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Am J Psychiatry. Author manuscript; available in PMC 2017 May 1.
Published in final edited form as:
PMCID: PMC4886342
NIHMSID: NIHMS783694

Common and Dissociable Mechanisms of Executive System Dysfunction Across Psychiatric Disorders in Youth

Sheila Shanmugan, B.A.,a Daniel H. Wolf, M.D., Ph.D.,a Monica E. Calkins, Ph.D.,a Tyler M. Moore, Ph.D.,a Kosha Ruparel, M.S.E.,a Ryan D. Hopson, B.A.,a Simon N. Vandekar, B.S.,a,b David R. Roalf, Ph.D.,a Mark A. Elliott, Ph.D.,c Chad Jackson, M.S.E.,a Efstathios D. Gennatas, MBBS,a Ellen Leibenluft, M.D.,d Daniel S. Pine, M.D.,d Russell T. Shinohara, Ph.D.,b Hakon Hakonarson, M.D., Ph.D.,e,f Ruben C. Gur, Ph.D.,a,b,g Raquel E. Gur, M.D., Ph.D.,a,b and Theodore D. Satterthwaite, M.D., M.A.a,*

Abstract

Objective

Disruption of executive function is present in many neuropsychiatric disorders. However, determining the specificity of executive dysfunction within these disorders is challenging given high comorbidity of conditions. Here we investigated executive system deficits in association with dimensions of psychiatric symptoms in youth using a working memory paradigm, hypothesizing that common and dissociable patterns of dysfunction would be present.

Methods

We studied 1,129 youths who completed a fractal n-back task during fMRI at 3T as part of the Philadelphia Neurodevelopmental Cohort. Factor scores of clinical psychopathology were calculated using an itemwise confirmatory bifactor model, describing overall psychopathology as well as four orthogonal dimensions of symptoms including anxious-misery (mood / anxiety), behavioral disturbance (ADHD / conduct), psychosis-spectrum symptoms, and fear (phobias). The impact of psychopathology dimensions on behavioral performance and executive system recruitment (2-back > 0-back) were examined using both multivariate (matrix regression) and mass-univariate (linear regression) analyses.

Results

Overall psychopathology was associated with both abnormal multivariate patterns of activation and a failure to activate executive regions within the cingulo-opercular control network including the frontal pole, cingulate cortex, and anterior insula. Additionally, psychosis-spectrum symptoms were associated with hypo-activation of left dorsolateral prefrontal cortex, whereas behavioral symptoms were associated with hypo-activation of fronto-parietal cortex and cerebellum. In contrast, anxious-misery symptoms were associated with widespread hyper-activation of the executive network.

Conclusions

These findings provide novel evidence that common and dissociable deficits within the brain’s executive system are present in association with dimensions of psychopathology in youth.

INTRODUCTION

Deficits of executive function are present in a wide range of psychiatric disorders including attention deficit hyperactivity disorder (ADHD) (1), conduct disorder (2), and psychotic disorders such as schizophrenia (3,4). Executive deficits negatively impact everyday functioning (5), and contribute to diminished quality of life in many clinical populations (6,7). Consequences of executive deficits may be particularly acute in childhood and adolescence, and include increased interpersonal conflict, decreased academic achievement, and risk-taking behavior (7,8).

Many studies have investigated the neural basis of executive impairments in individual psychiatric disorders. Working memory is one of the most commonly studied components of executive function. For example, meta-analyses in patients with ADHD demonstrate hypoactivation within a network of regions including the dorsolateral prefrontal cortex, anterior cingulate cortex, thalamus, superior parietal lobule, and precuneus (9,10). Patients with schizophrenia also fail to recruit portions of the executive network including the dorsolateral prefrontal cortex (1113). Likewise, conduct disorder is associated with a failure to activate a similar network of regions (14). Taken together, these studies suggest there are likely overlapping effects across disorders, with hypoactivation of the executive system being a common underlying brain phenotype. However, evidence exists for dissociable abnormalities among psychiatric disorders. For example, hyperactivation of executive regions have been reported in major depressive disorder and anxiety disorders (1517). Nonetheless, some heterogeneity in results exists, and such hyperactivation has also frequently been reported in schizophrenia (18).

A case-control design in a single disorder is the predominant approach in the studies reviewed above, and thus most studies are unable to directly evaluate whether common executive deficits exist across disorders. Furthermore, while the incidence of psychiatric co-morbidity is quite high, studies typically do not explicitly evaluate its impact. In epidemiological studies, approximately 40% of individuals meeting criteria for one diagnosis met criteria for at least one additional disorder in a different class (19). Thus, studies that use subjects with “pure” single diagnoses may not be representative. More recently, the neural basis of executive deficits across disorders have been investigated in an attempt to isolate regions of dysfunction specific to each diagnosis (20). However, studies comparing multiple clinical groups are typically hampered by small sample size, and few prior studies compared more than two disorders at a time. Furthermore, as psychiatric symptomatology exists on a continuum of normal to abnormal, dimensional analyses that cut across categorical clinical diagnoses may both enhance power and improve biological interpretability (21). Thus, studies in large samples evaluating the impact of multiple dimensions of psychopathology on executive functioning are necessary.

Accordingly, here we used a dimensional approach to examine executive dysfunction with a working memory fMRI task in a sample of 1,129 youth imaged as part of the Philadelphia Neurodevelopmental Cohort (22,23). Notably, the ascertainment strategy used in the Philadelphia Neurodevelopmental Cohort differs substantially from studies using clinical help-seeking samples, instead employing a population-based approach that examines symptoms in non-help seeking youth. While such a design will likely have lower symptom severity than clinical samples, a population-based approach may nonetheless be valuable for investigating dimensions of psychopathology. We hypothesized that there would be both common and dissociable deficits in executive recruitment associated with different dimensions of psychopathology. Specifically, we predicted that general psychopathology would be associated with executive hypo-activation regardless of clinical diagnosis. Furthermore, we predicted that specific dimensions of psychopathology would be linked to dissociable regional patterns of impairment within the executive network.

METHODS

Participants

As previously described, the Philadelphia Neurodevelopmental Cohort is a collaboration between the Center for Applied Genomics at the Children’s Hospital of Philadelphia and the Brain Behavior Laboratory at the University of Pennsylvania (22,23). The current report considers the entire cross-sectional sample of 1,601 subjects imaged as part of the Philadelphia Neurodevelopmental Cohort; of these, 1,462 completed the n-back task described below. Following subject exclusions including medical co-morbidity, task non-performance, and image quality assurance (see Supplementary Methods), the final sample included in analyses was n=1,129 (mean age = 15.5 years; SD = 3.4 years; 520 males). This sample thus constitutes a super-set of subjects previously included in reports that focused on effects of working memory performance (24) and psychosis-spectrum symptoms (25).

Clinical assessment

As previously detailed (26,27), psychopathology symptoms were evaluated using a structured screening interview (GOASSESS). Computerized algorithms used endorsement of symptoms, their frequency and duration, and the presence of distress or impairment to determine whether DSM-IV criteria were met (see Supplementary Methods). The frequency and relevant demographic data for each screening diagnosis considered are detailed in Table 1. As in large-scale epidemiologic datasets (19), co-morbidity was quite common, with more subjects meeting criteria for more than one category (n=529) than a single category (n=249).

Table 1
Demographic information by screening diagnostic category

Psychopathology factor analysis

In order to parse this co-morbidity into orthogonal dimensions of psychopathology, we performed factor analyses of item-level data from GOASSESS. To produce stable factor scores, analyses utilized all subjects for whom clinical data were available (n=9,498) rather than only the subset who completed neuroimaging (26,27). As initial exploratory factor analyses indicated correlated traits of psychopathology, we used a bifactor confirmatory model, which can produce orthogonal scores from correlated traits (28).

Methods regarding estimation of the bifactor model will be presented in detail elsewhere (also see Supplementary Methods). Briefly, the confirmatory bifactor model (Figure 1A) was estimated using a Bayesian estimator in Mplus. As predicted by theory and supported by initial exploratory models, the four factors primarily represent anxious-misery (mood & anxiety) symptoms, psychosis-spectrum symptoms, behavioral symptoms (conduct and ADHD), and fear symptoms (phobias). Additionally, the bifactor model estimated a general psychopathology factor, representing the overall burden of psychopathology while controlling for the presence of specific symptom dimensions. Importantly, all five factors (including general psychopathology) from the bifactor model are orthogonal and can be considered jointly in analysis of imaging data. Factors scores within each of the categorical screening categories were as expected given item-level loading (Figure 1B).

Figure 1
Bifactor model of common and divergent dimensions of psychopathology across categorical screening diagnoses

Task paradigm, image acquisition and image processing

Task paradigm, image acquisition, and pre-processing methods were as previously reported (24). Briefly, a fractal version of the n-back task (29) was used to probe executive system function across three levels of working memory load (Figure 2A). The primary behavioral measure was d’, a signal detection metric that limits the influence of response bias. Task performance (d’) across all levels of working memory load was related to categorical diagnosis from GOASSESS and dimensional factor scores using linear models while controlling for age and sex. Testing five dimensions of psychopathology were accounted for using Bonferroni correction.

Figure 2
Working memory task paradigm, behavioral performance and contrast of interest

fMRI, T1, and B0 images were acquired on the same scanner (Siemens 3T Tim Trio) for all subjects (see Supplementary Methods). Timeseries analysis of subject-level imaging data used FSL (30) to model three condition blocks (0-back, 1-back, and 2-back); the primary contrast was 2-back > 0-back, which robustly recruits the executive network (24). Subject-level statistical maps were distortion corrected, co-registered to the T1 image using boundary-based registration, and normalized to the MNI 152 1mm template using ANTs (31) and then downsampled to 2mm. All transformations were concatenated so only one interpolation was performed.

Multivariate group-level analysis: Global associations with psychopathology

As a first step, we evaluated the degree to which dimensions of psychopathology from the factor analysis impacted overall multivariate patterns of activation (see Figure S1). To do this, we used multivariate distance-based matrix regression, a statistical technique developed originally for large-scale ecologic datasets that has recently been used in image analysis (32); this was implemented using the vegan package in R (33). Subject-level activation maps are compared on a pairwise basis (Euclidean distance) to yield a distance matrix. Matrix regression is then used to test whether each phenotypic variable explains the distances among each participant’s activation patterns. In contrast to other multivariate methods, this approach allowed us to examine the influence of multiple dimensions of psychopathology simultaneously while also controlling for covariates (age, sex, and in-scanner motion). Multiple tests were accounted for using Bonferroni correction as above.

Mass-univariate group-level analysis: Regional associations with psychopathology

While the multivariate analysis detailed above provided an estimate of the degree to which dimensions of psychopathology impacted the global pattern of activation, this analysis does not evaluate regional effects. Accordingly, we next conducted a standard mass-univariate analysis using a general linear model implemented in FSL (30). We evaluated the effect of each dimension of psychopathology within this linear model, with covariates as above. Additionally, we investigated whether dimensions of psychopathology that showed a significant effect were significantly different from each other using an F-test across dimensions. Type I error control was provided by cluster correction using 10,000 Monte-Carlo simulations (voxel height of z > 3.09; cluster probability of p < 0.001; minimum cluster size of k=67) (34). All analyses described below used unmasked, whole-brain voxelwise data. Images were displayed using Caret.

Supplementary analyses

In order to evaluate whether potentially confounding factors influenced observed results, we conducted a series of supplementary analyses. This included a) removing the minority (11.4%) of subjects who were taking psychoactive medication; b) including task performance (d’) as a covariate; c) excluding subjects with poor performance (>7 non-responses) on the 2-back condition; and d) including mean accuracy on an out-of-scanner computerized neuropsychological battery, race, assessment-scan interval, maternal education, and in-scanner performance (d') together as covariates in one model.

RESULTS

Multiple dimensions of psychopathology impact task performance

As expected, increasing working memory load was associated with fewer correct responses to targets and increased false positive responses to foils (Figure 2B & C). These measures were integrated using the signal detection measure d’. As expected, d’ varied considerably by screening categorical diagnosis (see Table 1). When summarized as psychopathology dimensions, several factors significantly impacted working memory task performance (Figure 2D). Higher levels of both overall psychopathology (t[1124]=2.58, p=0.001) and behavioral symptoms (t[1124]=3.82, p=0.0001) were associated with lower working memory performance. In contrast, higher levels of anxious-misery symptoms were associated with a trend towards better working memory performance (t[1124]=2.45, p=0.015). There was not a significant relationship between fear or psychosis and working memory performance.

Overall psychopathology alters global patterns of executive system recruitment

As expected, the 2-back > 0-back contrast robustly recruited the entire executive network, and resulted in de-activation of non-executive regions (Figure 2E). We next evaluated whether dimensional psychopathology was associated with changes in this multivariate pattern of activation and de-activation using multivariate distance-based matrix regression. This procedure revealed that overall psychopathology was associated with a significant disturbance in the global pattern of executive system recruitment (p=0.009). Other symptom dimensions did not have a significant relationship; non-significant trends were observed for behavioral (p=0.044) and anxious-misery (p=0.092) symptoms.

Dimensions of psychopathology differentially impact regional executive activation

Multivariate distance-based matrix regression evaluates the presence of global multivariate effects, but is not sensitive to regional changes. Accordingly, we used mass-univariate generalized linear models to examine the relationship between dimensions of psychopathology and executive system activation (Figure 3, Table S1). As suggested by multivariate results, overall psychopathology had the most robust impact on executive activation. Higher levels of overall psychopathology were associated with diminished activation of bilateral frontal pole, anterior cingulate cortex, anterior insula, thalamus, and precuneus. Behavioral symptoms were associated with diminished activation of frontoparietal cortex as well as thalamus and cerebellum. Psychosis-spectrum symptoms were associated with diminished activation of the left dorsolateral prefrontal cortex. Fear (phobia symptoms) was associated with diminished activation of a marginally significant cluster of medial frontal cortex, but this effect did not survive supplementary analyses (see below) and was not evaluated further. Finally, in contrast to the hypo-activation described above, anxious-misery symptoms were associated with a marked hyper-activation of multiple executive regions including anterior cingulate cortex, dorsolateral prefrontal cortex, parietal cortex, and thalamus. Notably, a small area of overlapping significant effects across dimensions was seen in the left dorsolateral prefrontal cortex (MNI coordinates: x=−28, y=−6, z=58; k=10). Significant differential effects of each dimension were present in multiple executive regions (Figure 4, Table S2). This was driven by the divergent impact of global psychopathology (hypo-activation) and anxious misery (hyper-activation). We did not find any differences in default mode regions where task-induced deactivation was present.

Figure 3
Relationship of orthogonal dimensions of psychopathology with executive network recruitment
Figure 4
Differential effects of psychopathology on executive system activation

Supplementary analyses

Overall, convergent results were obtained from supplementary analyses where participants taking psychoactive medications were excluded (Table S3), when working memory performance was included as a covariate (Table S4), and when multiple additional covariates were included (Table S5). When participants with poor performance on the 2-back condition were excluded, psychosis-spectrum symptoms were associated with a marginally significant cluster of hyperactivation in the cingulate gyrus; otherwise results were similar (Table S6). As noted above, fear did not have a significant association with activation in any of the supplementary analyses.

DISCUSSION

In this large study of psychopathology in youth, we examined associations between diverse psychopathology and activation of the brain’s executive system during a working memory task. To account for the fact that psychiatric disorders are highly co-morbid, factor analysis of the psychopathology data using a bifactor model allowed us to examine both general psychopathology and orthogonal dimensions of psychopathology. Overall psychopathology was associated with a significant alteration of the global multivariate pattern of activation. Furthermore, dimensions of psychopathology significantly influenced regional patterns of activation in different ways. In contrast to the hypo-activation seen in association with overall psychopathology, anxious misery symptoms were associated with hyper-activation of the same network. Taken together, these data emphasize that dimensions of psychiatric symptomatology are associated with both common and distinct deficits within the executive system.

Evidence for common executive deficits across psychiatric syndromes in youth

The most robust finding in the present study is evidence of common executive deficits attributable to overall psychopathology present across categorical clinical diagnoses. This was observed on both a global and local scale: overall psychopathology was associated with an alteration of the global multivariate pattern of activation, driven by regional hypoactivation in a network of executive regions including the frontal pole, anterior cingulate, anterior insula, and precuneus. These results accord with a copious literature of case-control studies from multiple individual disorders, and moreover highlight the centrality of executive dysfunction across major psychiatric syndromes.

We were able to estimate the influence of overall psychopathology across disorders through the use of a bifactor analysis of the item-level responses from the psychopathology screening interview. This approach obviates several major obstacles to estimating common deficits across traditional categorical diagnoses. First, when co-morbidity is represented in standard models as shared variance, it is controlled for but cannot be estimated. Estimating overall psychopathology may be particularly important in youth where the pattern of psychopathology may not fit standard diagnostic criteria in context of ongoing development. Second, strongly non-random patterns of comorbidity exist between different disorders (19). For example, mood and anxiety disorders have a high rate of comorbidity, as do ADHD and conduct disorders. Third, the frequency of each diagnosis varies considerably; thus the statistical power to estimate the impact of each individual diagnosis is not equal. Fourth, categorical diagnoses are by definition dichotomous, and important dimensional effects cannot be examined.

Crucially, each of these problems can be overcome through modeling of latent dimensions of psychopathology. Here we used confirmatory factor analysis to estimate a bifactor model, which provides orthogonal scores for every subject in each psychopathology domain, as well as a score for general psychopathology. These orthogonal scores can thus be included in a single model that can estimate dissociable dimensions of psychopathology with equal power, as well as the impact of overall psychopathology through the general score. The regions implicated by overall psychopathology include the frontal pole, anterior cingulate cortex, and anterior insula. Notably, all these regions are part of the cingulo-opercular control network (also called the salience or ventral attention network), which is consistently identified as among the most reproducible large-scale functional brain networks both at rest and during task performance (35). This network is critical for cognitive control, and is particularly implicated in maintenance of task sets and error monitoring (36). These regions are among the most commonly impaired in case-control studies of schizophrenia, ADHD, and conduct disorder, suggesting that dysfunction of this network is not associated with one specific domain of psychopathology (4,10,37). Further support is provided from a large-scale meta-analysis by Goodkind et al., who found that gray matter loss in these cingulo-opercular regions was present across psychiatric disorders and associated with impaired executive function (38).

Orthogonal dimensions of psychopathology are associated with dissociable deficits

In addition to robust effects of overall psychopathology, we also identified both regionally and directionally dissociable effects of specific dimensions of psychopathology. Behavioral symptoms were associated with diminished activation of a network of regions including fronto-parietal cortex, thalamus, and cerebellum, whereas psychosis-spectrum symptoms were associated with hypo-activation of the left dorsolateral prefrontal cortex. The behavioral dimension loaded most prominently onto items assessing externalizing disorders such as ADHD, conduct disorder, and oppositional defiant disorder. Results are consistent with case-control studies in each of these disorders, which have demonstrated hypo-function of executive regions including most commonly fronto-parietal regions (10,20). Similarly, dysfunction of the dorsolateral prefrontal cortex is considered one of the cardinal impairments of psychosis (39). However, it should be noted that the effects of the psychosis spectrum observed here are more circumscribed than those typically reported; this may be due to the use of a population rather than a clinical sampling strategy, a focus on sub-threshold psychosis symptoms, and accounting for overall psychopathology through the bifactor model. However, consistent with the current results, in our previous report on psychosis-spectrum symptoms in this sample we observed that hypo-activation of executive regions was linked to cognitive performance but not to severity of positive psychotic symptoms, suggesting a substantial effect of overall impairment rather than psychosis per se (25).

In contrast to the diminished recruitment seen in other symptom domains, anxious-misery symptoms were associated with widespread hyper-activation of the executive network. Indeed, after accounting for overall psychopathology, anxious-misery symptoms were associated with a trend towards better working memory performance. However, executive hyper-activation is unlikely to be simply an epiphenomenon associated with working memory performance, as hyper-activation was still seen in sensitivity analyses that controlled for both in-scanner and out-of-scanner cognitive performance. This result is consistent with several studies of executive function in major depression and anxiety disorders that have found over-activation of executive regions (1517). In contrast to the deficient ability to recruit the executive network seen in association with other symptom dimensions, these data suggest that mood and anxiety symptoms are associated with an inefficient executive system, where higher levels of executive network activation occur. It should be noted that participants in our sample who screened positive for mood and anxiety disorders frequently also had high levels of overall psychopathology. As these dimensions have directionally opposite associations with executive recruitment, the relative presence of co-morbidity in a given sample could conceivably result in divergent results.

Limitations

Certain limitations of the present work should be noted. First, the young community-based sample studied here had diminished symptom severity compared to that found in typically older help-seeking samples drawn from clinical practices. Drawing from a different distribution of symptoms may result in reduced generalizability to clinical populations. However, the approach used here allowed us to accrue a larger, mainly unmedicated sample at a single site and scanner, and is well suited to investigating broad dimensions of psychopathology. A second important limitation is that this cross-sectional report does not include longitudinal data. As our prior work in this dataset has demonstrated that between-subject cross-sectional age effects are relatively subtle (24), within-subject longitudinal data will be particularly informative regarding whether specific symptom domains are associated with development of executive deficits over time. Third, certain important classes of psychopathology such as substance use that have been shown to impact executive function were not considered in the current analysis (40). Fourth, despite prior research showing abnormalities of the default mode network in multiple psychiatric disorders, we did not find significant effects in the default mode network; this may be due to the task paradigm employed. Finally, as previously noted (26,27), the highly-structured screening format of GOASSESS may have resulted in high sensitivity but relatively diminished specificity, leading to over-estimation of the frequency of several disorders. Alternatively, high rates of endorsed symptoms may accurately reflect this particular sample. Although factor-analytic results of the GOASSESS appear consistent with prior findings and lend support to the validity of the measure, ongoing longitudinal follow-up with semi-structured assessments applying DSM disorder criteria will allow further evaluation of the clinical relevance and stability of the current findings.

Conclusions

These data provide novel evidence regarding common and dissociable executive system deficits across multiple dimensions of psychopathology in young people. Results emphasize that executive dysfunction is present in association with overall psychopathology across traditional categorical psychiatric diagnoses, underscoring this system’s central relevance for circuit-based conceptualizations of neuropsychiatric disorders such as the NIMH Research Domain Criteria (RDoC) (21). These results may suggest that interventions seeking to enhance executive function may not fit well within the existing categorical diagnostic framework, and may be beneficial to individuals across diverse clinical syndromes where executive deficits are present. Future research employing longitudinal designs may motivate targeted early interventions that seek to mitigate executive dysfunction in youth before negative outcomes accrue.

Supplementary Material

Figure S1

Supplement

Tables S1-S6

Acknowledgments

Thanks to the acquisition and recruitment team: Karthik Prabhakaran, Jeff Valdez, Raphael Gerraty, Marisa Riley, Jack Keefe, Elliott Yodh, Nicholas DeLeo and Rosetta Chiavacci. Thanks to Frank Mentch for data management. Supported by RC2 grants from the National Institute of Mental Health MH089983 and MH089924 and P50MH096891. Support for developing statistical analyses (TDS, RTS, SNV,) was provided by a seed grant by the Center for Biomedical Computing and Image Analysis (CBICA) at Penn. Additional support was provided by K23MH098130 to TDS, R01MH101111 to DHW, K01MH102609 to DRR, K08MH079364 to MEC, R01NS085211 to RTS, T32MH065218-11 to SNV, and the Dowshen Program for Neuroscience. Raquel E. Gur served on Otsuka Advisory Board.

Footnotes

Previous presentation: Previously presented at the 2015 Annual Meeting of the Society for Biological Psychiatry in Toronto, Canada.

Disclosures: All other study authors report no competing interests.

REFERENCES

1. Barkley RA. Behavioral inhibition, sustained attention, and executive functions: Constructing a unifying theory of ADHD. Psychol Bull. 1997 Jan;121(1):65–94. [PubMed]
2. Sergeant JA, Geurts H, Oosterlaan J. How specific is a deficit of executive functioning for attention-deficit/hyperactivity disorder? Behav Brain Res. 2002 Mar 10;130(1–2):3–28. [PubMed]
3. Fusar-Poli P, Deste G, Smieskova R, Barlati S, Yung AR, Howes O, et al. Cognitive functioning in prodromal psychosis: A meta-analysis. Arch Gen Psychiatry. 2012 Jun;69(6):562–571. [PubMed]
4. Forbes NF, Carrick LA, McIntosh AM, Lawrie SM. Working memory in schizophrenia: A meta-analysis. Psychol Med. 2009 Jun;39(6):889–905. [PubMed]
5. Green MF. What are the functional consequences of neurocognitive deficits in schizophrenia? Am J Psychiatry. 1996 Mar;153(3):321–330. [PubMed]
6. Klassen AF, Miller A, Fine S. Health-related quality of life in children and adolescents who have a diagnosis of attention-deficit/hyperactivity disorder. Pediatrics. 2004 Nov;114(5):e541–e547. [PubMed]
7. Tolman AW, Kurtz MM. Neurocognitive predictors of objective and subjective quality of life in individuals with schizophrenia: A meta-analytic investigation. Schizophr Bull. 2012 Mar;38(2):304–315. [PMC free article] [PubMed]
8. Wehmeier PM, Schacht A, Barkley RA. Social and emotional impairment in children and adolescents with ADHD and the impact on quality of life. J Adolesc Health. 2010 Mar;46(3):209–217. [PubMed]
9. Hart H, Radua J, Nakao T, Mataix-Cols D, Rubia K. Meta-analysis of functional magnetic resonance imaging studies of inhibition and attention in attention-deficit/hyperactivity disorder: Exploring task-specific, stimulant medication, and age effects. JAMA Psychiatry. 2013 Feb;70(2):185–198. [PubMed]
10. Cortese S, Kelly C, Chabernaud C, Proal E, Di Martino A, Milham MP, Castellanos FX. Toward systems neuroscience of ADHD: A meta-analysis of 55 fmri studies. Am J Psychiatry. 2012 Oct;169(10):1038–1055. [PMC free article] [PubMed]
11. Weinberger DR, Berman KF, Zec RF. Physiologic dysfunction of dorsolateral prefrontal cortex in schizophrenia. I. Regional cerebral blood flow evidence. Arch Gen Psychiatry. 1986 Feb;43(2):114–124. [PubMed]
12. Barch DM, Carter CS, Braver TS, Sabb FW, MacDonald A, 3, Noll DC, Cohen JD. Selective deficits in prefrontal cortex function in medication-naive patients with schizophrenia. Arch Gen Psychiatry. 2001 Mar;58(3):280–288. [PubMed]
13. Minzenberg MJ, Laird AR, Thelen S, Carter CS, Glahn DC. Meta-analysis of 41 functional neuroimaging studies of executive function in schizophrenia. Arch Gen Psychiatry. 2009 Aug;66(8):811–822. [PMC free article] [PubMed]
14. Rubia K, Smith AB, Halari R, Matsukura F, Mohammad M, Taylor E, Brammer MJ. Disorder-specific dissociation of orbitofrontal dysfunction in boys with pure conduct disorder during reward and ventrolateral prefrontal dysfunction in boys with pure ADHD during sustained attention. Am J Psychiatry. 2009 Jan;166(1):83–94. [PubMed]
15. Fales CL, Barch DM, Burgess GC, Schaefer A, Mennin DS, Gray JR, Braver TS. Anxiety and cognitive efficiency: Differential modulation of transient and sustained neural activity during a working memory task. Cogn Affect Behav Neurosci. 2008 Sep;8(3):239–253. [PubMed]
16. Matsuo K, Glahn DC, Peluso MAM, Hatch JP, Monkul ES, Najt P, et al. Prefrontal hyperactivation during working memory task in untreated individuals with major depressive disorder. Mol Psychiatry. 2007 Feb;12(2):158–166. [PubMed]
17. Schöning S, Zwitserlood P, Engelien A, Behnken A, Kugel H, Schiffbauer H, et al. Working-memory fmri reveals cingulate hyperactivation in euthymic major depression. Hum Brain Mapp. 2009 Sep;30(9):2746–2756. [PubMed]
18. Callicott JH, Bertolino A, Mattay VS, Langheim FJ, Duyn J, Coppola R, et al. Physiological dysfunction of the dorsolateral prefrontal cortex in schizophrenia revisited. Cereb Cortex. 2000 Nov;10(11):1078–1092. [PubMed]
19. Merikangas KR, He J-P, Burstein M, Swanson SA, Avenevoli S, Cui L, et al. Lifetime prevalence of mental disorders in U.S. Adolescents: Results from the national comorbidity survey replication--adolescent supplement (NCS-A) J Am Acad Child Adolesc Psychiatry. 2010 Oct;49(10):980–989. [PMC free article] [PubMed]
20. Rubia K. "Cool" inferior frontostriatal dysfunction in attention-deficit/hyperactivity disorder versus"hot" ventromedial orbitofrontal-limbic dysfunction in conduct disorder: A review. Biol Psychiatry. 2011 Jun 15;69(12):e69–e87. [PubMed]
21. Insel T, Cuthbert B, Garvey M, Heinssen R, Pine DS, Quinn K, et al. Research domain criteria (rdoc): Toward a new classification framework for research on mental disorders. Am J Psychiatry. 2010 Jul;167(7):748–751. [PubMed]
22. Satterthwaite TD, Elliott MA, Ruparel K, Loughead J, Prabhakaran K, Calkins ME, et al. Neuroimaging of the philadelphia neurodevelopmental cohort. Neuroimage. 2014 Feb 1;86:544–553. [PMC free article] [PubMed]
23. Satterthwaite TD, Connolly JJ, Ruparel K, Calkins ME, Jackson C, Elliott MA, et al. The philadelphia neurodevelopmental cohort: A publicly available resource for the study of normal and abnormal brain development in youth. Neuroimage. 2015 Mar 31; [PMC free article] [PubMed]
24. Satterthwaite TD, Wolf DH, Erus G, Ruparel K, Elliott MA, Gennatas ED, et al. Functional maturation of the executive system during adolescence. J Neurosci. 2013 Oct 9;33(41):16249–16261. [PMC free article] [PubMed]
25. Wolf DH, Satterthwaite TD, Calkins ME, Ruparel K, Elliott MA, Hopson RD, et al. Functional neuroimaging abnormalities in youth with psychosis spectrum symptoms. JAMA Psychiatry. 2015 Mar 18; [PMC free article] [PubMed]
26. Calkins ME, Moore TM, Merikangas KR, Burstein M, Satterthwaite TD, Bilker WB, et al. The psychosis spectrum in a young U.S. Community sample: Findings from the philadelphia neurodevelopmental cohort. World Psychiatry. 2014 Oct;13(3):296–305. [PubMed]
27. Calkins ME, Merikangas KR, Moore TM, Burstein M, Behr MA, Satterthwaite TD, et al. The philadelphia neurodevelopmental cohort: Constructing a deep phenotyping collaborative. J Child Psychol Psychiatry. 2015 Apr 8; [PMC free article] [PubMed]
28. Reise SP. Invited paper: The rediscovery of bifactor measurement models. Multivariate Behav Res. 2012 Sep 1;47(5):667–696. [PMC free article] [PubMed]
29. Ragland JD, Turetsky BI, Gur RC, Gunning-Dixon F, Turner T, Schroeder L, et al. Working memory for complex figures: An fmri comparison of letter and fractal n-back tasks. Neuropsychology. 2002 Jul;16(3):370–379. [PMC free article] [PubMed]
30. Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM. FSL. Neuroimage. 2012 Aug 15;62(2):782–790. [PubMed]
31. Avants BB, Epstein CL, Grossman M, Gee JC. Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain. Med Image Anal. 2008 Feb;12(1):26–41. [PMC free article] [PubMed]
32. Shehzad Z, Kelly C, Reiss PT, Craddock R, Emerson JW, McMahon K, et al. A multivariate distance-based analytic framework for connectome-wide association studies. Neuroimage. 2014 Jun;93(Pt 1):74–94. [PMC free article] [PubMed]
33. Oksanen J, Kindt R, Legendre P, OHara B, Stevens MHH, Oksanen MJ, Suggests M. The vegan package. Community Ecology Package. 2007
34. Cox RW. AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res. 1996 Jun;29(3):162–173. [PubMed]
35. Power JD, Cohen AL, Nelson SM, Wig GS, Barnes KA, Church JA, et al. Functional network organization of the human brain. Neuron. 2011 Nov 17;72(4):665–678. [PMC free article] [PubMed]
36. Dosenbach NU, Fair DA, Miezin FM, Cohen AL, Wenger KK, Dosenbach RA, et al. Distinct brain networks for adaptive and stable task control in humans. Proc Natl Acad Sci U S A. 2007;104(26):11073–11078. [PubMed]
37. Salvadore G, Cornwell BR, Sambataro F, Latov D, Colon-Rosario V, Carver F, et al. Anterior cingulate desynchronization and functional connectivity with the amygdala during a working memory task predict rapid antidepressant response to ketamine. Neuropsychopharmacology. 2010 Jun;35(7):1415–1422. [PMC free article] [PubMed]
38. Goodkind M, Eickhoff SB, Oathes DJ, Jiang Y, Chang A, Jones-Hagata LB, et al. Identification of a common neurobiological substrate for mental illness. JAMA Psychiatry. 2015 Apr 1;72(4):305–315. [PMC free article] [PubMed]
39. Baker JT, Holmes AJ, Masters GA, Yeo BTT, Krienen F, Buckner RL, Öngür D. Disruption of cortical association networks in schizophrenia and psychotic bipolar disorder. JAMA Psychiatry. 2014 Feb;71(2):109–118. [PMC free article] [PubMed]
40. Whelan R, Watts R, Orr CA, Althoff RR, Artiges E, Banaschewski T, et al. Neuropsychosocial profiles of current and future adolescent alcohol misusers. Nature. 2014 Aug 14;512(7513):185–189. [PMC free article] [PubMed]