Since its inception in 2017, temporal interference stimulation (TIS) has attracted increasing attention as a novel neuromodulation approach with the potential to non-invasively target deep brain structures. As the field moves from initial biophysical validation toward broader experimental and translational applications, a macroscopic understanding of its developmental trajectory and thematic evolution is needed. In this study, we systematically mapped the scientific landscape of TIS research using bibliometric methods to characterize its knowledge structure, core themes, and emerging frontiers. The analysis shows that TIS research has expanded rapidly from foundational animal studies and biophysical mechanism validation toward computational head modeling, individualized electric field optimization, and early human applications. Current research is increasingly focused on cross-species scaling, stimulation dosimetry, comparative advantages over other neuromodulation techniques, precise targeting strategies, and potential physiological risks such as high-frequency conduction block. Overall, TIS is evolving from an exploratory biophysical concept into a promising but technically and physiologically complex neuromodulation tool. Overcoming current engineering and translational barriers, particularly through individualized modeling, rigorous optimization, and well-designed human studies, will be essential for establishing TIS as a reliable therapeutic intervention.

BACKGROUND:As common manifestations of depression, somatic symptoms are associated with treatment-resistant depression and a poor prognosis. Existing therapy for somatic symptoms, such as antidepressants and psychotherapy, have limited efficacy. Therefore, seeking effective and acceptable therapy for somatic symptoms is vital. OBJECTIVE:This study aimed to evaluate the efficacy and safety of transcranial direct current stimulation (tDCS) over the dorsomedial prefrontal cortex (dmPFC) in depressed patients with somatic symptoms. METHODS:The tDCS over the dmPFC was administrated to depressive patients for 2 weeks using a randomized, double-blind, sham-controlled design. Clinical symptoms were assessed at baseline, following tDCS, and 6 weeks after the treatment session. Structural neuroimaging data were collected to build individualized finite element models. Computed median and maximum current density values in the dmPFC and bilateral amygdala regions of interest (ROIs) were correlated with the alleviated somatic symptoms. RESULTS:Sixty-five patients were initially in the study; 57 completed the trial. The active group displayed greater improvements in somatic symptoms compared to the sham group after tDCS. During follow-up, the active group showed a higher responder ratio than the sham group, though not a greater improvement. Furthermore, there was a positive correlation between individualized electric fields of the right amygdala and changes in somatic symptoms in the active group. CONCLUSION:The tDCS targeting dmPFC was shown to be an effective and acceptable complementary therapy for depressed patients with somatic symptoms. The right amygdala electric field was associated with alleviation of somatic symptoms following tDCS treatment.

BACKGROUND:Resilience is increasingly conceptualized as a dynamic process rather than a static trait. The Mount Sinai Resilience Scale (MSRS) captures this process by assessing the frequency and subjective efficacy of malleable resources employed to manage stress. This study aimed to validate the Chinese MSRS (C-MSRS) and investigate the psychometric and network features of resilience in healthy and clinical populations. METHODS:The MSRS was translated and administered to 600 healthy adults and 95 patients. We utilized Exploratory and Confirmatory Factor Analyses, reliability and validity assessments, network analysis, and quadrant analysis to evaluate the psychometric properties and characterize clinical resilience profiles. RESULTS:The C-MSRS demonstrated satisfactory psychometric properties, yielding a 21-item, five-factor model. Network analysis identified the "Meaning and Purpose" dimension (specifically hope and growth mindset) as the central hub, functioning as a "motivational engine" that integrates other resilience resources. Clinical profiling revealed distinct phenotypes: depression was characterized by global deficits consistent with amotivation and helplessness (low frequency/low efficacy), whereas anxiety patients exhibited preserved motivational drive in social connections. Furthermore, "Meaning and Purpose" emerged as a core transdiagnostic factor negatively correlated with symptom severity. CONCLUSIONS:The C-MSRS is a robust, process-oriented instrument for the Chinese context. Our findings highlight resilience as an active, cognition-motivated process organized around hope and growth mindset. By capturing distinct resilience deficits in depression versus anxiety, the C-MSRS offers a precise tool for dissecting resilience mechanisms and guiding targeted interventions.

Background:Current treatments for major depressive disorder (MDD) are limited by efficacy and adverse effects. Transcranial alternating current stimulation (tACS) has emerged as a promising alternative. However, there is still a lack of systematic evaluation of its effectiveness. Recent studies suggest that currents greater than 7 milliamperes (mA) may be necessary to significantly alter potentials in deep brain regions associated with depression. We conducted a systematic review and exploratory meta-analysis to assess the overall efficacy and safety of tACS for MDD and to explore potential associations regarding current intensity by comparing high-intensity tACS (HI-tACS) and low-intensity tACS (LI-tACS) protocols. Methods:A systematic search of Embase, PubMed, Web of Science, the Cochrane Library, ClinicalTrials.gov, and WHO ICTRP was conducted from inception to January 5, 2026. Data were analyzed using a random-effects model, assessing changes in depressive scale scores, response and remission rates, discontinuation, and adverse events. Results:Randomized controlled trials with 438 participants were included. Compared to sham stimulation, active tACS was associated with a significant reduction in depressive symptoms (moderate effect size). In an exploratory subgroup analysis, HI-tACS was associated with a larger effect size (large effect) than LI-tACS (small effect), with lower heterogeneity observed within subgroups. The HI-tACS group also showed significantly higher response and remission rates. No significant differences in adverse events or treatment discontinuations were observed between the two subgroups. Conclusions:Our results suggest that tACS may be a potentially effective treatment for MDD. Data from exploratory subgroup analyses provide preliminary, hypothesis-generating evidence that higher intensity may be associated with improved outcomes. However, given the limited evidence base and substantial heterogeneity, as well as the fact that intensity co-varied with stimulation frequency and other parameters in the included trials, these findings do not establish a sole causal link for intensity. Future large-scale, controlled, multi-arm trials are needed to disentangle these factors and confirm these preliminary observations. The PROSPERO Registration:The study has been registered on https://www.crd.york.ac.uk/prospero/ (registration number: CRD42024589889; registration link: https://www.crd.york.ac.uk/PROSPERO/view/CRD42024589889).

Post-stroke aphasia (PSA) is characterized by persistent language impairment, but its underlying physiological mechanisms remain unclear. Glymphatic dysfunction has been implicated in post-stroke pathophysiology, but its role in PSA remains unclear. The cingulo-opercular network (CON) is a key network implicated in language recovery after stroke. Using blood-oxygen-level-dependent (BOLD) and cerebrospinal fluid (CSF) coupling as a putative non-invasive marker of glymphatic function, we examined global and CON-specific alterations in patients with PSA and their associations with language impairment and treatment-related recovery. We enrolled 79 patients with PSA and 72 healthy controls (HCs). Among the patients, 20 underwent transcranial electrical stimulation (TES) targeting key CON nodes. Global and CON-specific BOLD-CSF coupling were quantified. Language function was assessed using the Western Aphasia Battery (WAB) and the Aphasia Battery of Chinese (ABC). We compared BOLD-CSF coupling between groups and examined its associations with language performance and intervention-related changes. Compared with HCs, patients with PSA showed significantly reduced BOLD-CSF coupling at both the global level and within the CON (all p < 0.05). CON-specific coupling was positively associated with AQ (r = 0.27, p = 0.022), comprehension (r = 0.28, p = 0.018) and fluency (r = 0.23, p = 0.048), and showed a trend-level positive association with naming (r = 0.22, p = 0.056). After TES, the treated patients showed concurrent improvements in language performance and BOLD-CSF coupling. Increases in CON-specific coupling were positively associated with naming recovery (r = 0.59, p = 0.006), and this association remained significant after adjustment for covariates (r = 0.53, p = 0.041). AQ recovery was also associated with increased CON-specific coupling (r = 0.47, p = 0.034), with a trend-level association after adjustment (r = 0.51, p = 0.053). Patients with PSA showed altered BOLD-CSF coupling, particularly within the CON, and these alterations were associated with language impairment and treatment-related recovery. These findings provide preliminary evidence that CON-specific glymphatic dysfunction may be relevant to PSA and support further investigation of the CON as a potential therapeutic target for aphasia rehabilitation. Trial Registration NCT05502822, NCT07700537.

This study explores differential impairment patterns in resting-state functional connectivity (rs-FC) between the cingulo-opercular network (CON) and canonical language networks (LN) in post-stroke aphasia (PSA), emphasizing structure-function relationships mediated by white matter integrity. It specifically investigates how LN lesion topography modulates CON-LN connectivity and behavioral correlates. Sixty-three first-time PSA patients were divided into LN lesion-positive (LNL+, n = 31) and negative (LNL-, n = 32) groups based on lesion volume (>1 cm3) within the canonical LN. Using lesion-based individualized ROIs and group-based template rs-FC, the CON-LN rs-FC was correlated with verbal fluency and white matter integrity metrics, with particular focus on arcuate fasciculus (AF) and superior longitudinal fasciculus (SLF) subdivisions in the LNL+ and LNL- groups, respectively. Although no intergroup differences in CON-LN rs-FC were observed at baseline, distinct lesion-dependent patterns emerged. In LNL+ patients, individualized CON-LN rs-FC was positively correlated with verbal fluency (r = .504, p = .009) and negative correlation with the damage severity of the AF subdivisions (anterior: r = -.601, p < .001; long: r = -.426, p = .024) and SLF subdivisions (SLF II: r = -.483, p = .011; III: r = -.664, p < .001). However, the LNL- patients lacked significant structure-function-behavior associations. The group-based template analysis also detected no significant correlations in either group. Our findings implicate the critical role of CON-LN functional integration for language recovery in PSA patients with direct LN damage. The anterior/long AF segments and SLF II/III are key white matter substrates supporting this functional integration.

Somatic symptoms is an umbrella term that describes distressing somatic complaints occurring across a wide spectrum of diseases. However, their underlying neural mechanisms remain poorly understood. Elucidating their mechanisms could therefore benefit patients with diverse conditions. Using a recently validated lesion network mapping method and a large-scale healthy connectome database (n = 652, recruited in Anhui Province, China), we identified a somatic network from brain lesions causing somatic symptoms. The lesion-derived network was validated using independent multimodal neuroimaging signatures of somatic symptoms and interoceptive processing. It was further characterized by transcriptomic, neurochemical, and cognitive meta-analytic mapping. We further assessed the therapeutic potential of the somatic network by quantifying its spatial convergence with empirically validated neuromodulation targets. Finally, to determine whether the group-level network can predict personalized symptom severity across diagnoses, we built a normative model of gray matter volume using Gaussian process regression based on 1,342 healthy controls from Anhui. This model was then used to generate personalized atrophy maps in 399 local somatic patients with anxiety, depression, schizophrenia and bipolar disorder. We then assessed whether individual atrophy volume within the somatic network was associated with somatic symptom severity. We found that 21 heterogeneous lesions associated somatic symptoms occurred in many different brain locations but were characterized by a common brain network, with the hub region of the right insula and putamen. The somatic network demonstrated strong spatial alignment with multimodal somatic imaging abnormalities from 66 independent studies and interoceptive processing circuits from 69 task-fMRI studies. 11 effective brain stimulation targets were colocalization with the somatic network. In individual transdiagnostic patients, greater atrophy volume within the somatic network was significantly correlated with somatic symptom severity (r = 0.182, p = 0.004), but not with anxiety or depression symptoms. These convergent findings establish a unified somatic network framework, advancing both mechanistic understanding and precision medicine applications.

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.

Although the classical language cortex significantly contributes to post-stroke aphasia (PSA), non-language-specific cortex, such as the cerebellum, is increasingly implicated in language. However, the specific contributions of its subregions to PSA, particularly regarding distinct language dimensions, remain unclear. Given fluency as a core dimension, we investigated the functional and structural integrity of cerebellar language-related subregions to clarify their distinct roles in fluent (FA) versus non-fluent aphasia (nonFA). We enrolled a primary cohort of 81 PSA patients (46 nonFA, 35 FA), and 77 healthy controls (HCs), alongside an independent external validation cohort (Aphasia Recovery Cohort [ARC]; 23 nonFA, 22 FA). Using individualized functional connectivity (FC) and volumetric analyses based on the Multi-Domain Task Battery (MDTB) atlas, we found that nonFA patients exhibited significantly decreased FC between the classical language network (LN) and language-related cerebellar subregions (right MDTB 8 and 9; R_MDTB8/9-LN FC), alongside reduced right Crus II volume. Correlation analysis revealed that these neuroimaging indicators were positively associated with language scores in nonFA, while no such relationships were observed in FA. Furthermore, mediation analysis indicated that right Crus II volume statistically accounted for the observed association between R_MDTB8/9-LN FC and overall Aphasia Quotient (AQ). As the key findings were replicated in the ARC, our results provide compelling evidence that the functional connectivity strength and structural integrity of specific cerebellar subregions contribute to language fluency. Our findings support expanding models of PSA beyond cortical regions and suggest that cerebellar-targeted strategies may improve language rehabilitation outcomes.

BACKGROUND:Major depressive disorder (MDD) is associated with disrupted interhemispheric cooperation. However, the relationship between structural and functional alterations in interhemispheric cooperation in patients with MDD remains unclear. We investigated the associations between voxel-mirrored homotopic connectivity (VMHC) and radial diffusivity (RD) within the corpus callosum (CC) and their links to depressive symptoms in patients with MDD. METHODS:Sixty patients with MDD and 38 healthy controls (HCs) were assessed using resting-state functional MRI (rs-fMRI) and diffusion MRI (dMRI) to evaluate interhemispheric functional connectivity (VMHC) and structural integrity (RD) in the CC subregions. Group comparisons, correlation analyses, and mediation analyses were conducted to identify the significant differences, relationships, and indirect effects. RESULTS:Patients with MDD showed significantly reduced VMHC in the bilateral postcentral gyrus and lingual gyrus and increased RD in the CC subregions CC3, CC4, and CC5, indicating impaired functional and structural connectivity. Lower VMHC in the lingual gyrus was negatively correlated with depressive severity, whereas increased RD in the CC4 and CC5 was positively correlated with depressive symptoms. Mediation analysis revealed that the VMHC in the lingual gyrus fully mediated the relationship between RD in CC5 and depressive symptoms, suggesting a pathway through which structural impairments may affect mood through abnormal functional connectivity. LIMITATIONS:The cross-sectional design limits the assessment of changes over time, and focusing solely on interhemispheric connectivity may overlook other networks involved in MDD. CONCLUSION:These findings provide preliminary evidence for disrupted interhemispheric coordination in MDD, with both functional and structural connectivity impairments linked to depressive symptoms. The mediating effect of the VMHC in the lingual gyrus highlights the potential role of interhemispheric connectivity in the pathophysiology of MDD. Our results provide an integrative perspective on the functional and microstructural organization of the brain in patients with MDD.

BackgroundGastrointestinal (GI) symptoms are a common and burdensome dimension of major depressive disorder (MDD), yet their neurobiological underpinnings are poorly understood. It is unclear how the brain’s processing of visceral signals relates to the subjective experience of GI distress in depression. We aimed to identify a neural substrate for GI symptoms by examining functional connectivity (FC) between the insula and a network defined by gastric rhythms.MethodsWe first identified a gastric-related seed in the posterior insula (GD-pINS) using a large normative dataset of 652 healthy adults. Subsequently, 100 MDD patients—stratified into groups with (GD; n=58) and without (NGD; n=42) GI symptoms—and 80 healthy controls (HCs) were recruited. Using resting-state fMRI, we analyzed FC between the GD-pINS and the gastric network (GN). Group differences, clinical correlations, and the utility of FC features for patient classification via a support vector machine (SVM) were assessed.ResultsCompared to HCs, MDD patients as a whole showed reduced GD-pINS to GN connectivity. Paradoxically, GD patients exhibited relatively stronger connectivity than NGD patients. This symptom-specific enhancement was driven by pathways connecting the posterior insula to the secondary somatosensory cortex (SII). The strength of this insula-SII connection was positively correlated with GI symptom severity. An SVM classifier using these connectivity features distinguished between GD and NGD patients with high accuracy (AUC = 0.82).ConclusionsOur findings reveal a distinct neural signature for GI distress in depression, characterized by aberrant connectivity within an insula-somatosensory circuit. This circuit, which shows relative enhancement in symptomatic patients against a backdrop of globally reduced connectivity, may reflect a mechanism of somatosensory amplification. It represents a potential biomarker for patient stratification and a novel target for therapeutic intervention.

BackgroundNon-Suicidal Self-Injury (NSSI) is a primary risk factor for suicide, but objective biomarkers to assess this risk are urgently needed. The “prefrontal-limbic dysregulation” model provides a neurobiological framework for self-injurious behaviors. This study aimed to identify resting-state neural markers of suicidal ideation severity in adolescents with NSSI and to build a predictive model for individualized risk assessment.MethodsWe recruited 64 adolescent psychiatric inpatients with NSSI. Suicidal ideation was measured using the Beck Scale for Suicide Ideation (BSI). Resting-state functional MRI (rs-fMRI) was used to measure spontaneous brain activity via the amplitude of low-frequency fluctuation (ALFF). We performed a whole-brain correlation analysis between ALFF and BSI scores. A support vector regression (SVR) model was then developed using the identified neural feature to predict individual BSI scores.ResultsA significant negative correlation was found between BSI scores and ALFF values in the left Middle Frontal Gyrus (MFG). Lower spontaneous activity in this region was associated with more severe suicidal ideation. The SVR model, based on the left MFG ALFF values, successfully predicted individual BSI scores with significant accuracy (r = 0.492, p < 0.001), a finding confirmed by permutation testing.ConclusionDiminished resting-state activity in the left MFG is a key neural correlate of suicidal ideation severity in adolescents with NSSI. The functional activity of the left MFG is a promising biomarker for suicide risk assessment and may serve as a potential target for novel neuromodulatory therapies in this high-risk population.

BACKGROUND:The rising prevalence of depression imposes a heavy burden on individuals and society. Due to the complex pathogenesis of depression, there is a need to explore new diagnostic and prognostic biomarkers, as well as drug targets. METHODS:This study selected single nucleotide polymorphisms (SNPs) associated with depression and 2821 plasma protein level ratios to serve as instrumental variables (IVs). We employed a two-sample Mendelian randomization (MR) analysis and supplemented the sensitivity analysis with bidirectional MR analysis and Bayesian co-localization. Additionally, we utilized DrugBank to identify the targets and conducted a protein-protein interaction network analysis. RESULTS:Mendelian randomization analysis revealed that increased ADH4/GSTA1 (OR = 0.95; 95 % CI, 0.92-0.98; P = 0.0027), ADH4/KYNU (OR = 0.94; 95 % CI, 0.89-0.99; P = 0.0364), and ROBO2/SCARF2 (OR = 0.95; 95 % CI, 0.90-0.99; P = 0.0268) were associated with a decreased risk of depression, while elevated CD40/CD40LG (OR = 1.06; 95 % CI, 1.01-1.10; P = 0.0046), CD40/F11R (OR = 1.05; 95 % CI, 1.02-1.09; P = 0.0003), PLA2G15/PRCP (OR = 1.05; 95 % CI, 1.01-1.09; P = 0.0143), and PPP1R2/USP8 (OR = 1.04; 95 % CI, 1.00-1.08; P = 0.0385) were associated with an increased risk of depression. ADH4, GSTA1, CD40, CD40LG, F11R, PLA2G15, PPP1R2, USP8, and SCARF2 were identified as associated with therapeutic targets of existing depression medications, warranting further exploration. CONCLUSION:This study is the first to identify a causal relationship between the plasma protein level ratios and depression using MR analysis. These discoveries offer new perspectives for exploring the diagnosis, prognosis, and therapeutic targets of depression.

Background:The hippocampus has been widely reported to be involved in the neuropathology of major depressive disorder (MDD). All the previous researches adopted group-level hippocampus subregions atlas to investigate abnormal functional connectivities in MDD in absence of capturing individual variability. In addition, the molecular basis of functional impairments of hippocampal subregions in MDD remains elusive. Objective:We aimed to reveal functional disruptions and recovery of individual hippocampal subregions in MDD patients before and after ECT and linked these functional connectivity differences to transcriptomic profiles to reveal molecular mechanism. Methods:we used group guided individual functional parcellation approach to define individual subregions of hippocampus for each participant. Resting-state functional connectivity (FC) analysis of individual hippocampal subregions was conducted to investigate functional disruptions and recovery in MDD patients before and after ECT. Spatial association between functional connectivity differences and transcriptomic profiles was employed to reveal molecular mechanism. Results:MDD patients showed increased FCs of the left tail part of hippocampus with dorsolateral prefrontal cortex and middle temporal gyrus while decreased FC with primary visual cortex. These abnormal FCs in MDD patients were normalized after ECT. In addition, we found that functional disruptions of the left tail part of hippocampus in MDD were mainly related to synaptic signaling and transmission, ion transport, cell-cell signaling and neurogenesis. Conclusion:Our findings provide initial evidence for functional connectome disruption of individual hippocampal subregions and their molecular basis in MDD.

Non-invasive brain stimulation (NIBS) has the potential to treat generalized anxiety disorder (GAD). To assess the efficacy (response/remission/post-treatment continuous anxiety severity scores) and acceptability (failure to complete treatment for any reason) of NIBS, we searched PubMed, Web of Science, and the Cochrane Library (as of April 2024) for articles on NIBS for GAD and conducted a network meta-analysis of eight randomized trials (20 treatment arms, 405 participants). Data were pooled using standardized mean difference (SMD) and odds ratio (OR) with 95 % confidence interval (CI). Repetitive transcranial magnetic stimulation (rTMS) was the most widely studied treatment for GAD. The right dorsolateral prefrontal cortex (DLPFC) was the most common treatment target for GAD. High-frequency rTMS showed higher response rates (OR 291.40, 95 % CI 13.08 to 6490.21) and remission rates (OR 182.14, 95 % CI 8.72 to 3805.76) compared with other active therapies. Continuous theta burst stimulation (cTBS) greatly improved continuous post-treatment anxiety severity scores (SMD -2.56, 95 % CI -3.16 to -1.96). No significant differences in acceptability were found between the treatment strategies and the sham stimulation group. These findings provide evidence to consider NIBS techniques as alternative or adjunctive treatments for GAD.

BACKGROUND:Non-invasive brain stimulation (NIBS), including repetitive transcranial magnetic stimulation (rTMS), continuous theta-burst stimulation (cTBS), and transcranial direct current stimulation (tDCS), is an emerging intervention that has been used to treat various mental illnesses. However, previous studies have not comprehensively compared the efficacies of various NIBS modalities in alleviating anxiety symptoms among patients with generalized anxiety disorder (GAD). Therefore, this study conducted a systematic review and meta-analysis to assess the efficacy of NIBS for patients with GAD. METHODS:A systematic search of four major bibliographic databases (Embase, PubMed, Web of Science and The Cochrane Library) was conducted from inception dates to November 26, 2023 to identify eligible studies. The data were analyzed using a random-effects model. RESULTS:Seven randomized controlled trials (RCTs) were included in the meta-analysis. Significant differences were found in changes in Hamilton anxiety rating scale (HARS) scores, study-defined response, and remission between the intervention and control groups. Moreover, the intervention groups experienced a significantly higher frequency of headaches. CONCLUSION:The results revealed that interventions improved GAD compared to control groups. cTBS and rTMS exhibited better treatment efficacy than tDCS, which did not appear to have a significant therapeutic effect. Longer follow-up periods and larger sample sizes are required in future RCTs. TRIAL REGISTRATION:This meta-analysis was conducted in accordance with PRISMA guidelines and registered at PROSPERO (https://www.crd.york.ac.uk/PROSPERO/, CRD42023466285).

BACKGROUND:Electroconvulsive therapy (ECT) is an effective treatment for patients with major depressive disorder (MDD), but its underlying neural mechanisms remain largely unknown. The aim of this study was to identify changes in brain connectome dynamics after ECT in MDD and to explore their associations with treatment outcome. METHODS:We collected longitudinal resting-state functional magnetic resonance imaging data from 80 patients with MDD (50 with suicidal ideation [MDD-SI] and 30 without [MDD-NSI]) before and after ECT and 37 age- and sex-matched healthy control participants. A multilayer network model was used to assess modular switching over time in functional connectomes. Support vector regression was used to assess whether pre-ECT network dynamics could predict treatment response in terms of symptom severity. RESULTS:At baseline, patients with MDD had lower global modularity and higher modular variability in functional connectomes than control participants. Network modularity increased and network variability decreased after ECT in patients with MDD, predominantly in the default mode and somatomotor networks. Moreover, ECT was associated with decreased modular variability in the left dorsal anterior cingulate cortex of MDD-SI but not MDD-NSI patients, and pre-ECT modular variability significantly predicted symptom improvement in the MDD-SI group but not in the MDD-NSI group. CONCLUSIONS:We highlight ECT-induced changes in MDD brain network dynamics and their predictive value for treatment outcome, particularly in patients with SI. This study advances our understanding of the neural mechanisms of ECT from a dynamic brain network perspective and suggests potential prognostic biomarkers for predicting ECT efficacy in patients with MDD.

Although previous studies reported structural changes associated with electroconvulsive therapy (ECT) in major depressive disorder (MDD), the underlying molecular basis of ECT remains largely unknown. Here, we combined two independent structural MRI datasets of MDD patients receiving ECT and transcriptomic gene expression data from Allen Human Brain Atlas to reveal the molecular basis of ECT for MDD. We performed partial least square regression to explore whether/how gray matter volume (GMV) alterations were associated with gene expression level. Functional enrichment analysis was conducted using Metascape to explore ontological pathways of the associated genes. Finally, these genes were further assigned to seven cell types to determine which cell types contribute most to the structural changes in MDD patients after ECT. We found significantly increased GMV in bilateral hippocampus in MDD patients after ECT. Transcriptome-neuroimaging association analyses showed that expression levels of 726 genes were positively correlated with the increased GMV in MDD after ECT. These genes were mainly involved in synaptic signaling, calcium ion binding and cell-cell signaling, and mostly belonged to excitatory and inhibitory neurons. Moreover, we found that the MDD risk genes of CNR1, HTR1A, MAOA, PDE1A, and SST as well as ECT related genes of BDNF, DRD2, APOE, P2RX7, and TBC1D14 showed significantly positive associations with increased GMV. Overall, our findings provide biological and molecular mechanisms underlying structural plasticity induced by ECT in MDD and the identified genes may facilitate future therapy for MDD.