Brain Circuit-Derived Biotypes for Treatment Selection in Mood Disorders: A Critical Review and Illustration of a Functional Neuroimaging Tool for Clinical Translation.

Although the lifetime burden due to major depressive disorder is increasing, we lack tools for selecting the most effective treatments for each patient. One-third to one-half of patients with major depressive disorder do not respond to treatment, and we lack strategies for selecting among available treatments or expediting access to new treatment options. This critical review concentrates on functional neuroimaging as a modality of measurement for precision psychiatry. We begin by summarizing the current landscape of how functional neuroimaging-derived circuit predictors can forecast treatment outcomes in depression. Then, we outline the opportunities and challenges in integrating circuit predictors into clinical practice. We highlight one standardized and reproducible approach for quantifying brain circuit function at an individual level, which could serve as a model for clinical translation. We conclude by evaluating the prospects and practicality of employing neuroimaging tools, such as the one that we propose, in routine clinical practice.

Specific alterations of resting-state functional connectivity in the triple network related to comorbid anxiety in major depressive disorder.

The brain's default mode network (DMN) and the executive control network (ECN) switch engagement are influenced by the ventral attention network (VAN). Alterations in resting-state functional connectivity (RSFC) within this so-called triple network have been demonstrated in patients with major depressive disorder (MDD) or anxiety disorders (ADs). This study investigated alterations in the RSFC in patients with comorbid MDD and ADs to better understand the pathophysiology of this prevalent group of patients. Sixty-eight participants (52.9% male, mean age 35.3 years), consisting of 25 patients with comorbid MDD and ADs (MDD + AD), 20 patients with MDD only (MDD) and 23 healthy controls (HCs) were investigated clinically and with 3T resting-state fMRI. RSFC utilizing a seed-based approach within the three networks belonging to the triple network was compared between the groups. Compared with HC, MDD + AD showed significantly reduced RSFC between the ECN and the VAN, the DMN and the VAN and within the ECN. No differences could be found for the MDD group compared with both other groups. Furthermore, symptom severity and medication status did not affect RSFC values. The results of this study show a distinct set of alterations of RSFC for patients with comorbid MDD and AD compared with HCs. This set of dysfunctions might be related to less adequate switching between the DMN and the ECN as well as poorer functioning of the ECN. This might contribute to additional difficulties in engaging and utilizing consciously controlled emotional regulation strategies.

Canonical correlation analysis in high dimensions with structured regularization.

Canonical correlation analysis (CCA) is a technique for measuring the association between two multivariate data matrices. A regularized modification of canonical correlation analysis (RCCA) which imposes an ℓ2 penalty on the CCA coefficients is widely used in applications with high-dimensional data. One limitation of such regularization is that it ignores any data structure, treating all the features equally, which can be ill-suited for some applications. In this article we introduce several approaches to regularizing CCA that take the underlying data structure into account. In particular, the proposed group regularized canonical correlation analysis (GRCCA) is useful when the variables are correlated in groups. We illustrate some computational strategies to avoid excessive computations with regularized CCA in high dimensions. We demonstrate the application of these methods in our motivating application from neuroscience, as well as in a small simulation example.

A Cognitive Biotype of Depression and Symptoms, Behavior Measures, Neural Circuits, and Differential Treatment Outcomes: A Prespecified Secondary Analysis of a Randomized Clinical Trial.

Importance: Cognitive deficits in depression have been associated with poor functional capacity, frontal neural circuit dysfunction, and worse response to conventional antidepressants. However, it is not known whether these impairments combine together to identify a specific cognitive subgroup (or "biotype") of individuals with major depressive disorder (MDD), and the extent to which these impairments mediate antidepressant outcomes. Objective: To undertake a systematic test of the validity of a proposed cognitive biotype of MDD across neural circuit, symptom, social occupational function, and treatment outcome modalities. Design, Setting, and Participants: This secondary analysis of a randomized clinical trial implemented data-driven clustering in findings from the International Study to Predict Optimized Treatment in Depression, a pragmatic biomarker trial in which patients with MDD were randomized in a 1:1:1 ratio to antidepressant treatment with escitalopram, sertraline, or venlafaxine extended-release and assessed at baseline and 8 weeks on multimodal outcomes between December 1, 2008, and September 30, 2013. Eligible patients were medication-free outpatients with nonpsychotic MDD in at least the moderate range, and were recruited from 17 clinical and academic practices; a subset of these patients underwent functional magnetic resonance imaging. This prespecified secondary analysis was performed between June 10, 2022, and April 21, 2023. Main Outcomes and Measures: Pretreatment and posttreatment behavioral measures of cognitive performance across 9 domains, depression symptoms assessed using 2 standard depression scales, and psychosocial function assessed using the Social and Occupational Functioning Assessment Scale and World Health Organization Quality of Life scale were analyzed. Neural circuit function engaged during a cognitive control task was measured using functional magnetic resonance imaging. Results: A total of 1008 patients (571 [56.6%] female; mean [SD] age, 37.8 [12.6] years) participated in the overall trial and 96 patients participated in the imaging substudy (45 [46.7%] female; mean [SD] age, 34.5 [13.5] years). Cluster analysis identified what may be referred to as a cognitive biotype of 27% of depressed patients with prominent behavioral impairment in executive function and response inhibition domains of cognitive control. This biotype was characterized by a specific profile of pretreatment depressive symptoms, worse psychosocial functioning (d = -0.25; 95% CI, -0.39 to -0.11; P < .001), and reduced activation of the cognitive control circuit (right dorsolateral prefrontal cortex: d = -0.78; 95% CI, -1.28 to -0.27; P = .003). Remission was comparatively lower in the cognitive biotype positive subgroup (73 of 188 [38.8%] vs 250 of 524 [47.7%]; P = .04) and cognitive impairments persisted regardless of symptom change (executive function: ηp2 = 0.241; P < .001; response inhibition: ηp2 = 0.750; P < .001). The extent of symptom and functional change was specifically mediated by change in cognition but not the reverse. Conclusions and Relevance: Our findings suggest the presence of a cognitive biotype of depression with distinct neural correlates, and a functional clinical profile that responds poorly to standard antidepressants and instead may benefit from therapies specifically targeting cognitive dysfunction. Trial Registration: ClinicalTrials.gov Identifier: NCT00693849.

Machine Learning Prediction of Estimated Risk for Bipolar Disorders Using Hippocampal Subfield and Amygdala Nuclei Volumes.

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.

Long-term cortisol stress response in depression and comorbid anxiety is linked with reduced N-acetylaspartate in the anterior cingulate cortex.

OBJECTIVES: Major Depression (MDD) and anxiety disorders are stress-related disorders that share pathophysiological mechanisms. There is evidence for alterations of glutamate-glutamine, N-acetylaspartate (NAA) and GABA in the anterior cingulate cortex (ACC), a stress-sensitive region affected by hypothalamic-pituitary-adrenal axis (HPA). The aim was to investigate metabolic alterations in the ACC and whether hair cortisol, current stress or early life adversity predict them. METHODS: We investigated 22 patients with MDD and comorbid anxiety disorder and 23 healthy controls. Proton magnetic resonance spectroscopy was performed with voxels placed in pregenual (pg) and dorsal (d) ACC in 3 T. Analysis of hair cortisol was performed using liquid chromatography-tandem mass spectrometry (LC-MS/MS). RESULTS: The N-acetylaspartate/Creatin ratio (NAA/Cr) was reduced in patients in both pgACC (p = .040) and dACC (p = .016). A significant interactive effect of diagnosis and cortisol on both pg-NAA/Cr (F = 5.00, p = .033) and d-NAA/Cr (F = 7.86, p = .009) was detected, whereby in controls cortisol was positively correlated with d-NAA/Cr (r = 0.61, p = .004). CONCLUSIONS: Our results suggest a relationship between NAA metabolism in ACC and HPA axis activity as represented by long-term cortisol output.

C-reactive protein is related to a distinct set of alterations in resting-state functional connectivity contributing to a differential pathophysiology of major depressive disorder.

BACKGROUND: Several studies in major depressive disorder (MDD) have found inflammation, especially C-reactive protein (CRP), to be consistently associated with MDD and network dysfunction. The aim was to investigate whether CRP is linked to a distinct set of resting-state functional connectivity (RSFC) alterations. METHODS: For this reason, we investigated the effects of diagnosis and elevated blood plasma CRP levels on the RSFC in 63 participants (40 females, mean age 31.4 years) of which were 27 patients with a primary diagnosis of MDD and 36 healthy control-subjects (HC), utilizing a seed-based approach within five well-established RSFC networks obtained using fMRI. RESULTS: Of the ten network pairs examined, five showed increased between-network RSFC-values unambiguously connected either to a diagnosis of MDD or elevated CRP levels. For elevated CRP levels, increased RSFC between DMN and AN was found. Patients showed increased RSFC within DMN areas and between the DMN and ECN and VAN, ECN and AN and AN and DAN. CONCLUSIONS: The results of this study show dysregulated neural circuits specifically connected to elevated plasma CRP levels and independent of other alterations of RSFC in MDD. This dysfunction in neural circuits might in turn result in a certain immune-inflammatory subtype of MDD.

Convergence, preliminary findings and future directions across the four human connectome projects investigating mood and anxiety disorders.

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.

Coping Strategies, Neural Structure, and Depression and Anxiety During the COVID-19 Pandemic: A Longitudinal Study in a Naturalistic Sample Spanning Clinical Diagnoses and Subclinical Symptoms.

BACKGROUND: Although the COVID-19 pandemic has been shown to worsen anxiety and depression symptoms, we do not understand which behavioral and neural factors may mitigate this impact. To address this gap, we assessed whether adaptive and maladaptive coping strategies affect symptom trajectory during the pandemic. We also examined whether pre-pandemic integrity of brain regions implicated in depression and anxiety affect pandemic symptoms. METHODS: In a naturalistic sample of 169 adults (66.9% female; age 19-74 years) spanning psychiatric diagnoses and subclinical symptoms, we assessed anhedonia, tension, and anxious arousal symptoms using validated components (21-item Depression, Anxiety, and Stress Scale), coping strategies (Brief-Coping Orientation to Problems Experienced), and gray matter volume (amygdala) and cortical thickness (hippocampus, insula, anterior cingulate cortex) from magnetic resonance imaging T1-weighted scans. We conducted general linear mixed-effects models to test preregistered hypotheses that 1) maladaptive coping pre-pandemic and 2) lower structural integrity pre-pandemic would predict more severe pandemic symptoms; and 3) coping would interact with neural structure to predict pandemic symptoms. RESULTS: Greater use of maladaptive coping strategies was associated with more severe anxious arousal symptoms during the pandemic (p = .011, false discovery rate-corrected p [p FDR] = .035), specifically less self-distraction (p = .014, p FDR = .042) and greater self-blame (p = .002, p FDR = .012). Reduced insula thickness pre-pandemic predicted more severe anxious arousal symptoms (p = .001, p FDR = .027). Self-distraction interacted with amygdala volume to predict anhedonia symptoms (p = .005, p FDR = .020). CONCLUSIONS: Maladaptive coping strategies and structural variation in brain regions may influence clinical symptoms during a prolonged stressful event (e.g., COVID-19 pandemic). Future studies that identify behavioral and neural factors implicated in responses to global health crises are warranted for fostering resilience.

Relating whole-brain functional connectivity to self-reported negative emotion in a large sample of young adults using group regularized canonical correlation analysis.

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.

Reduced functional connectivity of default mode network subsystems in depression: Meta-analytic evidence and relationship with trait rumination.

Resting-state functional connectivity changes in the default mode network (DMN) of patients with major depressive disorder (MDD) have been linked to rumination. The DMN is divided into three subsystems: a midline Core, a dorsal medial prefrontal cortex (DMPFC) subsystem, and a medial temporal lobe (MTL) subsystem. We examined resting-state functional connectivity within and between DMN subsystems in MDD and its association with rumination. First, we conducted a meta-analysis on a large multi-site dataset of 618 MDD and 683 controls to quantify the differences in DMN subsystem functional connectivity between MDD and controls. Second, we tested the association of DMN subsystem functional connectivity and rumination in a sample of 115 unmedicated participants with symptoms of anxiety/depression and 48 controls. In our meta-analysis, only functional connectivity in the DMN Core was significantly reduced in MDD compared to controls (g = -0.246, CI = [-0.417; -0.074], pFDR = 0.048). Functional connectivity in the DMPFC subsystem and between the Core and DMPFC subsystems was slightly reduced but not significantly (g = -0.162, CI = [-0.310; -0.013], pFDR = 0.096; g = -0.249, CI = [-0.464; -0.034], pFDR = 0.084). Results were heterogeneous across sites for connectivity in the Core and between Core and DMPFC (I2 = 0.348 and I2 = 0.576 respectively). Prediction intervals consistently encompassed 0. In the independent sample we collected, functional connectivity within the DMN Core, DMPFC and between Core and DMPFC was not reduced in MDD compared to controls (all pFDR > 0.05). Trait rumination did not predict connectivity within and between DMN subsystems (all pFDR > 0.05). We conclude that MDD as a diagnostic category shows slightly reduced functional connectivity within the DMN Core, independent of illness duration, treatment, symptoms and trait rumination. However, this effect is small, highly variable and heterogeneous across samples, so that we could only detect it at the meta-analytic level, with a sample size of several hundreds. Our results indicate that reduced Core DMN connectivity has significant limitations as a potential clinical or prognostic marker for the diagnosis of MDD and might be more relevant to consider as a characteristic distinguishing a subgroup of individuals within this diagnostic category.

The Impact of Childhood Trauma on Developing Bipolar Disorder: Current Understanding and Ensuring Continued Progress.

Childhood trauma (CT) has been repeatedly linked to earlier onset and greater severity of bipolar disorder (BD) in adulthood. However, such knowledge is mostly based on retrospective and cross-sectional studies in adults with BD. The first objective of this selective review is to characterize the short-term effects of CT in the development of BD by focusing on studies in young people. The second objective is to describe the longer-term consequences of CT by considering studies with adult participants. This review first outlines the most prominent hypotheses linking CT exposure and the onset of BD. Then, it summarizes the psychological and biological risk factors implicated in the development of BD, followed by a discussion of original studies that investigated the role of CT in young people with early-onset BD, youths at increased risk of developing BD, or young people with BD with a focus on subclinical and clinical outcome measures. The review considers additional biological and psychological factors associated with a negative impact of CT on the long-term course of BD in later adulthood. Finally, we discuss how the integration of information of CT can improve ongoing early identification of BD and mitigate severe clinical expression in later adulthood.

DNA methylation differences in stress-related genes, functional connectivity and gray matter volume in depressed and healthy adolescents.

BACKGROUND: Studies in adult depressed patients have indicated that altered DNA methylation patterns at genes related to serotonin and HPA axis functioning (e.g., SLC6A4, FKBP5) are associated with changes in frontolimbic functional connectivity and structure. Here, we examined whether these associations can be generalized to adolescents. METHODS: 25 adolescents with depression (Mean age = 15.72 ± 0.94 SD; 20 girls) and 20 healthy controls (Mean age = 16.05 ± 1.5 SD; 16 girls) underwent a functional and structural magnetic resonance imaging protocol, which included a resting-state assessment and measures of brain morphometry. DNA was obtained from saliva. Levels of SLC6A4 and FKBP5 methylation were determined using pyrosequencing. RESULTS: SLC6A4 methylation was linked to amygdala-frontal operculum resting-state functional connectivity (rs-FC), regardless of diagnosis, and was differentially associated with inferior orbitofrontal gyrus (IFOG) gray matter (GM) volume in adolescents with depression and controls. Replicating and extending previous findings in adults, FKBP5 methylation was associated with IFOG GM volume in depressed and healthy adolescents, as well as orbitofrontal cortex (OFC)-rostral prefrontal cortex (RPFC) connectivity in healthy adolescents only. LIMITATIONS: Effects of medication use or genotype cannot be ruled out. Further, the relatively small sample size and predominately female sample may limit generalizability. CONCLUSIONS: These findings suggest that previously observed associations between SLC6A4 and FKBP5 methylation and frontolimbic processes in adult depressed patients can be in part generalized to adolescent patients. Further, findings suggest that measuring peripheral methylation at these genes deserves further attention as potential markers of typical and atypical development.

The human connectome project for disordered emotional states: Protocol and rationale for a research domain criteria study of brain connectivity in young adult anxiety and depression.

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.

Test-retest reliability of the human functional connectome over consecutive days: identifying highly reliable portions and assessing the impact of methodological choices.

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 (N = 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.

Aerobic exercise increases hippocampal subfield volumes in younger adults and prevents volume decline in the elderly.

Exercise improves both physical and mental health and increases neurogenesis in the dendate gyrus (DG) of the hippocampus. The aim of this study was to examine whether exercising, as compared to no change in regular physical activity, would impact on hippocampal volume, and in particular the core hippocampal structures, DG and cornu ammonis (CA) subfields, and whether any changes would be moderated by age. Thirty nine previously sedentary healthy participants were randomized to either a standardized progressive aerobic exercise program or to "no change" for 16 weeks. Mental health including profile of mood states (POMS), was assessed before and every 4 weeks during the program. Magnetic resonance imaging to examine hippocampal subfields was carried out before and after the program. Aerobic exercise resulted in a significant improvement of the POMS item 'vigour' compared to those in the control group. Overall left hippocampal and left CA4-DG volumes increased significantly in the exercise group while no significant changes were seen in the control group. Older adults in the control group demonstrated significant reductions in CA4-DG subfields over the study, whereas older adults in the exercise group did not show volume decline. These findings reinforce the literature that exercise has a beneficial effect on mental health and can prevent age-related volume decline. Exercise to Improve Resilience, https://register.clinicaltrials.gov/prs/app/action/LoginUser?ts=1&cx=-jg9qo4 , NCT02541136, Rec Ref 2011/45/13.

Connectivity of the Cognitive Control Network During Response Inhibition as a Predictive and Response Biomarker in Major Depression: Evidence From a Randomized Clinical Trial.

BACKGROUND: In treating major depressive disorder, we lack tests anchored in neurobiology that predict antidepressant efficacy. Cognitive impairments are a particularly disabling feature of major depressive disorder. We tested whether functional connectivity during a response-inhibition task can predict response to antidepressants and whether its changes over time are correlated to symptom changes. METHODS: We analyzed data from outpatients with major depressive disorder (n = 124) randomized to receive escitalopram, sertraline, or venlafaxine (8 weeks) and healthy control subjects (n = 59; age 18-65 years). Before and after treatment, participants were interviewed and scanned using functional magnetic resonance imaging, and functional connectivity was measured using generalized psychophysiological interaction during response inhibition (Go-NoGo task). We investigated the interaction between treatment type and response (≥50% reduction on self-reported symptoms), coupling differences between responders and nonresponders at baseline, their correlation with symptom improvement, and their changes in time. RESULTS: During response inhibition, connectivity between the dorsolateral prefrontal cortex/supramarginal gyrus and supramarginal gyrus/middle temporal gyrus was associated with response to sertraline and venlafaxine, but not escitalopram. Sertraline responders had higher functional connectivity between these regions compared with nonresponders, whereas venlafaxine responders had lower functional connectivity. For sertraline, attenuation of connectivity in the precentral and superior temporal gyri correlated with posttreatment symptom improvement. For venlafaxine, enhancement of connectivity between the orbitofrontal cortex and subcortical regions correlated with symptom improvement. CONCLUSIONS: Connectivity of the cognitive control circuit during response inhibition selectively and differentially predicts antidepressant treatment response and correlates with symptom improvement. These quantitative markers tied to the neurobiology of cognitive features of depression could be used translationally to predict and evaluate treatment response.

Interactive impact of childhood maltreatment, depression, and age on cortical brain structure: mega-analytic findings from a large multi-site cohort.

BACKGROUND: Childhood maltreatment (CM) plays an important role in the development of major depressive disorder (MDD). The aim of this study was to examine whether CM severity and type are associated with MDD-related brain alterations, and how they interact with sex and age. METHODS: Within the ENIGMA-MDD network, severity and subtypes of CM using the Childhood Trauma Questionnaire were assessed and structural magnetic resonance imaging data from patients with MDD and healthy controls were analyzed in a mega-analysis comprising a total of 3872 participants aged between 13 and 89 years. Cortical thickness and surface area were extracted at each site using FreeSurfer. RESULTS: CM severity was associated with reduced cortical thickness in the banks of the superior temporal sulcus and supramarginal gyrus as well as with reduced surface area of the middle temporal lobe. Participants reporting both childhood neglect and abuse had a lower cortical thickness in the inferior parietal lobe, middle temporal lobe, and precuneus compared to participants not exposed to CM. In males only, regardless of diagnosis, CM severity was associated with higher cortical thickness of the rostral anterior cingulate cortex. Finally, a significant interaction between CM and age in predicting thickness was seen across several prefrontal, temporal, and temporo-parietal regions. CONCLUSIONS: Severity and type of CM may impact cortical thickness and surface area. Importantly, CM may influence age-dependent brain maturation, particularly in regions related to the default mode network, perception, and theory of mind.

Longitudinal diffusion weighted imaging of limbic regions in patients with major depressive disorder after 6 years and partial to full remission.

The objective of this study was to determine the effect of major depressive disorder (MDD) on white matter microstructures after a 6-year period compared to healthy controls (HC). This study included a small sample size of 26 participants, including 14 patients with MDD clinically diagnosed at baseline, and 12 HCs. MRI brain scans were conducted at baseline and follow-up, 75.32 (±2.25) months after the initial scan. Tractography of 7 regions including the fornix, cingulum, superior longitudinal fasciculus, inferior fronto-occipital fasciculus and uncinate fasciculus were conducted using ExploreDTI software. Both groups showed significant reduction in tract integrity between time points. MDD diagnosis was shown to have an effect on longitudinal FA of the left dorsal cingulum and the left parahippocampal cingulum. A significant inverse relationship was found between ΔFA [baseline FA - follow-up FA] of the right uncinate fasciculus and the left rostral cingulum with ΔHAM-D [baseline HAM-D - follow-up HAM-D] within the MDD group. These preliminary findings support the hypothesis that limbic structures including the cingulum are involved in MDD pathophysiology and may be affected even after remission. Moreover, they indicate that recovery from depression symptoms may slow the rate of WM degradation associated with aging in these regions of interest.

Effects of early life adversity and FKBP5 genotype on hippocampal subfields volume in major depression.

BACKGROUND: Smaller hippocampus volume represents a consistent finding in major depression (MDD). Hippocampal neuroplasticity due to chronic stress might have differential effect on hippocampal subfields. We investigated the effects of the rs1360780 polymorphism of the hypothalamic-pituitary-axis related gene FKBP5 in combination with early life stress (ELA) on the structure of hippocampal subfields in MDD. METHODS: We assessed the hippocampal subfields volumes in 85/67 MDD/healthy controls. We investigated the effects of diagnosis, FKBP5 allelic status and their interaction as predictors of hippocampal subfield volumes as well as the effect of ELA and its interaction with FKBP5. RESULTS: MDD patients had smaller hippocampal volumes, in particular within the cornu ammonis (CA) and dentate gyrus (DG) regions. Patients exposed to ELA had larger hippocampi, in particular within the CA and DG. Among the patients exposed to ELA, the T allele carriers displayed lower volumes within the hippocampus-amygdala-transition-area (HATA) as those subjects homozygous for the C allele. LIMITATIONS: We pooled the subjects from 2 centers in order to increase the sample size. We did not include the cumulative lifetime exposure to medication. CONCLUSIONS: Hippocampal volume reductions in MDD were present particularly in the CA and DG. MDD with ELA display differential volume changes compared to MDD without ELA. The significant interaction between ELA and the rs1360780 polymorphism in HATA suggests a role of FKBP5 in the pathophysiology of structural alterations in depression.