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Neuroimaging studies point to neurostructural abnormalities in youth with anxiety disorders. Yet, findings are based on small-scale studies, often with small effect sizes, and have limited generalizability and clinical relevance. These issues have prompted a paradigm shift in the field towards highly powered (i.e., big data) individual-level inferences, which are data-driven, transdiagnostic, and neurobiologically informed. Here, we built and validated neurostructural machine learning (ML) models for individual-level inferences based on the largest-ever multi-site neuroimaging sample of youth with anxiety disorders (age: 10-25 years, N=3,343 individuals from 32 global sites), as compiled by three ENIGMA Anxiety Working Groups: Panic Disorder (PD), Generalized Anxiety Disorder (GAD), and Social Anxiety Disorder (SAD). ML classifiers were trained on MRI-derived regional measures of cortical thickness, surface area, and subcortical volumes to classify patients and healthy controls (HC) for each anxiety disorder separately and across disorders (transdiagnostic classification). Modest, yet robust, classification performance was achieved for PD vs. HC (AUC=0.62), but other disorder-specific and transdiagnostic classifications were not significantly different from chance. However, above chance-level transdiagnostic classifications were obtained in exploratory subgroup analyses of male patients vs. male HC, unmedicated patients vs. HC, and patients with low anxiety severity vs. HC (AUC 0.59-0.63). The above chance-level classifications were based on plausible and specific neuroanatomical features in fronto-striato-limbic and temporo-parietal regions. This study provides a realistic estimate of classification performance in a large, ecologically valid, multi-site sample of youth with anxiety disorders, and may as such serve as a benchmark.

Cognitive reappraisal is associated with reduced emotional distress; however, little is known about the nature of this relationship in autism. This study tested whether autistic traits moderate reappraisal success (i.e., the negative correlation between reappraisal use and emotional symptom severity). Emotional symptoms were assessed using measures of depression, anxiety, and stress. It was hypothesized that more severe autistic traits would be associated with weaker reappraisal success across all scales. Data were collected from 377 adults using an on-line survey. Structural equation models found moderation effects for depression and anxiety, but not stress. Contrary to hypotheses, more severe autistic traits were associated with stronger reappraisal success. These preliminary results support including reappraisal in emotion regulation treatments for individuals with autistic traits.

Intrinsic neural timescales (INT) reflect the duration for which brain areas store information. A posterior – anterior hierarchy of increasingly longer INT has been revealed in both typically developed individuals (TD), as well as patients diagnosed with autism spectrum disorder (ASD) and schizophrenia (SZ), though INT are, overall, shorter in both patient groups. In the present study, we attempted to replicate previously reported group differences by comparing INT of TD to ASD and SZ. We replicated the previously reported result showing reduced INT in the left lateral occipital gyrus and the right post-central gyrus in SZ compared to TD. For the first time, we also directly compared the INT of the two patient groups and found that these same two areas show significantly reduced INT in SZ compared to ASD. In ASD, significant correlations were found between INT and their clinical and phenotypic characteristics. Our results point to the left lateral occipital gyrus and the right post-central gyrus as holding potential for further diagnostic refinement of SZ.

The dynamics of the human brain span multiple spatial scales, from connectivity associated with a specific region/network to the global organization, each representing different brain mechanisms. Yet brain reconfigurations at different spatial scales are seldom explored and whether they are associated with the neural aspects of brain disorders is far from understood. In this study, we introduced a dynamic measure called step‐wise functional network reconfiguration (sFNR) to characterize how brain configuration rewires at different spatial scales. We applied sFNR to two independent datasets, one includes 160 healthy controls (HCs) and 151 patients with schizophrenia (SZ) and the other one includes 314 HCs and 255 individuals with autism spectrum disorder (ASD). We found that both SZ and ASD have increased whole‐brain sFNR and sFNR between cerebellar and subcortical/sensorimotor domains. At the ICN level, the abnormalities in SZ are mainly located in ICNs within subcortical, sensory, and cerebellar domains, while the abnormalities in ASD are more widespread across domains. Interestingly, the overlap SZ‐ASD abnormality in sFNR between cerebellar and sensorimotor domains was correlated with the reasoning‐problem‐solving performance in SZ (r = −.1652, p = .0058) as well as the Autism Diagnostic Observation Schedule in ASD (r = .1853, p = .0077). Our findings suggest that dynamic reconfiguration deficits may represent a key intersecting point for SZ and ASD. The investigation of brain dynamics at different spatial scales can provide comprehensive insights into the functional reconfiguration, which might advance our knowledge of cognitive decline and other pathophysiology in brain disorders., In this study, we found that both schizophrenia and autism have increased whole‐brain dynamic network reconfiguration and increased dynamic network reconfiguration associated with cerebellar and subcortical/sensorimotor domains. Interestingly, the diseases' overlapping abnormality in dynamic network reconfiguration between cerebellar and sensorimotor domains was correlated with the reasoning‐problem‐solving performance in schizophrenia as well as the Autism Diagnostic Observation Schedule in autism. Our findings suggest that dynamic functional connectivity deficits may represent a key intersecting point for schizophrenia and autism.

Individuals with generalized anxiety disorder (GAD) report poorer quality of life (QOL) than do nonanxious controls. Further, although positive affect (PA) and negative affect (NA) have been shown to predict QOL, no previous literature has tested this relationship in the context of individuals with GAD. In the present study, we evaluated the unique and interactive contributions of PA and NA on QOL within a sample of individuals diagnosed with GAD (N = 50). Specifically, a hierarchical regression was conducted to evaluate the unique contributions of PA, NA, and their interaction on QOL, over and above symptoms of depression. PA and depression symptoms were both significant predictors of QOL, whereas neither the main effect for NA nor the PA × NA interaction was statistically significant. Results suggest that, for those with GAD, PA uniquely contributes to QOL. Strategies to upregulate PA may be a useful treatment target for increasing QOL in individuals with GAD.

The excitation/inhibition (E/I) ratio has been shown to be elevated in both autism spectrum disorder (ASD) and schizophrenia (SZ), relative to neurotypical controls. However, the degree of E/I imbalance overlap and differentiation between SZ and ASD is not known. Our main objectives were therefore (1) to quantify group differences in the E/I ratio between controls, ASD and SZ, and (2) to assess the potential of the E/I ratio for differential diagnosis. We collected resting state fMRI (rsfMRI) and phenotypic data from 55 controls, 30 ASD, and 39 SZ, ages 18 to 35 (IQ>80). For each participant, we computed the Hurst exponent (H), an indicator of the E/I ratio, for the timecourses of 53 independent components covering the entire brain. Next, using Optimal Classification Trees (OCT), we ran a classification analysis on the two clinical groups using five incremental feature sets (i.e., models): (1) Positive and Negative Syndrome Scale (PANSS) and the Autism Diagnostic Observation Schedule (ADOS) only; (2) PANSS, ADOS, Bermond–Vorst Alexithymia Questionnaire (BVAQ), Empathy Quotient (EQ), and IQ; (3) H only; (4) H, PANSS and ADOS; (5) H, PANSS, ADOS, BVAQ, EQ and IQ. We observed decreased H (i.e., increase in E/I ratio) in ASD and SZ compared to controls, and in SZ compared to ASD in the Cerebellar, Sensorimotor, Visual and Cognitive Control networks. The OCT classification showed a consistent increase in discrimination accuracy across models between ASD and SZ, suggesting that the E/I ratio in combination with phenotypic measures can contribute to differential diagnosis in adults.

Social withdrawal in schizophrenia may be a result of "passive" motivation (reduced drive to engage) or "active" motivation (increased drive to avoid). We conducted a cross-sectional, between-subjects study using self-report measures and social cognition tasks to evaluate the relationships between motivational subtypes, social abilities, and social functioning in schizophrenia spectrum (n = 52, ages 19-34). Regression models showed significant differences in passive and active withdrawal. Passive, but not active, motivation predicted social functioning as measured by a clinical interview. This suggests that motivation, especially passive type, plays an important role in social withdrawal in schizophrenia. However, on a self-report measure of social functioning, neither passive nor active motivation predicted outcomes, suggesting a potential disconnect between observer versus self-report measures when assessing social motivation. Furthermore, performance on tasks of social abilities did not predict motivation, which supports the idea that motivation is distinct from social ability and should be specifically addressed in treatment.

Autism spectrum disorder (ASD) and schizophrenia (SZ) are separate clinical entities but share deficits in social–emotional processing and static neural functional connectivity patterns. We compared patients’ dynamic functional network connectivity (dFNC) state engagement with typically developed (TD) individuals during social–emotional processing after initially characterizing such dynamics in TD. Young adults diagnosed with ASD (n = 42), SZ (n = 41), or TD (n = 55) completed three functional MRI runs, viewing social–emotional videos with happy, sad, or neutral content. We examined dFNC of 53 spatially independent networks extracted using independent component analysis and applied k-means clustering to windowed dFNC matrices, identifying four unique whole-brain dFNC states. TD showed differential engagement (fractional time, mean dwell time) in three states as a function of emotion. During Happy videos, patients spent less time than TD in a happy-associated state and instead spent more time in the most weakly connected state. During Sad videos, only ASD spent more time than TD in a sad-associated state. Additionally, only ASD showed a significant relationship between dFNC measures and alexithymia and social–emotional recognition task scores, potentially indicating different neural processing of emotions in ASD and SZ. Our results highlight the importance of examining temporal whole-brain reconfiguration of FNC, indicating engagement in unique emotion-specific dFNC states.

Cognitive reappraisal is associated with reduced emotional distress; however, little is known about the nature of this relationship in autism. This study tested whether autistic traits moderate reappraisal success (i.e., the negative correlation between reappraisal use and emotional symptom severity). Emotional symptoms were assessed using measures of depression, anxiety, and stress. It was hypothesized that more severe autistic traits would be associated with weaker reappraisal success across all scales. Data were collected from 377 adults using an on-line survey. Structural equation models found moderation effects for depression and anxiety, but not stress. Contrary to hypotheses, more severe autistic traits were associated with stronger reappraisal success. These preliminary results support including reappraisal in emotion regulation treatments for individuals with autistic traits.

We present an exploratory cross-sectional analysis of the effect of season and weather on Freesurfer-derived brain volumes from a sample of 3,279 healthy individuals collected on two MRI scanners in Hartford, CT, USA over a 15 year period. Weather and seasonal effects were analyzed using a single linear regression model with age, sex, motion, scan sequence, time-of-day, month of the year, and the deviation from average barometric pressure, air temperature, and humidity, as covariates. FDR correction for multiple comparisons was applied to groups of non-overlapping ROIs. Significant negative relationships were found between the left- and right-cerebellum cortex and pressure (t = −2.25, p = 0.049; t = −2.771, p = 0.017). Significant positive relationships were found between left- and right-cerebellum cortex and white matter between the comparisons of January/June and January/September. Significant negative relationships were found between several subcortical ROIs for the summer months compared to January. An opposing effect was observed between the supra- and infra-tentorium, with opposite effect directions in winter and summer. Cohen’s d effect sizes from monthly comparisons were similar to those reported in recent psychiatric big-data publications, raising the possibility that seasonal changes and weather may be confounds in large cohort studies. Additionally, changes in brain volume due to natural environmental variation have not been reported before and may have implications for weather-related and seasonal ailments.

Highlights • Autism spectrum disorder (ASD) & schizophrenia (SZ) have mentalizing deficits. • Spatially constrained ICA reveals shared deficits in mentalizing default mode activity. • Mentalizing-related temporoparietal junction activity correlated with ADOS scores in ASD. • Mentalizing-related precuneus activity correlated with tendency to fantasize in SZ. • Both categorical and RDoC approaches to study neural deficits in SZ & ASD are supported., Schizophrenia and autism spectrum disorder (ASD) are nosologically distinct neurodevelopmental disorders with similar deficits in social cognition, including the ability to form mental representations of others (i.e., mentalizing). However, the extent of patient deficit overlap in underlying neural mechanisms is unclear. Our goal was to examine deficits in mentalizing task-related (MTR) activity modulation in schizophrenia and ASD and the relationship of such deficits with social functioning and psychotic symptoms in patients. Adults, ages 18–34, diagnosed with either ASD or schizophrenia, and typically developed controls (n = 30/group), performed an interactive functional MRI Domino task. Using independent component analysis, we analyzed game intervals known to stimulate mentalizing in the default mode network (DMN), i.e., medial prefrontal cortex (MPFC), posterior cingulate cortex (PCC), precuneus, and temporoparietal junction (TPJ), for group differences in MTR activity and associations between MTR activity and social and psychosis measures. Compared to controls, both schizophrenia and ASD groups showed MTR activity deficits in PCC and TPJ. In TPJ and MPFC, MTR activity modulation was associated with social communication impairments only in ASD. In precuneus, MTR activity was associated with increased self-reported fantasizing only in schizophrenia. In schizophrenia, we found no indication of over-mentalizing activity or an association between MTR activity and psychotic symptoms. Results suggest shared neural deficits between ASD and schizophrenia in mentalizing-associated DMN regions; however, neural organization might correspond to different dimensional social deficits. Our results therefore indicate the importance of examining both categorical-clinical diagnosis and social functioning dimensional constructs when examining neural deficits in schizophrenia and ASD.

Schizophrenia (SZ) and Autism Spectrum Disorder (ASD) show overlap in social cognitive and functioning impairments. Proposed predictors of social functioning (SF) and quality of life (QL) have been symptom severity, IQ and social cognition. Empathy has rarely been compared between ASD and SZ and its predictive power on functional outcomes is unclear. We investigated general, affective, and cognitive empathy in 46 SZ, 30 ASD and 51 healthy controls (HC) and examined their relationship to SF and QL in addition to IQ and symptoms. SZ and ASD shared deficits in general and cognitive empathy, and personal distress, but only SZ showed deficits in affective empathy. Both groups showed lower performance-based empathy scores and only ASD showed slower responses compared to HC. Negative symptoms predicted QL in both groups, the more negative symptoms the worse QL (ASD t=-3.22; SZ t= -3.43; p0.01), and only in ASD, IQ predicted QL, the higher the IQ the higher QL (t = 2.1; p0.05). In ASD only, negative symptoms predicted SF, the greater negative symptoms the worse SF (t=-3.45; p0.01), and communication deficits predicted SF, the higher deficits, the higher SF (t = 2.9; p0.01). Negative symptoms but not empathy were the shared predictors of functioning across ASD and SZ.

Despite over 400 peer-reviewed structural MRI publications documenting neuroanatomic abnormalities in bipolar disorder and schizophrenia, the confounding effects of head motion and the regional specificity of these defects are unclear. Using a large cohort of individuals scanned on the same research dedicated MRI with broadly similar protocols, we observe reduced cortical thickness indices in both illnesses, though less pronounced in bipolar disorder. While schizophrenia (n = 226) was associated with wide-spread surface area reductions, bipolar disorder (n = 227) and healthy comparison subjects (n = 370) did not differ. We replicate earlier reports that head motion (estimated from time-series data) influences surface area and cortical thickness measurements and demonstrate that motion influences a portion, but not all, of the observed between-group structural differences. Although the effect sizes for these differences were small to medium, when global indices were covaried during vertex-level analyses, between-group effects became nonsignificant. This analysis raises doubts about the regional specificity of structural brain changes, possible in contrast to functional changes, in affective and psychotic illnesses as measured with current imaging technology. Given that both schizophrenia and bipolar disorder showed cortical thickness reductions, but only schizophrenia showed surface area changes, and assuming these measures are influenced by at least partially unique sets of biological factors, then our results could indicate some degree of specificity between bipolar disorder and schizophrenia. Hum Brain Mapp, 2017. © 2017 Wiley Periodicals, Inc.

Over the recent years there has been a growing debate regarding the extent and nature of the overlap in neuropathology between schizophrenia (SZ) and autism spectrum disorder (ASD). Dynamic functional network connectivity (dFNC) is a recent analysis method that explores temporal patterns of functional connectivity (FC). We compared resting-state dFNC in SZ, ASD and healthy controls (HC), characterized the associations between temporal patterns and symptoms, and performed a three-way classification analysis based on dFNC indices.Resting-state fMRI was collected from 100 young adults: 33 SZ, 33 ASD, 34 HC. Independent component analysis (ICA) was performed, followed by dFNC analysis (window = 33 s, step = 1TR, k-means clustering). Temporal patterns were compared between groups, correlated with symptoms, and classified via cross-validated three-way discriminant analysis.Both clinical groups displayed an increased fraction of time (FT) spent in a state of weak, intra-network connectivity [p .001] and decreased FT in a highly-connected state [p .001]. SZ further showed decreased number of transitions between states [p .001], decreased FT in a widely-connected state [p .001], increased dwell time (DT) in the weakly-connected state [p .001], and decreased DT in the highly-connected state [p = .001]. Social behavior scores correlated with DT in the widely-connected state in SZ [r = 0.416, p = .043], but not ASD. Classification correctly identified SZ at high rates (81.8%), while ASD and HC at lower rates.Results indicate a severe and pervasive pattern of temporal aberrations in SZ (specifically, being "stuck" in a state of weak connectivity), that distinguishes SZ participants from both ASD and HC, and is associated with clinical symptoms.

Cognitive models of generalized anxiety disorder (GAD) propose that cognitive control, broadly construed, and inhibition specifically, play a role in the maintenance of GAD symptoms. However, few studies have explicitly investigated inhibition, and in particular “cold” (non-emotional) inhibition, and its relation to worry and anxiety severity in GAD. Adults with GAD (n = 35) and healthy controls (n = 21) completed computerized Stroop and Go/NoGo tasks, two widely-used tests of inhibition. GAD status predicted significantly worse (slower and less accurate) performance on the Stroop but not the Go/NoGo task. Clinician-rated anxiety severity predicted slower and less accurate Stroop performance over and above the effect of GAD diagnosis but did not predict Go/NoGo performance. Trait worry did not incrementally predict performance on either task. These findings provide qualified support for theoretical models of inhibition impairments in GAD and suggest that inhibition could be a promising target for novel neurocognitive interventions.

Generalized anxiety disorder (GAD) is characterized by emotion regulation difficulties, which are associated with abnormalities in neural circuits encompassing fronto-limbic regions including the dorsolateral prefrontal cortex (DLPFC). The aim of this study was to determine whether DLPFC neuromodulation improves emotion regulation in patients with GAD. This is a secondary analysis from a randomized-controlled trial comparing 30 sessions of low-frequency right-sided active (n=13) versus sham (n=12, sham coil) repetitive transcranial magnetic stimulation (rTMS) at the right DLPFC in patients with GAD. Results indicated statistically significant improvements in self-reported emotion regulation difficulties at posttreatment and 3-month follow-up in the active group only. Improvements were found primarily in the domains of goal-directed behaviors and impulse control and were significantly associated with a global clinician rating of improvement. These preliminary results support rTMS as a treatment for GAD and suggest improved emotion regulation as a possible mechanism of change.

BackgroundRepetitive transcranial magnetic stimulation (rTMS) holds promise for treating generalised anxiety disorder (GAD) but has only been studied in uncontrolled research.AimsThis is the first randomised controlled trial (clinicaltrials.gov: NCT01659736) to investigate the efficacy and neural correlates of rTMS in GAD.MethodTwenty five participants (activen= 13; sham,n= 12) enrolled. rTMS was targeted at the right dorsolateral prefrontal cortex (DLPFC, 1 Hz, 90% resting motor threshold).ResultsResponse and remission rates were higher in the activev.sham groups and there were significant group × time interactions for anxiety, worry and depressive symptoms, favouring activev.sham. In addition, right DLPFC activation during a decision-making gambling task increased at post-treatment for active rTMS only, and changes in neuroactivation correlated significantly with changes in worry symptoms.ConclusionsFindings provide preliminary evidence that rTMS may improve GAD symptoms in association with modifying neural activity in the stimulation site.

The ability to attribute mental states to others, or “mentalizing,” is posited to involve specific subnetworks within the overall default mode network (DMN), but this question needs clarification. To determine which default mode (DM) subnetworks are engaged by mentalizing processes, we assessed task-related recruitment of DM subnetworks. Spatial independent component analysis (sICA) applied to fMRI data using relatively high-order model (75 components). Healthy participants (n = 53, ages 17–60) performed two fMRI tasks: an interactive game involving mentalizing (Domino), a semantic memory task (SORT), and a resting state fMRI scan. sICA of the two tasks split the DMN into 10 subnetworks located in three core regions: medial prefrontal cortex (mPFC; five subnetworks), posterior cingulate/precuneus (PCC/PrC; three subnetworks), and bilateral temporoparietal junction (TPJ). Mentalizing events increased recruitment in five of 10 DM subnetworks, located in all three core DMN regions. In addition, three of these five DM subnetworks, one dmPFC subnetwork, one PCC/PrC subnetwork, and the right TPJ subnetwork, showed reduced recruitment by semantic memory task events. The opposing modulation by the two tasks suggests that these three DM subnetworks are specifically engaged in mentalizing. Our findings, therefore, suggest the unique involvement of mentalizing processes in only three of 10 DM subnetworks, and support the importance of the dmPFC, PCC/PrC, and right TPJ in mentalizing as described in prior studies. Hum Brain Mapp 36:3047–3063, 2015. © 2015 Wiley Periodicals, Inc.

Background Recent evidence suggests that repetitive transcranial magnetic stimulation (rTMS) might be effective in treating generalized anxiety disorder (GAD). Cognitive models of GAD highlight the role of intolerance of uncertainty (IU) in precipitating and maintaining worry, and it has been hypothesized that patients with GAD exhibit decision‐making deficits under uncertain conditions. Improving understanding of the neural mechanisms underlying cognitive deficits associated with IU may lead to the identification of novel rTMS treatment targets and optimization of treatment parameters. The current report describes two interrelated studies designed to identify and verify a potential neural target for rTMS treatment of GAD. Methods Study I explored the integrity of prefrontal cortex (PFC) and amygdala neural networks, which underlie decision making under conditions of uncertainty, in GAD. Individuals diagnosed with GAD (n = 31) and healthy controls (n = 20) completed a functional magnetic resonance imaging (fMRI) gambling task that manipulated uncertainty using high versus low error rates. In a subsequent randomized‐controlled trial (Study II), a subset of the GAD sample (n = 16) completed the fMRI gambling task again after 30 sessions of active versus sham rTMS (1 Hz, right dorsolateral prefrontal cortex) to investigate the modulation of functional networks and symptoms. Results In Study I, participants with GAD demonstrated impairments in PFC‐PFC and PFC‐amygdala functional connectivity (FC) mostly during the high uncertainty condition. In Study II, one region of interest pair, dorsal anterior cingulate (ACC) – subgenual ACC, showed “normalization” of FC following active, but not sham, rTMS, and neural changes were associated with improvement in worry symptoms. Conclusions These results outline a possible treatment mechanism of rTMS in GAD, and pave the way for future studies of treatment optimization.

Theory of mind (ToM) is the ability to understand mental states of others and it is crucial for building sensitivity to other persons or events. Measuring ToM is important for understanding and rehabilitating social cognitive impairments in persons with schizophrenia. The Social Attribution Task-Multiple Choice (SAT-MC) has been successfully employed to measure ToM between individuals with schizophrenia (SZ) and healthy controls (HC) in North America. Given that the SAT-MC uses geometric shapes, is nonverbal and less culturally loaded than other social cognition measures, it may serve for measuring ToM in schizophrenia across cultures. A total of 120 participants (30 per group; Korean SZ; Korean HC; North American SZ; North American HC) were selected from existing databases to examine the reliability and validity of the SAT-MC. Internal consistency, factor structure, measurement invariance, discriminant validity, and convergent/divergent validity were examined. The SAT-MC had good internal consistency regardless of the clinical and cultural group as evidence by Cronbach's α ≥ 0.78 in all groups. Confirmatory factor analysis confirmed the one-factor model with a good model fit (χ2 = 188.122, TLI = 0.958, CFI = 0.963, RMSEA = 0.045). The SAT-MC was sensitive to detect individual differences in ToM of SZ and HC, regardless of culture (p The SAT-MC is a reliable measure for evaluating ToM in both Koreans and North Americans with or without schizophrenia, supporting its potential utility in diverse language and cultures for schizophrenia research.

The second iteration of the Autism Brain Imaging Data Exchange (ABIDE II) aims to enhance the scope of brain connectomics research in Autism Spectrum Disorder (ASD). Consistent with the initial ABIDE effort (ABIDE I), that released 1112 datasets in 2012, this new multisite open-data resource is an aggregate of resting state functional magnetic resonance imaging (MRI) and corresponding structural MRI and phenotypic datasets. ABIDE II includes datasets from an additional 487 individuals with ASD and 557 controls previously collected across 16 international institutions. The combination of ABIDE I and ABIDE II provides investigators with 2156 unique cross-sectional datasets allowing selection of samples for discovery and/or replication. This sample size can also facilitate the identification of neurobiological subgroups, as well as preliminary examinations of sex differences in ASD. Additionally, ABIDE II includes a range of psychiatric variables to inform our understanding of the neural correlates of co-occurring psychopathology; 284 diffusion imaging datasets are also included. It is anticipated that these enhancements will contribute to unraveling key sources of ASD heterogeneity. ispartof: Scientific Data vol:4 ispartof: location:England status: published

Despite over 400 peer-reviewed structural MRI publications documenting neuroanatomic abnormalities in bipolar disorder and schizophrenia, the confounding effects of head motion and the regional specificity of these defects are unclear. Using a large cohort of individuals scanned on the same research dedicated MRI with broadly similar protocols, we observe reduced cortical thickness indices in both illnesses, though less pronounced in bipolar disorder. While schizophrenia (n = 226) was associated with wide-spread surface area reductions, bipolar disorder (n = 227) and healthy comparison subjects (n = 370) did not differ. We replicate earlier reports that head motion (estimated from time-series data) influences surface area and cortical thickness measurements and demonstrate that motion influences a portion, but not all, of the observed between-group structural differences. Although the effect sizes for these differences were small to medium, when global indices were covaried during vertex-level analyses, between-group effects became nonsignificant. This analysis raises doubts about the regional specificity of structural brain changes, possible in contrast to functional changes, in affective and psychotic illnesses as measured with current imaging technology. Given that both schizophrenia and bipolar disorder showed cortical thickness reductions, but only schizophrenia showed surface area changes, and assuming these measures are influenced by at least partially unique sets of biological factors, then our results could indicate some degree of specificity between bipolar disorder and schizophrenia. Hum Brain Mapp 38:3757-3770, 2017. © 2017 Wiley Periodicals, Inc.

We examined performance and functional magnetic resonance imaging activity in participants (n = 235) aged 17-81 years on a nonverbal recognition memory task, figural memory. Reaction time, error rate, and response bias measures indicated that the youngest and oldest participants were faster, made fewer errors, and showed a more conservative response bias than participants in the median age ranges. Encoding and Recognition phases activated a distributed bilateral network encompassing prefrontal, subcortical, lateral, and medial temporal and occipital regions. Activation during Encoding phase did not correlate with age. During Recognition, task-related activation for correctly identified targets (Hit-Targets) correlated linearly positively with age; nontask related activity correlated negative quadratically with age. During correctly identified distractors (Hit-Distractors) activity in task-related regions correlated positive linearly with age, nontask activity showed positive and negative quadratic relationships with age. Missed-Targets activity did not correlate with age. We concluded that figural memory performance and functional magnetic resonance imaging activity during Recognition but not Encoding was affected both by continued maturation of the brain in the early 20s and compensatory recruitment of additional brain regions during recognition memory in old age.

Trauma-related psychopathology has been associated with an intense emotional reaction to stressful event. Emotional responses have evolved to signal the presence of risks to be avoided or of rewards to be approached in the environment. Thus, individuals' sensitivity to signals of risk and reward may affect the level of stress vulnerability. Stress, however, can modify these sensitivities as well. In the current functional magnetic resonance imaging (fMRI) study, we prospectively probed the neural correlates of such sensitivities in 24 healthy soldiers by using an interactive game that encompasses risky and rewarding intervals both pre-exposure and post-exposure to stressful military service. As expected, risky and rewarding intervals elicited selective responses in the amygdala and nucleus accumbens (Nacc), respectively. Furthermore, increased post-traumatic stress disorder symptoms post-exposure (i.e., stress vulnerability) corresponded to greater amygdala's response to risk both pre-exposure and post-exposure and to decreased NAcc response to reward only post-exposure. By combining these regional responsivities post-exposure, we accurately identified all the most vulnerable soldiers. Imbalanced neural responsivity to risk and reward following exposure to stress may therefore constitute a marker for stress vulnerability. Such identification of vulnerability biomarkers can aid future diagnostic and therapeutic efforts by allowing early detection of vulnerability as well as follow up on patient's treatment progression.

Studies demonstrating selective brain networks subserving motivation and mentalization (i.e. attributing states of mind to others) during social interactions have not investigated their mutual independence. We report the results of two fMRI studies using a competitive game requiring players to use implicit ‘on-line’ mentalization simultaneously with motivational processes of gains and losses in playing against a human or a computer opponent. We delineate a network, consisting of bilateral temporoparietal junction, temporal pole (TP), medial prefrontal cortex (MPFC) and right fusiform gyrus, which is sensitive to the opponent's response (challenging>not challenging the player) and opponent type (human>computer). This network is similar to a known explicit ‘off-line’ mentalization circuit, suggesting its additional involvement in implicit ‘on-line’ mentalization, a process more applicable to real-life social interactions. Importantly, only MPFC and TP were selective to mentalization compared to motivation, highlighting their specific operation in attributing states of mind to others during social interactions.

Error-related brain activity has become an increasingly important focus of cognitive neuroscience research utilizing both event-related potentials (ERPs) and functional magnetic resonance imaging (fMRI). Given the significant time and resources required to collect these data, it is important for researchers to plan their experiments such that stable estimates of error-related processes can be achieved efficiently. Reliability of error-related brain measures will vary as a function of the number of error trials and the number of participants included in the averages. Unfortunately, systematic investigations of the number of events and participants required to achieve stability in error-related processing are sparse, and none have addressed variability in sample size. Our goal here is to provide data compiled from a large sample of healthy participants (n = 180) performing a Go/NoGo task, resampled iteratively to demonstrate the relative stability of measures of error-related brain activity given a range of sample sizes and event numbers included in the averages. We examine ERP measures of error-related negativity (ERN/Ne) and error positivity (Pe), as well as event-related fMRI measures locked to false alarms. We find that achieving stable estimates of ERP measures required four to six error trials and approximately 30 participants; fMRI measures required six to eight trials and approximately 40 participants. Fewer trials and participants were required for measures where additional data reduction techniques (i.e., principal component analysis and independent component analysis) were implemented. Ranges of reliability statistics for various sample sizes and numbers of trials are provided. We intend this to be a useful resource for those planning or evaluating ERP or fMRI investigations with tasks designed to measure error-processing.