Functional brain connectivity predictors of prospective substance use initiation and their environmental correlates.

Background: Early substance use initiation (SUI) places youth at substantially higher risk for later substance use disorders. Furthermore, adolescence is a critical period for the maturation of brain networks, the pace and magnitude of which are susceptible to environmental influences and may shape risk for SUI. Methods: We examined whether patterns of functional brain connectivity during rest (rsFC), measured longitudinally in pre-and-early adolescence, can predict future SUI. In an independent sub-sample, we also tested whether these patterns are associated with key environmental factors, specifically neighborhood pollution and socioeconomic dimensions. We utilized data from the Adolescent Brain Cognitive Development (ABCD) Study®. SUI was defined as first-time use of at least one full dose of alcohol, nicotine, cannabis, or other drugs. We created a control group (N = 228) of participants without SUI who were matched with the SUI group (N = 233) on age, sex, race/ethnicity, and parental income and education. Results: Multivariate analysis showed that whole-brain rsFC prior to SUI during 9-10 and 11-12 years of age successfully differentiated the prospective SUI and control groups. This rsFC signature was expressed more at older ages in both groups, suggesting a pattern of accelerated maturation in the SUI group in the years prior to SUI. In an independent sub-sample (N = 2,854) and adjusted for family socioeconomic factors, expression of this rsFC pattern was associated with higher pollution, but not neighborhood disadvantage. Conclusion: Brain functional connectivity patterns in early adolescence that are linked to accelerated maturation and environmental exposures can predict future SUI in youth.

Data-driven, generalizable prediction of adolescent sleep disturbances in the multisite Adolescent Brain Cognitive Development Study.

STUDY OBJECTIVES: Sleep disturbances are common in adolescence and associated with a host of negative outcomes. Here, we assess associations between multifaceted sleep disturbances and a broad set of psychological, cognitive, and demographic variables using a data-driven approach, canonical correlation analysis (CCA). METHODS: Baseline data from 9093 participants from the Adolescent Brain Cognitive Development (ABCD) Study were examined using CCA, a multivariate statistical approach that identifies many-to-many associations between two sets of variables by finding combinations for each set of variables that maximize their correlation. We combined CCA with leave-one-site-out cross-validation across ABCD sites to examine the robustness of results and generalizability to new participants. The statistical significance of canonical correlations was determined by non-parametric permutation tests that accounted for twin, family, and site structure. To assess the stability of the associations identified at baseline, CCA was repeated using 2-year follow-up data from 4247 ABCD Study participants. RESULTS: Two significant sets of associations were identified: (1) difficulty initiating and maintaining sleep and excessive daytime somnolence were strongly linked to nearly all domains of psychopathology (r2 = 0.36, p < .0001); (2) sleep breathing disorders were linked to BMI and African American/black race (r2 = 0.08, p < .0001). These associations generalized to unseen participants at all 22 ABCD sites and were replicated using 2-year follow-up data. CONCLUSIONS: These findings underscore interwoven links between sleep disturbances in early adolescence and psychological, social, and demographic factors.

Examining the association between posttraumatic stress disorder and disruptions in cortical networks identified using data-driven methods.

Posttraumatic stress disorder (PTSD) is associated with lower cortical thickness (CT) in prefrontal, cingulate, and insular cortices in diverse trauma-affected samples. However, some studies have failed to detect differences between PTSD patients and healthy controls or reported that PTSD is associated with greater CT. Using data-driven dimensionality reduction, we sought to conduct a well-powered study to identify vulnerable networks without regard to neuroanatomic boundaries. Moreover, this approach enabled us to avoid the excessive burden of multiple comparison correction that plagues vertex-wise methods. We derived structural covariance networks (SCNs) by applying non-negative matrix factorization (NMF) to CT data from 961 PTSD patients and 1124 trauma-exposed controls without PTSD. We used regression analyses to investigate associations between CT within SCNs and PTSD diagnosis (with and without accounting for the potential confounding effect of trauma type) and symptom severity in the full sample. We performed additional regression analyses in subsets of the data to examine associations between SCNs and comorbid depression, childhood trauma severity, and alcohol abuse. NMF identified 20 unbiased SCNs, which aligned closely with functionally defined brain networks. PTSD diagnosis was most strongly associated with diminished CT in SCNs that encompassed the bilateral superior frontal cortex, motor cortex, insular cortex, orbitofrontal cortex, medial occipital cortex, anterior cingulate cortex, and posterior cingulate cortex. CT in these networks was significantly negatively correlated with PTSD symptom severity. Collectively, these findings suggest that PTSD diagnosis is associated with widespread reductions in CT, particularly within prefrontal regulatory regions and broader emotion and sensory processing cortical regions.

Socioeconomic resources in youth are linked to divergent patterns of network integration and segregation across the brain's transmodal axis.

Socioeconomic resources (SER) calibrate the developing brain to the current context, which can confer or attenuate risk for psychopathology across the lifespan. Recent multivariate work indicates that SER levels powerfully influence intrinsic functional connectivity patterns across the entire brain. Nevertheless, the neurobiological meaning of these widespread alterations remains poorly understood, despite its translational promise for early risk identification, targeted intervention, and policy reform. In the present study, we leverage the resources of graph theory to precisely characterize multivariate and univariate associations between household SER and the functional integration and segregation (i.e., participation coefficient, within-module degree) of brain regions across major cognitive, affective, and sensorimotor systems during the resting state in 5,821 youth (ages 9-10 years) from the Adolescent Brain Cognitive Development (ABCD) Study. First, we establish that decomposing the brain into profiles of integration and segregation captures more than half of the multivariate association between SER and functional connectivity with greater parsimony (100-fold reduction in number of features) and interpretability. Second, we show that the topological effects of SER are not uniform across the brain; rather, higher SER levels are related to greater integration of somatomotor and subcortical systems, but greater segregation of default mode, orbitofrontal, and cerebellar systems. Finally, we demonstrate that the effects of SER are spatially patterned along the unimodal-transmodal gradient of brain organization. These findings provide critical interpretive context for the established and widespread effects of SER on brain organization, indicating that SER levels differentially configure the intrinsic functional architecture of developing unimodal and transmodal systems. This study highlights both sensorimotor and higher-order networks that may serve as neural markers of environmental stress and opportunity, and which may guide efforts to scaffold healthy neurobehavioral development among disadvantaged communities of youth.

Aberrant Effective Connectivity During Eye Gaze Processing Is Linked to Social Functioning and Symptoms in Schizophrenia.

BACKGROUND: Patients with schizophrenia show abnormal gaze processing, which is associated with social dysfunction. These abnormalities are related to aberrant connectivity among brain regions that are associated with visual processing, social cognition, and cognitive control. In this study, we investigated 1) how effective connectivity during gaze processing is disrupted in schizophrenia and 2) how this may contribute to social dysfunction and clinical symptoms. METHODS: Thirty-nine patients with schizophrenia/schizoaffective disorder (SZ) and 33 healthy control participants completed an eye gaze processing task during functional magnetic resonance imaging. Participants viewed faces with different gaze angles and performed explicit and implicit gaze processing. Four brain regions-the secondary visual cortex, posterior superior temporal sulcus, inferior parietal lobule, and posterior medial frontal cortex-were identified as nodes for dynamic causal modeling analysis. RESULTS: Both the SZ and healthy control groups showed similar model structures for general gaze processing. Explicit gaze discrimination led to changes in effective connectivity, including stronger excitatory, bottom-up connections from the secondary visual cortex to the posterior superior temporal sulcus and inferior parietal lobule and inhibitory, top-down connections from the posterior medial frontal cortex to the secondary visual cortex. Group differences in top-down modulation from the posterior medial frontal cortex to the posterior superior temporal sulcus and inferior parietal lobule were noted, such that these inhibitory connections were attenuated in the healthy control group but further strengthened in the SZ group. Connectivity was associated with social dysfunction and symptom severity. CONCLUSIONS: The SZ group showed notably stronger top-down inhibition during explicit gaze discrimination, which was associated with more social dysfunction but less severe symptoms among patients. These findings help pinpoint neural mechanisms of aberrant gaze processing and may serve as future targets for interventions that combine neuromodulation with social cognitive training.

Neurobehavioral indices of gaze perception are associated with social cognition across schizophrenia patients and healthy controls.

BACKGROUND: Gaze perception is a basic building block of social cognition, which is impaired in schizophrenia (SZ) and contributes to functional outcomes. Few studies, however, have investigated neural underpinnings of gaze perception and their relation to social cognition. We address this gap. METHOD: We recruited 77 SZ patients and 71 healthy controls, who completed various social-cognition tasks. During functional magnetic resonance imaging, participants (62 SZ, 54 controls) completed a gaze-perception task, where they judged whether faces with varying gaze angles were self-directed or averted; as a control condition, participants identified stimulus gender. Activation estimates were extracted based on (a) task versus baseline, (b) gaze-perception versus gender-identification, (c) parametric modulation by perception of stimuli as self-directed versus averted, and (d) parametric modulation by stimulus gaze angle. We used latent variable analysis to test associations among diagnostic group, brain activation, gaze perception, and social cognition. RESULTS: Preferential activation to gaze perception was observed throughout dorsomedial prefrontal cortex, superior temporal sulcus, and insula. Activation was modulated by stimulus gaze angle and perception of stimuli as self-directed versus averted. More precise gaze perception and higher task-related activation were associated with better social cognition. Patients with SZ showed hyperactivation within left pre-/postcentral gyrus, which was associated with more precise gaze perception and fewer symptoms and thus may be a compensatory mechanism. CONCLUSIONS: Neural and behavioral indices of gaze perception were related to social cognition, across patients and controls. This suggests gaze perception is an important perceptual building block for more complex social cognition. Results are discussed in the context of dimensional psychopathology and clinical heterogeneity. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Generalizable prediction of childhood ADHD symptoms from neurocognitive testing and youth characteristics.

Childhood attention-deficit/hyperactivity disorder (ADHD) symptoms are believed to result from disrupted neurocognitive development. However, evidence for the clinical and predictive value of neurocognitive assessments in this context has been mixed, and there have been no large-scale efforts to quantify their potential for use in generalizable models that predict individuals' ADHD symptoms in new data. Using data drawn from the Adolescent Brain Cognitive Development Study (ABCD), a consortium that recruited a diverse sample of over 10,000 youth (ages 9-10 at baseline) across 21 U.S. sites, we develop and test cross-validated machine learning models for predicting youths' ADHD symptoms using neurocognitive abilities, demographics, and child and family characteristics. Models used baseline demographic and biometric measures, geocoded neighborhood data, youth reports of child and family characteristics, and neurocognitive tests to predict parent- and teacher-reported ADHD symptoms at the 1-year and 2-year follow-up time points. Predictive models explained 15-20% of the variance in 1-year ADHD symptoms for ABCD Study sites that were left out of the model-fitting process and 12-13% of the variance in 2-year ADHD symptoms. Models displayed high generalizability across study sites and trivial loss of predictive power when transferred from training data to left-out data. Features from multiple domains contributed meaningfully to prediction, including neurocognition, sex, self-reported impulsivity, parental monitoring, and screen time. This work quantifies the information value of neurocognitive abilities and other child characteristics for predicting ADHD symptoms and provides a foundational method for predicting individual youths' symptoms in new data across contexts.

Neighborhood air pollution is negatively associated with neurocognitive maturation in early adolescence.

The ability to maintain focus and process task-relevant information continues developing during adolescence, but the specific physical environmental factors that influence this development remain poorly characterized. One candidate factor is air pollution. Evidence suggests that small particulate matter and NO2 concentrations in the air may negatively impact cognitive development in childhood. We assessed the relationship between neighborhood air pollution and the changes in performance on the n-back task, a test of attention and working memory, in the Adolescent Brain Cognitive Development (ABCD) Study's baseline (ages 9-10) and two-year-follow-up releases (Y2, ages 11-12; n = 5,256). In the behavioral domain, multiple linear regression showed that developmental change in n-back task performance was negatively associated with neighborhood air pollution (ß = -.044, t = -3.11, p = .002), adjusted for covariates capturing baseline cognitive performance of the child, their parental income and education, family conflicts, and their neighborhood's population density, crime rate, perceived safety, and Area Deprivation Index (ADI). The strength of the adjusted association for air pollution was similar to parental income, family conflict, and neighborhood ADI. In the neuroimaging domain, we evaluated a previously published youth cognitive composite Connectome-based Predictive Model (ccCPM), and again found that decreased developmental change in the strength of the ccCPM from pre- to early adolescence was associated with neighborhood air pollution (ß = -.110, t = -2.69, p = .007), adjusted for the covariates mentioned above and head motion. Finally, we found that the developmental change in ccCPM strength was predictive of the developmental change in n-back performance (r = .157, p < .001), and there was an indirect-only mediation where the effect of air pollution on change in n-back performance was mediated by the change in the ccCPM strength (ßindirect effect = -.013, p = .029). In conclusion, neighborhood air pollution is associated with lags in the maturation of youth cognitive performance and decreased strengthening of the brain networks supporting cognitive abilities over time.

Dissecting structural connectivity of the left and right inferior frontal cortex in children who stutter.

Inferior frontal cortex pars opercularis (IFCop) features a distinct cerebral dominance and vast functional heterogeneity. Left and right IFCop are implicated in developmental stuttering. Weak left IFCop connections and divergent connectivity of hyperactive right IFCop regions have been related to impeded speech. Here, we reanalyzed diffusion magnetic resonance imaging data from 83 children (41 stuttering). We generated connection probability maps of functionally segregated area 44 parcels and calculated hemisphere-wise analyses of variance. Children who stutter showed reduced connectivity of executive, rostral-motor, and caudal-motor corticostriatal projections from the left IFCop. We discuss this finding in the context of tracing studies from the macaque area 44, which leads to the need to reconsider current models of speech motor control. Unlike the left, the right IFCop revealed increased connectivity of the inferior posterior ventral parcel and decreased connectivity of the posterior dorsal parcel with the anterior insula, particularly in stuttering boys. This divergent connectivity pattern in young children adds to the debate on potential core deficits in stuttering and challenges the theory that right hemisphere differences might exclusively indicate compensatory changes that evolve from lifelong exposure. Instead, early right prefrontal connectivity differences may reflect additional brain signatures of aberrant cognition-emotion-action influencing speech motor control.

Resting-State Connectivity and Response to Psychotherapy Treatment in Adolescents and Adults With OCD: A Randomized Clinical Trial.

OBJECTIVE: Cortical-subcortical hyperconnectivity related to affective-behavioral integration and cortical network hypoconnectivity related to cognitive control have been demonstrated in obsessive-compulsive disorder (OCD); the study objective was to examine whether these connectivity patterns predict treatment response. METHODS: Adolescents (ages 12-17) and adults (ages 24-45) were randomly assigned to 12 sessions of exposure and response prevention (ERP) or stress management therapy (SMT), an active control. Before treatment, resting-state connectivity of ventromedial prefrontal cortical (vmPFC), cingulo-opercular, frontoparietal, and subcortical regions was assessed with functional MRI. OCD severity was assessed with the Yale-Brown Obsessive Compulsive Scale before, during, and after treatment. Usable fMRI and longitudinal symptom data were obtained from 116 patients (68 female; 54 adolescents; 60 medicated). RESULTS: ERP produced greater decreases in symptom scores than SMT. ERP was selectively associated with less vmPFC-subcortical (caudate and thalamus) connectivity in both age groups and primarily in unmedicated participants. Greater symptom improvement with both ERP and SMT was associated with greater cognitive-control (cingulo-opercular and frontoparietal) and subcortical (putamen) connectivity across age groups. Developmental specificity was observed across ERP and SMT treatments, such that greater improvements with ERP than SMT were associated with greater frontoparietal-subcortical (nucleus accumbens) connectivity in adolescents but greater connectivity between frontoparietal regions in adults. Comparison of response-predictive connections revealed no significant differences compared with a matched healthy control group. CONCLUSIONS: The results suggest that less vmPFC-subcortical connectivity related to affect-influenced behavior may be important for ERP engagement, whereas greater cognitive-control and motor circuit connectivity may generally facilitate response to psychotherapy. Finally, neural predictors of treatment response may differ by age.

Socioeconomic resources are associated with distributed alterations of the brain's intrinsic functional architecture in youth.

Little is known about how exposure to limited socioeconomic resources (SER) in childhood gets "under the skin" to shape brain development, especially using rigorous whole-brain multivariate methods in large, adequately powered samples. The present study examined resting state functional connectivity patterns from 5821 youth in the Adolescent Brain Cognitive Development (ABCD) study, employing multivariate methods across three levels: whole-brain, network-wise, and connection-wise. Across all three levels, SER was associated with widespread alterations across the connectome. However, critically, we found that parental education was the primary driver of neural associations with SER. These parental education associations with the developing connectome exhibited notable concentrations in somatosensory and subcortical regions, and they were partially accounted for by home enrichment activities, child's cognitive abilities, and child's grades, indicating interwoven links between parental education, child stimulation, and child cognitive performance. These results add a new data-driven, multivariate perspective on links between household SER and the child's developing functional connectome.

Differentiated nomological networks of internalizing, externalizing, and the general factor of psychopathology ('p factor') in emerging adolescence in the ABCD study.

BACKGROUND: Structural models of psychopathology consistently identify internalizing (INT) and externalizing (EXT) specific factors as well as a superordinate factor that captures their shared variance, the p factor. Questions remain, however, about the meaning of these data-driven dimensions and the interpretability and distinguishability of the larger nomological networks in which they are embedded. METHODS: The sample consisted of 10 645 youth aged 9-10 years participating in the multisite Adolescent Brain and Cognitive Development (ABCD) Study. p, INT, and EXT were modeled using the parent-rated Child Behavior Checklist (CBCL). Patterns of associations were examined with variables drawn from diverse domains including demographics, psychopathology, temperament, family history of substance use and psychopathology, school and family environment, and cognitive ability, using instruments based on youth-, parent-, and teacher-report, and behavioral task performance. RESULTS: p exhibited a broad pattern of statistically significant associations with risk variables across all domains assessed, including temperament, neurocognition, and social adversity. The specific factors exhibited more domain-specific patterns of associations, with INT exhibiting greater fear/distress and EXT exhibiting greater impulsivity. CONCLUSIONS: In this largest study of hierarchical models of psychopathology to date, we found that p, INT, and EXT exhibit well-differentiated nomological networks that are interpretable in terms of neurocognition, impulsivity, fear/distress, and social adversity. These networks were, in contrast, obscured when relying on the a priori Internalizing and Externalizing dimensions of the CBCL scales. Our findings add to the evidence for the validity of p, INT, and EXT as theoretically and empirically meaningful broad psychopathology liabilities.

Automated Brain Masking of Fetal Functional MRI with Open Data.

Fetal resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a critical new approach for characterizing brain development before birth. Despite the rapid and widespread growth of this approach, at present, we lack neuroimaging processing pipelines suited to address the unique challenges inherent in this data type. Here, we solve the most challenging processing step, rapid and accurate isolation of the fetal brain from surrounding tissue across thousands of non-stationary 3D brain volumes. Leveraging our library of 1,241 manually traced fetal fMRI images from 207 fetuses, we trained a Convolutional Neural Network (CNN) that achieved excellent performance across two held-out test sets from separate scanners and populations. Furthermore, we unite the auto-masking model with additional fMRI preprocessing steps from existing software and provide insight into our adaptation of each step. This work represents an initial advancement towards a fully comprehensive, open-source workflow, with openly shared code and data, for fetal functional MRI data preprocessing.

Widespread attenuating changes in brain connectivity associated with the general factor of psychopathology in 9- and 10-year olds.

Convergent research identifies a general factor ("P factor") that confers transdiagnostic risk for psychopathology. Large-scale networks are key organizational units of the human brain. However, studies of altered network connectivity patterns associated with the P factor are limited, especially in early adolescence when most mental disorders are first emerging. We studied 11,875 9- and 10-year olds from the Adolescent Brain and Cognitive Development (ABCD) study, of whom 6593 had high-quality resting-state scans. Network contingency analysis was used to identify altered interconnections associated with the P factor among 16 large-scale networks. These connectivity changes were then further characterized with quadrant analysis that quantified the directionality of P factor effects in relation to neurotypical patterns of positive versus negative connectivity across connections. The results showed that the P factor was associated with altered connectivity across 28 network cells (i.e., sets of connections linking pairs of networks); pPERMUTATION values < 0.05 FDR-corrected for multiple comparisons. Higher P factor scores were associated with hypoconnectivity within default network and hyperconnectivity between default network and multiple control networks. Among connections within these 28 significant cells, the P factor was predominantly associated with "attenuating" effects (67%; pPERMUTATION < 0.0002), i.e., reduced connectivity at neurotypically positive connections and increased connectivity at neurotypically negative connections. These results demonstrate that the general factor of psychopathology produces attenuating changes across multiple networks including default network, involved in spontaneous responses, and control networks involved in cognitive control. Moreover, they clarify mechanisms of transdiagnostic risk for psychopathology and invite further research into developmental causes of distributed attenuated connectivity.

Brain-wide functional connectivity patterns support general cognitive ability and mediate effects of socioeconomic status in youth.

General cognitive ability (GCA) is an individual difference dimension linked to important academic, occupational, and health-related outcomes and its development is strongly linked to differences in socioeconomic status (SES). Complex abilities of the human brain are realized through interconnections among distributed brain regions, but brain-wide connectivity patterns associated with GCA in youth, and the influence of SES on these connectivity patterns, are poorly understood. The present study examined functional connectomes from 5937 9- and 10-year-olds in the Adolescent Brain Cognitive Development (ABCD) multi-site study. Using multivariate predictive modeling methods, we identified whole-brain functional connectivity patterns linked to GCA. In leave-one-site-out cross-validation, we found these connectivity patterns exhibited strong and statistically reliable generalization at 19 out of 19 held-out sites accounting for 18.0% of the variance in GCA scores (cross-validated partial η2). GCA-related connections were remarkably dispersed across brain networks: across 120 sets of connections linking pairs of large-scale networks, significantly elevated GCA-related connectivity was found in 110 of them, and differences in levels of GCA-related connectivity across brain networks were notably modest. Consistent with prior work, socioeconomic status was a strong predictor of GCA in this sample, and we found that distributed GCA-related brain connectivity patterns significantly statistically mediated this relationship (mean proportion mediated: 15.6%, p < 2 × 10-16). These results demonstrate that socioeconomic status and GCA are related to broad and diffuse differences in functional connectivity architecture during early adolescence, potentially suggesting a mechanism through which socioeconomic status influences cognitive development.

The General Factor of Psychopathology in the Adolescent Brain Cognitive Development (ABCD) Study: A Comparison of Alternative Modeling Approaches.

Many models of psychopathology include a single general factor of psychopathology (GFP) or "p factor" to account for covariation across symptoms. The Adolescent Brain Cognitive Development (ABCD) Study provides a rich opportunity to study the development of the GFP. However, a variety of approaches for modeling the GFP have emerged, raising questions about how modeling choices impact estimated GFP scores. We used the ABCD baseline assessment (ages 9-10 years-old; N=11,875) of the parent-rated Child Behavior Checklist (CBCL) to examine the implications of modeling the GFP using items versus scales; using a priori CBCL scales versus data-driven dimensions; and using bifactor, higher-order, or single-factor models. Children's rank-ordering on the GFP was stable across models, with GFP scores similarly related to criterion variables. Results suggest that while theoretical debates about modeling the GFP continue, the practical implications of these choices for rank-ordering children and assessing external associations will often be modest.

Boost in Test-Retest Reliability in Resting State fMRI with Predictive Modeling.

Recent studies found low test-retest reliability in functional magnetic resonance imaging (fMRI), raising serious concerns among researchers, but these studies mostly focused on the reliability of individual fMRI features (e.g., individual connections in resting state connectivity maps). Meanwhile, neuroimaging researchers increasingly employ multivariate predictive models that aggregate information across a large number of features to predict outcomes of interest, but the test-retest reliability of predicted outcomes of these models has not previously been systematically studied. Here we apply 10 predictive modeling methods to resting state connectivity maps from the Human Connectome Project dataset to predict 61 outcome variables. Compared with mean reliability of individual resting state connections, we find mean reliability of the predicted outcomes of predictive models is substantially higher for all 10 modeling methods assessed. Moreover, improvement was consistently observed across all scanning and processing choices (i.e., scan lengths, censoring thresholds, volume- vs. surface-based processing). For the most reliable methods, the reliability of predicted outcomes was mostly, though not exclusively, in the "good" range (above 0.60). Finally, we identified three mechanisms that help to explain why predicted outcomes of predictive models have higher reliability than individual imaging features. We conclude that researchers can potentially achieve higher test-retest reliability by making greater use of predictive models.

Treatment-Specific Associations Between Brain Activation and Symptom Reduction in OCD Following CBT: A Randomized fMRI Trial.

OBJECTIVE: The authors sought to examine whether brain activity is associated with treatment response to cognitive-behavioral therapy (CBT) in adolescents and adults with obsessive-compulsive disorder (OCD), and whether any associations are treatment specific relative to an active control psychotherapy (stress management therapy; SMT). METHODS: Eighty-seven patients with OCD (age range 12-45 years; 57 female, 39 medicated) were randomly assigned to receive 12 weeks of CBT or SMT. Prior to treatment, functional MRI scans were conducted in patients performing an incentive flanker task, which probes brain activation to both cognitive control and reward processing. Voxelwise linear mixed-effects models examined whether baseline brain activation was differentially associated with change in scores on the Yale-Brown Obsessive Compulsive Scale (standard or Children's version) over the course of CBT or SMT treatment. RESULTS: Within the CBT group, a better treatment response was significantly associated with greater pretreatment activation within the right temporal lobe and rostral anterior cingulate cortex during cognitive control and within the ventromedial prefrontal, orbitofrontal, lateral prefrontal, and amygdala regions during reward processing. In contrast, reduced pretreatment activation within a largely overlapping set of regions was significantly associated with a better treatment response to SMT. CONCLUSIONS: The study findings demonstrate that associations between brain activation and treatment response were treatment specific to CBT relative to a control psychotherapy and that these associations were stable from adolescence to mature adulthood. Such treatment-specific associations are important for the development of biomarkers to personalize treatment in OCD.

Dynamic causal modeling of eye gaze processing in schizophrenia.

BACKGROUND: Abnormal eye gaze perception is related to symptoms and social functioning in schizophrenia. However, little is known about the brain network mechanisms underlying these abnormalities. Here, we employed dynamic causal modeling (DCM) of fMRI data to discover aberrant effective connectivity within networks associated with eye gaze processing in schizophrenia. METHODS: Twenty-seven patients (schizophrenia/schizoaffective disorder, SZ) and 22 healthy controls (HC) completed an eye gaze processing task during fMRI. Participants viewed faces with different gaze angles and performed explicit gaze discrimination (Gaze: "Looking at you?" yes/no) or implicit gaze processing (Gender: "male or female?"). Four brain regions, the secondary visual cortex (Vis), posterior superior temporal sulcus (pSTS), inferior parietal lobule (IPL), and posterior medial frontal cortex (pMFC) were identified as nodes for subsequent DCM analysis. RESULTS: SZ and HC showed similar generative model structure, but SZ showed altered connectivity for specific self-connections, inter-regional connections during all gaze processing (reduced excitatory bottom-up and enhanced inhibitory top-down connections), and modulation by explicit gaze discrimination (increased frontal inhibition of visual cortex). Altered effective connectivity was significantly associated with poorer social cognition and functioning. CONCLUSIONS: General gaze processing in SZ is associated with distributed cortical dysfunctions and bidirectional connectivity between regions, while explicit gaze discrimination involves predominantly top-down abnormalities in the visual system. These results suggest plausible neural mechanisms underpinning gaze processing deficits and may serve as bio-markers for intervention.

Associations between resting-state functional connectivity and treatment response in a randomized clinical trial for posttraumatic stress disorder.

BACKGROUND: Alterations in resting-state functional connectivity (rsFC) have been reported in posttraumatic stress disorder (PTSD). Here, we examined pre- and post-treatment rsFC during a randomized clinical trial to characterize alterations and examine predictors of treatment response. METHODS: Sixty-four combat veterans with PTSD were randomly assigned to prolonged exposure (PE) plus placebo, sertraline plus enhanced medication management, or PE plus sertraline. Symptom assessment and resting-state functional magnetic resonance imaging (fMRI) scans occurred before and after treatment. Twenty-nine trauma-exposed combat veterans without PTSD served as a control group at intake. Seed-based and region of interest (ROI)-to-ROI connectivities, as well as an exploratory connectome-based approach were used to analyze rsFC patterns. Based on previously reported findings, analyses focused on Salience Network (SN) and Default-Mode Network (DMN). RESULTS: At intake, patients with PTSD showed greater DMN-dorsal attention network (DAN) connectivity (between ventromedial prefrontal cortex and superior parietal lobule; family-wise error corrected p = .011), greater SN-DAN connectivity (between insula and middle frontal gyrus; corrected p = .003), and a negative correlation between re-experiencing symptoms and within-DMN connectivity (between posterior cingulate cortex (PCC) and middle temporal gyrus; corrected p < .001). We also found preliminary evidence for associations between rsFC and treatment response. Specifically, high responders (≥50% PTSD symptom improvement), compared with low responders, had greater SN-DMN segregation (i.e., less pre-treatment amygdala-PCC connectivity; p = .011) and lower pre-treatment global centrality (p = .042). CONCLUSIONS: Our findings suggest neural abnormalities in PTSD and may inform future research examining neural biomarkers of PTSD treatment response.