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. 2026 Jul 24;16:576. doi: 10.1038/s41398-026-04308-8

Network localization of brain structural and functional alterations induced by electroconvulsive therapy for depression

Shanwen Yao 1,2,3,#, Dao-Min Zhu 4,5,6,#, Huimin Zhu 1,2,3, Shuang Xu 1,2,3, Fan Mo 1,2,3, Jiajia Zhu 1,2,3,✉, Yongqiang Yu 1,2,3,✉
PMCID: PMC13683337  PMID: 42901907

Abstract

Extensive longitudinal neuroimaging research has investigated brain structural and functional changes following electroconvulsive therapy (ECT) for depression. However, neither the regional nor the global brain effects reported in previous research can satisfactorily explain all aspects of the antidepressant efficacy of ECT, pointing to a potentially more refined network-specific mechanism. We initially identified the locations of brain structural and functional alterations induced by ECT for depression from 34 published studies, including 15 voxel-based morphometry studies with 596 depressed individuals and 19 resting-state functional magnetic resonance imaging studies with 436 depressed individuals. By combining these affected brain locations with large-scale brain functional connectome datasets (1113 healthy and 255 clinical individuals), we then adopted a novel functional connectivity network mapping approach to construct ECT-modulated structural and functional alteration networks. Our data showed different and specific ECT-modulated structural and functional alteration networks, with the former predominantly involving the subcortical (hippocampus and amygdala), ventral attention (insula and operculum and temporoparietal junction) and somatomotor (precentral and postcentral gyrus) networks, while the latter mainly implicating the default network (medial prefrontal cortex and posterior cingulate cortex). These findings may not only reconcile prior discrepancy between the ECT-induced regional and global brain changes from a network perspective, but also suggest a dissociation between the brain structural and functional effects of ECT.

Subject terms: Depression, Prognostic markers

Introduction

Electroconvulsive therapy (ECT) has been an essential treatment option for severe mood and psychotic disorders for many decades, especially for major depressive episodes in the context of major depressive disorder (MDD) and bipolar disorder [1–7]. In terms of therapeutic efficacy, ECT is arguably considered the most effective treatment for major depression, with response rates and times superior to other available antidepressant therapies [3, 8–12]. Despite its high antidepressant efficacy, however, the prescription of ECT is limited to a specific subgroup of patients with many treatment failures. The lack of knowledge about its precise mode of action, in addition to concerns regarding its side effects, has raised questions about the scientific basis of ECT [1, 2, 7]. Thus, better understanding the exact nature of the neurobiological effects of ECT represents a critical step toward fostering the clinical application of ECT in depression treatment.

In recent years, extensive longitudinal neuroimaging research has investigated brain structural and functional changes following ECT [13–22]. However, small sample sizes, substantial heterogeneity of clinical populations, and variable analytic approaches have led to mixed results [1, 3, 23–30]. Many studies have documented that specific brain regions, particularly the hippocampus and amygdala, show volume increases and functional alterations after ECT [13–20, 31, 32]. These findings in humans have been confirmed by animal studies demonstrating ECT-induced neurogenesis, synaptogenesis, and angiogenesis in the hippocampus [33–36]. By contrast, other studies have reported that ECT elicits structural and functional alterations in widespread cortical and subcortical regions [14, 20–22, 37–41]. Nevertheless, neither the regional nor the global brain effects can satisfactorily explain all aspects of the antidepressant efficacy of ECT, pointing to a potentially more refined network-specific mechanism.

Most conventional neuroimaging studies and meta-analyses have focused on region specific alterations in brain structure and function that are associated with a mental disorder or a therapeutic strategy [42]. However, there is an increasing recognition that mental disorders map to connected brain networks better than they do to single brain regions [43–45]. The updated neuropathological understanding of mental disorders has recently given rise to the revolutionary concept of network-based interventions for precision psychiatry, emphasizing that existing treatments may exert their salutary effects in a network-dependent manner [46–48]. To achieve network localization, researchers have developed a novel functional connectivity network mapping (FCNM) approach [49–51] that has employed a combination of brain locations of interest (e.g., structural or functional alterations related to a disease or a treatment) and the brain functional connectome. This approach has enjoyed significant success in mapping mental disorders [51–58] and neuromodulatory interventions [59, 60] to specific brain networks, advancing our understanding of disease and treatment mechanisms from a network perspective. Despite this progress, investigations into network localization of brain changes induced by ECT for depression are crucially lacking.

To address this missing gap, we adopted the FCNM approach to investigate the brain network substrates underlying ECT for depression, potentially unifying the heterogeneous findings across prior neuroimaging studies from a network perspective. Specifically, we initially synthesized published literature to identify the locations of brain structural and functional alterations induced by ECT for depression. By combining these affected brain locations with large-scale resting-state functional magnetic resonance imaging (fMRI) datasets, we then adopted the FCNM approach to construct ECT-modulated structural and functional alteration networks, respectively. A schematic representation of the study design and analytical procedure is provided in Fig. 1. Building on prior evidence, we hypothesized that the ECT-modulated structural and functional alteration networks would differ in their spatial distributions.

Fig. 1. Study design and analytical procedure.

Fig. 1

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We initially synthesized published literature to identify the locations of brain structural and functional alterations induced by ECT for depression. By combining these affected brain locations with large-scale resting-state fMRI datasets (the HV and MDD datasets), we then adopted the FCNM approach to construct ECT-modulated structural and functional alteration networks, respectively. Specifically, spheres centered at each coordinate of a contrast were firstly created and merged together to generate a contrast-specific combined seed mask. Second, based on the resting-state fMRI data, we computed a contrast seed-to-whole brain rsFC map for each subject. Third, the subject-level rsFC maps were entered into a voxel-wise one-sample t-test to identify brain regions functionally connected to each contrast seed. Fourth, the resulting group-level t maps were thresholded and binarized at P < 0.05 corrected for multiple comparisons. Finally, the binarized maps of the contrasts were overlaid to produce 2 network probability maps, which were thresholded at 60% to yield ECT-modulated structural and functional alteration networks, respectively. ECT electroconvulsive therapy, FCNM functional connectivity network mapping, HV healthy volunteer, MDD major depressive disorder, rsFC resting-state functional connectivity.

Methods

Study search and selection

We performed a comprehensive and systematic literature search in PubMed and Web of Science to identify relevant studies examining longitudinal brain structural or functional alterations following ECT for depression (i.e., post- vs. pre-ECT) published before 1 December 2023, according to the Meta-analysis of Observational Studies in Epidemiology guidelines [61]. The study search and selection process is detailed in Methods and Figure S1 in the Supplementary materials. The protocol was registered on PROSPERO (https://www.crd.york.ac.uk/PROSPERO/, registration number: CRD42024502611). Since a single study may contain multiple contrasts (i.e., within-subject comparisons in brain structure or function between pre- and post-ECT), we focused our analysis on contrasts rather than studies. In cases where multiple publications or contrasts utilized the same group of participants, we assessed the neuroimaging measures used. If the contrasts reflected distinct imaging modalities or distinct functional paradigms, we treated them as independent inputs. Coordinates of peak voxels of significant clusters reported in each contrast were extracted, and coordinates in Talairach space were converted to Montreal Neurological Institute (MNI) space.

Brain functional connectome datasets

The resting-state fMRI datasets used for constructing the brain functional connectome included a healthy volunteer (HV) dataset and a MDD dataset. The HV dataset included 1113 healthy adults of Han Chinese (643 female, mean 32.66 ± 12.79 years, 1110 right-handedness), who were enrolled from local universities and communities through poster advertisements. The exclusion criteria included neuropsychiatric or severe somatic disorders, a history of head injury with consciousness loss, MRI contraindications, or a family history of psychiatric diseases among first-degree relatives. The MDD dataset included 255 MDD individuals of Han Chinese (161 female, mean 49.99 ± 10.48 years, 252 right-handedness). Patients were recruited consecutively from Affiliated Psychological Hospital of Anhui Medical University. Following the International Classification of Diseases (ICD-10), two well-trained clinical psychiatrists confirmed the diagnosis of depression. The exclusion criteria included 1) the presence of other psychiatric disorders such as substance-induced (e.g., drug and alcohol) mood disorder, bipolar disorder, anxiety disorders, schizophrenia, substance abuse or addiction; 2) a history of significant physical or neurological disease; 3) a history of head injury with loss of consciousness; 4) contraindications for MRI such as pregnancy. All patients were receiving their regular antidepressant medications, including selective serotonin reuptake inhibitors, serotonin-norepinephrine reuptake inhibitors, or noradrenergic and specific serotonergic antidepressants. This study was approved by the ethics committee of The First Affiliated Hospital of Anhui Medical University. Written informed consent was obtained from all participants after being given a complete description of the study. Demographic and clinical information of the two datasets is provided in Table S1 in the Supplementary materials.

fMRI data acquisition and preprocessing

Resting-state fMRI data of the two datasets were collected on a 3.0-Tesla MR system (Discovery MR750w, General Electric, Milwaukee, Wl, USA) with a 24-channel head coil. Earplugs were used to reduce scanner noise, and tight but comfortable foam padding was used to minimize head motion. During the scans, all participants were instructed to keep their eyes closed, relax but not fall asleep, think of nothing in particular, and move as little as possible. High-resolution 3D T1-weighted structural images were acquired by employing a brain volume sequence with the following parameters: repetition time (TR) = 8.5 ms; echo time (TE) = 3.2 ms; inversion time = 450 ms; flip angle (FA) = 12 °; field of view (FOV) = 256 mm × 256 mm; matrix = 256 × 256; slice thickness = 1 mm; no gap; 188 sagittal slices; acquisition time = 296 s. Resting-state blood-oxygen-level-dependent fMRI data were acquired using a gradient-echo single-shot echo planar imaging sequence with the following parameters: TR = 2000 ms; TE = 30 ms; FA = 90 °; FOV = 220 mm × 220 mm; matrix = 64 × 64; slice thickness = 3 mm; slice gap = 1 mm; 35 interleaved axial slices; 185 volumes; acquisition time = 370 s. Participants with poor image quality (e.g., visible artifacts) and incomplete brain coverage were excluded.

Resting-state fMRI data were preprocessed using Statistical Parametric Mapping software (SPM12, http://www.fil.ion.ucl.ac.uk/spm) and Data Processing & Analysis for Brain Imaging (DPABI, http://rfmri.org/dpabi) [62]. The first 10 volumes for each participant were discarded to allow the signal to reach equilibrium and the participants to adapt to the scanning noise. The remaining volumes were corrected for the acquisition time delay between slices. Then, realignment was performed to correct the motion between time points. Head motion parameters were computed by estimating the translation in each direction and the angular rotation on each axis for each volume. All participants’ BOLD data were within the defined motion thresholds (i.e., maximal translational or rotational motion parameters less than 2 mm or 2 ° for healthy subjects and less than 3 mm or 3 ° for MDD individuals). We also calculated frame-wise displacement (FD), which indexes the volume-to-volume changes in head position. Several nuisance covariates (the linear drift, the estimated motion parameters based on the Friston-24 model, the spike volumes with FD > 0.5 mm, the global signal, the white matter signal, and the cerebrospinal fluid signal) were regressed out from the data. Since global signal regression can enhance the detection of system-specific correlations and improve the correspondence to anatomical connectivity [63], we included this step in the preprocessing of resting-state fMRI data. Next, the datasets were band-pass filtered using a frequency range of 0.01–0.1 Hz. In the normalization step, individual structural images were firstly co-registered with the mean functional images; the transformed structural images were then segmented and normalized to MNI space using a high-level nonlinear warping algorithm, i.e., the diffeomorphic anatomical registration through exponentiated Lie algebra technique [64]. Then, each filtered functional volume was spatially normalized to MNI space using the deformation parameters estimated during the above step and resampled into a 3-mm isotropic voxel. Finally, all functional data were spatially smoothed with a Gaussian kernel of 6 × 6 × 6 mm3 full-width at half maximum.

Functional connectivity network mapping

The FCNM approach was employed to construct ECT-modulated brain alteration networks based on the extracted coordinates of pre- to post-ECT changes in brain structure or function (Fig. 1). First, 4-mm radius spheres centered at each coordinate of a contrast were created and merged together to generate a contrast-specific combined seed mask (henceforth referred to as the contrast seed) in accordance with previous FCNM research [49–51, 65–71]. Second, based on the preprocessed fMRI data of the HV and MDD datasets, we computed a contrast seed-to-whole brain functional connectivity (FC) map for each subject, by calculating Pearson’s correlation coefficients between time courses of the contrast seed and each voxel within the whole brain, followed by Fisher’s Z-transformation to improve normality. Third, the subject-level FC maps were entered into a voxel-wise one-sample t-test to identify brain regions functionally connected to each contrast seed. We only considered positive FC as the biological meaning of negative FC is still a matter of debate [63, 72]. Fourth, the resulting group-level t maps were thresholded and binarized at P < 0.05 corrected for multiple comparisons. Note that a voxel-level family-wise error rate (FWE) correction was used for the HV dataset, while a less stringent voxel-level false discovery rate (FDR) method was used for the MDD dataset due to its reduced sample size. Finally, the binarized maps of the contrasts were overlaid to produce 2 network probability maps, which were thresholded at 60% to yield ECT-modulated structural and functional alteration networks, respectively.

Statistical significance testing

To determine whether our ECT-modulated brain alteration networks were statistically different from those generated by chance, we repeated the above-described FCNM procedure 1000 times with random contrast seeds to yield 1000 null networks. We recorded the sizes of the 1000 null networks (i.e., the numbers of voxels within these networks) to build a null distribution. Finally, we compared the network size derived from the real contrast seeds with this null distribution to determine whether our results were different from random.

Relation to canonical brain networks

To ease interpretability, we tested the spatial associations between the ECT-modulated brain alteration networks and 8 well-established canonical brain networks. The 7 cortical networks were the visual, somatomotor, dorsal attention, ventral attention, limbic, frontoparietal, and default networks from the study by Yeo et al. [73]. The subcortical network was composed of the amygdala, hippocampus, basal ganglia, and thalamus from the Human Brainnetome Atlas [74]. The proportion of overlapping voxels between each ECT-modulated brain alteration network and a canonical network to all voxels within the corresponding canonical network was calculated to quantify their spatial relationship.

Validation analysis

First, to determine whether our findings were influenced by seed size, we repeated the FCNM procedure with 1-mm and 7-mm radius spheres. To quantify the spatial similarity between the ECT-modulated brain alteration networks from the main and validation analyses, we calculated a Dice coefficient, defined as 2 × (overlapping voxels) / [(network #1 voxels) + (network #2 voxels)]. Second, to examine the impact of overlap threshold selection, we repeated the FCNM procedure using 55 and 65% thresholds.

Results

Included studies

A total of 34 studies with 41 within-subject pre- vs. post-ECT contrasts were included in our analyses. Specifically, 15 voxel-based morphometry studies contained 16 brain structural contrasts from 596 depressed individuals and 19 resting-state fMRI studies contained 25 brain functional contrasts from 436 depressed individuals. Sample, treatment, and imaging information of the selected studies is provided in Tables S2 and S3 in the Supplementary materials.

ECT-modulated brain alteration networks

Analyses of both the HV and MDD datasets yielded similar ECT-modulated brain alteration networks, with subtle differences largely attributed to variation in sample sizes. The ECT-modulated structural alteration network was chiefly comprised the bilateral striatum, thalamus, hippocampus, amygdala, insula and operculum, temporoparietal junction, Heschl’s gyrus, precentral and postcentral gyrus, middle cingulate cortex, and temporal pole (Figs. 2a and 3a). The ECT-modulated functional alteration network was primarily composed of the bilateral medial prefrontal cortex, posterior cingulate cortex, precuneus, angular gyrus, and lateral temporal cortex (Figs. 2b and 3b). To avoid repetition, we focused subsequent analyses on the ECT-modulated brain alteration networks derived from the HV dataset.

Fig. 2. ECT-modulated brain alteration networks derived from the HV dataset.

Fig. 2

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a ECT-modulated structural alteration network. b ECT-modulated functional alteration network. ECT electroconvulsive therapy, HV healthy volunteer.

Fig. 3. ECT-modulated brain alteration networks derived from the MDD dataset.

Fig. 3

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a ECT-modulated structural alteration network. b ECT-modulated functional alteration network. ECT electroconvulsive therapy, MDD major depressive disorder.

Statistical significance of the ECT-modulated brain alteration networks

By generating 1000 null networks with random contrast seeds and comparing their sizes with that derived from the real contrast seeds, we observed that the ECT-modulated structural (9591 voxels, P = 0.016) and functional (5644 voxels, P = 0.014) alteration networks were significantly larger than most of the null networks (Figure S2 in the Supplementary materials), suggesting that our ECT-modulated brain alteration networks were statistically different from those generated by chance.

Relation to canonical brain networks

To better delineate the anatomical and functional characteristics of the ECT-modulated brain alteration networks, we examined their spatial associations with 8 well-established canonical brain networks (Fig. 4). We found that the ECT-modulated structural alteration network predominantly involved the subcortical (56.9%), ventral attention (54.7%), and somatomotor (40.8%) networks. By contrast, the ECT-modulated functional alteration network mainly implicated the default network (44.4%).

Fig. 4. ECT-modulated brain alteration networks in relation to canonical brain networks.

Fig. 4

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Polar plots illustrate the proportion of overlapping voxels between each ECT-modulated brain alteration network and a canonical network to all voxels within the corresponding canonical network. ECT electroconvulsive therapy.

Validation analysis

When repeating the FCNM procedure using 1-mm and 7-mm radius spheres, we observed that the resultant ECT-modulated structural and functional alteration networks were nearly identical to those using the 4-mm radius sphere (4-mm vs. 1-mm Dice coefficients: 0.97, 0.91; 4-mm vs. 7-mm Dice coefficients: 0.96, 0.89) (Figures S3 and S4 in the Supplementary materials). Using the 55 and 65% thresholds yielded ECT-modulated structural and functional alteration networks that had larger and smaller spatial extents than those based on the 60% threshold (Figures S5 and S6 in the Supplementary materials). However, the core areas of the ECT-modulated structural and functional alteration networks were still present across these thresholds.

Discussion

By a combination of the novel FCNM approach and large-scale brain functional connectome datasets, the current study investigated network localization of brain structural and functional alterations induced by ECT for depression. We found that the ECT-modulated structural alteration network predominantly involved the subcortical, ventral attention and somatomotor networks, while its functional counterpart mainly implicated the default network. These findings may not only reconcile prior discrepancy between the ECT-induced regional and global brain changes from a network perspective, but also suggest a dissociation between the brain structural and functional effects of ECT.

Prior neuroimaging studies and meta-analyses have identified brain regions associated with volume increases and functional alterations in patients with MDD following ECT [13–22], including widespread cortical and subcortical regions such as the parahippocampal gyrus, hippocampus, amygdala, entorhinal cortex, anterior cingulate cortex, and striatum. However, inconsistencies in the precise location and nature of these reported effects make it challenging to unify this research. Several possible explanations for the inconsistencies have been proposed, including small sample sizes, clinical heterogeneity, and methodological differences [30, 75]. In addition to these common concerns in neuroimaging research, earlier efforts have focused principally on regional brain effects, violating the major concept in modern neuroscience that neural processes in relation to a brain disorder or in response to a treatment do not act in isolation but rather rely on distributed, interconnected networks [43].

There is ample evidence that individuals with depression exhibit decreased gray matter volumes in widespread subcortical regions including the hippocampus, thalamus, amygdala, putamen, and caudate [76–80]. The hippocampus, which is an important part of the limbic system and develops connectivity with emotion-related brain regions (e.g., the prefrontal cortex and amygdala) [76, 81], constitutes the most commonly studied brain region in depression research. Reduced hippocampal volume is thought to be a neural scar marker of depression and a vulnerability marker for future episodes [82]. The amygdala is also assumed to be involved in the neurobiology of major depression in light of its pivotal role in negative emotional memory, the regulation of emotion, and the modulation of autonomic responses to emotion [83–86]. Consistent with our findings, ECT-induced volume increases in the hippocampus and amygdala have been widely reported, suggesting that the structural plasticity of the hippocampus and amygdala may serve as potential biomarkers for the development of fast-acting treatments [13–19, 87–89]. In addition, the subcortical nuclei have been engaged in the pathogenesis of psychomotor symptoms in depression [90–92]. Psychomotor disturbance has been considered as a predictor of ECT response in previous studies [91–95]. This invites the speculation that ECT may exert its antidepressant effects via eliciting the structural plasticity in the subcortical nuclei to improve psychomotor symptoms.

The ventral attention network is associated with the orientation of stimulus-driven attention and orients attention to emotionally salient stimuli, and its key brain regions include the ventrolateral prefrontal cortex, temporoparietal junction, and anterior insula [96–98]. Previous resting-state fMRI studies have shown that depression is associated with abnormal function in the ventral attention network [98–101] and its disruption may be an early feature or a biomarker of depression [98, 99]. Moreover, several studies have documented that depressed patients show cortical thickness and gray matter volume increases in the insula after ECT [39, 97]. Insular functional connectivity have been proved to act as biomarkers of the antidepressant effects of ECT in patients with major depression [102]. These pilot studies jointly work to support the involvement of the ventral attention network in the neural mechanism whereby ECT exerts its beneficial effects.

The somatomotor network is involved in processes of sensation, perception, and action [103, 104]. It is largely known that depressive symptoms are typically defined as mood disturbances, but can also manifest as a spectrum of sensorimotor impairments characterized by various somatic symptoms and psychomotor retardation [105]. Anomalies in the somatomotor network have been frequently reported in patients with depression [106–111] and these anomalies are thought to underlie the somatosensory, motor, emotional, and cognitive deficits associated with depression [112–114]. Furthermore, previous resting-state fMRI studies have demonstrated that the sensorimotor network shows treatment-related alterations after ECT for depression [115–119]. Prior task-based fMRI data have also revealed dysfunction of the auditory network and the treatment effects of ECT on impaired auditory processing in individuals suffering from depression [120, 121].

The ECT-modulated functional alteration network principally implicated the default network. The relationship between default network dysfunction and depression has long been well established, and substantial evidence indicates a prominent role of the default network in the pathophysiology of major depression [2, 108, 122–124]. The default network is mainly involved in self-referential thinking, which may provide a neural explanation for the common presentation of rumination in major depression [125–127]. Importantly, a large number of brain network studies have found that abnormal default network in major depression can be restored by ECT, suggesting the potential of the default network for ECT-related biomarker identification and outcome prediction [115, 119, 128–137].

There are several limitations in our work. First, most of the longitudinal studies included in our analysis have focused on the short-term impacts of ECT (1–3 days after treatment) on brain structure and function. The inclusion of more studies with a longer follow-up period might help reveal long-term brain network effects of ECT. Second, a less conservative overlapping threshold of 60% was employed to identify brain regions that were functionally connected to 60% of the contrast seeds. An important consideration regarding threshold selection arises from the fact that many sources of variance might preclude us from finding a common brain network at a more stringent threshold. Finally, this study might not mitigate concerns with respect to small sample sizes, heterogeneous clinical profiles, and variable analytical approaches that jointly lead to inconsistent neuroimaging findings related to ECT for psychiatric disorders. Continued efforts will be required to surmount these challenges in psychiatry.

In conclusion, the present work combined the novel FCNM approach with the brain functional connectome to examine network localization of brain structural and functional alterations induced by ECT for depression. Our data showed different and specific ECT-modulated structural and functional alteration networks, with the former predominantly involving the subcortical, ventral attention and somatomotor networks, while the latter mainly implicating the default network. These findings may not only reconcile prior discrepancy between the ECT-induced regional and global brain changes from a network perspective, but also suggest a dissociation between the brain structural and functional effects of ECT.

Supplementary information

Supplementary material (5MB, docx)

Author contributions

Jiajia Zhu and Yongqiang Yu performed the experimental design and the review and revision of the first draft. Shanwen Yao and Dao-min Zhu performed the data analysis and wrote the first draft. Shanwen Yao, Dao-min Zhu, Huimin Zhu, Shuang Xu and Fan Mo performed the related literature search as well as the visualization of the experimental results.

Funding

The study was supported by the National Natural Science Foundation of China (grant numbers: 82471952 and 82371928), the Anhui Provincial Natural Science Foundation (grant number: 2308085MH277), and the Scientific Research Foundation of Anhui Medical University (grant number: 2022xkj143).

Data availability

The data used in the preparation of this article are publicly available at https://github.com/mfmaplestory/FCNM/.

Code availability

The analysis codes used in the preparation of this article are publicly available at https://github.com/mfmaplestory/FCNM/.

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

The original studies providing the HV and MDD datasets received ethical approval from the relevant Institutional Review Boards (IRBs) or ethics committees. All participants in the HV and MDD datasets provided informed consent under protocols approved by The First Affiliated Hospital of Anhui Medical University. The datasets utilized in this study were anonymized to ensure participant confidentiality, containing no protected health information or personally identifiable images. All procedures involving human participants were performed in accordance with the relevant guidelines and regulations, in compliance with the ethical principles of the Declaration of Helsinki and its later amendments or comparable standards.

Footnotes

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

These authors contributed equally: Shanwen Yao, Dao-Min Zhu.

Contributor Information

Jiajia Zhu, Email: zhujiajia@ahmu.edu.cn.

Yongqiang Yu, Email: yuyongqiang@ahmu.edu.cn.

Supplementary information

The online version contains supplementary material available at https://doi.org/10.1038/s41398-026-04308-8.

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Associated Data

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Supplementary Materials

Supplementary material (5MB, docx)

Data Availability Statement

The data used in the preparation of this article are publicly available at https://github.com/mfmaplestory/FCNM/.

The analysis codes used in the preparation of this article are publicly available at https://github.com/mfmaplestory/FCNM/.

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