ENIGMA MDD: seven years of global neuroimaging studies of major depression through worldwide data sharing

Atlas of Gray Matter Volume Differences Across Psychiatric Conditions: A Systematic Review With a Novel Meta-Analysis That Considers Co-Occurring Disorders

BACKGROUND

Regional gray matter volume (GMV) differences between individuals with mental disorders and comparison participants may be confounded by co-occurring disorders. To disentangle disorder-specific GMV correlates, we conducted a large-scale multidisorder meta-analysis using a novel approach that explicitly models co-occurring disorders.

METHODS

We systematically reviewed voxel-based morphometry studies indexed in PubMed and Scopus up to January 2023 that compared adults with major mental disorders (anorexia nervosa, schizophrenia spectrum, anxiety, bipolar, major depressive, obsessive-compulsive, and posttraumatic stress disorders plus attention-deficit/hyperactivity, autism spectrum, and borderline personality disorders) with comparison participants. Two authors independently extracted data and assessed quality using the Newcastle-Ottawa Scale. We derived GMV correlates for each disorder using: 1) a multidisorder meta-analysis that accounted for all co-occurring mental disorders simultaneously and 2) separate standard meta-analyses for each disorder in which co-occurring disorders were ignored. We assessed the alterations' extent, intensity (effect size), and specificity (interdisorder correlations and transdiagnostic alterations) for both approaches.

RESULTS

We included 433 studies (499 datasets) involving 19,718 patients and 16,441 comparison participants (51% female, ages 20-67 years). We provide GMV correlate maps for each disorder using both approaches. The novel approach, which accounted for co-occurring disorders, produced GMV correlates that were more focal and disorder specific (less correlated across disorders and fewer transdiagnostic abnormalities).

CONCLUSIONS

This work offers the most comprehensive atlas of GMV correlates across major mental disorders. Modeling co-occurring disorders yielded more specific correlates, supporting this approach's validity. The atlas NIfTI maps are available online.

Peripheral MicroRNA Signatures in Adolescent Depression

Background

Adolescent depression is linked to enduring maladaptive outcomes, chronic severity of symptoms, and poor treatment response. Identifying epigenetic signatures of adolescent depression is urgently needed to improve early prevention and intervention strategies. MicroRNAs (miRNAs) are epigenetic regulators of adolescent neurodevelopmental processes, but their role as markers and mediators of adolescent depression is unknown.

Methods

Here, we examined miRNA profiles from dried blood spot samples of male and female adolescents with clinical depression and psychiatrically healthy male and female adolescents (N = 62). We processed and sequenced these samples using a small RNA protocol tailored for miRNA identification.

Results

We identified 9 differentially expressed (DE) miRNAs (adjusted p value < .05), all of which were upregulated in adolescents with depression. At future follow-ups post blood collection, expression of miR-3613-5p, mir-30c-2, and miR-942-5p were positively associated with depression severity but not anxiety, suggesting a stronger link to persistent depression symptoms. Expression of miR-32-5p inversely correlated with hippocampal volume, highlighting a potential neurobiological basis. Common predicted gene targets of the DE miRNAs are involved in neurodevelopment, cognitive processing, and depressive disorders.

Conclusions

These findings lay the groundwork for identifying adolescent peripheral miRNA markers that reflect neurodevelopmental pathways that shape lifelong psychopathology risk.

Structural alterations after repetitive transcranial magnetic stimulation in depression and the link to neurotransmitter profiles

BACKGROUND

Repetitive Transcranial Magnetic Stimulation (rTMS) is widely used to treat depression, showing good efficacy and tolerability. However, the neurobiological mechanisms of its antidepressant effects remain unclear. This study explores the potential impact of rTMS on brain structure in depressed patients and its link to neurotransmitter systems.

METHODS

Thirty-six MDD patients were randomized to receive 5 times per week for 3 weeks of active or sham rTMS targeting the dorsolateral prefrontal cortex (DLPFC) within a double-blind, sham-controlled trial. The Hamilton Depression Rating Scale-17 items (HAMD-17) was used to assess depressive symptoms at baseline and the end of 1 W, 2 W and 3 W after treatment. We analyzed the differences in efficacy between the two groups of patients at different time points, and the grey matter changes of the brain before and after treatment in both groups. In addition, we analyzed the spatial correlations between abnormal grey matter and the neurotransmitter receptors and transporters map.

RESULTS

Both the active and sham groups showed significant improvement in depression and anxiety symptoms following rTMS treatment, with the Active group demonstrating greater improvement. Additionally, the Active group exhibited increased grey matter volume in regions associated with the frontal-limbic network, and these changes were significantly correlated with the spatial distribution of D1 receptors.

CONCLUSION

This study suggests that rTMS targeting the left DLPFC produces antidepressant effects by enhancing structural plasticity in the frontal-limbic network, and that dopamine system modulation may underlie rTMS therapeutic effects. These findings provide insight into the neurobiological basis of rTMS for depression treatment.

The Task-Based fMRI Using von Zerssen Scale in Recurrent Depression Disorder: A Replication Study

Currently, translation into clinical practice of scientific knowledge on pathological reorganization of brain mechanisms in psychiatric disorders, as suggested by functional neuroimaging data, remains limited. This situation calls for the exploration of new approaches, which were recently proposed for the combined use of the simultaneous application of psychodiagnostic testing and fMRI scanning. Consequently, a self-rated psychodiagnostic scale was used as an experimental task during fMRI scanning for patients with major depressive disorder. Given the promising neuroimaging results obtained in these studies, in current research, our objective was to replicate these results and conduct an fMRI study using statements from the von Zerssen depression as experimental conditions. Eighteen patients with recurrent depressive disorder and healthy volunteers participated in the study to replicate the group size of previous research. The results obtained showed that patients with recurrent depressive disorder exhibited greater activity in the right precuneus and bilateral supramarginal gyrus than healthy controls while responding to diagnostically specific (DS) statements compared to diagnostically neutral (DN) ones. These findings replicate the main results of the original study and emphasize the potential of this approach in the field of translational psychiatry. In addition, they contribute to understanding the pathophysiological mechanisms of depression through the use of this unique fMRI paradigm.

L1CAM+ extracellular vesicles derived from the serum of adolescents with major depressive disorder induce depression-like phenotypes in adolescent mice

BACKGROUND

It has been reported that L1 cell adhesion molecule (L1CAM) antibody can capture neuron-derived extracellular vesicles (NDEVs) derived from peripheral blood. This antibody is significantly associated with occurrence of adult psychiatric disorders. However, the role and mechanism of L1CAM+ EVs (L1+ EVs) in adolescent with major depressive disorder (AMDD) is not well understood. This research aimed to explore the function and potential mechanism of L1+ EVs and miRNAs genes in AMDD.

METHODS

L1+ EVs derived from the serum of AMDD and healthy controls (HC) were transplanted into adolescent mice via tail vein. Their effects were explored using behavioral tests, hippocampal Nissl staining, and whole genome mRNA sequencing. MiRNAs expression in L1+ EVs was evaluated by whole-genome sequencing and qRT-PCR. Bioinformatics analysis was employed to explore the possible pathogenic molecular mechanisms of these miRNAs in AMDD.

RESULTS

Transplantation of L1+ EVs from AMDD induced depression-like behavior and hippocampal neuronal damage in adolescent mice and aberrant expression of 298 mRNA genes. The molecular signals related to MDD were enriched in the top pathways of the differentially expressed genes. Compared with HC, miR-375-3p and miR-200a-3p were upregulated in L1+ EVs from AMDD, miR-375-3p was also increased in the hippocampus of AMDD serum L1+ EVs-recipient mice. Bioinformatics analysis revealed that miR-375-3p might modulate the network of molecules associated with the MAPK pathway via protein interaction involving hippocampal differential genes Cadm2, Cacna2d1, and Casz1.

CONCLUSION

MiR-375-3p might contribute to L1+ EVs-induced AMDD. L1+ EVs miR-375-3p and miR-200a-3p could potentially serve as potential biomarkers for AMDD.

Depression is marked by differences in structural covariance between deep-brain nuclei and sensorimotor cortex

BACKGROUND

Depression impacts nearly 3% of the global adult population. Symptomatology is likely related to regions encompassing frontoparietal, somatosensory, and salience networks. Questions regarding deep brain nuclei (DBN), including the substantia nigra (STN), subthalamic nucleus (STN), and red nucleus (RN) remain unanswered.

METHODS

Using an existing structural neuroimaging dataset including 86 individuals (Baranger et al., 2021; nDEP = 39), frequentist and Bayesian logistic regressions assessed whether DBN volumes predict diagnosis, then structural covariance analyses in FreeSurfer tested diagnostic differences in deep brain volume and cortical morphometry covariance. Exploratory correlations tested relationships between implicated cortical regions and Hamilton Depression Rating Scale (HAM-D) scores.

RESULTS

Group differences emerged in deep brain/cortical covariance. Right RN volume covaried with left parietal operculum volume and central sulcus thickness, while left RN and right STN volumes covaried with right occipital pole volume. Positive relationships were observed within the unaffected group and negative relationships among those with depression. These cortical areas did not correlate with HAM-D scores. Simple DBN volumes did not predict diagnostic group.

CONCLUSION

Structural codependence between DBN and cortical regions may be important in depression, potentially for sensorimotor features. Future work should focus on causal mechanisms of DBN involvement with sensory integration.

Advancing the diagnosis of major depressive disorder: Integrating neuroimaging and machine learning

Major depressive disorder (MDD), a psychiatric disorder characterized by functional brain deficits, poses considerable diagnostic and treatment challenges, especially in adolescents owing to varying clinical presentations. Biomarkers hold substantial clinical potential in the field of mental health, enabling objective assessments of physiological and pathological states, facilitating early diagnosis, and enhancing clinical decision-making and patient outcomes. Recent breakthroughs combine neuroimaging with machine learning (ML) to distinguish brain activity patterns between MDD patients and healthy controls, paving the way for diagnostic support and personalized treatment. However, the accuracy of the results depends on the selection of neuroimaging features and algorithms. Ensuring privacy protection, ML model accuracy, and fostering trust are essential steps prior to clinical implementation. Future research should prioritize the establishment of comprehensive legal frameworks and regulatory mechanisms for using ML in MDD diagnosis while safeguarding patient privacy and rights. By doing so, we can advance accuracy and personalized care for MDD.

Dissecting heterogeneity in cortical thickness abnormalities in major depressive disorder: a large-scale ENIGMA MDD normative modelling study

Importance

Major depressive disorder (MDD) is highly heterogeneous, with marked individual differences in clinical presentation and neurobiology, which may obscure identification of structural brain abnormalities in MDD. To explore this, we used normative modeling to index regional patterns of variability in cortical thickness (CT) across individual patients.

Objective

To use normative modeling in a large dataset from the ENIGMA MDD consortium to obtain individualised CT deviations from the norm (relative to age, sex and site) and examine the relationship between these deviations and clinical characteristics.

Design setting and participants

A normative model adjusting for age, sex and site effects was trained on 35 CT measures from FreeSurfer parcellation of 3,181 healthy controls (HC) from 34 sites (40 scanners). Individualised z-score deviations from this norm for each CT measure were calculated for a test set of 2,119 HC and 3,645 individuals with MDD. For each individual, each CT z-score was classified as being within the normal range (95% of individuals) or within the extreme range (2.5% of individuals with the thinnest or thickest cortices).

Main outcome measures

Z-score deviations of CT measures of MDD individuals as estimated from a normative model based on HC.

Results

Z-score distributions of CT measures were largely overlapping between MDD and HC (minimum 92%, range 92-98%), with overall thinner cortices in MDD. 34.5% of MDD individuals, and 30% of HC individuals, showed an extreme deviation in at least one region, and these deviations were widely distributed across the brain. There was high heterogeneity in the spatial location of CT deviations across individuals with MDD: a maximum of 12% of individuals with MDD showed an extreme deviation in the same location. Extreme negative CT deviations were associated with having an earlier onset of depression and more severe depressive symptoms in the MDD group, and with higher BMI across MDD and HC groups. Extreme positive deviations were associated with being remitted, of not taking antidepressants and less severe symptoms.

Conclusions and relevance

Our study illustrates a large heterogeneity in the spatial location of CT abnormalities across patients with MDD and confirms a substantial overlap of CT measures with HC. We also demonstrate that individualised extreme deviations can identify protective factors and individuals with a more severe clinical picture.

Microstructural Alterations in Superficial White Matter Associated With Anhedonia and Suicidal Ideation in Major Depressive Disorder

BACKGROUND

Major depressive disorder (MDD) is characterized by impaired emotional and cognitive functions. Previous studies have focused on the long-range white matter bundles within the deep white matter connecting distant cortices. Less is known about the superficial white matter (SWM), which consists of short bundles connecting adjacent and precise cortices. Therefore, we investigated the differences in SWM between patients with MDD and healthy control participants (HCs) and its relationship with core clinical depressive symptoms.

METHODS

Probabilistic tractography was used to generate the SWM bundles in 62 antidepressant-free patients with MDD and 77 HCs. Diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) were used to compare the microstructural differences of SWM between the 2 groups. Correlations were calculated between the diffusion metrics in the SWM showing significant between-group differences and core clinical depressive symptoms.

RESULTS

Compared with HCs, patients with MDD showed DTI metric changes in the SWM bundles connecting frontal-parietal-temporal-occipital cortices. For the NODDI metrics, patients with MDD showed a lower neurite density index in the SWM bundles connecting frontal-parietal-temporal cortices. Here, the neurite density index in the SWM bundles connecting prefrontal-insula regions was significantly negatively correlated with anhedonia and suicidal ideation. Patients with MDD displayed a higher orientation dispersion index in the SWM bundles connecting parietal, occipital, and posterior cingulate cortices.

CONCLUSIONS

SWM plays a crucial role in the neuropathology of MDD. The decreased neurite density in the SWM connecting prefrontal-insula regions may underlie anhedonia and suicidal ideation. Furthermore, NODDI metrics may offer more specific detection of SWM microstructural abnormalities than DTI metrics.

Structural brain changes in the anterior cingulate cortex of major depressive disorder individuals with suicidal ideation: Evidence from the REST-meta-MDD project

BACKGROUND

Suicidal ideation (SI) is very common in patients with major depressive disorder (MDD). However, its neural mechanisms remain unclear. The anterior cingulate cortex (ACC) region may be associated with SI in MDD patients. This study aimed to elucidate the neural mechanisms of SI in MDD patients by analyzing changes in gray matter volume (GMV) in brain structures in the ACC region, which has not been adequately studied to date.

METHODS

According to the REST-meta-MDD project, this study subjects consisted of 235 healthy controls and 246 MDD patients, including 123 MDD patients with and 123 without SI, and their structural magnetic resonance imaging data were analyzed. The 17-item Hamilton Depression Rating Scale (HAMD) was used to assess depressive symptoms. Correlation analysis and logistic regression analysis were used to determine whether there was a correlation between GMV of ACC and SI in MDD patients.

RESULTS

MDD patients with SI had higher HAMD scores and greater GMV in bilateral ACC compared to MDD patients without SI (all p < 0.001). GMV of bilateral ACC was positively correlated with SI in MDD patients and entered the regression equation in the subsequent logistic regression analysis.

CONCLUSIONS

Our findings suggest that GMV of ACC may be associated with SI in patients with MDD and is a sensitive biomarker of SI.

Abnormal choroid plexus, hippocampus, and lateral ventricles volumes as markers of treatment-resistant major depressive disorder

AIM

One-third of patients with major depressive disorder (MDD) do not achieve full remission and have high relapse rates even after treatment, leading to increased medical costs and reduced quality of life and health status. The possible specificity of treatment-resistant depression (TRD) neurobiology is still under investigation, with risk factors such as higher inflammatory markers being identified. Given recent findings on the role of choroid plexus (ChP) in neuroinflammation and hippocampus in treatment response, the aim of the present study was to evaluate inflammatory- and trophic-related differences in these regions along with ventricular volumes among patients with treatment-sensitive depression (TSD), TRD, and healthy controls (HCs).

METHODS

ChP, hippocampal, and ventricular volumes were assessed in 197 patients with MDD and 58 age- and sex-matched HCs. Volumes were estimated using FreeSurfer 7.2. Treatment resistance status was defined as failure to respond to at least two separate antidepressant treatments. Region of interest volumes were then compared among groups.

RESULTS

We found higher ChP volumes in patients with TRD compared with patients with TSD and HCs. Our results also showed lower hippocampal volumes and higher lateral ventricular volumes in TRD compared with both patients without TRD and HCs.

CONCLUSIONS

These findings corroborate the link between TRD and neuroinflammation, as ChP volume could be considered a putative marker of central immune activity. The lack of significant differences in all of the region of interest volumes between patients with TSD and HCs may highlight the specificity of these features to TRD, possibly providing new insights into the specific neurobiological underpinnings of this condition.

Neurovascular dysfunction in psychiatric disorders: Underlying mechanisms and therapeutic approaches

BACKGROUND

Neurovascular interfaces, specifically the blood-brain barrier (BBB) and blood-retinal barrier (BRB), play pivotal roles in maintaining the homeostasis of the central nervous system (CNS). For a long time, these structures were seen only as a way of protection, but we currently know that they have a critical role in CNS (dys)function. Several studies have identified neurovascular alterations in early stages of brain and eye diseases, contributing to the pathophysiology of such conditions. More recently, interesting data have also highlighted the importance of neurovasculature in psychiatric disorders.

METHODS

Using the PubMed database, we brought together the evidence concerning the changes in BBB and BRB under psychiatric conditions, with a focus on anxiety, major depressive disorder (MDD), attention-deficit/hyperactivity disorder (ADHD) and drug abuse, specifically related with methamphetamine (METH) and cocaine consumption.

RESULTS

We summarized the main findings obtained from in vitro and animal studies, as well as clinical research that has been undertaken to identify neurovascular abnormalities upon such neuropsychiatric disorders. The drivers of barrier alterations were examined, namely the role of neuroinflammation, while reporting putative barrier-associated biomarkers of these disorders.

CONCLUSION

This review underscores the critical need for a deeper understanding of BBB and BRB function in neuropsychiatric conditions and their potential as therapeutic targets while elucidating the key players involved. The innovative approaches to managing these complex disorders are also addressed while bridging the gap concerning what is currently known regarding the association between neuropsychiatric conditions and their vascular implications.

Resting-state dynamic functional connectivity in major depressive disorder: A systematic review

As a novel measure, dynamic functional connectivity (dFC) provides insight into the dynamic nature of brain networks and their interactions in resting-state, surpassing traditional static functional connectivity in pathological conditions such as depression. Since a comprehensive review is still lacking, we then reviewed forty-five eligible papers to explore pathological mechanisms of major depressive disorder (MDD) from perspectives including abnormal brain regions and functional networks, brain state, topological properties, relevant recognition, along with longitudinal studies. Though inconsistencies could be found, common findings are: (1) From different perspectives based on dFC, default-mode network (DMN) with its subregions exhibited a close relation to the pathological mechanism of MDD. (2) With a corrupted integrity within large-scale functional networks and imbalance between them, longer fraction time in a relatively weakly-connected state may be a possible property of MDD concerning its relation with DMN. Abnormal transition frequencies between states were correlated to the severity of MDD. (3) Including dynamic properties in topological network metrics enhanced recognition effect. In all, this review summarized its use for clinical diagnosis and treatment, elucidating the non-stationary of MDD patients' aberrant brain activity in the absence of stimuli and bringing new views into its underlying neuro mechanism.

Psychiatric neuroimaging at a crossroads: Insights from psychiatric genetics

Thanks to methodological advances, large-scale data collections, and longitudinal designs, psychiatric neuroimaging is better equipped than ever to identify the neurobiological underpinnings of youth mental health problems. However, the complexity of such endeavors has become increasingly evident, as the field has been confronted by limited clinical relevance, inconsistent results, and small effect sizes. Some of these challenges parallel those historically encountered by psychiatric genetics. In past genetic research, robust findings were historically undermined by oversimplified biological hypotheses, mistaken assumptions about expectable effect sizes, replication problems, confounding by population structure, and shared biological patterns across disorders. Overcoming these challenges has contributed to current successes in the field. Drawing parallels across psychiatric genetics and neuroimaging, we identify key shared challenges as well as pinpoint relevant insights that could be gained in psychiatric neuroimaging from the transition that occurred from the candidate gene to (post) genome-wide "eras" of psychiatric genetics. Finally, we discuss the prominent developmental component of psychiatric neuroimaging and how that might be informed by epidemiological and omics approaches. The evolution of psychiatric genetic research offers valuable insights that may expedite the resolution of key challenges in psychiatric neuroimaging, thus potentially moving our understanding of psychiatric pathophysiology forward.

Spatiotemporal discoordination of brain spontaneous activity in major depressive disorder

BACKGROUND

Major depressive disorder (MDD) is a widespread mental health issue, impacting spatial and temporal aspects of brain activity. The neural mechanisms behind MDD remain unclear. To address this gap, we introduce a novel measure, spatiotemporal topology (SPT), capturing both the hierarchy and dynamic attributes of brain activity in depressive disorder patients.

METHODS

We analyzed fMRI data from 285 MDD inpatients and 141 healthy controls (HC). SPT was assessed by coupling brain gradient measurement and time delay estimation. A nested machine learning process distinguished between MDD and HC using SPT. Person's correlation tested the link between SPT's and symptom severity, and another machine learning method predicted the gap between patients' chronological and brain age.

RESULTS

SPT demonstrated significant differences between patients and healthy controls (F = 2.944, p < 0.001). Machine learning approaches revealed SPT's ability to discriminate between patients and healthy controls (Accuracy = 0.65, Sensitivity = 0.67, Specificity = 0.64). Moreover, SPT correlated with the severity of depression symptom (r = 0.32. pFDR = 0.045) and predicted the gap between patients' chronological age and brain age (r = 0.756, p < 0.001).

LIMITATIONS

Evaluation of brain dynamics was constrained by MRI temporal resolution.

CONCLUSIONS

Our study introduces SPT as a promising metric to characterize the spatiotemporal signature of brain function, providing insights into deviant brain activity associated with depressive disorders and advancing our understanding of their psychopathological mechanisms.

Current best practices and future opportunities for reproducible findings using large-scale neuroimaging in psychiatry

Research into the brain basis of psychopathology is challenging due to the heterogeneity of psychiatric disorders, extensive comorbidities, underdiagnosis or overdiagnosis, multifaceted interactions with genetics and life experiences, and the highly multivariate nature of neural correlates. Therefore, increasingly larger datasets that measure more variables in larger cohorts are needed to gain insights. In this review, we present current "best practice" approaches for using existing databases, collecting and sharing new repositories for big data analyses, and future directions for big data in neuroimaging and psychiatry with an emphasis on contributing to collaborative efforts and the challenges of multi-study data analysis.

Breakthroughs and challenges for generating brain network-based biomarkers of treatment response in depression

Treatment outcomes widely vary for individuals diagnosed with major depressive disorder, implicating a need for deeper understanding of the biological mechanisms conferring a greater likelihood of response to a particular treatment. Our improved understanding of intrinsic brain networks underlying depression psychopathology via magnetic resonance imaging and other neuroimaging modalities has helped reveal novel and potentially clinically meaningful biological markers of response. And while we have made considerable progress in identifying such biomarkers over the last decade, particularly with larger, multisite trials, there are significant methodological and practical obstacles that need to be overcome to translate these markers into the clinic. The aim of this review is to review current literature on brain network structural and functional biomarkers of treatment response or selection in depression, with a specific focus on recent large, multisite trials reporting predictive accuracy of candidate biomarkers. Regarding pharmaco- and psychotherapy, we discuss candidate biomarkers, reporting that while we have identified candidate biomarkers of response to a single intervention, we need more trials that distinguish biomarkers between first-line treatments. Further, we discuss the ways prognostic neuroimaging may help to improve treatment outcomes to neuromodulation-based therapies, such as transcranial magnetic stimulation and deep brain stimulation. Lastly, we highlight obstacles and technical developments that may help to address the knowledge gaps in this area of research. Ultimately, integrating neuroimaging-derived biomarkers into clinical practice holds promise for enhancing treatment outcomes and advancing precision psychiatry strategies for depression management. By elucidating the neural predictors of treatment response and selection, we can move towards more individualized and effective depression interventions, ultimately improving patient outcomes and quality of life.

Unveiling Promising Neuroimaging Biomarkers for Schizophrenia Through Clinical and Genetic Perspectives

Schizophrenia is a complex and serious brain disorder. Neuroscientists have become increasingly interested in using magnetic resonance-based brain imaging-derived phenotypes (IDPs) to investigate the etiology of psychiatric disorders. IDPs capture valuable clinical advantages and hold biological significance in identifying brain abnormalities. In this review, we aim to discuss current and prospective approaches to identify potential biomarkers for schizophrenia using clinical multimodal neuroimaging and imaging genetics. We first described IDPs through their phenotypic classification and neuroimaging genomics. Secondly, we discussed the applications of multimodal neuroimaging by clinical evidence in observational studies and randomized controlled trials. Thirdly, considering the genetic evidence of IDPs, we discussed how can utilize neuroimaging data as an intermediate phenotype to make association inferences by polygenic risk scores and Mendelian randomization. Finally, we discussed machine learning as an optimum approach for validating biomarkers. Together, future research efforts focused on neuroimaging biomarkers aim to enhance our understanding of schizophrenia.

Unraveling robust brain-behavior links of depressive complaints through granular network models for understanding heterogeneity

BACKGROUND

Depressive symptoms are highly prevalent, present in heterogeneous symptom patterns, and share diverse neurobiological underpinnings. Understanding the links between psychopathological symptoms and biological factors is critical in elucidating its etiology and persistence. We aimed to evaluate the utility of using symptom-brain network models to parse the heterogeneity of depressive complaints in a large adolescent sample.

METHODS

We used data from the third wave of the IMAGEN study, a multi-center panel cohort study involving 1317 adolescents (52.49 % female, mean ± SD age = 18.5 ± 0.7). Two network models were estimated: one including an overall depressive symptom severity sum score based on the Adolescent Depression Rating Scale (ADRS), and one incorporating individual ADRS item scores. Both networks included measures of cortical thickness in several regions (insula, cingulate, mOFC, fusiform gyrus) and hippocampal volume derived from neuroimaging.

RESULTS

The network based on individual item scores revealed associations between cortical thickness measures and specific depressive complaints, obscured when using an aggregate depression severity score. Notably, the insula's cortical thickness showed negative associations with cognitive dysfunction (partial cor. = -0.15); the cingulate's cortical thickness showed negative associations with feelings of worthlessness (partial cor. = -0.10), and mOFC was negatively associated with anhedonia (partial cor. = -0.05).

LIMITATIONS

This cross-sectional study relied on the self-reported assessment of depression complaints and used a non-clinical sample with predominantly healthy participants (19 % with depression or sub-threshold depression).

CONCLUSIONS

This study showcases the utility of network models in parsing heterogeneity in depressive complaints, linking individual complaints to specific neural substrates. We outline the next steps to integrate neurobiological and cognitive markers to unravel MDD's phenotypic heterogeneity.

Magnetic resonance imaging connectivity features associated with response to transcranial magnetic stimulation in major depressive disorder

Transcranial magnetic stimulation (TMS) is an FDA-approved neuromodulation treatment for major depressive disorder (MDD), thought to work by altering dysfunctional brain connectivity pathways, or by indirectly modulating the activity of subcortical brain regions. Clinical response to TMS remains highly variable, highlighting the need for baseline predictors of response and for understanding brain changes associated with response. This systematic review examined brain connectivity features, and changes in connectivity features, associated with clinical improvement following TMS in MDD. Forty-one studies met inclusion criteria, including 1097 people with MDD. Most studies delivered one of two types of TMS to left dorsolateral prefrontal cortex and measured connectivity using resting-state functional MRI. The subgenual anterior cingulate cortex was the most well-studied brain region, particularly its connectivity with the TMS target or with the "executive control network" of brain regions. There was marked heterogeneity in findings. There is a need for greater understanding of how cortical TMS modulates connectivity with, and the activity of, subcortical regions, and how these effects change within and across treatment sessions.

Neurobiology and systems biology of stress resilience

Stress resilience is the phenomenon that some people maintain their mental health despite exposure to adversity or show only temporary impairments followed by quick recovery. Resilience research attempts to unravel the factors and mechanisms that make resilience possible and to harness its insights for the development of preventative interventions in individuals at risk for acquiring stress-related dysfunctions. Biological resilience research has been lagging behind the psychological and social sciences but has seen a massive surge in recent years. At the same time, progress in this field has been hampered by methodological challenges related to finding suitable operationalizations and study designs, replicating findings, and modeling resilience in animals. We embed a review of behavioral, neuroimaging, neurobiological, and systems biological findings in adults in a critical methods discussion. We find preliminary evidence that hippocampus-based pattern separation and prefrontal-based cognitive control functions protect against the development of pathological fears in the aftermath of singular, event-type stressors [as found in fear-related disorders, including simpler forms of posttraumatic stress disorder (PTSD)] by facilitating the perception of safety. Reward system-based pursuit and savoring of positive reinforcers appear to protect against the development of more generalized dysfunctions of the anxious-depressive spectrum resulting from more severe or longer-lasting stressors (as in depression, generalized or comorbid anxiety, or severe PTSD). Links between preserved functioning of these neural systems under stress and neuroplasticity, immunoregulation, gut microbiome composition, and integrity of the gut barrier and the blood-brain barrier are beginning to emerge. On this basis, avenues for biological interventions are pointed out.

Behavioral Activation and Brain Network Changes in Depression

Behavioral activation (BA) is a well-established method of evidence-based treatment for depression. There are clear links between the neural mechanisms underlying reward processing and BA treatment for depressive symptoms, including anhedonia; however, integrated interpretations of these two domains are lacking. Here we examine brain imaging studies involving BA treatments to investigate how changes in brain networks, including the reward networks, mediate the therapeutic effects of BA, and whether brain circuits are predictors of BA treatment responses. Increased activation of the prefrontal and subcortical regions associated with reward processing has been reported after BA treatment. Activation of these regions improves anhedonia. Conversely, some studies have found decreased activation of prefrontal regions after BA treatment in response to cognitive control stimuli in sad contexts, which indicates that the therapeutic mechanism of BA may involve disengagement from negative or sad contexts. Furthermore, the decrease in resting-state functional connectivity of the default-mode network after BA treatment appears to facilitate the ability to counteract depressive rumination, thereby promoting enjoyable and valuable activities. Conflicting results suggest that an intact neural response to rewards or defective reward functioning is predictive of the efficacy of BA treatments. Increasing the benefits of BA treatments requires identification of the unique individual characteristics determining which of these conflicting findings are relevant for the personalized treatment of each individual with depression.

Lower motor performance is linked with poor sleep quality, depressive symptoms, and grey matter volume alterations

Motor performance (MP) is essential for functional independence and well-being, particularly in later life. However, the relationship between behavioural aspects such as sleep quality and depressive symptoms, which contribute to MP, and the underlying structural brain substrates of their interplay remains unclear. This study used three population-based cohorts of younger and older adults (n=1,950) from the Human Connectome Project-Young Adult (HCP-YA), HCP-Aging (HCP-A), and enhanced Nathan Kline Institute-Rockland sample (eNKI-RS). Several canonical correlation analyses were computed within a machine learning framework to assess the associations between each of the three domains (sleep quality, depressive symptoms, grey matter volume (GMV)) and MP. The HCP-YA analyses showed progressively stronger associations between MP and each domain: depressive symptoms (unexpectedly positive, r=0.13, SD=0.06), sleep quality (r=0.17, SD=0.05), and GMV (r=0.19, SD=0.06). Combining sleep and depressive symptoms significantly improved the canonical correlations (r=0.25, SD=0.05), while the addition of GMV exhibited no further increase (r=0.23, SD=0.06). In young adults, better sleep quality, mild depressive symptoms, and GMV of several brain regions were associated with better MP. This was conceptually replicated in young adults from the eNKI-RS cohort. In HCP-Aging, better sleep quality, fewer depressive symptoms, and increased GMV were associated with MP. Robust multivariate associations were observed between sleep quality, depressive symptoms and GMV with MP, as well as age-related variations in these factors. Future studies should further explore these associations and consider interventions targeting sleep and mental health to test the potential effects on MP across the lifespan.

The power of many brains: Catalyzing neuropsychiatric discovery through open neuroimaging data and large-scale collaboration

Recent advances in open neuroimaging data are enhancing our comprehension of neuropsychiatric disorders. By pooling images from various cohorts, statistical power has increased, enabling the detection of subtle abnormalities and robust associations, and fostering new research methods. Global collaborations in imaging have furthered our knowledge of the neurobiological foundations of brain disorders and aided in imaging-based prediction for more targeted treatment. Large-scale magnetic resonance imaging initiatives are driving innovation in analytics and supporting generalizable psychiatric studies. We also emphasize the significant role of big data in understanding neural mechanisms and in the early identification and precise treatment of neuropsychiatric disorders. However, challenges such as data harmonization across different sites, privacy protection, and effective data sharing must be addressed. With proper governance and open science practices, we conclude with a projection of how large-scale imaging resources and collaborations could revolutionize diagnosis, treatment selection, and outcome prediction, contributing to optimal brain health.

An epidemiological study of season of birth, mental health, and neuroimaging in the UK Biobank

Environmental exposures during the perinatal period are known to have a long-term effect on adult physical and mental health. One such influential environmental exposure is the time of year of birth which affects the amount of daylight, nutrients, and viral load that an individual is exposed to within this key developmental period. Here, we investigate associations between season of birth (seasonality), four mental health traits (n = 137,588) and multi-modal neuroimaging measures (n = 33,212) within the UK Biobank. Summer births were associated with probable recurrent Major Depressive Disorder (β = 0.026, pcorr = 0.028) and greater mean cortical thickness in temporal and occipital lobes (β = 0.013 to 0.014, pcorr<0.05). Winter births were associated with greater white matter integrity globally, in the association fibers, thalamic radiations, and six individual tracts (β = -0.013 to -0.022, pcorr<0.05). Results of sensitivity analyses adjusting for birth weight were similar, with an additional association between winter birth and white matter microstructure in the forceps minor and between summer births, greater cingulate thickness and amygdala volume. Further analyses revealed associations between probable depressive phenotypes and a range of neuroimaging measures but a paucity of interactions with seasonality. Our results suggest that seasonality of birth may affect later-life brain structure and play a role in lifetime recurrent Major Depressive Disorder. Due to the small effect sizes observed, and the lack of associations with other mental health traits, further research is required to validate birth season effects in the context of different latitudes, and by co-examining genetic and epigenetic measures to reveal informative biological pathways.

ER stress in mouse serotonin neurons triggers a depressive phenotype alleviated by ketamine targeting eIF2α signaling

Depression is a devastating mood disorder that causes significant disability worldwide. Current knowledge of its pathophysiology remains modest and clear biological markers are lacking. Emerging evidence from human and animal models reveals persistent alterations in endoplasmic reticulum (ER) homeostasis, suggesting that ER stress-related signaling pathways may be targets for prevention and treatment. However, the neurobiological basis linking the pathways involved in depression-related ER stress remains unknown. Here, we report that an induced model of ER stress in mouse serotonin (5-HT) neurons is associated with reduced Egr1-dependent 5-HT cellular activity and 5-HT neurotransmission, resulting in neuroplasticity deficits in forebrain regions and a depressive-like phenotype. Ketamine administration engages downstream eIF2α signaling to trigger rapid neuroplasticity events that rescue the depressive-like effects. Collectively, these data identify ER stress in 5-HT neurons as a cellular pathway involved in the pathophysiology of depression and show that eIF2α is critical in eliciting ketamine's fast antidepressant effects.

The difference in volumetric alternations of the orbitofrontal-limbic-striatal system between major depressive disorder and anxiety disorders: A systematic review and voxel-based meta-analysis

BACKGROUND

Major depressive disorder (MDD) and anxiety disorders (ANX) are psychiatric disorders with high mutual comorbidity rates that might indicate some shared neurobiological pathways between them, but they retain diverse phenotypes that characterize themselves specifically. However, no consistent evidence exists for common and disorder-specific gray matter volume (GMV) alternations between them.

METHODS

A systematic review and meta-analysis on voxel-based morphometry studies of patients with MDD and ANX were performed. The effect of comorbidity was explicitly controlled during disorder-specific analysis and particularly investigated in patient with comorbidity.

RESULTS

A total of 45 studies with 54 datasets comprising 2196 patients and 2055 healthy participants met the inclusion criteria. Deficits in the orbitofrontal cortex, striatum, and limbic regions were found in MDD and ANX. The disorder-specific analyses showed decreased GMV in the bilateral anterior cingulate cortex, right striatum, hippocampus, and cerebellum in MDD, while decreased GMV in the left striatum, amygdala, insula, and increased cerebellar volume in ANX. A totally different GMV alternation pattern was shown involving bilateral temporal and parietal gyri and left fusiform gyrus in patients with comorbidity.

LIMITATIONS

Owing to the design of included studies, only partial patients in the comorbid group had a secondary comorbidity diagnosis.

CONCLUSION

Patients with MDD and ANX shared a structural disruption in the orbitofrontal-limbic-striatal system. The disorder-specific effects manifested their greatest severity in distinct lateralization and directionality of these changes that differentiate MDD from ANX. The comorbid group showed a totally different GMV alternation pattern, possibly suggesting another illness subtype that requires further investigation.

The Dark and Gloomy Brain: Grey Matter Volume Alterations in Major Depressive Disorder-Fine-Grained Meta-Analyses

Background

While the brain correlates of major depressive disorder (MDD) have been extensively studied, there is no consensus conclusion so far. Various meta-analyses tried to determine the most consistent findings, but the results are often discordant for grey matter volume (GMV) atrophy and hypertrophy. Applying rigorous and stringent inclusion criteria and controlling for confounding factors, such as the presence of anxiety comorbidity, we carried out two novel meta-analyses on the existing literature to unveil MDD signatures.

Methods

A systematic literature search was performed up to January 2023. Seventy-three studies on MDD patients reporting GMV abnormalities were included in the first meta-analysis, for a total of 6167 patients and 6237 healthy controls (HC). To test the effects of anxiety comorbidity, we conducted a second meta-analysis, by adding to the original pure MDD sample a new cohort of MDD patients with comorbid anxiety disorders (308 patients and 342 HC). An activation likelihood estimation (ALE) analysis and a coordinate-based mapping approach separate for atrophy and hypertrophy were used to identify common brain structural alterations among patients.

Results

The pure MDD sample exhibited atrophy in the left insula, as well as hypertrophy in the bilateral amygdala and parahippocampal gyri. When we added patients with comorbid anxiety to the original sample, bilateral insula atrophy emerged, whereas the hypertrophy results were not replicated.

Conclusions

Our findings revealed important structural alterations in pure MDD patients, particularly in the insula and amygdala, which play key roles in sensory input integration and in emotional processing, respectively. Additionally, the amygdala and parahippocampal gyrus hypertrophy may be related to MDD functional overactivation to emotional stimuli, rumination, and overactive self-referential thinking. Conversely, the presence of anxiety comorbidity revealed separate effects which were not seen in the pure MDD sample, underscoring the importance of strict inclusion criteria for investigations of disorder-specific effects.

Interplay of early negative life events, development of orbitofrontal cortical thickness and depression in young adulthood

Background

Early negative life events (NLE) have long-lasting influences on neurodevelopment and psychopathology. Reduced orbitofrontal cortex (OFC) thickness was frequently associated with NLE and depressive symptoms. OFC thinning might mediate the effect of NLE on depressive symptoms, although few longitudinal studies exist. Using a complete longitudinal design with four time points, we examined whether NLE during childhood and early adolescence predict depressive symptoms in young adulthood through accelerated OFC thinning across adolescence.

Methods

We acquired structural MRI from 321 participants at two sites across four time points from ages 14 to 22. We measured NLE with the Life Events Questionnaire at the first time point and depressive symptoms with the Center for Epidemiologic Studies Depression Scale at the fourth time point. Modeling latent growth curves, we tested whether OFC thinning mediates the effect of NLE on depressive symptoms.

Results

A higher burden of NLE, a thicker OFC at the age of 14, and an accelerated OFC thinning across adolescence predicted young adults' depressive symptoms. We did not identify an effect of NLE on OFC thickness nor OFC thickness mediating effects of NLE on depressive symptoms.

Conclusions

Using a complete longitudinal design with four waves, we show that NLE in childhood and early adolescence predict depressive symptoms in the long term. Results indicate that an accelerated OFC thinning may precede depressive symptoms. Assessment of early additionally to acute NLEs and neurodevelopment may be warranted in clinical settings to identify risk factors for depression.

Intracortical Myelin in Youths at Risk for Depression

Background

Major depressive disorder (MDD) is a leading cause of disability. To understand why depression develops, it is important to distinguish between early neural markers of vulnerability that precede the onset of MDD and features that develop during depression. Recent neuroimaging findings suggest that reduced global and regional intracortical myelination (ICM), especially in the lateral prefrontal cortex, may be associated with depression, but it is unknown whether it is a precursor or a consequence of MDD. The study of offspring of affected parents offers the opportunity to distinguish between precursors and consequences by examining individuals who carry high risk at a time when they have not experienced depression.

Methods

We acquired 129 T1-weighted and T2-weighted scans from 56 (25 female) unaffected offspring of parents with depression and 114 scans from 63 (34 female) unaffected offspring of parents without a history of depression (ages 9 to 16 years). To assess scan quality, we calculated test-retest reliability. We used the scan ratios to calculate myelin maps for 68 cortical regions. We analyzed data using mixed-effects modeling.

Results

ICM did not differ between high and low familial risk youths in global (B = 0.06, SE = 0.03, p = .06) or regional (B = 0.05, SE = 0.03, p = .08) analyses. Our pediatric sample had high ICM reliability (intraclass correlation coefficient = 0.79; 95% CI, 0.55-0.88).

Conclusions

Based on our results, reduced ICM does not appear to be a precursor of MDD. Future studies should examine ICM in familial high-risk youths across a broad developmental period.

Revealing complexity: segmentation of hippocampal subfields in adolescents with major depressive disorder reveals specific links to cognitive dysfunctions

BACKGROUND

Hippocampal disruptions represent potential neuropathological biomarkers in depressed adolescents with cognitive dysfunctions. Given heterogeneous outcomes of whole-hippocampus analyses, we investigated subregional abnormalities in depressed adolescents and their associations with symptom severity and cognitive dysfunctions.

METHODS

MethodsSeventy-nine first-episode depressive patients (ag = 15.54 ± 1.83) and 71 healthy controls (age = 16.18 ± 2.85) were included. All participants underwent T1 and T2 imaging, completed depressive severity assessments, and performed cognitive assessments on memory, emotional recognition, cognitive control, and attention. Freesurfer was used to segment each hippocampus into 12 subfields. Multivariable analyses of variance were performed to identify overall and disease severity-related abnormalities in patients. LASSO regression was also conducted to explore the associations between hippocampal subfields and patients' cognitive abilities.

RESULTS

Depressed adolescents showed decreases in dentate gyrus, CA1, CA2/3, CA4, fimbria, tail, and molecular layer. Analyses of overall symptom severity, duration, self-harm behavior, and suicidality suggested that severity-related decreases mainly manifested in CA regions and involved surrounding subfields with disease severity increases. LASSO regression indicated that hippocampal subfield abnormalities had the strongest associations with memory impairments, with CA regions and dentate gyrus showing the highest weights.

CONCLUSIONS

Hippocampal abnormalities are widespread in depressed adolescents and such abnormalities may spread from CA regions to surrounding areas as the disease progresses. Abnormalities in CA regions and dentate gyrus among these subfields primarily link with memory impairments in patients. These results demonstrate that hippocampal subsections may serve as useful biomarkers of depression progression in adolescents, offering new directions for early clinical intervention.

Lifetime Exposure to Depression and Neuroimaging Measures of Brain Structure and Function

Importance

Despite decades of neuroimaging studies reporting brain structural and functional alterations in depression, discrepancies in findings across studies and limited convergence across meta-analyses have raised questions about the consistency and robustness of the observed brain phenotypes.

Objective

To investigate the associations between 6 operational criteria of lifetime exposure to depression and functional and structural neuroimaging measures.

Design, Setting, and Participants

This cross-sectional study analyzed data from a UK Biobank cohort of individuals aged 45 to 80 years who were enrolled between January 1, 2014, and December 31, 2018. Participants included individuals with a lifetime exposure to depression and matched healthy controls without indications of psychosis, mental illness, behavior disorder, and disease of the nervous system. Six operational criteria of lifetime exposure to depression were evaluated: help seeking for depression; self-reported depression; antidepressant use; depression definition by Smith et al; hospital International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) diagnosis codes F32 and F33; and Composite International Diagnostic Interview Short Form score. Six increasingly restrictive depression definitions and groups were defined based on the 6 depression criteria, ranging from meeting only 1 criterion to meeting all 6 criteria. Data were analyzed between January and October 2022.

Main Outcomes and Measures

Functional measures were calculated using voxel-wise fractional amplitude of low-frequency fluctuation (fALFF), global correlation (GCOR), and local correlation (LCOR). Structural measures were calculated using gray matter volume (GMV).

Results

The study included 20 484 individuals with lifetime depression (12 645 females [61.7%]; mean [SD] age, 63.91 [7.60] years) and 25 462 healthy controls (14 078 males [55.3%]; mean [SD] age, 65.05 [7.8] years). Across all depression criteria, individuals with lifetime depression displayed regionally consistent decreases in fALFF, LCOR, and GCOR (Cohen d range, -0.53 [95% CI, -0.88 to -0.15] to -0.04 [95% CI, -0.07 to -0.01]) but not in GMV (Cohen d range, -0.47 [95 % CI, -0.75 to -0.12] to 0.26 [95% CI, 0.15-0.37]). Hospital ICD-10 diagnosis codes F32 and F33 (median [IQR] difference in effect sizes, -0.14 [-0.17 to -0.11]) and antidepressant use (median [IQR] difference in effect sizes, -0.12 [-0.16 to -0.10]) were criteria associated with the most pronounced alterations.

Conclusions and Relevance

Results of this cross-sectional study indicate that lifetime exposure to depression was associated with robust functional changes, with a more restrictive depression definition revealing more pronounced alterations. Different inclusion criteria for depression may be associated with the substantial variation in imaging findings reported in the literature.

The molecular mechanism of polysaccharides in combating major depressive disorder: A comprehensive review

Major depressive disorder (MDD) is a complex psychiatric condition with diverse etiological factors. Typical pathological features include decreased cerebral cortex, subcortical structures, and grey matter volumes, as well as monoamine transmitter dysregulation. Although medications exist to treat MDD, unmet needs persist due to limited efficacy, induced side effects, and relapse upon drug withdrawal. Polysaccharides offer promising new therapies for MDD, demonstrating antidepressant effects with minimal side effects and multiple targets. These include neurotransmitter, neurotrophin, neuroinflammation, hypothalamic-pituitary-adrenal axis, mitochondrial function, oxidative stress, and intestinal flora regulation. This review explores the latest advancements in understanding the pharmacological actions and mechanisms of polysaccharides in treating major depression. We discuss the impact of polysaccharides' diverse structures and properties on their pharmacological actions, aiming to inspire new research directions and facilitate the discovery of novel anti-depressive drugs.

The prevalence of depression and anxiety among cardiovascular patients at University of Gondar specialized hospital using beck's depression inventory II and beck anxiety inventory: A cross-sectional study

Psychosocial issues are leading factor as well as consequences of cardiovascular disease. Identifying factors associated with depression facilitate service quality improvement for inpatients. This study assessed the prevalence and identified associated factors with depression and anxiety among patients with cardio vascular disease.

Method

An institution-based cross-sectional study was conducted with a convenience sample of 370 stable adult patients from June 1 to July 30, 2020 among cardiovascular disease patients at the University of Gondar Specialized Hospital Ethiopia. Data were collected by using structured questionnaires. Data analyses were conducted using SPSS version 21. The statistical significance declared at p-value <0.05.

Result

In this study, among 370 Cardiovascular diseases patients, 228 (61.6 %) suffer from anxiety, and 53.51 % (198) suffer with depression. There was a significant mean difference in the level of depression and anxiety between male and female Cardiovascular diseases patients. The females' scores of depression (mean = 28, p < 0.01) and anxiety (mean = 25.3, p < 0.01) were more than that of males 'scores of depression (mean = 15.1, p < 0.01) and anxiety (mean = 12.3, p < 0.01). Cardiovascular diseases patients aged greater than 60 years have the highest rate of prevalence of depression in all age group. Being in the age category of greater than 60 years was 1.16 (0.57-2.32) times more likely to have depression than the age category of 18-24 years. Depression and anxiety were significantly associated with being woman, widowed, being single, unable to read and write, and possess mental disorders history.

Challenges in Identifying Individualized Brain Biomarkers of Late Life Depression

Research into neuroimaging biomarkers for Late Life Depression (LLD) has identified neural correlates of LLD including increased white matter hyperintensities and reduced hippocampal volume. However, studies into neuroimaging biomarkers for LLD largely fail to converge. This lack of replicability is potentially due to challenges linked to construct variability, etiological heterogeneity, and experimental rigor. We discuss suggestions to help address these challenges, including improved construct standardization, increased sample sizes, multimodal approaches to parse heterogeneity, and the use of individualized analytical models.

Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures

Machine learning (ML) techniques have gained popularity in the neuroimaging field due to their potential for classifying neuropsychiatric disorders. However, the diagnostic predictive power of the existing algorithms has been limited by small sample sizes, lack of representativeness, data leakage, and/or overfitting. Here, we overcome these limitations with the largest multi-site sample size to date (N = 5365) to provide a generalizable ML classification benchmark of major depressive disorder (MDD) using shallow linear and non-linear models. Leveraging brain measures from standardized ENIGMA analysis pipelines in FreeSurfer, we were able to classify MDD versus healthy controls (HC) with a balanced accuracy of around 62%. But after harmonizing the data, e.g., using ComBat, the balanced accuracy dropped to approximately 52%. Accuracy results close to random chance levels were also observed in stratified groups according to age of onset, antidepressant use, number of episodes and sex. Future studies incorporating higher dimensional brain imaging/phenotype features, and/or using more advanced machine and deep learning methods may yield more encouraging prospects.

Chronic psychosocial stress triggers microglial-/macrophage-induced inflammatory responses leading to neuronal dysfunction and depressive-related behavior

Chronic environmental stress and traumatic social experiences induce maladaptive behavioral changes and is a risk factor for major depressive disorder (MDD) and various anxiety-related psychiatric disorders. Clinical studies and animal models of chronic stress have reported that symptom severity is correlated with innate immune responses and upregulation of neuroinflammatory cytokine signaling in brain areas implicated in mood regulation (mPFC; medial Prefrontal Cortex). Despite increasing evidence implicating impairments of neuroplasticity and synaptic signaling deficits into the pathophysiology of stress-related mental disorders, how microglia may modulate neuronal homeostasis in response to chronic stress has not been defined. Here, using the repeated social defeat stress (RSDS) mouse model we demonstrate that microglial-induced inflammatory responses are regulating neuronal plasticity associated with psychosocial stress. Specifically, we show that chronic stress induces a rapid activation and proliferation of microglia as well as macrophage infiltration in the mPFC, and these processes are spatially related to neuronal activation. Moreover, we report a significant association of microglial inflammatory responses with susceptibility or resilience to chronic stress. In addition, we find that exposure to chronic stress exacerbates phagocytosis of synaptic elements and deficits in neuronal plasticity. Importantly, by utilizing two different CSF1R inhibitors (the brain penetrant PLX5622 and the non-penetrant PLX73086) we highlight a crucial role for microglia (and secondarily macrophages) in catalyzing the pathological manifestations linked to psychosocial stress in the mPFC and the resulting behavioral deficits usually associated with depression.

Large-Scale Neuroimaging of Mental Illness

Neuroimaging has provided important insights into the brain variations related to mental illness. Inconsistencies in prior studies, however, call for methods that lead to more replicable and generalizable brain markers that can reliably predict illness severity, treatment course, and prognosis. A paradigm shift is underway with large-scale international research teams actively pooling data and resources to drive consensus findings and test emerging methods aimed at achieving the goals of precision psychiatry. In parallel with large-scale psychiatric genomics studies, international consortia combining neuroimaging data are mapping the transdiagnostic brain signatures of mental illness on an unprecedented scale. This chapter discusses the major challenges, recent findings, and a roadmap for developing better neuroimaging-based tools and markers for mental illness.

Vascular risk factors, white matter microstructure, and depressive symptoms: a longitudinal analysis in the UK Biobank

BACKGROUND

Cumulative burden from vascular risk factors (VRFs) has been associated with an increased risk of depressive symptoms in mid- and later life. It has been hypothesised that this association arises because VRFs disconnect fronto-subcortical white matter tracts involved in mood regulation, which puts older adults at higher risk of developing depressive symptoms. However, evidence for the hypothesis that disconnection of white matter tracts underlies the association between VRF burden and depressive symptoms from longitudinal studies is scarce.

METHODS

This preregistered study analysed longitudinal data from 6,964 middle-aged and older adults from the UK Biobank who participated in consecutive assessments of VRFs, brain imaging, and depressive symptoms. Using mediation modelling, we directly tested to what extend white matter microstructure mediates the longitudinal association between VRF burden and depressive symptoms.

RESULTS

VRF burden showed a small association with depressive symptoms at follow-up. However, there was no evidence that fractional anisotropy (FA) of white matter tracts mediated this association. Additional analyses also yielded no mediating effects using alternative operationalisations of VRF burden, mean diffusivity (MD) of single tracts, or overall average of tract-based white matter microstructure (global FA, global MD, white matter hyperintensity volume).

CONCLUSIONS

Our results lend no support to the hypothesis that disconnection of white matter tracts underlies the association between VRF burden and depressive symptoms, while highlighting the relevance of using longitudinal data to directly test pathways linking vascular and mental health.

[Applications and challenges of wearable electroencephalogram signals in depression recognition and personalized music intervention]

Rapid and accurate identification and effective non-drug intervention are the worldwide challenges in the field of depression. Electroencephalogram (EEG) signals contain rich quantitative markers of depression, but whole-brain EEG signals acquisition process is too complicated to be applied on a large-scale population. Based on the wearable frontal lobe EEG monitoring device developed by the authors' laboratory, this study discussed the application of wearable EEG signal in depression recognition and intervention. The technical principle of wearable EEG signals monitoring device and the commonly used wearable EEG devices were introduced. Key technologies for wearable EEG signals-based depression recognition and the existing technical limitations were reviewed and discussed. Finally, a closed-loop brain-computer music interface system for personalized depression intervention was proposed, and the technical challenges were further discussed. This review paper may contribute to the transformation of relevant theories and technologies from basic research to application, and further advance the process of depression screening and personalized intervention.

Ketamine and the neurobiology of depression: Toward next-generation rapid-acting antidepressant treatments

Ketamine has emerged as a transformative and mechanistically novel pharmacotherapy for depression. Its rapid onset of action, efficacy for treatment-resistant symptoms, and protection against relapse distinguish it from prior antidepressants. Its discovery emerged from a reconceptualization of the neurobiology of depression and, in turn, insights from the elaboration of its mechanisms of action inform studies of the pathophysiology of depression and related disorders. It has been 25 y since we first presented our ketamine findings in depression. Thus, it is timely for this review to consider what we have learned from studies of ketamine and to suggest future directions for the optimization of rapid-acting antidepressant treatment.

Dimensional subtyping of first-episode drug-naïve major depressive disorder: A multisite resting-state fMRI study

Major depressive disorder (MDD) is a heterogeneous syndrome, and understanding its neural mechanisms is crucial for the advancement of personalized medicine. However, conventional subtyping studies often categorize MDD patients into a single subgroup, neglecting the continuous interindividual variations. This implies a pressing need for a dimensional approach. 230 first-episode drug-naïve MDD patients and 395 healthy controls were obtained from 5 sites via the Rest-meta-MDD project. A Bayesian model was used to decompose the resting-state functional connectivity (RSFC) into multiple distinct RSFC patterns (refer to as "factors"), and each individual was allowed to express multiple factors to varying degrees (dimensional subtyping). The associations between demographic and clinical variables with the identified factors were calculated. We identified three latent factors with distinct but partially overlapping hypo- and hyper-RSFC patterns. Most participants co-expressed multiple latent factors. All factors shared abnormal RSFC involving the default mode network and frontoparietal network, but the directionality partially differed across factors. All factors were not significantly associated with demographic and clinical variables. These findings shed light on the interindividual variability in MDD and could form the basis for developing novel therapeutic approaches that capitalize on the heterogeneity of MDD.

Aberrant degree centrality profiles during rumination in major depressive disorder

Rumination is closely linked to the onset and maintenance of major depressive disorder (MDD). Prior neuroimaging studies have identified the association between self-reported rumination trait and the functional coupling among a network of brain regions using resting-state functional magnetic resonance imaging (MRI). However, little is known about the underlying neural circuitry mechanism during active rumination in MDD. Degree centrality (DC) is a simple metric to denote network integration, which is critical for higher-order psychological processes such as rumination. During an MRI scan, individuals with MDD (N = 45) and healthy controls (HC, N = 46) completed a rumination state task. We examined the interaction effect between the group (MDD vs. HC) and condition (rumination vs. distraction) on vertex-wise DC. We further characterized the identified brain region's functional involvement with Neurosynth and BrainMap. Network-wise seed-based functional connectivity (FC) analysis was also conducted for the identified region of interest. Finally, exploratory correlation analysis was conducted between the identified region of interest's network FCs and self-reported in-scanner affect levels. We found that a left superior frontal gyrus (SFG) region, generally overlapped with the frontal eye field, showed a significant interaction effect. Further analysis revealed its involvement with executive functions. FCs between this region, the frontoparietal, and the dorsal attention network (DAN) also showed significant interaction effects. Furthermore, its FC to DAN during distraction showed a marginally significant negative association with in-scanner affect level at the baseline. Our results implicated an essential role of the left SFG in the rumination's underlying neural circuitry mechanism in MDD and provided novel evidence for the conceptualization of rumination in terms of impaired executive control.

Subthreshold Depression: A Systematic Review and Network Meta-Analysis of Non-Pharmacological Interventions

Background

Subthreshold depression (StD) is considered to be the "precursor" stage of major depressive disorder (MDD), which could cause higher risk of suicide, disease burden and functional impairment. There have been various non-pharmacological interventions for StD. However, the comparison of their effectiveness still lacks sufficient evidence. We performed a systematic review and network meta-analysis to evaluate and rank the efficacy of multiple non-pharmacological interventions targeting StD.

Methods

We conducted a thorough search across various databases including PubMed, Medline, Embase, Web of Science and PsycINFO from inception to December 2022. All included studies were randomized controlled trials (RCTs) of non-pharmacological interventions for patients with StD compared with control group (CG). Several universal scales for measuring depression severity were used as efficacy outcomes. The surface under the cumulative ranking curve (SUCRA) was used to separately rank each intervention using the "Stata 17.0" software.

Results

A total of thirty-six trials were included, involving twenty-eight interventions and 7417 participants. The research found that most non-pharmacological interventions were superior to controls for StD. In each outcome evaluation by different scales for measuring depression, psychotherapy always ranked first in terms of treatment effectiveness, especially Problem-solving Therapy (PST), Behavioral Activation Therapy (BAT), Cognitive Behavioral Therapy (CBT)/Internet-based CBT (I-CBT)/Telephone-based CBT (T-CBT). Since different groups could not be directly compared, the total optimal intervention could not be determined.

Conclusion

Here, we show that psychotherapy may be the better choice for the treatment of StD. This study provides some evidence on StD management selection for clinical workers. However, to establish its intervention effect more conclusively, the content, format and operators of psychotherapy still require extensive exploration to conduct more effective, convenient and cost-effective implementation in primary healthcare. Notably, further research is also urgently needed to find the biological and neural mechanisms of StD by examining whether psychotherapy alters neuroplasticity in patients with StD.

Altered topology of individual brain structural covariance networks in major depressive disorder

BACKGROUND

The neurobiological pathogenesis of major depression disorder (MDD) remains largely controversial. Previous literatures with limited sample size utilizing group-level structural covariance networks (SCN) commonly generated mixed findings regarding the topology of brain networks.

METHODS

We analyzed T1 images from a high-powered multisite sample including 1173 patients with MDD and 1019 healthy controls (HCs). We used regional gray matter volume to construct individual SCN by utilizing a novel approach based on the interregional effect size difference. We further investigated MDD-related structural connectivity alterations using topological metrics.

RESULTS

Compared to HCs, the MDD patients showed a shift toward randomization characterized by increased integration. Further subgroup analysis of patients in different stages revealed this randomization pattern was also observed in patients with recurrent MDD, while the first-episode drug naïve patients exhibited decreased segregation. Altered nodal properties in several brain regions which have a key role in both emotion regulation and executive control were also found in MDD patients compared with HCs. The abnormalities in inferior temporal gyrus were not influenced by any specific site. Moreover, antidepressants increased nodal efficiency in the anterior ventromedial prefrontal cortex.

CONCLUSIONS

The MDD patients at different stages exhibit distinct patterns of randomization in their brain networks, with increased integration during illness progression. These findings provide valuable insights into the disruption in structural brain networks that occurs in patients with MDD and might be useful to guide future therapeutic interventions.

Childhood trauma and social support affect symptom profiles through cortical thickness abnormalities in major depressive disorder: A structural equation modeling analysis

BACKGROUND

Childhood trauma, low social support, and alexithymia are recognized as risk factors for major depressive disorder (MDD). However, the mechanisms of risk factors, symptoms, and corresponding structural brain abnormalities in MDD are not fully understood. Structural equation modeling (SEM) has advantages in studying multivariate interrelationships. We aim to illustrate their relationships using SEM.

METHODS

313 MDD patients (213 female; mean age 42.49 years) underwent magnetic resonance imaging and completed assessments. We integrated childhood trauma, alexithymia, social support, anhedonia, depression, anxiety, suicidal ideation and cortical thickness into a multivariate SEM.

RESULTS

We first established the risk factors-clinical phenotype SEM with an adequate fit. Cortical thickness results show a negative correlation of childhood trauma with the left middle temporal gyrus (MTG) (p = 0.012), and social support was negatively correlated with the left posterior cingulate cortex (PCC) (p < 0.001). The final good fit SEM (χ2 = 32.92, df = 21, χ2/df = 1.57, CFI = 0.962, GFI = 0.978, RMSEA = 0.043) suggested two pathways, with left PCC thickness mediating the relationship between social support and suicidal ideation, and left MTG thickness mediating between childhood trauma and anhedonia/anxiety.

CONCLUSION

Our findings provide evidence for the impact of risk factor variables on the brain structure and clinical phenotype of MDD patients. Insufficient social support and childhood trauma might lead to corresponding cortical abnormalities in PCC and MTG, affecting the patient's mood and suicidal ideation. Future interventions should aim at these nodes.

Transdiagnostic MRI markers of psychopathology following traumatic brain injury: a systematic review and network meta-analysis protocol

INTRODUCTION

Psychopathology following traumatic brain injury (TBI) is a common and debilitating consequence that is often associated with reduced functional and psychosocial outcomes. There is a lack of evidence regarding the neural underpinnings of psychopathology following TBI, and whether there may be transdiagnostic neural markers that are shared across traditional psychiatric diagnoses. The aim of this systematic review and meta-analysis is to examine the association of MRI-derived markers of brain structure and function with both transdiagnostic and specific psychopathology following moderate-severe TBI.

METHODS AND ANALYSIS

A systematic literature search of Embase (1974-2022), Ovid MEDLINE (1946-2022) and PsycINFO (1806-2022) will be conducted. Publications in English that investigate MRI correlates of psychopathology characterised by formal diagnoses or symptoms of psychopathology in closed moderate-severe TBI populations over 16 years of age will be included. Publications will be excluded that: (a) evaluate non-MRI neuroimaging techniques (CT, positron emission tomography, magnetoencephalography, electroencephalogram); (b) comprise primarily a paediatric cohort; (c) comprise primarily penetrating TBI. Eligible studies will be assessed against a modified Joanna Briggs Institute Critical Appraisal Instrument and data will be extracted by two independent reviewers. A descriptive analysis of MRI findings will be provided based on qualitative synthesis of data extracted. Quantitative analyses will include a meta-analysis and a network meta-analysis where there are sufficient data available.

ETHICS AND DISSEMINATION

Ethics approval is not required for the present study as there will be no original data collected. We intend to disseminate the results through publication to a high-quality peer-reviewed journal and conference presentations on completion.

PROSPERO REGISTRATION NUMBER

CRD42022358358.

Neuroinflammation in the Amygdala Is Associated With Recent Depressive Symptoms

BACKGROUND

Converging evidence suggests that elevated inflammation may contribute to depression. Yet, the link between peripheral inflammation and neuroinflammation in depression is unclear. Here, using data from the UK Biobank, we estimated associations among depression, C-reactive protein (CRP) as a measure of peripheral inflammation, and neuroinflammation as indexed by diffusion basis spectral imaging-based restricted fraction (DBSI-RF).

METHODS

DBSI-RF was derived from diffusion-weighted imaging data (N = 11,512) for whole-brain gray matter (global-RF), and regions of interest in the bilateral amygdala (amygdala-RF) and hippocampus (hippocampus-RF), and CRP was estimated from blood (serum) samples. Self-reported recent depression symptoms were measured using a 4-item assessment. Linear regressions were used to estimate associations between CRP and DBSI-RFs with depression while adjusting for the following covariates: age, sex, body mass index, smoking, drinking, and medical conditions.

RESULTS

Elevated CRP was associated with higher depression symptoms (β = 0.04, false discovery rate-corrected p < .005) and reduced global-RF (β = -0.03, false discovery rate-corrected p < .001). Higher amygdala-RF was associated with elevated depression-an effect resilient to added covariates and CRP (β = 0.02, false discovery rate-corrected p < .05). Interestingly, this association was stronger in individuals with a lifetime history of depression (β = 0.07, p < .005) than in those without (β = 0.03, p < .05). Associations between global-RF or hippocampus-RF with depression were not significant, and no DBSI-RF indices indirectly linked CRP with depression (i.e., mediation effect).

CONCLUSIONS

Peripheral inflammation and DBSI-RF neuroinflammation in the amygdala are independently associated with depression, consistent with animal studies suggesting distinct pathways of peripheral inflammation and neuroinflammation in the pathophysiology of depression and with investigations highlighting the role of the amygdala in stress-induced inflammation and depression.