motor dysfunction, presenting as an altered quantity or quality of movements, is common in late life depression (LLD) but poorly understood. We characterized motor dysfunction with a multimodal clinical-experimental assessment and investigated associations with depressive symptoms, psychotropic medication and falls.

75 participants ≥60 yr., 34 patients with LLD and 41 healthy controls, from the Leuven LLD study completed affective, cognitive and motor assessments including the Montgomery-Åsberg Depression Rating Scale (MADRS), Apathy Evaluation Scale, mini-mental state examination, neuropsychological battery, parkinsonism scale (MDS-UPDRS part III), ataxia scale (SARA), speech and gait analysis. A digital tablet drawing task tracked how fast patients initiated and executed movements with variable complexity and cueing. Past year fall history was collected. Multiple linear regression analyses evaluated associations of motor outcome with assessments scores, depending on diagnostic group, with age, exercise frequency, education duration as covariates. Movement initiation and execution were investigated with linear mixed models including the same covariates. Psychotropic medication effects and statistical predictors of falls were analyzed in patients.

patients (mean age 73.3 ± SD6.0 yr., MADRS 27.9 ± 11.1, 42 % fall incidence) performed worse than controls on all motor assessments which was overall not significantly associated with depression severity, apathy, cognitive dysfunction, antipsychotic or benzodiazepine use. Patients executed movements slower than controls, but movement initiation was similar if cueing was provided. Patients' SARA and gait outcome related to past year fall incidence.

motor dysfunction in LLD patients was not clearly related to core depressive symptoms or cognitive dysfunction. Cueing may facilitate movement initiation in LLD.

NCT03849417.

Clinical and preclinical studies have identified somatostatin (SST)-positive interneurons as critical elements that regulate the vulnerability to stress-related psychiatric disorders. Conversely, disinhibition of SST neurons in mice results in resilience to the behavioral effects of chronic stress. Here, we established a low-dose chronic chemogenetic protocol to map these changes in positively and negatively motivated behaviors to specific brain regions. AAV-hM3Dq-mediated chronic activation of SST neurons in the prelimbic cortex (PLC) had antidepressant drug-like effects on anxiety- and anhedonia-like motivated behaviors in male but not female mice. Analogous manipulation of the ventral hippocampus (vHPC) had such effects in female but not male mice. Moreover, the activation of SST neurons in the PLC of male mice and the vHPC of female mice resulted in stress resilience. Activation of SST neurons in the PLC reversed prior chronic stress-induced defects in motivated behavior in males but was ineffective in females. Conversely, activation of SST neurons in the vHPC reversed chronic stress-induced behavioral alterations in females but not males. Quantitation of c-Fos+ and FosB+ neurons in chronic stress-exposed mice revealed that chronic activation of SST neurons leads to a paradoxical increase in pyramidal cell activity. Collectively, these data demonstrate that GABAergic microcircuits driven by dendrite targeting interneurons enable sex- and brain-region-specific neural plasticity that promotes stress resilience and reverses stress-induced anxiety- and anhedonia-like motivated behavior. The data provide a rationale for the lack of antidepressant efficacy of benzodiazepines and superior efficacy of dendrite-targeting, low-potency GABAA receptor agonists, independent of sex and despite striking sex differences in the relevant brain substrates.

Insomnia affects a substantial proportion of the population and frequently co-occurs with mental illnesses including depression and anxiety. However, the neurobiological correlates of these disorders remain unclear. Here we review magnetic resonance imaging (MRI) studies assessing structural and functional brain associations with depressive and anxiety symptoms in insomnia disorder (ID; n = 38), insomnia symptoms in depressive and anxiety disorders (n = 14), and these symptoms in the general populations (n = 3). The studies on insomnia disorder consistently showed overlapping (salience network: insula and anterior cingulate cortex) and differential MRI correlation patterns between depressive (thalamus, orbitofrontal cortex and its associated functional connectivity) and anxiety (functional connectivity associated with default mode network) symptoms. The insula was also consistently identified as indicating the severity of insomnia symptoms in depressive disorder. In contrast, findings for other regions related to insomnia symptoms in both depressive and anxiety disorders were generally inconsistent across studies, partly due to variations in methods and patient cohorts. In the general population, brain regions in the default mode network provided a functional link between insomnia and depressive symptoms. These findings underscore both the shared and distinct neural correlates among depression, anxiety, and insomnia, providing potential avenues for the clinical management of these conditions.

Thrombotic events striking the central nervous system are clinical criteria for the antiphospholipid syndrome (APS). Besides these, neuropsychiatric non-APS criteria manifestations are increasingly described in patients with persistently positive antiphospholipid antibodies (aPL). Among these are psychiatric manifestations. Animal models mainly describe hyperactive behavior and anxiety associated with hippocampal abnormalities. Cases of associations with psychosis, mood disorders, bipolarity, anxiety, obsessive-compulsive behavior, and depression have been reported but are still rare. Systematic human clinical association studies are concordant with a risk of psychosis, depression (simple to major), and anxiety disorders, but these are limited and of inconstant methodological quality. Brain imaging in patients, also insufficiently investigated, shows early signs of hypoperfusion and of subtle diffuse white matter changes compatible with an alteration of the axonal structure and changes in the myelin sheath. Direct interactions of aPL with the brain cells, both on cell lines and on animal and human brain biopsies, targeting both glial cells, astrocytes, and neurons, can be demonstrated. These clusters of arguments make the association between psychiatric diseases and aPL increasingly plausible. However, a considerable amount of clinical research must still be performed in accordance with the highest standards of methodological quality. The therapeutic management of this association, in terms of both prevention and cure, currently remains unresolved.

The heterogeneity of major depressive disorder (MDD) complicates the selection of effective treatments. While more studies have identified cluster-based MDD subtypes, they often overlook individual variability within subtypes. To address this, we applied latent dirichlet allocation to decompose resting-state functional connectivity (FC) into latent factors. It allows patients to express varying degrees of FC across multiple factors, retaining inter-individual variability. We enrolled 226 patients and 100 healthy controls to identify latent factors and examine their distinct patterns of hyper- and hypo-connectivity. We investigated the association between these connectivity patterns and treatment preferences. Additionally, we compared demographic characteristics, clinical symptoms, and longitudinal symptom improvements across the identified factors. We identified three factors. Factor 1, characterized by inter-network hyperconnectivity of the default mode network (DMN), was associated with treatment response to antidepressant monotherapy. Additionally, factor 1 was more frequently expressed by younger and highly educated patients, with significant improvements in cognitive symptoms. Conversely, factor 3, characterized by inter-networks and intra-networks hypoconnectivity of DMN, was associated with treatment response when combining antidepressants with stimulation therapy. Factor 2, characterized by global hypoconnectivity without DMN, was associated with higher baseline depression severity and anxiety symptoms. These three factors showed distinct treatment preferences and clinical characteristics. Importantly, our results suggested that patients with DMN hyperconnectivity benefited from monotherapy, while those with DMN hypoconnectivity benefited from combined treatments. Our approach allows for a unique composition of factors in each individual, potentially facilitating the development of more personalized treatment-related biomarkers.

Understanding the heterogeneity of Alzheimer's disease (AD) is crucial for advancing precision medicine specifically tailored to this disorder. Recent research has deepened our understanding of AD heterogeneity; however, translating these insights from bench to bedside via neuroimaging heterogeneity frameworks presents significant challenges. In this review, we systematically revisit prior studies and summarize the existing methodology of data-driven neuroimaging studies for AD heterogeneity. We organized the current methodology into 1) a subtyping clustering strategy for patients with AD, and we also subdivided it into subtyping analysis based on cross-sectional multimodal neuroimaging profiles and the identification of long-term disease progression from short-term datasets; 2) a stratified strategy that integrates neuroimaging measures with biomarkers; and 3) individual-specific abnormal patterns based on the normative model. Then, we evaluated the characteristics of these studies along 2 dimensions: 1) the understanding of pathology and 2) clinical application. We systematically address the limitations, challenges, and future directions of research into AD heterogeneity. Our goal is to enhance the neuroimaging heterogeneity framework for AD, thereby facilitating its transition from bench to bedside.

Electroconvulsive therapy (ECT) is one of the most effective treatments for depression. ECT induces volume changes in the amygdala, a key center of anxiety. However, the clinical relevance of ECT-induced changes in amygdala volume remains uncertain. We hypothesized that nuclei-specific amygdala volumes and anxiety symptoms in depression could explain the clinical correlates of ECT-induced volume changes. To test this hypothesis, we enrolled patients with depression who underwent ECT (N = 20) in this multicenter observational study and collected MRI data at three time points: before and after treatment and a 6-month follow-up. Patients who received medication (N = 52), cognitive behavioral therapy (N = 63), or transcranial magnetic stimulation (N = 20), and healthy participants (N = 147) were included for comparison. Amygdala nuclei were identified using FreeSurfer and clustered into three subdivisions to enhance reliability and interpretability. Anxiety symptoms were quantified using the anxiety factor scores derived from the Hamilton Depression Rating Scale. Before treatment, basolateral and basomedial subdivisions of the right amygdala were smaller than those of healthy controls. The volumes of the amygdala subdivisions increased after ECT and decreased during the follow-up period, but the volumes at 6-month follow-up were larger than those observed before treatment. These volume changes were specific to ECT. Long-term volume changes in the right basomedial amygdala correlated with improvements in anxiety symptoms. Baseline volumes in the right basolateral amygdala correlated with long-term improvements in anxiety symptoms. These findings demonstrate that clinical correlates of ECT-induced amygdala volume changes are existent, but in a nucleus and symptom-specific manner.

Posttraumatic stress disorder (PTSD) is a heterogenous disorder with frequent diagnostic comorbidity. Research has deciphered this heterogeneity by identifying PTSD subtypes and their neural biomarkers. This review summarizes current approaches, symptom-based group-level and data-driven approaches, for generating PTSD subtypes, providing an overview of current PTSD subtypes and their neural correlates. Additionally, we systematically assessed studies to evaluate the influence of comorbidity on PTSD subtypes and the predictive utility of biotypes for treatment outcomes. Following the PRISMA guidelines, a systematic search was conducted to identify studies employing brain imaging techniques, including functional magnetic resonance imaging (fMRI), structural MRI, diffusion-weighted imaging (DWI), and electroencephalogram (EEG), to identify biomarkers of PTSD subtypes. Study quality was assessed using the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. We included 53 studies, with 44 studies using a symptom-based group-level approach, and nine studies using a data-driven approach. Findings suggest biomarkers across the default-mode network (DMN) and the salience network (SN) throughout multiple subtypes. However, only six studies considered comorbidity, and four studies tested the utility of biotypes in predicting treatment outcomes. These findings highlight the complexity of PTSD's heterogeneity. Although symptom-based and data-driven methods have advanced our understanding of PTSD subtypes, challenges remain in addressing the impact of comorbidities and the limited validation of biotypes. Future studies with larger sample sizes, brain-based data-driven approaches, careful account for comorbidity, and rigorous validation strategies are needed to advance biologically grounded biotypes across mental disorders.

Precise stratification of patients into homogeneous disease subgroups could address the heterogeneity of phenotypes and enhance understanding of the pathophysiology underlying specific subtypes. Existing literature on subtyping patients with major depressive disorder (MDD) mainly utilized clinical features only. Genomic and imaging data may improve subtyping, but advanced methods are required due to the high dimensionality of features.

We propose a novel disease subtyping framework for MDD by integrating brain structural features, genotype-predicted expression levels in brain tissues, and clinical features. Using a multi-view biclustering approach, we classify patients into clinically and biologically homogeneous subgroups. Additionally, we propose approaches to identify causally relevant genes for clustering.

We verified the reliability of the subtyping model by internal and external validation. High prediction strengths (PS) (average PS: 0.896, minimum: 0.854), a measure of generalizability of the derived clusters in independent datasets, support the validity of our approach. External validation using patient outcome variables (treatment response and hospitalization risks) confirmed the clinical relevance of the identified subgroups. Furthermore, subtype-defining genes overlapped with known susceptibility genes for MDD and were involved in relevant biological pathways. In addition, drug repositioning analysis based on these genes prioritized promising candidates for subtype-specific treatments.

Our approach successfully stratified MDD patients into subgroups with distinct clinical prognoses. The identification of biologically and clinically meaningful subtypes may enable more personalized treatment strategies. This study also provides a framework for disease subtyping that can be extended to other complex disorders.

Deep Brain Stimulation (DBS) for treatment-resistant depression (TRD) results in about 50 % response and 30 % remission. Response prediction may optimize patient selection, which is currently based on expert opinion. Here, we provide a comprehensive and systematic overview of the available data and explore if expert opinion is supported quantitatively. PubMed, Cochrane, Embase and PsycInfo were searched and studies reporting on DBS for TRD were included if they 1) reported on pre-operative predictors of response or 2) compared responders vs. non-responders. Meta-analysis was performed on outcomes with k ≥ 4 studies reporting data separately for responder/non-responder groups. From 7766 screened references, we included 22 studies on 294 unique patients. Seven studies were included for meta-analysis. No significant responder/non-responder differences were found in meta-analysis, but a trend toward shorter duration of current episode in responders (-0.97 years, p = 0.076, k = 6, n = 105) was found. Neuroimaging studies showed relative hyperactivity of the SCC area in responders (k = 2, n = 41), without volumetric differences (k = 2, n = 85). Multiple other structural, metabolic and (neuro)psychological predictors were found in small samples and have not yet been replicated. We conclude that there is no quantitative support for any of the response predictors based on expert opinion, although depressive chronicity and prior treatment responsiveness may be of interest for future research. For clinical practice, this suggests that patients should not be excluded based on the reviewed characteristics. More than a dozen predictors across structural, metabolic and (neuro)psychological outcomes are presented, awaiting replication and prospective validation in future trials.

Major depressive disorder (MDD) is a prevalent and intricate mental health condition characterized by a wide range of symptoms. A fundamental challenge in understanding MDD lies in elucidating the brain mechanisms underlying the complexity and diversity of these symptoms, particularly the heterogeneity reflected in individual differences and subtype variations within brain networks.

To address this problem, we explored the brain network topology using resting-state functional magnetic resonance imaging (rs-fMRI) data from a cohort of 797 MDD patients and 822 matched healthy controls (HC). Utilizing normative modeling of HC, we quantified individual deviations in brain network degree centrality among MDD patients. Through k-means clustering of these deviation profiles, we identified two clinically meaningful MDD subtypes. Moreover, we employed Neurosynth to analyze the cognitive correlates of these subtypes.

Subtype 1 exhibited positive deviations of degree centrality in the limbic (LIM), frontoparietal (FPN), and default mode networks (DMN), but negative deviations in the visual (VIS) and sensorimotor networks (SMN), positively correlating with higher cognitive functions and negatively with basic perceptual processes. In contrast, subtype 2 demonstrated opposing patterns, characterized by negative deviations in degree centrality of the LIM, FPN, and DMN and positive deviations of the VIS and SMN, along with inverse cognitive associations.

Our findings underscore the heterogeneity within MDD, revealing two distinct patterns of network topology between unimodal and transmodal networks, offering a valuable reference for personalized diagnosis and treatment strategies.

There is a surprising lack of neuroimaging studies of depression that not only identify the whole brain causal connectivity features but also explore whether these features have neurobiological correlates.

Three graph neural networks (GNN) models were applied to three types of causal connectomes (CCs): granger causality, regression DCM (rDCM), and TwoStep, obtained from a total of 1296 young adult participants in three large-scale datasets.

GNN models showed better performance for predicting depression when using causal connectomes such as TwoStep (average precision score, 0.882), granger causality (0.878), or rDCM (0.853) compared with using functional connectomes like Pearson's (0.850) and partial (0.823) correlation. Notably, nodal features derived only from rDCM and TwoStep showed spatial associations with positron emission tomography measures of receptors for neurotransmitters such as dopamine and serotonin. Further analysis revealed the shared directed edges among the subject's edge features, which included cortical causal connections in networks such as the default mode, control, dorsal attention, peripheral visual, and parietofrontal networks.

The classification performance of leave-one-site-out cross-validation did not achieve a similar level with that of 10-fold cross-validation.

Our findings suggest that the connectomes derived from CCs using GNN, rather than functional connectomes, provide more accurate and neurobiologically relevant information for depression. Moreover, the observed spatial heterogeneity of this relevance and subject-specific edge features emphasizes the complexity of depression. These results have the potential to advance our understanding of depression's nature and potentially contribute to precision psychiatry by aiding in its diagnosis and treatment.

The heterogeneity of major depressive disorder (MDD) significantly hinders its effective and optimal clinical outcomes. This study aimed to identify MDD subtypes by adopting a data-driven approach and assessing validity based on symptomatology and neuroimaging.

A total of 259 patients with MDD and 92 healthy controls were enrolled in this cross-sectional study. Latent profile analysis (LPA) was used to identify MDD subtypes based on validated clinical symptoms. To examine whether there were differences between these identified MDD subtypes, network analysis was used to test any differences in symptom patterns between these subtypes. We also compared neural activity between these identified MDD subtypes and tested whether certain neural activities were related to individual subtypes. This MDD subtyping was further tested in an independent dataset that contains 86 patients with MDD.

Five MDD subtypes with distinct depressive symptom patterns were identified using the LPA model, with the 5-class model selected as the optimal classification solution based on its superior fit indices (AIC = 6656.296, aBIC = 6681.030, entropy = 0.917, LMR p = 0.3267, BLRT p < 0.001). The identified subtypes include atypical-like depression, two melancholic depression (moderate and severe) subtypes with distinct patterns on feeling anxious, and two anhedonic depression subtypes (moderate and severe) with different manifestations on weight/appetite loss. The reproducibility of the classification was also confirmed. Significant differences in symptom structures between melancholic and two anhedonic subtypes, and between anhedonic and atypical subtypes were observed (all p < 0.05). Furthermore, these identified subtypes had differential neural activities in both regional spontaneous neural activity (pFWE < 0.005) and functional connectivity between different brain regions (pFDR < 0.005), linked to different clinical symptoms (FDR q < 0.05).

The network analysis and neuroimaging tests support the existence and validity of the identified MDD subtypes, each exhibiting unique clinical manifestations and neural activity patterns. The categorisation of these subtypes sheds light on the heterogeneity of depression and suggest that personalised treatment and management strategies tailored to specific subtypes may enhance intervention strategies in clinical settings.

National Natural Science Foundation of China (NSFC) and China Scholarship Council (CSC).

Precision psychiatry aims to improve routine clinical practice by integrating biological, clinical, and environmental data. Many studies have been performed in different areas of research on major depressive disorder, bipolar disorder, and schizophrenia. Neuroimaging and electroencephalography findings have identified potential circuit-level abnormalities predictive of treatment response. Protein biomarkers, including IL-2, S100B, and NfL, and the kynurenine pathway illustrate the role of immune and metabolic dysregulation. Circadian rhythm disturbances and the gut microbiome have also emerged as critical transdiagnostic contributors to psychiatric symptomatology and outcomes. Moreover, advances in genomic research and polygenic scores support the perspective of personalized risk stratification and medication selection. While challenges remain, such as data replication issues, prediction model accuracy, and scalability, the progress so far achieved underscores the potential of precision psychiatry in improving diagnostic accuracy and treatment effectiveness.

Patients with depression vary from one-another in their clinical and neuroimaging presentation, yet the relationship between clinical and neuroimaging sources of variation is poorly understood. Determining sources of heterogeneity in depression is important to gain insights into its diverse and complex neural etiology. This study aims to test if depression heterogeneity is characterized by subgroups that differ both clinically and neurobiologically and/or whether multiple neuroimaging profiles give rise to the same clinical presentation.

This study utilizes population-based data from the UK Biobank over multiple imaging sites. Clinically dissociated groups were selected to isolate clinical characteristics of depression (symptoms of anhedonia, depressed mood, and somatic disturbance; severity indices of lifetime chronicity and acute impairment; and late onset). Residual neuroimaging heterogeneity within each group was assessed using neuroimaging driven clustering.

The clinically dissociated subgroups had significantly larger neuroimaging normative deviations than a comparison heterogeneous group and had distinct neuroimaging profiles from each other. Imaging driven clustering within each clinically dissociated group identified two stable subtypes within the acute impairment group that differed significantly in cognitive ability, despite identical clinical profiles.

The study identified distinct neuroimaging profiles related to particular clinical depression features that may explain inconsistencies in the literature and sub-clusters within the acute impairment group with cognitive differences that were only differentiable by neuroimaging. Our results provide evidence that multiple neuroimaging profiles may give rise to the same clinical presentation, emphasizing the presence of complex interactions between clinical and neuroimaging sources of heterogeneity.

There is increasing awareness of interindividual variability in brain function, with potentially major implications for repetitive transcranial magnetic stimulation (rTMS) efficacy. We perform a secondary analysis using data from a double-blind randomized controlled 4-week trial of 20 Hz active versus sham rTMS to dorsolateral prefrontal cortex (DLPFC) during a working memory task in participants with schizophrenia. We hypothesized that rTMS would change local functional activity and variability in the active group compared with sham.

83 participants were randomized in the original trial, and offered neuroimaging pre- and post-treatment. Of those who successfully completed both scans (n = 57), rigorous quality control left n = 42 (active/sham: n = 19/23), who were included in this analysis. Working memory-evoked activity during an N-Back (3-Back vs 1-Back) task was contrasted. Changes in local brain activity were examined from an 8 mm ROI around the rTMS coordinates. Individual variability was examined as the mean correlational distance (MCD) in brain activity pattern from each participant to others within the same group.

We observed an increase in task-evoked left DLPFC activity in the active group compared with sham (F1,36 = 5.83, False Discovery Rate (FDR))-corrected P = .04). Although whole-brain activation patterns were similar in both groups, active rTMS reduced the MCD in activation pattern compared with sham (F1,36 = 32.57, P < .0001). Reduction in MCD was associated with improvements in attention performance (F1,16 = 14.82, P = .0014, uncorrected).

Active rTMS to DLPFC reduces individual variability of brain function in people with schizophrenia. Given that individual variability is typically higher in schizophrenia patients compared with controls, such reduction may "normalize" brain function during higher-order cognitive processing.

Brain networks are composed of nodes representing neural elements, such as brain regions, and edges indicating functional or anatomical connections between these nodes. By shifting our focus from traditional node-centric perspectives to examining second-order similarity patterns between pairs of network edges, we captured and illuminated the co-fluctuation profiles between brain regions, revealing overlapping communities and the intensity of interactions within brain networks. Specifically, we mapped edge-centric networks and then computed edge-community normalized entropy and edge functional connectivity (eFC) to assess perturbations in normal brain network organization associated with major depressive disorder (MDD). Sample data were sourced from a cohort of 400 MDD patients and 441 healthy controls. Edge-community entropy was measured by clustering edge time series derived from resting-state functional magnetic resonance imaging data, while eFC was quantified using the Pearson correlation coefficient between edge time series. Our results showed that MDD patients exhibited increased entropy in the subcortical and frontoparietal networks and decreased eFC within the visual and sensory-motor networks compared to controls. These differences were less evident in first-episode drug-naive patients. However, in recurrent patients, the same abnormalities were observed and the entropy of subcortical network was positively correlated with depression severity, while the eFC of visual network was negatively correlated with depression and anxiety scores. This study provides new insights into the abnormal changes in MDD from a spatiotemporal flexibility and diversity perspective based on high-order edge-centric networks and offering potential novel biomarkers for MDD.

Neuroimaging-based subtyping is increasingly used to explain heterogeneity in psychiatric disorders. However, the clinical utility of these subtyping efforts remains unclear, and replication has been challenging. Here we examined how the choice of neuroimaging measures influences the derivation of neuro-subtypes and the consequences for clinical delineation. On a clinically heterogeneous dataset (total n = 566) that included controls (n = 268) and cases (n = 298) of psychiatric conditions, including individuals diagnosed with post-traumatic stress disorder (PTSD), traumatic brain injury (TBI), and comorbidity of both (PTSD&TBI), we identified neuro-subtypes among the cases using either structural, resting-state, or task-based measures. The neuro-subtypes for each modality had high internal validity but did not significantly differ in their clinical and cognitive profiles. We further show that the choice of neuroimaging measures for subtyping substantially impacts the identification of neuro-subtypes, leading to low concordance across subtyping solutions. Similar variability in neuro-subtyping was found in an independent dataset (n = 1642) comprised of major depression disorder (MDD, n = 848) and controls (n = 794). Our results suggest that the highly anticipated relationships between neuro-subtypes and clinical features may be difficult to discover.

Efforts to elucidate subtypes within depression have yet to establish a consensus. In this study, we aimed to rigorously compare different subtyping approaches in the same participant space to quantitatively test agreement across subtyping approaches and determine whether the different approaches are sensitive to different sources of heterogeneity in depression.

We implemented 6 different data-driven subtyping methods developed in previous work using the same UK Biobank participants (n = 2276 participants with depression, n = 1595 healthy control participants). The 6 approaches include 2 symptom-based, 2 structural neuroimaging-based, and 2 functional neuroimaging-based techniques. The resulting subtypes were compared based on participant assignment, stability, and sensitivity to subtype differences in demographics, general health, clinical characteristics, neuroimaging, trauma, cognition, genetics, and inflammation markers.

We found almost no agreement between the resulting subtypes of the 6 approaches (mean adjusted Rand index [ARI] = 0.006), even within data domains. This finding was largely driven by differences in input feature set (mean ARI = 0.005) rather than clustering algorithm (mean ARI = 0.23). However, each approach had relatively high internal stability across bootstraps (ARI = 0.36-0.89); most approaches performed above null; and most approaches were sensitive to relevant phenotypes within their data domain.

Despite marginal overlap between approaches, we found the subtyping approaches to be internally consistent. These results explain why previous studies found strong evidence for subtypes within their analysis but with very little convergence between studies. We recommend that in future work, investigators incorporate systematic comparisons between their approach and alternative/previous approaches to facilitate consensus on depression subtypes.

Understanding the neurobiology of resilient emotion regulation following adversities is critical for addressing mental health problems globally. However, the functional neurobiology of resilience has rarely been studied in low- and middle-income countries, which comprise 90% of the world's population and experience more consistent adversities. Here, we describe how we are investigating the neurodevelopment of resilient emotion regulation in adolescents (anticipated N = 525) from a South African birth cohort recruited from a low-income, high-adversity township. Across 2 longitudinal time points (13-14 and 15-16 years), magnetic resonance imaging, behavior, and self-report measures from adolescents and their caregivers are collected. These data are complemented by existing developmental histories (from the prenatal period to 8 years). The culturally adapted measures, protocols, and analytic plans for investigating resilient emotion regulation are presented. By characterizing neurodevelopmental correlates of adolescent resilience from an understudied low- and middle-income country, this research will provide deeper insights into mental health globally.

Depression has been linked to cortico-limbic brain regions, and ketamine is known for its rapid antidepressant effects. However, how these brain regions encode depression collaboratively and how ketamine regulates these regions to exert its prompt antidepressant effects through mesoscale brain-wide fluctuations remain elusive. In this study, we used a multidisciplinary approach, including multi-region in vivo recordings in mice, chronic social defeat stress (CSDS), and machine learning, to construct a Mesoscale Brain-Wide Fluctuation Analysis platform (MBFA-platform). This platform analyzes the mesoscale brain-wide fluctuations of multiple brain regions from the perspective of local field potential oscillations and network dynamics. The decoder results demonstrate that our MBFA platform can accurately classify the Control/CSDS and ketamine/saline-treated groups based on neural oscillation and network activities among the eight brain regions. We found that multiple-region LFPs patterns are disrupted in CSDS-induced social avoidance, with the basolateral amygdala playing a key role. Ketamine primarily exerts the compensatory effects through network dynamics, contributing to its rapid antidepressant effect. These findings highlight the MBFA platform as an interdisciplinary tool for revealing mesoscale brain-wide fluctuations underlying complex emotional pathologies, providing insights into the etiology of psychiatry. Furthermore, the platform's evaluation capabilities present a novel approach for psychiatric therapeutic interventions.

Understanding the brain mechanisms underlying adolescent sleep patterns and their impact on psychophysiological development is complex. We applied sparse canonical correlation analysis (sCCA) to data from 3,222 adolescents in the Adolescent Brain Cognitive Development (ABCD) study, integrating sleep characteristics with multimodal imaging. This reveals two key sleep-brain dimensions: one linking later sleep onset and shorter duration to decreased subcortical-cortical connectivity and another associating a higher heart rate and shorter light sleep with lower brain volumes and connectivity. Hierarchical clustering identifies three biotypes: biotype 1 has delayed, shorter sleep with a higher heart rate; biotype 3 has earlier, longer sleep with a lower heart rate; and biotype 2 is intermediate. These biotypes also differ in cognitive performance and brain structure and function. Longitudinal analysis confirms these differences from ages 9 to 14, with biotype 3 showing consistent cognitive advantages. Our findings offer insights into optimizing sleep routines for better cognitive development.

Anhedonia, a core feature of major depressive disorder (MDD), presents significant treatment challenges with conventional methods. Circuit-targeted, personalized repetitive transcranial magnetic stimulation (rTMS) has shown potentiation by focusing on disruptions in specific networks related to anhedonia. However, how rTMS modulates brain network dynamics in anhedonia is not yet fully understood. This research sought to explore these effects using EEG microstate analysis. In this double-blind, randomized, sham-controlled study, resting-state functional MRI was employed to pinpoint the left dorsolateral prefrontal cortex (DLPFC) region that exhibited the strongest functional connectivity to the nucleus accumbens (NAcc), used as the target for rTMS stimulation. Rest-state EEG data from 49 depressive patients with anhedonia(active=26, sham=23) were analyzed both at baseline and after treatment. In addition, a group of 15 healthy participants was included to serve as baseline controls. Resting-state EEG data were collected at baseline and post-treatment. Using polarity-insensitive k-means clustering, EEG microstates were segmented into five categories (A-E). Circuit-targeted rTMS significantly alleviated symptoms of anhedonia and depression. Compared to healthy controls, patients with anhedonia showed reduced microstate B and C occurrence, along with increased microstate D duration. After rTMS targeting the DLPFC-NAcc pathway, the active treatment group exhibited normalization of microstate C occurrence and a reduction in microstate E duration. Notably, the increase in microstate C was significantly correlated with improvements in anticipatory anhedonia, and these changes were observed specifically in treatment responders. The findings suggest that microstate C is linked to anhedonia and could serve as a reliable biomarker for personalized rTMS treatment. These results provide insights into the neural mechanisms underlying rTMS for anhedonia and highlight the potential of EEG microstate analysis in guiding personalized treatment strategies for depression.

The long-term consequences of concussion are still being uncovered but have been linked to disruptions in cognition and psychological well-being. Previous studies focusing on the association between concussion history and structural changes in the brain have reported inconsistent results. We sought to examine the effect of concussion history on cortical volume with a focus on functional networks. These networks are associated with many of the functions that can be disrupted in those with an extensive concussion history. We collected baseline behavioral data including the Immediate Post-Concussion Assessment and Cognitive Testing, a self-report measure of the number of diagnosed concussions, and structural MRI in college athletes (n=296; 263 men and 33 women, age range 17-24). Behavioral measures were collected by members of the Department of Athletics concussion management team using a standardized protocol as part of their on-boarding process. Collegiate athletes in the present study who self-reported concussion history did not report different baseline symptoms and did not exhibit consistent differences in cognitive performance relative to those who reported no concussion history. We found that concussion history was not related to cortical volume at the network or region level, even when we compared participants with two or more concussions to those with no concussion history. We did identify relationships between cortical volume in the visual network and dorsal attention network with cognitive performance. In addition to comparing cortical volume between individuals with and without reported concussion history, we also examined whether cortical volume changes could be observed within individuals from baseline to acutely following concussion. We found that network level cortical volume did not change within subjects from baseline measurement to acutely post-concussion. Together, these results suggest that both self-reported concussion history and acute concussion effects are not associated with changes in cortical volume in young adult athletes.

Cachexia, a severe wasting syndrome associated with inflammatory conditions, often leads to multiorgan failure and death. Patients with cachexia experience extreme fatigue, apathy, and clinical depression, yet the biological mechanisms underlying these behavioral symptoms and their relationship to the disease remain unclear. In a mouse cancer model, cachexia specifically induced increased effort-sensitivity, apathy-like symptoms through a cytokine-sensing brainstem-to-basal ganglia circuit. This neural circuit detects elevated interleukin-6 (IL-6) at cachexia onset and translates inflammatory signals into decreased mesolimbic dopamine, thereby increasing effort sensitivity. We alleviated these apathy-like symptoms by targeting key circuit nodes: administering an anti-IL-6 antibody treatment, ablating cytokine sensing in the brainstem, and optogenetically or pharmacologically boosting mesolimbic dopamine. Our findings uncovered a central neural circuit that senses systemic inflammation and orchestrates behavioral changes, providing mechanistic insights into the connection between chronic inflammation and depressive symptoms.

Current interventions for major depressive disorder (MDD) demonstrate limited and heterogeneous efficacy, highlighting the need for improving the precision of treatment. Although findings have been mixed, resting-state functional connectivity (rsFC) at baseline shows promise as a predictive biomarker. This meta-analysis evaluates the evidence for baseline rsFC as a predictor of treatment outcomes of MDD interventions.

We included MDD literature published between 2012 and 2024 that used antidepressants, non-invasive brain stimulation, and cognitive behavioral therapy. Pearson correlations or their equivalents were analyzed between baseline rsFC and treatment outcome. Nodes were categorized according to the type of brain networks they belong to, and pooled coefficients were generated for rsFC connections reported by more than three studies.

Among the 16 included studies and 892 MDD patients, data from nine studies were used to generate pooled coefficients for the rsFC connection between the frontoparietal network (FPN) and default mode network (DMN), and within the DMN (six studies each, with three overlapping studies, involving 534 and 300 patients, respectively). The rsFC between the DMN and FPN had a pooled predictability of -0.060 (p = 0.171, fixed effect model), and the rsFC within the DMN had a pooled predictability of 0.207 (p < 0.001, fixed effect model). The rsFC between the DMN and FPN and the rsFC within the DMN had a larger effect in predicting the outcome of non-invasive brain stimulation (-0.215, p < 0.001, fixed effect model) and antidepressants (0.315, p < 0.001, fixed effect model), respectively. Heterogeneity was observed in both types of rsFC, study design, sample characteristics and data analysis pipeline.

Baseline rsFC within the DMN and between the DMN and FPN demonstrated a small but differential predictive effect on the outcome of antidepressants and non-invasive brain stimulation, respectively. The small predictability of rsFC suggested that rsFC between the FPN and DMN and the rsFC within the DMN might not be a good biomarker for predicting treatment outcome. Future research should focus on exploring treatment-specific predictions of baseline rsFC and its predictive utility for other types of MDD interventions.

The review was pre-registered at PROSPERO CRD42022370235 (33).

The human brain exhibits a modular and hierarchical structure, spanning low-order sensorimotor to high-order cognitive/affective systems. What is the mechanistic significance of this organization for brain dynamics and information processing properties? We investigated this question using rare simultaneous multimodal electrophysiology (stereotactic and scalp electroencephalography - EEG) recordings in 36 patients with drug-resistant focal epilepsy during presurgical intracerebral electrical stimulation (iES) (323 stimulation sessions). Our analyses revealed an anatomical gradient of excitability across the cortex, with stronger iES-evoked EEG responses in high-order compared to low-order regions. Mathematical modeling further showed that this variation in excitability levels results from a differential dependence on recurrent feedback from non-stimulated regions across the anatomical hierarchy, and could be extinguished by suppressing those connections in-silico. High-order brain regions/networks thus show an activity pattern characterized by more inter-network functional integration than low-order ones, which manifests as a spatial gradient of excitability that is emergent from, and causally dependent on, the underlying hierarchical network structure. These findings offer new insights into how hierarchical brain organization influences cognitive functions and could inform strategies for targeted neuromodulation therapies.

Attention deficit hyperactivity disorder (ADHD) is a common psychiatric disorder in children during their early school years. While many researchers have explored automated ADHD detection methods, developing accurate, rapid, and reliable approaches remains challenging.

This study proposes a graph convolutional neural network (GCN)-based ADHD detection framework utilizing multi-domain electroencephalogram (EEG) features. First, time-domain and frequency-domain features are extracted via long short-term memory (LSTM) and convolutional neural network (CNN) models, respectively. Second, a novel functional connectivity matrix is constructed by fusing phase lag index (PLI) and coherence (COH) features to simultaneously capture phase synchrony and signal intensity consistency between brain regions. Finally, a GCN model integrates these time-frequency features with topological patterns from the connectivity matrix for ADHD classification.

Evaluated on two EEG datasets, the proposed method achieved average accuracies of 97.29% and 96.67%, outperforming comparative models (XGBoost, LightGBM, AdaBoost, random forest). Visualization experiments further revealed distinct brain connectivity distributions between ADHD patients and healthy controls.

The fused functional connectivity matrix surpasses traditional single-metric approaches in characterizing brain interactions. By synergistically combining time, frequency, and topological features, the GCN framework enables more precise ADHD detection. This method demonstrates potential for assisting neurologists in clinical diagnosis while providing interpretable neurophysiological insights.

Ketamine has shown promise in rapidly improving symptoms of depression and most notably treatment-resistant depression (TRD). However, given the heterogeneity of TRD, biobehavioral markers of treatment response are necessary for the personalized prescription of intravenous ketamine. Heterogeneity in depression can be manifested in discrete patterns of functional connectivity (FC) in default mode, ventral affective, and cognitive control networks. This study employed a data-driven approach to parse FC during positive mood processing to characterize subgroups of patients with TRD prior to infusion and determine whether these connectivity-based subgroups could predict subsequent antidepressant response to ketamine compared to saline infusion. 152 adult patients with TRD completed a baseline assessment of FC during positive mood processing and were randomly assigned to either ketamine or saline infusion. The assessment utilized Subgroup-Group Iterative Multiple Model Estimation to recover directed connectivity maps and applied Walktrap algorithm to determine data-driven subgroups. Depression severity was assessed pre- and 24-hr postinfusion. Two connectivity-based subgroups were identified: Subgroup A (n = 110) and Subgroup B (n = 42). We observed that treatment response was moderated by an infusion type by subgroup interaction (p = .040). For patients receiving ketamine, subgroup did not predict treatment response (β = -.326, p = .499). However, subgroup predicted response for saline patients. Subgroup B individuals, relative to A, were more likely to be saline responders at 24-hr postinfusion (β = -2.146, p = .007). Thus, while ketamine improved depressive symptoms uniformly across both subgroups, this heterogeneity was a predictor of placebo response. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

People with psychosis exhibit thalamo-cortical hyperconnectivity and cortico-cortical hypoconnectivity with sensory networks, however, it remains unclear if this applies to all sensory networks, whether it arises from other illness factors, or whether such differences could form the basis of a viable biomarker. To address the foregoing, we harnessed data from the Human Connectome Early Psychosis Project and computed resting-state functional connectivity (RSFC) matrices for 54 healthy controls and 105 psychosis patients. Primary visual, secondary visual ("visual2"), auditory, and somatomotor networks were defined via a recent brain network partition. RSFC was determined for 718 regions via regularized partial correlation. Psychosis patients-both affective and non-affective-exhibited cortico-cortical hypoconnectivity and thalamo-cortical hyperconnectivity in somatomotor and visual2 networks but not in auditory or primary visual networks. When we averaged and normalized the visual2 and somatomotor network connections, and subtracted the thalamo-cortical and cortico-cortical connectivity values, a robust psychosis biomarker emerged (p = 2e-10, Hedges' g = 1.05). This "somato-visual" biomarker was present in antipsychotic-naive patients and did not depend on confounds such as psychiatric comorbidities, substance/nicotine use, stress, anxiety, or demographics. It had moderate test-retest reliability (ICC = 0.62) and could be recovered in five-minute scans. The marker could discriminate groups in leave-one-site-out cross-validation (AUC = 0.79) and improve group classification upon being added to a well-known neurocognition task. Finally, it could differentiate later-stage psychosis patients from healthy or ADHD controls in two independent data sets. These results introduce a simple and robust RSFC biomarker that can distinguish psychosis patients from controls by the early illness stages.

Decisions about when to change antidepressant treatment are complex and benefit from accurate prediction of treatment outcome. Prognostic accuracy can be enhanced by incorporating repeated assessments of symptom severity collected during treatment. Participants (n = 714) from the Genome-Based Therapeutic Drugs for Depression study received escitalopram or nortriptyline over 12 weeks. Remission was defined as scoring ≤ 7 on the Hamilton Rating Scale. Predictors included demographic, clinical, and genetic variables (at 0 weeks) and measures of symptom severity (at 0, 2, 4, and 6 weeks). Longitudinal descriptors extracted with growth curves and topological data analysis were used to inform prediction of remission. Repeated assessments produced gradual and drug-specific improvements in predictive performance. By Week 4, models' discrimination in all samples reached levels that might usefully inform treatment decisions (area under the receiver operating curve (AUC) = 0.777 for nortriptyline; AUC = 0.807 for escitalopram; AUC = 0.794 for combined sample). Decisions around switching or modifying treatments for depression can be informed by repeated symptom assessments collected during treatment, but not until 4 weeks after the start of treatment.