Clinically useful brain imaging for neuropsychiatry: How can we get there?

Predictive modelling of brain disorders with magnetic resonance imaging: A systematic review of modelling practices, transparency, and interpretability in the use of convolutional neural networks

Brain disorders comprise several psychiatric and neurological disorders which can be characterized by impaired cognition, mood alteration, psychosis, depressive episodes, and neurodegeneration. Clinical diagnoses primarily rely on a combination of life history information and questionnaires, with a distinct lack of discriminative biomarkers in use for psychiatric disorders. Symptoms across brain conditions are associated with functional alterations of cognitive and emotional processes, which can correlate with anatomical variation; structural magnetic resonance imaging (MRI) data of the brain are therefore an important focus of research, particularly for predictive modelling. With the advent of large MRI data consortia (such as the Alzheimer's Disease Neuroimaging Initiative) facilitating a greater number of MRI-based classification studies, convolutional neural networks (CNNs)-deep learning models well suited to image processing tasks-have become increasingly popular for research into brain conditions. This has resulted in a myriad of studies reporting impressive predictive performances, demonstrating the potential clinical value of deep learning systems. However, methodologies can vary widely across studies, making them difficult to compare and/or reproduce, potentially limiting their clinical application. Here, we conduct a qualitative systematic literature review of 55 studies carrying out CNN-based predictive modelling of brain disorders using MRI data and evaluate them based on three principles-modelling practices, transparency, and interpretability. We propose several recommendations to enhance the potential for the integration of CNNs into clinical care.

Resting-state connectivity subtype of comorbid PTSD and alcohol use disorder moderates improvement from integrated prolonged exposure therapy in Veterans

BACKGROUND

Posttraumatic stress disorder (PTSD) and alcohol use disorder (AUD) are highly comorbid and are associated with significant functional impairment and inconsistent treatment outcomes. Data-driven subtyping of this clinically heterogeneous patient population and the associated underlying neural mechanisms are highly needed to identify who will benefit from psychotherapy.

METHODS

In 53 comorbid PTSD/AUD patients, resting-state functional magnetic resonance imaging was collected prior to undergoing individual psychotherapy. We used a data-driven approach to subgroup patients based on directed connectivity profiles. Connectivity subgroups were compared on clinical measures of PTSD severity and heavy alcohol use collected at pre- and post-treatment.

RESULTS

We identified a subgroup of patients associated with improvement in PTSD symptoms from integrated-prolonged exposure therapy. This subgroup was characterized by lower insula to inferior parietal cortex (IPC) connectivity, higher pregenual anterior cingulate cortex (pgACC) to posterior midcingulate cortex connectivity and a unique pgACC to IPC path. We did not observe any connectivity subgroup that uniquely benefited from integrated-coping skills or subgroups associated with change in alcohol consumption.

CONCLUSIONS

Data-driven approaches to characterize PTSD/AUD subtypes have the potential to identify brain network profiles that are implicated in the benefit from psychological interventions - setting the stage for future research that targets these brain circuit communication patterns to boost treatment efficacy.

Shared and unique brain network features predict cognitive, personality, and mental health scores in the ABCD study

How individual differences in brain network organization track behavioral variability is a fundamental question in systems neuroscience. Recent work suggests that resting-state and task-state functional connectivity can predict specific traits at the individual level. However, most studies focus on single behavioral traits, thus not capturing broader relationships across behaviors. In a large sample of 1858 typically developing children from the Adolescent Brain Cognitive Development (ABCD) study, we show that predictive network features are distinct across the domains of cognitive performance, personality scores and mental health assessments. On the other hand, traits within each behavioral domain are predicted by similar network features. Predictive network features and models generalize to other behavioral measures within the same behavioral domain. Although tasks are known to modulate the functional connectome, predictive network features are similar between resting and task states. Overall, our findings reveal shared brain network features that account for individual variation within broad domains of behavior in childhood.

Brain SPECT as an Imaging Biomarker for Evaluating Effects of Novel Treatments in Psychiatry-A Case Series

The difficulties of evaluating patients with complex neuropsychiatric conditions and prescribing appropriate treatments are well known. Imaging complements clinical assessments and allows a clinician to narrow the differential diagnosis by facilitating accurate and efficient evaluation. This is particularly relevant to neuropsychiatric conditions that are often diagnosed using a trial-and error process of exclusion. Single Photon Emission Computed Tomography (SPECT) is a functional brain imaging procedure that allows practitioners to measure the functional changes of gray matter structures based on regional cerebral blood flow (rCBF). The accurate diagnosis and treatment selection in psychiatry is challenging due to complex cases and frequent comorbidities. However, such complex neuropsychiatric conditions are increasingly benefitting from new treatment approaches, in addition to established medications. Among these are combination transcranial magnetic stimulation with ketamine infusions (CTK), hyperbaric oxygen therapy (HBOT) and perispinal administration of etanercept (PSE). This article provides readers with six case study examples that demonstrate how brain SPECT imaging can be used, both as a diagnostic tool, and as a potential biomarker for monitoring and evaluating novel treatments for patients with complex neuropsychiatric conditions. Six patients were assessed in our clinic and baseline brain SPECT imagesTourettes and a long history of alcohol were visually compared with SPECT images collected after periods of treatment with CTK or HBOT followed by PSE. This retrospective review demonstrates the clinical utility of these novel treatments and describes how SPECT imaging can complement standard diagnostic assessments. A novel display technique for SPECT images is described and we argue that SPECT imaging can be used for monitoring biomarker for clinical change.

Brain Activation During Processing of Depression Emotion in College Students With Premenstrual Syndrome in China: Preliminary Findings

BACKGROUND

This study aimed to investigate the neural substrates of processing depression emotion in premenstrual syndrome (PMS) and healthy subjects of college students using blood oxygenation level-dependent functional magnetic resonance imaging (BOLD-fMRI).

METHODS

During BOLD-fMRI scanning, 13 PMS patients and 15 healthy controls (HC) performed a picture visual stimulation task during luteal and follicular phases, in which participants and HC were asked to see pictures containing depression and non-depression emotions. Simultaneously, self-rating depression scales (SDS) were employed to evaluate the emotional status of participants.

RESULTS

Compared to HC, right inferior occipital gyrus, right middle occipital gyrus, right lingual gyrus, right fusiform gyrus, right inferior temporal gyrus, cerebelum_crus1_R, cerebelum_6_R, culmen, the cerebellum anterior lobe, tuber, and cerebellar tonsil of PMS patients showed enhanced activation. In contrast, sub-lobar, sub-gyral, extra-nuclear, right orbit part of superior frontal gyrus, right middle temporal gyrus, right orbit part of inferior frontal gyrus, limbic lobe, right insula, bilateral anterior and adjacent cingulate gyrus, bilateral caudate, caudate head, bilateral putamen, and left globus pallidus exhibited decreased activation.

CONCLUSION

The findings indicate that abnormal functional regulation of brain regions such as occipital lobe and cerebellum leads to abnormal changes in emotional regulation, cognitive ability, and attention distribution in PMS patients, implying significant central pathogenesis.

Testing the Stability and Validity of an Executive Dysfunction Classification Using Task-Based Assessment in Children and Adolescents

OBJECTIVE

It is unclear if pediatric executive dysfunction assessed only with cognitive tasks predicts clinically relevant outcomes independently of psychiatric diagnoses. This study tested the stability and validity of a task-based classification of executive function.

METHOD

A total of 2,207 individuals (6-17 years old) from the Brazilian High-Risk Cohort Study participated in this study (1,930 at baseline, 1,532 at follow-up). Executive function was measured using tests of working memory and inhibitory control. Dichotomized age- and sex-standardized performances were used as input in latent class analysis and receiver operating curves to create an executive dysfunction classification (EDC). The study tested EDC's stability over time, association with symptoms, functional impairment, a polymorphism in the CADM2 gene, polygenic risk scores (PRS), and brain structure. Analyses covaried for age, sex, social class, IQ, and psychiatric diagnoses.

RESULTS

EDC at baseline predicted itself at follow-up (odds ratio [OR] = 5.11; 95% CI 3.41-7.64). Participants in the EDC reported symptoms spanning several domains of psychopathology and exhibited impairment in multiple settings, including more adverse school events (OR = 2.530; 95% CI 1.838-3.483). Children in the EDC presented higher attention-deficit/hyperactivity disorder and lower educational attainment PRS at baseline; higher schizophrenia PRS at follow-up; and lower chances of presenting a polymorphism in a gene previously linked to high performance in executive function (CADM2 gene). They also exhibited smaller intracranial volumes and smaller bilateral cortical surface areas in several brain regions.

CONCLUSION

Task-based executive dysfunction is associated with several validators, independently of psychiatric diagnoses and intelligence. Further refinement of task-based assessments might generate clinically useful tools.

Neuroimaging Association Scores: reliability and validity of aggregate measures of brain structural features linked to mental disorders in youth

In genetics, aggregation of many loci with small effect sizes into a single score improved prediction. Nevertheless, studies applying easily replicable weighted scores to neuroimaging data are lacking. Our aim was to assess the reliability and validity of the Neuroimaging Association Score (NAS), which combines information from structural brain features previously linked to mental disorders. Participants were 726 youth (aged 6-14) from two cities in Brazil who underwent MRI and psychopathology assessment at baseline and 387 at 3-year follow-up. Results were replicated in two samples: IMAGEN (n = 1627) and the Healthy Brain Network (n = 843). NAS were derived by summing the product of each standardized brain feature by the effect size of the association of that brain feature with seven psychiatric disorders documented by previous meta-analyses. NAS were calculated for surface area, cortical thickness and subcortical volumes using T1-weighted scans. NAS reliability, temporal stability and psychopathology and cognition prediction were analyzed. NAS for surface area showed high internal consistency and 3-year stability and predicted general psychopathology and cognition with higher replicability than specific symptomatic domains for all samples. They also predicted general psychopathology with higher replicability than single structures alone, accounting for 1-3% of the variance, but without directionality. The NAS for cortical thickness and subcortical volumes showed lower internal consistency and less replicable associations with behavioural phenotypes. These findings indicate the NAS based on surface area might be replicable markers of general psychopathology, but these links are unlikely to be causal or clinically useful yet.

Brain structural associations with depression in a large early adolescent sample (the ABCD study®)

BACKGROUND

Depression is the leading cause of disability worldwide with > 50% of cases emerging before the age of 25 years. Large-scale neuroimaging studies in depression implicate robust structural brain differences in the disorder. However, most studies have been conducted in adults and therefore, the temporal origins of depression-related imaging features remain largely unknown. This has important implications for understanding aetiology and informing timings of potential intervention.

METHODS

Here, we examine associations between brain structure (cortical metrics and white matter microstructural integrity) and depression ratings (from caregiver and child), in a large sample (N = 8634) of early adolescents (9 to 11 years old) from the US-based, Adolescent Brain and Cognitive Development (ABCD) Study®. Data was collected from 2016 to 2018.

FINDINGS

We report significantly decreased global cortical and white matter metrics, and regionally in frontal, limbic and temporal areas in adolescent depression (Cohen's d = -0⋅018 to -0⋅041, β = -0·019 to -0⋅057). Further, we report consistently stronger imaging associations for caregiver-reported compared to child-reported depression ratings. Divergences between reports (caregiver vs child) were found to significantly relate to negative socio-environmental factors (e.g., family conflict, absolute β = 0⋅048 to 0⋅169).

INTERPRETATION

Depression ratings in early adolescence were associated with similar imaging findings to those seen in adult depression samples, suggesting neuroanatomical abnormalities may be present early in the disease course, arguing for the importance of early intervention. Associations between socio-environmental factors and reporter discrepancy warrant further consideration, both in the wider context of the assessment of adolescent psychopathology, and in relation to their role in aetiology.

FUNDING

Wellcome Trust (References: 104036/Z/14/Z and 220857/Z/20/Z) and the Medical Research Council (MRC, Reference: MC_PC_17209).

Neural correlates of extinguished threat recall underlying the commonality between pediatric anxiety and irritability

BACKGROUND

Anxiety and irritability frequently co-occur in youth and are mediated by aberrant threat responses. However, empirical evidence on neural mechanisms underlying this co-occurrence is limited. To address this, we apply data-driven latent phenotyping to data from a prior report of a well-validated threat extinction recall fMRI paradigm.

METHODS

Participants included 59 youth (28 anxiety disorder, 31 healthy volunteers; Mage=13.15 yrs) drawn from a transdiagnostic sample of 331 youth, in which bifactor analysis was conducted to derive latent factors representing shared vs. unique variance of dimensionally-assessed anxiety and irritability. Participants underwent threat conditioning and extinction. Approximately three weeks later, during extinction recall fMRI, participants made threat-safety discriminations under two task conditions: current threat appraisal and explicit recall of threat contingencies. Linear mixed-effects analyses examined associations of a "negative affectivity" factor reflecting shared anxiety and irritability variance with whole-brain activation and task-dependent amygdala connectivity.

RESULTS

During recall of threat-safety contingencies, higher negative affectivity was associated with greater prefrontal (ventrolateral/ventromedial, dorsolateral, orbitofrontal), motor, temporal, parietal, and occipital activation. During threat appraisal, higher negative affectivity was associated with greater amygdala-inferior parietal lobule connectivity to threat/safety ambiguity.

LIMITATIONS

Sample included only healthy youth and youth with anxiety disorders. Results may not generalize to other diagnoses for which anxiety and irritability are also common, and our negative affectivity factor should be interpreted as anxiety disorders with elevated irritability. Reliability of some subfactors was poor.

CONCLUSIONS

Aberrant amygdala-prefrontal-parietal circuitry during extinction recall of threat-safety stimuli may be a mechanism underlying the co-occurrence of pediatric anxiety and irritability.

Neuropsychiatric symptoms and brain morphology in patients with mild cognitive impairment and Alzheimer's disease with dementia

We present associations between neuropsychiatric symptoms (NPS) and brain morphology in a large sample of patients with mild cognitive impairment (MCI) and Alzheimer's disease with dementia (AD dementia).Several studies assessed NPS factor structure in MCI and AD dementia, but we know of no study that tested for associations between NPS factors and brain morphology. The use of factor scores increases parsimony and power. For transparency, we performed an additional analysis with selected Neuropsychiatric Inventory - Questionnaire (NPI-Q) items. Including regional cortical thickness, cortical and subcortical volumes, we examined associations between NPS and brain morphology across the whole brain in an unbiased fashion. We reported both statistical significance and effect sizes, using linear models adjusted for multiple comparisons by false discovery rate (FDR). Moreover, we included an interaction term for diagnosis and could thereby compare associations of NPS and brain morphology between MCI and AD dementia.We found an association between the factor elation and thicker right anterior cingulate cortex across MCI and AD dementia. Associations between the factors depression to thickness of the banks of the left superior temporal sulcus and psychosis to the left post-central volume depended on diagnosis: in MCI these associations were positive, in AD dementia negative.Our findings indicate that NPS in MCI and AD dementia are not exclusively associated with atrophy and support previous findings of associations between NPS and mainly frontotemporal brain structures.

OBJECTIVES

Neuropsychiatric symptoms (NPS) are common in mild cognitive impairment (MCI) and Alzheimer’s disease with dementia (AD dementia), but their brain structural correlates are unknown. We tested for associations between NPS and MRI-based cortical and subcortical morphometry in patients with MCI and AD dementia.

DESIGN

Cross-sectional.

SETTINGS

Conducted in Norway.

PARTICIPANTS

Patients with MCI (n = 102) and AD dementia (n = 133) from the Memory Clinic and the Geriatric Psychiatry Unit at Oslo University Hospital.

MEASUREMENTS

Neuropsychiatric Inventory – Questionnaire (NPI-Q) severity indices were reduced using principal component analysis (PCA) and tested for associations with 170 MRI features using linear models and false discovery rate (FDR) adjustment. We also tested for differences between groups. For transparency, we added analyses with selected NPI-Q items.

RESULTS

PCA revealed four factors: elation, psychosis, depression, and motor behavior.FDR adjustment revealed a significant positive association (B = 0.20, pFDR < 0.005) between elation and thickness of the right caudal anterior cingulate cortex (ACC) across groups, and significant interactions between diagnosis and psychosis (B = −0.48, pFDR < 0.0010) on the left post-central volume and between diagnosis and depression (B = −0.40, pFDR < 0.005) on the thickness of the banks of the left superior temporal sulcus. Associations of apathy, anxiety, and nighttime behavior to the left temporal lobe were replicated.

CONCLUSIONS

The positive association between elation and ACC thickness suggests that mechanisms other than atrophy underly elation. Interactions between diagnosis and NPS on MRI features suggest different mechanisms of NPS in our MCI and AD dementia samples. The results contribute to a better understanding of NPS brain mechanisms in MCI and AD dementia.

The effect of mental health on social capital: An instrumental variable analysis

Although a large body of literature has examined the effect of social capital on health and theoretical models suggest a reciprocal relationship between the two variables, there are relatively few studies that have investigated the effect of mental health on social capital. This paper evaluates the impact of mental health on the stock of social capital using data from the cross-sectional 2012 (N = 21,844) and 2002 (N = 31,089) Canadian Community Health Survey - Mental Health editions. Mental health was measured retrospectively as self-rated mental health, past year mental health conditions, and past 30-day psychological distress. Given the reciprocal relationship, we used an instrumental variable approach with family history of mental health problems as the instrument and examined forms of social capital - sense of belonging and workplace social support - that are largely measures of social capital provided by non-family members in the community and workplace. The analysis suggests there are large and significant associations between measures of mental health and both outcomes, which persist in the instrumental variable analyses. These findings highlight the urgent need for policy makers to implement greater prevention and treatment of poor mental health, and provide greater support for individuals with poor mental health so they can build and maintain their social capital.

Stroop-related cerebellar and temporal activation is correlated with negative affect and alcohol use disorder severity

Impairment in cognitive control in alcohol use disorder (AUD) contributes to difficulty controlling alcohol use and, in many populations, difficulties with emotion regulation. However, the most reliable and robust marker of clinically-relevant deficits in cognitive control in AUD is unclear. Our aims were to measure relationships between BOLD signal during a Stroop task and AUD severity and change in BOLD signal and change in drinking over three weeks. We also aimed to explore the relationships between BOLD signal and subjective negative affect. Thirty-three individuals with AUD underwent a multisensory Stroop task during functional magnetic resonance imaging (fMRI), as well as a battery of neuropsychological tests and self-report assessments of negative affect and AUD severity. Greater activation in temporal gyrus and cerebellum during incongruent trials compared to congruent trials was observed, and percent signal change (incongruent minus congruent) in both clusters was positively correlated with AUD severity and self-reported negative affect. Neuropsychological task performance and self-reported impulsivity were not highly correlated with AUD severity. Hierarchical regression analyses indicated that percent signal change (incongruent minus congruent) in cerebellum was independently associated with negative affect after controlling for recent and chronic drinking. In a subset of individuals (n = 23) reduction in cerebellar percent signal change (incongruent minus congruent) was correlated with increases in percent days abstinent over 3 weeks. BOLD activation during this Stroop task may therefore be an important objective marker of AUD severity and negative affect. The potential importance of the cerebellum in emotion regulation and AUD severity is highlighted.

Charting brain growth in tandem with brain templates at school age

Brain growth charts and age-normed brain templates are essential resources for researchers to eventually contribute to the care of individuals with atypical developmental trajectories. The present work generates age-normed brain templates for children and adolescents at one-year intervals and the corresponding growth charts to investigate the influences of age and ethnicity using a common pediatric neuroimaging protocol. Two accelerated longitudinal cohorts with the identical experimental design were implemented in the United States and China. Anatomical magnetic resonance imaging (MRI) of typically developing school-age children (TDC) was obtained up to three times at nominal intervals of 1.25 years. The protocol generated and compared population- and age-specific brain templates and growth charts, respectively. A total of 674 Chinese pediatric MRI scans were obtained from 457 Chinese TDC and 190 American pediatric MRI scans were obtained from 133 American TDC. Population- and age-specific brain templates were used to quantify warp cost, the differences between individual brains and brain templates. Volumetric growth charts for labeled brain network areas were generated. Shape analyses of cost functions supported the necessity of age-specific and ethnicity-matched brain templates, which was confirmed by growth chart analyses. These analyses revealed volumetric growth differences between the two ethnicities primarily in lateral frontal and parietal areas, regions which are most variable across individuals in regard to their structure and function. Age- and ethnicity-specific brain templates facilitate establishing unbiased pediatric brain growth charts, indicating the necessity of the brain charts and brain templates generated in tandem. These templates and growth charts as well as related codes have been made freely available to the public for open neuroscience (https://github.com/zuoxinian/CCS/tree/master/H3/GrowthCharts).

Using structural MRI to identify bipolar disorders - 13 site machine learning study in 3020 individuals from the ENIGMA Bipolar Disorders Working Group

Bipolar disorders (BDs) are among the leading causes of morbidity and disability. Objective biological markers, such as those based on brain imaging, could aid in clinical management of BD. Machine learning (ML) brings neuroimaging analyses to individual subject level and may potentially allow for their diagnostic use. However, fair and optimal application of ML requires large, multi-site datasets. We applied ML (support vector machines) to MRI data (regional cortical thickness, surface area, subcortical volumes) from 853 BD and 2167 control participants from 13 cohorts in the ENIGMA consortium. We attempted to differentiate BD from control participants, investigated different data handling strategies and studied the neuroimaging/clinical features most important for classification. Individual site accuracies ranged from 45.23% to 81.07%. Aggregate subject-level analyses yielded the highest accuracy (65.23%, 95% CI = 63.47-67.00, ROC-AUC = 71.49%, 95% CI = 69.39-73.59), followed by leave-one-site-out cross-validation (accuracy = 58.67%, 95% CI = 56.70-60.63). Meta-analysis of individual site accuracies did not provide above chance results. There was substantial agreement between the regions that contributed to identification of BD participants in the best performing site and in the aggregate dataset (Cohen's Kappa = 0.83, 95% CI = 0.829-0.831). Treatment with anticonvulsants and age were associated with greater odds of correct classification. Although short of the 80% clinically relevant accuracy threshold, the results are promising and provide a fair and realistic estimate of classification performance, which can be achieved in a large, ecologically valid, multi-site sample of BD participants based on regional neurostructural measures. Furthermore, the significant classification in different samples was based on plausible and similar neuroanatomical features. Future multi-site studies should move towards sharing of raw/voxelwise neuroimaging data.

Imaging of Posttraumatic Stress Disorder

This review summarizes current knowledge obtained from psychoradiological studies of posttraumatic stress disorder (PTSD). We first focus on 3 key anatomic structures (hippocampus, amygdala, and medial prefrontal cortex) and the functional circuits to which they contribute. In addition, we discuss the triple-network model, a widely accepted neurobiological model of PTSD that explains the vast majority of neuroimaging findings, as well as their interactions and relationships to functional disruptions in PTSD.

Multimodal canonical correlation reveals converging neural circuitry across trauma-related disorders of affect and cognition

Trauma-related disorders of affect and cognition (TRACs) are associated with a high degree of diagnostic comorbidity, which may suggest that these disorders share a set of underlying neural mechanisms. TRACs are characterized by aberrations in functional and structural circuits subserving verbal memory and affective anticipation. Yet, it remains unknown how the neural circuitry underlying these multiple mechanisms contribute to TRACs. Here, in a sample of 47 combat Veterans, we measured affective anticipation using functional magnetic resonance imaging (fMRI), verbal memory with fluorodeoxyglucose positron emission tomography (FDG-PET), and grey matter volume with structural magnetic resonance imaging (sMRI). Using a voxel-based multimodal canonical correlation analysis (mCCA), the set of neural measures were statistically integrated, or fused, with a set of TRAC symptom measures including mild traumatic brain injury (mTBI), posttraumatic stress, and depression severity. The first canonical correlation pair revealed neural convergence in clusters encompassing the middle frontal gyrus and supplemental motor area, regions implicated in top-down cognitive control and affect regulation. These results highlight the potential of leveraging multivariate neuroimaging analysis for linking neurobiological mechanisms associated with TRACs, paving the way for transdiagnostic biomarkers and targets for treatment.

Imaging and Genetic Approaches to Inform Biomarkers for Anxiety Disorders, Obsessive-Compulsive Disorders, and PSTD

Anxiety disorders are the most common mental health problem in the world and also claim the highest health care cost among various neuropsychiatric disorders. Anxiety disorders have a chronic and recurrent course and cause significantly negative impacts on patients' social, personal, and occupational functioning as well as quality of life. Despite their high prevalence rates, anxiety disorders have often been under-diagnosed or misdiagnosed, and consequently under-treated. Even with the correct diagnosis, anxiety disorders are known to be difficult to treat successfully. In order to implement better strategies in diagnosis, prognosis, treatment decision, and early prevention for anxiety disorders, tremendous efforts have been put into studies using genetic and neuroimaging techniques to advance our understandings of the underlying biological mechanisms. In addition to anxiety disorders including panic disorder, generalised anxiety disorder (GAD), specific phobias, social anxiety disorders (SAD), due to overlapping symptom dimensions, obsessive-compulsive disorder (OCD), and post-traumatic stress disorder (PTSD) (which were removed from the anxiety disorder category in DSM-5 to become separate categories) are also included for review of relevant genetic and neuroimaging findings. Although the number of genetic or neuroimaging studies focusing on anxiety disorders is relatively small compare to other psychiatric disorders such as psychotic disorders or mood disorders, various structural abnormalities in the grey or white matter, functional alterations of activity during resting-state or task conditions, molecular changes of neurotransmitter receptors or transporters, and genetic associations have all been reported. With continuing effort, further genetic and neuroimaging research may potentially lead to clinically useful biomarkers for the prevention, diagnosis, and management of these disorders.

An open resource for transdiagnostic research in pediatric mental health and learning disorders

Technological and methodological innovations are equipping researchers with unprecedented capabilities for detecting and characterizing pathologic processes in the developing human brain. As a result, ambitions to achieve clinically useful tools to assist in the diagnosis and management of mental health and learning disorders are gaining momentum. To this end, it is critical to accrue large-scale multimodal datasets that capture a broad range of commonly encountered clinical psychopathology. The Child Mind Institute has launched the Healthy Brain Network (HBN), an ongoing initiative focused on creating and sharing a biobank of data from 10,000 New York area participants (ages 5–21). The HBN Biobank houses data about psychiatric, behavioral, cognitive, and lifestyle phenotypes, as well as multimodal brain imaging (resting and naturalistic viewing fMRI, diffusion MRI, morphometric MRI), electroencephalography, eye-tracking, voice and video recordings, genetics and actigraphy. Here, we present the rationale, design and implementation of HBN protocols. We describe the first data release (n=664) and the potential of the biobank to advance related areas (e.g., biophysical modeling, voice analysis).