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). Accurate perception of negative emotions predictsfunctional capacity in schizophrenia.Psychiatry Research, 216,6 –11.
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This article is intended solely for the personal use of the individual user and is not to be disseminated broadly. 405 CONNECTIVIY ASSOCIATED WITH SOCIAL FUNCTIONING