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The gene expressing the FK506 binding protein 51 (FKBP5) is involved in the regulation of glucocorticoid receptor sensitivity. The rs1360780 SNP in this gene (T allele vs C homozygous) has been found to be associated with major depressive disorder (MDD). The aim of our study was to investigate whether this polymorphism might be associated with altered brain structure and function in a cohort of 40 patients with MDD and 43 healthy controls. A functional magnetic resonance imaging (fMRI) emotional attention task was employed. Diffusion tensor imaging (DTI) was also conducted, extracting mean diffusivity (MD) and fractional anisotropy (FA) from brain areas that showed functional differences between patients expressing the two alleles of the rs1360780 SNP. Finally, the effect of the interaction of childhood adversity as measured by the Childhood trauma Questionnaire (CTQ) and rs1360780 allele status was analyzed in relation to DTI measures using a general linear model. All results presented are family-wise error (FWE) corrected. Functional interactions were found between genotype and diagnosis (p<0.01). Patients carrying the high-risk allele, compared with patients not carrying it, showed reduced activity in the rolandic operculum, Heschl gyrus, insula, parahippocampal gyrus, posterior cingulate cortex, inferior frontal gyrus (p<0.05 for all measures); and increased MD and reduced FA measures in many of these regions (p<0.05). An interaction between CTQ scores and allele status was associated with DTI changes in the insula, rolandic operculum, and inferior frontal gyrus. Here, the presence of both the high-risk allele and higher CTQ scores was associated with higher MD and lower FA values (p<0.05). In conclusion, MDD patients expressing the T allele of rs1360780, compared with C homozygous patients, exhibit functional and structural differences in areas involved in emotional perception and inhibition. The interaction between the T allele and childhood maltreatment explained our structural findings in these regions, suggesting that their altered maturation and function might be influenced by early chronic stress in the presence of this genetic trait.

The goal of our study was to use functional connectivity to map brain function to self-reports of negative emotion. In a large dataset of healthy individuals derived from the Human Connectome Project (N = 652), first we quantified functional connectivity during a negative face-matching task to isolate patterns induced by emotional stimuli. Then, we did the same in a complementary task-free resting state condition. To identify the relationship between functional connectivity in these two conditions and self-reports of negative emotion, we introduce group regularized canonical correlation analysis (GRCCA), a novel algorithm extending canonical correlations analysis to model the shared common properties of functional connectivity within established brain networks. To minimize overfitting, we optimized the regularization parameters of GRCCA using cross-validation and tested the significance of our results in a held-out portion of the data set using permutations. GRCCA consistently outperformed plain regularized canonical correlation analysis. The only canonical correlation that generalized to the held-out test set was based on resting state data (r = 0.175, permutation test p = 0.021). This canonical correlation loaded primarily on Anger-aggression. It showed high loadings in the cingulate, orbitofrontal, superior parietal, auditory and visual cortices, as well as in the insula. Subcortically, we observed high loadings in the globus pallidus. Regarding brain networks, it loaded primarily on the primary visual, orbito-affective and ventral multimodal networks. Here, we present the first neuroimaging application of GRCCA, a novel algorithm for regularized canonical correlation analyses that takes into account grouping of the variables during the regularization scheme. Using GRCCA, we demonstrate that functional connections involving the visual, orbito-affective and multimodal networks are promising targets for investigating functional correlates of subjective anger and aggression. Crucially, our approach and findings also highlight the need of cross-validation, regularization and testing on held out data for correlational neuroimaging studies to avoid inflated effects.

Through the Human Connectome Project (HCP) our understanding of the functional connectome of the healthy brain has been dramatically accelerated. Given the pressing public health need, we must increase our understanding of how connectome dysfunctions give rise to disordered mental states. Mental disorders arising from high levels of negative emotion or from the loss of positive emotional experience affect over 400 million people globally. Such states of disordered emotion cut across multiple diagnostic categories of mood and anxiety disorders and are compounded by accompanying disruptions in cognitive function. Not surprisingly, these forms of psychopathology are the leading cause of disability worldwide. The Research Domain Criteria (RDoC) initiative spearheaded by NIMH offers a framework for characterizing the relations among connectome dysfunctions, anchored in neural circuits and phenotypic profiles of behavior and self-reported symptoms. Here, we report on our Connectomes Related to Human Disease protocol for integrating an RDoC framework with HCP protocols to characterize connectome dysfunctions in disordered emotional states, and present quality control data from a representative sample of participants. We focus on three RDoC domains and constructs most relevant to depression and anxiety: 1) loss and acute threat within the Negative Valence System (NVS) domain; 2) reward valuation and responsiveness within the Positive Valence System (PVS) domain; and 3) working memory and cognitive control within the Cognitive System (CS) domain. For 29 healthy controls, we present preliminary imaging data: functional magnetic resonance imaging collected in the resting state and in tasks matching our constructs of interest (“Emotion”, “Gambling” and “Continuous Performance” tasks), as well as diffusion-weighted imaging. All functional scans demonstrated good signal-to-noise ratio. Established neural networks were robustly identified in the resting state condition by independent component analysis. Processing of negative emotional faces significantly activated the bilateral dorsolateral prefrontal and occipital cortices, fusiform gyrus and amygdalae. Reward elicited a response in the bilateral dorsolateral prefrontal, parietal and occipital cortices, and in the striatum. Working memory was associated with activation in the dorsolateral prefrontal, parietal, motor, temporal and insular cortices, in the striatum and cerebellum. Diffusion tractography showed consistent profiles of fractional anisotropy along known white matter tracts. We also show that results are comparable to those in a matched sample from the HCP Healthy Young Adult data release. These preliminary data provide the foundation for acquisition of 250 subjects who are experiencing disordered emotional states. When complete, these data will be used to develop a neurobiological model that maps connectome dysfunctions to specific behaviors and symptoms.

In this paper we provide an overview of the rationale, methods, and preliminary results of the four Connectome Studies Related to Human Disease investigating mood and anxiety disorders. The first study, “Dimensional connectomics of anxious misery” (HCP-DAM), characterizes brain-symptom relations of a transdiagnostic sample of anxious misery disorders. The second study, “Human connectome Project for disordered emotional states” (HCP-DES), tests a hypothesis-driven model of brain circuit dysfunction in a sample of untreated young adults with symptoms of depression and anxiety. The third study, “Perturbation of the treatment resistant depression connectome by fast-acting therapies” (HCP-MDD), quantifies alterations of the structural and functional connectome as a result of three fast-acting interventions: electroconvulsive therapy, serial ketamine therapy, and total sleep deprivation. Finally, the fourth study, “Connectomes related to anxiety and depression in adolescents” (HCP-ADA), investigates developmental trajectories of subtypes of anxiety and depression in adolescence. The four projects use comparable and standardized Human Connectome Project magnetic resonance imaging (MRI) protocols, including structural MRI, diffusion-weighted MRI, and both task and resting state functional MRI. All four projects also conducted comprehensive and convergent clinical and neuropsychological assessments, including (but not limited to) demographic information, clinical diagnoses, symptoms of mood and anxiety disorders, negative and positive affect, cognitive function, and exposure to early life stress. The first round of analyses conducted in the four projects offered novel methods to investigate relations between functional connectomes and self-reports in large datasets, identified new functional correlates of symptoms of mood and anxiety disorders, characterized the trajectory of connectome-symptom profiles over time, and quantified the impact of novel treatments on aberrant connectivity. Taken together, the data obtained and reported by the four Connectome Studies Related to Human Disease investigating mood and anxiety disorders describe a rich constellation of convergent biological, clinical, and behavioral phenotypes that span the peak ages for the onset of emotional disorders. These data are being prepared for open sharing with the scientific community following screens for quality by the Connectome Coordinating Facility (CCF). The CCF also plans to release data from all projects that have been pre-processed using identical state-of-the-art pipelines. The resultant dataset will give researchers the opportunity to pool complementary data across the four projects to study circuit dysfunctions that may underlie mood and anxiety disorders, to map cohesive relations among circuits and symptoms, and to probe how these relations change as a function of age and acute interventions. This large and combined dataset may also be ideal for using data-driven analytic approaches to inform neurobiological targets for future clinical trials and interventions focused on clinical or behavioral outcomes.

Countless studies have advanced our understanding of the human brain and its organization by using functional magnetic resonance imaging (fMRI) to derive network representations of human brain function. However, we do not know to what extent these “functional connectomes” are reliable over time. In a large public sample of healthy participants ( = 833) scanned on two consecutive days, we assessed the test-retest reliability of fMRI functional connectivity and the consequences on reliability of three common sources of variation in analysis workflows: atlas choice, global signal regression, and thresholding. By adopting the intraclass correlation coefficient as a metric, we demonstrate that only a small portion of the functional connectome is characterized by good (6–8%) to excellent (0.08–0.14%) reliability. Connectivity between prefrontal, parietal, and temporal areas is especially reliable, but also average connectivity within known networks has good reliability. In general, while unreliable edges are weak, reliable edges are not necessarily strong. Methodologically, reliability of edges varies between atlases, global signal regression decreases reliability for networks and most edges (but increases it for some), and thresholding based on connection strength reduces reliability. Focusing on the reliable portion of the connectome could help quantify brain trait-like features and investigate individual differences using functional neuroimaging. We quantified the reliability of fMRI functional connectivity in a large sample of healthy participants ( = 833) scanned over two consecutive days. We also assessed the consequences on reliability of atlas choice, global signal regression and thresholding. Only a small portion of the functional connectome has good (6–8%) to excellent (0.08–0.14%) reliability. Connectivity between prefrontal, parietal and temporal areas is especially reliable and average connectivity within known networks has good reliability. While unreliable edges are generally weak, reliable edges are not necessarily strong. Reliability of edges varies between atlases. Global signal regression decreases reliability for networks and most edges (but increases it for some). Thresholding based on connection strength reduces reliability. Focusing on reliable portions of the connectome could help investigate individual differences using functional neuroimaging.

Magnetic resonance imaging (MRI) is a valuable translational tool that can be used to investigate alterations in brain structure and function in both patients and animal models of disease. Regional changes in brain structure, functional connectivity, and metabolite concentrations have been reported in depressed patients, giving insight into the networks and brain regions involved, however preclinical models are less well characterized. The development of more effective treatments depends upon animal models that best translate to the human condition and animal models may be exploited to assess the molecular and cellular alterations that accompany neuroimaging changes. Recent advances in preclinical imaging have facilitated significant developments within the field, particularly relating to high resolution structural imaging and resting-state functional imaging which are emerging techniques in clinical research. This review aims to bring together the current literature on preclinical neuroimaging in animal models of stress and depression, highlighting promising avenues of research toward understanding the pathological basis of this hugely prevalent disorder.

The pathophysiology of bipolar disorder (BD) remains mostly unclear. Yet, a valid biomarker is necessary to improve upon the early detection of this serious disorder. Patients with manifest BD display reduced volumes of the hippocampal subfields and amygdala nuclei. In this pre-registered analysis, we used structural MRI (n = 271, 7 sites) to compare volumes of hippocampus, amygdala and their subfields/nuclei between help-seeking subjects divided into risk groups for BD as estimated by BPSS-P, BARS and EPIbipolar. We performed between-group comparisons using linear mixed effects models for all three risk assessment tools. Additionally, we aimed to differentiate the risk groups using a linear support vector machine. We found no significant volume differences between the risk groups for all limbic structures during the main analysis. However, the SVM could still classify subjects at risk according to BPSS-P criteria with a balanced accuracy of 66.90% (95% CI 59.2–74.6) for 10-fold cross-validation and 61.9% (95% CI 52.0–71.9) for leave-one-site-out. Structural alterations of the hippocampus and amygdala may not be as pronounced in young people at risk; nonetheless, machine learning can predict the estimated risk for BD above chance. This suggests that neural changes may not merely be a consequence of BD and may have prognostic clinical value.

The Stria medullaris (SM) Thalami is a discrete white matter tract that directly connects frontolimbic areas to the habenula, allowing the forebrain to influence midbrain monoaminergic output. Habenular dysfunction has been shown in various neuropsychiatric conditions. However, there exists a paucity of research into the habenula's principal afferent tract, the SM. Diffusion-weighted tractography may provide insights into the properties of the SM , opening up investigation of this tract in conditions of monoamine dysregulation such as depression, schizophrenia, addiction and pain. We present a reliable method for reconstructing the SM using diffusion-weighted imaging, and examine the effects of age and gender on tract diffusion metrics. We also investigate reproducibility of the method through inter-rater comparisons. In consultation with neuroanatomists, a Boolean logic gate protocol was developed for use in to extract the SM from constrained spherical deconvolution based whole brain tractography. Particular emphasis was placed on the reproducibility of the tract, attention to crossing white matter tract proximity and anatomical consistency of anterior and posterior boundaries. The anterior commissure, pineal gland and mid point of the thalamus were defined as anatomical fixed points used for reconstruction. Fifty subjects were scanned using High Angular Resolution Diffusion Imaging (HARDI; 61 directions, -value 1500 mm ). Following constrained spherical deconvolution whole brain tractography, two independent raters isolated the SM. Each output was checked, examined and cleaned for extraneous streamlines inconsistent with known anatomy of the tract by the rater and a neuroanatomist. A second neuroanatomist assessed tracts for face validity. The SM was reconstructed with excellent inter-rater reliability for dimensions and diffusion metrics. Gender had no effect on the dimensions or diffusion metrics, however radial diffusivity (RD) showed a positive correlation with age. Reliable identification and quantification of diffusion metrics of the SM invites further exploration of this key habenula linked structure in neuropsychiatric disorders such as depression, anxiety, chronic pain and addiction. The accurate anatomical localization of the SM may also aid preoperative stereotactic localization of the tract for deep brain stimulation (DBS) treatment.

The gene for the glucocorticoid receptor regulator FK506 binding protein 5 (FKBP5) plays a role for risk, response to treatment, and changes in brain areas in major depressive disorder (MDD). Chronic stress is associated with lower methylation of FKBP5. Our aim was to investigate whether methylation of FKBP5 reflected exposure to childhood adversity in MDD and controls and whether it was associated with structure and function of emotional processing regions. FKBP5 intron 7 GR response element region methylation and rs1360780 allelic status were assessed from whole blood in 56 MDD adults and 50 controls. Using magnetic resonance imaging, we assessed gray matter concentration of selected areas and their function during valence recognition of emotional images. Childhood adversity was investigated using the Childhood Trauma Questionnaire. In MDD patients carrying the high-risk T allele of rs1360780, lower methylation of FKBP5 was predicted by childhood adversity (F=4.95, p=0.04). In all participants, lower FKBP5 intron methylation levels were associated with reduced gray matter concentration in the inferior frontal orbital gyrus bilaterally (Wald chi-square=11.93, pFDR <0.01) and, in MDD, with its bilaterally higher activation during valence recognition (Wald chi-square=5.58, p=0.02). Activation of this region, regardless of side, was found to be lower in MDD compared to controls (Wald chi-square=3.88, p=0.049) and to be inversely correlated with depression severity (Wald chi-square=4.65, p=0.03). Our findings support the hypothesis that, in genetically predisposed individuals carrying a high-risk variant of the gene, childhood maltreatment might induce demethylation of FKBP5. This is in turn associated with structural and functional changes in the inferior frontal orbital gyrus, a relevant area for the clinical symptoms of MDD.

There is an urgent need to derive quantitative measures based on coherent neurobiological dysfunctions or ‘biotypes’ to enable stratification of patients with depression and anxiety. We used task-free and task-evoked data from a standardized functional magnetic resonance imaging protocol conducted across multiple studies in patients with depression and anxiety when treatment free ( n  = 801) and after randomization to pharmacotherapy or behavioral therapy ( n  = 250). From these patients, we derived personalized and interpretable scores of brain circuit dysfunction grounded in a theoretical taxonomy. Participants were subdivided into six biotypes defined by distinct profiles of intrinsic task-free functional connectivity within the default mode, salience and frontoparietal attention circuits, and of activation and connectivity within frontal and subcortical regions elicited by emotional and cognitive tasks. The six biotypes showed consistency with our theoretical taxonomy and were distinguished by symptoms, behavioral performance on general and emotional cognitive computerized tests, and response to pharmacotherapy as well as behavioral therapy. Our results provide a new, theory-driven, clinically validated and interpretable quantitative method to parse the biological heterogeneity of depression and anxiety. Thus, they represent a promising approach to advance precision clinical care in psychiatry. Personalized brain circuit measures quantified using a new imaging technology in 801 patients with depression and anxiety identify six biotypes with unique symptoms, behaviors and responses to different types of treatment.