Multimodal neuroimaging changes and their behavioral, genetic, and neurotransmitter correlates in electroconvulsive therapy for major depressive disorder

Multimodal neuroimaging changes and their behavioral, genetic, and neurotransmitter correlates in electroconvulsive therapy for major depressive disorder | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Multimodal neuroimaging changes and their behavioral, genetic, and neurotransmitter correlates in electroconvulsive therapy for major depressive disorder Zuxing Wang, Ruifeng Shi, Yikai Dou, Ying He, Cui Yuan, Yaoxia Liu, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7327954/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Electroconvulsive therapy (ECT) is an effective treatment for major depressive disorder (MDD), yet its underlying mechanisms remain unclear. This study investigated the antidepressant effects of ECT through a multimodal neuro-image meta-analysis combined with functional, genetic, and neurotransmitter assessments. Resting-state functional magnetic resonance imaging (fMRI) and voxel-based morphometry (VBM) data were analyzed using seed-based d mapping with permutation of subject images (SDM-PSI) to identify changes in brain activation and gray matter volume (GMV) before and after ECT. Further analysis of regions with altered activation and GMV was conducted using Neurosynth, postmortem gene expression data, and receptor/transporter distribution maps to explore molecular underpinnings. The whole-brain multimodal meta-analysis included 291 patients from resting-state fMRI studies and 302 patients from VBM studies. Results showed increased activation and GMV in the left angular gyrus (AG) following ECT. Functional annotation linked the left AG to memory, attention, and perceptual processing. Gene expression analysis identified TFAP2B and OTX2 as the most highly expressed genes in this region. Notably, ECT-induced changes in brain activation and GMV were positively correlated with 5-HT1a receptor and dopamine transporter distribution. These findings suggest the left AG is a key region mediating ECT's effects. Neurotransmitter analysis further indicates that ECT may exert its antidepressant action by modulating neurotransmitter systems, offering insights into the neural and molecular basis of its therapeutic efficacy in MDD. Health sciences/Diseases/Psychiatric disorders/Depression Biological sciences/Neuroscience major depressive disorder electroconvulsive therapy resting-state fMRI voxel-based morphometry multimodal meta-analysis Figures Figure 1 Figure 2 Figure 3 Introduction Major depressive disorder (MDD) is a common psychiatric condition characterized by persistent low mood, loss of interest, and cognitive and somatic symptoms [ 1 ], resulting in significant social and economic burdens [ 2 ]. Electroconvulsive therapy (ECT) is an effective treatment for severe and drug-resistant MDD, known for a rapid onset and relatively high response rates [ 3 ]. Despite decades of research, the mechanisms underlying ECT's effects remain unclear. Recently, neuroimaging has emerged as a crucial tool for investigating the neural basis of MDD and the effects of ECT on brain function [ 4 – 6 ]. Many studies have explored ECT's impact on both structural and functional brain changes in depression [ 7 – 9 ]. A meta-analysis showed increased volumes in the hippocampus, amygdala, and caudate nucleus following ECT treatment [ 10 ]. However, the association between structural changes and symptom improvement remains unclear [ 11 – 13 ]. Evidence suggests that adults with depression show abnormal activity in the striatum and frontal cortex [ 14 – 16 ]. Functional magnetic resonance imaging (fMRI) has been used to assess how ECT affects brain activity. Some studies report decreased activity in the superior frontal gyrus and increased activity in the middle frontal gyrus (MFG) after ECT [ 17 , 18 ], though Wang et al. and Qiu et al. found conflicting results [ 19 , 20 ]. Additionally, the hippocampus and amygdala—critical for emotional memory and stress response—have also been central to several ECT studies [ 21 , 22 ]. Brain activity in the amygdala, parahippocampal gyrus, and fusiform gyrus has been shown to change after ECT [ 9 , 19 ]. Moreover, ECT responders showed increased cerebral blood volume in the right middle hippocampal region, which connects functionally to the hippocampus-thalamus-striatum network [ 4 ], with dopaminergic synaptic activity extending throughout the striatum [ 23 ]. Enhanced dopaminergic neurotransmission may help explain ECT’s role in mood disorders [ 24 , 25 ]. However, Mortel et al. reported no changes in brain function after ECT [ 26 ]. Nevertheless, most of these findings come from small sample sizes (N < 30), which may contribute to inconsistent or unstable results [ 27 ]. These inconsistencies highlight the complex neural changes associated with ECT. ECT has been shown to influence several neurotransmitters, including serotonin, catecholamines (dopamine, epinephrine, norepinephrine) [ 28 ], and glutamate, which play key roles in central nervous system signaling. Genetic studies have explored treatment responses to ECT by investigating polymorphisms in the serotonin transporter gene (5-HTTLPR) and the norepinephrine transporter gene (NET) [ 29 ]. I Additionally, changes in default mode network (DMN) static functional connectivity (FC) after ECT have been linked to the expression of seven genes, including AP1S2, CDC123, and SNAPC3 [ 30 ], although knowledge in this area remains limited. Therefore, this study used a meta-analysis based on multimodal whole-brain coordinates to explore the effects of ECT on resting-state brain activity and brain structure in patients with depression. To improve interpretation of these effects, we also performed behavioral, genetic, and neurotransmitter analyses. By synthesizing data from multiple sources, we aim to clarify the neural mechanisms of ECT and provide a more integrated understanding of its impact on brain function and structure in MDD. Methods Literature search and study selection We searched PubMed, Embase, and Web of Science for English-language publications on resting-state fMRI or voxel-based morphometry (VBM) and ECT for MDD, published before May 31, 2025. We conducted a comprehensive search regardless of country of origin or article type. Additionally, we reviewed relevant review articles and the reference lists of all retrieved studies for further information. The search used the following keywords: (“MDD” OR “major depressive disorder” OR “unipolar depression” OR “depressive disorder” OR “depression”) AND (“amplitude of low frequency fluctuation” OR “ALFF” OR “low frequency fluctuation” OR “LFF” OR “amplitude of low frequency oscillation” OR “LFO” OR “regional homogeneity” OR “ReHo” OR “structural magnetic resonance imaging” OR “morphometry” OR “voxel-based” OR “voxel-wise” OR “voxel-based morphometry” OR “VBM” OR “high-resolution imaging” OR “structural neuroimaging” OR “grey matter”) AND (“electroconvulsive therapy” OR “ECT”). The search formulas are available in the Supplementary Information. This study was registered with PROSPERO (CRD420251086496), and we followed the PRISMA guidelines (http://www.prisma-statement.org). The inclusion criteria were as follows: (1) Studies comparing resting-state fMRI or VBM research before and after ECT in patients aged 18–65 with MDD, (2) results reported in MNI or Talairach coordinate systems, (3) whole-brain analysis to avoid region selection bias, (4) for overlapping samples, only the study with the largest sample size was included. Exclusion criteria include: (1) No reported peak coordinates, (2) presence of other neuropsychiatric disorders or severe somatic diseases. Data extraction The data extraction form systematically collected variables from each included study. These included: the first author’s last name and publication year; sample characteristics such as total sample size for both MDD patients and healthy controls, number of female participants, and mean age with standard deviation (SD); details on the Depression Assessment Scale (DAS) used and scores, and whether it was the first depressive episode; electrotherapy specifics such as the number of sessions, stimulation site, type of anesthesia, and any muscarinic drugs administered; MRI parameters including scanning field strength, rs-fMRI analytical methods (e.g., ALFF, ReHo), analysis software, and statistical thresholds; stereotactic coordinates and spatial reference system; and effect size indicators (t-statistic, z-score, or p-value). The literature search, study evaluation, and selection were conducted independently by two researchers. Any discrepancies were resolved by consulting a third investigator, ensuring consensus on all decisions. Voxel-wise meta-analysis We used seed-based d mapping with permutation of subject images (SDM-PSI) (version 6.23, http://www.sdmproject.com/ ) to perform separate voxel-based meta-analyses of resting-state fMRI activation and VBM maps in MDD patients prior to ECT treatment [31]. Figures 1A shows the workflow of the main analyses. All analytical procedures followed the SDM tutorial guidelines. SDM employs restricted maximum likelihood estimation to balance unbiasedness and efficiency [32, 33]. First, we extracted peak coordinates reflecting brain activation differences before and after ECT. Second, a non-normalized anisotropic Gaussian kernel with a full-width half-maximum (FWHM) of 20 mm was used to enhance analysis sensitivity and specificity. Variance and effect size signature plots were generated for each dataset within a gray matter mask based on peak coordinates. Third, mean maps were created by averaging dataset maps voxel-wise, weighted by the square root of the sample size to give more weight to larger studies. Results were considered significant if p < 0.0025 (uncorrected), SDM z-score ≥ 1, and cluster size ≥ 10 voxels [34-36]. These criteria were set to minimize false positives. Results were visualized using statistically significant SDM plots generated in MRIcron (available at http://people.cas.sc.edu/rorden/mricron/). Multimodal meta-analysis To identify brain regions with both gray matter volume (GMV) and resting-state functional activity abnormalities in MDD patients pre- and post-ECT, we summarized intergroup comparisons using a multimodal meta-analysis plot [37]. Brain regions showing multimodal effects were identified by calculating overlapping p-values for GMV and resting-state activity. The SDM method accounted for noise in the meta-analysis estimates. The voxel-level threshold was set at p < 0.0025 due to the four-tailed analysis [38]. Functional annotation and Genetic analysis The distinct behavioral functions and genetic levels of the identified brain regions were analyzed using the Brain Annotation Toolbox (BAT) [39]. The BAT provides methods for performing functional and genetic annotation analyses on neuroimaging data, leveraging activation maps from Neurosynth [40] and gene expression data from the Allen Human Brain Atlas (AHBA) [41]. The NeuroSynth decoder (https://neurosynth.org/decode/) enables reverse inference decoding of activation-term associations, aiding in the exploration of the relationships between brain regions and various psychological processes. By integrating brain imaging data, text mining techniques, and machine learning methods, the NeuroSynth decoder calculates probability mappings between brain regions and psychological terms [40]. Additionally, the AHBA offers a comprehensive 'all genes-all structures' profile of the human brain [41]. Following the steps outlined in the BAT user manual, we conducted functional annotation and genetic characterization analyses. The top ten functional characterizations of each region were extracted and plotted according to their correlation strength, with all requiring a permutation test p -value of less than 0.05. Genetic expression of the identified regions enhances our understanding of brain development, risk factors for depression, and potential treatment options. The top ten genetic expressions for each region were also extracted and plotted based on their highest levels, all meeting the criterion of a permutation test p -value less than 0.05. Neurotransmitter analysis Finally, we assessed the topographic association between regions of significantly altered spontaneous activation and the distribution of various receptor/transporter systems. Using JuSpace v1.5 ( https://github.com/juryxy/JuSpace ) [42], we analyzed the spatial correlation between MRI-based measures of abnormal brain activation and PET- or SPECT-derived maps covering multiple neurotransmitter systems. Specifically, we focused on the 5-hydroxytryptamine (5-HT1a, 5-HT1b, and 5-HT2a) [43] and dopamine (D1 and D2) [44, 45] receptors, as well as dopamine [46] and noradrenaline [47] transporters, all of which have been closely linked to depression. We calculated Pearson correlation coefficients between the significantly altered brain activation observed in MDD patients post-ECT treatment and the distribution of these selected receptors and transporters. A permutation test (10,000 permutations) provided precise p -values to determine if the average correlation observed in participants significantly deviated from a null distribution. Results were corrected for multiple comparisons using the false discovery rate (FDR), with significance defined at an FDR-corrected p < 0.05 [42]. Heterogeneity and publication bias Heterogeneity and sensitivity analyses followed standard procedures. Inter-study heterogeneity was assessed using the I ² index, which quantifies the proportion of variance due to between-study differences [48]. Extreme heterogeneity is indicated by I ² values between 75% and 100%, large heterogeneity corresponds to I ² values between 50% and 75%, moderate heterogeneity reflects I² values between 25% and 50%, and low heterogeneity is indicated by I ² values between 0% and 25% [49]. Egger’s test was used to detect publication bias, with p < 0.05 considered significant [50]. Reliability analysis To test the reliability of voxel-level meta-analysis results, we performed a whole-brain sensitivity analysis using the jackknife method, repeating the analysis while omitting one dataset at a time [32]. The statistical threshold remained consistent across iterations. These analyses were only performed when significant activation differences were observed before and after ECT in MDD patients. Results Study characteristics This meta-analysis included a total of 21 studies, of which 10 examined resting-state functional brain activity and 11 examined GMV ( Figures 1B, 1C) . The functional analysis included 291 patients with MDD, with an average age of 39.11 years (69% female). Electrode placements for ECT varied: 7 studies used bifrontal placement, 1 used bilateral temporal lobe placement, and another employed bifrontal-temporal placement. One study did not specify the electrode placement. Most studies administered around 8 ECT treatments; however, 3 studies did not report the number of treatments, and 1 study included only a single session. The structural analysis included 302 patients with MDD (mean age: 45.62 years; 56% female). Electrode placements were distributed as follows: 6 studies used right unilateral (RUL) placement, 1 used bilateral placement, 1 used forehead placement, 1 used bifrontal placement, 1 used bifrontal-temporal placement [51], and 1 study did not report the placement method [52]. Except for one study that lacked treatment count, the average number of ECT sessions exceeded eight in the remaining studies. The coordinates and t-value files that have been collected can be accessed digitally via the following link: https://osf.io/n5c7t/. The thresholded whole-brain activation and convergence maps mentioned below are also accessible online at https://neurovault.org/collections/21645/#. Detailed demographic and study characteristics are provided in Table 1 . Main meta-analyses Functional meta-analysis Meta-analysis of resting-state functional imaging showed that, compared with pre-ECT, post-ECT treatment resulted in increased activation in the left inferior frontal gyrus (IFG), left angular gyrus (AG), right cerebellum, and right middle frontal gyrus (MFG). No brain regions showed decreased activation. No significant heterogeneity was observed among these regions, and Egger's test indicated no publication bias (all p-values > 0.05). Details are in Table 2 and Figure 2A . Jackknife sensitivity analysis showed that changes in neural activation were reproducible, with significance maintained in at least seven studies ( Supplementary Table S1 ). Structural meta-analysis As shown in Table 2 and Figure 2B , ECT-induced structural changes in MDD patients included increased GMV in the right temporal pole, superior temporal gyrus, right insula, left parahippocampal gyrus, right medial and medial orbital superior frontal gyrus, left AG, and the inferior parietal gyrus (excluding supramarginal and angular areas). No significant heterogeneity or publication bias was observed (all p-values > 0.05). Jackknife sensitivity analysis confirmed reproducibility in at least nine experiments ( Supplementary Table S2 ). Multimodal analysis of functional and structural abnormalities As shown in Table 2 and Figure 2C, multimodal analysis revealed overlapping brain regions with enhanced structure and function after ECT compared to baseline. Specifically, the left AG (peak MNI = -48, -64, 36; p < 0.001; 32 voxels). Behavioral characterization, genetic expression and neurotransmitter distribution When we input the left AG into the BAT, we found that it was functionally enriched in terms related to Memory,’ ‘Attention,’ and ‘Perception’ ( Figure 3A ). Genetic expression analyses revealed Transcription Factor AP-2 Beta (TFAP2B), Orthodenticle Homeobox 2 (OTX2), Serine Peptidase Inhibitor Kazal Type 6 (SPINK6) and Nik Related Kinase (NRK) were among the top ten most expressed genes in the left AG ( Figure 3B ). The full names of all associated genes, along with the proteins they encode and their functions, are provided in Supplemental Table S3 . To examine the neurochemical basis of significantly altered multimodal patterns in the left AG between pre- and post-MDD states, we analyzed their associations with neurotransmitter distributions. We found a significant positive correlation between these multimodal changes and the distribution of 5-HT1a receptors (Fisher’s z = 0.27, FDR-corrected p < 0.01), as well as a significant positive correlation with dopamine transporters (Fisher’s z = 0.23, FDR-corrected p = 0.01) ( Figure 3C ). Discussion To our knowledge, this is the first whole-brain, multimodal meta-analysis of brain function and structure changes in patients with MDD before and after ECT. We found that ECT increased both functional activity and GMV in multiple brain regions. These included the prefrontal cortex (left IFG, right MFG, medial and orbitofrontal regions of the right superior frontal gyrus), parietal lobe (left AG and left inferior parietal gyrus), temporal lobe (right temporal pole extending to the superior temporal gyrus, left parahippocampal gyrus), as well as the right insula and cerebellum. Multimodal analysis revealed overlapping structural and functional changes in the left AG. Functional decoding further showed that activity in the left AG was closely associated with memory, attention, and perception. Gene expression analysis indicated high levels of transcription factors TFAP2B and OTX2, among others, in the left AG. Additionally, we found significant positive correlations between these multimodal changes and the distribution of 5-HT1a receptors and dopamine transporters. Based on the functional meta-analysis, ECT significantly enhanced neural activity in the left IFG, left AG, right cerebellum, and right MFG. The IFG and MFG are key hubs of the frontoparietal control network (FPN), which supports higher-order cognitive functions such as attention regulation, goal-directed behavior, and response inhibition [ 53 ]. Prior studies suggest that individuals with suicidal symptoms often show hyperconnectivity within the FPN [ 54 ], and that ECT can normalize this excessive connectivity [ 55 ]. Meanwhile, in MDD, the default mode network (DMN) typically shows hyperactivity and instability [ 56 ]. Since the left AG is a core hub of the posterior DMN [ 57 ], increased AG activity after ECT may reflect improved self-referential processing. Although the cerebellum has traditionally been linked to motor coordination, recent research has highlighted its role in emotion regulation, due to its connections with the prefrontal cortex and limbic structures like the amygdala and hippocampus [ 58 , 59 ]. Importantly, the brain regions modulated by ECT span both the FPN and DMN, suggesting cross-network regulatory effects. This aligns with previous findings showing that ECT enhances modularity and stability in whole-brain networks, especially within the FPN and DMN [ 60 – 63 ]. These network changes are strongly linked to reductions in depressive symptoms, including suicidal ideation. The structural meta-analysis revealed ECT-induced GMV increases in the right temporal pole, insula, left parahippocampal gyrus, AG, and frontoparietal cortex. These areas are central to emotional and cognitive processing. The temporal pole anchors the temporal-frontal emotion circuit; the insula integrates bodily states with emotion; the parahippocampal gyrus links memory with spatial context; and the frontoparietal cortex is implicated in pathological self-focus and blunted reward sensitivity [ 64 – 66 ]. ECT-induced seizures activate neurotrophic pathways, particularly upregulating brain-derived neurotrophic factor (BDNF) [ 67 ]. As a key modulator of synaptic plasticity, BDNF promotes dendritic spine formation, increases dendritic branching, and strengthens synaptic connectivity [ 68 ]. BDNF levels are typically reduced in MDD [ 69 ]. Longitudinal MRI studies suggest that GMV increases following ECT are transient, typically returning to baseline within 1–6 months [ 70 ]. This temporary change may relate to cognitive side effects, as both GMV changes and cognitive deficits tend to be short-lived [ 11 ]. Furthermore, some studies have found that volume changes do not correlate with clinical improvement [ 71 ], suggesting that symptom relief is more likely driven by optimized network interactions rather than changes in isolated regions [ 72 ]. Thus, neuroplasticity may manifest as sustained structural-functional synergy, rather than as isolated morphological shifts. Our multimodal meta-analysis identified concurrent increases in functional activity and GMV in the left AG, consistent with prior ECT research [ 73 , 74 ]. The left AG, part of the inferior parietal lobule, acts as a cross-modal hub connecting the DMN, central executive network (CEN), and language network [ 57 ]. In MDD, this region shows abnormal connectivity (e.g., hyperactive DMN) and GMV reduction [ 75 , 76 ], contributing to cognitive control deficits (rumination) and negative self-focus [ 77 ]. After ECT, GMV in the left AG increased. Animal studies show that electroconvulsive seizures stimulate dendritic branching, synapse formation, and glial proliferation [ 78 ]. A recent quantitative MRI study measuring myelin, iron, and water content before, during, and after ECT found that longitudinal changes were likely linked to myelin plasticity, not edema [ 79 ]. This may be mediated by increased BDNF expression and release [ 80 ]. ECT may also transiently open the blood–brain barrier, facilitating the movement of neurotrophic factors like BDNF and improving communication between the AG and frontal/temporal regions. At the same time, enhanced spontaneous neural activity (e.g., ALFF/fALFF) in the AG reflects improved local processing. [ 81 ]. Prior research has shown that ECT increases functional connectivity between the left AG and regions such as the bilateral inferior temporal gyrus and bilateral MFG [ 74 ]. As a key DMN node [ 82 ], the AG’s enhanced activity may help decouple the DMN from excessive integration, promoting dynamic reorganization with the CEN and improving cognitive rigidity. This synergistic mechanism may explain the dual benefits of ECT on working memory (via AG-CEN integration) and autobiographical memory (via AG-DMN regulation). Neurotransmitter analysis showed that 5-HT1a receptor enrichment may sensitize the AG to ECT’s synaptic remodeling, boosting GMV through BDNF [ 83 ]. High dopamine transporter expression enhances dopaminergic transmission in the AG. Increased dopaminergic neurotransmission after ECT has been reported in both animal and human studies [ 80 ]. Dopamine stabilizes neural signaling in brain networks, helping regulate the dynamics of the DMN and somatomotor network (SMN), and making the previously unstable MDD brain more stable [ 61 , 84 ]. This stabilization can reduce tendencies toward negative emotions and maladaptive memory patterns in MDD patients [ 85 , 86 ]. is one of the most highly expressed genes in the left AG and may be a key regulator of depression risk [ 87 ]. Overall, ECT may form a structure–function positive feedback loop through the 5-HT/DA neurotransmitter system and the BDNF–mTOR pathway, ultimately transforming the AG from a pathological node into a therapeutic response hub. It is important to acknowledge the limitations of our meta-analysis. First, while the brain operates as an integrated network, we focused only on localized activation due to the limited number of studies on resting-state functional connectivity and brain networks. This may have reduced the comprehensiveness of our findings. Second, the number of ECT sessions varied across studies. Although most reported 8–12 sessions, Du et al.’s study involved only a single session [ 88 ]. Third, the sites of ECT stimulation differed, though all aimed to induce therapeutic seizures. Due to the limited number of included studies, subgroup analyses were not feasible. In addition, some studies combined medicated and unmedicated patients, introducing potential interactions between medication effects and ECT efficacy. Future studies should aim to include patients with consistent medication statuses to minimize heterogeneity and reduce the risk of Type II errors [ 89 ]. Finally, none of the included studies had extended follow-up periods. Longitudinal studies are recommended to further explore the neuroimaging mechanisms underlying ECT’s effects on depression. Conclusions In summary, our findings suggest that ECT leads to convergent functional and structural changes in the left AG in patients with MDD. Functional decoding highlighted the left AG’s critical role in memory, attention, and perception. Genetic and neurotransmitter analyses indicated that ECT-related activity changes were closely linked to the expression of genes such as TFAP2B, OTX2, and SPINK6, and to neurotransmitter density—particularly 5-HT and dopamine. These results may contribute to a more comprehensive understanding of the physiological and pathological mechanisms of depression, and the neuroimaging basis of ECT’s antidepressant effects. Declarations AUTHOR CONTRIBUTIONS Contributors: ZXW, RFS and YKD conceived and designed the study. ZXW and YHS supervised the study. ZXW, YKD and RFS performed the statistical analysis. YH and CY carried out data cleaning and material support. ZXW and RFS drafted the manuscript. Conflict of Interest: There is no conflict of interest to declare. Funding: This work was supported by the Sichuan Science and Technology Program (2024NSFSC1564), Postdoctoral Fellowship Program of CPSF under Grant Number GZC2023180, the China Postdoctoral Science Foundation under Grant Number 2024M762243，and the Postdoctoral Research Fund of West China Hospital，Sichuan University (2024HXBH046). References Marx W, Penninx B, Solmi M, et al. Major depressive disorder. Nat Rev Dis Primers. 2023. 9(1): 44. Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Psychiatry. 2022. 9(2): 137-150. Mutz J, Vipulananthan V, Carter B, Hurlemann R, Fu C, Young AH. Comparative efficacy and acceptability of non-surgical brain stimulation for the acute treatment of major depressive episodes in adults: systematic review and network meta-analysis. BMJ. 2019. 364: l1079. Leaver AM, Vasavada M, Kubicki A, Wade B, Loureiro J, Hellemann G, et al. 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Tables Table 1 : Characteristics of the included studies Author (year) Country Patients (N/Age/ M/ F) Controls (N/Age/ M/ F) Main diagnosis ECT parameters meters Scanner Method of analysis Software space coordinate No. of treatments Stimulation site Anesthetic/muscle relaxation Rs-fMRI Qiu et al.,2019 China 24/31.33±10.79 /10/14 14/33.29±10.36 /4/10 MDD 8 / Sodium thiopental/ succinylcholine HAMD 3.0-Tesla fALFF SPM MNI Liu et al.,2015 China 23/30.57±9.43 /9/14 / MDD 8 bitemporal Propofol/ succinylcholine HAMD 3.0-Tesla ALFF REST v1.8 MNI Kong et al.,2017 China 13/63.0±4.9 /2/11 / MDD / bifrontal Propofol/ succinylcholine HAMD 3.0-Tesla ALFF/ ReHo REST/ DPARSF MNI Wang et al.,2023 China 26/27.73±7.59 /3/23 32/29.63±7.53 /10/22 MDD 8-12 bifrontal Propofol/ succinylcholine HAMD -17 3.0-Tesla ALFF /fALFF/ ReHo DPABI V7.0 MNI Zhang et al.,2020 China 46/40.65±12.62 /10/36 33/36.82±11.48 /8/25 MDD 6-12 bifrontal / HDRS 3.0-Tesla ALFF DPARSF MNI Mo et al.,2020 China 28/37.18±11.53 /12/16 20/ 38.50±10.32/9/11 MDD / bifrontal Propofol/ succinylcholine HAMD 3.0-Tesla ReHo SPM12 MNI Argyelan et al.,2016 USA 16/48.5±13.6 /11/5 10/45.6±13.1 /5/5 MDD 8 bifrontal Methohexital Ketamine/ succinylcholine HAMD 3.0-Tesla fALFF SPM5 MNI Du et al.,2015 China 11/35.36±10.49 /9/2 11/42.73±11.88 /7/4 MDD 1 bifrontotemporal Propofol/ succinylcholine / 3.0-Tesla ALFF /fALFF SPM8 MNI Qian et al.,2025 China 46/38.20±11.84 /9/37 38/38.03±11.43 /15/23 MDD / bifrontal Isoproterenol/ succinylcholine HAMD 3.0-Tesla ALFF DPABIS MNI Wu et al.,2025 China 58/38.62±12.07 /15/43 42/35.00±11.61 /8/34 MDD 8 bifrontal Propofol/ succinylcholine HRSD 3.0-Tesla / SPM12 MNI VBM Xu et al.,2019 China 11/39.27±7.84 /5/6 12/39.08±7.4 /6/6 MDD 7.91 forehead Propofol/ Succinylcholine HAMD 3.0-Tesla GMV SPM12 Talairach Depping et al.,2017 Germany 12/46.3±11.3 /4/8 16/40.1±10.3 /8/8 MDD 10.6 RUL Etomidate/ Succinylcholine HAMD 3.0-Tesla GMV SPM8 MNI Long et al.,2025 China 29/40.03±14.87 /11/18 37/33.25±11.44 /16/21 TRD / RUL / HDRS 3.0-Tesla GMV MRIQC toolbox MNI Redlich et al.,2016 Germany 23/45.7±9.8 /9/14 21/43.7±11.2 /8/13 MDD 9-12 RUL Methohexital sodium or propofol /Succinylcholine BDI /HDRS 3.0-Tesla GMV SPM8 MNI Sartorius et al.,2018 Germany 92/50.4±12.4 /50/42 43/49.53±11.74 /21/22 MDE 12 RUL/ BIL/LART / HAMD 3.0-Tesla GMV SPM8 MNI Zhang et al.,2021 China 34/40.53±13.44 /4/30 33/35.27±11.56 7/26 MDD 6-12 bifrontal Propofol/ Succinylcholine HDRS 3.0-Tesla GMV SPM8 MNI Wu et al.,2022 China 42/37.90±10.71 /9/23 42/36.00±11.93 /10/32 MDD 6-12 / Propofol/ Succinylcholine HRSD 3.0-Tesla GMV SPM MNI Cano et al.,2023 USA 15/42.93±14.87 /7/8 / TRD 12.07 RUL Methohexital/ Succinylcholine QIDS 3.0-Tesla GMV SPM12 MNI Ota et al.,2015 Japan 15/52.1±14.4 /9/6 / MDD 9 BIL Propofol/ Succinylcholine HAMD 1.5-Tesla GMV SPM8 MNI Cano et al.,2017 Spain 12/59.17±8.02 6/6 10/54.4±8.37 /5/5 TRD 9 bifrontotemporal Thiopental/ Succinylcholine HRSD 3.0-Tesla GMV SPM12 MNI Borgers et al.,2023 Germany 17/47.47±10.94 /8/9 21/40.57±12.98 /10/11 MDD 12.71 RUL / HDRS 3.0-Tesla GMV SPM12 MNI Abbreviation: ECT, electroconvulsive therapy; HAMD, Hamilton Rating Scale for Depression; HDRS, Hamilton Depression Rating Scale; ALFF, Amplitude of low-frequency fluctuations; fALFF, fractional amplitude of low-frequency fluctuations; dALFF, dynamic amplitude of low-frequency fluctuation; ReHo, Regional homogeneity; MNI, Montreal Neurological Institute; MDD, major depressive disorder; MDE, major depressive episodes; TRD, treatment-resistant depression, RUL, right unilateral; BIL, bilateral, LART, left anterior right temporal, GMV, gray matter volume; VBM, voxel-based morphometry; Rs-fMRI, resting-state functional magnetic resonance imaging. Table 2 : Meta-analysis results of VBM and resting-state functional brain activity differences before and after ECT treatment in patients with MDD. Local maximum region MNI coordinates (x, y, z) SDM-Z p Cluster size/ voxels Breakdown (Number of voxels) Jackknife sensitivity Heterogeneity I 2 Publish bias Egger’s tests Resting-state functional activity Post-ECT > Pre-ECT Left inferior frontal gyrus, orbital part -38, 50, -12 4.520 0.000003099 166 Left middle frontal gyrus, orbital part (105) Left inferior frontal gyrus, orbital part (43) 9/10 1.94% 0.897 Left angular gyrus, BA 39 -48, -64, 36 3.585 0.000168622 50 Left angular gyrus, BA 39 (50) 8/10 0.06% 0.886 Right cerebellum, hemispheric lobule VI, BA 37 36, -48, -24 3.313 0.000461519 29 Right cerebellum, hemispheric lobule VI, BA 37 (22) 7/10 0.93% 0.970 Right middle frontal gyrus, orbital part 44, 48, -14 3.350 0.000404716 27 Right middle frontal gyrus, orbital part (20) 7/10 2.62% 0.933 Post-ECT Pre-ECT Right temporal pole, superior temporal gyrus 32, 8, -26 5.658 ~0 991 Right temporal pole, superior temporal gyrus (157) Right amygdala (153) Right striatum (69) Right inferior network (88) Right parahippocampal gyrus (82) Right hippocampus (65) Anterior commissure (23) Right insula (17) Right median network, cingulum (14) 11/11 7.89% 0.627 Right insula, BA 48 46, -4, -2 5.759 ~0 881 Right insula (421) Right rolandic operculum (103) Right superior temporal gyrus (87) Right lenticular nucleus, putamen (81) Right heschl gyrus (49) Corpus callosum (45) Right temporal pole, superior temporal gyrus (12) Right fronto-insular tract (11) 11/11 4.15% 0.789 Left parahippocampal gyrus -14, 2, -20 5.655 ~0 388 Left parahippocampal gyrus (146) Left amygdala (29) Left hippocampus (22) Left fusiform gyrus (14) Left striatum (13) Left olfactory cortex (13) 11/11 2.36% 0.897 Right superior frontal gyrus, medial, BA 10 4,46,0 4.561 0.000002563 319 Right anterior cingulate / paracingulate gyri (111) Left anterior cingulate / paracingulate gyri (107) Right superior frontal gyrus, medial (80) Corpus callosum (10) 10/11 4.72% 0.344 Right superior frontal gyrus, medial orbital, BA 11 4, 26, -12 4.809 0.000000775 263 Right superior frontal gyrus, medial orbital (58) Right gyrus rectus (44) Corpus callosum (38) Right striatum (32) Right olfactory cortex (21) Left anterior cingulate / paracingulate gyri (19) 10/11 7.90% 0.276 Left inferior parietal (excluding supramarginal and angular) gyri, BA 40 -50, -52,46 4.628 0.000001848 227 Left inferior parietal (excluding supramarginal and angular) gyri, BA 40 (225) 9/11 3.89% 0.387 Left angular gyrus, BA 39 -44, -62,38 4.239 0.000011206 144 Left angular gyrus (137) 11/11 2.04% 0.715 Right inferior parietal (excluding supramarginal and angular) gyri, BA 40 42, -54,46 4.317 0.000007868 60 Right inferior parietal (excluding supramarginal and angular) gyri (46) Right angular gyrus (13) 9/11 1.52% 0.809 VBM AND resting-state functional activity overlapping Left angular gyrus, BA 39 -48, -64,36 3.585 0.000168622 32 Left angular gyrus, BA 39 (32) Abbreviations: MDD, major depressive disorder; ECT, electroconvulsive therapy; MNI, Montreal Neurological Institute; BA, Brodman areas; VBM, voxel-based morphometry. Additional Declarations The authors have declared there is NO conflict of interest to disclose Supplementary Files supplementarymaterials.docx 1.Search formulas2.Jackknife sensitivity analysis (Table S1-S2) 3.The full name and role of genes (Table S3) Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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Yuan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Cui","middleName":"","lastName":"Yuan","suffix":""},{"id":523102405,"identity":"2ae0fc8c-f1a9-4de1-8ae8-bcd3cabb14dd","order_by":5,"name":"Yaoxia Liu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yaoxia","middleName":"","lastName":"Liu","suffix":""},{"id":523102406,"identity":"cb1fee2a-57a3-43e5-80ef-607f0fe030cb","order_by":6,"name":"Xiaoxia Wang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Xiaoxia","middleName":"","lastName":"Wang","suffix":""},{"id":523102407,"identity":"7e0b56f6-702c-471e-ab41-d5c27d1dc0ff","order_by":7,"name":"Dong Yang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Dong","middleName":"","lastName":"Yang","suffix":""},{"id":523102408,"identity":"a15a7819-bc8c-4cc9-a7c7-444cadae1647","order_by":8,"name":"Daotao Lan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Daotao","middleName":"","lastName":"Lan","suffix":""},{"id":523102409,"identity":"fe92729e-96b5-4151-ac68-a459903c9b1c","order_by":9,"name":"Yunqiong Wang","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yunqiong","middleName":"","lastName":"Wang","suffix":""},{"id":523102410,"identity":"a83dd128-0faa-4626-b65b-96762ac64428","order_by":10,"name":"Yihan Su","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yihan","middleName":"","lastName":"Su","suffix":""}],"badges":[],"createdAt":"2025-08-08 13:56:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7327954/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7327954/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93543065,"identity":"bc089312-4ef5-4a70-bfd7-e2ab5e4acb3a","added_by":"auto","created_at":"2025-10-15 02:41:24","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3338650,"visible":true,"origin":"","legend":"","description":"","filename":"Figure.docx","url":"https://assets-eu.researchsquare.com/files/rs-7327954/v1/4f95f00eb7b3a707bbec0c2a.docx"},{"id":93544753,"identity":"f9d27112-10d5-4c62-aa40-cfd24e37d623","added_by":"auto","created_at":"2025-10-15 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02:41:24","extension":"html","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":235182,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7327954/v1/41a47d2f346a0ad8901ca175.html"},{"id":93543060,"identity":"0820e432-234a-4df8-b0da-72c1c9387676","added_by":"auto","created_at":"2025-10-15 02:41:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":880554,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagrams of this study. \u003cstrong\u003eA)\u003c/strong\u003e Work flow of main analyses in the current study.\u003cstrong\u003e B) \u003c/strong\u003eFlowchart of the resting-state fMRI studies before and after ECT treatment in patients with MDD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eC) \u003c/strong\u003eFlowchart of the VBM studies before and after ECT treatment in patients with MDD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviation\u003c/strong\u003e: ECT, Electroconvulsive Therapy; SDM-PSI, Seed-based d mapping with permutation of subject images; BAT, Brain Annotation Toolbox; VBM, voxel-based morphometry; fMRI, functional magnetic resonance imaging; ALFF, Amplitude of low-frequency fluctuations; ReHo, Regional homogeneity.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7327954/v1/0d37ca604e5aa52dfb0b79c3.png"},{"id":93544012,"identity":"c497d543-2a10-4f25-a07a-1ad65df677d8","added_by":"auto","created_at":"2025-10-15 02:49:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1425782,"visible":true,"origin":"","legend":"\u003cp\u003eMeta-analyses of resting-state brain functional activity and VBM changes before and after ECT treatment in MDD patients. \u003cstrong\u003eA) \u003c/strong\u003eDifferences in resting-state brain functional activity before and after ECT treatment in MDD patients. \u003cstrong\u003eB)\u003c/strong\u003eDifferences in VBM before and after ECT treatment in MDD patients. \u003cstrong\u003eC)\u003c/strong\u003eMultimodal overlap of differences in resting-state brain functional activity and VBM before and after ECT treatment in MDD patients. Regions with increased resting-state brain functional activity or VBM values are shown in red.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviation:\u003c/strong\u003e MDD, Major Depressive Disorder; ECT, Electroconvulsive therapy; VBM, voxel-based morphometry.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7327954/v1/a71bf709ae92b10d5e88c7d4.png"},{"id":93543064,"identity":"2cd5adf4-463d-44f8-93f7-f3559457ac41","added_by":"auto","created_at":"2025-10-15 02:41:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":307045,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA) \u003c/strong\u003eResults of functional annotation of the left angular gyrus.\u003cstrong\u003e B) \u003c/strong\u003eGenetic level analysis showing the top ten genes identified in key regions of the left angular gyrus. \u003cstrong\u003eC) \u003c/strong\u003ePearson correlation coefficients between brain regions significantly activated by ECT and neurotransmitter systems (Fisher's z-transformation).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviation:\u003c/strong\u003e ECT, Electroconvulsive Therapy; 5-HT, 5-Hydroxytryptamine; DAT, Dopamine Transporters; NAT, Noradrenaline Transporter.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7327954/v1/a21779420976d5c580b871ca.png"},{"id":100356583,"identity":"a753f6ed-658a-4d3a-b361-5bc66e295156","added_by":"auto","created_at":"2026-01-16 07:15:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3786162,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7327954/v1/00d2c297-666b-426c-9c62-41868420a35a.pdf"},{"id":93544010,"identity":"9d402615-7a57-42fe-af26-05dbe884aedf","added_by":"auto","created_at":"2025-10-15 02:49:24","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":25461,"visible":true,"origin":"","legend":"\u003cp\u003e1.Search formulas2.Jackknife sensitivity analysis (Table S1-S2) 3.The full name and role of genes (Table S3)\u003c/p\u003e","description":"","filename":"supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-7327954/v1/7f2b7d319355d0093a11139c.docx"}],"financialInterests":"The authors have declared there is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"Multimodal neuroimaging changes and their behavioral, genetic, and neurotransmitter correlates in electroconvulsive therapy for major depressive disorder","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMajor depressive disorder (MDD) is a common psychiatric condition characterized by persistent low mood, loss of interest, and cognitive and somatic symptoms [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], resulting in significant social and economic burdens [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Electroconvulsive therapy (ECT) is an effective treatment for severe and drug-resistant MDD, known for a rapid onset and relatively high response rates [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Despite decades of research, the mechanisms underlying ECT's effects remain unclear.\u003c/p\u003e\u003cp\u003eRecently, neuroimaging has emerged as a crucial tool for investigating the neural basis of MDD and the effects of ECT on brain function [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Many studies have explored ECT's impact on both structural and functional brain changes in depression [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. A meta-analysis showed increased volumes in the hippocampus, amygdala, and caudate nucleus following ECT treatment [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. However, the association between structural changes and symptom improvement remains unclear [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Evidence suggests that adults with depression show abnormal activity in the striatum and frontal cortex [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Functional magnetic resonance imaging (fMRI) has been used to assess how ECT affects brain activity. Some studies report decreased activity in the superior frontal gyrus and increased activity in the middle frontal gyrus (MFG) after ECT [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], though Wang et al. and Qiu et al. found conflicting results [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Additionally, the hippocampus and amygdala\u0026mdash;critical for emotional memory and stress response\u0026mdash;have also been central to several ECT studies [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Brain activity in the amygdala, parahippocampal gyrus, and fusiform gyrus has been shown to change after ECT [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Moreover, ECT responders showed increased cerebral blood volume in the right middle hippocampal region, which connects functionally to the hippocampus-thalamus-striatum network [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], with dopaminergic synaptic activity extending throughout the striatum [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Enhanced dopaminergic neurotransmission may help explain ECT\u0026rsquo;s role in mood disorders [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. However, Mortel et al. reported no changes in brain function after ECT [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Nevertheless, most of these findings come from small sample sizes (N\u0026thinsp;\u0026lt;\u0026thinsp;30), which may contribute to inconsistent or unstable results [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. These inconsistencies highlight the complex neural changes associated with ECT.\u003c/p\u003e\u003cp\u003eECT has been shown to influence several neurotransmitters, including serotonin, catecholamines (dopamine, epinephrine, norepinephrine) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], and glutamate, which play key roles in central nervous system signaling. Genetic studies have explored treatment responses to ECT by investigating polymorphisms in the serotonin transporter gene (5-HTTLPR) and the norepinephrine transporter gene (NET) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. I Additionally, changes in default mode network (DMN) static functional connectivity (FC) after ECT have been linked to the expression of seven genes, including AP1S2, CDC123, and SNAPC3 [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], although knowledge in this area remains limited.\u003c/p\u003e\u003cp\u003eTherefore, this study used a meta-analysis based on multimodal whole-brain coordinates to explore the effects of ECT on resting-state brain activity and brain structure in patients with depression. To improve interpretation of these effects, we also performed behavioral, genetic, and neurotransmitter analyses. By synthesizing data from multiple sources, we aim to clarify the neural mechanisms of ECT and provide a more integrated understanding of its impact on brain function and structure in MDD.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eLiterature search and study selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe searched PubMed, Embase, and Web of Science for English-language publications on resting-state fMRI or voxel-based morphometry (VBM) and ECT for MDD, published before May 31, 2025. We conducted a comprehensive search regardless of country of origin or article type. Additionally, we reviewed relevant review articles and the reference lists of all retrieved studies for further information. The search used the following keywords: (\u0026ldquo;MDD\u0026rdquo; OR \u0026ldquo;major depressive disorder\u0026rdquo; OR \u0026ldquo;unipolar depression\u0026rdquo; OR \u0026ldquo;depressive disorder\u0026rdquo; OR \u0026ldquo;depression\u0026rdquo;) AND (\u0026ldquo;amplitude of low frequency fluctuation\u0026rdquo; OR \u0026ldquo;ALFF\u0026rdquo; OR \u0026ldquo;low frequency fluctuation\u0026rdquo; OR \u0026ldquo;LFF\u0026rdquo; OR \u0026ldquo;amplitude of low frequency oscillation\u0026rdquo; OR \u0026ldquo;LFO\u0026rdquo; OR \u0026ldquo;regional homogeneity\u0026rdquo; OR \u0026ldquo;ReHo\u0026rdquo; OR \u0026ldquo;structural magnetic resonance imaging\u0026rdquo; OR \u0026ldquo;morphometry\u0026rdquo; OR \u0026ldquo;voxel-based\u0026rdquo; OR \u0026ldquo;voxel-wise\u0026rdquo; OR \u0026ldquo;voxel-based morphometry\u0026rdquo; OR \u0026ldquo;VBM\u0026rdquo; OR \u0026ldquo;high-resolution imaging\u0026rdquo; OR \u0026ldquo;structural neuroimaging\u0026rdquo; OR \u0026ldquo;grey matter\u0026rdquo;) AND (\u0026ldquo;electroconvulsive therapy\u0026rdquo; OR \u0026ldquo;ECT\u0026rdquo;). The search formulas are available in the Supplementary Information. This study was registered with PROSPERO (CRD420251086496), and we followed the PRISMA guidelines (http://www.prisma-statement.org).\u003c/p\u003e\n\u003cp\u003eThe inclusion criteria were as follows:\u0026nbsp;(1) Studies comparing resting-state fMRI or VBM research before and after ECT in patients aged 18\u0026ndash;65 with MDD, (2) results reported in MNI or Talairach coordinate systems, (3) whole-brain analysis to avoid region selection bias, (4) for overlapping samples, only the study with the largest sample size was included. Exclusion criteria include: (1)\u0026nbsp;No reported peak coordinates, (2) presence of other neuropsychiatric disorders or severe somatic diseases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData extraction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data extraction form systematically collected variables from each included study. These included: the first author\u0026rsquo;s last name and publication year; sample characteristics such as total sample size for both MDD patients and healthy controls, number of female participants, and mean age with standard deviation (SD); details on the Depression Assessment Scale (DAS) used and scores, and whether it was the first depressive episode; electrotherapy specifics such as the number of sessions, stimulation site, type of anesthesia, and any muscarinic drugs administered; MRI parameters including scanning field strength, rs-fMRI analytical methods (e.g., ALFF, ReHo), analysis software, and statistical thresholds; stereotactic coordinates and spatial reference system; and effect size indicators (t-statistic, z-score, or p-value). The literature search, study evaluation, and selection were conducted independently by two researchers. Any discrepancies were resolved by consulting a third investigator, ensuring consensus on all decisions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVoxel-wise meta-analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used seed-based d mapping with permutation of subject images (SDM-PSI) (version 6.23,\u0026nbsp;\u003ca href=\"http://www.sdmproject.com/\" target=\"_new\"\u003ehttp://www.sdmproject.com/\u003c/a\u003e) to perform separate voxel-based meta-analyses of resting-state fMRI activation and VBM maps in MDD patients prior to ECT treatment [31]. \u003cstrong\u003eFigures 1A\u0026nbsp;\u003c/strong\u003eshows the workflow of the main analyses. All analytical procedures followed the SDM tutorial guidelines. SDM employs restricted maximum likelihood estimation to balance unbiasedness and efficiency [32, 33]. First, we extracted peak coordinates reflecting brain activation differences before and after ECT. Second, a non-normalized anisotropic Gaussian kernel with a full-width half-maximum (FWHM) of 20 mm was used to enhance analysis sensitivity and specificity. Variance and effect size signature plots were generated for each dataset within a gray matter mask based on peak coordinates. Third, mean maps were created by averaging dataset maps voxel-wise, weighted by the square root of the sample size to give more weight to larger studies. Results were considered significant if p \u0026lt; 0.0025 (uncorrected), SDM z-score \u0026ge; 1, and cluster size \u0026ge; 10 voxels [34-36]. These criteria were set to minimize false positives. Results were visualized using statistically significant SDM plots generated in MRIcron (available at\u0026nbsp;\u003ca href=\"http://people.cas.sc.edu/rorden/mricron/).\"\u003ehttp://people.cas.sc.edu/rorden/mricron/).\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultimodal meta-analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify brain regions with both gray matter volume (GMV) and resting-state functional activity abnormalities in MDD patients pre- and post-ECT, we summarized intergroup comparisons using a multimodal meta-analysis plot [37]. Brain regions showing multimodal effects were identified by calculating overlapping p-values for GMV and resting-state activity. The SDM method accounted for noise in the meta-analysis estimates. The voxel-level threshold was set at p \u0026lt; 0.0025 due to the four-tailed analysis [38].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional annotation and Genetic analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe distinct behavioral functions and genetic levels of the identified brain regions were analyzed using the Brain Annotation Toolbox (BAT) [39]. The BAT provides methods for performing functional and genetic annotation analyses on neuroimaging data, leveraging activation maps from Neurosynth [40] and gene expression data from the Allen Human Brain Atlas (AHBA) [41]. The NeuroSynth decoder (https://neurosynth.org/decode/) enables reverse inference decoding of activation-term associations, aiding in the exploration of the relationships between brain regions and various psychological processes. By integrating brain imaging data, text mining techniques, and machine learning methods, the NeuroSynth decoder calculates probability mappings between brain regions and psychological terms [40]. Additionally, the AHBA offers a comprehensive \u0026apos;all genes-all structures\u0026apos; profile of the human brain [41]. Following the steps outlined in the BAT user manual, we conducted functional annotation and genetic characterization analyses. The top ten functional characterizations of each region were extracted and plotted according to their correlation strength, with all requiring a permutation test\u003cem\u003e\u0026nbsp;p\u003c/em\u003e-value of less than 0.05. Genetic expression of the identified regions enhances our understanding of brain development, risk factors for depression, and potential treatment options. The top ten genetic expressions for each region were also extracted and plotted based on their highest levels, all meeting the criterion of a permutation test \u003cem\u003ep\u003c/em\u003e-value less than 0.05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNeurotransmitter analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFinally, we assessed the topographic association between regions of significantly altered spontaneous activation and the distribution of various receptor/transporter systems. Using JuSpace v1.5 (\u003ca href=\"https://github.com/juryxy/JuSpace\"\u003ehttps://github.com/juryxy/JuSpace\u003c/a\u003e) [42], we analyzed the spatial correlation between MRI-based measures of abnormal brain activation and PET- or SPECT-derived maps covering multiple neurotransmitter systems. Specifically, we focused on the 5-hydroxytryptamine (5-HT1a, 5-HT1b, and 5-HT2a) [43] and dopamine (D1 and D2) [44, 45] receptors, as well as dopamine [46] and noradrenaline [47] transporters, all of which have been closely linked to depression. We calculated Pearson correlation coefficients between the significantly altered brain activation observed in MDD patients post-ECT treatment and the distribution of these selected receptors and transporters. A permutation test (10,000 permutations) provided precise \u003cem\u003ep\u003c/em\u003e-values to determine if the average correlation observed in participants significantly deviated from a null distribution. Results were corrected for multiple comparisons using the false discovery rate (FDR), with significance defined at an FDR-corrected\u003cem\u003e\u0026nbsp;p\u003c/em\u003e \u0026lt; 0.05 [42].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHeterogeneity and publication bias\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHeterogeneity and sensitivity analyses followed standard procedures. Inter-study heterogeneity was assessed using the \u003cem\u003eI\u003c/em\u003e\u003cem\u003e\u0026sup2;\u003c/em\u003e index, which quantifies the proportion of variance due to between-study differences [48]. Extreme heterogeneity is indicated by \u003cem\u003eI\u003c/em\u003e\u003cem\u003e\u0026sup2;\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003evalues between 75% and 100%, large heterogeneity corresponds to \u003cem\u003eI\u003c/em\u003e\u003cem\u003e\u0026sup2;\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003evalues between 50% and 75%, moderate heterogeneity reflects \u003cem\u003eI\u0026sup2;\u0026nbsp;\u003c/em\u003evalues between 25% and 50%, and low heterogeneity is indicated by\u003cem\u003e\u0026nbsp;I\u003c/em\u003e\u003cem\u003e\u0026sup2;\u003c/em\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003evalues between 0% and 25%\u0026nbsp;[49]. Egger\u0026rsquo;s test was used to detect publication bias, with p \u0026lt; 0.05 considered significant\u0026nbsp;[50].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReliability analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo test the reliability of voxel-level meta-analysis results, we performed a whole-brain sensitivity analysis using the jackknife method, repeating the analysis while omitting one dataset at a time [32]. The statistical threshold remained consistent across iterations. These analyses were only performed when significant activation differences were observed before and after ECT in MDD patients.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eStudy characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis meta-analysis included a total of 21 studies, of which 10 examined resting-state functional brain activity and 11 examined GMV (\u003cstrong\u003eFigures 1B, 1C)\u003c/strong\u003e. The functional analysis included 291 patients with MDD, with an average age of 39.11 years (69% female). Electrode placements for ECT varied: 7 studies used bifrontal placement, 1 used bilateral temporal lobe placement, and another employed bifrontal-temporal placement. One study did not specify the electrode placement. Most studies administered around 8 ECT treatments; however, 3 studies did not report the number of treatments, and 1 study included only a single session.\u003c/p\u003e\n\u003cp\u003eThe structural analysis included 302 patients with MDD (mean age: 45.62 years; 56% female). Electrode placements were distributed as follows: 6 studies used right unilateral (RUL) placement, 1 used bilateral placement, 1 used forehead placement, 1 used bifrontal placement, 1 used bifrontal-temporal placement [51], and 1 study did not report the placement method [52]. Except for one study that lacked treatment count, the average number of ECT sessions exceeded eight in the remaining studies. The coordinates and t-value files that have been collected can be accessed digitally via the following link: https://osf.io/n5c7t/. The thresholded whole-brain activation and convergence maps mentioned below are also accessible online at https://neurovault.org/collections/21645/#. Detailed demographic and study characteristics are provided in\u003cstrong\u003e\u0026nbsp;Table 1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMain meta-analyses\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional meta-analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMeta-analysis of resting-state functional imaging showed that, compared with pre-ECT, post-ECT treatment resulted in increased activation in the left inferior frontal gyrus (IFG), left angular gyrus (AG), right cerebellum, and right middle frontal gyrus (MFG). No brain regions showed decreased activation. No significant heterogeneity was observed among these regions, and Egger\u0026apos;s test indicated no publication bias (all p-values \u0026gt; 0.05). Details are in \u003cstrong\u003eTable 2\u003c/strong\u003e and\u003cstrong\u003e\u0026nbsp;Figure 2A\u003c/strong\u003e. Jackknife sensitivity analysis showed that changes in neural activation were reproducible, with significance maintained in at least seven studies (\u003cstrong\u003eSupplementary Table S1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStructural meta-analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in \u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;Figure 2B\u003c/strong\u003e, ECT-induced structural changes in MDD patients included increased GMV in the right temporal pole, superior temporal gyrus, right insula, left parahippocampal gyrus, right medial and medial orbital superior frontal gyrus, left AG, and the inferior parietal gyrus (excluding supramarginal and angular areas). No significant heterogeneity or publication bias was observed (all p-values \u0026gt; 0.05). Jackknife sensitivity analysis confirmed reproducibility in at least nine experiments (\u003cstrong\u003eSupplementary Table S2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultimodal analysis of functional and structural abnormalities\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in \u003cstrong\u003eTable 2\u003c/strong\u003e and \u003cstrong\u003eFigure 2C,\u0026nbsp;\u003c/strong\u003emultimodal analysis revealed overlapping brain regions with enhanced structure and function after ECT compared to baseline. Specifically, the left AG (peak MNI = -48, -64, 36; \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001; 32 voxels).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBehavioral characterization, genetic expression and neurotransmitter distribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhen we input the left AG into the BAT, we found that it was functionally enriched in terms related to Memory,\u0026rsquo; \u0026lsquo;Attention,\u0026rsquo; and \u0026lsquo;Perception\u0026rsquo; (\u003cstrong\u003eFigure 3A\u003c/strong\u003e). Genetic expression analyses revealed Transcription Factor AP-2 Beta (TFAP2B), Orthodenticle Homeobox 2 (OTX2), Serine Peptidase Inhibitor Kazal Type 6 (SPINK6) and Nik Related Kinase (NRK) were among the top ten most expressed genes in the left AG (\u003cstrong\u003eFigure 3B\u003c/strong\u003e). The full names of all associated genes, along with the proteins they encode and their functions, are provided in \u003cstrong\u003eSupplemental Table S3\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo examine the neurochemical basis of significantly altered multimodal patterns in the left AG between pre- and post-MDD states, we analyzed their associations with neurotransmitter distributions. We found a significant positive correlation between these multimodal changes and the distribution of 5-HT1a receptors (Fisher\u0026rsquo;s \u003cem\u003ez\u003c/em\u003e = 0.27, FDR-corrected \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01), as well as a significant positive correlation with dopamine transporters (Fisher\u0026rsquo;s \u003cem\u003ez\u003c/em\u003e = 0.23, FDR-corrected \u003cem\u003ep\u003c/em\u003e = 0.01) (\u003cstrong\u003eFigure 3C\u003c/strong\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo our knowledge, this is the first whole-brain, multimodal meta-analysis of brain function and structure changes in patients with MDD before and after ECT. We found that ECT increased both functional activity and GMV in multiple brain regions. These included the prefrontal cortex (left IFG, right MFG, medial and orbitofrontal regions of the right superior frontal gyrus), parietal lobe (left AG and left inferior parietal gyrus), temporal lobe (right temporal pole extending to the superior temporal gyrus, left parahippocampal gyrus), as well as the right insula and cerebellum. Multimodal analysis revealed overlapping structural and functional changes in the left AG. Functional decoding further showed that activity in the left AG was closely associated with memory, attention, and perception. Gene expression analysis indicated high levels of transcription factors TFAP2B and OTX2, among others, in the left AG. Additionally, we found significant positive correlations between these multimodal changes and the distribution of 5-HT1a receptors and dopamine transporters.\u003c/p\u003e\u003cp\u003eBased on the functional meta-analysis, ECT significantly enhanced neural activity in the left IFG, left AG, right cerebellum, and right MFG. The IFG and MFG are key hubs of the frontoparietal control network (FPN), which supports higher-order cognitive functions such as attention regulation, goal-directed behavior, and response inhibition [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Prior studies suggest that individuals with suicidal symptoms often show hyperconnectivity within the FPN [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], and that ECT can normalize this excessive connectivity [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Meanwhile, in MDD, the default mode network (DMN) typically shows hyperactivity and instability [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Since the left AG is a core hub of the posterior DMN [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e], increased AG activity after ECT may reflect improved self-referential processing. Although the cerebellum has traditionally been linked to motor coordination, recent research has highlighted its role in emotion regulation, due to its connections with the prefrontal cortex and limbic structures like the amygdala and hippocampus [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Importantly, the brain regions modulated by ECT span both the FPN and DMN, suggesting cross-network regulatory effects. This aligns with previous findings showing that ECT enhances modularity and stability in whole-brain networks, especially within the FPN and DMN [\u003cspan additionalcitationids=\"CR61 CR62\" citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. These network changes are strongly linked to reductions in depressive symptoms, including suicidal ideation.\u003c/p\u003e\u003cp\u003eThe structural meta-analysis revealed ECT-induced GMV increases in the right temporal pole, insula, left parahippocampal gyrus, AG, and frontoparietal cortex. These areas are central to emotional and cognitive processing. The temporal pole anchors the temporal-frontal emotion circuit; the insula integrates bodily states with emotion; the parahippocampal gyrus links memory with spatial context; and the frontoparietal cortex is implicated in pathological self-focus and blunted reward sensitivity [\u003cspan additionalcitationids=\"CR65\" citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. ECT-induced seizures activate neurotrophic pathways, particularly upregulating brain-derived neurotrophic factor (BDNF) [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. As a key modulator of synaptic plasticity, BDNF promotes dendritic spine formation, increases dendritic branching, and strengthens synaptic connectivity [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. BDNF levels are typically reduced in MDD [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Longitudinal MRI studies suggest that GMV increases following ECT are transient, typically returning to baseline within 1\u0026ndash;6 months [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. This temporary change may relate to cognitive side effects, as both GMV changes and cognitive deficits tend to be short-lived [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Furthermore, some studies have found that volume changes do not correlate with clinical improvement [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e], suggesting that symptom relief is more likely driven by optimized network interactions rather than changes in isolated regions [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. Thus, neuroplasticity may manifest as sustained structural-functional synergy, rather than as isolated morphological shifts.\u003c/p\u003e\u003cp\u003eOur multimodal meta-analysis identified concurrent increases in functional activity and GMV in the left AG, consistent with prior ECT research [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. The left AG, part of the inferior parietal lobule, acts as a cross-modal hub connecting the DMN, central executive network (CEN), and language network [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. In MDD, this region shows abnormal connectivity (e.g., hyperactive DMN) and GMV reduction [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e], contributing to cognitive control deficits (rumination) and negative self-focus [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. After ECT, GMV in the left AG increased. Animal studies show that electroconvulsive seizures stimulate dendritic branching, synapse formation, and glial proliferation [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. A recent quantitative MRI study measuring myelin, iron, and water content before, during, and after ECT found that longitudinal changes were likely linked to myelin plasticity, not edema [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. This may be mediated by increased BDNF expression and release [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eECT may also transiently open the blood\u0026ndash;brain barrier, facilitating the movement of neurotrophic factors like BDNF and improving communication between the AG and frontal/temporal regions. At the same time, enhanced spontaneous neural activity (e.g., ALFF/fALFF) in the AG reflects improved local processing. [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. Prior research has shown that ECT increases functional connectivity between the left AG and regions such as the bilateral inferior temporal gyrus and bilateral MFG [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. As a key DMN node [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e], the AG\u0026rsquo;s enhanced activity may help decouple the DMN from excessive integration, promoting dynamic reorganization with the CEN and improving cognitive rigidity. This synergistic mechanism may explain the dual benefits of ECT on working memory (via AG-CEN integration) and autobiographical memory (via AG-DMN regulation). Neurotransmitter analysis showed that 5-HT1a receptor enrichment may sensitize the AG to ECT\u0026rsquo;s synaptic remodeling, boosting GMV through BDNF [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]. High dopamine transporter expression enhances dopaminergic transmission in the AG. Increased dopaminergic neurotransmission after ECT has been reported in both animal and human studies [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. Dopamine stabilizes neural signaling in brain networks, helping regulate the dynamics of the DMN and somatomotor network (SMN), and making the previously unstable MDD brain more stable [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e]. This stabilization can reduce tendencies toward negative emotions and maladaptive memory patterns in MDD patients [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]. is one of the most highly expressed genes in the left AG and may be a key regulator of depression risk [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e]. Overall, ECT may form a structure\u0026ndash;function positive feedback loop through the 5-HT/DA neurotransmitter system and the BDNF\u0026ndash;mTOR pathway, ultimately transforming the AG from a pathological node into a therapeutic response hub.\u003c/p\u003e\u003cp\u003eIt is important to acknowledge the limitations of our meta-analysis. First, while the brain operates as an integrated network, we focused only on localized activation due to the limited number of studies on resting-state functional connectivity and brain networks. This may have reduced the comprehensiveness of our findings. Second, the number of ECT sessions varied across studies. Although most reported 8\u0026ndash;12 sessions, Du et al.\u0026rsquo;s study involved only a single session [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. Third, the sites of ECT stimulation differed, though all aimed to induce therapeutic seizures. Due to the limited number of included studies, subgroup analyses were not feasible. In addition, some studies combined medicated and unmedicated patients, introducing potential interactions between medication effects and ECT efficacy. Future studies should aim to include patients with consistent medication statuses to minimize heterogeneity and reduce the risk of Type II errors [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e]. Finally, none of the included studies had extended follow-up periods. Longitudinal studies are recommended to further explore the neuroimaging mechanisms underlying ECT\u0026rsquo;s effects on depression.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, our findings suggest that ECT leads to convergent functional and structural changes in the left AG in patients with MDD. Functional decoding highlighted the left AG\u0026rsquo;s critical role in memory, attention, and perception. Genetic and neurotransmitter analyses indicated that ECT-related activity changes were closely linked to the expression of genes such as TFAP2B, OTX2, and SPINK6, and to neurotransmitter density\u0026mdash;particularly 5-HT and dopamine. These results may contribute to a more comprehensive understanding of the physiological and pathological mechanisms of depression, and the neuroimaging basis of ECT\u0026rsquo;s antidepressant effects.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eContributors: ZXW, RFS and YKD conceived and designed the study. ZXW and YHS supervised the study. ZXW, YKD and RFS performed the statistical analysis. YH and CY carried out data cleaning and material support. ZXW and RFS drafted the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u0026nbsp;\u003c/strong\u003eThere is no conflict of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis work was supported by the Sichuan Science and Technology Program (2024NSFSC1564), Postdoctoral Fellowship Program of CPSF under Grant Number GZC2023180, the China Postdoctoral Science Foundation under Grant Number 2024M762243，and the Postdoctoral Research Fund of West China Hospital，Sichuan University (2024HXBH046).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMarx W, Penninx B, Solmi M, et al. Major depressive disorder. 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Biol Psychiatry 2009;66:886-97. https://doi.org/10.1016/j.biopsych.2009.06.021. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e: Characteristics of the included studies\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 65px;\"\u003e\n \u003cp\u003eAuthor\u003c/p\u003e\n \u003cp\u003e(year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 59px;\"\u003e\n \u003cp\u003eCountry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 96px;\"\u003e\n \u003cp\u003ePatients\u003c/p\u003e\n \u003cp\u003e(N/Age/\u0026nbsp;M/ F)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 105px;\"\u003e\n \u003cp\u003eControls\u003c/p\u003e\n \u003cp\u003e(N/Age/\u0026nbsp;M/ F)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 61px;\"\u003e\n \u003cp\u003eMain \u0026nbsp; \u0026nbsp;diagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 270px;\"\u003e\n \u003cp\u003eECT parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 51px;\"\u003e\n \u003cp\u003emeters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 59px;\"\u003e\n \u003cp\u003eScanner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 54px;\"\u003e\n \u003cp\u003eMethod of analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 61px;\"\u003e\n \u003cp\u003eSoftware\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 65px;\"\u003e\n \u003cp\u003espace coordinate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003eNo. of treatments\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eStimulation site\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eAnesthetic/muscle relaxation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"13\" style=\"width: 945px;\"\u003e\n \u003cp\u003eRs-fMRI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eQiu et al.,2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e24/31.33\u0026plusmn;10.79\u003c/p\u003e\n \u003cp\u003e/10/14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e14/33.29\u0026plusmn;10.36\u003c/p\u003e\n \u003cp\u003e/4/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eMDD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eSodium thiopental/ succinylcholine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eHAMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e3.0-Tesla\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003efALFF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eSPM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eMNI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eLiu et al.,2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e23/30.57\u0026plusmn;9.43\u003c/p\u003e\n \u003cp\u003e/9/14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eMDD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003ebitemporal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003ePropofol/\u003c/p\u003e\n \u003cp\u003esuccinylcholine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eHAMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e3.0-Tesla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eALFF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eREST\u003c/p\u003e\n \u003cp\u003ev1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eMNI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eKong et al.,2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e13/63.0\u0026plusmn;4.9\u003c/p\u003e\n \u003cp\u003e/2/11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eMDD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003ebifrontal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003ePropofol/\u003c/p\u003e\n \u003cp\u003esuccinylcholine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eHAMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e3.0-Tesla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eALFF/\u003c/p\u003e\n \u003cp\u003eReHo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eREST/\u003c/p\u003e\n \u003cp\u003eDPARSF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eMNI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eWang et al.,2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e26/27.73\u0026plusmn;7.59\u003c/p\u003e\n \u003cp\u003e/3/23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e32/29.63\u0026plusmn;7.53\u003c/p\u003e\n \u003cp\u003e/10/22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eMDD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e8-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003ebifrontal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003ePropofol/\u003c/p\u003e\n \u003cp\u003esuccinylcholine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eHAMD\u003c/p\u003e\n \u003cp\u003e-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e3.0-Tesla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eALFF\u003c/p\u003e\n \u003cp\u003e/fALFF/\u003c/p\u003e\n \u003cp\u003eReHo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;DPABI V7.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eMNI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eZhang et al.,2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e46/40.65\u0026plusmn;12.62\u003c/p\u003e\n \u003cp\u003e/10/36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e33/36.82\u0026plusmn;11.48\u003c/p\u003e\n \u003cp\u003e/8/25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eMDD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e6-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003ebifrontal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eHDRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e3.0-Tesla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eALFF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eDPARSF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eMNI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eMo et al.,2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e28/37.18\u0026plusmn;11.53\u003c/p\u003e\n \u003cp\u003e/12/16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e20/ 38.50\u0026plusmn;10.32/9/11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eMDD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003ebifrontal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003ePropofol/\u003c/p\u003e\n \u003cp\u003esuccinylcholine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eHAMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e3.0-Tesla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eReHo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eSPM12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eMNI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eArgyelan et al.,2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e16/48.5\u0026plusmn;13.6\u003c/p\u003e\n \u003cp\u003e/11/5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e10/45.6\u0026plusmn;13.1\u003c/p\u003e\n \u003cp\u003e/5/5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eMDD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003ebifrontal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eMethohexital\u003c/p\u003e\n \u003cp\u003eKetamine/\u003c/p\u003e\n \u003cp\u003esuccinylcholine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eHAMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e3.0-Tesla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003efALFF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eSPM5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eMNI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eDu et al.,2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e11/35.36\u0026plusmn;10.49\u003c/p\u003e\n \u003cp\u003e/9/2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e11/42.73\u0026plusmn;11.88\u003c/p\u003e\n \u003cp\u003e/7/4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eMDD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;bifrontotemporal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003ePropofol/\u003c/p\u003e\n \u003cp\u003esuccinylcholine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e3.0-Tesla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eALFF\u003c/p\u003e\n \u003cp\u003e/fALFF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eSPM8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eMNI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eQian et al.,2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e46/38.20\u0026plusmn;11.84\u003c/p\u003e\n \u003cp\u003e/9/37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e38/38.03\u0026plusmn;11.43\u003c/p\u003e\n \u003cp\u003e/15/23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eMDD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003ebifrontal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eIsoproterenol/\u003c/p\u003e\n \u003cp\u003esuccinylcholine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eHAMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e3.0-Tesla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eALFF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eDPABIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eMNI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eWu et al.,2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e58/38.62\u0026plusmn;12.07\u003c/p\u003e\n \u003cp\u003e/15/43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e42/35.00\u0026plusmn;11.61\u003c/p\u003e\n \u003cp\u003e/8/34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eMDD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003ebifrontal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003ePropofol/\u003c/p\u003e\n \u003cp\u003esuccinylcholine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eHRSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e3.0-Tesla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eSPM12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eMNI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"13\" style=\"width: 945px;\"\u003e\n \u003cp\u003eVBM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eXu et al.,2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e11/39.27\u0026plusmn;7.84\u003c/p\u003e\n \u003cp\u003e/5/6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e12/39.08\u0026plusmn;7.4\u003c/p\u003e\n \u003cp\u003e/6/6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eMDD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e7.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;forehead\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003ePropofol/\u003c/p\u003e\n \u003cp\u003eSuccinylcholine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eHAMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e3.0-Tesla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eGMV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eSPM12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eTalairach\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eDepping et al.,2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e12/46.3\u0026plusmn;11.3\u003c/p\u003e\n \u003cp\u003e/4/8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e16/40.1\u0026plusmn;10.3\u003c/p\u003e\n \u003cp\u003e/8/8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eMDD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e10.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eRUL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eEtomidate/\u003c/p\u003e\n \u003cp\u003eSuccinylcholine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eHAMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e3.0-Tesla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eGMV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eSPM8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eMNI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eLong et al.,2025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e29/40.03\u0026plusmn;14.87\u003c/p\u003e\n \u003cp\u003e/11/18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e37/33.25\u0026plusmn;11.44\u003c/p\u003e\n \u003cp\u003e/16/21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eTRD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eRUL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eHDRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e3.0-Tesla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eGMV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eMRIQC toolbox\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eMNI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eRedlich et al.,2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e23/45.7\u0026plusmn;9.8\u003c/p\u003e\n \u003cp\u003e/9/14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e21/43.7\u0026plusmn;11.2\u003c/p\u003e\n \u003cp\u003e/8/13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eMDD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e9-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eRUL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eMethohexital sodium or propofol\u003c/p\u003e\n \u003cp\u003e/Succinylcholine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eBDI\u003c/p\u003e\n \u003cp\u003e/HDRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e3.0-Tesla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eGMV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eSPM8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eMNI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eSartorius et al.,2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e92/50.4\u0026plusmn;12.4\u003c/p\u003e\n \u003cp\u003e/50/42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e43/49.53\u0026plusmn;11.74\u003c/p\u003e\n \u003cp\u003e/21/22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eMDE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eRUL/\u003c/p\u003e\n \u003cp\u003eBIL/LART\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eHAMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e3.0-Tesla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eGMV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eSPM8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eMNI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eZhang et al.,2021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e34/40.53\u0026plusmn;13.44\u003c/p\u003e\n \u003cp\u003e/4/30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e33/35.27\u0026plusmn;11.56\u003c/p\u003e\n \u003cp\u003e7/26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eMDD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e6-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003ebifrontal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003ePropofol/\u003c/p\u003e\n \u003cp\u003eSuccinylcholine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eHDRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e3.0-Tesla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eGMV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eSPM8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eMNI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eWu et al.,2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e42/37.90\u0026plusmn;10.71\u003c/p\u003e\n \u003cp\u003e/9/23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e42/36.00\u0026plusmn;11.93\u003c/p\u003e\n \u003cp\u003e/10/32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eMDD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e6-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003ePropofol/\u003c/p\u003e\n \u003cp\u003eSuccinylcholine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eHRSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e3.0-Tesla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eGMV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eSPM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eMNI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eCano et al.,2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e15/42.93\u0026plusmn;14.87\u003c/p\u003e\n \u003cp\u003e/7/8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eTRD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e12.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eRUL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eMethohexital/\u003c/p\u003e\n \u003cp\u003eSuccinylcholine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eQIDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e3.0-Tesla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eGMV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eSPM12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eMNI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eOta et al.,2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003eJapan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e15/52.1\u0026plusmn;14.4\u003c/p\u003e\n \u003cp\u003e/9/6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eMDD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eBIL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003ePropofol/\u003c/p\u003e\n \u003cp\u003eSuccinylcholine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eHAMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e1.5-Tesla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eGMV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eSPM8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eMNI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eCano et al.,2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e12/59.17\u0026plusmn;8.02\u003c/p\u003e\n \u003cp\u003e6/6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e10/54.4\u0026plusmn;8.37\u003c/p\u003e\n \u003cp\u003e/5/5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eTRD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003ebifrontotemporal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003eThiopental/\u003c/p\u003e\n \u003cp\u003eSuccinylcholine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eHRSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e3.0-Tesla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eGMV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eSPM12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eMNI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eBorgers et al.,2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 96px;\"\u003e\n \u003cp\u003e17/47.47\u0026plusmn;10.94\u003c/p\u003e\n \u003cp\u003e/8/9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e21/40.57\u0026plusmn;12.98\u003c/p\u003e\n \u003cp\u003e/10/11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eMDD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e12.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003eRUL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 107px;\"\u003e\n \u003cp\u003e/\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 51px;\"\u003e\n \u003cp\u003eHDRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 59px;\"\u003e\n \u003cp\u003e3.0-Tesla\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003eGMV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eSPM12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003eMNI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviation: ECT, electroconvulsive therapy; HAMD, Hamilton Rating Scale for Depression; HDRS, Hamilton Depression Rating Scale; ALFF, Amplitude of low-frequency fluctuations; fALFF, fractional amplitude of low-frequency fluctuations; dALFF, dynamic amplitude of low-frequency fluctuation; ReHo, Regional homogeneity; MNI, Montreal Neurological Institute; MDD, major depressive disorder; MDE, major depressive episodes; TRD, treatment-resistant depression, RUL, right unilateral; BIL, bilateral, LART, left anterior right temporal, GMV, gray matter volume; VBM, voxel-based morphometry; Rs-fMRI, resting-state functional magnetic resonance imaging.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e: Meta-analysis results of VBM and resting-state functional brain activity differences before and after ECT treatment in patients with MDD.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"938\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003eLocal maximum\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eregion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003eMNI coordinates\u003c/p\u003e\n \u003cp\u003e(x, y, z)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003eSDM-Z\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 79px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 69px;\"\u003e\n \u003cp\u003eCluster size/\u0026nbsp;\u003c/p\u003e\n \u003cp\u003evoxels\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 338px;\"\u003e\n \u003cp\u003eBreakdown (Number of voxels)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003eJackknife\u0026nbsp;\u003c/p\u003e\n \u003cp\u003esensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003eHeterogeneity\u003c/p\u003e\n \u003cp\u003eI\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003ePublish bias Egger\u0026rsquo;s tests\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"16\" style=\"width: 938px;\"\u003e\n \u003cp\u003eResting-state functional activity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"16\" style=\"width: 938px;\"\u003e\n \u003cp\u003ePost-ECT \u0026gt; Pre-ECT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003eLeft inferior frontal gyrus, orbital part\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-38, 50, -12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003e4.520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.000003099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 69px;\"\u003e\n \u003cp\u003e166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 338px;\"\u003e\n \u003cp\u003eLeft middle frontal gyrus, orbital part (105)\u003c/p\u003e\n \u003cp\u003eLeft inferior frontal gyrus, orbital part (43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e9/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e1.94%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.897\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003eLeft angular gyrus, BA 39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e-48, -64, 36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003e3.585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.000168622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 69px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 338px;\"\u003e\n \u003cp\u003eLeft angular gyrus, BA 39 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e8/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.06%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.886\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003eRight cerebellum, hemispheric lobule VI, BA 37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e36, -48, -24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003e3.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.000461519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 69px;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 338px;\"\u003e\n \u003cp\u003eRight cerebellum, hemispheric lobule VI, BA 37 (22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e7/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e0.93%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.970\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003eRight middle frontal gyrus, orbital part\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 85px;\"\u003e\n \u003cp\u003e44, 48, -14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003e3.350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.000404716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 69px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 338px;\"\u003e\n \u003cp\u003eRight middle frontal gyrus, orbital part (20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 67px;\"\u003e\n \u003cp\u003e7/10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 61px;\"\u003e\n \u003cp\u003e2.62%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 73px;\"\u003e\n \u003cp\u003e0.933\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"16\" valign=\"top\" style=\"width: 938px;\"\u003e\n \u003cp\u003ePost-ECT \u0026lt; Pre-ECT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"16\" valign=\"top\" style=\"width: 938px;\"\u003e\n \u003cp\u003e(None)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"16\" valign=\"top\" style=\"width: 938px;\"\u003e\n \u003cp\u003eVBM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"16\" valign=\"top\" style=\"width: 938px;\"\u003e\n \u003cp\u003ePost-ECT \u0026gt; Pre-ECT\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003eRight temporal pole, superior temporal gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 78px;\"\u003e\n \u003cp\u003e32, 8, -26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003e5.658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 80px;\"\u003e\n \u003cp\u003e~0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 340px;\"\u003e\n \u003cp\u003eRight temporal pole, superior temporal gyrus (157)\u003c/p\u003e\n \u003cp\u003eRight amygdala (153)\u003c/p\u003e\n \u003cp\u003eRight striatum (69)\u003c/p\u003e\n \u003cp\u003eRight inferior network (88)\u003c/p\u003e\n \u003cp\u003eRight parahippocampal gyrus (82)\u003c/p\u003e\n \u003cp\u003eRight hippocampus (65)\u003c/p\u003e\n \u003cp\u003eAnterior commissure (23)\u003c/p\u003e\n \u003cp\u003eRight insula (17)\u003c/p\u003e\n \u003cp\u003eRight median network, cingulum (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e11/11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 62px;\"\u003e\n \u003cp\u003e7.89%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.627\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003eRight insula, BA 48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 78px;\"\u003e\n \u003cp\u003e46, -4, -2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003e5.759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 80px;\"\u003e\n \u003cp\u003e~0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 340px;\"\u003e\n \u003cp\u003eRight insula (421)\u003c/p\u003e\n \u003cp\u003eRight rolandic operculum (103)\u003c/p\u003e\n \u003cp\u003eRight superior temporal gyrus (87)\u003c/p\u003e\n \u003cp\u003eRight lenticular nucleus, putamen (81)\u003c/p\u003e\n \u003cp\u003eRight heschl gyrus (49)\u003c/p\u003e\n \u003cp\u003eCorpus callosum (45)\u003c/p\u003e\n \u003cp\u003eRight temporal pole, superior temporal gyrus (12)\u003c/p\u003e\n \u003cp\u003eRight fronto-insular tract\u0026nbsp;(11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e11/11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 62px;\"\u003e\n \u003cp\u003e4.15%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.789\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003eLeft parahippocampal gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 78px;\"\u003e\n \u003cp\u003e-14, 2, -20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003e5.655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 80px;\"\u003e\n \u003cp\u003e~0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 340px;\"\u003e\n \u003cp\u003eLeft parahippocampal gyrus (146)\u003c/p\u003e\n \u003cp\u003eLeft amygdala (29)\u003c/p\u003e\n \u003cp\u003eLeft hippocampus (22)\u003c/p\u003e\n \u003cp\u003eLeft fusiform gyrus (14)\u003c/p\u003e\n \u003cp\u003eLeft striatum (13)\u003c/p\u003e\n \u003cp\u003eLeft olfactory cortex (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e11/11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 62px;\"\u003e\n \u003cp\u003e2.36%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.897\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003eRight superior frontal gyrus, medial, BA 10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 78px;\"\u003e\n \u003cp\u003e4,46,0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003e4.561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.000002563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 340px;\"\u003e\n \u003cp\u003eRight anterior cingulate / paracingulate gyri (111)\u003c/p\u003e\n \u003cp\u003eLeft anterior cingulate / paracingulate gyri (107)\u003c/p\u003e\n \u003cp\u003eRight superior frontal gyrus, medial (80)\u003c/p\u003e\n \u003cp\u003eCorpus callosum (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e10/11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 62px;\"\u003e\n \u003cp\u003e4.72%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.344\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003eRight superior frontal gyrus, medial orbital, BA 11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 78px;\"\u003e\n \u003cp\u003e4, 26, -12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003e4.809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.000000775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 340px;\"\u003e\n \u003cp\u003eRight superior frontal gyrus, medial orbital (58)\u003c/p\u003e\n \u003cp\u003eRight gyrus rectus (44)\u003c/p\u003e\n \u003cp\u003eCorpus callosum (38)\u003c/p\u003e\n \u003cp\u003eRight striatum (32)\u003c/p\u003e\n \u003cp\u003eRight olfactory cortex (21)\u003c/p\u003e\n \u003cp\u003eLeft anterior cingulate / paracingulate gyri (19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e10/11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 62px;\"\u003e\n \u003cp\u003e7.90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.276\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003eLeft inferior parietal (excluding supramarginal and angular) gyri, BA 40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 78px;\"\u003e\n \u003cp\u003e-50, -52,46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003e4.628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.000001848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 340px;\"\u003e\n \u003cp\u003eLeft inferior parietal (excluding supramarginal and angular) gyri, BA 40 (225)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e9/11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 62px;\"\u003e\n \u003cp\u003e3.89%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.387\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003eLeft angular gyrus, BA 39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 78px;\"\u003e\n \u003cp\u003e-44, -62,38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003e4.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.000011206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 340px;\"\u003e\n \u003cp\u003eLeft angular gyrus (137)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e11/11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 62px;\"\u003e\n \u003cp\u003e2.04%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.715\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003eRight inferior parietal (excluding supramarginal and angular) gyri, BA 40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 78px;\"\u003e\n \u003cp\u003e42, -54,46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003e4.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.000007868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 340px;\"\u003e\n \u003cp\u003eRight inferior parietal (excluding supramarginal and angular) gyri (46)\u003c/p\u003e\n \u003cp\u003eRight angular gyrus (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e9/11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 62px;\"\u003e\n \u003cp\u003e1.52%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.809\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"16\" style=\"width: 938px;\"\u003e\n \u003cp\u003eVBM AND resting-state functional activity overlapping\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 104px;\"\u003e\n \u003cp\u003eLeft angular gyrus, BA 39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 78px;\"\u003e\n \u003cp\u003e-48, -64,36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003e3.585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 80px;\"\u003e\n \u003cp\u003e0.000168622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 340px;\"\u003e\n \u003cp\u003eLeft angular gyrus, BA 39 (32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 62px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: MDD, major depressive disorder; ECT, electroconvulsive therapy; MNI, Montreal Neurological Institute; BA, Brodman areas; VBM, voxel-based morphometry.\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"major depressive disorder, electroconvulsive therapy, resting-state fMRI, voxel-based morphometry, multimodal, meta-analysis","lastPublishedDoi":"10.21203/rs.3.rs-7327954/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7327954/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eElectroconvulsive therapy (ECT) is an effective treatment for major depressive disorder (MDD), yet its underlying mechanisms remain unclear. This study investigated the antidepressant effects of ECT through a multimodal neuro-image meta-analysis combined with functional, genetic, and neurotransmitter assessments. Resting-state functional magnetic resonance imaging (fMRI) and voxel-based morphometry (VBM) data were analyzed using seed-based d mapping with permutation of subject images (SDM-PSI) to identify changes in brain activation and gray matter volume (GMV) before and after ECT. Further analysis of regions with altered activation and GMV was conducted using Neurosynth, postmortem gene expression data, and receptor/transporter distribution maps to explore molecular underpinnings. The whole-brain multimodal meta-analysis included 291 patients from resting-state fMRI studies and 302 patients from VBM studies. Results showed increased activation and GMV in the left angular gyrus (AG) following ECT. Functional annotation linked the left AG to memory, attention, and perceptual processing. Gene expression analysis identified TFAP2B and OTX2 as the most highly expressed genes in this region. Notably, ECT-induced changes in brain activation and GMV were positively correlated with 5-HT1a receptor and dopamine transporter distribution. These findings suggest the left AG is a key region mediating ECT's effects. Neurotransmitter analysis further indicates that ECT may exert its antidepressant action by modulating neurotransmitter systems, offering insights into the neural and molecular basis of its therapeutic efficacy in MDD.\u003c/p\u003e","manuscriptTitle":"Multimodal neuroimaging changes and their behavioral, genetic, and neurotransmitter correlates in electroconvulsive therapy for major depressive disorder","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-15 02:41:19","doi":"10.21203/rs.3.rs-7327954/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e2896d9b-814a-4720-bf50-80fbe079d10c","owner":[],"postedDate":"October 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55604758,"name":"Health sciences/Diseases/Psychiatric disorders/Depression"},{"id":55604759,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2026-01-08T16:14:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-15 02:41:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7327954","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7327954","identity":"rs-7327954","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}