Distinct antidepressant therapies act on a common brain network

Domain: neuroscience
Published: 2026-01-03
Editorial outcome: major_revision

Abstract
Depression disorder is a global health crisis, due to its high prevalence. Yet clinical treatments are inadequate. Uncovering the neural correlates of depression remission is a vital step toward developing effective therapies. Using a method termed therapeutic network mapping, we performed secondary analyses on the data of 16 longitudinal experiments and found that the network of the amygdala was commonly modulated by three antidepressant therapies (pharmacotherapy, electroconvulsive therapy, and magnetic seizure therapy). This therapeutic network was i) validated by two independent treatment datasets of patients with depression and a prospective preclinical experiment on male rats, ii) associated with networks of depression-related genes, neurotransmitters, and cognitions, and iii) also the target network of deep brain stimulation and repetitive transcranial magnetic stimulation (rTMS). Finally, a longitudinal rTMS experiment indicated that strengthened therapeutic network connectivity is associated with higher depressant alleviation. Taken together, our findings demonstrate that distinct antidepressant therapies act on a common network with potential targets for developing effective therapies.

Introduction
Depression disorder represents a severe global health crisis, owing to its high prevalence1,2. It is a leading contributor to disability and premature mortality in the world3,4. Pharmacotherapy and psychotherapy are first-line treatments for depression disorder. However, the therapeutic effects typically require weeks to months of continuous treatment to manifest, and approximately 30% of patients do not respond to these treatments5. These refractory patients need more intensive interventions6, such as electroconvulsive therapy (ECT), repetitive transcranial magnetic stimulation (rTMS), and magnetic seizure therapy (MST). Although these clinical therapies are effective, their mechanisms remain largely unknown.

Longitudinal imaging studies can provide important clues to understanding the remission mechanism of depression7. Relative to the highly variable workflows for functional magnetic resonance imaging (fMRI)8, methods in structural studies are more homogeneous. However, the findings of such studies are inconsistent. For instance, MST was found to increase gray matter volume (GMV) in the bilateral paracentral lobule9, but ECT studies found restricted volume changes in the hippocampus–amygdala complex10,11 or widespread alterations in the brain12. The diversity of these findings between and within therapies has been a major obstacle to understanding the mechanism of depression remission. Regarding these findings as functionally isolated regions, coordinate-based meta-analytic approaches such as activation likelihood estimation are powerful tools for identifying the most consistent results13,14. Nonetheless, it is increasingly evident that brain regions linked to symptom remission are not acting in isolation. Rather, they are linked through distributed functional and structural brain circuits7,15–17. For instance, the targets of deep brain stimulation (DBS) and rTMS for depression belong to the same functional brain network18. Here, we hypothesized that the heterogeneous brain changes across different depression therapies aggregate in a common brain network.

Lesion network mapping is an effective way to determine the network effect of local changes19. This technique uses brain lesions as seeds to delineate the resting-state functional network of specific symptoms based on a normative connectome. This powerful and broadly applicable technique20 has been extended by replacing brain lesions with coordinates of brain structural atrophy21–23 and brain stimulation sites18,24 as seeds. Adapted from this technique, we propose a new method—called therapeutic network mapping—to identify the common network modulated by different therapies for depression. Briefly, we transformed the brain regions that longitudinally changed after treatment to networks through normative resting-state functional connectomes25,26 and explored the common connectivity across distinct therapies.

This study aimed to test whether distinct antidepressant therapies act on a common brain network. This network would be (i) derived from longitudinal pharmacotherapy, ECT, and MST experiments through therapeutic network mapping, (ii) validated by independent longitudinal datasets, (iii) annotated by depression-related genetic, molecular, and cognition networks, and (iv) tested for generalizability to DBS and rTMS.

Results
Longitudinal experiments
A systematic search identified 298 studies investigating GMV changes after depression treatment (Supplementary Fig. 1; Supplementary Table 1). Ultimately, 15 studies with 16 independent experiments (11 ECT experiments, four pharmacotherapy experiments, and one MTS experiment) fulfilled our criteria. Here, a “study” refers to an individual scientific paper and an “experiment” indicates a single contrast of interest in a given study27. Data between experiments were independent. Collectively, these experiments included 533 depression patients (296 females; mean age ± standard deviation [SD], 54.66 ± 15.90 years) and 169 healthy controls (95 females; mean age ± SD, 43.70 ± 14.27 years) (Supplementary Table 2). Coordinates with longitudinal VBM changes in these experiments were used to map the therapeutic network of depression (see the study design in Fig. 1).

A common network modulated by distinct therapies
In the 16 experiments, we found 144 coordinates with increased GMV after treatment. These coordinates were heterogeneously distributed across the whole brain (Fig. 2a). We transformed these coordinates into experiment-specific networks using a local-to-network mapping method on a healthy functional connectome (Fig. 2b)25,26. All 16 networks showed common connectivity within a subregion of the left amygdala (100% overlap). More than 85% of experiments showed common connectivity with the bilateral insula, hippocampus, parahippocampal gyrus, and amygdala (termed the sensitivity map; Fig. 3a). Part of this common connectivity was specific to depression, compared to random networks and non-depression networks (termed the specificity maps; two-sample t-test family-wise error [FWE] corrected P < 0.05; Fig. 3b; Supplementary Tables 3–5; Supplementary Fig. 2). Conjunction analysis identified two clusters (termed hub regions) in the bilateral amygdala that were both sensitive (>85% in the sensitivity map) and specific to depression remission (P < 0.05 in both specificity maps, Fig. 3c). We computed the whole-brain connectivity network of these hub regions using the local-to-network mapping method (Fig. 3d). The resulting map indicated the common network in which regions were associated with depression remission, thus it was hereafter referred to as depression therapeutic network (DTN).

The whole-brain pattern of this DTN could be robustly identified when different sizes was used to define the seed spheres, when the connectome dataset changed, when GMV-decreased coordinates were also included as seeds, when studies with moderate or severe patients were analyzed separately, when ECT and non-ECT experiments were analyzed separately, when only psychiatry disorders were used in the specificity analysis, when different thresholds were used for sensitivity analysis, and when including five additional experiments in updating the experiment search in literature (see Supplementary Figs. 3–4).

Retrospective clinical validation
Group-level validation. GMV and cortical thickness (CTh) are two common measures of brain morphology. We did not use CTh studies to establish the DTN because the vertex-wise CTh analysis did not cover the subcortical structures and cerebellum. Here, the CTh findings were adopted as an independent validation dataset. In the literature, we identified CTh increases after treatment reported in five depression experiments and six non-depression experiments (Supplementary Figs. 5–6; Supplementary Tables 6–7). The networks of cortical regions where CTh increased after treatment were computed using local-to-network mapping for each experiment. The DTN showed higher spatial similarity with the networks in depression experiments (mean ± SD = 0.67 ± 0.17, two-sided one-sample t-test, t = 8.65, df = 4, P = 1 × 10−3, 95% CI = [0.4530, 0.8814]) than in non-depression experiments (mean ± SD = 0.11 ± 0.20, two-sided one-sample t-test, t = 1.35, df = 5, P = 0.236, 95% CI = [−0.0975, 0.3123]; two-sided two-sample t-test, t = 4.99, df = 9, P = 8 × 10−4, 95% CI = [0.3058, 0.8139]; Fig. 4a).

Individual-level validation. The individual-level validation analysis was implemented in two independent datasets (AHMU data, n = 40; USTC data, n = 54) of depression patients treated with ECT28 and a control group (21 Parkinson’s patients29). For each patient, we first identified the local areas showing increased GMV after treatment and then transformed the local changes into networks through local-to-network mapping. The networks in both depression cohorts exhibited significantly spatial correlation with the DTN (AHMU, mean ± SD = 0.39 ± 0.30, two-sided one-sample t-test, t = 8.16, df = 39, P = 1 × 10−4, 95% CI = [0.2921, 0.4846]; USTC, mean ± SD = 0.47 ± 0.30, two-sided one-sample t-test, t = 11.55, df = 53, P = 1 × 10-4, 95% CI = [0.3899, 0.5537]), while the control networks did not (mean ± SD = 0.14 ± 0.35, t = 1.89, df = 20, P = 0.07, 95% CI = [−0.0151, 0.3008]). The DTN showed a higher similarity to the depression networks than to the control networks (USTC cohort, two-sided two-sample t-test, t = 4.08, df = 73, P = 1 × 10−4, 95% CI = [0.1682, 0.4897]; AHMU cohort, two-sided two-sample t-test, t = 2.87, df = 59, P = 6 × 10−3, 95% CI = [0.0740, 0.4166]; Fig. 4b).

Prospective preclinical validation
To test the causal role of the DTN function in depression treatment, we designed a prospective treatment experiment with a rat model of depression. The model rats (n = 20) displayed typical depressive-like phenotypes as evidenced by the forced swimming test (FST; two-sided paired t-test, t = 12.87, df = 19, P = 8 × 10−11, 95% CI = [125.6, 174.5]; Fig. 5a). We randomly assigned the rats into an experimental group (n = 10) and a control group (n = 10). Amygdala function was temporally inhibited in the experimental group by injecting muscimol. The amygdala in the control group were injected with saline (Fig. 5b). After 5 days of electroconvulsive shock (ECS) treatment, we found a significant time-by-group interaction (F[1,16] = 29.88, P = 5 × 10−5, 95% CI = [79.3, 179.7]; Fig. 5b) for the immobility time in the FST. Post hoc analysis indicated that the treatment effect of ECS on immobility time (control group, post-ECS vs. pre-ECS t = 5.77, adjusted P = 6 × 10−5) was blocked by inhibiting amygdala function (experiment group, post-ECS vs. pre-ECS, t = 1.96, adjusted P = 0.13). We additionally performed three supplementary experiments on depressant rats. See the method and results in Supplementary Preclinical Experiment.

Genetic and molecular mechanism
To explore the genetic mechanism involved in depression treatment, we tested the spatial similarity between the DTN and networks of depression-risk genes. We adopted 218 depression-risk genes from the largest genome-wide meta-analysis study30. A transcriptomic map of these genes was computed using the “abagen” toolbox (v.0.1.3; https://github.com/rmarkello/abagen) and then transformed into networks through local-to-network mapping. Ninety of these networks showed significant spatial correlation with the DTN (P FDR < 0.05 after Moran’s test of spatial autocorrelation31; Fig. 6a, Supplementary Table 8). Enrichment analysis in Metascape (http://metascape.org) further indicated that these genes were significantly enriched in six ontology terms: “positive regulation of immune response”, “cell morphogenesis”, “regulation of response to biotic stimulus”, “spinal cord development”, “cell-cell adhesion”, and “platelet formation” (P FDR < 0.05). Additionally, the DTN showed significant spatial correlation with eight neurotransmitter networks (Fig. 6b): three dopamine transporter/receptor, two serotonin transporters/receptors, one noradrenaline transporter, one vesicular acetylcholine transporter, and one fluorodopa (P FDR < 0.05 after Moran’s test).

Cognition relevance
To explore the cognitive relevance of the DTN network, we investigated the spatial similarity between the DTN and 123 cognitive networks. We derived the activation maps of these cognitive terms using Neurosynth (https://github.com/neurosynth/neurosynth). Among the 123 cognitive networks, 21 and 24 networks showed significant positive correlation and anti-correlation with the DTN, respectively (P FDR < 0.05 after Moran’s test; Fig. 6c, Supplementary Table 9). Functions of the 21 positive networks were associated with psychomotor retardation (such as “movement”, “facial expression”, and “speech” terms). The 24 anti-correlated networks were associated with memory (such as “episodic memory”, “memory retrieval”, and “recognition” terms) and executive functions (such as “decision”, “intention”, and “strategy” terms)32.

Generalizability
The DTN was derived from pharmacotherapy, ECT, and MST experiments. We tested whether the DTN is also the target of other antidepressant therapies, such as DBS and TMS. In the literature, we found 11 effective brain sites for DBS33 and 13 for rTMS treatment33–35 (Supplementary Tables 10–11). These DBS sites showed stronger positive connectivity in the DTN (mean ± SD = 14.3 ± 7.7) than random networks (P = 0.008 in permutation tests; Fig. 7a). The rTMS targets showed stronger anti-correlation (mean ± SD = −11.1 ± 9.5) in the DTN than random networks (P < 0.005 in permutation tests; Fig. 7b). On longitudinal data of 24 major depression patients who received rTMS around the positive node of the DTN36, we found that functional connectivity changes within DTN were significantly correlated with symptom improvement as measured by the 17-item Hamilton Depression Scale (HAMD-17) (partial Pearson’s correlation r = 0.56, df = 16, P = 0.016; Fig. 7c). No significant correlation was found between the DTN change and improvement in other symptoms, including the Hamilton Anxiety Scale, Patient Health Questionnaire-15, Ruminative Response Scale, Pain Vigilance and Awareness Questionnaire, Insomnia Severity Index, and Temporal Experience of Pleasure Scale (Supplementary Table 12). These findings suggest that the DTN is specific to depression symptoms.

Methods
This study was carried out under the Declaration of Helsinki and approved by the institutional review board of Anhui Medical University and exempted from obtaining informed consent based on the secondary use of research data.

Literature search
We performed a systematic literature search for longitudinal studies investigating GMV changes in depression patients after clinical treatment. Coordinates with increased GMV after treatment were extracted for further analysis. The search followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines68 (see Supplementary Table 1 for the search strategy). The inclusion criteria were as follows: (1) involving participants diagnosed with depression; (2) utilizing whole-brain VBM analyses; (3) reporting GMV longitudinal increases with treatment; (4) reporting coordinates in Montreal Neurological Institute (MNI) or Talairach space; and (5) performing whole-brain correction for multiple comparisons. The exclusion criteria were (1) studies that did not report results in the form of standardized coordinates; (2) cross-sectional studies; (3) results that were not significant in longitudinal treatment; and (4) studies that were a review or a meta-analysis. Talairach coordinates were converted into MNI space using GingerALE software (version 3.0.2 www.brainmap.org).

We also checked the reference lists and review articles to identify studies that we may have missed. Two researchers independently searched these articles. In case of inconsistent opinions, the two researchers reached a consensus through discussion under the guidance of the corresponding author.

Therapeutic network mapping
The therapeutic network mapping method is an adaptation of lesion network mapping19. It comprises sensitivity, specificity, conjunction analyses, and network mapping. A critical method throughout these analyses is local-to-network mapping, which computes the brain network of local areas using a population-based functional connectome (Fig. 2b). The local areas refer to coordinates with GMV increases after depression treatment or to a whole-brain map weighted by other metrics, such as the density of a given neurotransmitter, or by the expression level of a given gene. The functional connectome comprised resting-state fMRI data of 652 healthy Chinese participants (termed the healthy connectome; mean age ± standard error = 22.9 ± 5.53 years old, 334 females)25. Acquisition and preprocessing of the fMRI data have been described previously29,69. Local-to-network mapping involved two steps. First, the average (or weighted average) signal of the local areas was correlated with that of each brain voxel using Pearson’s correlation on the functional connectome data. Then, a one-sample t-test was used to identify voxels that were significantly connected to the local areas (voxel-wise FWE-corrected P < 0.01). This t-map was defined as the brain network of the local areas.

Sensitivity analysis. We created spherical regions of interest (ROIs; 6 mm in diameter) centered at each coordinate reported by depression experiments, and combined multiple coordinates from one experiment to generate an experiment-specific seed image. All the experiment-specific seeds were transformed to networks by local-to-network mapping. These networks were binarized (voxel-wise FWE P < 0.01) and overlapped (i.e., summed and divided by the number of experiments) to show the common connections across all experiments. The sensitivity map refers to the resulting map with a 0.85 threshold.

Specificity analysis. To determine the connections specific to depression remission, we compared the networks from 16 depression experiments to two groups of control networks. One was from non-depression experiments. Coordinates with GMV increases after treatment (the inclusion criteria and search strategies are described in the Supplementary Methods) were transformed to networks through local-to-network mapping. The other group comprised random networks. These were networks of randomly redistributed seeds from the 16 depression experiments. The control networks were compared to 16 depression networks by 5000 permutation tests. A cluster-level P < 0.05 (with the cluster-forming threshold at voxel-level FWE P < 0.001) was considered statistically significant.

Conjunction analysis. Finally, we performed a conjunction analysis to identify regions that were both sensitive (connected to >85% of depression remission regions) and specific (voxels surviving specificity analyses) to depression remission, as in previous studies70,71. The regions identified by the conjunction analysis were hub regions for depression remission.

Network mapping. The resting-state functional connectivity of hub regions defines a human brain circuit that encompasses GMV changes causing depression remission. We derived this circuit using the hub regions as a seed through local-to-network mapping. We refer to this hub connectivity map as our DTN (a t-map with voxel-wise FWE P < 0.01).

Robustness analysis
Multiple control analyses were conducted to test the robustness of the DTN. First, to ensure that the DTN was independent of the seed size, we repeated the analysis using a larger sphere size of 10 mm. Second, to ensure that the DTN was independent of the connectome data used for the local-to-network mapping, we re-computed the DTN using an older healthy connectome (mean age ± SD = 55.45 ± 13.71; age range: 30–89) and a connectome of 365 patients with depression. We also re-computed the DTN separately for the depression patients in pre-treatment and post-remission stages (Supplementary Methods). Third, ECT accounted for more than half of the experiments (11 in 16) that we used for therapeutic network mapping. To ensure that the DTN was not solely driven by ECT coordinates, we re-computed the DTN using ECT and non-ECT experiments, respectively. Fourth, to ensure that the DTN was independent of the threshold of the sensitivity map, we re-computed the DTN using another two thresholds, 80 and 90%. Fifth, to examine whether the DTN exhibits distinct features across different stages of depression, we re-computed the DTN using data from studies on moderate and severe depression separately. The severity of depressive disorder in all available longitudinal experiments was assessed using the Hamilton Depression Rating Scale (HAMD) or Montgomery–Åsberg Depression Rating Scale (MADRS). Depression severity was stratified according to the HAMD as moderate (scores, 17–23) and severe (scores, ≥24); according to the MADRS as moderate (scores, 20–34) and severe (scores, 35–60). Sixth, we re-performed the specificity analysis by restricting the control network to psychiatric disorders. Finally, we re-computed the DTN using seed images that combined both GMV-increased and -decreased coordinates. We also conducted an updated systematic literature search for longitudinal studies reporting GMV changes in patients with depression following clinical treatment, covering publications up to June 2025. More details of these robust analysis can be found in the Supplementary Tables 14–17.

Retrospective clinical validation
Group-level validation. We searched for longitudinal experiments investigating CTh changes after treatment (the inclusion criteria and detailed search strategies are described in the Supplemental Methods; Supplementary Tables 6–7) and classified them into depression and non-depression groups. Coordinates with increased CTh were extracted from these experiments and transformed into networks using local-to-network mapping. We hypothesized that the DTN would align more with the CTh networks in depression experiments than in non-depression experiments. To quantify this, we correlated the DTN with all CTh networks using spatial correlation (Pearson’s correlation) and compared the coefficient values between depression and non-depression groups using a two-sample t-test.

Individual-level validation. To validate the DTN at the individual level, we included longitudinal structural MRI data of depression (AHMU data, n = 40; USTC data, n = 54)28 and Parkinson’s disease (n = 21)29 from previous treatment studies. We first computed GMV changes after treatment using the SPM12 toolbox Computational Anatomy Toolbox12. We processed the data in the same way as in the previous study29. Then, the differential brain map of GMV (post-treatment vs. pre-treatment) of each patient was transformed into a network through local-to-network mapping. Finally, these networks were correlated with the DTN using voxel-wise spatial correlation (Pearson’s correlation). The correlation coefficients were compared between depression (AHMU and USTC data) and Parkinson’s disease groups using a two-sample t-test. We predicted that the DTN would have higher spatial similarity with the individual networks changed from depression treatment than from Parkinson’s disease treatment.

Prospective preclinical validation
Young male adult Sprague Dawley rats (6–8 weeks of age) were used for this experiment. First, the learned helplessness model was used to develop depression rats72, and the FST was used to assess the severity of depression. Over the next 5 days of treatment, we injected muscimol and saline to the bilateral basolateral amygdala for rats in the experimental and control groups, respectively. The injections were performed 30 min before each ECS. Finally, a post-treatment FST was performed 48 h after the last ECS. Ten rats each were allocated to the experimental and control groups. For detailed information regarding this main experiment and three supplementary experiments, see the Supplementary Preclinical Experiment.

Network annotation
Gene. We identified 269 risk genes of depression from the largest genome-wide association study (GWAS) for depression30. 218 of the 269 genes were available in the Allen Human Brain Atlas (AHBA) (http://human.brain-map.org). In line with our previous studies67,73, gene expression maps were constructed from the left brain of AHBA using the “abagen” toolbox (v.0.1.3; https://github.com/abagen). The AHBA is based on 3702 spatially distinct samples from six post-mortem brains (age = 42.50 ± 13.38 years; male/female = 5/1) and includes expression maps of 218 depression-risk genes30. The microarray probes were re-annotated74, and probes that did not match a valid Entrez ID were discarded. Probes with intensity less than a background in ≥50.00% of samples across donors were discarded, yielding 31,569 probes. When multiple probes indexed the expression of the same gene, we selected the probe with the most consistent pattern of regional variation across donors (that is, with the highest differential stability)75. Gene expression values were then normalized across tissue samples using an identical procedure and computed in left-brain ROIs. These ROIs included 160 regions from the HCP-MMH atlas76 and nine cerebellum and four subcortical regions from the automatic anatomical labeling template77. We chose only ROIs in the left hemisphere because the microarray probes in the right hemisphere are not available in two of the AHBA brains. Samples assigned to the same ROI were averaged separately for each donor, and then across donors, yielding expression maps of 218 risk genes. These expression maps were transformed into brain networks through local-to-network mapping.

Neurotransmitter. Neurotransmitter density maps were obtained from previous positron emission tomography (PET) studies of healthy individuals, including dopamine receptors (D1R 78, D2R 55), serotonin receptors (5-HT1AR 79, 5-HT1BR 80, 5-HT2AR 80, 5-HT4R 80), a dopamine transporter (DAT 81), a serotonin transporter (SERT 80), fluorodopa (F-DOPA; a reflection of presynaptic dopamine synthesis capacity), a γ-aminobutyric acid type A receptor (GABAAR 81), a norepinephrine transporter (NAT 82), a vesicular acetylcholine transporter (VAChT 55), and a metabotropic glutamate receptor type 5 (mGluR5 55). The distribution maps of neurotransmitter receptors/transporters were transformed into brain networks through local-to-network mapping.

Cognition. The neural correlates of different cognitive processes have been reported in many fMRI studies. A meta-analytic tool, Neurosynth, synthesized the findings in more than 15,000 studies searching for high-frequency keywords (https://github.com/neurosynth/neurosynth, using the volumetric association test maps83). In this study, we focused primarily on cognitive function and therefore limited the terms of interest to 123 cognitive or behavioral terms, which were selected from the Cognitive Atlas, a public ontology of cognitive science84. Terms ranged from umbrella terms (e.g., “memory”) to specific cognitive processes (e.g., “episodic memory”) and behaviors (e.g., “speech production”). The coordinates reported by Neurosynth in the Schaefer-100 atlas55,85 were transformed into brain networks through local-to-network mapping.

Null model for spatial correlation
The spatial correlation between networks was tested against a null model to correct for spatial autocorrelation using Moran’s test86 in the Neuromaps toolbox31 (https://github.com/netneurolab/neuromaps). Specifically, the spatial correlation was derived across 398 brain regions, including 360 cortical regions from the HCP-MMH atlas76 and 38 cerebellum and subcortical structures from an automatic anatomical labeling template77. A P-value was defined as the percentage of 5000 randomly permuted results that were stronger than the real result. Findings that survived Moran’s test were further corrected for FDR using the Benjamini–Hochberg method (PFDR) if multiple correlation analyses were performed.

Generalizability
We tested whether the 11 DBS and 13 rTMS sites aligned with the DTN by performing a permutation test. Specifically, we randomly redistributed the hub regions of DTN 2000 times and generated 2000 random networks through local-to-network mapping. We extracted the average value of the DBS and rTMS sites (spherical ROIs with a diameter of 6 mm) independently in these random networks. A P-value was defined as the percentage of 1000 random results that were stronger than the DTN.

To test whether the DTN can explain the outcome variability of depression treatment. We included 24 medical-refractory depression patients who received rTMS treatment from a previous study36. We computed the whole-brain functional connectivity (Pearson’s correlation) of the DTN (positive part) via rs-fMRI data before and following rTMS for each patient. The average functional connectivity changes within the DTN (positive part) were correlated with HAMD-17 improvement rates using partial Pearson’s correlation, controlling for age, gender, education, Hamilton Anxiety Scale, HAMA, and disease duration. To test the specificity, we also computed the correlations between the connectivity changes in the DTN and other symptom improvements using partial correlation analysis.

Statistical analysis
Statistical analyses were performed in MATLAB version 2018a (MathWorks). Image-based analyses were performed with SPM12 software (www.fil.ion.ucl.ac.uk/spm), and the results were corrected for multiple comparisons in the whole brain. For all analyses, a two-sided P-value < 0.05 after correction was considered significant.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The investigators of “Distinct antidepressant therapies act on a common brain network” assess structural brain changes associated with 16 unique ECT, MST, and pharmacotherapy investigations. These regions converged on a subregion of the left amygdala based on rs-fMRI from over 600 healthy controls labeled as the “depression remission network” (DRN). Retrospective validation included “spatial similarity” with cortical thickness changes and two independent ECT datasets. Prospective validation included a pre-translational mouse model with inhibited amygdala functioning, transcriptomic map of depression-risk genes, and cognitive relevance (activation maps from cognitive terms from Neurosynth). Finally, DBS and TMS targets aligned with DRN.\nThis well-written manuscript includes several strengths. The different layers (meta-analytic, genetic, cognitive, and pre-translational) and secondary analyses suggest convergence on the DRN. The remission network mapping also appears innovative.\nSeveral considerations for review:\n1. The manuscript is composed of secondary analyses. While this interesting from a hypothesis-generating perspective, this type of analyses does detract from the overall impact.\n2. The selected nodes related are pre-/post-structural changes and not related to antidepressant outcomes. The ECT-imaging literature demonstrate that the ECT-mediated volumetric change is widely distributed but was not related to depression outcomes (see 2019 Ousdal Biological Psychiatry). The authors focus on volumetric change from ECT, MST, and pharmacotherapy, but these changes do not appear to be related to depression outcomes. Since this is the foundation for the development of the DRN, this may be a “shaky” foundation.\n3. After selecting the nodes based on structural changes, the authors then use resting state fMRI from > 600 healthy controls to identify the anatomic area of convergence. Depressed patients may have different connectivity patterns both during and after the depressive episode that may not align with the DRN.\n4. The pre-translational model simply demonstrates the widely accepted importance of the amygdala in depression and does not add any relevance to the “network”, which is based on the amygdala forming a hub of volumetric changes in discrete brain areas.\n5. The genetic and cognitive relevance data do not provide direct support for the DRN.\n6. The TMS experiments are interesting but were not used to prospectively test the role of a DRN “target”.", "reviewer_2": "This is a well-written manuscript that makes an interesting adaptation to the lesion network mapping method. The authors introduce \"local-to-network mapping\" by first identifying regions showing GMV increases following treatment in depression, then using these coordinates as seeds to identify hubs that change concurrently with the seed regions. The resulting hub—bilateral amygdala—was then used to identify a functional network related to treatment effect in the normative connectome, termed the \"depressive remission network\" (DRN). The authors validate the DRN for repeatability and disease specificity, and explore its correlation with gene expression and cognition. There are several methodological concerns require attention before its conclusions can be fully supported.\n1.The authors' decision to focus exclusively on increased GMV as the local changes for DRN requires further justification. As noted in the introduction, different treatments produce bidirectional changes in brain volume. Why weren't both increased and decreased GMV sites included? While the authors mention conducting an analysis with GMV-decreased coordinates, these results are absent from both the main text and supplementary materials.\n2.The study's treatment-related GMV increases derive predominantly from ECT studies, which typically involve treatment-resistant patients with severe depression, while pharmacotherapy studies generally include patients with milder symptoms. This fundamental clinical heterogeneity raises critical questions about the DRN's universality across the depression severity spectrum. Have the authors investigated whether the DRN hub architecture remains consistent between treatment responders and non-responders, or between severely and mildly depressed patients? This distinction is crucial for determining whether the identified network represents a universal depression remission pathway or merely reflects treatment-specific neuroplastic changes that vary with illness severity.\n3.The age range of participants in studies reporting increased GMV was quite broad (30-70 years), while the normative connectome data came from individuals aged 20-30 years. Given the well-established impact of age on functional connectivity, how did the authors control for potential age-related confounds?\n4.For the specificity validation, the authors compared the DRN with random networks and non-depression networks. It would be more informative to restrict the non-depression cohorts to other mood disorders, which would better clarify whether the depression remission network is specific to depression rather than mood disorders more broadly.\n5.The authors link structural brain changes directly to therapeutic efficacy without empirically establishing this connection. When selecting studies showing GMV increases post-treatment, did they account for treatment response? Research like Redlich et al. (2016, JAMA Psychiatry) found that GMV increases didn't correlate with clinical improvement, suggesting these changes might be treatment byproducts rather than mechanisms. Why wasn't treatment response included in the review criteria? If non-responders show similar GMV changes as responders, these regions cannot legitimately constitute a 'remission network.' This raises the question: do treatment responders and non-responders share the same DRN architecture?\"\n6.For the MDD connectome data, please provide more detailed demographics, including disease duration, number of recurrent episodes, and medication status.\n7.On page 16, line 408, under \"prospective preclinical validation,\" supplementary preclinical experiments are mentioned but not included in the supplementary materials. Please provide this information.\n8.Could the authors elaborate on the potential clinical significance and applications of this method? What insights does this study offer to the depression research community?", "reviewer_3": "This is a relevant study exploring the effect of common antidepressant therapies (pharmacotherapy, electroconvulsive therapy, and magnetic seizure therapy) in brain networks. The data presented provide new evidence about the neurobiological basis of antidepressant therapy.\nI provide below a more detailed review of the preclinical experiment, as well as some additional general comments.\nGeneral:\n1. It is not clear in the Abstract and Introduction section that this is actually a literature review study (i.e., secondary use of research data). This should be clearer mentioned for the reader in the beginning sections of the manuscripts.\n2. Introduction, first paragraph: One additional aspect that could be highlighted is the delayed effect of antidepressant treatment, especially the pharmacotherapy.\n3. Discussion, fifth paragraph: Study limitation is mentioned in terms of absence of studies evaluating psychotherapy and DBS adn rTMS that fulfilled the inclusion criteria, so that these aspects were not assessed in the present study. However, it looks like the literature search was performed on march/2022 and February-March/2023. I was wondering if some study fulfilling your criteria for these parameters was published after that.\nPreclinical study:\nThe animal model for assessing depressive-like behaviors used the learned helplessness model (LHM) and forced swimming test (FST). Briefly, the LHM was used to develop depression in the rats, and the FST was used to assess the severity of depression. Some concerns are raised by this reviewer, especially regarding the experimental design.\n1. Figure 5: Graph indicate 8-9 animals/group in Post-ECS data. However, n=10/group is mentioned in the text for this study. Accordingly, pre-ECS data have n=10/group. The authors should include detailed description in case some animals were lost along the protocol.\n2. By the information provided in the text it looks like the animals in Pre-ECS condition were not subjected to BLA treatment (treatment onset was after that). If this is true, the data presentation should be reviewed since the way it is shown gives to the reader the wrong idea that the amygdala was inhibited both pre- and post-ECS.\n3. A control group not subjected to ECS or BLA treatment is missing. This group would provide relevant information regarding the long-term depressive-like effect of LHM assessed in the FST. In other words, saline+ECS group presents a decrease in immobility, but how the authors can ensure that this decrease is not related to a progressive time-dependent reduction of the depressive-like effect caused by LHM. Please clarify.\n4. The effect of repeated BLA treatment independent of ECS would also be relevant to provide information that the change in immobility in the FST is related to depressive-like behavior rather than an unspecific behavioral change in the FST.", "reviewer_4": "This manuscript identifies a network centered on the amygdala that the authors claim is specifically associated with symptomatic remission in depression across three therapeutic modalities: pharmacotherapy, magnetic seizure therapy, and electroconvulsive therapy. The study is impressively comprehensive. It builds upon data derived from a meta-analysis of multiple studies using longitudinal structural neuroimaging to assess brain changes associated with antidepressant treatments—potentially reflecting the neurobiological mechanisms of response. These data are then used for functional connectivity analyses employing robust, validated methods such as lesion network mapping. The authors further support their findings with a wide range of complementary analyses to test the robustness, generalizability, and specificity of the results, as well as preclinical studies aimed at deepening our understanding of the neurobiological mechanisms underlying treatment response. They also perform associations with gene expression maps, neurotransmitter distributions, and cognitive function profiles, thereby furthering a mechanistic interpretation of antidepressant efficacy.\nNonetheless, I have two major concerns that temper my enthusiasm for this work.\nFirst, the brain regions identified by the authors as reactive to antidepressant treatment in the individual studies do not show—or at least are not shown to show—a significant association with clinical remission. This is particularly common in studies of electroconvulsive therapy (the most represented treatment modality in this analysis), where anatomical changes are often not reliably associated with clinical improvement. While I assume that clinical remission was significant in all studies included (this should be also explicitly reported), this does not necessarily imply that remission was related to the structural changes the authors selected as the basis for their analysis. In light of this, the term \"remission network mapping\" used by the authors becomes somewhat misleading.\nSecond, the authors appear to overlook a critical distinction between structural plasticity (changes in neuronal morphology) and functional plasticity (changes in synaptic efficacy). The study begins with structural changes and moves on to examine functional connectivity, implicitly treating both types of plasticity as two sides of the same coin. This equivalence is problematic. Structural plasticity is more readily observable in certain brain regions (for instance, limbic areas—where the authors find their effects—tend to exhibit detectable structural changes more easily than other cortical regions; see Butz et al., 2009; Kolb et al., 2003; Neufeld et al., 2009). This introduces a bias into the functional connectivity analyses and may obscure functional plasticity in other brain regions that could also be relevant for clinical response. These other regions could serve as seeds of interest from which to define functionally relevant networks more accurately associated with antidepressant effects.\nBeyond these central concerns, I also have some reservations regarding the analytical strategies and their interpretation. For instance, I am not fully convinced that showing a higher similarity between maps derived from VBM and cortical thickness studies in depression—compared to other conditions—constitutes sufficient validation. While it is expected that such similarity might be greater in depression studies, this does not necessarily indicate meaningful convergence unless the magnitude and spatial distribution of the correlation are clearly reported and interpreted.\nLikewise, I question the strategy of comparing networks derived from ECT vs. non-ECT studies to the overall network as a way of addressing potential confounding. Since this comparison involves a part-to-whole relationship, it likely inflates the correlation. Would it not be more appropriate to compare the ECT and non-ECT networks directly, rather than in relation to the network incorporating all treatment types?\nRegarding the analysis of genetic and molecular mechanisms, the authors report a series of significant correlations, but these are only briefly discussed. The interpretation feels somewhat opportunistic—as though any significant result could have been included, regardless of its specific biological meaning.\nFinally, the brain regions forming the core of the identified network are barely discussed. A more detailed description of these regions and their potential roles in depressive pathology would significantly enrich the discussion and provide valuable insight into the implications of the findings."}, "author_response": "Response to reviewers:\nDistinct antidepressant therapies act on a common\nbrain network\nReviewer #1 (Remarks to the Author):\nThe investigators of “Distinct antidepressant therapies act on a common brain\nnetwork” assess structural brain changes associated with 16 unique ECT, MST,\nand pharmacotherapy investigations. These regions converged on a subregion\nof the left amygdala based on rs-fMRI from over 600 healthy controls labeled\nas the “depression remission network” (DRN). Retrospective validation\nincluded “spatial similarity” with cortical thickness changes and two\nindependent ECT datasets. Prospective validation included a pre-translational\nmouse model with inhibited amygdala functioning, transcriptomic map of\ndepression-risk genes, and cognitive relevance (activation maps from\ncognitive terms from Neurosynth). Finally, DBS and TMS targets aligned with\nDRN.\nThis well-written manuscript includes several strengths. The different layers\n(meta-analytic, genetic, cognitive, and pre-translational) and secondary\nanalyses suggest convergence on the DRN. The remission network mapping\nalso appears innovative.\nResponse:\nWe thank the reviewer for the kind and insightful comments. According to the\nsuggestions, we have performed additional experiments and analyses which\ngreatly improved the quality of our manuscript.\nSeveral considerations for review:\n1. The manuscript is composed of secondary analyses. While this interesting\nfrom a hypothesis-generating perspective, this type of analyses does detract\nfrom the overall impact.\nResponse:\nWe appreciate the reviewer's valuable suggestion. As indicated in the revised\nAbstract, this study presents secondary analyses of existing data. The\nmodifications have been implemented accordingly.\n“Depression disorder is a global health crisis, due to its high prevalence. Yet\nclinical treatments are inadequate. Uncovering the neural correlates of\ndepression remission is a vital step toward developing effective therapies.\nUsing a new method termed therapeutic network mapping, we performed\nsecondary analyses on the data of 16 longitudinal experiments and found that\nthe network of the amygdala was commonly modulated by three\nantidepressant therapies (pharmacotherapy, electroconvulsive therapy, and\nmagnetic seizure therapy).”\n2. The selected nodes related are pre-/post-structural changes and not related\nto antidepressant outcomes. The ECT-imaging literature demonstrate that the\nECT-mediated volumetric change is widely distributed but was not related to\ndepression outcomes (see 2019 Ousdal Biological Psychiatry). The authors\nfocus on volumetric change from ECT, MST, and pharmacotherapy, but these\nchanges do not appear to be related to depression outcomes. Since this is the\nfoundation for the development of the DRN, this may be a “shaky” foundation.\nResponse:\nWe appreciate the reviewer's insightful comment and fully agree with the\nreviewer that brain-phenotype association analysis represents a promising\napproach for investigating the mechanisms underlying clinical symptoms.\nAccording to your suggestion, we did a systematic search for\nimaging-symptom association studies. We identified 376 studies investigating\nthe association of brain volume changes and depression improvement.\nHowever, no significant association was found (Supplementary Table 13;\nSupplementary Figure 7). A major reason is the sample size in these studies is\nfar from the requirement of obtaining a reproducible result. Marek et al.'s (2022)\nstudy in Nature demonstrated that cohort sizes numbering in the thousands\nare required to establish reproducible brain-behavior relationships. Thus, the\nnegative finding (such as the work of Ousdal et al.) in brain-phenotype\nassociation analysis cannot exclude the possibility that the volume changes\nare involved in depression remission.\nAs the reviewer noted, few studies in literature found significant correlation\nfindings between imaging changes and symptom alleviation. The inadequate\nof data makes the network mapping analysis impossible. To avoid any\nmisinterpretation, we have changed the “depression remission network” to\n“depression therapeutic network (DTN)” and added a limitation at the revised\nmanuscript.\nRevised limitation:\n“In this study, we converged the heterogeneous longitudinal findings in\nantidepressant experiments on a common therapeutic network, which was\nfurther validated by retrospective and prospective experiments. From a\nnetwork perspective, we addressed the knowledge gaps among clinical\ntherapies, risk gene expression, neurotransmitter systems, and cognitive\nfunctions. Despite these advantages, some limitations should be mentioned.\nFirst, the finding of DTN is based on regions with GMV changes. A better basis\nwould be regions showing GMV change as well as association with symptom\nimprovement. However, few studies have reported significant association\nfindings62,63 (Supplementary Table 13; Supplementary Figure 7). This may be\npartly caused by the inadequate sample size which should be larger than\nthousands to observe reproducible brain-phenotype associations64. Another\nfuture direction is comparing treatment responders and non-responders, which\nmay further elucidate the relationship between DTN and symptom\nremission.….”\nSupplementary Table 13: Search strategy for imaging-symptom association\nstudies\nDatabase PubMed\nDate of search 2025.6.15\nSearch strategy Search term: (depressive disorder OR depression OR MDD OR major\ndepressive disorder) AND (voxel-based morphometry OR VBM OR gray\nmatter OR gray matter volume OR gray matter concentration) AND\n(treatment OR therapy OR therapeutic* OR antidepressant)\nPublication data: up to 2025.6\nSpecies: Humans\nLanguage: English\nItems found 376\nPRISMA 2020 flow diagram for new systematic reviews which included searches of databases and registers only\nIdentification of studies via databases and registers\nn\no ita\nc ifitn\ne\nR ec\nD\no\na\nrd\nta\ns\nb\nid\na\ne\nse\nn\ns\nti f\n(\nie\nn\nd\n=\nf r\n3\no\n7\nm\n6\n:\n)\nR\nsc\ne\nr\nc\ne D\n(\no\ne\nn\nu\nr\nn\nd\n=\np i\ns\nn\n0\nli g c\n)\nr e\n: a\nm\nte\no\nr\nv\ne\ne\nc\nd\no r\nb\nd\ne\ns\nf o\nre\nre\nm oved\nd\nI\nRecords screened Records excluded\n(n = 376) (n = 290)\nReports sought for retrieval Reports not retrieved\ng\nn\n(n = 51) (n = 0)\nin\ne\ne\nrc\nS\nR\n(n\ne\n=\np o\n5\nr\n1\nts\n)\nassessed for eligibility Rep\n1\no\n.\nr\nN\nts\no\ne\nn\nx\n-\nc\nd\nl\ne\nu\np\nd\nr\ne\ne\nd\ns s\n(n\nio n\n=\ns\n5\nu\n1\nb\n):\nj ects\n(n = 4)\n2. Non-longitudinal studies (n\n= 3)\n3. No analysis of structural\nchanges and symptom\nimprovement (n = 36)\n4. No significant results (n =\n8)\nd\ne\nd Studies included\nu\nlc (n = 0)\nn\nI\nSupplementary Figure 7: Work flow for identifying imaging-symptom association\nstudies.\nReference\nMarek et al., 2022 Nature Reproducible brain-wide association studies require\nthousands of individuals.\n3. After selecting the nodes based on structural changes, the authors then use\nresting state fMRI from > 600 healthy controls to identify the anatomic area of\nconvergence. Depressed patients may have different connectivity patterns\nboth during and after the depressive episode that may not align with the DRN.\nResponse:\nWe thank the reviewer for raising this important point. We agree that\ndepressed patients may exhibit altered functional connectivity both during\nacute episodes and after remission, which could, in principle, differ from\npatterns observed in healthy subjects. Our decision to use resting-state fMRI\ndata from a large cohort of over 600 healthy controls was motivated by the\ngoal of minimizing disease-related confounds. Using healthy connectome\nallows us to avoid network alterations driven by illness severity, medication\nstatus, or disease duration. In addition, the healthy dataset offered a\nsubstantially larger sample size compared to available patient dataset, thereby\nincreasing the robustness and reproducibility of the DTN. It is worth noting that\nthis approach is consistent with prior network mapping studies in\nneuropsychiatric populations, where healthy connectome data have been\nsuccessfully used to localize symptom or treatment targets networks (Siddiqi\nSH, et al. Nat Hum Behav. 2020; Fox MD. N Engl J Med. 2018). Nevertheless,\nwe also incorporated depression connectome data to further verify the\nrobustness of the DTN. In addition, in response to the reviewers’ comment, we\nfurther validated it using resting-state functional data from depressive patients\nbefore and after remission.\nThe revised method in main text.\n“Multiple control analyses were conducted to test the robustness of the\nDTN. First, to ensure that the DTN was independent of the seed size, we\nrepeated the analysis using a larger sphere size of 10 mm. Second, to ensure\nthat the DTN was independent of the connectome data used for the\nlocal-to-network mapping, we re-computed the DTN using a connectome of\n365 patients with depression. We also re-computed the DTN separately for the\ndepression patients in pre-treatment and post-remission stages (details can be\nfound in the Supplementary Methods). …...” (see the results in Supplementary\nFigure 4b)\nThe new connectome information in the Supplementary Method:\n“A total of 70 patients were included in longitudinal connectome dataset\n(52 females; mean age ± standard deviation, 37.24±13.63 years; mean\npre-treatment HAMD score ± standard deviation, 24.24±5.75; mean\npost-treatment HAMD score ± standard deviation, 6.27±4.18). all participants\nprovided written informed consent before the experiments. High-resolution\nstructural images of brain anatomy and resting-state functional MRI (rs-fMRI)\ndata were acquired for all individuals. The scanning parameters and\nprocessing of this data are consistent with those used for healthy connectome\ndata.”\nSupplementary Figure 4 The robust test of depression therapeutic network\n(DTN). The network pattern of DTN exhibited strong stability and reliability to\nmultiple parameter changes. a. The DTN showed strong stability when switching to\nusing the ECT (spatial correlation r = 0.988) and non-ECT (r = 0.867) experiments\ndata, with themselves also exhibiting high similarity (r = 0.868). b. The DTN showed\nstrong stability when switching to using different human connectome datasets,\nincluding connectome of healthy cohort over 30 years old (r = 0.975) and connectome\nof depressive patient cohort (r = 0.990). This similarity persisted when distinguishing\nbetween the pre- (r = 0.991) and post-treatment (r = 0.975) connectome, with\nthemselves also exhibiting high similarity (r = 0.987). c. The DTN pattern persisted\neven when the control group was restricted to patients with non-depressive psychiatric\ndisorders in the specificity analysis (r = 0.965). d. The DTN demonstrated high\nstability when the seed regions were altered, including updating the search for\ndepressant therapeutic experiment in literature (r = 0.995), merging the coordinates\nwith decreased volumes (r = 0.998), and changing the sphere size (10-mm diameter, r\n= 0.999). e. The DTN pattern persisted across different sensitivity map thresholds\n(threshold ≥80%, r = 0.989; threshold ≥90%, r = 0.986). f. The DTN remained highly\nstable across moderate (r = 0.933) and severe depression (r = 0.962) experiments,\nwith the themselves also exhibiting strong spatial similarity (r = 0.902). Given the\nlimited number of experiments in subgroup analyses of ECT vs. non-ECT and\nmoderate vs. severe depression, specificity analyses were not feasible; thus, regions\nexceeding 85% in the sensitivity map were defined as hub region to construct the\nDTN.\nReference:\nSiddiqi SH, et al. Nat Hum Behav. 2020, Brain stimulation and brain\nlesions converge on common causal circuits in neuropsychiatric disease.\nFox MD. N Engl J Med. 2018, Mapping Symptoms to Brain Networks with\nthe Human Connectome.\n4. The pre-translational model simply demonstrates the widely accepted\nimportance of the amygdala in depression and does not add any relevance to\nthe “network”, which is based on the amygdala forming a hub of volumetric\nchanges in discrete brain areas.\nResponse:\nWe thank the reviewer for this insightful comment. The depression therapeutic\nnetwork (DTN) is a probability map showing regions differentially related to the\ntherapeutic mechanism. The amygdala and visual cortex exhibit markedly\ndistinct importance in the DTN. While the amygdala shows the highest\nprobability, the visual cortex shows extremely low involvement in the DTN.\nIn the original preclinical experiment, we have demonstrated the importance of\namygdala in treatment. To validate the network specificity, we conducted an\nadditional control experiment on visual cortex in the revised version. In this\ncontrol experiment, visual cortex was temporally inhibited by injecting\nmuscimol. In line with the weak contribution of visual cortex in the DTN,\ninhibition of this control region failed to abolish the treatment effect induced by\nECS.\nWe have now included the description of this control experiment and its results\nin the revised “Supplementary Preclinical Experiment” (Figure S4). We also\ndiscussed the network specificity in the revised Discussion as follows.\n“In a prospective preclinical experiment, we found that the treatment effect of\nECS could be significantly blocked by inhibiting amygdala function. It\ndemonstrated a causal role of amygdala function in alleviating depression\nsymptoms. Conversely, inhibition of the visual cortex – a region with minimal\nDTN connectivity – did not alter ECS efficacy. These contrastive outcomes\nestablished the DTN as probability mapping of brain regions hierarchically\nassociated with antidepressant mechanisms. The high rank regions in the\nmedial DTN included the medial temporal lobe, orbitofrontal cortex, anterior\nand middle cingulate cortex, and insular lobule. All these are typically\ncomponent of the limbic system which is a core network of emotion and show\natrophy in the largest worldwide imaging study of depression49. Particularly,\nthe functional expansion in anterior cingulate cortex may serve a trait-like\nfeature of depression43. The high rank regions in the lateral DTN were\ndistributed across the multiple canonical brain networks50, including the ventral\nlateral prefrontal cortex in frontostriatal network, perirolandic regions in\nsomatomotor network, supramarginal gyrus and temporo-occipital junction in\nattention network. Despite the highlight of multiple regions, it should be noted\nthat only the amygdala constitutes the invariant core node in the\nantidepressant therapeutics, while the peripherally connected regions provided\nmodulatory rather than determinant inputs. This is why we inhibited the\namygdala function rather than other regions in the main preclinical experiment.\nBut it is worth testing whether simultaneously stimulation on multiple DTN\nregions could also achieve the goal of antidepressant. For instance, one may\nplace multiple electrodes to shape an electric field precisely targeting the\nDTN51.”\nFigure S4. Inhibiting the visual cortex does not alter the antidepressant-like effect of\nECS. Left panel: Immobility time in the FST significantly increased after induction of\nlearned helplessness (pair-t test, t = 27.12, P < 0.0001), confirming successful\nestablishment of the depression model. Right panel: No significant time-by-group\ninteraction effect was found in immobility time, indicating that suppression of the\nvisual cortex does not affect ECS efficacy. (from Supplementary Preclinical\nExperiment)\n5. The genetic and cognitive relevance data do not provide direct support for\nthe DRN.\nResponse:\nWe agree with the reviewer that validating or supporting the newly identified\nnetwork is important. Thus, we tested the DTN with independent data from\ngroup level, individual level and a prospective preclinical trial (Figure 4 and 5).\nMeanwhile, the functional annotation of a new network is also important, since\nthe DTN is distinct from canonical brain networks. Thus, we additionally\nperformed the genetic and cognitive analyses to explore its genetic\nmechanism and behavior relevance. In all, the “genetic and cognitive\nrelevance data” was designed for annotating the DTN rather than supporting\nthe DTN. To clarify this objective, we have expanded the discussion\naccordingly.\n“In spite of the clinical efficacy of antidepressant therapies, their mechanism of\naction is still largely unknown. We investigated the biological mechanism by\nassociating the therapeutic network with a series of networks of\ndepression-risk genes and pharmacotherapy-targeted neurotransmitter\nsystems. The therapeutic network significantly aligned with networks of 90\ngenes and 13 neurotransmitters. The genetic findings are consistent with the\npharmacogenomics findings that genetic variation is predictive of\nantidepressant response 52, 53. Enrichment analysis further summarized these\ngenes into six ontology terms. All these terms were associated with the\nplasticity of neural system, such as ‘immune response’, ‘cell morphogenesis’\nand ‘response to biotic stimulus’. The immune finding is in line with the\nevidence that dysregulated immune responses contributes to depression\npathogenesis and immune targets can improve depressive symptoms 54. The\nmorphogenesis may be a manifestation of the neural activation in\nantidepressant treatment55 and contribute to the increased GMV 10-12. These\nsuggest that part of the depression-risk genes may mediate the antidepressant\ntherapies by activating neuroplasticity. In molecular level, neurotransmitter\ntransporter/receptor may affect the response of different regions to\nantidepressant treatment 56. This is expected with pharmacotherapy, since\nsuch drugs are designed to modulate neurotransmitter systems, such as\nserotonin and noradrenaline transporters/receptors. Our findings suggest that\nthis neurotransmitter-mediated mechanism may be generalized to other\nnon-pharmacotherapies. For instance, ECT decreased the serotonin-1A\nreceptor binding in regions of the DTN, such as the amygdala, anterior\ncingulate cortex, hippocampus, and insula 57; the TMS-induced long-term\npotentiation and long-term depression were mediated by dopamine receptors\n58. In this study, we linked genetic and neurotransmitter factors through\nimaging networks. These multi-level factors may interact through molecular\ncrosstalk mechanisms to modulate depression pathogenesis and remission\ntrajectories. Future studies may trace their interaction in rodent models of\ndepression. Pharmacogenomics studies focusing on candidate genes involved\nin drug metabolism may show how the biological mechanism affect the\ntreatment outcomes in behavior and cognition59.”\n6. The TMS experiments are interesting but were not used to prospectively test\nthe role of a DRN “target”.\nResponse:\nWe thank the reviewer for this insight comment. Yes. The TMS experiment was\nnot prospectively designed to test the role of a depression therapeutic network\n(DTN) target. An ideal prospective test would be a double-blinded clinical trial\nwith two arms. One is DTN-guided precision rTMS. The other is traditional\nrTMS with the 5-cm rule. We have discussed this in the revised version as\nfollows.\n“In this study, we converged the heterogeneous longitudinal findings in\nantidepressant experiments on a common therapeutic network, which was\nfurther validated by retrospective and prospective experiments. From a\nnetwork perspective, we addressed the knowledge gaps among clinical\ntherapies, risk gene expression, neurotransmitter systems, and cognitive\nfunctions. Despite these advantages, some limitations should be mentioned.\nFirst,.…Finally, we incorporate a retrospective rather than prospective rTMS\ndata to show the clinical potential of DTN. An ideal prospective demonstration\nwould be a double-blinded randomized controlled trial with two parallel arms:\none employing DTN-guided precision targeting, and the other utilizing the\nconventional 5-cm rule for target localization.”\nReviewer #2 (Remarks to the Author):\nThis is a well-written manuscript that makes an interesting adaptation to the\nlesion network mapping method. The authors introduce \"local-to-network\nmapping\" by first identifying regions showing GMV increases following\ntreatment in depression, then using these coordinates as seeds to identify\nhubs that change concurrently with the seed regions. The resulting\nhub—bilateral amygdala—was then used to identify a functional network\nrelated to treatment effect in the normative connectome, termed the\n\"depressive remission network\" (DRN). The authors validate the DRN for\nrepeatability and disease specificity, and explore its correlation with gene\nexpression and cognition. There are several methodological concerns require\nattention before its conclusions can be fully supported.\nResponse:\nWe thank the reviewer for the kind comments and great suggestions which\nhave significantly improved the quality of our manuscript.\n1.The authors' decision to focus exclusively on increased GMV as the local\nchanges for DRN requires further justification. As noted in the introduction,\ndifferent treatments produce bidirectional changes in brain volume. Why\nweren't both increased and decreased GMV sites included? While the authors\nmention conducting an analysis with GMV-decreased coordinates, these\nresults are absent from both the main text and supplementary materials.\nResponse:\nWe sincerely thank the reviewer for this important and insightful comment. We\nacknowledge that treatment-induced brain volume changes can be\nbidirectional, and it is indeed important to consider both increases and\ndecreases in GMV. However, our initial focus on increased GMV was based on\ntwo primary reasons: (1) Among the 16 included treatment experiments, only 2\nreported clusters of decreased GMV following intervention. Due to the very\nlimited number of experiments, there were not enough coordinates to support\na meaningful network-level analysis of decreased GMV. Therefore, the\nexclusion of GMV-decrease data was due to factors beyond our control rather\nthan any theoretical bias. (2) Prior studies have suggested that increased GMV\nmay more directly reflect treatment-related neuroplasticity (e.g., Gbyl et al,\nActa Psychiatr Scand, 2018). Nevertheless, we recognize the importance of\nthe minority findings, namely the decreases in gray matter volume following\ntreatment. In response to the reviewer’s suggestion, the coordinates indicating\nGMV decreases from the two experiments were also included and analyzed\ntogether with those indicating increases. We have made a significant revision\nto the relevant sections. The description of the analysis now reads.\n“Multiple control analyses were conducted to test the robustness of the\nDTN. First, to ensure that the DTN was independent of the seed size, we\nrepeated the analysis using a larger sphere size of 10 mm. …… Finally, we\nre-computed the DTN using seed images that combined both GMV-increased\nand -decreased coordinates.” (see the results in Supplementary Figure 4d)\nSupplementary Figure 4. The robust test of depression therapeutic network\n(DTN). The network pattern of DTN exhibited strong stability and reliability to\nmultiple parameter changes. a. The DTN showed strong stability when switching to\nusing the ECT (spatial correlation r = 0.988) and non-ECT (r = 0.867) experiments\ndata, with themselves also exhibiting high similarity (r = 0.868). b. The DTN showed\nstrong stability when switching to using different human connectome datasets,\nincluding connectome of healthy cohort over 30 years old (r = 0.975) and connectome\nof depressive patient cohort (r = 0.990). This similarity persisted when distinguishing\nbetween the pre- (r = 0.991) and post-treatment (r = 0.975) connectome, with\nthemselves also exhibiting high similarity (r = 0.987). c. The DTN pattern persisted\neven when the control group was restricted to patients with non-depressive psychiatric\ndisorders in the specificity analysis (r = 0.965). d. The DTN demonstrated high\nstability when the seed regions were altered, including updating the search for\ndepressant therapeutic experiment in literature (r = 0.995), merging the coordinates\nwith decreased volumes (r = 0.998), and changing the sphere size (10-mm diameter, r\n= 0.999). e. The DTN pattern persisted across different sensitivity map thresholds\n(threshold ≥80%, r = 0.989; threshold ≥90%, r = 0.986). f. The DTN remained highly\nstable across moderate (r = 0.933) and severe depression (r = 0.962) experiments,\nwith the themselves also exhibiting strong spatial similarity (r = 0.902). Given the\nlimited number of experiments in subgroup analyses of ECT vs. non-ECT and\nmoderate vs. severe depression, specificity analyses were not feasible; thus, regions\nexceeding 85% in the sensitivity map were defined as hub region to construct the\nDTN.\nReference\nGbyl et al, Acta Psychiatr Scand, 2018, Electroconvulsive therapy\nincreases brain volume in major depression: a systematic review and\nmeta-analysis\n2.The study's treatment-related GMV increases derive predominantly from\nECT studies, which typically involve treatment-resistant patients with severe\ndepression, while pharmacotherapy studies generally include patients with\nmilder symptoms. This fundamental clinical heterogeneity raises critical\nquestions about the DRN's universality across the depression severity\nspectrum. Have the authors investigated whether the DRN hub architecture\nremains consistent between treatment responders and non-responders, or\nbetween severely and mildly depressed patients? This distinction is crucial for\ndetermining whether the identified network represents a universal depression\nremission pathway or merely reflects treatment-specific neuroplastic changes\nthat vary with illness severity.\nResponse:\nWe thank the reviewer for this insightful comment. We also consider it\nimportant to investigate the therapeutic network mechanisms in\ntreatment-responsive patients. However, the longitudinal experiments did not\nprovide subgroup data (i.e., responder vs. non-responder) supporting us to\ndirectly assess the consistency of the depressant therapeutic network (we\nrenamed the depression remission network) hub architecture across these\nclinically distinct populations. In contrast, the severity of depressive disorder\nwas available in all longitudinal experiments assessed by the Hamilton\nDepression Rating Scale (HAMD) or Montgomery–Åsberg Depression Rating\nScale (MADRS).\nDepression severity was stratified according to the HAMD as follows\n(Zimmerman et al., J Affect Disord, 2013): mild (scores, 8–16), moderate\n(scores, 17–23), and severe (scores, ≥24). Based on the MADRS (Snaith et al.,\nBr J Psychiatry, 1986), severity was stratified as: mild (scores, 7–19),\nmoderate (scores, 20–34), and severe (scores, 35–60). In line with reviewers’\nsuggestion, we stratified the depression cohort into moderate and severe\nsubgroups and delineated the therapeutic networks for each separately. We\nthen correlated each of these subgroup-specific networks with the DTN and\nfurther performed a direct comparison between the moderate and severe\nnetworks. These analyses consistently demonstrated that the DTN remained\nhighly stable across depression severity spectrum, suggesting that the\nidentified DTN is not confined to a particular illness stage but instead reflects a\nrobust and shared mechanisms.\nWe have made a significant revision to the relevant sections. The description\nof the analysis now reads.\n“Multiple control analyses were conducted to test the robustness of the\nDTN. …... Fifth, to examine whether the DTN exhibits distinct features across\ndifferent stages of depression, we re-computed the DTN using data from\nstudies on moderate and severe depression separately. The severity of\ndepressive disorder in all available longitudinal experiments was assessed\nusing the Hamilton Depression Rating Scale (HAMD) or Montgomery–Åsberg\nDepression Rating Scale (MADRS). Depression severity was stratified\naccording to the HAMD as moderate (scores, 17–23) and severe (scores, ≥24);\naccording to the MADRS as moderate (scores, 20–34) and severe (scores,\n35–60). …...” (see the results in Supplementary Figure 4f)\nSupplementary Figure 4. The robust test of depression therapeutic network\n(DTN). The network pattern of DTN exhibited strong stability and reliability to\nmultiple parameter changes. a. The DTN showed strong stability when switching to\nusing the ECT (spatial correlation r = 0.988) and non-ECT (r = 0.867) experiments\ndata, with themselves also exhibiting high similarity (r = 0.868). b. The DTN showed\nstrong stability when switching to using different human connectome datasets,\nincluding connectome of healthy cohort over 30 years old (r = 0.975) and connectome\nof depressive patient cohort (r = 0.990). This similarity persisted when distinguishing\nbetween the pre- (r = 0.991) and post-treatment (r = 0.975) connectome, with\nthemselves also exhibiting high similarity (r = 0.987). c. The DTN pattern persisted\neven when the control group was restricted to patients with non-depressive psychiatric\ndisorders in the specificity analysis (r = 0.965). d. The DTN demonstrated high\nstability when the seed regions were altered, including updating the search for\ndepressant therapeutic experiment in literature (r = 0.995), merging the coordinates\nwith decreased volumes (r = 0.998), and changing the sphere size (10-mm diameter, r\n= 0.999). e. The DTN pattern persisted across different sensitivity map thresholds\n(threshold ≥80%, r = 0.989; threshold ≥90%, r = 0.986). f. The DTN remained highly\nstable across moderate (r = 0.933) and severe depression (r = 0.962) experiments,\nwith the themselves also exhibiting strong spatial similarity (r = 0.902). Given the\nlimited number of experiments in subgroup analyses of ECT vs. non-ECT and\nmoderate vs. severe depression, specificity analyses were not feasible; thus, regions\nexceeding 85% in the sensitivity map were defined as hub region to construct the\nDTN.\nReference:\nZimmerman et al., J Affect Disord, 2013, Severity classification on the\nHamilton Depression Rating Scale.\nSnaith et al., Br J Psychiatry, 1986, Grade scores of the\nMontgomery-Asberg Depression and the Clinical Anxiety Scales.\n3.The age range of participants in studies reporting increased GMV was quite\nbroad (30-70 years), while the normative connectome data came from\nindividuals aged 20-30 years. Given the well-established impact of age on\nfunctional connectivity, how did the authors control for potential age-related\nconfounds?\nResponse:\nWe appreciate the reviewer’s thoughtful comment and fully agree that age can\ninfluence resting-state functional connectivity. In line with the reviewers’\nsuggestions, we conducted a control analysis in which the healthy connectome\ndataset was replaced with one matched in age range (middle to late adulthood)\nto the depressive patients. The description of the control analysis now reads.\nThe revised method of this analysis:\n“Multiple control analyses were conducted to test the robustness of the\nDTN. First, to ensure that the DTN was independent of the seed size, we\nrepeated the analysis using a larger sphere size of 10 mm. Second, to ensure\nthat the DTN was independent of the connectome data used for the\nlocal-to-network mapping, we re-computed the DTN using an older healthy\nconnectome (mean age ± SD = 55.45 ± 13.71; age range:30–89) and a\nconnectome of 365 patients with depression.” (see the results in\nSupplementary Figure 4b)\nSupplementary Figure 4. The robust test of depression therapeutic network\n(DTN). The network pattern of DTN exhibited strong stability and reliability to\nmultiple parameter changes. a. The DTN showed strong stability when switching to\nusing the ECT (spatial correlation r = 0.988) and non-ECT (r = 0.867) experiments\ndata, with themselves also exhibiting high similarity (r = 0.868). b. The DTN showed\nstrong stability when switching to using different human connectome datasets,\nincluding connectome of healthy cohort over 30 years old (r = 0.975) and connectome\nof depressive patient cohort (r = 0.990). This similarity persisted when distinguishing\nbetween the pre- (r = 0.991) and post-treatment (r = 0.975) connectome, with\nthemselves also exhibiting high similarity (r = 0.987). c. The DTN pattern persisted\neven when the control group was restricted to patients with non-depressive psychiatric\ndisorders in the specificity analysis (r = 0.965). d. The DTN demonstrated high\nstability when the seed regions were altered, including updating the search for\ndepressant therapeutic experiment in literature (r = 0.995), merging the coordinates\nwith decreased volumes (r = 0.998), and changing the sphere size (10-mm diameter, r\n= 0.999). e. The DTN pattern persisted across different sensitivity map thresholds\n(threshold ≥80%, r = 0.989; threshold ≥90%, r = 0.986). f. The DTN remained highly\nstable across moderate (r = 0.933) and severe depression (r = 0.962) experiments,\nwith the themselves also exhibiting strong spatial similarity (r = 0.902). Given the\nlimited number of experiments in subgroup analyses of ECT vs. non-ECT and\nmoderate vs. severe depression, specificity analyses were not feasible; thus, regions\nexceeding 85% in the sensitivity map were defined as hub region to construct the\nDTN.\n4.For the specificity validation, the authors compared the DRN with random\nnetworks and non-depression networks. It would be more informative to\nrestrict the non-depression cohorts to other mood disorders, which would\nbetter clarify whether the depression remission network is specific to\ndepression rather than mood disorders more broadly.\nResponse:\nWe thank the reviewer for this valuable suggestion. It would be more\ninformative to restrict the non-depression cohorts to other mood disorders.\nHowever, no longitudinal study of mood disorders was found when we\nsystematically searched for control studies in literature. Since mood problem is\ncommon in psychiatry disorders, we conducted a supplementary analysis by\nrestricting the non-depression cohort to psychiatric disorders only.\nThe method of this analysis:\n“Multiple control analyses were conducted to test the robustness of the\nDTN……Sixth, we re-performed the specificity analysis by restricting the\ncontrol network to non-depression psychiatric disorders. Finally, although we\nstrongly believed that GMV increases rather than decreases are likely the\nneural basis of depression remission, we re-computed the DTN using seed\nimages that combined both GMV-increased and -decreased coordinates.” (see\nthe results in Supplementary Figure 4c)\nSupplementary Figure 4. The robust test of depression therapeutic network\n(DTN). The network pattern of DTN exhibited strong stability and reliability to\nmultiple parameter changes. a. The DTN showed strong stability when switching to\nusing the ECT (spatial correlation r = 0.988) and non-ECT (r = 0.867) experiments\ndata, with themselves also exhibiting high similarity (r = 0.868). b. The DTN showed\nstrong stability when switching to using different human connectome datasets,\nincluding connectome of healthy cohort over 30 years old (r = 0.975) and connectome\nof depressive patient cohort (r = 0.990). This similarity persisted when distinguishing\nbetween the pre- (r = 0.991) and post-treatment (r = 0.975) connectome, with\nthemselves also exhibiting high similarity (r = 0.987). c. The DTN pattern persisted\neven when the control group was restricted to patients with non-depressive psychiatric\ndisorders in the specificity analysis (r = 0.965). d. The DTN demonstrated high\nstability when the seed regions were altered, including updating the search for\ndepressant therapeutic experiment in literature (r = 0.995), merging the coordinates\nwith decreased volumes (r = 0.998), and changing the sphere size (10-mm diameter, r\n= 0.999). e. The DTN pattern persisted across different sensitivity map thresholds\n(threshold ≥80%, r = 0.989; threshold ≥90%, r = 0.986). f. The DTN remained highly\nstable across moderate (r = 0.933) and severe depression (r = 0.962) experiments,\nwith the themselves also exhibiting strong spatial similarity (r = 0.902). Given the\nlimited number of experiments in subgroup analyses of ECT vs. non-ECT and\nmoderate vs. severe depression, specificity analyses were not feasible; thus, regions\nexceeding 85% in the sensitivity map were defined as hub region to construct the\nDTN.\n5.The authors link structural brain changes directly to therapeutic efficacy\nwithout empirically establishing this connection. When selecting studies\nshowing GMV increases post-treatment, did they account for treatment\nresponse? Research like Redlich et al. (2016, JAMA Psychiatry) found that\nGMV increases didn't correlate with clinical improvement, suggesting these\nchanges might be treatment byproducts rather than mechanisms. Why wasn't\ntreatment response included in the review criteria? If non-responders show\nsimilar GMV changes as responders, these regions cannot legitimately\nconstitute a 'remission network.' This raises the question: do treatment\nresponders and non-responders share the same DRN architecture?\"\nResponse:\nWe thank the reviewer for raising these important points. In our study,\ntreatment response was not used as a formal inclusion criterion for identifying\nGMV increases because all longitudinal experiments included in our study did\nnot provide separate comparative analyses for responders and\nnon-responders. We therefore did not impose treatment response as an\ninclusion criterion. Nevertheless, all experiments included in our study reported\na significant group-level reduction in depression severity following treatment\n(see Supplementary Table 2).\nWe fully agree with the reviewer that brain-phenotype association analysis\nrepresents a promising approach for investigating the mechanisms underlying\nclinical symptoms. According to your suggestion, we did a systematic search\nfor imaging-symptom association studies. We identified 376 studies\ninvestigating the association of brain volume changes and depression\nimprovement. However, no significant association was found (Supplementary\nTable 13; Supplementary Figure 7). A major reason is the sample size in these\nstudies is far from the requirement of obtaining a reproducible result. Marek et\nal.'s (2022) study in Nature demonstrated that cohort sizes numbering in the\nthousands are required to establish reproducible brain-behavior relationships. Thus,\nthe negative finding in brain-phenotype association analysis cannot exclude the\npossibility that the volume changes are involved in depression remission.\nAs the reviewer noted, few studies in literature found significant correlation\nfindings between imaging changes and symptom alleviation. The inadequate\nof data makes the network mapping analysis impossible. To avoid any\nmisinterpretation, we have changed the “depression remission network” to\n“depression therapeutic network (DTN)” and added a limitation at the revised\nmanuscript.\nRevised limitation:\n“In this study, we converged the heterogeneous longitudinal findings in\nantidepressant experiments on a common therapeutic network, which was\nfurther validated by retrospective and prospective experiments. From a\nnetwork perspective, we addressed the knowledge gaps among clinical\ntherapies, risk gene expression, neurotransmitter systems, and cognitive\nfunctions. Despite these advantages, some limitations should be mentioned.\nFirst, the finding of DTN is based on regions with GMV changes. A better basis\nwould be regions showing GMV change as well as association with symptom\nimprovement. However, few studies reported significant association\nfindings62,63 (Supplementary Table 13; Supplementary Figure 7). This may be\npartly caused by the inadequate sample size which should be larger than\nthousands to observe reproducible brain-phenotype associations64. Another\nfuture direction is comparing treatment responders and non-responders, which\nmay further elucidate the relationship between DTN and symptom\nremission.…….”\nSupplementary Table 13: Search strategy for imaging-symptom association\nstudies\nDatabase PubMed\nDate of search 2025.6.15\nSearch strategy Search term: (depressive disorder OR depression OR MDD OR major\ndepressive disorder) AND (voxel-based morphometry OR VBM OR gray\nmatter OR gray matter volume OR gray matter concentration) AND\n(treatment OR therapy OR therapeutic* OR antidepressant)\nPublication data: up to 2025.6\nSpecies: Humans\nLanguage: English\nItems found 376\nPRISMA 2020 flow diagram for new systematic reviews which included searches of databases and registers only\nIdentification of studies via databases and registers\nn\no ita\nc ifitn\ne\nR ec\nD\no\na\nrd\nta\ns\nb\nid\na\ne\nse\nn\ns\nti f\n(\nie\nn\nd\n=\nf r\n3\no\n7\nm\n6\n:\n)\nR\nsc\ne\nr\nc\ne D\n(\no\ne\nn\nu\nr\nn\nd\n=\np i\ns\nn\n0\nli g c\n)\nr e\n: a\nm\nte\no\nr\nv\ne\ne\nc\nd\no r\nb\nd\ne\ns\nf o\nre\nre\nm oved\nd\nI\nRecords screened Records excluded\n(n = 376) (n = 290)\nReports sought for retrieval Reports not retrieved\ng\nn\n(n = 51) (n = 0)\nin\ne\ne\nrc\nS\nReports assessed for eligibility Reports excluded (n = 17):\n(n = 22) 1.No whole-brain voxel-\nbased analysis (n = 3)\n2.No longitudinal study (n =8)\n3.No sufficient report of\nresults (n = 6)\nd\ne d Studies included\nu lc (n = 0)\nn\nI\nSupplementary Figure 7: Work flow for identifying imaging-symptom association\nstudies.\nReference\nMarek et al., 2022 Nature Reproducible brain-wide association studies require\nthousands of individuals.\n6.For the MDD connectome data, please provide more detailed demographics,\nincluding disease duration, number of recurrent episodes, and medication\nstatus.\nResponse:\nWe apologize that these points were not made clear in the manuscript. We\nhave made a significant revision to the relevant sections.\nSupplementary Table 16: Demographic and clinical variables of MDD connectome\nDemographic and clinical variables Recurrent First-episode\nSample size 281 84\nSex: female (%) 191 (68) 64 (76)\nMean age ± SD (years) 28.92±12.10 26.73±13.25\nMean Disease duration ± SD (Month) 56.37±64.49 11.29±15.15\nMean HAMD Score ± SD 20.77±6.30 24.02±5.53\nMean number episodes ± SD 2.67±1.54 -\nMedication use (%) 233 (83) 28 (33)\n7. On page 16, line 408, under \"prospective preclinical validation,\"\nsupplementary preclinical experiments are mentioned but not included in the\nsupplementary materials. Please provide this information.\nResponse:\nWe apologize for the missing of “supplementary preclinical experiments” file. It\nhas been uploaded in the system this time.\n8. Could the authors elaborate on the potential clinical significance and\napplications of this method? What insights does this study offer to the\ndepression research community?\nResponse:\nWe thank the reviewer for this insightful suggestion. Despite the widespread\nclinical application of various antidepressant therapies, the underlying\nmechanisms of action remain poorly understood, significantly impeding\ntreatment optimization efforts. Our study makes a significant contribution by\nelucidating a network-based mechanism across antidepressant therapies. By\nprecisely modulate the depression network, future studies may develop more\neffective antidepressant therapies. Please see the added discussion as\nfollows.\n“……Here, our finding unified five different antidepressant therapies in a\ncommon network mechanism. It provides a basis to optimize the current\nelectromagnetic therapies. According to a newly proposed network-based\nframework41, the identification of this DTN is the first step to develop a\npatient-specific treatment targets that address the unique symptom. Future\nstudies should adapt this group-level network to a personalized one by using\nindividual fMRI data42, 43. Then, the personalized DTN could be precisely\nmodulated by optimizing the location of electrode plates in transcranial\nelectronic stimulation or the coil position of rTMS.”\nReviewer #3 (Remarks to the Author):\nThis is a relevant study exploring the effect of common antidepressant\ntherapies (pharmacotherapy, electroconvulsive therapy, and magnetic seizure\ntherapy) in brain networks. The data presented provide new evidence about\nthe neurobiological basis of antidepressant therapy. I provide below a more\ndetailed review of the preclinical experiment, as well as some additional\ngeneral comments.\nResponse:\nWe thank the reviewer for the kind comments and great suggestions which\nhave significantly improved the quality of our manuscript.\nGeneral:\n1. It is not clear in the Abstract and Introduction section that this is actually a\nliterature review study (i.e., secondary use of research data). This should be\nclearer mentioned for the reader in the beginning sections of the manuscripts.\nResponse:\nThank you for this valuable suggestion. In the revised Abstract, we have\nclearly stated that this manuscript is composed of secondary analyses. Please\nsee the modified version below.\n“Depression disorder is a global health crisis, due to its high prevalence. Yet\nclinical treatments are inadequate. Uncovering the neural correlates of\ndepression remission is a vital step toward developing effective therapies.\nUsing a new method termed therapeutic network mapping, we performed a\nsecondary analysis on the data of 16 longitudinal experiments and found that\nthe network of the amygdala was commonly modulated by three\nantidepressant therapies (pharmacotherapy, electroconvulsive therapy, and\nmagnetic seizure therapy).”\n2. Introduction, first paragraph: One additional aspect that could be highlighted\nis the delayed effect of antidepressant treatment, especially the\npharmacotherapy.\nResponse:\nThank you for this valuable suggestion. We have added this limitation of\ncurrent antidepressant treatment in the first paragraph as follows.\n“Depression disorder represents a severe global health crisis, owing to its high\nprevalence 1, 2. It is a leading contributor to disability and premature mortality in\nthe world 3, 4. Pharmacotherapy and psychotherapy are first-line treatments for\ndepression disorder. However, the therapeutic effects typically require weeks\nto months of continuous treatment to manifest and approximately 30% of\npatients do not respond to these treatments 5. These refractory patients need\nmore intensive interventions 6, such as electroconvulsive therapy (ECT),\nrepetitive transcranial magnetic stimulation (rTMS), and magnetic seizure\ntherapy (MST).”\n3. Discussion, fifth paragraph: Study limitation is mentioned in terms of\nabsence of studies evaluating psychotherapy and DBS and rTMS that fulfilled\nthe inclusion criteria, so that these aspects were not assessed in the present\nstudy. However, it looks like the literature search was performed on\nmarch/2022 and February-March/2023. I was wondering if some study fulfilling\nyour criteria for these parameters was published after that.\nResponse:\nWe greatly appreciate the reviewer for this thoughtful suggestion. Updating the\nliterature search is essential for validating the robustness and generalizability\nof the DTN. To this end, we updated the literature search to include studies\npublished up to June 2025.\nThe method of this updated literature search:\n“Multiple control analyses were conducted to test the robustness of the\nDTN. First, to ensure that the DTN was independent of the seed size, we\nrepeated the analysis using a larger sphere size of 10 mm.……Finally, we\nre-computed the DTN using seed images that combined both GMV-increased\nand -decreased coordinates. We also conducted an updated systematic\nliterature search for longitudinal studies reporting GMV changes in patients\nwith depression following clinical treatment, covering publications up to June\n2025 (see Supplementary Table 9 for details of the updated search strategy).”\n“The whole-brain pattern of this DTN could be robustly identified when\ndifferent sizes was used to define the seed spheres, when the connectome\ndataset changed, when GMV-decreased coordinates were also included as\nseeds, when studies with moderate or severe patients were analyzed\nseparately, when ECT and non-ECT experiments were analyzed separately,\nwhen psychiatry disorders were used in the specificity analysis, when different\nthresholds were used for sensitivity analysis, and when including five\nadditional experiments in updating the experiment search in literature (see\nSupplementary Figures 3-4).”\nSupplementary Figure 4 The robust test of depression therapeutic network (DTN). The\nnetwork pattern of DTN exhibited strong stability and reliability to multiple parameter changes. a.\nThe DTN showed strong stability when switching to using the ECT (spatial correlation r = 0.988)\nand non-ECT (r = 0.867) experiments data, with themselves also exhibiting high similarity (r =\n0.868). b. The DTN showed strong stability when switching to using different human connectome\ndatasets, including connectome of healthy cohort over 30 years old (r = 0.975) and connectome of\ndepressive patient cohort (r = 0.990). This similarity persisted when distinguishing between the\npre- (r = 0.991) and post-treatment (r = 0.975) connectome, with themselves also exhibiting high\nsimilarity (r = 0.987). c. The DTN pattern persisted even when the control group was restricted to\npatients with non-depressive psychiatric disorders in the specificity analysis (r = 0.965). d. The\nDTN demonstrated high stability when the seed regions were altered, including updating the\nsearch for depressant therapeutic experiment in literature (r = 0.995), merging the coordinates with\ndecreased volumes (r = 0.998), and changing the sphere size (10-mm diameter, r = 0.999). e. The\nDTN pattern persisted across different sensitivity map thresholds (threshold ≥80%, r = 0.989; threshold ≥90%, r = 0.986). f. The DTN remained highly stable across moderate (r = 0.933) and\nsevere depression (r = 0.962) experiments, with the themselves also exhibiting strong spatial\nsimilarity (r = 0.902). Given the limited number of experiments in subgroup analyses of ECT vs.\nnon-ECT and moderate vs. severe depression, specificity analyses were not feasible; thus, regions\nexceeding 85% in the sensitivity map were defined as hub region to construct the\nDTN.\nSupplementary Table 14: Updated search strategy for studies reporting gray matter\nvolume changes after treatment in depression patients\nDatabase PubMed\nDate of search 2025.6.17\nSearch strategy Search term: (depressive disorder OR depression OR MDD\nOR major depressive disorder) AND (voxel-based morphometry\nOR VBM OR gray matter OR gray matter volume OR gray\nmatter concentration) AND (treatment OR therapy OR\ntherapeutic* OR antidepressant)\nPublication data: 2022–2025.6\nSpecies: Humans\nLanguage: English\nItems found 104\nIdentification of studies via databases and registers\nn\no ita\nc ifitn\ne\nR ec\nD\no\na\nrd\nta\ns\nb\nid\na\ne\nse\nn\ns\nti f\n(\nie\nn\nd\n=\nf r\n1\no\n0\nm\n4\n:\n)\nR\nsc\ne\nr\nc\ne D\n(\no\ne\nn\nu\nr\nn\nd\n=\np i\ns\nn\n0\nli g c\n)\nr e\n: a\nm\nte\no\nr\nv\ne\ne\nc\nd\no r\nb\nd\ne\ns\nf o\nre\nre\nm oved\nd\nI\nRecords screened Records excluded\n(n = 104) (n = 82)\nReports sought for retrieval Reports not retrieved\ng\nn\n(n = 22) (n = 0)\nin\ne\ne\nrc\nS\nReports assessed for eligibility Reports excluded (n = 17):\n(n = 22) 1.No whole-brain voxel-\nbased analysis (n = 3)\n2.No longitudinal study (n =8)\n3.No sufficient report of\nresults (n = 6)\nd\ne d Studies included\nu lc (n = 5)\nn\nI\nSupplementary Figure 7: Work flow for identifying updated studies investigating gray\nmatter changes in depression patients.\nSupplementary Table 15: Updated experiments reporting gray matter volume increase\nin depression patients\nDepression patients Controls\nReference P\nSample Treatment Pre-treatmen Post-treatm Sample (corrected)\nAge Age\n(F) modality t ent (F)\nVan47 88(39) 50.2±15.7 ECT 24.4±6.2# 17.4±12.8 27(11) 60.7±8.0 P<0.05\nKawashima48 18(9) 61.9±16.0 ECT 27.6±7.3* 9.7±6.0 11(5) 44.9±7.9 P<0.05\nBorgers49 17(9) 47.5±10.9 ECT 25.2±3.9* 11.1±6.6 33(17) 36.1±11.7 P<0.05\nLong50 127(66) 36.6±11.8 FAT 18.8±5.3* 11.8±5.6 37(21) 33.3±11.4 P<0.05\nPing51 20(16) 30.2±9.0 TMS 18.2±2.3* 4.9±1.8 14(13) 29.9±9.9 P<0.05\nData are presented as mean (standard deviations). The corrected p value indicates a\nsignificant increase in gray matter volume after treatment. ECT=Electroconvulsive\ntherapy; FAT=Fast-acting treatments (including ECT, ketamine infusion and total\nsleep deprivation); TMS=Transcranial magnetic stimulation. *The severity of\ndepressive disorder was assessed using the Hamilton Depression Rating Scale\n(HAMD). #The severity of depressive disorder was assessed using the Montgomery–\nÅsberg Depression Rating Scale (MADRS).\nPreclinical study:\nThe animal model for assessing depressive-like behaviors used the learned\nhelplessness model (LHM) and forced swimming test (FST). Briefly, the LHM\nwas used to develop depression in the rats, and the FST was used to assess\nthe severity of depression. Some concerns are raised by this reviewer,\nespecially regarding the experimental design.\n1. Figure 5: Graph indicate 8-9 animals/group in Post-ECS data. However,\nn=10/group is mentioned in the text for this study. Accordingly, pre-ECS data\nhave n=10/group. The authors should include detailed description in case\nsome animals were lost along the protocol.\nResponse:\nWe appreciate the reviewer’s careful check. All groups initially included n = 10\nanimals. For the post-treatment analyses, one animal per group was excluded\nbecause post-hoc verification showed that the injection/electrode/cannula\nplacement was outside the intended target region. Thus, the correct final\nsample size is n = 9 per group for the post-treatment dataset.\nThe appearance of n = 8 in experiment group in Figure 5 was caused by a\nplotting/occlusion artifact in which one individual data point overlapped with the\nbar and was hidden due to plotting order. We have re-exported Figure 5; the\ncorrected figure now clearly displays n = 9 per group for post-treatment.\nFigure 5. Inhibiting the amygdala function can attenuate the treatment effects of\nelectroconvulsive shock (ECS) on depression models. a, We produce rat models of\ndepression by creating learned helplessness. The experiment was performed in two-way\nshuttle boxes with an electric shock unit. The model rats showed longer immobility time in\nthe forced swimming test (FST) than baseline (paired t-test, t = 12.87, P ＜ 0 .0001). b,Th\ne model rats were received a 5-day treatment. In each treatment day, muscimol (for exper\niment group) or saline (for control group) were injected in the bilateral BLA before ECS. T\nhe example injection sites in BLA are illustrated in the histological map. After the 5-day tre\natment, the immobility time in FST significantly decreased in the control group while that i\nn the experiment group did not change. The mixed model of two-way analysis of variance\nshows significant time by group interaction (F\n(1,16)\n= 29.88, P＜ 0.0001). No tably, one\nrat in each group was discarded in the analysis because of the unqualified cannula locatio\nn. Data are prese nted as the mean ± standard error. **** P < 0.001, ns: P＞ 0.05. Created\nin BioR ender. Juan, Z. (2025) https://BioRender.com/vrueuil\n2. By the information provided in the text it looks like the animals in Pre-ECS\ncondition were not subjected to BLA treatment (treatment onset was after that).\nIf this is true, the data presentation should be reviewed since the way it is\nshown gives to the reader the wrong idea that the amygdala was inhibited both\npre- and post-ECS.\nResponse:\nWe thank the reviewer for this valuable comment. The original descriptions in\nthe text and presentation may lead to misunderstanding. In the revised version,\nwe give more description showing the relation between “ECS” and “treatment”.\nThe “treatment” represents a 5-day ECS (once per day). In each treatment day,\nwe injected insaline or muscimol to BLA and then delivered ECS. Thus, all BLA\ninjection were performed before ECS. Please see the modified Figure 5 and\nFigure S1.\nFigure 5. Inhibiting the amygdala function can attenuate the treatment effects of\nelectroconvulsive shock (ECS) on depression models. a, We produce rat models of\ndepression by creating learned helplessness. The experiment was performed in two-way\nshuttle boxes with an electric shock unit. The model rats showed longer immobility time in\nthe forced swimming test (FST) than baseline (paired t-test, t = 12.87, P ＜ 0 .0001). b,Th\ne model rats received a 5-day treatment. In each treatment day, muscimol (for ex\nperiment gr oup) or saline (for control group) were injected in the bilateral BLA before ECS\n. The example injection sites in BLA are illustrated in the histological map. After the 5-day\ntreatment, the immobility time in FST significantly decreased in the control group while tha\nt in the experiment group did not change. The mixe d model of two-way analysis of varian\nce shows significant time by group interaction (F\n(1,16)\n= 29.88, P＜ 0.0001). Notably,\none rat in each group was discarded in the analysis because of the unqualified cannula lo\ncation. Data are p resented as the mean ± standard error. **** P < 0.001, ns : P＞0.05. Cre\nated in BioRender. Juan, Z. (2025) https://BioRender.com/vrueuil\nFigure S1. Schematic of the main experiment.\n3. A control group not subjected to ECS or BLA treatment is missing. This\ngroup would provide relevant information regarding the long-term\ndepressive-like effect of LHM assessed in the FST. In other words,\nsaline+ECS group presents a decrease in immobility, but how the authors can\nensure that this decrease is not related to a progressive time-dependent\nreduction of the depressive-like effect caused by LHM. Please clarify.\nResponse:\nWe thank the reviewer for this valuable comment. T o rule out the possibility tha\nt the observed reduction in immobility following ECS reflects a spontaneous re\nmission of depressive-like behavior over time, we assessed the time course of\nbehavioral changes following LHM induction in the absence of any treatment. I\nmmobility time in the FST was significantly increased immediately after LHM in\nduction (Day 1) and remained on Days 8 an d 28. These findings support the lo\nng-term validity of the LHM model and confirm that the reduction in im\nmobility observed in treatment groups is unlikely due to time-dependent re\ncovery. C r e a ted in BioRender. Juan, Z. (2025) https://BioRender.com/n94a024\nFigure S3. Depressive-like behavior. Immobility time in FST significantly increased\nafter LHM induction (Day 1) and remained at Days 8 and 28. Data are presented as\nmean ± SD (n = 9 per group); *** P < 0.001.\n4. The effect of repeated BLA treatment independent of ECS would also be\nrelevant to provide information that the change in immobility in the FST is\nrelated to depressive-like behavior rather than an unspecific behavioral\nchange in the FST.\nResponse:\nWe thank the reviewer for this insightful comment. The BLA treatment was\nperformed by using microinjection, a standard, minimally invasive but\nnon-lesional technique for transient focal inactivation. In principle, this\nmicroinjection technique itself would not produce any brain damage and\nconsequent behavior problem. To exclude the possibility that BLA inhibition\nmay induce unspecific behavioral change, we performed a supplementary\nexperiment. We compared depression rats treated with muscimol or insaline\nfor 5 days, and then compared their behavior in the open-field test. As shown\nin Figure S5, neither of the four behavior measures shows a significant\ndifference between groups.\nFigure S5. Open-field test. Two depression rat groups received saline and muscimol\ninjection in the bilateral BLA. The open-field test was performed after 5-day\ntreatment. Neither of the four open-field test measures shows a significant difference\nbetween groups.\nReviewer #4 (Remarks to the Author):\nThis manuscript identifies a network centered on the amygdala that the\nauthors claim is specifically associated with symptomatic remission in\ndepression across three therapeutic modalities: pharmacotherapy, magnetic\nseizure therapy, and electroconvulsive therapy. The study is impressively\ncomprehensive. It builds upon data derived from a meta-analysis of multiple\nstudies using longitudinal structural neuroimaging to assess brain changes\nassociated with antidepressant treatments—potentially reflecting the\nneurobiological mechanisms of response. These data are then used for\nfunctional connectivity analyses employing robust, validated methods such as\nlesion network mapping. The authors further support their findings with a wide\nrange of complementary analyses to test the robustness, generalizability, and\nspecificity of the results, as well as preclinical studies aimed at deepening our\nunderstanding of the neurobiological mechanisms underlying treatment\nresponse. They also perform associations with gene expression maps,\nneurotransmitter distributions, and cognitive function profiles, thereby\nfurthering a mechanistic interpretation of antidepressant efficacy. Nonetheless,\nI have two major concerns that temper my enthusiasm for this work.\nResponse:\nWe thank the reviewer for their kind comments and valuable suggestions.\nAdditional supplementary experiments and analyses have been performed and\nshown in the revised version. We believe these modifications significantly\nimprove the quality of our manuscript.\nFirst, the brain regions identified by the authors as reactive to antidepressant\ntreatment in the individual studies do not show—or at least are not shown to\nshow—a significant association with clinical remission. This is particularly\ncommon in studies of electroconvulsive therapy (the most represented\ntreatment modality in this analysis), where anatomical changes are often not\nreliably associated with clinical improvement. While I assume that clinical\nremission was significant in all studies included (this should be also explicitly\nreported), this does not necessarily imply that remission was related to the\nstructural changes the authors selected as the basis for their analysis. In light\nof this, the term \"remission network mapping\" used by the authors becomes\nsomewhat misleading.\nResponse:\nWe appreciate this insightful comment and fully agree with your comment that\nbrain-phenotype association analysis represents a promising approach for\ninvestigating the mechanisms underlying clinical symptoms. According to your\nsuggestion, we did a systematic search for imaging-symptom association\nstudies. We identified 376 studies investigating the association of brain volume\nchanges and depression improvement. However, no significant association\nwas found (Supplementary Table 13; Supplementary Figure 7). A major reason\nis the sample size in these studies is far from the requirement of obtaining a\nreproducible result. Marek et al.'s (2022) study in Nature demonstrated that\ncohort sizes numbering in the thousands are required to establish reproducible\nbrain-behavior relationships. Thus, the negative finding in brain-phenotype\nassociation analysis cannot exclude the possibility that the volume changes\nare involved in depression remission.\nAs the reviewer noted, few studies in literature found significant correlation\nfindings between imaging changes and symptom alleviation. The inadequate\nof data makes the network mapping analysis impossible. To avoid any\nmisinterpretation, we have changed the “depression remission network” to\n“depression therapeutic network (DTN)” and added a limitation at the revised\nmanuscript.\nRevised limitation:\n“In this study, we converged the heterogeneous longitudinal findings in\nantidepressant experiments on a common therapeutic network, which was\nfurther validated by retrospective and prospective experiments. From a\nnetwork perspective, we addressed the knowledge gaps among clinical\ntherapies, risk gene expression, neurotransmitter systems, and cognitive\nfunctions. Despite these advantages, some limitations should be mentioned.\nFirst, the finding of DTN is based on regions with GMV changes. A better basis\nwould be regions showing GMV change as well as association with symptom\nimprovement. However, few studies have reported significant association\nfindings62, 63 (Supplementary Table 13; Supplementary Figure 7). This may be\npartly caused by the inadequate sample size which should be larger than\nthousands to observe reproducible brain-phenotype associations64. Another\nfuture direction is comparing treatment responders and non-responders, which\nmay further elucidate the relationship between DTN and symptom\nremission.…...”\nSupplementary Table 13: Search strategy for imaging-symptom association\nstudies\nDatabase PubMed\nDate of search 2025.6.15\nSearch strategy Search term: (depressive disorder OR depression OR MDD OR major\ndepressive disorder) AND (voxel-based morphometry OR VBM OR gray\nmatter OR gray matter volume OR gray matter concentration) AND\n(treatment OR therapy OR therapeutic* OR antidepressant)\nPublication data: up to 2025.6\nSpecies: Humans\nLanguage: English\nItems found 376\nPRISMA 2020 flow diagram for new systematic reviews which included searches of databases and registers only\nIdentification of studies via databases and registers\nn\no ita\nc ifitn\ne\nR ec\nD\no\na\nrd\nta\ns\nb\nid\na\ne\nse\nn\ns\nti f\n(\nie\nn\nd\n=\nf r\n3\no\n7\nm\n6\n:\n)\nR\nsc\ne\nr\nc\ne D\n(\no\ne\nn\nu\nr\nn\nd\n=\np i\ns\nn\n0\nli g c\n)\nr e\n: a\nm\nte\no\nr\nv\ne\ne\nc\nd\no r\nb\nd\ne\ns\nf o\nre\nre\nm oved\nd\nI\nRecords screened Records excluded\n(n = 376) (n = 290)\nReports sought for retrieval Reports not retrieved\ng\nn\n(n = 51) (n = 0)\nin\ne\ne\nrc\nS\nR\n(n\ne\n=\np o\n5\nr\n1\nts\n)\nassessed for eligibility Rep\n1\no\n.\nr\nN\nts\no\ne\nn\nx\n-\nc\nd\nl\ne\nu\np\nd\nr\ne\ne\nd\ns s\n(n\nio n\n=\ns\n5\nu\n1\nb\n):\nj ects\n(n = 4)\n2. Non-longitudinal studies (n\n= 3)\n3. No analysis of structural\nchanges and symptom\nimprovement (n = 36)\n4. No significant results (n =\n8)\nd\ne\nd Studies included\nu\nlc (n = 0)\nn\nI\nSupplementary Figure 7: Work flow for identifying imaging-symptom association\nstudies.\nReference\nMarek et al., 2022 Nature Reproducible brain-wide association studies require\nthousands of individuals.\nSecond, the authors appear to overlook a critical distinction between structural\nplasticity (changes in neuronal morphology) and functional plasticity (changes\nin synaptic efficacy). The study begins with structural changes and moves on\nto examine functional connectivity, implicitly treating both types of plasticity as\ntwo sides of the same coin. This equivalence is problematic. Structural\nplasticity is more readily observable in certain brain regions (for instance,\nlimbic areas—where the authors find their effects—tend to exhibit detectable\nstructural changes more easily than other cortical regions; see Butz et al.,\n2009; Kolb et al., 2003; Neufeld et al., 2009). This introduces a bias into the\nfunctional connectivity analyses and may obscure functional plasticity in other\nbrain regions that could also be relevant for clinical response. These other\nregions could serve as seeds of interest from which to define functionally\nrelevant networks more accurately associated with antidepressant effects.\nResponse:\nWe understand the reviewer’s concerns, which likely stem from insufficient\nclarification of the assumptions underlying the therapeutic network mapping\n(TNM) methodology. TNM is an adaptation of lesion network mapping (LNM).\nCentral to both methods is the hypothesis that “local changes” can exert global\neffects through network-wide “interactions”, consistent with the brain's\nconnectome organization.\n1) The “local changes” defined in this study are structural changes after\ntreatment which may represent structural plasticity. Although functional\nplasticity could also be used as “local changes” in theory, we did not do this\nbecause of the highly heterogeneous methodological variations in functional\nstudies. Thus, this study only considered the structural plasticity and\noverlooked the functional plasticity.\n2) The other key word of TNM is the “interactions” which was usually estimated\nby functional connectivity analysis. Notably, both LNM and TNM did not\npresume that the functional connectivity of “local changes” alters in patients.\nIn other words, the functional connectivity is a way to identify the potential\ndistant effect of local changes rather than a measure of functional plasticity.\nTo avoid misunderstanding, we listed the overlook of functional plasticity as a\nlimitation in the revised manuscript as follows.\n“In this study, we converged the heterogeneous longitudinal findings in\nantidepressant experiments on a common therapeutic network, which was\nfurther validated by retrospective and prospective experiments. From a\nnetwork perspective, we addressed the knowledge gaps among clinical\ntherapies, risk gene expression, neurotransmitter systems, and cognitive\nfunctions. Despite these advantages, some limitations should be mentioned.\nFirst, …… Fourth, this study only considered the structural plasticity while\noverlooked the functional plasticity in antidepressant treatment because of the\nhighly heterogeneous methodological variations in functional studies. As the\nfunctional processing become more and more unified, their findings are\nnecessary to be incorporated with our current work.”\nBeyond these central concerns, I also have some reservations regarding the\nanalytical strategies and their interpretation. For instance, I am not fully\nconvinced that showing a higher similarity between maps derived from VBM\nand cortical thickness studies in depression—compared to other\nconditions—constitutes sufficient validation. While it is expected that such\nsimilarity might be greater in depression studies, this does not necessarily\nindicate meaningful convergence unless the magnitude and spatial distribution\nof the correlation are clearly reported and interpreted.\nResponse:\nWe sincerely thank the reviewer for this insightful comment. Our group-level\nvalidation analysis focused primarily on statistical significance, without\nshowing the magnitude and spatial distribution of the correlation between DTN\nand each validation network. All these information has been added in the\nrevised a supplementary figure. All the five depression network showed similar\npattern as the DTN (all P < 0.05), while only one of the non-depression\nnetwork showed significant correlation with the DTN.\nSupplementary Figure 7: Group-level validation of depression therapeutic\nnetwork. We collected data from 11 longitudinal experiments (5 depression\nand 6 non-depression) that investigated cortical thickness (CTh) changes\nfollowing treatment. The networks identified in the depression experiments all\nexhibited higher spatial similarity with the DTN (P < 0.05), whereas only one of\nthe non-depression experiments showed such similarity.\nLikewise, I question the strategy of comparing networks derived from ECT vs.\nnon-ECT studies to the overall network as a way of addressing potential\nconfounding. Since this comparison involves a part-to-whole relationship, it\nlikely inflates the correlation. Would it not be more appropriate to compare the\nECT and non-ECT networks directly, rather than in relation to the network\nincorporating all treatment types?\nResponse:\nWe thank the reviewer for highlighting this issue and apologize for having\noverlooked the part-to-part spatial correlation comparison in our initial analysis.\nAs recommended by the reviewer, and as an extension of our initial analysis,\nwe directly calculated the spatial correlation between the ECT and non-ECT\nnetworks (r = 0.868). The high spatial correlation indicated that the DTN was\nnot driven by a particular treatment subtype. (Supplementary Figure 4a)\nSupplementary Figure 4 The robust test of depression therapeutic network\n(DTN). The network pattern of DTN exhibited strong stability and reliability to\nmultiple parameter changes. a. The DTN showed strong stability when switching to\nusing the ECT (spatial correlation r = 0.988) and non-ECT (r = 0.867) experiments\ndata, with themselves also exhibiting high similarity (r = 0.868). b. The DTN showed\nstrong stability when switching to using different human connectome datasets,\nincluding connectome of healthy cohort over 30 years old (r = 0.975) and connectome\nof depressive patient cohort (r = 0.990). This similarity persisted when distinguishing\nbetween the pre- (r = 0.991) and post-treatment (r = 0.975) connectome, with\nthemselves also exhibiting high similarity (r = 0.987). c. The DTN pattern persisted\neven when the control group was restricted to patients with non-depressive psychiatric\ndisorders in the specificity analysis (r = 0.965). d. The DTN demonstrated high\nstability when the seed regions were altered, including updating the search for\ndepressant therapeutic experiment in literature (r = 0.995), merging the coordinates\nwith decreased volumes (r = 0.998), and changing the sphere size (10-mm diameter, r\n= 0.999). e. The DTN pattern persisted across different sensitivity map thresholds\n(threshold ≥80%, r = 0.989; threshold ≥90%, r = 0.986). f. The DTN remained highly\nstable across moderate (r = 0.933) and severe depression (r = 0.962) experiments,\nwith the themselves also exhibiting strong spatial similarity (r = 0.902). Given the\nlimited number of experiments in subgroup analyses of ECT vs. non-ECT and\nmoderate vs. severe depression, specificity analyses were not feasible; thus, regions\nexceeding 85% in the sensitivity map were defined as hub region to construct the\nDTN.\nRegarding the analysis of genetic and molecular mechanisms, the authors\nreport a series of significant correlations, but these are only briefly discussed.\nThe interpretation feels somewhat opportunistic—as though any significant\nresult could have been included, regardless of its specific biological meaning.\nResponse:\nWe sincerely appreciate this comment. The major finding of this study is the\ndepression therapeutic network (DTN). To explain the biological mechanism of\nthis new network, we associated it with a number of genetic and molecular\nnetworks. We acknowledge that the biological interpretations of these\ncorrelations were not sufficiently developed in the original manuscript. This\nwas partly due to space constraints. However, we fully agree these findings\nwarrant deeper mechanistic exploration. Please see the modified discussion\nas follows.\n“In spite of the clinical efficacy of antidepressant therapies, their mechanism of\naction is still largely unknown. We investigated the biological mechanism by\nassociating the therapeutic network with a series of networks of\ndepression-risk genes and pharmacotherapy-targeted neurotransmitter\nsystems. The therapeutic network significantly aligned with networks of 90\ngenes and 13 neurotransmitters. The genetic findings are consistent with the\npharmacogenomics findings that genetic variation is predictive of\nantidepressant response 52, 53. Enrichment analysis further summarized these\ngenes into six ontology terms. All these terms were associated with the\nplasticity of neural system, such as ‘immune response’, ‘cell morphogenesis’\nand ‘response to biotic stimulus’. The immune finding is in line with the\nevidence that dysregulated immune responses contributes to depression\npathogenesis and immune targets can improve depressive symptoms 54. The\nmorphogenesis may be a manifestation of the neural activation in\nantidepressant treatment55 and contribute to the increased GMV 10-12. These\nsuggest that part of the depression-risk genes may mediate the antidepressant\ntherapies by activating neuroplasticity. In molecular level, neurotransmitter\ntransporter/receptor may affect the response of different regions to\nantidepressant treatment 56. This is expected with pharmacotherapy, since\nsuch drugs are designed to modulate neurotransmitter systems, such as\nserotonin and noradrenaline transporters/receptors. Our findings suggest that\nthis neurotransmitter-mediated mechanism may be generalized to other\nnon-pharmacotherapies. For instance, ECT decreased the serotonin-1A\nreceptor binding in regions of the DTN, such as the amygdala, anterior\ncingulate cortex, hippocampus, and insula 57; the TMS-induced long-term\npotentiation and long-term depression were mediated by dopamine receptors\n58. In this study, we linked genetic and neurotransmitter factors through\nimaging networks. These multi-level factors may interact through molecular\ncrosstalk mechanisms to modulate depression pathogenesis and remission\ntrajectories. Future studies may trace their interaction in rodent models of\ndepression. Pharmacogenomics studies focusing on candidate genes involved\nin drug metabolism may show how the biological mechanism affect the\ntreatment outcomes in behavior and cognition59.”\nFinally, the brain regions forming the core of the identified network are barely\ndiscussed. A more detailed description of these regions and their potential\nroles in depressive pathology would significantly enrich the discussion and\nprovide valuable insight into the implications of the findings.\nResponse:\nWe thank the reviewer for this insightful suggestion. In the revised version, we\nhave discussed the core regions of identified network and their relation with\ndepressive pathology and treatment.\n“The bilateral amygdala are the hub regions of the DTN. They play a prominent\nrole in processing emotions through connections with diverse brain regions\nsuch as the cingulate cortex and hippocampus. Their function and interaction\nhave been shown to relate to depression remission in previous studies. For\ninstance, enhanced amygdala–cingulate connectivity was associated with\nbetter mood in depression patients 44. DBS works by modulating amygdala\nconnectivity with sensorimotor and cingulate cortices 45. Psilocybin therapy\nrevives emotional responsiveness by normalizing amygdala function and its\nconnectivity with medial prefrontal cortices 46, 47. More recently, the\nelectrophysiological signal of amygdala has been used to develop a\npersonalized close-loop therapy for depression patients 48. In a prospective\npreclinical experiment, we found that the treatment effect of ECS could be\nsignificantly blocked by inhibiting amygdala function. It demonstrated a causal\nrole of amygdala function in alleviating depression symptoms. Conversely,\ninhibition of the visual cortex – a region with minimal DTN connectivity – did\nnot alter ECS efficacy. These contrastive outcomes established the DTN as\nprobability mapping of brain regions hierarchically associated with\nantidepressant mechanisms. The high rank regions in the medial DTN\nincluded the medial temporal lobe, orbitofrontal cortex, anterior and middle\ncingulate cortex, and insular lobule. All these are typically component of the\nlimbic system which is a core network of emotion and show atrophy in the\nlargest worldwide imaging study of depression49. Particularly, the functional\nexpansion in anterior cingulate cortex may serve a trait-like feature of\ndepression43. The high rank regions in the lateral DTN were distributed across\nthe multiple canonical brain networks50, including the ventral lateral prefrontal\ncortex in frontostriatal network, perirolandic regions in somatomotor network,\nsupramarginal gyrus and temporo-occipital junction in attention network.\nDespite the highlight of multiple regions, it should be noted that only the\namygdala constitutes the invariant core node in the antidepressant\ntherapeutics, while the peripherally connected regions provided modulatory\nrather than determinant inputs. This is why we inhibited the amygdala function\nrather than other regions in the preclinical experiment. But it is worth testing\nwhether simultaneously stimulation on multiple DTN regions could also\nachieve the goal of antidepressant. For instance, one may place multiple\nelectrodes to shape an electric field precisely targeting the DTN51.”"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have been responsive to reviewers' comments. The improved manuscript will be a valuable addition to the depression-imaging literature.", "reviewer_2": "I appreciate the authors’ thorough revisions and clarifications, which address most of my major concerns. By adapting the lesion network mapping approach, this study links treatment-related structural changes to altered functional networks and identifies a common depression therapeutic network (DTN) centered on the bilateral amygdala. The DTN has potential utility for personalizing TMS and DBS targets.\nMinor comments:\n1.Methods: Could the authors elaborate on how multiple seed regions were combined into a single network via local-to-network mapping for each experiment when constructing experiment-specific network?\n2.Figure 3: Panels c and d appear to be mislabeled. Please correct.", "reviewer_3": "All the issues I raised were adequately addressed, including the addition of new experiments. The revised manuscript shows substantial improvement.", "reviewer_4": "The authors have done a very good job of thoroughly addressing my initial comments. I appreciate the work and time they have invested and congratulate them on the result."}, "author_response": "Response to reviewers:\nDistinct antidepressant therapies act on a common\nbrain network\nREVIEWERS' COMMENTS\nReviewer #1 (Remarks to the Author):\nThe authors have been responsive to reviewers' comments. The improved manuscript will\nbe a valuable addition to the depression-imaging literature.\nResponse:\nWe thank the reviewer for the kind comments. We will keep making effects to\nimprove depression treatment.\nReviewer #2 (Remarks to the Author):\nI appreciate the authors’ thorough revisions and clarifications, which address most of my\nmajor concerns. By adapting the lesion network mapping approach, this study links\ntreatment-related structural changes to altered functional networks and identifies a\ncommon depression therapeutic network (DTN) centered on the bilateral amygdala. The\nDTN has potential utility for personalizing TMS and DBS targets.\nResponse:\nWe thank the reviewer for these positive comments. We will keep making\neffects to improve depression treatment.\nMinor comments:\n1.Methods: Could the authors elaborate on how multiple seed regions were combined into\na single network via local-to-network mapping for each experiment when constructing\nexperiment-specific network?\nResponse:\nWe thank the reviewer for this comment. The construction of\nexperiment-specific network could be detailed in three steps.\nFirst, we generated one sphere mask for each coordinate reported in each\nexperiment. If one experiment reported two or more coordinates, we combined\nall the sphere masks into one single image, termed experiment-specific seed\nimage. Second, we computed the seed-to-whole brain functional connectivity\nusing the resting-state fMRI data of 652 healthy subjects. The seed is\nexperiment-specific seed image. The results of this step were 652 functional\nconnectivity maps. Third, we performed one-sample t-test on the 652\nconnectivity maps. The resulting t map is the experiment-specific network.\n2.Figure 3: Panels c and d appear to be mislabeled. Please correct.\nResponse:\nWe thank the reviewer for pointing out the omissions we made. We have\ncorrected it in the revised version.\nReviewer #3 (Remarks to the Author):\nAll the issues I raised were adequately addressed, including the addition of new\nexperiments. The revised manuscript shows substantial improvement.\nResponse:\nWe thank the reviewer for the kind comments. We will keep making effects to\nimprove depression treatment.\nReviewer #4 (Remarks to the Author):\nThe authors have done a very good job of thoroughly addressing my initial comments. I\nappreciate the work and time they have invested and congratulate them on the result.\nResponse:\nWe thank the reviewer for the kind comments. We will keep making effects to\nimprove depression treatment."}]

Decision letter