independent of the choices made during the task, although participants were not explicitly told about the waiting period at the end of the task that was responsible for ensuring approximately equal task duration. The waiting period was defined as 660 s minus the sum of all larger reward delays that the participant experienced throughout the task. Although this manipulation ensured that total pro- grammed waiting time did not substantially differ across partici- pants, differences in participant response latency nonetheless al- lowed for some variability in total task time. At the end of the task, participants exchanged whole dollar amounts of coins for paper currency. Clinical Characteristics Symptoms were evaluated by trained raters (Ventura, Green, Shaner, & Liberman, 1993) using four subscales from the Positive and Negative Syndrome Scale (PANSS;Kay, Opler, & Linden- mayer, 1989): Positive, Excitement, Disorganized, Depression/ Anxiety; and two subscales from the Clinical Assessment Inter- view for Negative Symptoms (CAINS): Motivation and Pleasure (MAP) and Expression. Neurocognition The MATRICS Consensus Cognitive Battery (MCCB;Nuech- terlein & Green, 2006) includes 10 tests to measure seven domains of cognition: speed of processing, attention/vigilance, working memory, verbal memory, visual memory, reasoning and problem solving, and social cognition. StandardizedTscores were com- puted for each of the seven domains, correcting for age and gender; an overall composite score was examined. Subjective Value of Money Index To obtain an index of subjective valuation of money (in the absence of any delay), participants were asked to: “rate how valuable (i.e., how important) the following amounts of money are to you” (Goldstein et al., 2007;Martin-Soelch et al., 2001). Par- ticipants rated 7 monetary amounts (US$ 10, 20, 50, 100, 200, 500, 1,000) on a scale from 0 (not at all valuable)to10(extremely valuable). The rating for $10 was subtracted from the rating for $1,000 to represent subjective sensitivity to gradations in monetary value; the lower the value, the less the sensitivity (i.e., more similar value ratings from highest and lowest amounts). Statistical Analyses Following preliminary analyses that compared demographic and other characteristics between the groups, the primary analyses were conducted in four stages. The first stage evaluated whether there were qualitative differences in the orderliness and shape of the DD data for both groups. Orderlinesswas assessed using established criteria (M. W. Johnson & Bickel, 2008): starting with the second delay value (1 day for the hypothetical task; 5 s for the QDOT), no indifference point value could exceed the immediately preceding indifference point value by more than 0.2. If the data for a discounting task for a particular individual violated this criterion, the discounting task was flagged as being relatively less orderly for that participant. Passing the criterion indicates a relatively orderly pattern of stable and/or decreasing value across all increases in delay. Shapeof the discounting curves was analyzed by using the Akaike information criterion (Akaike, 1974;Bozdogan, 1987; Burnham & Anderson, 2003) to determine whether the hyperbolic model expressed inEquation 1(Mazur, 1987) or an exponential model traditionally assumed in economics and expressed inEqua- tion 2, was more likely to be the correct model: IND 1 (1 kD), (1) IND e kD . (2) In each model, IND is the indifference point expressed as a proportion of the delayed reward amount,Dis the delay to receipt of the reward, andk, a free parameter, is the discounting rate. In Equation 2,erepresents the constant Euler’s number. The hyper- bolic model accounts for dynamic inconsistency or “irrationality” in the form of empirically observed preference reversals over time that are not accounted for by the exponential model (L. Green et al., 1994). The second stage evaluated quantitative differences between the groups on the DD tasks. The extent of discounting was determined using area under the curve (AUC;Myerson, Green., & Waru- sawitharana, 2001); AUC values can range from 0 –1, with greater values indicating less DD, or greater preference for larger-later rewards. The AUC data were normally distributed and parametric statistical tests were used. Group comparisons were done with a repeated-measures analysis of variance using task as the within- subject variable and group as the between-subjects variable. The third stage evaluated whether DD (i.e., AUC for each task) was associated with symptoms, neurocognition, antipsychotic dose equivalents, and subjective valuation of money using Pearson correlation coefficients. Schizophrenia subgroups based on nico- 304 HORAN, JOHNSON, AND GREEN tine, alcohol, cannabis, and other substance use status were com- pared with pairedttests. The fourth stage examined test–retest reliability within the schizophrenia group with Pearson correlations and paired-samples ttests between DD performance at Times 1 and 2. Results Demographic, Neurocognitive, and Clinical Data Descriptive data is presented inTable 1. The groups did not differ in age, sex, ethnicity, or parental education. As expected, the schizophrenia had significantly lower education and neurocogni- tive functioning than controls. The schizophrenia group showed mild to moderate levels of symptoms on the PANSS and CAINS. Regarding substance use, the schizophrenia group had a higher proportion of cigarette smokers than controls, 2(1, 201) 45.95, p .001. Since the exclusion criteria for other types of substances differed across groups, between-groups comparisons were not conducted. Finally, the schizophrenia showed significantly smaller scores than controls on the subjective value of money index; this reflected the schizophrenia group assigning relatively higher val-ues than controls for the small amount ($10) while the group ratings were virtually identical for the high amount ($1,000). Between-Groups Comparisons Discounting data orderliness and shape.The large majority of participant data sets for both tasks provided data that was orderly across all delay indifference points. For the QDOT, 85.5% of the schizophrenia group and 92.9% of controls passed the criterion. For the hypothetical discounting task, 84.0% of the schizophrenia group and 95.8% of controls passed the criterion. The proportion of participants with less-orderly data did not sig- nificantly differ between group for the QDOT, 2(1, 201) 2.35, p .17, but was higher in the schizophrenia than the control group for the hypothetical task, 2(1, 201) 6.13,p .01. Regarding the shape of discounting functions, Akaike informa- tion criterion analysis showed that in all four conditions examined (both Tasks both Groups), the hyperbolic model had 99.99% probability of being the correct model over the exponential model. Thus, the DD data were qualitatively similar across groups in terms of shape, demonstrating a hyperbolic discounting function on both tasks. Table 1 Demographic and Clinical Data for Schizophrenia (n 131) and Control (n 70) Groups Sample characteristics Schizophrenia Controls Group comparisons Age 48.7 (11.3) 48.0 (8.6)t(199) .45,p .66 Sex (% male) 68 59 2(1,201) 1.75,p .22 Education 13.1 (1.9) 14.6 (1.8)t(199) 5.30,p .001 Parental education 12.8 (3.0) 13.1 (3.2)t(199) .66,p .52 Ethnicity (% Hispanic) 19 21 2(1,201) .21,p .71 Race (%) 2(5,201) 1.75,p .35 American Indian/Alaskan 1 0 Asian 6 4 Hawaiian/Pacific Islander 1 6 Black/African American 32 33 White 55 51 More than one race 5 6 MCCB Overall Composite 33.1 (12.2) 45.8 (10.3)t(199) 7.32,p .001 Symptoms CAINS Motivation and Pleasure 16.0 (7.3) CAINS Expressive 5.0 (4.1) PANSS Positive 18.5 (7.7) PANSS Disorganized 12.5 (4.5) PANSS Excitement 5.4 (6.9) PANSS Depression/Anxiety 7.2 (2.8) Lifetime substance use disorder status a,b Lifetime alcohol abuse or dependence (%) 30 7 Lifetime cannabis abuse or dependence (%) 30 3 Lifetime other substance abuse or dependence (%) 37 2 Current cigarette smoker (%) 59 7 Subjective value of money index 3.3 (2.9) 4.1 (2.7)t(199) 2.01,p .04 Note.Standard deviations appear in parentheses. MCCB Matrics Consensus Neurocognitive Battery; CAINS Clinical Assessment Interview for Negative Symptoms; PANSS Positive and Negative Symptoms Scale. aExclusion criteria for alcohol, cannabis, and other use disorder differed across groups: control participants were excluded for any current or past substance dependence disorders whereas participants with schizophrenia were only excluded for current substance dependence disorders (past substance dependence disorders were allowed). Regarding substance abuse disorders, participants in both groups were excluded for current substance abuse disorders (past substance abuse disorders were allowed for both groups). bFor substance use disorders among patients, breakdowns (% of patient sample) for substance abuse vs. dependence are: alcohol abuse (7%), dependence (27%); cannabis abuse (17%), dependence (13%); other abuse (9%), dependence (28%). 305 DELAY DISCOUNTING IN SCHIZOPHRENIA Discounting rates.Figure 1shows median indifference points with best-fitting hyperbolic curves. The upper panel shows data from the QDOT and the lower panel shows data from the hypothetical discounting task (descriptive statistics are presented in supplemental Table 1). Comparisons using AUC indicated that there was a significant main effect for task,F(1, 199) 163.01,p .001, p2 .450, a nonsignificant main effect for group,F(1, 199) 1.26,p .26, p2 .006, and a significant Task Group interaction,F(1, 199) 4.95,p .03, p2 .024. Follow-up comparisons for the QDOT indicated that the schizophrenia group (M .49,SD .22) showed significantly greater discounting than controls (M .57,SD .22),t(199) 2.39,p .02,d .34. However, for the hypothetical DD task, the schizophrenia group (M .27,SD .25) did not significantly differ from controls (M .25,SD .23),t(199) .60,p .54,d .09. Correlations between the experiential and hypothetical DD tasks (using AUC) were rel- atively small in both the schizophrenia (r .24,p .005) and control (r .19,p .11) groups. The results of between-groups comparisons for the AUC were unchanged after excluding participants with less-orderly data for either task: a significant Task Group interaction,F(1, 157) 4.06,p .04, p2 .025, reflected greater discounting in the schizophrenia group than controls on the QDOT, t(157) 2.04,p .04,d .33, and a nonsignificant group difference on the hypothetical task,t(157) .46,p .65, d .07. Associations With Other Variables Within the Schizophrenia Group Results are shown inTable 2. Contrary to predictions, DD was not associated with negative symptoms; there were only nonsig- nificant trend level correlations between the QDOT and MAP negative symptoms, and between the hypothetical DD task expres- sive negative symptoms. There were no significant correlations with other types of symptoms or neurocognitive composite scores, or with CPZ equivalents or the monetary valuation index. A supplemental analysis also revealed no significant correlations with any of the MCCB subdomain scores (supplemental Table 2). Regarding substances, within the schizophrenia group, smokers (M .22,SD .19) showed significantly greater discounting than nonsmokers (M .33,SD .31) on the hypothetical DD task, t(129) 2.44,p .01,d .43, but these subgroups did not significantly differ on the QDOT,t(129) .24,p .83, d .04. Among controls, the small subgroup of smokers did not significantly differ from nonsmokers on either DD task (ts 1.67, ps .05). For the QDOT, when the schizophrenia versus control comparison was restricted only to nonsmokers, the schizophrenia group continued to show significantly greater DD than controls, t(118) 2.00,p .04,d .37. Similarly, for the hypothetical DD task, the groups still did not significantly differ,t(118) 1.43, p .16,d .26. For other substances, within the schizophrenia group, there were no significant differences among subgroups with versus without alcohol, cannabis, or other substance use disorders, (ts 1.13, ps .05; see supplemental Table 3). Test–Retest Reliability Within the Schizophrenia Group The DD functions for the schizophrenia group at the 1-month retest are displayed inFigure 2. The mean AUC for the schizo- phrenia group at retest for the QDOT (M .49,SD .24) and the hypothetical DD task (M .30,SD .30) were very similar to their baseline means reported above. The test–retest correlations were large and significant for the QDOT,r .70,p .001, and the hypothetical DD task,r .67,p .001. Further, mean differences across testing occasions were nonsignificant with small effect sizes for both the QDOT,t(121) .15,p .75,d .01, and the hypothetical DD task,t(121) 1.22,p .21,d .11. Discussion The schizophrenia and control groups had qualitatively similar DD functions, but quantitatively, the schizophrenia group showed a significantly greater DD than controls on the experiential task, and normal DD on the hypothetical task. The schizophrenia group’s performance on the DD tasks was generally not associated with a range of potential confounds. In addition, test–retest reli- ability was examined for the schizophrenia group and was good on both tasks. These findings provide the first evidence of impaired DD in schizophrenia using an experiential paradigm that parallels tasks used in animal research much more closely than conventional human paradigms. While not all aspects of reward processing are impaired in schizophrenia (e.g.,Horan, Foti, Hajcak, Wynn, & Green, 2012;Llerena, Wynn, Hajcak, Green, & Horan, 2016), Figure 1.Delay discounting functions for the schizophrenia and control groups for the (a) experiential and (b) hypothetical delay discounting tasks. 306 HORAN, JOHNSON, AND GREEN these findings suggest alterations do extend to a DD context that involves real rewards and real delay periods. As described below, the schizophrenia group’s pattern of altered experiential and nor- mal hypothetical DD likely reflects the fact the these tasks differed on several key dimensions, including reward type (real vs. unreal), reward magnitude (cents vs. hundreds of dollars), and delay time frame (minutes with actual waiting periods vs. decades with no waiting periods). Regarding qualitative analyses, the shape and orderliness of the DD data were generally similar across groups. In line with a prior report (Ahn et al., 2011), the schizophrenia group showed typical hyperbolic discounting functions across tasks. Further, a large majority ( 84%) demonstrated orderly data for both DD tasks. The proportion with less-orderly data on the hypothetical, though not the experiential, task was significantly larger than controls (similar to (Weller et al., 2014)). However, the main study findings were unchanged after removing the subset of participants from both groups with less-orderly data. In this first study of experiential DD in schizophrenia, the schizophrenia group showed quantitatively greater discounting than controls for actual monetary rewards delivered in real time. Diminished discounting on this and similar experiential tasks has been reported in other clinical populations, including cocaine dependence, attention deficit/hyperactivity disorder (ADHD), and smokers (M. W. Johnson, 2012;Reynolds, 2006a;Rosch & Mostofsky, 2016). Experiential tasks appear to tap into a rather different aspect of DD than hypothetical tasks. For example, the correlation between hypothetical and experiential DD tasks was relatively small in both groups. Several studies have also reported relatively low convergence between these tasks (M. W. Johnson, 2012;Krishnan-Sarin et al., 2007;Melanko et al., 2009) and one found altered experiential but not hypothetical discounting in ADHD (Rosch & Mostofsky, 2016). There were no quantitative group differences for the hypothet- ical DD task and this study included the largest schizophrenia and control samples examined to date. Our finding on this task isconsistent with two prior studies (including the second largest study (MacKillop & Tidey, 2011;Wing et al., 2012), but incon- sistent with four others that found greater hypothetical DD in schizophrenia (Ahn et al., 2011;Heerey et al., 2011;Heerey et al., 2007;Weller et al., 2014). The rather substantial methodological differences across the few DD studies make it difficult to pinpoint why three studies found normal DD but four did not. Since all prior studies included chronically ill samples, and all except one (Ahn et al., 2011) examined outpatients, the discrepancies across studies are not attributable to these participant characteristics. However, the tasks and data analytic approaches varied widely. For example, across the seven studies, the maximum delayed reward magnitude ranged from $86 to $1,000, and the maximum delayed reward duration ranged from a few months up to 50 years. Further re- search will want to systematically assess the impact of these parameters on hypothetical DD in schizophrenia. For example, it could be informative to examine how individuals with schizophre- nia perform on a hypothetical task with reward magnitudes and delay intervals that correspond to those in the experiential task. The current study considered a wide range of potentially con- founding factors on DD and found that their impact was small. The only relevant factor was smoking status. Smokers showed greater hypothetical DD than nonsmokers, which converges with prior findings from the general population (MacKillop et al., 2011) and schizophrenia (MacKillop & Tidey, 2011;Wing et al., 2012). However, we still found the pattern of altered experiential and Table 2 Correlations Between Delay Discounting Tasks and Symptoms, Neurocognition, Antipsychotic Dosage Equivalents, and Subjective Valuation of Money Within the Schizophrenia Group (n 131) VariableQDOT experiential delay discounting taskHypothetical delay discounting task CAINS Motivation and Pleasure .16 † .10 CAINS Experiential .12 .16† PANSS Positive symptoms .02 .10 PANSS Disorganization symptoms .01 .07 PANSS Excited symptoms .01 .01 PANSS Depression/Anxiety .03 .01 MCCB Overall Composite .05 .11 CPZ equivalents .10 .05 Subjective value of money .07 .11 Note.QDOT Quick Discounting Operant Task; CAINS Clinical Assessment Interview for Negative Symptoms; PANSS Positive and Negative Symptoms Scale; MCCB Matrics Consensus Neurocognitive Battery; CPZ chlorpromazine. †p .07. Figure 2.Delay discounting functions within the schizophrenia group at Times 1 and 2 for the (a) experiential and (b) hypothetical delay discount- ing tasks. 307 DELAY DISCOUNTING IN SCHIZOPHRENIA normal hypothetical DD in schizophrenia when we limited our analyses to nonsmokers. There were no significant associations between DD and other substances, symptoms, or antipsychotic medication dosages. Given the conceptual link between reward processing and negative symptoms (Reddy et al., 2015), it is somewhat puzzling that alterations in DD, particularly on the experiential task, did not significantly correlate with higher clini- cally rated negative symptoms. Although some studies have found that neuroscience-based reward and decision making tasks are associated with negative symptoms (e.g.,Barch, Treadway, & Schoen, 2014;Gold et al., 2013;Strauss et al., 2014) a number of studies by our group and others failed to detect such relationships (Green, Horan, Barch, & Gold, 2015;Horan et al., 2015). The reason for these discrepancies is not year clear. We have suggested that there are complex intervening steps on the causal pathway between the relatively discrete processes measured by decision- making tasks and the broad aspects of experience and behavior that are captured by clinical rating scales, which may substantially diminish direct correlations (M. F. Green et al., 2015). DD also showed no significant associations with global or particular do- mains (e.g., working memory) of neurocognition. This does not support prior suggestions that DD disturbances in schizophrenia reflect problems in the representation and maintenance of reward value (Heerey et al., 2007). The schizophrenia group’s pattern of altered experiential but normal hypothetical DD was also not attributable to differences in the test–retest reliabilities of the tasks. The test–retest correlations of approximately .70 for both tasks are similar to prior reports in healthy samples (Matusiewicz, Carter, Landes, & Yi, 2013;Smits, Stein, Johnson, Odum, & Madden, 2013;Weafer, Baggott, & de Wit, 2013) and the group means showed good stability across occasions. These findings, in conjunction with the lack of associ- ations with symptoms, suggest the DD tasks are measuring rela- tively stable traits among individuals with schizophrenia. These properties support the use of the experiential DD task as a perfor- mance measure of decision-making impairment in clinical trials for schizophrenia (M. F. Green et al., 2015). Its potential useful- ness for clinical trials is bolstered by evidence that it is sensitive to state-related changes, such as sleep deprivation, dopamine agonist administration in Parkinson’s disease, alcohol administration, and methylphenidate administration in ADHD (Reynolds et al., 2006; Reynolds & Schiffbauer, 2004;Shiels et al., 2009;Voon et al., 2010). One might have expected the schizophrenia group to show greater difficulties for hypothetical, distant rewards in light im- paired abstract thinking and longer-term prospection associated with this disorder (Eack & Keshavan, 2008;Fioravanti, Carlone, Vitale, Cinti, & Clare, 2005;Goodby & MacLeod, 2016). How- ever, the pattern found in the current study may relate to partici- pant and task characteristics. Regarding participant characteristics, since schizophrenia is associated with decreased socioeconomic status (Werner, Malaspina, & Rabinowitz, 2007) and many in the schizophrenia group were receiving limited fixed incomes, the schizophrenia group may have valued immediately available, real (albeit small) rewards more than controls. This possibility is bol- stered by our finding that the schizophrenia group assigned higher value ratings than controls for the lowest value ($10) but similar ratings for the highest value ($1,000) on the subjective valuation of money index, and with previous research showing greater dis-counting in lower income adults (L. Green, Myerson, Lichtman, Rosen, & Fry, 1996). Although individual differences in subjective valuation ratings did not significantly correlate with performance on the DD tasks, this factor remains a possible contributor (Re- imers, Maylor, Stewart, & Chater, 2009). Regarding task characteristics,Paglieryi (2013)postulated key differences between hypothetical tasks and experiential tasks, be- yond reward magnitude and delay length. Whereas hypothetical tasks merely involve postponing receipt of a reward with no constraints on how subjects spend their time during the intervening delay, the waiting period in experiential tasks comes with associ- ated costs. These include direct costs, such as boredom or discom- fort, and opportunity costs, such as valuable activities that the participant could be engaged in if not forced to wait. The relevance of such costs was demonstrated in a recent study that found DD rates increased as an orderly function of the constraints on what people could do (e.g., freely surf the net on the computer vs. sit at the computer and do nothing) during the delay interval on a hypothetical task (P. S. Johnson et al., 2015). Perhaps the individ- uals with schizophrenia in our study were hyperresponsive to the associated costs of doing nothing in the delay period and experi- enced alterations in their cost/benefit calculations. For example, schizophrenia is associated with an elevated tendency to experi- ence negative affect/arousal and boredom (Cohen & Minor, 2010; Gerritsen, Goldberg, & Eastwood, 2015), as well as altered decision-making on tasks that involve weighing the relative effort expenditure costs against monetary rewards (M. F. Green et al., 2015). Studies that manipulate the constraints, or obtain subjective ratings/psychophysiological measures, during delay intervals could shed light on the possible impact of these costs in DD in schizophrenia. Strengths of the current study include the large clinical sample, use of two different types of DD tasks, rigorous evaluation of data integrity, examination of many potential confounds, and evalua- tion of test–retest reliability. However, the study has some limita- tions and highlights areas in need of further study. First, partici- pants with schizophrenia were taking medications at clinical dosages. Although dosage equivalents were not related to DD, the impact of medications remains unclear. Second, the schizophrenia sample was chronically ill and it is unknown whether similar DD patterns would be evident in younger or high-risk samples. Third, the order of DD task administration was not counterbalanced, so we are unable to examine potential order effects. Fourth, although performance on the tasks was not related to subjective valuation of money, we did not obtain objective measures to evaluate whether income or socioeconomic status was associated with DD task performance. Fifth, this study only assessed monetary rewards and it is unknown whether similar patterns would be found for other primary (e.g., food) or secondary (e.g., social, health) reinforcers. 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(2014). Delay discounting and task performance consistency inpatients with schizophrenia.Psychiatry Research, 215,286 –293.http:// dx.doi.org/10.1016/j.psychres.2013.11.013 Werner, S., Malaspina, D., & Rabinowitz, J. (2007). Socioeconomic status at birth is associated with risk of schizophrenia: Population-based mul- tilevel study.Schizophrenia Bulletin, 33,1373–1378.http://dx.doi.org/ 10.1093/schbul/sbm032 Wing, V. C., Moss, T. G., Rabin, R. A., & George, T. P. (2012). Effects of cigarette smoking status on delay discounting in schizophrenia and healthy controls.Addictive Behaviors, 37,67–72.http://dx.doi.org/10 .1016/j.addbeh.2011.08.012 Received June 13, 2016 Revision received November 8, 2016 Accepted November 16, 2016 Members of Underrepresented Groups: Reviewers for Journal Manuscripts Wanted If you are interested in reviewing manuscripts for APA journals, the APA Publications and Communications Board would like to invite your participation. Manuscript reviewers are vital to the publications process. As a reviewer, you would gain valuable experience in publishing. The P&C Board is particularly interested in encouraging members of underrepresented groups to participate more in this process. If you are interested in reviewing manuscripts, please write APA Journals at [email protected] Please note the following important points: •To be selected as a reviewer, you must have published articles in peer-reviewed journals. The experience of publishing provides a reviewer with the basis for preparing a thorough, objective review. •To be selected, it is critical to be a regular reader of the five to six empirical journals that are most central to the area or journal for which you would like to review. Current knowledge of recently published research provides a reviewer with the knowledge base to evaluate a new submission within the context of existing research. •To select the appropriate reviewers for each manuscript, the editor needs detailed information. Please include with your letter your vita. In the letter, please identify which APA journal(s) you are interested in, and describe your area of expertise. Be as specific as possible. For example, “social psychology” is not sufficient—you would need to specify “social cognition” or “attitude change” as well. •Reviewing a manuscript takes time (1– 4 hours per manuscript reviewed). If you are selected to review a manuscript, be prepared to invest the necessary time to evaluate the manuscript thoroughly. APA now has an online video course that provides guidance in reviewing manuscripts. To learn more about the course and to access the video, visit http://www.apa.org/pubs/authors/review- manuscript-ce-video.aspx. 311 DELAY DISCOUNTING IN SCHIZOPHRENIA
Annotated Bibliography, Introduction, and Summary Paragraph: Seeking the Truth
Is Longer Treatment Better? A Comparison Study of 3 Versus 6 Months Cognitive Remediation in Schizophrenia Mariachiara Buonocore IRCCS San Raffaele Scientific Institute Marta Bosia Universita `Vita-Salute San Raffaele and IRCCS San Raffaele Scientific Institute Margherita Bechi IRCCS San Raffaele Scientific Institute Marco Spangaro Universita `Vita-Salute San Raffaele and IRCCS San Raffaele Scientific Institute Silvia Cavedoni Universita `Vita-Salute San Raffaele Federica Cocchi, Carmelo Guglielmino, Laura Bianchi, and Antonella Rita Mastromatteo IRCCS San Raffaele Scientific Institute Roberto Cavallaro IRCCS San Raffaele Scientific Institute and Universita ` Vita-Salute San Raffaele Objective:Despite its extensive use for treating cognitive deficits in schizophrenia, computer-assisted cognitive remediation (CACR) currently lacks a standardized protocol. Duration is an important feature to be defined, as it may contribute to heterogeneous outcome. This study compares 2 treatment durations, 3 versus 6 months, to analyze their effects on both cognition and daily functioning.Method:Fifty-seven outpatients with schizophrenia received 3 months of CACR and 41 received 6 months of CACR. All patients were assessed at baseline and after 3 and 6 months with the Brief Assessment for Cognition in Schizophrenia and with the Quality of Life Scale (QLS).Results:Repeated measures ANOVA showed significant improvements in all cognitive domains after 3 months. A significant effect of treatment duration was observed only for executive functions, with significantly higher scores among patients treated for 6 months. Significant improvements in QLS were also observed after 6 months in both groups, with a significant time by treatment interaction for QLS Total Score.Conclusions:Results confirm the efficacy of 3-months CACR in terms of both cognitive and functional improvements, suggesting that an extended intervention may lead to further benefits in executive functions and daily functioning. General Scientific Summary Computer Assisted Cognitive Remediation (CACR) improves cognition in patients with schizophre- nia. It is not clear how long the treatment should be to see significant effects on functioning. Our study compares 2 treatments duration: 3- versus 6-months CACR. Results shows that 3-months CACR seems to be adequate to improve cognitive deficits in patients with schizophrenia, despite executive functions and quality of life might need a longer treatment to improve. Keywords:daily functioning, executive functioning, psychosis, quality of life, rehabilitation Neurocognitive impairment represents a core feature of schizophrenia and appears to be strongly correlated with global functional outcome (Bowie et al., 2006). Current treatments ofcognitive dysfunction involve both pharmacological and non- pharmacological interventions. Among the latter, research has increasingly studied the effects of computer-assisted cognitive This article was published Online First February 2, 2017. Mariachiara Buonocore, Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute; Marta Bosia, Universita `Vita-Salute San Raffaele and School of Medicine, IRCCS San Raffaele Scientific Institute; Margherita Bechi, Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute; Marco Spangaro, Universita `Vita-Salute San Raffaele and School of Medicine, IRCCS San Raffaele Scientific Institute; Silvia Cavedoni, School of Psychology, Universita `Vita-Salute San Raffaele; Federica Cocchi, CarmeloGuglielmino, Laura Bianchi, and Antonella Rita Mastromatteo, Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute; Roberto Cavallaro, Department of Clinical Neurosciences, IRCCS San Raffaele Sci- entific Institute, and Department of School of Medicine, Universita `Vita-Salute San Raffaele. Correspondence concerning this article should be addressed to Marta Bosia, Department of Clinical Neurosciences, IRCCS San Raffaele Scientific Institute, Via Stamira d’Ancona 20, Milan, Italy. E-mail:[email protected] This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. Neuropsychology© 2017 American Psychological Association 2017, Vol. 31, No. 4, 467– 4730894-4105/17/$12.00http://dx.doi.org/10.1037/neu0000347 467 remediation (CACR), a rehabilitation treatment consisting of computer based cognitive exercises, plus individual or group instructions to improve cognitive deficits, with the goal of durability and generalization to everyday life abilities (Mueller et al., 2015). Several studies showed the effectiveness of this intervention (Cavallaro et al., 2009;Gomar et al., 2015; McGurk et al., 2007;Wykes et al., 2002), although some negative results have also been reported (Dickinson et al., 2010; Gomar et al., 2015). Despite the amount of literature, research methods involving CACR do not follow a standardized proto- col, leading to high heterogeneity and thus difficulties in draw- ing conclusions. Differences between studies might lie in both methodological aspects, such as the use of restorative or com- pensatory strategies, drill-and-practice rather than drill-and- strategy exercises, specific learning strategies, such as fixed versus individually tailored exercises, as well as in patients clinical status (Barlati et al., 2013;Penades et al., 2006; Rauchensteiner et al., 2011;Sartory et al., 2005). Moreover, frequency and duration of treatment are important features that needs to be taken into account, as there is still high heteroge- neity: from 10 –12 sessions delivered over four weeks (Rauchensteiner et al., 2011), to 100 hours, three sessions per week (Kurtz et al., 2009) until up to more than 120 hours over two years (Eack et al., 2013). Although these aspects have been pointed out since early studies, they are still poorly addressed. To our knowledge, only one study has explicitly questioned how intense CACR should be to be effective (Choi & Medalia, 2005), showing an association between higher treatment inten- sity and greater cognitive improvement, specifically on sus- tained attention. Duration of treatment, in terms of number of weeks, has been analyzed as possible predictor of cognitive improvement in a few meta-analyses (Radhakrishnan et al., 2016;Wykes et al., 2002), with negative results. However, a head-to-head comparison of interventions with different dura- tion is lacking. Moreover, questions about CACR administra- tion are important in light of the relationship between global function and cognitive improvement after CACR. It is not yet clear how extensive the cognitive improvement has to be to support an overall functional enhancement. A stable improve- ment of functional outcome may require a lasting effect of cognitive improvements. We could hypothesize that a longer training may be better to the “restoration”, especially of more complex cognitive functions, such as executive functions. In addition, if the long-term goal is to improve daily functioning, then it could be useful to consider whether the patient’s quality of life may benefit from a longer training and whether there is a relationship between the cognitive improvement and the func- tional outcome. Despite these considerations, CACR duration has so far rarely been in the limelight and there are still open questions: how long should the training last in order to be effective? How long has to be to show an impact on daily functioning? Our aim is to evaluate whether a longer training may lead to greater improvement in both cognition and daily functioning. To pursue this goal, we compared two CACR protocols with different duration: 36 sessions of computer aided training that lasted three months versus 72 sessions that lasted six months. Method Participants One hundred twenty-nine Caucasian outpatients diagnosed with schizophrenia according to theDiagnostic and Statistical Manual of Mental Disorders, fourth edition, text revision (DSM–IV–TR; American Psychiatric Association, 2000) were recruited at the Psychiatric Rehabilitation Unit of IRCCS San Raffaele Hospital, Milan, Italy. After a complete description of the study, informed consent to participation was obtained. The protocol followed the principles of the Declaration of Helsinki and was approved by the local Ethical Committee. To be included, patients had to satisfyDSM–IV–TRdiagnostic criteria for schizophrenia and the following conditions: (a) Treat- ment with a stable dose of the same antipsychotic therapy for at least three months, with a good response (30% or more reduction of Positive and Negative Syndrome Scale-PANSS Total score); (b) No evidence of substance dependence or abuse, comorbid diagno- ses on Axis I or II, epilepsy, or any other major neurological illness or perinatal trauma, or mental retardation; and (c) All patients remained on the same antipsychotic medication throughout the course of the study. Patients needing drug or dose changes during the study were excluded. Study Design In this study, we compared data from two different protocols performed at different time points through a sequential approach. The first was a randomized single blind trial to analyze the effect of 3-months CACR, as compared to placebo, proving the effec- tiveness of the 3-months intervention (Cavallaro et al., 2009). Following these results, we designed an open label trial to evalu- ated the effectiveness of 6-months CACR, recruiting a sample of patients matched for age and education. Within the time-frame of this second study, all eligible patients meeting inclusion criteria were assigned to the 6-months CACR protocol. In particular, all patients followed, for at least six months, a Standard Rehabilitation Treatment (SRT). Then, added to SRT was one of the following protocols: (a) 3-months CACR: 36 computerized sessions that lasted for 3 months. After CACR, this group continued standard rehabilitation (SRT) for 3 months; and (b) 6-months CACR: 72 computerized sessions that lasted for 6 months. CACR sessions had a frequency of three times a week, each one lasted about 45 min. All patients were assessed at baseline (T0), before starting CACR, within one week after the end of 36 CACR sessions (T1), and within one week after the end of further 36 CACR sessions or three months of SRT alone (T2). All interventions were conducted by trained psychologists. Assessments Patients were assessed for psychopathology, cognitive perfor- mance and daily functioning before and after CACR. At baseline, IQ was also evaluated and basic information, such as age, sex, education, duration of illness and medication, were collected. Psychopathological assessment.Psychopathology was as- sessed by means of the Positive and Negative Syndrome Scale This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 468 BUONOCORE ET AL. (PANSS;Kay et al., 1987), administered by psychiatrists trained on PANSS rating and calibration. Neuropsychological assessment.Cognitive performance was assessed with the Brief Assessment of Cognition in Schizophrenia (BACS), a broad neuropsychological battery evaluating core cog- nitive domains that are typically impaired in schizophrenia (Keefe et al., 2004). It consists of the following tests: verbal memory (words recall), working memory (digit sequencing), token motor task (psychomotor speed and coordination), speed of processing (symbol coding), verbal fluency (semantic and letter production), and planning (Tower of London). Analyses were performed ont scores for each BACS subtest and on meantscore, as a measure of global cognition. BACStscores were calculated from Italian normative data (Anselmetti et al., 2008). Intellectual functioning was assessed by means of the Wechsler Adult Intelligence Scale—Revised (WAIS–R), Italian version (Wechsler, 1998). The scales were administered by trained psychologists. Daily functioning outcome assessment.Daily functioning was assessed with the Quality of Life Scale (QLS;Heinrichs et al., 1984), a semistructured interview of 21 items evaluating three different areas of social functioning: (a) interpersonal relationships (items 1– 8), assessing the ability of the patient to establish and maintain social relationships; (b) instrumental role (items 9 –12), evaluating the ability to obtain and maintain a job, to study and to collaborate in everyday housework; and (c) self-directedness (items 13–21), assessing planning abilities, personal autonomy, affective and cognitive functioning, and motivation level. Information on daily functioning was obtained by the patient and reviewed with an informant (typically the patient’s mother or another first-degree relative). The scale was administered by trained rehabilitation therapists. Treatments Standard rehabilitation program (SRT).The rehabilitation program focused on the main community goals of social abilities, work, and autonomy. The SRT included noncognitive subpro- grams of IPT (Verbal Communication, Social Skill Training and Problem Solving;Brenner et al., 1994), social skills training pro- grams for residential, vocational, and recreational functioning (Roder et al., 2002), and psychoeducation (Bechdolf et al., 2004; Rund et al., 1994). Computer-aided training.The computer-aided training em- ployed the Cogpack Software (Marker, 1987-2007). This program includes domain-specific neurocognitive exercises, aimed at train- ing specific cognitive areas known to be impaired in schizophre- nia: verbal memory, verbal fluency, psychomotor speed and coor- dination, executive function, working memory, and attention (for more details seeCavallaro et al., 2009). Analysis Following verification of normal distribution of the assessed variables through Kolmogorov–Smirnov test, analysis of variance (ANOVA) and chi square test (for dichotomic variables) were performed on demographic, clinical, daily functioning, neuropsy- chological, and IQ variables to evaluate differences between groups.The effect of CACR treatment on neuropsychological perfor- mance and daily functioning was evaluated by means of repeated measures ANOVAs. Pre-to-post treatment changes in cognition, assessed by BACS, were analyzed with repeated measures ANOVA (3 3,p .05, two-tailed) entering BACS subtests and totaltscores as dependent variables, time as fixed factor (with the three levels T0, T1 and T2), and treatment group as independent variable. Fisher LSD post hoc test followed. Pre-to-post treatment changes in daily functioning, assessed by QLS, were analyzed with repeated measures ANOVA (3 3,p .05, two-tailed) entering QLS subscales and total scores as depen- dent variables, time as fixed factor and treatment group as inde- pendent variable. Fisher LSD post hoc test followed. Bartlett test was also performed on significant results, to verify homogeneity of variance. Pre-to-post treatment changes in both cognition and daily func- tioning were also assessed in term of Cohen’sdeffect-sizes for each treatment group. The STATISTICA Software for Windows, version 8 (StatSoft Inc., Tulsa, OK) was used to perform the statistical analyses. Results A group of 57 patients received 3-months CACR, whereas a group of 41 patients received 6-months CACR intervention. Fif- teen patients (seven in the first group and five in the second one) dropped out. Reasons for dropping out were worsening of symp- toms or change of residence. The dropouts were excluded from analyses. All patients were on antipsychotic monotherapy and treatment was distributed as follow: 29% Risperidone, 13% Hal- operidol, 32% Clozapine, 11% Olanzapine, 7% Aripiprazole, 7% Paliperidone, 1% Fluphenazine. The mean dose, derived from chlorpromazine equivalents (CPZ-eq) using the conversion pro- posed by Davis (Davis & Chen, 2004), was 265.67 182.Table 1shows demographic and clinical characteristics of the sample at baseline. There were no statistically significant differences be- tween groups at baseline observation for any of the variables studied, except for Psychomotor coordination (F 4.63,p .033), with higher scores in patients treated with 3-months CACR. Posttest Evaluations Treatment effects on cognition.Analyses showed a signifi- cant time effect for all the functions evaluated, as well as a significant time by treatment group interaction only for executive functions in particular planning abilities, evaluated by means of the Tower of London (F 2.65;df 2;p .03, seeTable 2for details). Specifically, within the 3-months treatment group, post hoc analysis showed a significant improvement from T0 to T1 in the following cognitive functions: verbal memory (p .0001), work- ing memory (p .04), verbal fluency (p .001), processing speed (p .0001), and planning (p .05). Further significant improve- ments in cognitive functions were not observed from T1 to T2. For the 6-months treatment group, post hoc analysis also showed a significant improvement at three months (T0 –T1) for the following functions: verbal memory (p .0001), working memory (p .0001), verbal fluency (p .04), processing speed (p .03), and planning (p .0001). This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 469 DURATION OF COGNITIVE REMEDIATION IN SCHIZOPHRENIA Further significant improvements in cognitive functions were not observed from T1 to T2. Finally, post hoc analysis showed a significant difference be- tween treatment groups in executive performance at T2, with higher scores among patients treated for 6-months CACR com- pared to the group treated with 3-months CACR (p .03). Treatment effects on daily functioning.Analysis showed a significant time effect for all the functions evaluated, as well as a significant time by treatment group interaction for QLS Total Score (F 6.89;df 2;p .0001; seeTable 2for details). Within the 3-months treatment group, post hoc analysis did not show a significant improvement at three months (from T0 to T1) for any QLS subscale, whereas significant improvements at six months (from T0 to T2) were observed for QLS Relationships (p .002); QLS Work (p .04); QLS Self directedness (p .0001); QLS Total score (p .0001). Within the six months treatment group as well, post hoc analysis did not show any significant improvement at three months, while significant improvements at six months were observed QLS Re- lationships (p .0001), QLS Work (p .0001), QLS Self direct- edness (p .0001), and QLS Total score (p .0001). Post hoc analysis showed no significant differences between treatment groups. Effect Sizes of Improvement Within the 3-months treatment group the following effect-sizes of improvement were observed: Verbal memory (0.48), Working memory (0.14), Psychomotor coordination (0.33), Verbal fluency (0.30), Processing speed (0.31), Planning (0.22), BACS Composite Score (0.42), QLS relationships (0.23), QLS work (0.20), QLS self-directedness (0.25) and QLS Total Score (0.30). Within the 6-months treatment group the following effect-sizes of improvement were observed: Verbal memory (0.57), Working memory (0.28), Psychomotor coordination (0.16), Verbal fluency (0.27), Processing speed (0.23), Planning (0.61), BACS CompositeScore (0.54), QLS relationships (0.45), QLS work (0.52), QLS self-directedness (0.58), and QLS Total Score (0.71). Discussion CACR has been extensively employed as a rehabilitation treat- ment and vast amount of research has been conducted since its first applications (Cella et al., 2015). However, to date CACR admin- istration does not follow a standardized protocol, which, in addi- tion to treatment’s heterogeneous characteristics, might explain uneven results in research (Barlati et al., 2013). This issue high- lights even more the necessity of better defining treatment’s fea- tures to support more standardized protocols for this intervention. Surprisingly, over the past 10 years only one study has explicitly questioned which characteristics could have made CACR the most effective treatment, in terms of intensity (Choi & Medalia, 2005), whereas duration has been explored only quantitatively through meta-analyses (McGurk et al., 2007). The present study focuses on CACR duration, investigating whether longer cycles of treatment could further extend the ben- efits of CACR with respect to both cognition and daily function- ing. For this purpose, we compared two groups attending the same CACR rehabilitation, respectively for three and six months, eval- uating both cognitive domains and daily functioning at baseline, three months and six months. In analyzing the effect of the two treatment groups (3 vs. 6 months) on cognition, we found a significant improvement in both groups and a significant between-groups difference only for exec- utive functions, in the subcomponent of planning, as evaluated by the Tower of London. Supporting previous literature (Bosia et al., 2007,2014;Cavallaro et al., 2009;Poletti et al., 2010), we ob- served a significant global change in cognition at three months for all patients, thus confirming that 3-months CACR is adequate to strengthen impaired functions. This result is also consistent with previous meta-analyses showing an adequate improvement in cog- nitive functions already after a brief period of CACR, even though Table 1 Sociodemographic and Clinical Data at Baseline of Patients in the 3 Months and 6 Months Treatment Variable3-months treatment (mean SD)6-months treatment (mean SD)ANOVA/ 2 F/ 2 p N57 41 Males/Females 37/20 21/20 Age 33.82 9.89 34.21 9.56 .04 .95 Education (yrs) 11.75 2.30 11.55 2.89 .53 .59 Age of onset 22.98 5.39 24.58 7.06 2.27 .10 Illness duration (yrs) 10.84 5.42 10.20 8.30 .63 .59 Antipsychotic dose (CPZ Eq) 315.15 27.88 222.81 39.42 3.66 .06 PANSS – Total score 50.00 8.84 54.53 7.67 2.76 .06 PANSS – Positive scale 14.78 4.63 15.73 5.13 4.22 .06 PANSS – Negative 20.24 6.33 21.80 5.24 .56 .56 PANSS – General 34.30 8.89 38.60 6.39 2.26 .10 IQ Verbal 91.43 14.35 89.40 10.46 1.11 .33 IQ Performance 84.15 13.52 82.17 12.93 .27 .75 IQ Total 86.95 12.74 85.33 9.25 .74 .47 Note. CPZ Eq chlorpromazine equivalents; PANSS Positive and Negative Syndrome Scale. Degree of freedom 1 for analyses. This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 470 BUONOCORE ET AL. the duration is often uneven among the studies (McGurk et al., 2007). When addressing further improvement from three to six months, the analysis showed a significant change only for execu- tive functions in the group of patients treated with 6-months CACR. These patients reached significantly higher scores in ex- ecutive functions at six months than the group treated with 3-months, whereas no significant differences were observed in other cognitive domains at six months. Beyond further supporting global efficacy of 3-months CACR on cognition, these findings suggest that most cognitive functions, such as verbal memory, working memory, psychomotor coordina- tion, and fluency, do not obtain additional benefit from a training lengthening. We can hypothesize that these skills reach a plateau with a 36-session treatment, and then maintain themselves stable over time, with no significant progressions (Cavallaro et al., 2009; Poletti et al., 2010). Interestingly, the only cognitive function that seems to benefit from a treatment extension is planning ability, as patients receiving the 6-months CACR showed significantly higher scores after 72 CACR sessions, compared to those attending only 36 CACR sessions. Planning, a subcomponent of executive functions, is a complex ability that requires evaluation of a se- quence of thoughts and actions to achieve a goal and thus selection and application of the best strategy. Planning can be defined as a higher cognitive function, relying on a broad neural network that involves also other specific and lower level cognitive functions (i.e., memory and attention), directly trained through CACR. In this view, our result suggests that higher-level functions may need a longer training to be “restructured” and that this further poten- tiation may be boosted by the improvement in lower level func- tions, obtained with 3-months CACR. These findings are particu- larly encouraging: they represent empirical evidence of how a longer treatment can lead to a higher benefit in an important aspect of cognition, such as planning. Addressing daily functioning, as evaluated by means of QLS, the analysis showed a significant improvement over time for all subscales and Total score, with a significant Time Treatment group interaction for Total Score. In details, we did not observe any significant change in QLS from baseline to T1 (three months), although we detected significant improvements in all QLS sub- scales and Total score at six months among all patients. This result is in line with previous work (Bosia et al., 2007) suggesting that a 3-months treatment is not long enough to see generalization of results on patients’ global functioning, as cognitive exercise may restore the patient’s cognitive scaffold, intended as the target on which classical rehabilitation acts to rebuild skills. Results also showed a significant effect of treatment group on QLS Total Score improvement, with patients treated with 6 months CACR reaching higher scores, although not significantly different from the group treated with 3 months CACR. In sum, results confirm the efficacy of 3-months CACR in terms of both cognitive and functional improvements, as well as evi- dencing that higher functions, such as planning abilities, may further benefit from an extended intervention with possible impact on daily functioning. However, there are some limitations to this study that should be pointed out. First of all, this is not a randomized study, which would have been the best design to compare treatment effects. Second, a control computerized condition was not included for patients treated with 3-months CACR, thus we cannot exclude that Table 2 t Score of Neuropsychological and Functional Measures of Patients at T0, T1, and T2, Separated According to Treatment Groups Measure3-months CACR 6-months CACR T G T T0 (mean SD) T1 (mean SD) T2 (mean SD) T0 (mean SD) T1 (mean SD) T2 (mean SD)FpFp BACS Verbal memory 36.01 10.99 40.83 11.45 42.20 12.65 34.78 13.17 40.81 11.43 41.99 11.75 23.17 .0001 1.59 .17 Working memory 35.89 10.09 38.58 10.89 37.94 13.31 33.93 8.80 39.36 9.81 36.52 10.03 5.27 .0001 1.35 .24 Psychomotor coordination 33.76 14.39 35.37 1.65 38.65 12.81 26.57 12.52 27.99 11.11 28.84 14.53 .80 .44 1.64 .16 Verbal fluency 36.84 8.53 39.78 9.5 39.54 10.67 34.68 9.38 36.90 8.0 37.19 9.76 8.04 .0001 .25 .90 Processing speed 29.52 12.76 33.54 11.47 33.03 1.72 28.40 1.71 31.29 10.46 31.17 12.94 7.75 .0001 .52 .72 Planning 34.76 14.31 37.95 12.63 36.91 14.5 33.76 11.35 41.26 12.41 42.97 10.92 7.30 .0001 2.65 .03 Composite Score 34.03 7.54 37.12 7.79 37.99 5.56 32.57 7.25 36.18 6.3 36.54 7.37 31.95 .0001 .50 .60 QLS Relationships 19.25 1.13 19.97 1.15 21.27 1.15 20.53 1.31 23.33 1.33 24.10 1.33 9.53 .0001 2.66 .07 Work 4.00 .85 4.20 .88 5.30 1.00 2.93 .99 4.06 1.02 5.66 1.15 6.55 .0001 1.48 .20 Self-directedness 25.42 1.22 26.05 1.25 27.90 1.22 27.33 1.41 29.46 1.44 31.70 1.41 9.99 .0001 3.20 .05 Total score 48.67 2.83 50.22 2.53 54.47 2.67 50.80 2.83 56.86 2.92 61.46 3.08 15.82 .0001 6.89 .0001 Note. CACR computer-assisted cognitive remediation; BACS Brief Assessment of Cognition in Schizophrenia; QLS Quality of Life Scale. ANOVA effects for time (T) and Time Treatment Group Interaction (G T) are also reported. Degree of freedom 2 for all analyses. This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 471 DURATION OF COGNITIVE REMEDIATION IN SCHIZOPHRENIA the differences between the two treatment groups could depend on aspecific effect of “computer exercising.” Third, the QLS scale used for the evaluation of daily functioning was originally created to assess deficit symptoms (Heinrichs et al., 1984), although it could be useful in future studies to use a more performance based scale like the UCSD Performance based Skills assessment (Patter- son et al., 2001). Moreover, it is worth noting that examining individual subtests of the BACS, although allowing a more in- depth evaluation of specific cognitive functions, may hamper the statistical power of results. This is the first study specifically addressing CACR duration with respect to both cognition and daily functioning. Defining the appropriate CACR duration is of great interest, as it would help the clinician to have a more precise idea of the resources to be invested in each patient to optimize outcome. Although further research is needed to clarify this important issue, including follow-up studies evaluating the maintenance of results over time, these results suggest that 3-months CACR could be proposed as a standard protocol, whereas the 6-months CACR is worth extending to a selected group of patients with more prominent deficit in executive functions persisting after the 3-months CACR. References American Psychiatric Association. 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Annotated Bibliography, Introduction, and Summary Paragraph: Seeking the Truth
Evidence of Systematic Attenuation in the Measurement of Cognitive Deficits in Schizophrenia Michael L. Thomas University of California San Diego and VA San Diego Healthcare System, San Diego, California Virginie M. Patt San Diego State University/University of California San Diego Andrew Bismark University of California San Diego and VA San Diego Healthcare System, San Diego, California Joyce Sprock University of California San Diego Melissa Tarasenko, Gregory A. Light, and Gregory G. Brown University of California San Diego and VA San Diego Healthcare System, San Diego, California Cognitive tasks that are too hard or too easy produce imprecise measurements of ability, which, in turn, attenuates group differences and can lead to inaccurate conclusions in clinical research. We aimed to illustrate this problem using a popular experimental measure of working memory—the N-back task—and to suggest corrective strategies for measuring working memory and other cognitive deficits in schizo- phrenia. Samples of undergraduates (n 42), community controls (n 25), outpatients with schizo- phrenia (n 33), and inpatients with schizophrenia (n 17) completed the N-back. Predictors of task difficulty—including load, number of word syllables, and presentation time—were experimentally manipulated. Using a methodology that combined techniques from signal detection theory and item response theory, we examined predictors of difficulty and precision on the N-back task. Load and item type were the 2 strongest predictors of difficulty. Measurement precision was associated with ability, and ability varied by group; as a result, patients were measured more precisely than controls. Although difficulty was well matched to the ability levels of impaired examinees, most task conditions were too easy for nonimpaired participants. In a simulation study, N-back tasks primarily consisting of 1- and 2-back load conditions were unreliable, and attenuated effect size (Cohen’sd) by as much as 50%. The results suggest that N-back tasks, as commonly designed, may underestimate patients’ cognitive deficits as a result of nonoptimized measurement properties. Overall, this cautionary study provides a template for identifying and correcting measurement problems in clinical studies of abnormal cognition. General Scientific Summary Patients’ cognitive deficits can appear smaller than they truly are as a result of measurement artifacts. This study suggests that a measure commonly used to assess working memory deficits in schizo- phrenia can produce unreliable and attenuated estimates of ability because most items are too easy. The methodology presented is general, and can be used by investigators to determine whether cognitive tasks used in research are appropriately calibrated for the samples under investigation. Keywords:effect size, N-back, reliability, schizophrenia, working memory deficits Supplemental materials:http://dx.doi.org/10.1037/abn0000256.supp This article was published Online First March 9, 2017. Michael L. Thomas, Department of Psychiatry, University of California San Diego, and VISN-22 Mental Illness, Research, Education, and Clinical Center (MIRECC), VA San Diego Healthcare System, San Diego, Cali- fornia; Virginie M. Patt, Joint Doctoral Program in Clinical Psychology, San Diego State University/University of California San Diego; Andrew Bismark, Department of Psychiatry, University of California San Diego, and VISN-22 Mental Illness, Research, Education, and Clinical Center (MIRECC), VA San Diego Healthcare System; Joyce Sprock, Department of Psychiatry, University of California San Diego; Melissa Tarasenko, Gregory A. Light, and Gregory G. Brown, Department of Psychiatry, University of California San Diego, and VISN-22 Mental Illness, Research,Education and Clinical Center (MIRECC), VA San Diego Healthcare System. Research reported in this publication was supported, in part, by the National Institute of Mental Health of the National Institutes of Health under award numbers R01 MH065571, R01 MH042228, and K23 MH102420. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Correspondence concerning this article should be addressed to Michael L. Thomas, Department of Psychiatry, University of California San Diego, 9500 Gilman Drive MC: 0738, La Jolla, CA 92093-0738. E-mail: [email protected] This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. Journal of Abnormal Psychology© 2017 American Psychological Association 2017, Vol. 126, No. 3, 312–3240021-843X/17/$12.00http://dx.doi.org/10.1037/abn0000256 312 Cognitive tasks that are too hard or easy produce imprecise measurements (Lord, 1980), confound studies of differential def- icit (Chapman & Chapman, 1973), and complicate translational research (Callicott et al., 2000;Manoach et al., 1999). Researchers have explicitly recommended that task difficulty be a main crite- rion used to select neurobehavioral probes (Gur, Erwin, & Gur, 1992), and problems associated with using tests with nonoptimized item properties have been known for many years (Lord & Novick, 1968). Despite this, the relative match, or mismatch, between ability and difficulty is rarely discussed in applied research, likely because there have been few demonstrations of its practical con- sequences. In this paper, we illustrate these problems using a popular experimental measure of working memory—the N-back task—and suggest strategies for precisely measuring working memory and other cognitive deficits in schizophrenia. The meth- odology applied is general, and can inform future studies of abnormal cognition in schizophrenia and other neurocognitive disorders. Item Difficulty and Measurement Error Ability estimates are most precise when item difficulty is closely matched to ability (Embretson, 1996;Lord, 1980). To understand why, it is important to distinguish between classic and modern conceptualizations of measurement error. Classical test theory defines measurement error as the square root of one minus the ratio of true score variance to observed score variance: stan- dard error of measurement. As such, measurement error in classi- cal test theory is a constant. Modern psychometrics—particularly item response theory (IRT;Lord, 1980)— on the other hand, defines measurement error as the standard deviation of the esti- mate of ability: standard error of estimate. As such, estimates of measurement error in IRT may vary over scores within a popula- tion (Embretson, 1996); specifically, error is often a “U”-shaped function of ability. Although unequal precision is not a desirable property, it is, unfortunately, a real and ever-present one that may go unnoticed by researchers using classical methods (e.g., split- half reliability or coefficient alpha). This problem occurs because items that are too hard or too easy produce little systematic variation in observed test scores (Lord, 1980); in extreme cases, tests may show “floor” or “ceiling” effects (i.e., when all exam- inees within a particular range of the ability distribution receive the same score;Haynes, Smith, & Hunsley, 2011). There are practical consequences of administering tests with item difficulties that are poorly matched to ability. It is an axiom of psychometric theory that associations between variables are attenuated to the extent that measures of those variables are unre- liable (Haynes et al., 2011;Spearman, 1904). Moreover, because reliability is a function of the standard errors associated with individual estimates of ability obtained within a sample (Embret- son, 1996;Lord, 1955), and because, as noted above, error often varies with ability, samples with different mean abilities—such as patients and healthy controls— can be measured with unequal reliability. As a result, associations between ability and outcome, as well as changes in ability, can appear relatively smaller in one group when compared to the other purely due to a measurement confound. IRT can be used to identify and correct these problems (Thomas, 2011). Unfortunately, the approach is rarely used in neuropsycho-logical test development, and the formal use of IRT in small-scale neurocognitive research is unprecedented. AsStrauss (2001,p.12) noted, IRT’s large sample requirements— usually several hundred to thousand participants—implies that the “method does not seem practical for testing specific, theoretically based hypotheses.” However, with the use of alternative, less statistically demanding measurement models, it is possible to utilize certain applications from IRT in small-scale research (e.g.,Thomas, Brown, Thomp- son, et al., 2013). We describe one such model next. Measurement Approach A limiting factor in the application of IRT to measures of abnormal cognition has been the disconnect between measurement models that are popular in item response theory and measurem- ent models that are popular in cognitive assessment. In particular, most applications of item response theory rely on unidimensional measurement models (i.e., models in which a single person vari- able is thought to influence item responses), with only a small portion of studies using multidimensional approaches (i.e., models in which multiple person variables are thought to influence item responses;Thomas, 2011). Applications of the latter that have been published are generally exploratory (e.g.,Thomas, Brown, Gur, et al., 2013). Measurement models used in cognitive assess- ment, in contrast, are often multidimensional, theory-based, and rely heavily on experimental cognitive research. A prime example is the equal variance signal detection theory (SDT;Snodgrass & Corwin, 1988) model, which is commonly used to score data from recognition memory tasks (e.g.,Kane, Conway, Miura, & Colflesh, 2007;Ragland et al., 2002). The SDT model, shown inFigure 1, distinguishes between two classes of items: targets and foils. Targets are repeated (or old) items that the examinee is expected to remember. Foils are nonrepeated (or new) items that the examinee is not expected to remember. The SDT model assumes that the presentation of target or foil items during testing invokes a sense of familiarity that can be represented as unimodal, symmetric probability distributions with identical vari- ances but different means. The distance between distributions is a measure of discriminability (d=), and is often the primary outcome score of interest.d=can reflect perceptual, memory, or other types of sensitivity to the detection of signal against a backdrop of noise (Witt, Taylor, Sugovic, & Wixted, 2015). However, because the Figure 1.Equal variance, signal detection theory model. T mean of the distribution of familiarity for targets; F mean of the distribution of familiarity for foils;d= Tminus F(discrimination);C criterion; C center value of the criterion relative the midpoint between Tand F (bias). This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 313 ATTENUATION OF COGNITIVE DEFICITS IN SCHIZOPHRENIA familiarity distributions of targets and foils often overlap, the SDT model assumes that examinees must establish a criterion, or level of familiarity, beyond which items will be classified as targets. It is useful to define a measure of bias as the value of the criterion relative to the midpoint between target and foil distributions (C center ).C center can reflect both perceptual and response biases (Witt et al., 2015). The primary advantage of using the SDT measurement model in studies of abnormal cognition is the ability to disentangle sensitivity from bias. Previous work has shown that the SDT model can be formulated as a generalized linear model with coefficients representing exam- inee ability and item difficulty (DeCarlo, 1998,2011). In other work (Thomas et al., 2016), and in theAppendix, we show that this model is also equivalent to a multidimensional IRT model, thus linking a valuable body of psychometric research and technical literature to the measurement of a general class of cognitive constructs. Moreover, because this framework assumes certain item properties based on theory, and allows others to be estimated as a function of task properties, sample size demands are greatly reduced. Researchers can use the approach to investigate standard error of ability estimates, even in relatively small samples, pro- vided that the cognitive tasks used are scored using the SDT framework. The application of modern psychometric ideas to SDT scoring of test data in experimental studies of abnormal cognition would provide tangible evidence of the problems associated with admin- istering items and tests that poorly match difficulty to ability. Next, we describe one domain of assessment that is ripe for the appli- cation of these ideas: the assessment of working memory deficits in schizophrenia. Working Memory Deficits in Schizophrenia Decreased brain volume, altered morphology, and impaired functioning in brain regions associated with complex cognitive processes (e.g., prefrontal cortex, limbic and paralimbic structures, and temporal lobe) are common in patients diagnosed with schizo- phrenia (Brown & Thompson, 2010;Levitt, Bobrow, Lucia, & Srinivasan, 2010), and are linked to a host of cognitive deficits, including impaired attention, language, executive functioning, pro- cessing speed, and memory (Bilder et al., 2000;Kalkstein, Hur- ford, & Gur, 2010;Reichenberg & Harvey, 2007). Cognitive deficits are core, treatment-refractory, even endophenotypic traits that might prove useful in identifying targets for the next genera- tion of psychological and pharmacological therapies (Brown et al., 2007;Gur et al., 2007;Hyman & Fenton, 2003;Insel, 2012;Lee et al., 2015). Working memory is a core deficit in patients diagnosed with schizophrenia (Barch & Smith, 2008;Kalkstein et al., 2010;Lee & Park, 2005). Although the construct has been characterized by several evolving theories (Atkinson & Shiffrin, 1968;Baddeley & Hitch, 1974;Cowan, 1988), it can generally be defined as, “those mechanisms or processes that are involved in the control, regula- tion and active maintenance of task-relevant information in the service of complex cognition” (Miyake & Shah, 1999, p. 450). The construct has been intensively studied in cognitive psychology (Baddeley, 1992;Cowan, 1988), neuroscience (Owen, McMillan, Laird, & Bullmore, 2005), and clinical neuropsychology (Lezak, Howieson, Bigler, & Tranel, 2012). Deficits in working memoryalso occur in several other neurological and psychiatric disorders including attention-deficit/hyperactivity disorder (Engelhardt, Nigg, Carr, & Ferreira, 2008), autism (Williams, Goldstein, Car- penter, & Minshew, 2005), dementia (Salmon & Bondi, 2009), depression (Christopher & MacDonald, 2005), traumatic brain injury (Vallat-Azouvi, Weber, Legrand, & Azouvi, 2007), and posttraumatic stress disorder (Shaw et al., 2009). The N-back task, which asks examinees to monitor a continuous stream of stimuli and respond each time an item is repeated from Nitems before, is one popular measure of working memory deficits in schizophrenia. N-Back tasks were introduced to study serial learning and short-term retention of rapidly changing infor- mation (Kirchner, 1958;Mackworth, 1959;Welford, 1952).Figure 2shows an example of a 2-back task (i.e., load orN 2) using words as stimuli. Examinees are asked to respond to targets but not to foils or lures (i.e., items that have been repeated from some lag other thanN[e.g., a 3-back item presented during a 2-back condition; seeFigure 2] and thus should not be responded to). The N-back task gained popularity as an experimental working mem- ory paradigm in the 1990s (Cohen & Servan-Schreiber, 1992; Gevins & Cutillo, 1993;Gevins et al., 1990;Jonides et al., 1997), and has since been widely adapted, using stimuli varying across modality, including letters, digits, words, shapes, pictures, faces, locations, auditory tones, and even odors (Owen et al., 2005). These diverse versions of the N-back task have been shown to require both stimulus-specific processes as well as recruit common brain regions (Nystrom et al., 2000;Owen et al., 2005;Ragland et al., 2002). Although experimental versions of the N-back task are popular in schizophrenia and neuroimaging research—to the point of being considered a “gold standard” paradigm (Glahn et al., 2005;Kane & Engle, 2002;Owen et al., 2005), and have even shown efficacy for use in cognitive remediation (Jaeggi, Busch- kuehl, Jonides, & Perrig, 2008)— questions nevertheless remain about the psychometric properties of these tasks (e.g.,Jaeggi, Buschkuehl, Perrig, & Meier, 2010). Several investigators have reported only moderate, weak, and even nonsignificant associations between N-back performance and Figure 2.Example of a 2-back run from the N-back task. Examinees are asked to respond whenever a word is repeated from 2 words before. Items repeated from 2-back are targets, items that are repeated, but not from 2-back are referred to as lures, and nonrepeated items are referred to as foils. This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 314 THOMAS ET AL. performance on prototypical working memory paradigms such as measures of simple and complex span (e.g.,Jacola et al., 2014; Jaeggi et al., 2010;Kane & Engle, 2002;Miller, Price, Okun, Montijo, & Bowers, 2009;Shamosh et al., 2008;Shelton, Elliott, Matthews, Hill, & Gouvier, 2010). One possible cause for the N-back’s poor validity is poor reliability. Reliability estimates reported in the literature have ranged from poor to good (e.g., Jaeggi et al., 2010;Kane et al., 2007;Salthouse, Atkinson, & Berish, 2003;Shelton et al., 2010) and appear to depend on N-back load condition and stimulus modality (e.g.,Jaeggi et al., 2010; Salthouse et al., 2003). Indeed, in a study examining the split-half reliability of the N-back task under different load manipulations, Jaeggi et al. (2010)concluded that, “the N-back task does not seem to be a useful measure of individual differences in working mem- ory [capacity], due to its low reliability.” However, the N-back’s poor, or at least inconsistent, reliability may be a function of poorly matched examinee ability and item difficulty. Current Study In this study our first aim was to determine how task manipu- lations influence difficulty and precision on the N-back. This was accomplished by using techniques from IRT to quantify measure- ment error for estimates ofd=andC center produced by a SDT measurement model. As noted above, measurement error varies when item difficulty is not well matched to the full range of ability within a sample. Because the N-back appears to have a restricted range of difficulty (i.e., few load conditions), and because reliabil- ity estimates reported in the literature have varied substantially from sample to sample, we hypothesized that error in empirical estimates ofd=andC center would vary as a function of ability. Oursecond aim was to use this information to explore the potential impact of imprecision on observed group differences in clinical studies of working memory deficits in schizophrenia. We hypoth- esized that mismatched ability and difficulty would lead to atten- uated precision and effect size. That is, if item difficulty on the N-back is well matched to the abilities of healthy controls or patients, but not both, this should result in unequal precision between groups. Furthermore, because mismatched ability and difficulty increases measurement error, and because measurement error attenuates effect size, we also assumed that restricted range of item difficulty would result in lower effect size for one group when compared to the other. Method Participants We sought to study a heterogeneous sample in order to maxi- mize variance in working memory ability. The sample comprised two cognitively healthy groups— undergraduates (N 42) and community controls (N 25)—and two groups of patients diag- nosed with either schizophrenia or schizoaffective disorder— out- patients (N 33) and inpatients (N 17). Undergraduates were recruited from an experimental subject pool, outpatients and com- munity controls were recruited from the general community, and inpatients were recruited from a locked long-term care facility. Demographic characteristics of the samples are reported inTable 1. Written consent was obtained from all participants. Patients were assessed on their capacity to provide informed consent. When relevant, consent was obtained from court-ordered conservators. Table 1 Demographic and Clinical Characteristics Characteristic Undergraduates Community controls Outpatients Inpatients N42 25 33 17 Age (SD) 21.07 (2.11) 38.24 (12.39) 44.94 (11.63) 37.88 (11.13) Male 16 (38%) 10 (40%) 19 (58%) 9 (53%) Female 26 (62%) 15 (60%) 14 (42%) 8 (47%) Hispanic 12 (29%) 2 (8%) 9 (27%) 4 (24%) Race White 13 (32%) 13 (52%) 16 (48%) 12 (71%) Black 0 (0%) 4 (16%) 4 (12%) 0 (0%) Asian 18 (45%) 4 (16%) 0 (0%) 2 (12%) American Indian 0 (0%) 0 (0%) 0 (0%) 1 (6%) Multiracial 2 (5%) 4 (16%) 13 (39%) 2 (12%) Other 7 (18%) 0 (0%) 0 (0%) 0 (0%) Education (SD) 15 (1.18) 15.12 (2.15) 12.09 (2.26) 11.47 (2.03) Parental Education (SD) a — 13.88 (1.81) 12.46 (2.52) 14.10 (2.16) Std. WRAT — 106.38 (8.06) 93.53 (12.38) 93.5 (13.49) Age of Onset — — 22.06 (7.2) 19.62 (5.32) Hospitalizations b — — 9.62 (10.18) 16.71 (9.52) GAF — — 41.34 (4.23) 28.24 (4.98) SAPS Total — — 6.34 (3.73) 6.44 (5.19) SANS Total — — 14.66 (4.12) 5.88 (3.54) c Note. Two undergraduates declined to report their race. SAPS Scale for the Assessment of Positive Symptoms; SANS Scale for the Assessment of Negative Symptoms. “—” implies that data were not collected; WRAT Wide Range Achievement Test; GAF General Assessment of Functioning. aBased on average of mother and father. bBased on self-report. cAvolition-Apathy and Anhedonia- Asociality Scores for inpatients were based on work, social, and recreational participation within the inpatient facility, and thus are likely smaller (better) than would be observed in the community. This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 315 ATTENUATION OF COGNITIVE DEFICITS IN SCHIZOPHRENIA Research procedures were reviewed and approved by the UC San Diego Human Subjects Protection Program (protocol numbers 071831, 080435, 101497, and 130874). Diagnoses (or lack thereof) were verified using the patient and nonpatient editions of the Structured Clinical Interview forDiag- nostic and Statistical Manual of Mental Disorders, fourth edition, text revision (DSM–IV–TR;First, Spitzer, Gibbon, & Williams, 2002a;First, Spitzer, Gibbon, & Williams, 2002b) for both patient groups and community controls, respectively, and by using a self-report questionnaire for the undergraduates. Exclusion criteria included inability to understand consent and self-reported nonflu- ent English speaker, previous significant head injury (i.e., loss of consciousness 30 min, residual neurological symptoms, or ab- normal neuroimaging finding), neurological illness, and severe systemic illness. Patients and community controls were excluded if they had a history of alcohol or substance abuse or dependence within the preceding one month, or had a positive illicit drug toxicology screen at the time of testing. Patients were also ex- cluded if they did not meet diagnostic criteria for schizophrenia or schizoaffective disorder, or if they reported current mania. Under- graduates and community controls were also excluded if they reported any history of psychosis, current Cluster A personality disorder, current Axis I mood disorder, history of psychosis in a first degree family member, or current treatment with any antipsy- chotic or other psychoactive medication. Cognitive Task An N-back task using words as stimuli designed specifically for the purposes of this study was administered to all participants. We generated a list of words using an online word pool database (Wilson, 1988), saved each word’s letter, syllable, and frequency count, and then removed any offensive words and personal names. This left us with a stimulus pool of 32,236 English words taken from all parts of speech. Next, we generated one hundred 40-word lists containing 32 foil and 8 target or lure item types, so that 1 out of every 5 words presented, on average, was either a target or a lure. Words were randomly selected from the word pool. 1To prevent examinees from guessing the order and rate at which targets and lures were presented, a script written inR(R Core Team, 2013) was used to pseudorandomize the order of stimulus presentation (although the order was held constant over examin- ees). We experimentally manipulated three crossed factors: N-back load (3-levels: 1, 2, or 3), number of word syllables (3-levels: 1, 2, or 3), and presentation time (3-levels: 500ms, 1,500ms, or 2,500ms followed by a blank screen to attain a fixed presentation rate of one word every 3,000ms). 2We did not include a 0-back load condition (i.e., where examinees are asked to respond anytime a key word is shown) because we felt that the condition may differ not just quantitatively, but also qualitatively from load conditions that require both active maintenance and continuous updating of newly encoded information. Although load manipulations are common, syllable length and presentation time are generally fixed over items on N-back tasks; however, we reasoned that— because these ma- nipulations can increase pressure on encoding and maintenance processes—they might produce a wider range of item difficulty for the N-back task which could benefit measurement precision over- all.We generated unique 40-word lists for each combination of factors. In addition to the experimentally manipulated factors, word frequency and item count within runs were included in all analyses. At an administration time of two minutes per list, we could not administer all unique combinations of factor levels to each participant. Therefore, we used incomplete counterbalancing of conditions. Participants were administered nine lists each with the requirement that they should receive all levels of each factor. A short set of instructions followed by a practice trial with feed- back preceded each new N-back load condition. Participants were encouraged to take short breaks after each run. The task was administered online using a web application designed and pro- grammed for neurocognitive task administration and lasted ap- proximately 25 to 30 min per participant. Words were presented in large black font on a light gray background with minimal screen distraction. The protocol was the same for all participants except inpatients, who were administered only six N-back lists (three 1-back followed by three 2-back) due to time and fatigue con- straints. Analyses Model.All analyses were conducted within the context of SDT. In equal variance SDT models, the probability of responding to stimuli can be expressed using the following generalized linear model (seeDeCarlo, 1998 Appendix): 1(P(U ij 1)) C center,i Z jd i2, (1) where 1 is the inverse cumulative distribution function for the normal distribution; P(U ij 1) is the probability that individuali responds positively (presses the button) to itemj;Z jis a binary variable equal to 1 if itemjis a target and 1 if it is a foil or lure; d i=is the ability of individualito discriminate between target and foil or lure items; andC center,i represents individuali’s bias. In order to be consistent with IRT, the SDT model can be modified to express the probability of correct answers (as opposed to the probability of responding) and to include the notion of item diffi- culty (seeAppendix): 1(P(X ij 1)) j Z jCcenter,i d i2, (2) where P(X ij 1) is the probability of a correct answer for indi- vidualion itemj, and jrepresents the easiness of itemj. Task difficulty. j,d i=, andC center,i vary over items and ex- aminees and can be specified as random effects in a mixed-effects model. Accordingly, we analyzed the item accuracy data using 1We also explored the effect of including words with a similar spelling (n-grams) as the items presentedNwords before (e.g., “DOG” – “CAT” – “DIG” in a 2-back condition). However, early analyses suggested that these items did not add difficulty to the N-back task over and above lures, and were highly variable in terms of difficulty level. For simplicity, these items were removed from all analyses. 2We also manipulated the number of word letters to determine whether syllable and letter effects were independent. Because syllables and letters are correlated, the word letter factor was only partially crossed with the word syllable factor (i.e., 3, 4, or 5 letters for 1-syllable words; 5, 6, or 7 letters for 2-syllable words; and 7, 8, or 9 letters for 3-syllable words). Results suggested that number of word letters did not significantly improve model fit when number of word syllables had already been accounted for. This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 316 THOMAS ET AL. generalized linear mixed modeling (GLMM; seeHox, 2010for a review of multilevel or mixed-effects models) and the lme4 pack- age forR(Bates, Maechler, Bolker, & Walker, 2014). To inves- tigate predictors of task difficulty within an SDT scoring frame- work, we added fixed effect predictors of item accuracy to Equation 2. The predictors of interest included N-back load, num- ber of word syllables, presentation time, and item count within each run (all centered). The effect of item type was also explored, although the effects are complex to dissociate. In the SDT model, values ofd=, andC center determine the difficulty of targets and foils; item difficulty is negatively associated withd=for both targets and foils, and negatively associated withC center for foils but positively associated with Ccenter for targets (seeEquation 2). In the current approach, the means of the random effects deter- mined the difficulty of targets and foils. Lure difficulty was de- termined the same as foil difficulty, except for a dummy-coded “Lure” variable that captured added difficulty due to the complex- ity of lures. Centered and log-transformed word frequency was included as a covariate. The combined model had the form: 1(P(X ij 1)) j Z jCcenter,i d i2 N-back j b 3 Word Syllables j b 4 Presentation Time j b 5 Word Frequency j b 6 Lure j b 7 (3) where j,di=, andC center ,iwere all treated as random effects, and all other terms were fixed effects with values varying depending on itemj. The model does not have an intercept term so as to allow the means ofd= iandC center ,ito be nonzero (as they should be). Measurement precision.In the GLMM approachd=and C center are modeled as random effects, which are equivalent to latent abilities in IRT (de Boeck et al., 2011). Individual values of d=andC center for all examinees were derived using maximum a posteriori (MAP) estimates. To quantify measurement error for these estimates, we extracted their posterior standard deviations (PSDs). Both MAPs and PSDs are produced by the lme4 R package. PSD, which is interpreted similarly to standard error of estimate, provides an index of measurement (im)precision based on the observed data. Measurement precision based on the model and fitted parameter estimates was quantified using information functions for multidimensional item response theory models (Reckase, 2009). Information, the inverse of squared standard error, is a statistic that reflects precision in ability estimates. We produced information functions for all combinations of item type by N-back factor levels focusing only ond=while holdingC center to the mean value in the sample. Effect size.Finally, we simulated data that would allow us to obtain estimates of the expected attenuation in group difference effect size (Cohen’sd) given different combinations of N-back load conditions. This consisted of the following steps. Step 1: We simulated normally distributedd=andC center values hypothetically obtained from samples of nonimpaired and impaired individuals withd=means fixed to 0.0 and 0.8SDsbelow the overall sample mean in the current study respectively (i.e., corresponding to a Cohen’sdvalue of 0.8 [large effect]). Step 2: We created a pool of N-back items based on specific combinations of task difficulty factors (see below). Step 3: We calculatedd=for each participant in the simulated data using conventional formulas (Snodgrass &Corwin, 1988). Step 4: We calculated Spearman-Brown-corrected split-half reliability (Rel. S.B. ) and Cohen’sdstatistics. Step 5: Repeated Steps 2 through 4 for the following N-back load condi- tions: all 1-back, all 2-back, all 3-back, mix of 1- and 2-back, mix of 1- and 3-back, mix of 2- and 3-back, and mix of 1-, 2-, and 3-back. Importantly, the same total number of item responses were assumed in each simulation (240) hypothetically corresponding to 12 min of testing. To improve efficiency, each run had a distribu- tion of 60% foils, 20% targets, and 20% lures. The mean for the nonimpaired simulation group was fixed to the unweighted grand mean of the sample, as opposed to the sample mean of controls, to account for any demographic mismatch between outpatients and community controls in the current study (see below). We used the Spearman-Brown-corrected split-half reliability so that our results would be consistent with studies of N-back reliability reported in the literature (e.g.,Jaeggi et al., 2010). The simulation was pro- grammed inR. Results Demographic Characteristics We compared the samples on key characteristics to determine demographic similarity. Because undergraduates are not expected to be demographically similar to patients or community controls, comparisons were restricted only to the latter groups. The samples did not significantly differ with respect to age,F(2, 72) 3.12,ns, 2 .08, or gender, 2(2;N 75) 1.80;ns; c .16. Moreover, although the groups differed in terms of education,F(2, 72) 19.01,p .001, 2 .35, they did not significantly differ in terms of mean level of parent education,F(2, 46) 2.79,ns, 2 .11. Descriptive Accuracy Results Figure 3shows mean accuracy results (i.e., the proportion of correct answers) broken down by N-back load, item type, and group. It is notable that accuracies were generally well over 50% and many were above 75%. Undergraduates generally performed better than community controls, followed by outpatients, and then inpatients. Foils were the easiest item type, and lures were the most difficult. Items became consistently harder as N-back load in- creased. Ability and Task Difficulty GLMM parameter estimates are reported inTable 2. The mean empirical estimate of discriminability (d=) was 4.20 in the sample, suggesting that the N-back task was moderately easy overall. The mean empirical estimate of bias (C center ) was 0.78, suggesting that foils were much easier—more often responded to correctly—than targets. The lure effect was significantly negative, indicating that lures were much more difficult than foils. Increasing N-back load, word syllables, and item count, as well as decreasing presentation time all predicted significantly worse accuracy. The effect of word frequency was not statistically significant. Interestingly, the stan- dard deviation of empirically estimated item easiness ( ) was small, suggesting that N-back difficulty was dominated by task rather than individual item features. This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 317 ATTENUATION OF COGNITIVE DEFICITS IN SCHIZOPHRENIA Measurement Precision Table 3reports mean estimates (MAPs) ofd=andC center as well as measurement errors (PSDs) within each sample. (Note that these results do not attempt to control for demographic covariates.) Ability and measurement precision varied over populations.Figure 4shows estimates ofd=plotted against the errors of those estimates for undergraduates, community controls, and outpatients (inpa- tients were omitted from the figure because, as a result of being administered fewer items [see methods], PSDs associated with inpatients’ ability estimates are higher than other groups). The figure also shows approximate values of reliability corresponding to each PSD level. 3Error appears to be a nonlinear function of ability level; PSDs were generally lower for examinees with low versus high values ofd=. The PSDs generally suggest good or even excellent measurement precision in the sample; this is mainly due to the high number of N-back runs administered. To further explore measurement precision we created information functions for combinations of N-back load and item type, holding all other task factors at their median values. The results are shown in Figure 5. For interpretability, the information functions (represented by solid, dashed, and dotted lines corresponding to 1, 2, and 3-back loads, respectively) are superimposed over the implied distributions of d=for undergraduates, community controls, outpatients, and inpa- tients. As can be seen, the information functions generally peak atd= values that are lower than the mean of each distribution of ability; this is particularly true of foils and all 1-back conditions. The results suggest that the N-back task was too easy to provide precise, or at least efficient, estimates ofd=for participants with average to above average ability. Moreover, foils provided almost no useful informa-tion about ability. Targets at 3-back and lures at 2 and 3-back were the most informative across all groups. Effect Size Results of the effect size simulation are reported inTable 4. Reliability was consistently worse for the nonimpaired group. Reliability overall was closely tied to N-back difficulty. The sim- ulations that used all 1-back conditions and a combination of both 1- and 2-back conditions both produced unacceptably low reliabili- ties, and Cohen’sdeffect size values were severely attenuated for these simulations dropping by 0.37 (46%) and 0.30 (37%) respec- tively (i.e., from large to small and medium effects). The two best performing simulations were those that used all 3-back conditions and a mix of 2- and 3-back conditions. Both produced moderate reliabilities (0.75 and 0.70, respectively), and the simulated atten- uations in Cohen’sdwere 0.18 (22%) and 0.21 (26%) respectively. Discussion The results of this study demonstrate that reliability and mea- sured group differences are both attenuated when cognitive tasks are not well matched to ability within the samples under investi- gation. These problems were demonstrated using a task commonly 3It has been noted by several authors that, given the classical test theory definition that standard error of measurement equals the standard deviation of scores times the square root of one minus reliability, the average standard error of estimate needed in order to achieve adequate, good, or excellent reliability can be calculated. Figure 3.Item accuracy by group, item type, and N-back.nrefers to the number of observed item responses. This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 318 THOMAS ET AL. used to study working memory deficits in schizophrenia: the N-back task. We found that N-back load and item type were the two primary determinants of task difficulty. Difficulty increased along with N-back load, and lures and targets were both much harder than foils. Task conditions were maximally informative within the low average to highly impaired spectrum of ability. In a simulation study, we found that N-back tasks composed entirely of low load conditions (i.e., 1- and 2-back) were highly unreliable, and may reduce the observed effect size by half. Strengths and Limitations Strengths of the study include its novel statistical methodology, the heterogeneous sample, and the use of an experimental design to study task features on the N-back. However, results should be interpreted in light of several limitations. First, our sample and design did not provide data that would be sufficient to examine the dimensionality and construct validity of the N-back task. This topic is discussed in detail below. Second, it is common in psy- chometrics to examine detailed fit statistics to determine how well the theoretical model matches the observed data (Swaminathan, Hambleton, & Rogers, 2007). Although general markers of model fit were good (see supplemental material), we lacked appropriate data to examine item-level fit statistics (i.e., too few responses per item). Third, although common in the literature, we did not includea 0-back load condition, which is sometimes used to form contrast measures which, in theory, control for variance that is irrelevant to the target construct (e.g., attention and motivation). This was because we felt that the 0-back condition—where examinees are typically asked to respond anytime a key word is shown—may differ not just quantitatively, but also qualitatively from load conditions that require both active maintenance and continuous updating of newly encoded information. Fourth, although patients and community controls did not significantly differ in terms of age and gender, controls reported higher education. The difference in education is a common finding that almost certainly reflects, at least in part, a consequence of mental illness. The groups were, however, matched on parental education, which may be a better indicator of premorbid demographic similarity. Nonetheless, to the extent that demographic factors exaggerated differences in work- ing memory between groups, unequal reliability as well as atten- uation in effect size between groups may have been overestimated. Table 2 Generalized Linear Mixed-Effects Model Parameter Estimates MAP est. random effectsMS 2 Discriminability (d=) 4.20 1.03 Bias (C center ) .78 .09 Item easiness ( ) .01 .02 Fixed effectsbSE CI 95% exp(b)r xy.z p N-back load .552 .028 [ .607, .497] .58 .24 .001 Word syllables .094 .027 [ .146, .042] .91 .04 .001 Presentation time .062 .025 [ .111, .013] .94 .02 .014 Item count .009 .002 [ .013, .006] .99 .06 .001 Word frequency .023 .012 [ .048, .001] .98 .02 .059 Lure 2.228 .068 [ 2.362, 2.094] .11 .40 .001 Note. MAP maximum a posteriori;b estimate of regression coefficient;SE standard error of estimate; CI 95% 95% confidence interval; exp(b) coefficients scaled in log-odds;r xy.z partial correlation coefficients. Table 3 Mean Estimates and Error for Discriminability (d=) and Bias (C Center ) by Group Variable UndergraduatesCommunity controls Outpatients Inpatients Discriminability (d=) Estimate 4.78 4.61 3.72 3.14 Error (PSD) .44 .45 .34 .48 Bias (C center ) Estimate .71 .86 .72 .94 Error (PSD) .20 .20 .16 .21 Note. PSD posterior standard deviation. Figure 4.Estimates of discriminability (d=) against the measurement error (PSD) of each estimate. PSD posterior standard deviation. Data for inpatients were omitted because, as a result of being administered fewer items by design (see methods), PSDs associated with inpatients’ ability estimates are higher than other groups. This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 319 ATTENUATION OF COGNITIVE DEFICITS IN SCHIZOPHRENIA Finally, although our goal was to illustrate a general measurement concern, some results may be specific to characteristics of the current study. However, we purposely collected data from four separate populations and chose a variety of task manipulations in order to increase the range of ability and difficulty under investi- gation. As a practical guide, researchers may wish to compare their samples’ accuracy statistics to our results (seeFigure 3). Significance and Implications An N-back task with an appropriate number of items, that also includes 2- and 3-back conditions, as well as targets, lures, and foils, is expected to provide reliable, moderately efficient estimates of working memory ability in chronic patients with schizophrenia; the same task, however, is expected to provide less reliable estimates of ability in healthy controls. Because validity coefficients are attenuated by unreliability, associations between N-back scores and outcomes (or predictors) of cognitive impairment can appear weaker in healthy controls when compared with patients with schizophrenia simply because of this measurement artifact. Moreover, the dependence of reliability upon ability has been shown to bring potential confounds instudies of differential deficit (i.e., differences in cognitive abilities between groups;Chapman & Chapman, 1973;Strauss, 2001). Within the framework of IRT, precision is maximized when predictable variance is maximized. Item information is greatest when the probability of a correct response is 0.50 for dichotomous item responses with no guessing. The common use of SDT to score N-back data in the literature implicitly assumes that examinees do not guess, but rather that response behavior is driven entirely by discriminability (d=) and bias (C center ). Thus, the simple observa- tion that the majority of examinees performed far better than 50% on most N-back items (seeFigure 3) suggests that the test does not produce optimally precise or efficient estimates of ability. The pattern of measurement error (seeFigure 4) was consistent across samples, suggesting that measurement error was a function of ability but not population. It is reasonable to ask, then, how N-back task manipulations might be altered in such a way to improve the match between ability in difficulty across groups. Our results suggest that researchers should consider using more diffi- cult versions of the N-back task in cognitive studies meant to precisely measure a wide range of individual differences in work- Figure 5.Information functions for all combinations of N-back load by item type holding all other task factors at their median values. For interpretability, the information functions (represented by solid, dashed, and dotted lines corresponding to 1, 2, and 3-back loads) are superimposed over the implied distributions of discriminability (d=) in undergraduates, community controls, outpatients, and inpatients. Table 4 Simulation Results N-back loadAll Rel. S.B. Nonimpaired Rel. S.B. Impaired Rel. S.B. Simulated Cohen’sdMeasured Cohen’sdAttenuation in Cohen’sd All 1-back .41 .30 .42 .80 .43 .37 All 2-back .61 .50 .62 .80 .54 .26 All 3-back .75 .68 .75 .80 .62 .18 Mix of 1- & 2-back .53 .42 .54 .80 .50 .30 Mix of 1- & 3-back .64 .55 .64 .80 .58 .22 Mix of 2- & 3-back .70 .61 .70 .80 .59 .21 Mix of 1-, 2-, & 3-back .64 .53 .64 .80 .56 .24 Note. Rel. S.B. Spearman-Brown corrected split-half reliability. This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 320 THOMAS ET AL. ing memory ability. This is especially true in clinical studies that include healthy controls as a comparison group, or in studies meant to evaluate change over time. Considering the samples as a whole, our results (e.g.,Figure 5) suggest that some examinees with below average ability, most examinees with average ability, and nearly all examinees with above average ability might be measured more efficiently and precisely with additional 4- and possibly even 5-back load conditions. Alternative possibilities for increasing item difficulty without increasing N-back load should also be considered. This might include the use of nonword stimuli, a greater proportion of lures, or dual N-back tasks (Jaeggi et al., 2003). The use of pseudowords (pronounceable word-like letter strings) has particular appeal given that pseudowords tend to have a more pronounced word syllable effect (Valdois et al., 2006) and produce higher false-alarm rates (Greene, 2004) relative to words. There are, however, two major cautions to consider when eval- uating these recommendations. First, efficient measurement, as is expected to result from administering more difficult items, could come at the cost of tolerability. Parenthetically, we have observed that participants’ reports of mental workload during the N-back task tend to be high even when performance is very good. Four- and especially 5-back runs may cause participants to prematurely discontinue testing, and thus tolerability must be weighed against the benefits of efficient measurement. Second, and perhaps more challenging, manipulating stimulus factors, especially factors other than N-back load, might fundamentally change the task in a way that threatens construct validity. Indeed, there is a longstanding debate regarding the relative merits of manipulating task difficulty to improve the precision of cognitive measures (seeStrauss, 2001). Changing task difficulty to improve reliability could come at the expense of validity. There are likely several overlapping cognitive processes engaged by the N-back: (a) processes meant to maintain goal and task relevant information without passive/external support—for example, en- coding, storage, and rehearsal; (b) processes meant to manipulate information so as to meet task demands—for example, updating, ordering, and matching; and (c) processes involved in response execution—for example, bias and inhibition (Cohen et al., 1994; Cohen et al., 1997;Jonides et al., 1997;Kane et al., 2007;Lezak et al., 2012;Oberauer, 2005;Wager & Smith, 2003). Because N-back scores likely reflect a weighted composite of these pro- cesses, and because manipulating task difficulty could upset this weighting, the dimensionality of observed scores might vary over conditions (but seeReise, Moore, & Haviland, 2010). From this perspective, it might be argued that task difficulty should only be manipulated if the dimensionality and construct validity of mea- sures can be preserved across conditions. Researchers interested in investigating, and dissociating, spe- cific deficits using experimental cognitive approaches (seeMac- Donald & Carter, 2002), may prefer to compare performance scores produced by task conditions that are thought to isolate specific cognitive processes (e.g.,Ragland et al., 2002). Unfortu- nately, under such circumstances—where difficulty is held con- stant within, but might differ between, experimental conditions— the amount of nonerror or informative variance in test scores that is directly related to impaired neurocognitive processes might vary over conditions, thus leading to the presently detailed reliability and effect size confounds. As noted byMacDonald and Carter (2002, pp. 880 – 881), “The challenge for researchers from theexperimental cognitive approach is to ensure that their measures of cognitive processes produce an adequate amount of variance so that they are sensitive to the presence of an impairment.” There are two general solutions to this problem. First, researchers can explicitly seek to create process-pure or process-isolating tasks that nonetheless have a wide range of difficulty. Second, researchers can develop mathematical cognitive and psychometric measurement models that link manipulations of item difficulty to specific cognitive processes (e.g.,Brown, Patt, Sawyer, & Thomas, 2016;Brown, Turner, Mano, Bolden, & Thomas, 2013;Embretson, 1984), thereby allowing for the optimization of measurement precision through dif- ficulty manipulations while also accounting for the changing dimen- sionality of observed test scores. To this end, further work is needed to determine how best to manipulate task difficulty and model re- sponse processes on the N-back and other experimental cognitive measures being used in studies of abnormal cognition in schizophre- nia (e.g.,Barch & Smith, 2008). Conclusion This study has demonstrated how task difficulty affects both reliability and effect size measures of group differences. Although concerns related to mismatched ability and difficulty have been known in the psychometric literature for many years—and ac- knowledged using classical psychometric methods in schizophre- nia research (Chapman & Chapman, 1973)—this study is among the first to show the practical, negative consequences of mis- matched ability and difficulty using modern psychometric meth- ods. The problems can be overcome, in part, by administering tasks that include a wide range of difficulty to avoid psychometric floor and ceiling effects. 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Working memory after severe traumatic brain injury.Journal of the International Neuropsycho- logical Society, 13,770 –780.http://dx.doi.org/10.1017/S1355617707070993 Wager, T. D., & Smith, E. E. (2003). Neuroimaging studies of working memory: A meta-analysis.Cognitive, Affective & Behavioral Neurosci- ence, 3,255–274.http://dx.doi.org/10.3758/CABN.3.4.255Welford, A. T. (1952). An apparatus for use in studying serial performance. The American Journal of Psychology, 65,91–97.http://dx.doi.org/10 .2307/1418834 Williams, D. L., Goldstein, G., Carpenter, P. A., & Minshew, N. J. (2005). Verbal and spatial working memory in autism.Journal of Autism and Developmental Disorders, 35,747–756.http://dx.doi.org/10.1007/ s10803-005-0021-x Wilson, M. D. (1988). The MRC Psycholinguistic Database: Machine Readable Dictionary, Version 2.Behavioural Research Methods, Instru- ments and Computers, 20,6 –11. Witt, J. K., Taylor, J. E. T., Sugovic, M., & Wixted, J. T. (2015). Signal detection measures cannot distinguish perceptual biases from re- sponse biases.Perception, 44,289 –300.http://dx.doi.org/10.1068/ p7908 Appendix Derivation of Model Used in Analyses Thisappendixprovides the derivations of the generalized linear model used in all analyses. Equal variance SDT (DeCarlo, 1998; Snodgrass & Corwin, 1988) first assumes that the distributions of familiarity for targets and foils (or lures) can modeled by two normal distributions (mean Tand F, respectively) with equal variance (seeFigure 2). The discrimination parameterd=represents the distance between the two distributions: d T F (A1) The decision criterion,C, represents the threshold at which individuals may judge that an item looks familiar enough to respond. C can be centered with respect to the mid-point between the two distributions: C center C T F 2(A2) The probability of responding given that a target was presented, P(U 1 |Target), corresponds mathematically to the area under the target distribution that is to the right of the criterion: 1(P(U 1 |Target)) T C(A3) where 1 is the inverse cumulative distribution function for the normal distribution. Similarly, the probability of responding given that a foil (or lure) was presented, P(U 1 |Foil), corresponds mathematically to the area under the foil distribution that is to the right of the criterion: 1(P(U 1 |Foil)) F C(A4) Using binary variableZ 1if the test item is a target and Z 1 if the test item is a foil (or lure),Equations A3andA4can be combined into the formula that appears in Appendix A of DeCarlo (1998): 1(p(U 1 |Z)) ( F C) 1 Z2 ( T C) Z 12 C center d 2Z(A5) To align the approach with IRT, the model can also be formu- lated to predict the probably of a correct response. A new binary variableXwas thus introduced so thatX 1 for a correct response andX 0 for an incorrect response. Using the property that 1(1 P) 1(P), and knowing that responding is correct when a target is presented whereas nonresponding is correct when a foil is presented,Equation A5yielded 1(P(X 1 |Target)) 1(P(U 1 |Z 1)) C center d 2 1(P(X 1 |Foil)) 1(1 P(U 1 |Z 1)) C center d 2 (A6) These equations were combined, leading to: 1(P(X 1 |Z)) ZC center d 2(A7) To account for item differences in easiness (overjofJitems) and person differences in ability (overiofNpeople), as in IRT, we added the term as well as subscripts to each parameter to arrive at our final equation: 1(P(X ij 1)) j Z jCcenter,i d i2(A8) In this form, the model is functionally equivalent to a multidi- mensional IRT model, but appears superficially distinct because of the use of notation this is common in SDT but not IRT (see Thomas et al., 2016). Received July 11, 2016 Revision received December 5, 2016 Accepted December 9, 2016 This document is copyrighted by the American Psychological Association or one of its allied publishers. This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 324 THOMAS ET AL.
Annotated Bibliography, Introduction, and Summary Paragraph: Seeking the Truth
C. J. Herold et al.: Co gnitive Performance in Schizophren iaGeroPsych (20 17), 30 (1), 35 –44© 20 17 Hogrefe Full-Length Research Report Cognitive Performance in Patients with Chronic Schizophrenia Across the Lifespan Christina Josefa Herold 1, Lena Anna Schmid 1, Marc Montgomery Lässer 1, Ulrich Seidl 2, and Johannes Schröder 1,3 1Section of Geriatric Psychiatry, Department of General Psychiatry, University of Heidelberg, Heidelberg, Germany2Center for Mental Health, Klinikum Stuttgart, Stuttgart, Germany3Institute of Gerontology, University of Heidelberg, Heidelberg, Germany DOI 10.1024/1662-9647/a000164 Abstract:Chronic schizophrenia involves neuropsychological deficits that primarily strike executive functions and episodic memory. Our study investigated these deficits throughout the lifespan in patients with chronic schizophrenia and in healthy controls. Important neuropsycholog- ical functions were tested in 94 patients and 66 healthy controls, who were assigned to three age groups. Compared with the healthy controls, patients performed significantly poorer on all tests applied. Significant age effects occurred on all tests except the digit span forward, with older subjects scoring well below the younger ones. With respect to cognitive flexibility, age effects were more pronounced in the patients. These findings underline the importance of cognitive deficits in chronic schizophrenia and indicate that diminished cognitive flexibility shows age-associated differences. Keywords:schizophrenia, cognition, aging, executive functions, memory Cognitive impairment is a hallmark of schizophrenia. The pattern of deficits and their relationship to psychosocial functioning have been illustrated in a large number of neuropsychological studies over the past few decades (Dickinson & Gold, 2008; Green, 1996; Schröder, Tittel, Stockert, & Karr, 1996). It is generally assumed that cognitive function is often already below average in premorbid periods (Reichenberg et al., 2006; Woodberry, Giu- liano, & Seidman, 2008) and decreases with manifestation of the disease (Bilder et al., 2000; Mesholam-Gately, Giuliano, Goff, Faraone, & Seidman, 2009). The respective deficits continue in patients with chronic schizophrenia, including those in whom symptoms have partially remitted (Barbarotto, Castignoli, Pasetti, & Laiacona, 2001; Heinrichs & Zakzanis, 1998). Cognitive and functional losses occur with normal aging in the entire population. The frontal-lobe hypothesis (West, 1996) posits that the frontal lobe is particularly susceptible to age-re- lated deterioration in healthy adults. This assumption is sup- ported by neuroimaging data that demonstrate both structural and functional changes in the frontal lobe with aging (Hazlett et al., 1998; Raz et al., 1997; Salat et al., 2004). In addition, neuropsychological studies describe a worsening of frontal ex- ecutive functions with aging in healthy adults (Salthouse, At- kinson, & Berish, 2003; Sorel & Pennequin, 2008). The question of the extent to which this decline of frontal functions with age also applies to patients with chronic schizo- phrenia remains unresolved. While some studies indicate thatcertain cognitive domains such as information processing and executive functioning might bear a greater risk of worsening with age (Bowie, Reichenberg, McClure, Leung, & Harvey, 2008; Fucetola et al., 2000; Irani et al., 2012; Loewenstein, Czaja, Bowie, & Harvey, 2012), others did not find any differ- ential aging effects (Heaton et al., 2001; Hijman, Hulshoff Pol, Sitskoorn,&Kahn,2003;Mockler,Riordan,&Sharma, 1997). These divergent findings may reflect methodological dif- ferences between the studies, which are detailed in the Discus- sion section below. Recently, Kirkpatrick et al. (2008) estab- lished the hypothesis that schizophrenia is a syndrome of ac- celerated aging – as already conceptualized by the term “dementia praecox” (Kraepelin, 1913) – since cognitive deficits in chronic schizophrenia primarily strike those domains that are typically affected in the physiological aging process. This hypothesis also conforms to the frontal cortex changes fre- quently described in patients with schizophrenia in neuroimag- ing studies (Bachmann et al., 2004; Buchsbaum et al., 1982; DeLisi, Szulc, Bertisch, Majcher, & Brown, 2006; Schröder, Buchsbaum et al., 1996). Despite the renewed interest in cog- nition in old age schizophrenia, considerable controversy still lingers over this topic. The current study examines the association between age and cognitive performance in chronic schizophrenia. We concen- trate specifically on patients and psychiatrically healthy controls ranging in age from young adulthood to old age. © 2017 HogrefeGeroPsych (2017), 30 (1), 35–44 We hypothesized that patients with chronic schizophrenia of all ages show substantial cognitive deficits. In addition, we expected these deficits to worsen with age. This effect should primarily involve executive functions, while episodic memory deficits should remain more stable. Methods Subjects and Procedures Cognitive performance was assessed in healthy subjects and patients with chronic schizophrenia in the age range 18 to 82 years. The patients and healthy controls were each subdivided into three age groups (“young”≤34 years, “middle” 35–49 years, and “older”≥50 years). 94 patients with chronic or subchronic schizophrenia ac- cording to DSM-IV (American Psychiatric Association, 2000) were recruited from three psychiatric long-term units (n= 40) and a mental state hospital (n= 54). All patients were in a stable condition and had received antipsychotic therapy; dosage was evaluated in mg chlorpromazine (CPZ) equivalents (Woods, 2003). The diagnosis was established by experienced psychia- trists. Inclusion criteria for patients were (1) a diagnosis of schizophrenia according to DSM-IV (American Psychiatric As- sociation, 2000), (2) German as the primary language, and (3) a minimum of 8 years school education. Patients with late onset schizophrenia with a manifestation of the disease after age 45 were not included as this condition may have involved a differ- ent etiology (Howard, Rabins, Seeman, & Jeste, 2000; Schmid, Lässer, & Schröder, 2011). Further exclusion criteria included a history of any neurological condition affecting the central ner- vous system, head injury, or substance abuse. Healthy controls (n= 66) were recruited among the hospital staff and through advertisements in a newspaper. The Mini In- ternational Neuropsychiatric Interview (interrater and retest- reliability Cohen’sκ> 0.75, Sheehan et al., 1998) and the Beck Depression Inventory II (Cronbach’sα= 0.89, retest-reliability r= 0.78, Hautzinger, Keller, & Kühner, 2006) were performed to screen controls for current psychopathology. They were care- fully matched to patients with respect to age and sex (main effect “diagnosis,”p> .30). Informed consent was obtained from all participants after the study had been fully explained. The study was approved by the local ethics committee. Measures Symptoms were assessed using the Brief Psychiatric Rating Scale (BPRS, 18 items, maximum score = 108, interrater-reli- abilityr= 0.8; Ligon & Thyer, 2000; Mass, Burmeister, &Krausz, 1997; Overall & Gorham, 1962), the Scale for the As- sessment of Positive Symptoms (SAPS) and the Scale for the Assessment of Negative Symptoms (SANS) (34 and 25 items respectively, maximum global score = 20 and 25 respectively, interrater-reliabilityr= 0.63 andr= 0.52 for SAPS and SANS respectively, Cronbach’sαSAPS = 0.77–0.91 and SANS = 0.83–0.92; Andreasen & Olsen, 1982; Norman, Malla, Cor- tese, & Diaz, 1996). Important neuropsychological domains typically involved in chronic schizophrenia were assessed by using a comprehensive test battery. Therefore, verbal learning and memory, short-term and working memory, processing speed, and cognitive flexibil- ity were taken into account; the Mini-Mental State Examination (MMSE, maximum score = 30, retest-reliabilityr= 0.80, Cron- bach’sα= 0.91) was used as a screening instrument for cogni- tive ability (Folstein, Folstein, & McHugh, 1975; Marioni, Chat- field, Brayne, & Matthews, 2011). All subjects completed the logical memory subtests of the Wechsler Memory Scale (Här- ting et al., 2000) to assess verbal learning and memory (logical memory I and logical memory II, maximum score each = 50, retest-reliabilityr= 0.79, interrater-reliabilityr= 0.99), and the digit span forward and backward subtests, assessing short-term and working memory (each maximum score = 12, retest-reli- abilityr= 0.83). As an index of processing speed and cognitive flexibility, we used the scores of the Trail Making Test (TMT A – max. 180 s, TMT B – max. 240 s, retest-reliabilityr= 0.74 andr= 0.43 for TMT A and B, respectively (Conway Greig, Nicholls, Wexler, & Bell, 2004; Reitan, 1992)). Statistical Analyses The effects of diagnosis and age were examined using multivar- iate analyses of variance (MANOVA) with diagnosis (patients, controls) and age group (young, middle, older) as the between- group factors, and the different demographical/clinical charac- teristics and the cognitive parameters as the dependent vari- ables, while controlling for years of education in the latter. These analyses were followed by Bonferroni posthoc tests. An αlevel of 0.05 (two-tailed) was used for all statistical tests. Analyses were conducted by means of the Predictive Analy- sis Soft Ware (PASW/SPSS 18.0). Results Sample Characteristics In a first step, demographic and clinical characteristics of the three age groups were tested for significant group differences (Table 1). Patient and control groups showed only minor, non- significant differences with regard to age and sex (main effect 36C. J. Herold et al.: Cognitive Performance in Schizophrenia GeroPsych (2017), 30 (1), 35–44© 2017 Hogrefe C. J. Herold et al.: Cognitive Performance in Schizophrenia37 © 2017 HogrefeGeroPsych (2017), 30 (1), 35–44 “diagnosis,”p> .30), while the healthy subjects had received a significantly longer school education than the patients (mean of years of educationM= 12.47 (SD= 2.78) vs.M= 13.58 (SD = 2.30),F(1, 154) = 6.755,p=.010,η 2= 0.042). Further analysis of the patient group revealed that the three age groups did not differ in dosage of antipsychotic medication (CPZ equivalents), negative and positive symptoms, with a trend-level significant effect for SAPS global score, indicating more distinctive positive symptoms in the young patient group, F(2, 91) = 2.870,p= .062. With respect to BPRS sum score a significant effect for “age,”F(2, 91) = 5.238,p= .007, shows additionally evidence for a more severe psychopathology in younger patient groups. Posthoc tests revealed significant dif- ferences between the older patients and both patient groups of middle (p= .015) and young (p= .032) age. As expected, significant differences were noticeable with re- gard to illness duration,F(2, 91) = 64.016,p< .001, the patient groups differed each withp< .001, and age at onset of the illness,F(2, 91) = 5.631,p= .005. Posthoc tests showed a sig- nificant difference between young and old patients (p= .004), whereas other comparisons failed to reach significance (p> .09). There was a significant age cohort effect for dwelling status, χ² = 9.542,p= .008, with middle and older patients being moreoften hospitalized in comparison to young patients at the time point of study. Age Effects on Cognitive Performance In a second step it was shown that, compared with the healthy controls, patients performed lower on all tests applied (Figure 1). Test performance tended to be lower in the oldest than the young and middle-aged groups. With respect to TMT B, this effect was more pronounced in the patient groups in whom a sharper decline of performance with age became evident (Fig- ure 2). These findings were confirmed by a MANOVA (Table 2) which yielded a significant main effect for “diagnosis,” F(7, 147) = 19.227,p< .001,η 2= 0.478. Further comparisons revealed significant differences between patients and healthy controls for all neuropsychological tests applied, thus indicat- ing a pronounced performance deficit of the patients (0.04 >p = .000). The main effect for “age” reached significance level too, F(14, 296) = 4.280,p< .001,η 2= 0.168, with older subjects being more impaired (0.03 >p= .000), except for digit span forward (p= .145). Figure 1.Neuropsychological profiles of patients (black lines) and healthy controls (gray lines). Raw test scores of all cognitive parameters were transformed toz-scores, based on the norm values of the specific test. 38C. J. Herold et al.: Cognitive Performance in Schizophrenia GeroPsych (2017), 30 (1), 35–44© 2017 Hogrefe Posthoc tests revealed that MMSE performance was signifi- cantly lower for old than for young subjects (p= .007), while the difference between groups of old and middle-aged subjects failed to reach significance (p= .062). In case of logical memory I, the old subjects had significant impairments in contrast to the young subjects (p= .044); in the case of logical memory II, the old subjects showed marked deficits in comparison to both younger groups (p< .02). Working memory performance, as indicated by digit span backward, was significantly reduced in the old subject group in contrast to the young group (p= .003). Figure 2.TMT A (above) and TMT B (below) performance as a function of age for patients (black lines) and healthy subjects (gray lines). C. J. Herold et al.: Cognitive Performance in Schizophrenia39 © 2017 HogrefeGeroPsych (2017), 30 (1), 35–44 Information processing speed, assessed via TMT A, was also significantly impaired in the older subjects compared to both younger groups (p< .001). With respect to cognitive flexibility, as assessed using TMT B, significant differences between each age group and the other groups were evident (.05 >p= .000). The interaction “diagnosis × age” showed a significant effect for TMT B,F(2, 153) = 4.869,p= .009, with trend-level signif- icance for TMT A,F(2, 153) = 2.716,p= .069. A trend-level significant “diagnosis × age” interaction with respect to MMSE also appeared, additionally indicating a cognitive deterioration in older patients,F(2, 153) = 2.774,p= .066. Discussion The present study revealed three major findings regarding cog- nitive impairment of patients with chronic schizophrenia: (1) a confirmation of broad deficits in a variety of important neu- ropsychological domains which (2) apply to all life periods from young adulthood to early age; and (3) evidence that cognitive flexibility is particularly affected in the older patients. The poorer test performance of patients with chronic schiz- ophrenia in comparison to healthy subjects covers a wide range of cognitive domains. This was particularly evident with regard to verbal learning and memory, wherez-scores nearly reached the mark ofz= –1.5 for all age groups. Information processing speed and cognitive flexibility were impaired to a comparable extent with a considerable stronger dip in the older patients. In contrast, short-term memory remained rather spared with performance still ranging in low average levels. These results corroborate findings from previous studies on cognitive deficits in young and middle-aged patients with chron- ic schizophrenia (Heinrichs & Zakzanis, 1998; Irani et al., 2012) and extend them for an older group. One of the studies investigating cognition in schizophrenia over a wide age range was conducted by Fucetola et al. (2000), who examined 87 patients and 94 healthy controls assigned to three groups with an average age ofM= 30.0 (SD= 3.6),M=41.1(SD= 4.2) andM= 58.3 (SD= 5.6) years in the patient groups andM= 28.5 (SD= 4.4),M=41.3(SD= 3.8) andM= 62.5 (SD= 7.2) years in the control groups, respectively. Cognitive deficits in the patient group involved verbal memory, perceptual motor skills, and abstraction, withz-scores below –1 throughout the three age groups. As in the present study, performance in mem- ory and learning, information processing, and cognitive flexi- bility was well within the range of that typically obtained in older patients with a diagnosis of mild cognitive impairment (Sattler, 2012). At this point it should be emphasized that even the marked deficits typically observed in older patients with chronic schiz- ophrenia are not directly comparable to the impairments char- acteristic of neurodegenerative illnesses such as Alzheimer’sdisease (AD), since declarative memory remains relatively spared and does not further deteriorate with progression of the disease. As in the present study, a consistent pattern of neuro- psychological deficits was already described by McBride et al. (2002) and Ting et al. (2009). The MMSE scores of our patient group were – though reduced and at trend-level deteriorating with increasing age – not comparable to that of patients with AD (Barth, Schönknecht, Pantel, & Schröder, 2005; Dos San- tos et al., 2011). These findings parallel results from a review of neuropathological studies, which concluded that AD pathol- ogy does not occur more frequently among patients with schiz- ophrenia than in the general population (Niizato, Genda, Na- kamura, Iritani, & Ikeda, 2001). While a wealth of studies investigated cognitive performance in schizophrenia in general, only few authors focused on the potential interaction effects between age and illness with regard to cognitive functioning. The present study demonstrated that older patients showed a significantly poorer performance in cognitive flexibility compared to their younger counterparts. Along with this, a trend toward significant interaction of diag- nosis with age was found for information processing. In con- trast, none of the other cognitive domains examined showed such a differential effect of aging in the patient group compared to the healthy controls. In the study cited above, Fucetola et al. (2000) found similar age-related performance differences be- tween patients and controls across various domains, while a significant interaction was restricted to abstract thinking as as- sessed on the Wisconsin Card Sorting Test. In a recent study, Irani et al. (2012) tested two groups of 624 patients with schiz- ophrenia and healthy controls on a computerized version of the Continuous Performance Test and on a Letter-N-Back Test and came to similar conclusions. Compared with the healthy controls, the patients showed significantly lower values regard- less of age in most indices of cognitive performance. However, the older group under investigation showed a reduced speed but not accuracy in the N-back task compared to the younger patients. This indicates that the executive component of work- ing memory performance was predominantly affected. Loewen- stein et al. (2012) analyzed age-associated cognitive differences in a sample of 226 patients with chronic schizophrenia and 834 healthy controls, which were compiled from different data- bases. All participants were older than 40 years; the clinical course of the disorder was not further specified. The study yielded greater age effects for patients than for controls on mea- sures of information processing i.e., the TMT A, the Stroop and the Digit Symbol Test, which also assess at least to a certain extent cognitive flexibility. The results of our study indicate that patients with chronic schizophrenia show a slope of cognitive decline with advancing age similar to controls in all cognitive domains except for cog- nitive flexibility as a typical executive function. Although this effect was rather small, it clearly refers to progressive cerebral changes in normal aging, which particularly strike the frontal lobes (DeCarli et al., 2005; Raz et al., 1997; Salat et al., 2004). 40C. J. Herold et al.: Cognitive Performance in Schizophrenia GeroPsych (2017), 30 (1), 35–44© 2017 Hogrefe Longitudinally, the extent of progressive brain tissue decrease in patients with schizophrenia is found to be twice that of healthy subjects and particularly affects frontal areas (Hulshoff Pol & Kahn, 2008; Olabi et al., 2011). Similar significant re- ductions in superior frontal gyrus and orbitofrontal regions were observed in a small male sample of young patients with schizophrenia and older healthy subjects in comparison to a young healthy control group (Convit et al., 2001). Moreover, gray matter decreases in frontal cortex were greater in chronic than in first-episode schizophrenia (Chan, Di, McAlonan, & Gong, 2011; Ellison-Wright, Glahn, Laird, Thelen, & Bullmore, 2008). Except for cognitive flexibility, our pattern of findings with rather stable deficits across different groups is consistent with the results of previous studies. Mockler et al. (1997) confirmed widespread cognitive deficits, but did not report any significant age effects on cognitive functioning in 62 patients with chronic schizophrenia between 18 and 69 years of age. However, the majority of patients were below 50 years, and just 6 patients formed the oldest group (60 to 69 years). Moreover, executive functions were not specifically addressed. Similarly, Hijman et al. (2003) who compared performance on four subtests of the Wechsler Adult Intelligence Test between 112 patients with chronic schizophrenia and 70 healthy controls (age range: 16 to 56 years) did not describe a significant interaction effect of age with group, while patients performed worse on all subtests. The oldest group (46 to 56 years) comprised 17 patients; the majority of patients were below 46 years of age. Performance on the subtest picture arrangement, which shares aspects of executive functioning, decreased with age, a process which ap- peared to be slightly more pronounced in the patient group. Bowie and colleagues (2008) also reported deficits in a number of important neuropsychological domains including psychomo- tor speed and cognitive flexibility. Performance levels compare to the “middle-aged” and “older” patient subgroups investigat- ed in the present study. However, Bowie et al. (2008) recruited a group of old patients (50–85 years), but did not include younger patients with chronic schizophrenia. In light of the re- duced life expectancy of patients with chronic schizophrenia (Laursen, 2011), the subgroup of old patients (70–85 years) may represent a number of survivors who either had a more favorable course of the disorder or were less vulnerable to its consequences during the aging process. The study showed ev- idence for age-associated cognitive decline on the more com- plex components of an information-processing test, which Bow- ie et al. (2008) alternatively referred to “the course of illness and the processing demands of the cognitive measure of inter- est.” However, their results mirror our findings because they did not only show a significant age-associated decline in the TMT A, but also a similar although nonsignificant trend toward for the TMT B in the patients. In the present study, executive functions were only ad- dressed by using the TMT, while other tests such as the Wis- consin Card Sorting Test were not applied. Because of reducedcognitive capacity of especially the older patients, we restricted our cognitive assessment to a few tests. While groups were carefully matched for age and sex, in the patients years of education were significantly reduced, which may be expected in a group of patients with a chronic course of the disease, of whom 20–60% were hospitalized. For this reason years of education were controlled for in the MANOVA. Negative symptoms differed nonsignificantly between the pa- tient groups, while positive symptoms (trend-level only) and BPRS total score were lower in older than younger patients. These differences correspond to the amelioration of acute schizophrenic symptoms with increasing age (Schmid et al., 2011), already described by Bleuler (1949). That the older pa- tients are nonetheless severely affected is indicated by their dwelling status, illustrating that older patients are more often institutionalized. Given that age and duration of illness coincide because of onset of the disease in early adulthood and the exclusion of patients with late onset schizophrenia, the three age groups differed significantly with respect to illness duration. The mar- ginal, albeit significant group difference of age at illness onset, determined on basis of the patients’ history and case notes, may well be explained by the fact that the youngest group per defi- nitionem does not comprise patients with a later onset, which is also reflected by the respective standard deviations. Data concerning the predominant treatment of the patients in the past were unfortunately not available. At the time of as- sessment the majority of the patients were receiving atypical antipsychotics only or typical and atypical antipsychotic medi- cation in combination. Potential medication effects cannot be entirely excluded as patients were examined cross-sectionally, although the three patient groups showed only marginally, non- significant differences with respect to CPZ equivalents. Simi- larly, significant medication effects were not identified in the large meta-analysis by Irani and colleagues (2011). In contrast, other studies indicate a beneficial impact of atypical (Guilera, Pino, Gómez-Benito, & Rojo, 2009; Thornton, Van Snellen- berg, Sepehry, & Honer, 2006; Woodward, Purdon, Meltzer, & Zald, 2005) and typical (Davidson et al., 2009; Mishara & Goldberg, 2004; Schröder, Tittel et al., 1996) antipsychotic medication on cognition in schizophrenia. Especially the latter findings are important given that particularly the older patients of our sample might have received mainly classical antipsychot- ics in the past. Additional factors other than age are likely to affect cognitive flexibility: The large meta-analysis cited above (Irani et al., 2011) revealed a significant role for both demographic (age, sex, education, race) and clinical factors (living status, age of onset, duration of illness, clinical symptoms). From a clinical standpoint, co-morbid somatic conditions and other life-style factors should also be added in longitudinal studies, as physical illnesses like the metabolic syndrome, which increases in inci- dence with rising age and is associated with cognitive deterio- ration (Schröder & Pantel, 2011), are more common in patients C. J. Herold et al.: Cognitive Performance in Schizophrenia41 © 2017 HogrefeGeroPsych (2017), 30 (1), 35–44 with schizophrenia (Oud & Meyboom-de Jong, 2009; Sebas- tian & Beer, 2007). The results of the present cross-sectional study underline the importance of cognitive deficits in chronic schizophrenia and indicate that diminished cognitive flexibility undergoes age-as- sociated differences, which can be assigned to frontal lobe changes. This pattern of cognitive deficits facilitates the differ- entiation from neurodegenerative diseases such as mild cogni- tive impairment and AD and underlines the need for appropri- ate training programs for elderly patients with chronic schizo- phrenia. Declaration of Conflicts of Interest The authors declare that no conflicts of interest exist. Acknowledgments The study was supported by the Dietmar Hopp Foundation, Germany. References American Psychiatric Association. (2000).Diagnostic and statistical manual of mental disorders – DSM-IV-TR. Washington, DC: American Psychiatric Association. Andreasen, N. C., & Olsen, S. (1982). 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A meta-analysis of neuropsychological change to clozapine, olanzapine, quetiapine, and risperidone in schizophrenia.Inter- national Journal of Neuropsychopharmacology, 8, 457–472. doi 10.1017/S146114570500516X Manuscript received: 17.07.2015 Manuscript accepted after revision: 17.12.2015 Dipl.-Psych. Dr. Christina Herold Section of Geriatric Psychiatry University of Heidelberg Voßstr. 4 69115 Heidelberg Germany [email protected] 44C. J. Herold et al.: Cognitive Performance in Schizophrenia GeroPsych (2017), 30 (1), 35–44© 2017 Hogrefe