Major depressive disorder (MDD) is common and disabling, yet reported brain structural differences vary across studies. Here we performed a large vertex-wise (point-by-point) meta-analysis of cortical thickness and surface area using harmonized magnetic resonance imaging processing across 64 cohorts from the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) MDD and Depression Imaging Research Consortium (DIRECT) consortia (5,736 patients; 6,538 controls). We show significantly lower cortical thickness in patients with MDD in multiple brain regions, including the inferior parietal, lateral occipital, superior parietal, medial and lateral orbitofrontal, anterior and posterior cingulate, and precentral gyri, with cortical surface area showing no significant differences. Effects were most pronounced in adults with acute depression, whereas adolescents showed no significant case-control differences. Antidepressant medication use at scanning was associated with more extensive thinning, although effect sizes remained modest (mostly |Cohen's d| < 0.20). This high-resolution, globally generalizable map can support studies of mechanisms and help evaluate structural markers of the clinical course and treatment response.

Major depressive disorder (MDD) remains clinically diagnosed based on subjective symptoms rather than objective neurobiological markers, which limits diagnostic accuracy and the ability to tailor treatment. We present an ensemble hybrid framework that integrates graph neural networks (GNN) with unsupervised clustering to classify and subtype MDD using resting-state functional connectivity (rs-fMRI) profiles. A GNN was trained to distinguish MDD from healthy controls using functional connectivity derived brain graphs, and the resulting subject level embeddings were clustered to uncover subtype structure. We evaluated the approach on two public multisite cohorts, REST-meta-MDD (China; N = 1,604; 17 sites) and SRPBS (Japan; N = 446; 4 sites), using leave-one-site-out cross-validation and cross-national transfer. The classifier achieved 0.73 leave-one-site-out accuracy on REST-meta-MDD and retained 0.78 sensitivity when transferred from the Chinese to the Japanese cohort, outperforming BrainIB and CI GNN under the same protocol. To mitigate site related confounds, we applied a standardized preprocessing pipeline and ComBat harmonization. Clustering consistently identified three MDD subtypes with distinct connectivity signatures involving the default mode network and cerebellum, the insula-cingulum temporal circuit, and frontostriatal circuitry. These findings provide a reproducible and biologically interpretable stratification of MDD. Prospective studies will be needed to link these subtypes to treatment response and other clinically meaningful outcomes.

Major Depressive Disorder (MDD) is highly heterogeneous, limiting treatment efficacy. Despite efforts to delineate patient heterogeneity through subtyping, current approaches remain limited by noise, lack of clinical applicability, and insufficient external validation. Crucially, they focus on subtyping while neglecting staging information (e.g., illness duration). We developed BrainCVAE, a contrastive variational autoencoder, to disentangle MDD-specific neural features. Applying BrainCVAE to fALFF-derived resting-state fMRI from 1590 patients and 1308 controls identified two subtypes: Subtype 1 with hyperactivity in visual, attention, and default mode networks, and Subtype 2 with hypoactivity. Subtypes were validated in 1276 patients across independent centers. Subtype 1 showed superior responsiveness to pharmacological (SSRIs, SNRIs) and non-pharmacological (rTMS) interventions. Cross-sectional analyses revealed subtype-specific differences in DMN profiles across illness duration: Subtype 1 shifted from hyper- to hypoactivity, whereas Subtype 2 remained consistently hypoactive. In an independent dataset, illness duration correlated negatively with symptom reduction (r = -0.5565, 95% CI = (-0.8123, -0.1210), p = 0.0165). Datasets were ethically approved and registered on ClinicalTrials.gov: XJ_QG (NCT05577481, May 24, 2023), SAINT (NCT04653337, Oct 21, 2020), XJ_KG (NCT05544071, May 24, 2023). Integrating subtyping with illness staging bridges neurobiological heterogeneity and disease progression, providing a clinically actionable framework for precision treatment in MDD.

Major depressive disorder (MDD) imposes significant global health burdens, yet its underlying neural mechanisms remain elusive. Traditional static functional metrics inadequately capture the brain’s dynamic nature, motivating the exploration of dynamic functional metrics to understand both the temporal and spatial reconfigurations of brain networks in MDD. Leveraging the Depression Imaging Research Consortium (DIRECT) dataset, this study conducted vertex-wise dynamic analyses in a large cohort of MDD patients (n = 1660) and healthy controls (n = 1341). We identified significant alterations in temporal stability across the brain, with MDD patients exhibiting increased stability in higher-order association areas (e.g., frontoparietal and default mode networks) and decreased stability in primary sensory-motor regions. Among the regions showing altered temporal stability, brain-symptom relationships were further explored. We identified a set of brain regions including the superior frontal gyrus, postcentral gyrus and superior insular sulcus, which were potentially involved in the common abnormal dFC network and associated with insomnia, feelings of guilt, and insight symptoms in MDD. By incorporating advanced vertex-wise dynamic functional analyses and a large sample size, this study provides insights into the neural mechanisms of MDD, emphasizing the value of dynamic approaches for identifying biomarkers. Future longitudinal and task-based studies are promising to elucidate causal relationships and refine personalized therapeutic interventions targeting specific dynamic dysfunctions in MDD.

Functional magnetic resonance imaging (fMRI) allows real-time observation of brain activity through blood oxygen level-dependent (BOLD) signals and is extensively used in studies related to sex classification, age estimation, behavioral measurements prediction, and mental disorder diagnosis. However, the application of deep learning techniques to brain fMRI analysis is hindered by the small sample size of fMRI datasets. Transfer learning offers a solution to this problem, but most existing approaches are designed for large-scale 2D natural images. The heterogeneity between 4D fMRI data and 2D natural images makes direct model transfer infeasible. This study proposes a novel geometric mapping-based fMRI transfer learning method that enables transfer learning from 2D natural images to 4D fMRI brain images, bridging the transfer learning gap between fMRI data and natural images. The proposed Multi-scale Multi-domain Feature Aggregation (MMFA) module extracts effective aggregated features and reduces the dimensionality of fMRI data to 3D space. By treating the cerebral cortex as a folded Riemannian manifold in 3D space and mapping it into 2D space using surface geometric mapping, we make the transfer learning from 2D natural images to 4D brain images possible. Moreover, the topological relationships of the cerebral cortex are maintained with our method, and calculations are performed along the Riemannian manifold of the brain, effectively addressing signal interference problems. The experimental results based on the Human Connectome Project (HCP) dataset demonstrate the effectiveness of the proposed method. Our method achieved state-of-the-art performance in sex classification, age estimation, and behavioral measurement prediction tasks. Moreover, we propose a cascaded transfer learning approach for depression diagnosis, and proved its effectiveness on 23 depression datasets. In summary, the proposed fMRI transfer learning method, which accounts for the structural characteristics of the brain, is promising for applying transfer learning from natural images to brain fMRI images, significantly enhancing the performance in various fMRI analysis tasks.

Long-term exposure to a high-altitude (HA) hypoxic environment induces cognitive impairments, yet the underlying temporal mechanisms remain elusive. This longitudinal study investigated brain functional alterations associated with cognitive changes in 49 college freshmen relocated from sea level to Tibet, with comprehensive cognitive assessments and magnetic resonance imaging (MRI) at baseline and 2- and 4-year follow-ups. Resting-state fMRI quantified changes in regional homogeneity (ReHo), amplitude of low-frequency fluctuations (ALFF)/fractional ALFF (fALFF), static functional connectivity (sFC), and dynamic FC (dFC). Behavioral data confirmed persistent cognitive deficits, while neuroimaging analyses revealed biphasic patterns (initial suppression then partial/full recovery) in ReHo, ALFF/fALFF, and sFC. Notably, dFC variability in the right orbital middle frontal gyrus (ORBmid.R) and Heschl's gyrus (HES.R) increased at 2 years and remained elevated, with this alteration strongly correlated with cognitive changes. Our findings highlight that elevated dFC variability in two brain regions is a key contributor to chronic hypoxia-induced cognitive impairments.

Antidepressant efficacy for major depressive disorder (MDD) remains limited, with the neural mechanisms underlying treatment response poorly understood. The default mode network (DMN), particularly the connectivity between the medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC), has been implicated in MDD pathophysiology and may be linked to treatment outcomes. However, its potential as a biomarker for antidepressant response has not been validated. Here, we investigate the relationship between DMN connectivity and antidepressant treatment response in MDD. Resting-state fMRI data from four large MDD cohorts (n = 4271) were analyzed using Granger causality to examine directional effective connectivity (EC) within the DMN. Linear mixed-effects models compared EC between recurrent MDD patients, first-episode drug-naïve patients, and healthy controls. We also examined associations between EC, medication use, illness duration, depressive symptoms, and treatment outcomes. Additionally, Support Vector Machine (SVM) classifiers and support vector regression (SVR) were trained using EC from mPFC to PCC to predict treatment response. Our results revealed that recurrent MDD patients exhibited significantly reduced EC from mPFC to PCC compared to healthy controls and first-episode patients, with this reduction correlating with antidepressant medication use and illness duration. Importantly, DMN connectivity was associated with treatment improvement rather than core depressive symptoms, including suicide, anhedonia, or emotional blunting. Crucially, EC from mPFC to PCC predicted antidepressant treatment response, and SVM classifiers demonstrated high predictive accuracy for therapeutic outcomes. In conclusion, reduced EC from mPFC to PCC may serve as a biomarker for antidepressant treatment response in MDD, offering insights into MDD neurobiology and supporting the clinical potential of DMN connectivity measures for guiding treatment decisions. The SAINT, Xijing_QG, and Xijing_KG datasets were approved by the Ethics Committee of the First Affiliated Hospital, Fourth Military Medical University (approval numbers: KY20202066-F-1, XJLL-KY20222111, and KY20222165-F-1, respectively) and registered with clinicaltrials.gov (identifiers: NCT 04653337, NCT 05577481, and NCT 05544071, respectively).

Imaging-based automatic diagnosis of major depressive disorder (MDD) has received widespread attention in precision medicine. Increasing evidence suggests that the pathophysiology of MDD is associated with the abnormality in brain connectome, which could be an effective biomarker for classification. However, previous studies suffered from small number of samples and large multi-site imaging divergences, as well as irregular graph architectures of the connectome, which challenges the diagnostic classification of MDD. Here, we propose a novel graph convolution network with sparse pooling (GCNSP) to learn the hierarchical features of the connectome graph to improve MDD classification. We applied the model to a multi-site functional MRI sample (33 sites with 3335 subjects, the largest functional imaging dataset of MDD to date), and perform transfer learning classification for each site using the pre-trained GCNSP on remaining sites to fit cross-site divergences, achieving an average accuracy of 70.14%. Moreover, hierarchical dysfunction of default mode network (DMN) is detected by the GCNSP in the patients. The interaction between DMN and frontoparietal network exhibit high discriminative power between patients and controls. Accordingly, this study may provide an effective pipeline for multi-site diagnostic classification and improve our understanding of hierarchical clues of brain network dysfunction in neuropsychiatric disorders.

Objective Although previous studies have reported structural brain alterations in major depressive disorder (MDD) patients at risk of suicide, no wide consensus has been reached. This study aimed to elucidate structural brain differences between MDD patients with and without suicide risk using data from the DIRECT Consortium, advancing our understanding of the neurophysiological mechanisms underlying suicide risk in MDD patients. Methods A total of 203 healthy controls (HCs), 208 MDD patients without suicide risk (MDD-NSR), and 376 MDD patients with suicide risk (MDD-SR) were included. T1-weighted MRI data were processed using DPABISurf to quantify cortical surface area, thickness, and cortical gray matter (CGM) volume. Results The MDD-SR and MDD-NSR groups demonstrated reduced surface area in the right orbitofrontal cortex (OFC), and reduced CGM volume in the left ACC and the right OFC compared with the HC group. The left ACC CGM volume decreased in a gradient, with the MDD-SR group showing significantly lower values than both the MDD-NSR and HC groups. While the SVM model showed limited differentiation between MDD-SR and MDD-NSR, the left ACC CGM volume was identified as the most critical feature in SHAP analysis. Conclusion MDD patients with suicide risk exhibited alterations in cortical surface area and CGM volume in the frontal lobe. The CGM volume reduction in the left ACC may serve as a potential biomarker for predicting suicide risk in MDD patients.

Major Depressive Disorder (MDD) has emerged as a major global public health challenge. Recently, Stanford neuromodulation therapy, a paradigm of individualized target-transcranial magnetic stimulation (IT-TMS) focusing on the subgenual anterior cingulate cortex (sgACC), has exhibited significant therapeutic benefits in treating MDD. The pregenual anterior cingulate cortex (pgACC), alongside the sgACC, is also an emotional subregion of the anterior cingulate cortex (ACC). This study aims to explore the safety and efficacy of IT-TMS targeting the pgACC for the treatment of MDD. This is a randomized, double-blind, sham-controlled trial involving 68 patients with MDD randomly assigned to an IT-TMS group and a sham group. The IT-TMS group receives individualized intermittent theta-burst stimulation targeted at the region in the left dorsolateral prefrontal cortex (DLPFC) with the strongest negative functional connectivity to the pgACC. The sham group undergoes a well-accepted 90°-reversed sham stimulation procedure. The primary outcome is the percentage change in the 17-item Hamilton Rating Scale for Depression (HAMD-17) score from baseline to the end of treatment. In the final intention-to-treat analysis, 31 participants from the IT-TMS group and 34 from the sham group were included. From baseline to the end of IT-TMS treatment, the mean percentage reduction in HAMD-17 score was 64.3

Major depressive disorder (MDD) has been increasingly characterized as a network dysconnectivity syndrome. Although single-subject morphological networks are advantageous in studying the brain connectome, extant research on MDD is limited by either small samples or a lack of integration of multi-feature across different morphological features. We used the largest structural MRI data from 1442 MDD patients and 1277 controls to construct individual-level cortical morphological networks based on cortical thickness (CT), cortical volume (CV), surface area (SA), and sulcal depth (SD). Group comparisons in interregional morphological connectivity (MC) and graph-theoretical nodal properties were performed. Furthermore, support vector machine (SVM) was applied to evaluate whether the network alterations could distinguish patients from controls. As a result, MDD patients presented widespread alterations in MC, with distinct alteration patterns observed across four morphological networks. Specifically, CT-based networks exhibited reduced MC primarily within and between higher-order networks involving the default mode and frontoparietal networks, whereas CV-based networks showed increased MC predominantly within the default mode network. By contrast, both SA- and SD-based networks demonstrated enhanced MC mainly within and between lower-order networks implicating the somatomotor and visual networks. Similar patterns of MC alterations were observed in first-episode, drug-naive MDD patients. Concurrently, nodal property analysis revealed increased betweenness centrality in multiple cortical regions in MDD. Moreover, SVM models based on the altered MC achieved moderate-to-good classification performance in distinguishing patients from controls. Overall, our findings of individual-level morphological network alterations in depressed patients may corroborate the dysconnectivity hypothesis of MDD and could further inform its more accurate diagnosis.

Developing new diagnostic models based on the underlying biological mechanisms rather than subjective symptoms for psychiatric disorders is an emerging consensus. Recently, machine learning (ML)-based classifiers using functional connectivity (FC) for psychiatric disorders and healthy controls (HCs) are developed to identify brain markers. However, existing ML-based diagnostic models are prone to overfitting (due to insufficient training samples) and perform poorly in new test environments. Furthermore, it is difficult to obtain explainable and reliable brain biomarkers elucidating the underlying diagnostic decisions. These issues hinder their possible clinical applications. In this work, we propose BrainIB, a new graph neural network (GNN) framework to analyze functional magnetic resonance images (fMRI), by leveraging the famed information bottleneck (IB) principle. BrainIB is able to identify the most informative edges in the brain (i.e., subgraph) and generalizes well to unseen data. We evaluate the performance of BrainIB against three baselines and seven state-of-the-art (SOTA) brain network classification methods on three psychiatric datasets and observe that our BrainIB always achieves the highest diagnosis accuracy. It also discovers the subgraph biomarkers that are consistent with clinical and neuroimaging findings. The source code and implementation details of BrainIB are freely available at the GitHub repository (https://github.com/SJYuCNEL/brain-and-Information-Bottleneck).

BACKGROUND:Cortical morphological alterations are evident in major depressive disorder (MDD), yet the underlying neurobiological processes that contribute to their characteristic spatial pattern remain unclear. METHODS:Large-scale, multisite structural magnetic resonance imaging data from a homogeneous Chinese cohort of 1442 patients with MDD and 1277 healthy control participants were used to calculate cortical morphological measures, which were compared between groups to determine cortical morphological alterations in MDD. A connectome constraint model was then used to examine whether the structural connectome shapes MDD-related cortical morphological alterations, followed by performance of a network diffusion model to identify the epicenters. RESULTS:Group comparisons demonstrated a broadly distributed cortical thickness (CT) reduction in MDD, with the prefrontal cortex affected more prominently. Based on the normative structural connectome, we derived the estimated CT alteration of each brain node according to its connected neighbors and found a strong spatial correlation between the empirical and estimated CT alterations, indicating structural connectome constraint on cortical atrophy in MDD. Concurrently, we identified the left lateral prefrontal cortex as the putative epicenter of cortical atrophy. Moreover, analyses across first-episode, early-stage, and chronic MDD subgroups revealed reduced connectome constraint with increasing illness duration. Additionally, our results were robust against several methodological variations and were largely reproducible in the cross-ethnic ENIGMA (Enhancing Neuro Imaging Genetics through Meta Analysis) cohort of 1902 patients with MDD and 7658 control participants. CONCLUSIONS:These findings represent a substantial advance in our understanding of the network-based spread of cortical atrophy in MDD and highlight the prospect of the left prefrontal cortex as a key target for early interventions.

Hemispheric lateralization, recognized as a pivotal feature in both the structural and functional organization of the human brain, may undergo alterations in specific psychiatric disorders. However, the time-varying patterns of hemispheric lateralization in individuals with major depressive disorder (MDD) and the relationship between these patterns and gene expression profiles remain largely unexplored thus far. Using a large multi-site resting-state functional magnetic resonance imaging (rs-fMRI) data encompassing 2611 participants (1660 MDD patients and 1341 healthy controls), we examined MDD-related abnormalities in dynamic laterality and its association with clinical symptoms, meta-analytic cognitive functions, and neurotransmitter receptor profiles, respectively. And the biological basis behind these changes was investigated through gene enrichment analysis and cell-specific analysis. Here we found revealed pronounced fluctuations in lateralization primarily in the regions in default mode network, attention network and control network in MDD patients when compared to healthy controls. In addition, these fluctuations exhibited significant correlations with higher-order cognition terms and the distributions of disease related neurotransmitters. Further, through gene enrichment and cell-specific analysis, we identified a molecular genetic basis for these changes, highlighting synaptic function-related genes and neuronal cells. Collectively, these results demonstrated robust altered brain lateralization patterns in MDD and its molecular genetic basis, providing new clues to understand the pathophysiology of MDD.

Repetitive transcranial magnetic stimulation (rTMS) is widely used in the treatment of major depressive disorder (MDD). Stanford Accelerated Intelligent Neuromodulation Therapy (SAINT) is currently the latest effective treatment option that can rapidly antidepressant and alleviate suicidal ideation. However, its mechanism of action is unclear. In this study, we applied regional homogeneity (ReHo) and dynamic functional connectivity (DFC) analyses to investigate the temporal similarity of signal fluctuations and dynamic properties of functional connectivity in 26 MDD patients. ReHo analysis revealed alterations of synchronicity of neuronal oscillations after treatment in the default mode network, subcutaneous nucleus network, frontoparietal network, etc. DFC analysis showed that there were two different states of connectivity. In addition, SAINT also induced increased number of state transitions and enhanced DFC variability in the default mode network, subcutaneous nucleus network, frontoparietal network, etc. Furthermore, correlation analysis revealed a significant negative correlation between DFC variability and baseline scale scores. Finally, we built a machine learning model to predict treatment efficacy based on baseline characteristics and demonstrated that neural activity and brain functional connectivity features at baseline effectively predicted outcomes following SAINT treatment. These findings enhance our understanding of the neurological changes in MDD patients undergoing SAINT, offering potential imaging markers for predicting rTMS treatment efficacy.

AIMS:Major depressive disorder (MDD) is a common psychiatric disorder whose causes and manifestations are diverse and numerous. To facilitate targeted therapeutic interventions, we characterized the abnormalities in effective connectivity within the cognitive-affective (CCN-AN) circuits to identify predictive biomarkers of TMS efficacy based on a large multicenter dataset and an independent dataset from patients receiving TMS. METHODS:Both functional and effective connectivity (FC, EC) were analyzed. As there was only one significant connection observed in FC, classification based on the differences in EC was performed using REST-meta-MDD. Furthermore, correlations between these abnormal connectivity and depression severity, as well as depression and suicidality alleviation, were calculated to determine their predictive implications for TMS efficacy using an independent dataset. RESULTS:Overall increased connectivity from the AN to the CCN and decreased connectivity from the CCN to the AN in MDD were observed using EC. These disruptions drove the classification accuracy up to 79.1%. Furthermore, the connection from the right inferior parietal lobule (IPL. R) to the right amygdala (AMYG.R) was negatively correlated with depression scores. Notably, the IPL connectivity to the anterior cingulate cortex (ACC) and the AMYG.R were closely correlated with depression and suicidal ideation alleviation following TMS treatment. CONCLUSIONS:These findings suggest that MDD is characterized by disruptions in both top-down and bottom-up emotion regulation systems. Notably, the key abnormal connectivities, particularly those from the IPL to ACC and AMYG, could predict the efficacy of TMS treatment. This insight refines MDD diagnosis and paves the way for more precise targeted interventions in the future.

Background The subgenual anterior cingulate cortex (sgACC) plays a central role in the pathophysiology of major depressive disorder (MDD), and its functional interactive profile with the left dorsal lateral prefrontal cortex (DLPFC) is associated with transcranial magnetic stimulation (TMS) treatment outcomes. Nevertheless, previous research on sgACC functional connectivity (FC) in MDD has yielded inconsistent results, partly due to small sample sizes and limited statistical power. Furthermore, calculating sgACC-FC to target TMS on an individual level is challenging because of the low signal-to-noise ratio and the poor replicability of individualized functional brain images. Methods Leveraging a large multi-site cross-sectional sample (1660 MDD patients vs. 1341 healthy controls) from Phase II of the Depression Imaging REsearch ConsorTium (DIRECT), we systematically delineated case-control difference maps of sgACC-FC. Then, in a sample of 25 individuals with treatment-resistant depression who had received repetitive TMS (rTMS) treatment, we examined the relationship between case-control differences in FCs between sgACC and their specific TMS targets and treatment outcomes. Next, we tested whether the position of the group mean FC-based target (previously determined in healthy participants) differed in MDD patients. Finally, we developed a dual regression (DR) based approach to integrate group-level statistical maps with individual-level spontaneous brain activity to evaluate individualized TMS target localization in MDD. We tested this approach in a sample of 16 individuals who had received rTMS. Results We found enhanced sgACC-DLPFC FC in MDD patients. The magnitude of case-control differences in FC between sgACC and TMS targets was related to clinical improvement. We found different peak sgACC anticorrelation locations in mean FC maps of MDD patients and HCs. More effective TMS targets were closer to individualized DR-based loci than to group-level targets. Conclusion In summary, we reliably delineated MDD-related abnormalities of sgACC-FC profiles in a large independently ascertained sample and demonstrated the potential impact of such case-control differences on FC-guided localization of TMS targets. The proposed individualized approach for TMS targeting has the potential to improve TMS treatment outcome and warrants prospective clinical trials.

Repetitive transcranial magnetic stimulation (rTMS) is a common non-invasive treatment for medication-resistant major depressive disorder (MDD). It utilizes continuous and adjustable magnetic stimulation to modulate neural circuits implicated in the pathogenesis of depression. Nevertheless, constructing a universal and effective predictive factor for forecasting treatment outcomes remains challenging. To address this, we first collect neuroimaging data and five depression scales from 26 medication-resistant MDD patients before and after rTMS treatment. Then we propose a novel framework for predicting treatment effects precisely, which combines open-loop control and neural manifold estimation. This framework utilizes the geometric information of the manifold as a biomarker to predict the therapeutic efficacy of rTMS. Experiments based on the clinical dataset demonstrate the effectiveness and robustness of our framework.

Previous studies in small samples have identified inconsistent cortical abnormalities in major depressive disorder (MDD). Despite genetic influences on MDD and the brain, it is unclear how genetic risk for MDD is translated into spatially patterned cortical vulnerability. Here, we initially examined voxel-wise differences in cortical function and structure using the largest multi-modal MRI data from 1660 MDD patients and 1341 controls. Combined with the Allen Human Brain Atlas, we then adopted transcription-neuroimaging spatial correlation and the newly developed ensemble-based gene category enrichment analysis to identify gene categories with expression related to cortical changes in MDD. Results showed that patients had relatively circumscribed impairments in local functional properties and broadly distributed disruptions in global functional connectivity, consistently characterized by hyper-function in associative areas and hypo-function in primary regions. Moreover, the local functional alterations were correlated with genes enriched for biological functions related to MDD in general (e.g., endoplasmic reticulum stress, mitogen-activated protein kinase, histone acetylation, and DNA methylation); and the global functional connectivity changes were associated with not only MDD-general, but also brain-relevant genes (e.g., neuron, synapse, axon, glial cell, and neurotransmitters). Our findings may provide important insights into the transcriptomic signatures of regional cortical vulnerability to MDD.