Major Depressive Disorder with Non-Suicidal Self-Injury in Adolescents: Altered Morphological Inverse Divergence Gradient in Brain Structure and Gene Expression Patterns

Major Depressive Disorder with Non-Suicidal Self-Injury in Adolescents: Altered Morphological Inverse Divergence Gradient in Brain Structure and Gene Expression Patterns | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Major Depressive Disorder with Non-Suicidal Self-Injury in Adolescents: Altered Morphological Inverse Divergence Gradient in Brain Structure and Gene Expression Patterns Yan Zhang, Xinyuan Hu, Wei Wang, Jing Wang, Hongying Li, Jianlei Zhu, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8994845/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Adolescents with major depressive disorder (MDD) and non-suicidal self-injury (NSSI) face serious challenges to their mental and physical health. The development of this condition is influenced by genetic, environmental, and brain development factors. This study aimed to explore brain structural network abnormalities and their transcriptional mechanisms in adolescents with MDD and NSSI using the Morphometric Inverse Divergence Gradient Network for the first time. Methods A total of 241 adolescents with MDD participated in this study, including 134 individuals with a history of non-suicidal self-injury and 107 without. Using high-resolution T1-weighted magnetic resonance imaging, we constructed the principal morphometric gradient network, which reflects the similarity of cortical morphology between different brain regions. Partial least squares regression analysis was conducted to link imaging findings with neurotransmitter receptor distributions and gene expression patterns. Results Adolescents with NSSI showed higher morphometric gradient values in the left caudal middle frontal gyrus and bilateral inferior temporal gyri, and lower values in the left lingual gyrus and right superior parietal lobule. These structural changes were slightly associated with functional alterations in the dorsal attention network. Further analysis revealed associations with the distribution of nine neurotransmitter receptor types, including serotonin 5-HT1A, GABAa, glutamate, and dopamine receptors. The first component of partial least squares regression analysis explained 39.53% of the spatial variance in the morphometric gradient and identified key genes related to NSSI, such as GABRA5 and MGST1. Enrichment analysis showed that positively correlated genes were involved in synaptic signaling, neurodevelopment, and mood disorder pathways, and were enriched in astrocytes and cortical layers I, II, and V. Negatively correlated genes were linked to chromatin regulation and cytoskeletal metabolism, and were mainly expressed in cortical layers III and IV. Conclusions Adolescents with MDD and non-suicidal self-injury display specific abnormalities in brain structural gradients that are closely associated with neurotransmitter receptor distributions and gene expression profiles. These findings provide multi-level evidence for understanding the neurobiological mechanisms underlying non-suicidal self-injury. Major depressive disorder Non-suicidal self-injury Adolescent Morphometric Inverse Divergence Gradient network Gene expression Neuroreceptors Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Background Major depressive disorder (MDD) is a chronic, recurrent psychological disorder caused by multiple factors, including genetic, environmental, and neurobiological influences. It is not only a leading cause of global disability but also a core contributor to the disease burden among adolescents [ 1 , 2 ].The prevalence of non-suicidal self-injury (NSSI) is significantly higher in adolescents with MDD, with studies showing that approximately 40%–60% of these patients engage in NSSI [ 3 , 4 ]. Although NSSI is defined as deliberate self-inflicted damage to body tissues without suicidal intent, more than 66% of adolescents who attempt suicide have a history of NSSI [ 5 , 6 ]. Epidemiological studies further reveal that the characteristics and prevalence of NSSI among adolescents are consistent across different countries [ 7 , 8 ]. This global phenomenon not only poses a serious threat to adolescents' physical and mental health but is also considered one of the strongest predictors of suicidal behavior [ 9 , 10 , 11 ]. In adolescent MDD patients, NSSI can often be identified through self-harming behaviors and visible scars. While these outward manifestations provide clues for clinical diagnosis, they fail to capture the underlying neurobiological mechanisms driving such behaviors. Therefore, studying brain structure abnormalities is important for understanding the underlying mechanisms of the disease and informing more effective therapeutic strategies. Many studies have shown that MDD and NSSI are linked to abnormalities in cortical structure and disrupted brain networks [ 12 , 13 , 14 ]. These changes are especially evident in the structural connectome [ 15 , 16 , 17 ]. The structural connectome, which represents the brain's large-scale network of anatomical connections, plays a vital role in coordinating cognitive, emotional, and behavioral functions. Despite these advances, there is still a lack of research on the specific structural connectome abnormalities in MDD patients with NSSI. The differences between these patients and MDD patients without NSSI also remain unclear. Addressing these gaps is essential for advancing our understanding of the distinct neural mechanisms underlying NSSI in the context of adolescent MDD. Several methods used to analyze brain structural connectivity networks in Major Depressive Disorder (MDD) and Non-Suicidal Self-Injury (NSSI) have been applied to analyze the brain structural networks in recent years [ 18 – 20 ]. These include white matter network analysis based on Diffusion Weighted Imaging (DWI) [ 4 ], Structural Covariance Networks (SCN) [ 18 – 20 ], Morphometric Similarity Networks (MSN) [ 21 ], and Morphometric Inverse Discrete Networks (MIND) [ 22 ]. While each method has contributed significantly to understanding brain structural networks, they also present notable limitations. For instance, DWI’s fiber tractography, although capable of objective quantitative assessment, has drawbacks when comparing the strength of long distance links [ 23 ]. SCN works well in group-level analysis, but fails in individualized characterization [ 24 , 25 ]. Dynamic time warping mapping is applicable to individualized analysis but relies heavily on longitudinal cohort data and has limited application [ 26 ]. MSN maps multimodal structural features of brain regions into structural vectors and connects morphometric measures with connectomic networks. The statistical associations between brain structure and morphometric features are found, and it provides theoretical support for research on brain structure, cortical gene expression and genetics. While MSN has its main advantages, it simplifies the rich data at the cortical vertices into a single statistical measure for regional features. This is inevitably a loss, and is also assumed to be completely uniform. Moreover, it assumes that cortical morphometric feature variability is completely uniform, which is not true, and reduces the network stability. On the contrary, the recently proposed MIND approach measures the divergence of five morphometric measurements at the vertex level to construct morphometric similarity networks. Compared to earlier methods, MIND is much more stable, better cortical matching, and better genetic relationships. The key advantage is that it requires only T1-weighted MRI data to construct reliable and effective MIND networks. This makes MIND more practical in practice. More diverse MRI measures are needed in future to create more meaningful networks, but early brain partitioning and cortical parcellation was guided by morphogenetic gradients. These gradients quantify the spatial organization of cortical structures as they vary on the gradient. This captures key features of cortical organization and the information of gene expression, cell structure, myelin distribution, and evolutionary growth. Thus, it provides more detailed structural network information [ 27 , 28 ]. MDD is a clinically undetermined disease, and there are a number of genetic variants for MDD, some of which are important in presynaptic regulation and cognitive function [ 29 , 30 , 31 ]. Scientists have studied for whole-brain gene expression data of Allen Human Brain Atlas database linking macro-structural brain disorders and specific transcriptional expression patterns with the effect of genes on brain networks [ 21 , 32 , 33 ]. Recently, researchers have identified significant differences in gene expression, enriched pathways, and expression patterns in the MSN network space of MDD patients [ 16 ], and our results suggest that gradient similarity network research is necessary to recognize neuroimaging biomarkers and assess the impact of genes. Most of these studies, however, investigated MSN networks. For NSSI patients, the abnormal mechanisms and their dependence on disease-related gene expression are not well explored. In contrast, the MIND network exhibits higher biological validity and can more accurately reflect the coupling relationship between cortical structure and gene co-expression. This study aims to construct a new MIND gradient network to evaluate the differences in brain structural network gradient changes between NSSI MDD and without NSSI patients. Taking these changes into account using transcriptomic data, we explore how molecular organization and microstructural changes in brain morphometric coordination in NSSI patients can affect the brain morphometries of MDD’s patients. In particular, we measure the MIND measures of cortical morphometric similarity at regional and global level and use this to formulate the principal MIND gradients (MIND-Gd). In this study, we examine differences in the hierarchical structure of the brain structural networks gradients between NSI patients and those without NSS. These gradients are further mapped onto brain functional networks and cortical cellular networks to investigate their potential functional implications and cellular-level changes. Additionally, we analyze abnormal neurochemical signaling of changes in the principal MIND gradient and their possible influence on the pathological process of NSSD patients. To further elucidate the molecular mechanisms underlying these changes, we compared change in the principal MIND to changes in in vivo receptor distribution maps (generated with the JuSpace toolbox). We then performed PLS regression analysis to link changes in principal MIND gradient to anatomical gene expression patterns and selected genes closely related. Permutation tests were used to detect genes that are strongly related. Finally, we conducted gene ontology pathway analysis, cell type enrichment analysis, and cortical layer enrichment analysis to further validate our findings. These analyses aimed to confirm whether transcriptional changes are consistent with principal MIND gradient alterations. Figure 1 . presents the overall analytical framework of this study. 2. Methods 2.1 Participants This study retrospectively included 3D T1-weighted magnetic resonance imaging (3D T1WI) data from 241 adolescent patients with MDD diagnosed according to ICD-10 criteria at the Shandong Mental Health Center. Based on clinical assessments of NSSI, patients were divided into an NSSI group (134 cases) and without NSSI group (107 cases). The severity of depressive symptoms was quantified using the 17-item Hamilton Depression Rating Scale (HDRS-17), with a baseline score of ≥ 18 required for inclusion. All patients were of Han ethnicity and showed no clear lesions or developmental abnormalities on MRI scans (see Additional file 2: Sheet S1 for details). As this study is a retrospective analysis and all data were obtained from an imaging database, informed consent was waived. The study was reviewed and approved by the Ethics Committee of the Shandong Mental Health Center (Approval Number: KYSJWLL2024-1-075). 2.2 Imaging acquisition and preprocessing Imaging data were collected using a 3.0 Tesla uMR 790 MRI scanner (United Imaging Healthcare, China). High-resolution T1-weighted anatomical images were acquired with a 3D gradient-recalled echo sequence (GRE-FSP). Specific imaging parameters are provided in Additional file 1: Table S1 . Surface-based preprocessing of 3D T1 images was performed using FreeSurfer software (v7.4.1, http://surfer.nmr.mgh.harvard.edu/ ) [ 34 ]. The preprocessing steps included skull stripping, tissue segmentation, division of hemispheres and subcortical structures, and generation of gray-white matter and pial surfaces. The preprocessed images were initially reviewed by two experienced radiologists with over 10 years of experience. Euler numbers were calculated for each segmented T1-weighted image, and images with Euler numbers less than − 270 were excluded (see Additional file 1: Text). Total intracranial volume (TIV) was also calculated [ 35 , 36 ]. 2.3 Construction of the MIND-Gd The cortical surface was divided into 308 spatially continuous regions (D-K 308 atlas) [ 37 , 38 ] by segmenting 68 cortical regions from the Desikan-Killiany (D-K) atlas. A recursive algorithm was used to ensure that each region had a similar surface area (approximately 500 mm²). The segmentation results of the D-K atlas were then individually mapped onto the cortical surface of each participant. Five morphological features were extracted: gray matter volume (GMV), cortical thickness (CT), surface area (SA), mean curvature (MC), and sulcal depth (SD) [ 22 ]. These features were standardized using z-score normalization to account for distributional differences.Next, the transformed Kullback-Leibler (KL) divergence was used to calculate the MIND similarity metrics between any two cortical regions. This resulted in a 308×308 MIND similarity matrix for each participant (see Fig. 1 , Additional file 1: Text and Fig. S1 ). The BrainSpace toolbox was then used to preprocess the MIND statistical gradients [ 39 ]. Since the principal gradient is closely related to basic cortical properties and functional mapping [ 40 ], this study focused on the principal morphological statistical gradient (n_components = 1) and its changes associated with NSSI in MDD. Finally, the principal gradient of the group-level MIND similarity matrix was generated (see Additional file 1: Text and Table S2 ). 2.4 Analysis of Regional MIND-Gd To explore differences in the principal MIND gradient across 308 cortical nodes between groups, we performed regression analysis using a general linear model (GLM). Age, sex, and the interaction term age×sex were included as covariates to control for their effects. Two-tailed t-tests were then conducted. To preserve spatial continuity, multiple comparison corrections were performed using a cluster-level false discovery rate (Cluster-FDR) method, with a significance threshold set at p < 0.05 (see Additional file 1: Fig. S2 ). To further clarify changes in the principal MIND gradient within a broader cortical classification framework, all 308 cortical regions were mapped onto two classic cortical classification schemes: the Yeo-7 atlas, which is based on resting-state networks [ 41 ], and the von Economo atlas, which is based on cytoarchitectonic features [ 42 ]. For this purpose, the mean principal MIND gradient was calculated for all regions within the Yeo-7 networks and von Economo classifications. Subsequently, GLM models were applied to analyze MIND gradient differences between the NSSI and non-NSSI groups while controlling for the same covariates. A significance threshold of p < 0.05 was set at each level of analysis. 2.5 Association of neurotransmitter receptors with MIND-Gd To elucidate the molecular mechanisms underlying NSSI-related changes in regional MIND-Gd, we conducted cross-modal association analyses by examining the spatial correlations between case-control t-maps and neurotransmitter receptor distribution maps. The neurotransmitter receptor data were derived from PET imaging-based datasets provided by the JuSpace toolbox [ 43 , 44 ].Specifically, neurotransmitter maps were first mapped onto the DK-308 brain atlas. Spatial correlations were then calculated between the case-control t-maps and each neurotransmitter receptor map. Significance levels were assessed through 10,000 spin tests. Multiple comparisons were corrected using the Bonferroni method, with a significance threshold set at p < 0.05(see Additional file 1: Text and Table S3). 2.6 Gene expressional profiles preprocessing Gene expression profile data were obtained from postmortem brain tissue samples of six individuals, covering 3,702 spatially distributed gene loci. The data were sourced from the AHBA database ( http://human.brain-map.org ) [ 45 ]. Since only two samples in the AHBA dataset included data from the right hemisphere, this study focused exclusively on the left hemisphere. The AHBA dataset was preprocessed using the Python-based abagen toolbox ( https://github.com/rmarkello/abagen ). Gene expression profile data were mapped onto 152 brain regions of the left hemisphere in the DK-308 atlas. This resulted in a transcriptional expression matrix containing 152 brain regions and 15,632 corresponding genes [ 46 ]. 2.7 Transcription-neuroimaging association region PLS analysis To establish the association between gene expression profiles and regional MIND-Gd changes in NSSI patients, partial least squares (PLS) regression analysis was performed. The expression levels of 15,632 genes across 152 brain regions were used as predictor variables, while the case-control t-value differences of 152 MIND-Gd regions served as response variables [ 47 , 48 ]. This analysis identified the linear combination of gene expressions that best predicted regional MIND-Gd changes.To assess whether the covariance explained by the PLS components between the MIND-Gdt-statistical map and the transcriptome exceeded chance levels, spatial autocorrelation analysis with 10,000 permutation iterations was conducted. Given the lack of consensus on the most predictive PLS components, the first or second components (PLS1 or PLS2) are commonly selected as optimal low-dimensional explanations of high-dimensional covariance. In this study, we selected PLS components with a variance explanation rate > 30% and statistical significance above the threshold (p spin <0.0001) [ 49 ]. Additionally, the significance of each gene's contribution to the PLS components was assessed using 10,000 bootstrap iterations. The z-values were calculated by dividing the expression weights by their bootstrap standard errors, and all genes were ranked based on their weights [ 21 , 50 ]. For subsequent analyses, only genes with statistical significance after false discovery rate (FDR) correction (p_fdr < 0.0001) were retained.Finally, Spearman correlation analysis was used to examine the spatial relationship between the MIND-Gdt-statistical map and PLS scores, with statistical significance validated through 10,000 permutation iterations. To further explore the relationship between NSSI-related gene expression and changes in the principal MIND gradient, genes closely associated with NSSI based on previous literature [ 51 , 52 ] were screened. Genes related to neurotransmitter function that overlapped with the significant genes identified by the PLS+ method were analyzed for their spatial relationship with the case-control t-value differences of the principal MIND gradient in the left hemisphere. 2.8 Enrichment analysis To further analyze the disease-related pathways associated with significant PLS+/− genes, we selected statistically significant genes from the first PLS component (PLS1+/−) and performed functional annotation using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway databases available on the Metascape platform ( https://metascape.org/gp/index.html#/main/step1 ) [ 53 ].Next, to localize NSSI-related genes identified through PLS regression to specific cortical regions, we conducted cortical structure enrichment analysis. Marker genes for six cortical regions were obtained from published transcriptomic studies [ 54 ].To further validate the cell-specificity of genes associated with regional MIND-Gd changes, we performed enrichment analysis by comparing the significant PLS1+/− gene lists with gene sets from seven types of cortical cells, including microglia, oligodendrocytes, endothelial cells, astrocytes, oligodendrocyte precursor cells (OPCs), excitatory neurons, and inhibitory neurons [ 21 ].All enrichment analyses were corrected for significance using the Bonferroni method, with p < 0.05 set as the threshold for significance. Statistical significance was further evaluated using 10,000 spin tests. 2.9 Null model To address potential confounding effects caused by spatial autocorrelation [ 50 , 55 ], we used a spin test technique based on null models. This method generates a set of Pearson correlation coefficients under the null hypothesis by randomly rotating the spherical projection of spatial maps while maintaining their spatial topology. Specifically, 10,000 spin permutations were applied to cortical regions to construct a null distribution model. The p spin value was then calculated, defined as the proportion of null correlation coefficients that exceeded the observed value. 2.10 Robustness analysis To ensure the reliability and robustness of our findings, we conducted five additional analyses: (a) We verified the robustness of the case-control principal MIND gradient by regressing out the effect of total intracranial volume (TIV); (b) We validated the robustness of MIND-Gd network construction using Spearman correlation analysis; (c) We assessed the impact of cluster size thresholds (n = 5, 10, 15, 20) on network topology features. This step further confirmed the reliability of the case-control MIND-Gd difference statistics; (d) We randomly divided NSSI patients into two groups and compared MIND-Gd differences between the two NSSI subgroups and the control group without NSSI; (e) We evaluated the robustness of enrichment results using gene category enrichment analysis (GCEA) based on integrated null models [ 56 ]. 3. Results 3.1 Data samples and characteristics This study included 241 adolescent participants (mean age: 15.91 ± 2.15) who passed data quality control. Among them, 134 patients had MDD with NSSI (mean age: 15.41 ± 1.98), and 107 patients had MDD without NSSI (mean age: 16.53 ± 2.21). Details about image quality control, demographic information, clinical data, and global data residual analysis are provided in Additional file 1: Fig. S3, Table S4 and Table S5. The mean residuals were close to zero, which confirmed that the model was unbiased. 3.2 Case control differences in MIND-Gd In the regional principal MIND gradient, areas with similar connectivity patterns are shown in the same color. As illustrated in Fig. 2 a, the variance explained by the principal MIND gradient was generally similar between the NSSI and non-NSSI groups. Both groups exhibited higher values in the occipital cortex and lower values in the frontal and temporoparietal cortices. This pattern reflects the hierarchical organization of brain tissues. We then used a general linear model (GLM) with age, sex, and age × sex as covariates to compare the regional principal MIND gradient between the two groups (details in Additional file 1: Table S6). The case-control t-map showed that positive t-values indicate an increase in the principal MIND gradient in MDD patients, while negative t-values indicate a decrease. After controlling for covariates, the average distribution of the principal MIND gradient differed significantly between the two groups (two-sample Kolmogorov-Smirnov test, p = 1.11×10 − 4 , Fig. 2 b). The regional comparison revealed that NSSI patients showed an increase in the principal MIND gradient in the left caudal middle frontal (part4), left inferior temporal (part2), left middle temporal (part2), and right inferior temporal (part2) regions. In contrast, a decrease was observed in the left lingual (part3) and right superior parietal (part1) regions (Fig. 2 c and Additional file 1: Table S7). Additionally, after adjusting for the same covariates, we found a significant negative correlation between the average regional MIND gradient in non-NSSI patients and the case-control t-map (r = − 0.708, p < 0.01, Fig. 2 d). This result suggests that highly connected regions tend to show larger case-control differences. Moreover, the case-control differences were more pronounced when the MIND gradient scores were at extreme values. To further interpret the case-control differences in regional MIND gradients, we applied two existing cortical parcellation methods: the Yeo 7 functional network atlas and the von Economo cytoarchitectonic atlas. In the Yeo functional network, MDD patients with NSSI showed a marginally significant decrease in the principal MIND gradient within the dorsal attention network compared to those without NSSI (Fig. 2 e, Additional file 1: Fig. S4 and Table S8). In the von Economo cytoarchitectonic atlas, no significant changes in the principal MIND gradient were observed in cortical cell types between the two groups (Fig. 2 f, Additional file 1: Fig. S4 and Table S9). 3.3 Association of neurotransmitter receptor systems with MIND-Gd alterations We examined the association between regional MIND-Gd changes and whole-brain cortical neurotransmitter receptor maps. Among the 30 neurotransmitter maps provided by JuSpace, we observed that the case-control t-map showed significant spatial correlations with 9 neurotransmitters (Fig. 3 and Additional file 2: Sheet S2). 3.4 Transcription-neuroimaging associations We used the principal MIND gradient transcription matrix (152 brain regions × 15,632 genes) to identify gene expression patterns. Partial least squares regression (PLS) revealed that PLS component 1 explained 39.53% of the macroscopic structural variance. This value was significantly higher than random expectations (p spin <0.0001). Bootstrapping (n = 10,000) was used to assess the contributions of PLS1 and PLS2 to the case-control t-values. PLS1 contributed 73.12% (Additional file 2: Sheet S3 and Additional file 1: Fig. S5). Further analysis showed that the weighted gene expression map of PLS1 increased progressively from the precentral gyrus to the inferior temporal gyrus (Fig. 4 a). The PLS1 score showed a significant positive spatial correlation with the case-control t-map. (r = 0.557, p spin <0.001, Fig. 4 b). This indicates that genes contributing to PLS1 were overexpressed in regions where the principal MIND gradient increased in MDD patients with NSSI. Using z-scores, we identified 2,209 PLS1 + genes (Z > 4.19, BH-FDR corrected p < 0.0001) and 2,110 PLS1 − genes (Z < − 4.19, BH-FDR corrected p spin <0.0001) (Fig. 4 c, Additional file 2: Sheet S4). Since NSSI-related gene databases are limited, we combined findings from previous studies [ 57 , 58 ]. From the PLS1 + gene list, we identified two genes closely related to NSSI in MDD: GABRA5 and MSGT1. Both genes showed significant positive spatial correlations with the case-control t-map (p spin <0.0001, BH-FDR corrected, Fig. 4 d-e). This suggests that these genes were overexpressed in the non-NSSI group compared to the NSSI group. 3.5 Annotation of PLS-weighted genes for regional MIND-Gd changes To further elucidate the functional characteristics of genes associated with the regional principal MIND gradient changes, we used Metascape software to compared the functional enrichment results with the PLS1+/-gene list (Additional file 2: Sheet S4). The PLS1 + genes were significantly enriched in synaptic signaling and neurodevelopment-related pathways (Fig. 5 a-b), whereas the PLS1- genes were predominantly associated with chromatin regulation, cytoskeletal organization and nucleic acid metabolism processes (Fig. 5 c-d). Aligned the human diseases with the PLS1 genes, We found that the PLS1 + genes were significantly enriched in neurological and psychiatric disorders such as depression and epilepsy, whereas the PLS1- genes were enriched in neurodevelopmental disorders including autism and developmental delay (Fig. 5 e-f). The information aboved indicated that neurodevelopmental abnormalities, synaptic signaling abnormalities, and metabolic abnormalities may collectively participate in the process of principal MIND gradient changes in adolescent MDD patients with NSSI. 3.6 Cortical layer enrichment related to regional MIND-Gd changes Using cortical gene markers from previous studies, we established the relationship between PLS1+/- genes and different cortical layers (L1-L6) through laminar gene marker analysis (Additional file 2: Sheet S5). Notably, PLS1 + genes were significantly enriched in cortical layer I (gene overlap number = 87, BH-FDR corrected p = 0.015), layer II (gene overlap number = 140, BH-FDR corrected p < 0.001), and layer V (gene overlap number = 30, BH-FDR corrected p < 0.001). In contrast, PLS1 − genes were significantly enriched in layer III (gene overlap number = 63, BH-FDR corrected p < 0.001) and layer IV (gene overlap number = 83, BH-FDR corrected p < 0.001). There was no overlap in the enrichment of PLS1+/− genes across different cortical layers (Fig. 6 a-b). 3.7 Specific cell types enrichment related to regional MIND-Gd changes Given the complex interactions among different cell types in the brains of adolescents with MDD and NSSI, we further explored the cell-specific changes in the principal MIND gradient. To refine our analysis, we included seven types of central nervous system cells (Additional file 2: Sheet S6).The cell-type-specific expression analysis revealed significant associations between PLS1 + genes and excitatory neurons (gene overlap number = 238, BH-FDR corrected p < 0.001), astrocytes (gene overlap number = 212, BH-FDR corrected p < 0.001), and inhibitory neurons (gene overlap number = 173, BH-FDR corrected p < 0.001). In contrast, PLS1 − genes were significantly enriched in excitatory neurons (gene overlap number = 185, BH-FDR corrected p = 0.009) and inhibitory neurons (gene overlap number = 163, BH-FDR corrected ) (Fig. 6 c-d). Notably, both PLS1+/−genes were enriched in excitatory and inhibitory neurons. However, only PLS1 + genes showed a significant association with astrocytes. This suggests that the enrichment of PLS1 − genes may reflect a compensatory response in specific cell types during the disease process. 3.8 Sensitivity and robustness analyses We verified the reliability of our findings using various sensitivity and robustness analysis strategies, including: (a) Examining MIND case-control differences that were not sensitive to TIV (r = 1.000, p spin <0.001, Additional file 1: Fig. S6); (b) Using Spearman correlation instead of Pearson correlation to construct the MIND-Gd network and further validate the reliability of case-control differences. The results showed that the case-control differences in the principal MIND gradient based on Spearman correlation were highly reproducible (r = 0.999, p spin <0.001, Additional file 1: Fig. S7) ; (c) Evaluating network topological features under different cluster size thresholds (n = 5,10,15,20). The results indicated that the statistical differences in MIND-Gd between cases and controls remained highly consistent across thresholds. No significant differences were observed in clustering distribution patterns, node connectivity, or global topological metrics across thresholds (p > 0.05, Additional file 1: Table S10 and Fig. S8) ; (d) Dividing patients with NSSI into two random subgroups (subgroup 1 and subgroup 2) and assessing their MIND-Gd differences compared to the non-NSSI group. The case-control t-maps showed positive spatial correlations with subgroup 1 (r = 0.912, p spin =0.002) and subgroup 2 (r = 0.933, p spin <0.001, Additional file 1: Fig. S9) ; (e) Relatively reliable gene enrichment results (Additional file 1: Text and Additional file 2: Sheet S7). 4. Discussion To date, no studies have specifically investigated brain structural and network changes in adolescent MDD patients with or without NSSI. This study is the first to systematically explore brain structural changes and potential molecular mechanisms in adolescent MDD patients with and without NSSI. We achieved this using an integrated analytical approach. By constructing cortical structural similarity gradients based on the principal MIND gradient network, we identified clear case-control differences between the two groups. The results identified distinct alterations in neurotransmitter activity and structural connectivity among NSSI patients, highlighting the unique neurobiological features associated with this high-risk population. Using partial least squares (PLS) analysis, we found that the differences in MIND-Gd maps between cases and controls were significantly correlated with cortical gene expression maps.Through neurotransmitter-related analyses, we identified two key genes that may play a pivotal role in the pathophysiology of NSSI. Gene enrichment analysis showed that PLS1 + genes were significantly enriched in metabolic pathways. In contrast, PLS1 − genes were enriched in synaptic signaling pathways. Additionally, cortical laminar analysis revealed that PLS1 + genes were enriched in cortical layers I, II, and V. Meanwhile, PLS1 − genes were enriched in layers III and IV. Cell-type analysis further showed that PLS1 + genes were significantly enriched in astrocytes.These findings highlight the abnormal MIND-Gd phenotypes in adolescent MDD patients with NSSI. They also provide new insights into the relationship between large-scale structural abnormalities and microscopic transcriptional patterns during disease progression. The MIND network uses large-scale, multidimensional, vertex-level structural MRI data to construct a unified network for assessing cortical structural similarity. Compared with the MSN phenotype, the MIND phenotype is more reliable, better aligned with cellular structural characteristics, and more sensitive in detecting individual differences in human brain structural connectivity [ 22 ]. Furthermore, it isclosely linked to fundamental cortical properties, such as gene expression, cellular structure, myelin architecture, and evolutionary expansion [ 29 , 36 ]. Adolescent MDD, whether accompanied by NSSI or not, is a disorder that disrupts neuroplasticity through multiple factors. These include genetics, gene-environment interactions, neuroendocrine dysfunction, and inflammation [ 58 , 59 ]. To explore the differences between MDD patients with and without NSSI, we applied a neuroimaging model called the principal MIND gradient. Our results revealed region-specific alterations in the principal MIND gradient.‌ Specifically, our results showed that the principal MIND gradient ‌increased in frontal and temporal regions but decreased in parietal and occipital regions‌. Increased gradients suggest reduced structural differentiation, while decreased gradients indicate greater structural segregation in typically connected areas. In these regions, the left occipital area is part of the principal visual cortex, which processes visual information at its initial stage. The bilateral temporal pathways are involved in visual memory and object recognition. The right parietal region integrates spatial attention, visual input, and somatosensory information. Meanwhile, the left frontal region is responsible for attention regulation, decision-making, and executive functions [60, 61 , 62].We observed that the visual functional system plays a key role in these regions. This supports the hypothesis that visual dysfunction may be an important clinical feature of MDD [63]. Additionally, when analyzing the functions of these regions, we found that NSSI might be linked to the immaturity of rational decision-making during adolescence. This immaturity, combined with hyperactivity in the limbic system, could lead to emotional dysregulation triggered by visual perception and memory [64, 65 , 66]. We also identified attention-related mechanisms in NSSI [67]. Specifically, the right superior parietal lobule, a core node of the dorsal attention network (DAN), works with the left caudal middle frontal gyrus and the left middle temporal gyrus to regulate goal-directed attention. In our study, the marginal significance of DAN network edges further supported this finding. Notably, no cortical expression was detected in these regions.These findings suggest that adolescents with NSSI may exhibit compensatory responses in cognitive task processing and information transmission before adulthood. This could be closely related to the physiological, psychological, and social changes unique to adolescence [68, 69].Finally, our reproducibility analysis confirmed that the case-control differences in t-values were not influenced by factors such as total intracranial volume (TIV), correlation analysis methods, or cluster size thresholds. This demonstrates the robustness of our results. We analyzed how neurotransmitter receptors are distributed along the principal MIND gradient. The results showed that the spatial pattern of case-control t-maps was linked to the cortical density of nine receptors. These receptors primarily act through G-protein-coupled receptor (GPCR) signaling pathways. GPCR signaling plays a pivotal role in regulating neuronal communication, synaptic plasticity, and emotional processing, all of which are critical for understanding the pathophysiology of mood disorders and self-injurious behaviors. This highlights the importance of examining receptor-mediated signaling in the context of structural and functional brain alterations, suggesting that dysregulation of GPCR pathways may contribute to the neural mechanisms underlying psychiatric conditions. Among these receptors, studies on 5-HT neurotransmitters are the most extensive in self-injury research. Dysfunction of 5-HT1A and 5-HT4 receptors is closely related to emotional regulation disorders. Low 5-HT levels may increase the risk of self-injury [ 70 ]. Impaired GABA signaling disrupts the balance between excitation and inhibition in neural circuits. This imbalance can lead to anxiety and impulsive self-injurious behavior [ 71 ]. Dysfunction of the metabotropic glutamate receptor (mGluR5) may reduce emotional regulation and impulse control, increasing the risk of self-injury and suicide [ 72 ]. It may also worsen emotional and behavioral disorders by interfering with GABAergic neuron function. Reduced D2 receptor density may weaken an individual’s ability to experience pleasure from healthy activities. In contrast, the “release” after self-injury activates dopamine release in the striatum. This creates a pain-reward reinforcement loop, driving individuals to seek temporary emotional relief through self-injury [ 73 ]. Similarly, dysfunction of the µ-opioid receptor (MU) may link to pain-related pleasure and addiction [ 74 , 75 ]. The findings of this study also highlighted the role of dopamine and opioid systems in the development of NSSI. Specifically, we observed ‌reduced D2 receptor density‌, which may weaken the ability to experience pleasure from healthy activities, and ‌dysfunction of the MU‌, which could link to pain-related pleasure and addiction—both aligning with and extending previous findings [76, 77]. We performed pathway enrichment analysis using partial least squares regression (PLS). This analysis revealed key transcriptional features of the related genes. In the PLS1 + gene set, synapse-related functions were significantly enriched. These include "glutamatergic synapse" (GO:0060079), "chemical synaptic transmission" (GO:0007268), and "regulation of trans-synaptic signaling" (GO:0099177). Key genes, such as GABRA5, SYT17, KCNMB4, EFNB3, and SSTR1, are involved in synaptic receptor function, vesicle release, and maintaining signal balance [ 78 , 79 , 80 ]. The PLS1 + gene set also plays a role in neural development. For example, DPYSL3 is involved in axon guidance. It works together with cytoskeletal regulation genes like TMSB10 and IFT22 to regulate the growth and direction of neuronal projections [ 81 , 82 , 83 ]. This gene set is also linked to several disease phenotypes. These include mood disorders, epilepsy, and cognitive impairments. This suggests that the PLS1 + gene set may influence NSSI mechanisms in adolescent MDD patients through a "synapse-development-behavior" network. The PLS1- gene set showed strong associations with transcriptional and epigenetic regulation. It was enriched in pathways such as "transcription coregulator activity" (GO:0003712), "chromatin remodeling" (GO:0006338), and "chromatin binding" (GO:0003682). Core genes in this set include CUX1, NCOA3, and RORA. These genes regulate transcription and chromatin accessibility [ 84 , 85 , 86 ]. Additionally, the PLS1- gene set was enriched in pathways related to "centrosome" (GO:0005813), "microtubule binding" (GO:0015631), and "DNA metabolic process" (GO:0006259). Genes such as TUBD1, CEP95, EEPD1, and AGO1 are involved in structural maintenance and metabolic regulation [ 87 , 88 , 89 ]. Phenotypic analysis revealed that PLS1- genes are closely linked to neurodevelopmental disorders. These include intellectual and language delays, autism-related behaviors, and morphological abnormalities. Neural connectivity and metabolic abnormalities may play key roles in the development and progression of MDD with NSSI. The PLS1 + and PLS1- gene sets appear to have distinct roles. The former is mainly involved in synaptic and developmental regulation, while the latter focuses on basic cellular processes. These findings provide a framework for understanding their mechanisms and identifying potential pathways. We also identified two genes, GABRA5 and MSGT1, that overlap with neurotransmitter-related analysis. Studies show that variations in GABRA5 significantly increase the genetic risk of NSSI. These variations may promote self-injury tendencies by altering gray matter volume in the temporal lobe and disrupting the social-emotional network [ 90 ]. Clinically, polygenic risk scores based on GABRA5 could help identify adolescents at high risk for NSSI. However, environmental factors, such as childhood trauma, should also be considered. MSGT1 regulates B-cell activation and cytokine release, such as IL-6. This is closely related to the inflammatory hypothesis of MDD [ 91 ]. Through the combined analysis of cortical layer markers and single-cell expression data, we found that PLS1+/- genes are differentially enriched across cortical layers. Further analysis of gene enrichment in different brain cell types showed that both PLS1+/- are enriched in excitatory and inhibitory neurons. This finding implies that these genes may contribute to the fine-tuning of synaptic transmission and network oscillations, which are critical for maintaining cognitive flexibility and emotional regulation - processes frequently disrupted in mood disorders. However, PLS1 + also shows significant expression in astrocytes. This result aligns with the neurotransmitter analysis, which identified shared cell types enriched in both PLS1+/-. It also highlights the regulatory role of astrocytes in maintaining neuronal and synaptic physiology during brain homeostasis. Combined with the findings on the MSGT1 gene, this supports the inflammation hypothesis of MDD. This hypothesis suggests that cytokines released by astrocytes mediate neuroinflammation [ 92 , 93 ]. Overall, our results suggest that neurons, astrocytes, and their unique connectivity patterns within affected brain regions might offer critical neurobiological insights into the pathophysiological mechanisms underlying adolescent MDD with NSSI. This underscores the vital importance of early intervention strategies during this vulnerable developmental period. Given that early intervention during this vulnerable phase may mitigate long-term disease progression and reduce the severity of psychiatric outcomes, targeted therapeutic approaches based on identified neurobiological markers could offer more precise and effective treatment strategies. The ultimate goal of this study is to identify robust neurobiological signatures that can inform both diagnostic classification and intervention timing, with the potential to develop personalized treatment protocols that address the specific neural circuit abnormalities associated with adolescent MDD and NSSI. Such neurobiological markers may serve as objective measures for monitoring treatment response and predicting clinical outcomes, thereby advancing our understanding of the neural mechanisms underlying these conditions and facilitating the development of more targeted therapeutic interventions. While this study makes significant contributions to elucidating the neural correlates of mood disorders through its innovative application of the principal MIND gradient in investigating brain structural changes, several limitations warrant consideration.‌ First, we investigated brain structural changes by a new MIND gradient, but the MIND network was built using T1 image and 5 morphological features [28,94]. Future studies should take into account larger scale microstructural indices. The combination of these with multimodal imaging information would help to investigate the hierarchical organization of morphological feature.Second, a relatively small sample size limits the generality of our results.More samples are needed to assess individual differences in MIND gradients.Third, we did not discuss some clinical variables that are known to be playing a significant role in MDD from NSSIs, such as impulsive personality traits, childhood experience, and social conditions. The study should consider them for their role in the disease dynamics.Finally, we used gene expression profiles from the AHBA database. They are affected by age, sex, and ethnicity [95]. Moreover, only left hemisphere data were used, which may have affected our results in this study.Future studies could be used with whole-brain transcriptomic datasets matching age,sex, and the ethnicity. Replication by similar studies could also be made. 5. Conclusion In conclusion, this study's findings strongly support our hypothesis that significant alterations in the principal MIND gradient exist between adolescent MDD patients with and without NSSI. ‌Our results delineate a clear regional pattern: the gradient is markedly increased in frontal and temporal regions, while it is decreased in parietal and occipital regions.‌ Crucially, these alterations not only exhibit striking spatial correlations with specific neuroreceptor maps but are also highly enriched in neurobiologically relevant pathways and preferentially expressed across distinct cortical layers and cell types. Taken together, our findings offer a groundbreaking perspective on structural coordination changes in adolescent MDD patients with NSSI. This may introduce a novel endophenotype, opening a valuable research avenue for deeper exploration of the disorder's complex mechanisms. Abbreviations AHBA Allen Human Brain Atlas Cluster-FDR Cluster-level False Discovery Rate CT Cortical Thickness DAN Dorsal Attention Network DWI Diffusion Weighted Imaging FDR False Discovery Rate GLM General Linear Model GMV Gray Matter Volume MC Mean Curvature MDD Major Depressive Disorder MIND Morphometric Inverse Divergence MIND-Gd Morphometric Inverse Divergence Gradient MSN Morphometric Similarity Network MRI Magnetic Resonance Imaging NSSI Non-Suicidal Self-Injury PLS Partial Least Squares Regression PLS1+/PLS1- Genes PLS component 1 positive/negative weight genes ICD-10 International Classification of Diseases, 10th Revision SA Surface Area SD Sulcal Depth SCN Structural Covariance Networks Declarations Ethics approval and consent to participate This study was conducted in accordance with the Declaration of Helsinki. Ethical approval was obtained from the Ethics Committee of the Shandong Mental Health Center (ref: KYSJWLL2024-1-075). As this was a retrospective analysis utilizing data from an imaging database, the requirement for informed consent was waived. Consent for publication Not applicable. Availability of data and materials The code for calculating the MIND could be found on the github: https://github.com/isebenius/MIND The toolbox for calculating the Gradient could be found on the github: https://github.com/MICA-MNI/BrainSpace The code for PLS analysis could be available at the github: https://github.com/SarahMorgan/Morphometric_Similarity_SZ The code for the computation of spatial permutation testing could be available at the github: https://github.com/frantisekvasa/rotate_parcellation Human gene expression data that support the findings of this study are available in the Allen Brain Atlas: http://human.brain-map.org/static/download The codes for gene expression analysis can be found at https://github.com/rmarkello/abagen The Metascape of gene enrichment analysis is available at http://metascape.org/ Other data will be made available on request. Competing interests Not applicable. Funding 1.National Natural Science Foundation of China (grant 62406128) 2.Shandong Provincial Medical and Health Science and Technology Project (2024), No. 202403091056 3.Sichuan VS Mental Health Survey and Early Intervention, SCDVA (2023), No. 05 Authors' contributions Yan Zhang: Writing – original draft, Writing – review & editing, Data curation, Methodology, Visualization Formal analysis, Conceptualization. Xinyuan Hu: Writing – original draft, Data curation, Methodology, Formal analysis, Visualization. Wei Wang: Writing – review & editing, Formal analysis, Visualization, Investigation. Jing Wang: Writing – review & editing, Funding acquisition, Software, Visualization, Methodology, Resources. Hongying Li: Visualization, Funding acquisition, Methodology, Resources. Jianlei Zhu: Project administration, Methodology, Investigation. Yumei Wan: Writing – review & editing, Investigation. Fei Liu: Data curation, Validation. Shiyue Tao: Data curation, Formal analysis. Duanwei Wang: Investigation, Resources. Xiangtao Lin: Supervision, Investigation, Conceptualization. Yingying Zhang: Investigation, Resources. Hongmei Zhang: Writing – review & editing, Methodology, Resources, Methodology, Investigation. Yuandong Gong: Writing – review & editing, Funding acquisition, Supervision, Formal analysis, Resources, Investigation, Conceptualization. Acknowledgements Not applicable. References Filatova EV, Shadrina MI, Slominsky PA. Major depression: one brain, one disease, one set of intertwined processes. Cells. 2021; doi:10.3390/cells10061283. Gore FM, Bloem PJ, Patton GC, Ferguson J, Joseph V, Coffey C, et al. Global burden of disease in young people aged 10–24 years: a systematic analysis. The Lancet. 2011; doi:10.1016/S0140-6736(11)60512-6. Li Y, Wan Z, Gong X, Wen L, Sun T, Liu J, et al. The association between child maltreatment, cognitive reappraisal, negative coping styles, and non-suicidal self-injury in adolescents with major depressive disorder. BMC Psychiatry. 2024; doi:10.1186/s12888-024-06041-2. Hu C, Jiang W, Wu Y, Wang M, Lin J, Chen S, et al. Microstructural abnormalities of white matter in the cingulum bundle of adolescents with major depression and non-suicidal self-injury. Psychol Med. 2024; doi:10.1017/S003329172300291X. Hooley JM, Fox KR, Boccagno C. Nonsuicidal self-injury: diagnostic challenges and current perspectives. Neuropsychiatr Dis Treat. 2020; doi:10.2147/NDT.S198806. Voss C, Hoyer J, Venz J, Pieper L, Beesdo‐Baum K. Non‐suicidal self‐injury and its co‐occurrence with suicidal behavior: an epidemiological study among adolescents and young adults. Acta Psychiatr Scand. 2020; doi:10.1111/acps.13237. Plener PL, Libal G, Keller F, Fegert JM, Muehlenkamp JJ. An international comparison of adolescent non-suicidal self-injury (NSSI) and suicide attempts: Germany and the USA. Psychol Med. 2009; doi:10.1017/S0033291708005114. Brown RC, Plener PL. Non-suicidal self-injury in adolescence. Curr Psychiatry Rep. 2017; doi:10.1007/s11920-017-0767-9. Mars B, Heron J, Klonsky ED, Moran P, O’Connor RC, Tilling K, et al. Predictors of future suicide attempt among adolescents with suicidal thoughts or non-suicidal self-harm: a population-based birth cohort study. Lancet Psychiatry. 2019; doi:10.1016/S2215-0366(19)30030-6. Mannekote Thippaiah S, Shankarapura Nanjappa M, Gude JG, Voyiaziakis E, Patwa S, Birur B, et al. Non-suicidal self-injury in developing countries: a review. Int J Soc Psychiatry. 2021; doi:10.1177/0020764020943627. Lawrence HR, Balkind EG, Ji JL, Burke TA, Liu RT. Mental imagery of suicide and non-suicidal self-injury: a meta-analysis and systematic review. Clin Psychol Rev. 2023; doi:10.1016/j.cpr.2023.102302. Pang X, Wu D, Wang H, Zhang J, Yu Y, Zhao Y, et al. Cortical morphological alterations in adolescents with major depression and non-suicidal self-injury. NeuroImage Clin. 2024; doi:10.1016/j.nicl.2024.103701. Liang S, Xue K, Wang W, Yu W, Ma X, Luo S, et al. Altered brain function and clinical features in patients with first-episode, drug naïve major depressive disorder: a resting-state fMRI study. Psychiatry Res Neuroimaging. 2020; doi:10.1016/j.pscychresns.2020.111134. Zhang J, Wu D, Wang H, Yu Y, Zhao Y, Zheng H, et al. Large-scale functional network connectivity alterations in adolescents with major depression and non-suicidal self-injury. Behav Brain Res. 2025; doi:10.1016/j.bbr.2025.115443. Yang H, Chen X, Chen ZB, Li L, Li XY, Castellanos FX, et al. Disrupted intrinsic functional brain topology in patients with major depressive disorder. Mol Psychiatry. 2021; doi:10.1038/s41380-021-01247-2. Xue K, Guo L, Zhu W, Liang S, Xu Q, Ma L, et al. Transcriptional signatures of the cortical morphometric similarity network gradient in first-episode, treatment-naive major depressive disorder. Neuropsychopharmacology. 2023; doi:10.1038/s41386-022-01474-3. Xue K, Liu F, Liang S, Guo L, Shan Y, Xu H, et al. Brain connectivity and transcriptomic similarity inform abnormal morphometric similarity patterns in first-episode, treatment-naïve major depressive disorder. J Affect Disord. 2025; doi:10.1016/j.jad.2024.11.021. Zielinski BA, Gennatas ED, Zhou J, Seeley WW. Network-level structural covariance in the developing brain. Proc Natl Acad Sci. 2010; doi:10.1073/pnas.1003109107. Singh MK, Kesler SR, Hadi Hosseini SM, Kelley RG, Amatya D, Hamilton JP, et al. Anomalous gray matter structural networks in major depressive disorder. Biol Psychiatry. 2013; doi:10.1016/j.biopsych.2013.03.005. Repple J, Mauritz M, Meinert S, De Lange SC, Grotegerd D, Opel N, et al. Severity of current depression and remission status are associated with structural connectome alterations in major depressive disorder. Mol Psychiatry. 2020; doi:10.1038/s41380-019-0603-1. Li J, Seidlitz J, Suckling J, Fan F, Ji GJ, Meng Y, et al. Cortical structural differences in major depressive disorder correlate with cell type-specific transcriptional signatures. Nat Commun. 2021; doi:10.1038/s41467-021-21943-5. Sebenius I, Seidlitz J, Warrier V, Bethlehem RAI, Alexander-Bloch A, Mallard TT, et al. Robust estimation of cortical similarity networks from brain MRI. Nat Neurosci. 2023; doi:10.1038/s41593-023-01376-7. Donahue CJ, Sotiropoulos SN, Jbabdi S, Hernandez-Fernandez M, Behrens TE, Dyrby TB, et al. Using diffusion tractography to predict cortical connection strength and distance: a quantitative comparison with tracers in the monkey. J Neurosci. 2016; doi:10.1523/JNEUROSCI.0493-16.2016. Alexander-Bloch A, Giedd JN, Bullmore E. Imaging structural co-variance between human brain regions. Nat Rev Neurosci. 2013; doi:10.1038/nrn3465. Wang J, He Y. Toward individualized connectomes of brain morphology. Trends Neurosci. 2024; doi:10.1016/j.tins.2023.11.011. Sun H, Sun Q, Li Y, Zhang J, Xing H, Wang J. Mapping individual structural covariance network in development brain with dynamic time warping. Cereb Cortex. 2024; doi:10.1093/cercor/bhae039. Huntenburg JM, Bazin PL, Margulies DS. Large-scale gradients in human cortical organization. Trends Cogn Sci. 2018; doi:10.1016/j.tics.2017.11.002. Yang S, Wagstyl K, Meng Y, Zhao X, Li J, Zhong P, et al. Cortical patterning of morphometric similarity gradient reveals diverged hierarchical organization in sensory-motor cortices. Cell Rep. 2021; doi:10.1016/j.celrep.2021.109582. Havinga PJ, Boschloo L, Bloemen AJP, Nauta MH, De Vries SO, Penninx BWJH, et al. Doomed for disorder? High incidence of mood and anxiety disorders in offspring of depressed and anxious patients: a prospective cohort study. J Clin Psychiatry. 2017; doi:10.4088/JCP.15m09936. Meng X, Navoly G, Giannakopoulou O, Levey DF, Koller D, Pathak GA, et al. Multi-ancestry genome-wide association study of major depression aids locus discovery, fine mapping, gene prioritization and causal inference. Nat Genet. 2024; doi:10.1038/s41588-023-01596-4. Liu M, Wang L, Zhang Y, Dong H, Wang C, Chen Y, et al. Investigating the shared genetic architecture between depression and subcortical volumes. Nat Commun. 2024; doi:10.1038/s41467-024-52121-y. Anderson KM, Collins MA, Kong R, Fang K, Li J, He T, et al. Convergent molecular, cellular, and cortical neuroimaging signatures of major depressive disorder. Proc Natl Acad Sci. 2020; doi:10.1073/pnas.2008004117. Wang H, Zhao Q, Zhang Y, Ma J, Lei M, Zhang Z, et al. Shared genetic architecture of cortical thickness alterations in major depressive disorder and schizophrenia. Prog Neuropsychopharmacol Biol Psychiatry. 2024; doi:10.1016/j.pnpbp.2024.111121. Fischl B. FreeSurfer. NeuroImage. 2012; doi:10.1016/j.neuroimage.2012.01.021. Rosen AFG, Roalf DR, Ruparel K, Blake J, Seelaus K, Villa LP, et al. Quantitative assessment of structural image quality. NeuroImage. 2018; doi:10.1016/j.neuroimage.2017.12.059. Monereo-Sánchez J, De Jong JJA, Drenthen GS, Beran M, Backes WH, Stehouwer CDA, et al. Quality control strategies for brain MRI segmentation and parcellation: practical approaches and recommendations - insights from the Maastricht study. NeuroImage. 2021; doi:10.1016/j.neuroimage.2021.118174. Desikan RS, Ségonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage. 2006; doi:10.1016/j.neuroimage.2006.01.021. Romero-Garcia R, Atienza M, Clemmensen LH, Cantero JL. Effects of network resolution on topological properties of human neocortex. NeuroImage. 2012; doi:10.1016/j.neuroimage.2011.10.086. Vos De Wael R, Benkarim O, Paquola C, Lariviere S, Royer J, Tavakol S, et al. BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets. Commun Biol. 2020; doi:10.1038/s42003-020-0794-7. Wagstyl K, Larocque S, Cucurull G, Lepage C, Cohen JP, Bludau S, et al. BigBrain 3D atlas of cortical layers: cortical and laminar thickness gradients diverge in sensory and motor cortices. PLOS Biol. 2020; doi:10.1371/journal.pbio.3000678. Thomas Yeo BT, Krienen FM, Sepulcre J, Sabuncu MR, Lashkari D, Hollinshead M, et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol. 2011; doi:10.1152/jn.00338.2011. Von Economo C, Koskinas GN. Atlas of cytoarchitectonics of the adult human cerebral cortex. Basel, Switzerland: Karger; 2008. Dukart J, Holiga S, Rullmann M, Lanzenberger R, Hawkins PCT, Mehta MA, et al. JuSpace: a tool for spatial correlation analyses of magnetic resonance imaging data with nuclear imaging derived neurotransmitter maps. Hum Brain Mapp. 2021; doi:10.1002/hbm.25244. Yang C, Zhang L, Liu J, Li K, Li S, Yang Z, et al. More severe brain network hierarchy disorganization in treatment-naive deficit compared to non-deficit schizophrenia and underlying neurotransmitter associations. Schizophr Bull. 2026; doi:10.1093/schbul/sbae231. Hawrylycz MJ, Lein ES, Guillozet-Bongaarts AL, Shen EH, Ng L, Miller JA, et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature. 2012; doi:10.1038/nature11405. Markello RD, Arnatkeviciute A, Poline JB, Fulcher BD, Fornito A, Misic B. Standardizing workflows in imaging transcriptomics with the abagen toolbox. eLife. 2021; doi:10.7554/eLife.72129. Krishnan A, Williams LJ, McIntosh AR, Abdi H. Partial least squares (PLS) methods for neuroimaging: a tutorial and review. NeuroImage. 2011; doi:10.1016/j.neuroimage.2010.07.034. Abdi H, Williams LJ. Partial least squares methods: partial least squares correlation and partial least square regression. In: Reisfeld B, Mayeno AN, editors. Computational Toxicology. Totowa, NJ, USA: Humana Press; 2013. p. 549–79. doi:10.1007/978-1-62703-059-5_23. Mao H, Xu M, Wang H, Liu Y, Wang F, Gao Q, et al. Transcriptional patterns of brain structural abnormalities in CSVD-related cognitive impairment. Front Aging Neurosci. 2024; doi:10.3389/fnagi.2024.1503806. Váša F, Seidlitz J, Romero-Garcia R, Whitaker KJ, Rosenthal G, Vértes PE, et al. Adolescent tuning of association cortex in human structural brain networks. Cereb Cortex. 2018; doi:10.1093/cercor/bhx249. Drevets WC, Wittenberg GM, Bullmore ET, Manji HK. Immune targets for therapeutic development in depression: towards precision medicine. Nat Rev Drug Discov. 2022; doi:10.1038/s41573-021-00368-1. Chawla A, Cakmakci D, Fiori LM, Zang W, Maitra M, Yang J, et al. Single-nucleus chromatin accessibility profiling identifies cell types and functional variants contributing to major depression. Nat Genet. 2025; doi:10.1038/s41588-025-02249-4. Zhou Y, Zhou B, Pache L, Chang M, Khodabakhshi AH, Tanaseichuk O, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 2019; doi:10.1038/s41467-019-09234-6. He Z, Han D, Efimova O, Guijarro P, Yu Q, Oleksiak A, et al. Comprehensive transcriptome analysis of neocortical layers in humans, chimpanzees and macaques. Nat Neurosci. 2017; doi:10.1038/nn.4548. Alexander-Bloch AF, Shou H, Liu S, Satterthwaite TD, Glahn DC, Shinohara RT, et al. On testing for spatial correspondence between maps of human brain structure and function. NeuroImage. 2018; doi:10.1016/j.neuroimage.2018.05.070. Liu S, Zhao W, Li Y, Li X, Li J, Cao H, et al. Improve cognition of depressive patients through the regulation of basal ganglia connectivity: combined medication using Shuganjieyu capsule. J Psychiatr Res. 2020; doi:10.1016/j.jpsychires.2020.01.013. Fulcher BD, Arnatkeviciute A, Fornito A. Overcoming false-positive gene-category enrichment in the analysis of spatially resolved transcriptomic brain atlas data. Nat Commun. 2021; doi:10.1038/s41467-021-22862-1. Otte C, Gold SM, Penninx BW, Pariante CM, Etkin A, Fava M, et al. Major depressive disorder. Nat Rev Dis Primer. 2016; doi:10.1038/nrdp.2016.65. Liu J, Guan J, Xiong J, Wang F. Effects of transcranial magnetic stimulation combined with sertraline on cognitive level, inflammatory response and neurological function in depressive disorder patients with non-suicidal self-injury behavior. Actas Esp Psiquiatr. 2024; doi:10.62641/aep.v52i1.1542. Kang DW, Wang SM, Na HR, Park SY, Kim NY, Lee CU, et al. Differences in cortical structure between cognitively normal East Asian and Caucasian older adults: a surface-based morphometry study. Sci Rep. 2020; doi:10.1038/s41598-020-77848-8. Yomogida Y, Matsumoto M, Aoki R, Sugiura A, Phillips AN, Matsumoto K. The neural basis of changing social norms through persuasion. Sci Rep. 2017; doi:10.1038/s41598-017-16572-2. Patil AU, Ghate S, Madathil D, Tzeng OJL, Huang HW, Huang CM. Static and dynamic functional connectivity supports the configuration of brain networks associated with creative cognition. Sci Rep. 2021; doi:10.1038/s41598-020-80293-2. Lu F, Cui Q, Huang X, Li L, Duan X, Chen H, et al. Anomalous intrinsic connectivity within and between visual and auditory networks in major depressive disorder. Prog Neuropsychopharmacol Biol Psychiatry. 2020; doi:10.1016/j.pnpbp.2020.109889. Plener PL, Bubalo N, Fladung AK, Ludolph AG, Lulé D. Prone to excitement: adolescent females with non-suicidal self-injury (NSSI) show altered cortical pattern to emotional and NSS-related material. Psychiatry Res Neuroimaging. 2012; doi:10.1016/j.pscychresns.2011.12.012. Huang Q, Xiao M, Ai M, Chen J, Wang W, Hu L, et al. Disruption of neural activity and functional connectivity in adolescents with major depressive disorder who engage in non-suicidal self-injury: a resting-state fMRI study. Front Psychiatry. 2021; doi:10.3389/fpsyt.2021.571532. Zhou Y, Yu R, Ai M, Cao J, Li X, Hong S, et al. A resting state functional magnetic resonance imaging study of unmedicated adolescents with non-suicidal self-injury behaviors: evidence from the amplitude of low-frequency fluctuation and regional homogeneity indicator. Front Psychiatry. 2022; doi:10.3389/fpsyt.2022.925672. Lu X, Zhang Y, Zhong S, Lai S, Yan S, Song X, et al. Cognitive impairment in major depressive disorder with non-suicidal self-injury: association with the functional connectivity of frontotemporal cortex. J Psychiatr Res. 2024; doi:10.1016/j.jpsychires.2024.07.008. Wang H, Wen S, Wang Y, Zhou Y, Niu B. Rumination, loneliness, and non-suicidal self-injury among adolescents with major depressive disorder: the moderating role of resilience. Soc Sci Med. 2025; doi:10.1016/j.socscimed.2024.117512. Niu HM, Zhang ZM, Mu XM, Zhao HJ. The characteristics and influencing factors of nonsuicidal self-injury of adolescents with depressive disorder in China: a meta-analysis. J Nerv Ment Dis. 2023; doi:10.1097/NMD.0000000000001641. Wood EK, Kruger R, Day JP, Day SM, Hunter JN, Neville L, et al. A nonhuman primate model of human non-suicidal self-injury: serotonin-transporter genotype-mediated typologies. Neuropsychopharmacology. 2022; doi:10.1038/s41386-021-00994-8. Deligiannidis KM, Clayton AH. Patient-specific considerations, the GABA pathway, and new clinical trial data on neuroactive steroids in MDD and PPD. J Clin Psychiatry. 2023; doi:10.4088/JCP.SG22045SU1C. Davis MT, Hillmer A, Holmes SE, Pietrzak RH, DellaGioia N, Nabulsi N, et al. In vivo evidence for dysregulation of mGluR5 as a biomarker of suicidal ideation. Proc Natl Acad Sci. 2019; doi:10.1073/pnas.1818871116. Qing Xu, Haoming Xu, Shuhan Li. Alpha-2-macroglobulin (A2M) and dopamine receptor D2 (DRD2) expression analysis and influence of separation from parents in childhood on the suicide and self-injury behavior and psychological adjustment in adolescence. Cell Mol Biol. 2023; doi:10.14715/cmb/2022.69.1.12. Störkel LM, Karabatsiakis A, Hepp J, Kolassa IT, Schmahl C, Niedtfeld I. Salivary beta-endorphin in nonsuicidal self-injury: an ambulatory assessment study. Neuropsychopharmacology. 2021; doi:10.1038/s41386-020-00914-2. Bresin K, Gordon KH. Endogenous opioids and nonsuicidal self-injury: a mechanism of affect regulation. Neurosci Biobehav Rev. 2013; doi:10.1016/j.neubiorev.2013.01.020. Mantas I, Saarinen M, Xu ZQD, Svenningsson P. Update on GPCR-based targets for the development of novel antidepressants. Mol Psychiatry. 2022; doi:10.1038/s41380-021-01040-1. Monfared RV, Alhassen W, Truong TM, Gonzales MAM, Vachirakorntong V, Chen S, et al. Transcriptome profiling of dysregulated GPCRs reveals overlapping patterns across psychiatric disorders and age-disease interactions. Cells. 2021; doi:10.3390/cells10112967. Romero-Garcia R, Warrier V, Bullmore ET, Baron-Cohen S, Bethlehem RAI. Synaptic and transcriptionally downregulated genes are associated with cortical thickness differences in autism. Mol Psychiatry. 2019; doi:10.1038/s41380-018-0023-7. Zhu XN, Liu XD, Sun S, Zhuang H, Yang JY, Henkemeyer M, et al. Ephrin-B3 coordinates timed axon targeting and amygdala spinogenesis for innate fear behaviour. Nat Commun. 2016; doi:10.1038/ncomms11096. Brenner R, Chen QH, Vilaythong A, Toney GM, Noebels JL, Aldrich RW. BK channel β4 subunit reduces dentate gyrus excitability and protects against temporal lobe seizures. Nat Neurosci. 2005; doi:10.1038/nn1573. Van Der Ende EL, In ‘T Veld SGJG, Hanskamp I, Van Der Lee S, Dijkstra JIR, Hok-A-Hin YS, et al. CSF proteomics in autosomal dominant Alzheimer’s disease highlights parallels with sporadic disease. Brain. 2023; doi:10.1093/brain/awad213. Zhao H, Chen P, Gao X, Huang Z, Yang P, Shen H. Spatiotemporal proteomic and transcriptomic landscape of DAT+ dopaminergic neurons development and function. iScience. 2025; doi:10.1016/j.isci.2025.112115. Sasayama D, Hiraishi A, Tatsumi M, Kamijima K, Ikeda M, Umene-Nakano W, et al. Possible association of CUX1 gene polymorphisms with antidepressant response in major depressive disorder. Pharmacogenomics J. 2013; doi:10.1038/tpj.2012.18. Rybczyński P, Cacala R, Cepil Z, Fic E, Romanska W, Marczak L, et al. Contrasting effects of clozapine and risperidone on cholesterol metabolism, synaptic proteins, and transcriptional regulation in human LUHMES neurons. Mol Neurobiol. 2026; doi:10.1007/s12035-025-05515-y. Logue MW, Baldwin C, Guffanti G, Melista E, Wolf EJ, Reardon AF, et al. A genome-wide association study of post-traumatic stress disorder identifies the retinoid-related orphan receptor alpha (RORA) gene as a significant risk locus. Mol Psychiatry. 2013; doi:10.1038/mp.2012.113. Szklarczyk D, Gable AL, Nastou KC, Lyon D, Kirsch R, Pyysalo S, et al. The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021; doi:10.1093/nar/gkaa1074. Liu S, Jia L, Quan B, Rong G, Li M, Xie R, et al. Coiled-coil domain-containing protein 45 is a potential prognostic biomarker and is associated with immune cell enrichment of hepatocellular carcinoma. Dis Markers. 2022; doi:10.1155/2022/7745315. Nickoloff JA, Sharma N, Taylor L, Allen SJ, Lee SH, Hromas R. Metnase and EEPD1: DNA repair functions and potential targets in cancer therapy. Front Oncol. 2022; doi:10.3389/fonc.2022.808757. Niu Y, Qian Q, Li J, Gong P, Jiao X, Mao X, et al. De novo variants in AGO1 recapitulate a heterogeneous neurodevelopmental disorder phenotype. Clin Genet. 2022; doi:10.1111/cge.14114. Sun Y, Zhao G, Zhang Y, Lu Z, Kang Z, Sun J, et al. Multitrait GWAS of non-suicidal self-injury and the polygenetic effects on child psychopathology and brain structures. Cell Rep Med. 2025; doi:10.1016/j.xcrm.2025.102119. Morabito S, Miyoshi E, Michael N, Shahin S, Martini AC, Head E, et al. Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer’s disease. Nat Genet. 2021; doi:10.1038/s41588-021-00894-z. González-Arias C, Sánchez-Ruiz A, Esparza J, Sánchez-Puelles C, Arancibia L, Ramírez-Franco J, et al. Dysfunctional serotonergic neuron-astrocyte signaling in depressive-like states. Mol Psychiatry. 2023; doi:10.1038/s41380-023-02269-8. Zhao YF, Verkhratsky A, Tang Y, Illes P. Astrocytes and major depression: the purinergic avenue. Neuropharmacology. 2022; doi:10.1016/j.neuropharm.2022.109252. King DJ, Wood AG. Clinically feasible brain morphometric similarity network construction approaches with restricted magnetic resonance imaging acquisitions. Netw Neurosci. 2020; doi:10.1162/netn_a_00123. Mogil LS, Andaleon A, Badalamenti A, Dickinson SP, Guo X, Rotter JI, et al. Genetic architecture of gene expression traits across diverse populations. PLOS Genet. 2018; doi:10.1371/journal.pgen.1007586. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1.docx Additionalfile2.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8994845","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":603451693,"identity":"324b57c6-086d-4e7f-95cc-7afacdb91d36","order_by":0,"name":"Yan Zhang","email":"","orcid":"","institution":"Shandong Mental Health Center, Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Zhang","suffix":""},{"id":603451694,"identity":"60b02f01-55ba-436c-a529-5aac9d797eb7","order_by":1,"name":"Xinyuan Hu","email":"","orcid":"","institution":"Shandong Provincial Key Medical and Health Laboratory of Digital Psychiatry, Shandong Mental Health Center, Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Xinyuan","middleName":"","lastName":"Hu","suffix":""},{"id":603451695,"identity":"a71cbcbf-202b-45b8-ac87-972edba85ce8","order_by":2,"name":"Wei Wang","email":"","orcid":"","institution":"The Second Veterans Hospital of Sichuan","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Wang","suffix":""},{"id":603451696,"identity":"3cc9f570-6792-4307-826f-8f1cf811cb01","order_by":3,"name":"Jing Wang","email":"","orcid":"","institution":"School of Information Science and Engineering, University of Jinan","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Wang","suffix":""},{"id":603451697,"identity":"d6596d3e-fc7d-4192-86e8-b13decffdc5a","order_by":4,"name":"Hongying Li","email":"","orcid":"","institution":"The Second Veterans Hospital of Sichuan","correspondingAuthor":false,"prefix":"","firstName":"Hongying","middleName":"","lastName":"Li","suffix":""},{"id":603451698,"identity":"577b1bf4-bc2f-4eeb-b2ff-629cc79d17c4","order_by":5,"name":"Jianlei Zhu","email":"","orcid":"","institution":"Shandong Mental Health Center, Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Jianlei","middleName":"","lastName":"Zhu","suffix":""},{"id":603451699,"identity":"597eb21f-af2f-4b11-979c-5fbea34381f2","order_by":6,"name":"Yumei Wan","email":"","orcid":"","institution":"Shandong Mental Health Center, Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Yumei","middleName":"","lastName":"Wan","suffix":""},{"id":603451700,"identity":"906bdf1a-e55f-4bc4-a001-ee7c9f0e7c14","order_by":7,"name":"Fei Liu","email":"","orcid":"","institution":"Shandong Mental Health Center, Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Liu","suffix":""},{"id":603451701,"identity":"7e9f2737-5ec1-46e5-ba87-171833641c1b","order_by":8,"name":"Shiyue Tao","email":"","orcid":"","institution":"Shandong Mental Health Center, Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Shiyue","middleName":"","lastName":"Tao","suffix":""},{"id":603451702,"identity":"092ae40f-8366-4006-9d79-a8b743696698","order_by":9,"name":"Duanwei Wang","email":"","orcid":"","institution":"Shandong Mental Health Center, Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Duanwei","middleName":"","lastName":"Wang","suffix":""},{"id":603451703,"identity":"797038ca-a837-4a5c-94ad-a63fabf24e35","order_by":10,"name":"Xiangtao Lin","email":"","orcid":"","institution":"Cheeloo College of Medicine, Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Xiangtao","middleName":"","lastName":"Lin","suffix":""},{"id":603451704,"identity":"da59f4e5-12ab-4cdd-bc3f-8f3243ebbc3f","order_by":11,"name":"Yingying Zhang","email":"","orcid":"","institution":"Medical Management Service Center of Health Commission of Shandong Province","correspondingAuthor":false,"prefix":"","firstName":"Yingying","middleName":"","lastName":"Zhang","suffix":""},{"id":603451705,"identity":"74fde001-4661-42b0-b5b7-54d50b3f06c4","order_by":12,"name":"Hongmei Zhang","email":"","orcid":"","institution":"The Second Veterans Hospital of Sichuan","correspondingAuthor":false,"prefix":"","firstName":"Hongmei","middleName":"","lastName":"Zhang","suffix":""},{"id":603451706,"identity":"3fa01105-5f16-4865-b3e7-8ab97e2b11ab","order_by":13,"name":"Yuandong Gong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYBAC+/b+hw8+VEjI8bM3EKnFgOcMs+GMMxbGkj0HiNUikcMmzdtWkbjhRgKRWswZcg8bzmCTSNxw8/HGGww1NtEEtVg2nEt88IFHwnjm7bRiC4ZjabkNBPUcbDA2nCEhIdt3O8dMgrHhMBFaDjOYSfMYABXfPEOkFoNjPEAtCRKKE27wEKlFsoct2XDGAQlgIAP9kkCMX/jlHx988PFfHTAqD2+88aHGhgi/IDtSIoEU5RAtpOoYBaNgFIyCkQEApcJDU+sVWAYAAAAASUVORK5CYII=","orcid":"","institution":"Shandong Provincial Key Medical and Health Laboratory of Digital Psychiatry, Shandong Mental Health Center, Shandong University","correspondingAuthor":true,"prefix":"","firstName":"Yuandong","middleName":"","lastName":"Gong","suffix":""}],"badges":[],"createdAt":"2026-02-28 11:23:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8994845/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8994845/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104781724,"identity":"1e9f9d1e-d39c-4a7c-88b2-5c963e5d7fbb","added_by":"auto","created_at":"2026-03-17 07:56:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3591157,"visible":true,"origin":"","legend":"\u003cp\u003eThe workflow of this study. \u003cstrong\u003ea.\u003c/strong\u003e MIND Gradient network construction. Regional morphological features (GMV, CT, SA, MC, SD) were extracted and standardized across all vertices. The MIND similarity statistic was calculated to generate the MIND network as a 308 × 308 matrix. Then, the regional MIND values were established by averaging all the connections of the 308 cortical areas without applying any threshold. Finally, the diffusion embedding algorithm was applied to decompose the affinity matrix and the first gradient map was acquired. \u003cstrong\u003eb.\u003c/strong\u003eNeurotransmitter receptors analysis. Based on the Juspace neurotransmitter maps and case control t maps, we conducted cross-modal association analyses by examining the spatial correlations. \u003cstrong\u003ec. \u003c/strong\u003eGene expression. The expression value in the left hemisphere region of each gene was extracted from the AHBA database, and the gene expression matrix could be obtained. \u003cstrong\u003ed.\u003c/strong\u003e Transcriptional analysis. PLS regression was used to identify imaging transcriptomic associations. Evaluate the relationship between brain gene expression and principal MIND gradient changes in MDD patients with NSSI through PLS regression, analyze the functional pathways enrichment and disease-related enrichment of PLS weighted genes, and assess the transcriptional characteristics of cortex and cell types. Further explore the genetic susceptibility to mental disorders.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8994845/v1/87547db5873474bd313ca385.png"},{"id":104572365,"identity":"9278c857-63b9-4e98-9d9a-44b0ca2e1134","added_by":"auto","created_at":"2026-03-13 13:00:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3694851,"visible":true,"origin":"","legend":"\u003cp\u003eNSSI-related regional changes in principal MIND gradient map. \u003cstrong\u003ea.\u003c/strong\u003e The principal MIND gradient pattern in MDD patients with NSSI and controls. Regions with similar connectivity patterns show similar colors. And the variance explained was generally similar between the NSSI and non-NSSI groups: Both groups exhibited higher values in the occipital cortex and lower values in the frontal and temporoparietal cortices. \u003cstrong\u003eb.\u003c/strong\u003e The histogram shows the distributions of principal MIND gradient in the NSSI and control group after regressing out the effect of age, sex, and age × sex. \u003cstrong\u003ec.\u003c/strong\u003e Region-wise statistical comparisons.NSSI patients showed an increase in the left caudal middle frontal (part4), left inferior temporal (part2), left middle temporal (part2), and right inferior temporal (part2) regions. In contrast, a decrease was observed in the left lingual (part3) and right superior parietal (part1) regions. \u003cstrong\u003ed. \u003c/strong\u003eThe scatterplot of the mean control and case-control \u003cem\u003et\u003c/em\u003e-map. The case-control \u003cem\u003et\u003c/em\u003e-values exhibited a negative spatial correlation with the regional MIND gradient values (r =− 0.708, p\u003csub\u003espin\u003c/sub\u003e =0.002).\u003cstrong\u003e e.\u003c/strong\u003e Functional community-based absolute t-value (left, Yeo 7 functional networks) and cytoarchitecture-based absolute t-value (right, von Economo classes) of the principal MIND gradient. Left showed a marginally significant decrease in the DAN (p = 0.07). * indicate marginally significant (p=0.05~0.10). VN: visual network, DMN: default mode network, FPN: fronto-parietal network, LN: limbic network, VAN: ventral attention network, DAN: dorsal attention network, SMN: somato-motor network, Prim motor: primary motor cortex, Insula: insular cortex, Limbic: limbic regions, Prim sens: primary sensory cortex, Sec sens: second sensory cortex, Asso1: association cortex1, Asso2: association cortex2.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8994845/v1/7846f8d955822704dce6b686.png"},{"id":104572366,"identity":"0bb9d238-3ba6-4dfe-ae28-7fe9d36b7786","added_by":"auto","created_at":"2026-03-13 13:00:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1926092,"visible":true,"origin":"","legend":"\u003cp\u003eSignificant spatially correlated neurotransmitter scatter plot. The above 9 neurotransmitters were analyzed using Spearman correlation analysis and subjected to 10000 permutation tests. The scatter plots of neurotransmitters with statistical significance (p\u003csub\u003espin\u003c/sub\u003e\u0026lt;0.05) were displayed with a 95% confidence interval range.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8994845/v1/fed3c97eac54bb4363e38473.png"},{"id":104572371,"identity":"7b133e6d-a9b8-4f73-99be-dfb30fd0153e","added_by":"auto","created_at":"2026-03-13 13:00:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1826648,"visible":true,"origin":"","legend":"\u003cp\u003eTranscriptional signatures associated with case-control regional changes in principal MIND gradient and two prominent genes. \u003cstrong\u003ea.\u003c/strong\u003e The case-control t-map of the regionally principal MIND gradient scores in the left hemisphere and the weighted gene expression map of regional PLS1 scores in the left hemisphere. The two maps have similar distributions, both showing a gradually increasing pattern from the parietal lobe to the frontotemporal lobe. \u003cstrong\u003eb. \u003c/strong\u003eThe scatterplot showing significant positive spatial correlations between PLS1 scores and the case-control \u003cem\u003et\u003c/em\u003e-value maps in the principal MIND gradient (Spearman’s \u003cem\u003er \u003c/em\u003e= 0.557, p\u003csub\u003espin\u003c/sub\u003e \u0026lt;0.001). \u003cstrong\u003ec. \u003c/strong\u003eA total of 2209 PLS1+ genes (\u003cem\u003eZ \u003c/em\u003e\u0026gt; 4.19, p_FDR\u003cem\u003e \u003c/em\u003e\u0026lt; 0.0001) and 2110 PLS2- genes (\u003cem\u003eZ \u003c/em\u003e\u0026lt; − 4.19, p_FDR \u0026lt; 0.0001) were identified by ranked Z scores. \u003cstrong\u003ed-e. \u003c/strong\u003eGABRA5 and MGST1 gene expression maps in the left hemisphere reveal distinctly opposing distribution patterns. The scatterplot demonstrates a significant positive spatial correlation between GABRA5 expression and the case-control t-value maps within the principal MIND gradient (Spearman’s r = 0.545, p \u0026lt; 0.0001), alongside a significant negative spatial correlation for MGST1 expression (Spearman’s r = -0.448, p \u0026lt; 0.0001).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8994845/v1/55e6a1132b63c2812cb86298.png"},{"id":104781315,"identity":"af2fcdf3-8768-41ff-a5ed-b6b7d9620c30","added_by":"auto","created_at":"2026-03-17 07:55:23","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2416023,"visible":true,"origin":"","legend":"\u003cp\u003eFunctional enrichment analysis of PLS1 weighted genes. \u003cstrong\u003ea.\u0026amp; c.\u003c/strong\u003e The bubble diagram showing the enrichment of GO terms and pathway annotation for the PLS1 +/- genes. The x-axis represents -log10(p-value) corrected by BH-FDR method. The size of bubble represents the number of genes included in each GO term and pathway. \u003cstrong\u003eb. \u0026amp; d.\u003c/strong\u003e Metascape analysis showed the enrichment network comprised of inter-cluster and intra-cluster similarities of enriched terms and pathways for PLS1+/- (d) genes. The node represents a term or pathway, and the size of node represents the number of genes included in each GO term and pathway. Lines among nodes represent links among terms and pathways, and colors represent different clusters. \u003cstrong\u003ee. \u0026amp; f.\u003c/strong\u003e The bubble diagram shows the enrichment of disease-related pathways associated with the PLS1 +/- genes. The x-axis represents the -log10(p-value) corrected by the BH-FDR method, and the size of each bubble represents the number of disease-related genes in each pathway (data source: DisGeNET).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8994845/v1/a0bcfa41651e54b90e1ab404.png"},{"id":104572369,"identity":"1f2dbecb-1af0-4657-8826-0987332945a0","added_by":"auto","created_at":"2026-03-13 13:00:54","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":391370,"visible":true,"origin":"","legend":"\u003cp\u003eCortical layer and Cell-type specific enrichment analysis of the PLS1 +/- gene list. \u003cstrong\u003ea. \u0026amp; b.\u003c/strong\u003e The number of overlapped genes with PLS1+/- genes for each cortical layer. The PLS1+ gene list was significantly enriched in cortical layers I, II and V, while the PLS1- gene list was significantly enriched in layers III and IV. There was no overlap in the enrichment regions between the two gene sets. \u003cstrong\u003ec. \u0026amp; d.\u003c/strong\u003e The number of overlapped genes with PLS1+/- genes for each cell type. PLS1+/- genes were enriched in both excitatory and inhibitory neurons, but an increase in PLS1+ genes was significantly associated with astrocytes.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8994845/v1/65b1bfe04b595a06c70a346a.png"},{"id":105567031,"identity":"b1f7f647-1701-446c-92dd-93579db09e83","added_by":"auto","created_at":"2026-03-27 12:58:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":14866969,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8994845/v1/9566f974-4bd7-4299-9ac8-728eee0efe7a.pdf"},{"id":104572370,"identity":"762c24ba-2186-48c7-b819-8c20aa3eb84c","added_by":"auto","created_at":"2026-03-13 13:00:54","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2393092,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8994845/v1/687d682df0928dd0dfa02440.docx"},{"id":104572368,"identity":"8d60b558-f3b3-4e1c-8e49-e806b08aac1f","added_by":"auto","created_at":"2026-03-13 13:00:54","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":823126,"visible":true,"origin":"","legend":"","description":"","filename":"Additionalfile2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8994845/v1/68b09786597b458966dc3084.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Major Depressive Disorder with Non-Suicidal Self-Injury in Adolescents: Altered Morphological Inverse Divergence Gradient in Brain Structure and Gene Expression Patterns","fulltext":[{"header":"1. Background","content":"\u003cp\u003eMajor depressive disorder (MDD) is a chronic, recurrent psychological disorder caused by multiple factors, including genetic, environmental, and neurobiological influences. It is not only a leading cause of global disability but also a core contributor to the disease burden among adolescents [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].The prevalence of non-suicidal self-injury (NSSI) is significantly higher in adolescents with MDD, with studies showing that approximately 40%\u0026ndash;60% of these patients engage in NSSI [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Although NSSI is defined as deliberate self-inflicted damage to body tissues without suicidal intent, more than 66% of adolescents who attempt suicide have a history of NSSI [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Epidemiological studies further reveal that the characteristics and prevalence of NSSI among adolescents are consistent across different countries [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This global phenomenon not only poses a serious threat to adolescents' physical and mental health but is also considered one of the strongest predictors of suicidal behavior [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn adolescent MDD patients, NSSI can often be identified through self-harming behaviors and visible scars. While these outward manifestations provide clues for clinical diagnosis, they fail to capture the underlying neurobiological mechanisms driving such behaviors. Therefore, studying brain structure abnormalities is important for understanding the underlying mechanisms of the disease and informing more effective therapeutic strategies. Many studies have shown that MDD and NSSI are linked to abnormalities in cortical structure and disrupted brain networks [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. These changes are especially evident in the structural connectome [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The structural connectome, which represents the brain's large-scale network of anatomical connections, plays a vital role in coordinating cognitive, emotional, and behavioral functions. Despite these advances, there is still a lack of research on the specific structural connectome abnormalities in MDD patients with NSSI. The differences between these patients and MDD patients without NSSI also remain unclear. Addressing these gaps is essential for advancing our understanding of the distinct neural mechanisms underlying NSSI in the context of adolescent MDD.\u003c/p\u003e \u003cp\u003eSeveral methods used to analyze brain structural connectivity networks in Major Depressive Disorder (MDD) and Non-Suicidal Self-Injury (NSSI) have been applied to analyze the brain structural networks in recent years [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. These include white matter network analysis based on Diffusion Weighted Imaging (DWI) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], Structural Covariance Networks (SCN) [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], Morphometric Similarity Networks (MSN) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], and Morphometric Inverse Discrete Networks (MIND) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. While each method has contributed significantly to understanding brain structural networks, they also present notable limitations. For instance, DWI\u0026rsquo;s fiber tractography, although capable of objective quantitative assessment, has drawbacks when comparing the strength of long distance links [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. SCN works well in group-level analysis, but fails in individualized characterization [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Dynamic time warping mapping is applicable to individualized analysis but relies heavily on longitudinal cohort data and has limited application [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. MSN maps multimodal structural features of brain regions into structural vectors and connects morphometric measures with connectomic networks. The statistical associations between brain structure and morphometric features are found, and it provides theoretical support for research on brain structure, cortical gene expression and genetics. While MSN has its main advantages, it simplifies the rich data at the cortical vertices into a single statistical measure for regional features. This is inevitably a loss, and is also assumed to be completely uniform. Moreover, it assumes that cortical morphometric feature variability is completely uniform, which is not true, and reduces the network stability. On the contrary, the recently proposed MIND approach measures the divergence of five morphometric measurements at the vertex level to construct morphometric similarity networks. Compared to earlier methods, MIND is much more stable, better cortical matching, and better genetic relationships. The key advantage is that it requires only T1-weighted MRI data to construct reliable and effective MIND networks. This makes MIND more practical in practice. More diverse MRI measures are needed in future to create more meaningful networks, but early brain partitioning and cortical parcellation was guided by morphogenetic gradients. These gradients quantify the spatial organization of cortical structures as they vary on the gradient. This captures key features of cortical organization and the information of gene expression, cell structure, myelin distribution, and evolutionary growth. Thus, it provides more detailed structural network information [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMDD is a clinically undetermined disease, and there are a number of genetic variants for MDD, some of which are important in presynaptic regulation and cognitive function [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Scientists have studied for whole-brain gene expression data of Allen Human Brain Atlas database linking macro-structural brain disorders and specific transcriptional expression patterns with the effect of genes on brain networks [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Recently, researchers have identified significant differences in gene expression, enriched pathways, and expression patterns in the MSN network space of MDD patients [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and our results suggest that gradient similarity network research is necessary to recognize neuroimaging biomarkers and assess the impact of genes. Most of these studies, however, investigated MSN networks. For NSSI patients, the abnormal mechanisms and their dependence on disease-related gene expression are not well explored. In contrast, the MIND network exhibits higher biological validity and can more accurately reflect the coupling relationship between cortical structure and gene co-expression.\u003c/p\u003e \u003cp\u003eThis study aims to construct a new MIND gradient network to evaluate the differences in brain structural network gradient changes between NSSI MDD and without NSSI patients. Taking these changes into account using transcriptomic data, we explore how molecular organization and microstructural changes in brain morphometric coordination in NSSI patients can affect the brain morphometries of MDD\u0026rsquo;s patients. In particular, we measure the MIND measures of cortical morphometric similarity at regional and global level and use this to formulate the principal MIND gradients (MIND-Gd). In this study, we examine differences in the hierarchical structure of the brain structural networks gradients between NSI patients and those without NSS. These gradients are further mapped onto brain functional networks and cortical cellular networks to investigate their potential functional implications and cellular-level changes. Additionally, we analyze abnormal neurochemical signaling of changes in the principal MIND gradient and their possible influence on the pathological process of NSSD patients. To further elucidate the molecular mechanisms underlying these changes, we compared change in the principal MIND to changes in in vivo receptor distribution maps (generated with the JuSpace toolbox). We then performed PLS regression analysis to link changes in principal MIND gradient to anatomical gene expression patterns and selected genes closely related. Permutation tests were used to detect genes that are strongly related. Finally, we conducted gene ontology pathway analysis, cell type enrichment analysis, and cortical layer enrichment analysis to further validate our findings. These analyses aimed to confirm whether transcriptional changes are consistent with principal MIND gradient alterations. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. presents the overall analytical framework of this study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Participants\u003c/h2\u003e \u003cp\u003eThis study retrospectively included 3D T1-weighted magnetic resonance imaging (3D T1WI) data from 241 adolescent patients with MDD diagnosed according to ICD-10 criteria at the Shandong Mental Health Center. Based on clinical assessments of NSSI, patients were divided into an NSSI group (134 cases) and without NSSI group (107 cases). The severity of depressive symptoms was quantified using the 17-item Hamilton Depression Rating Scale (HDRS-17), with a baseline score of \u0026ge;\u0026thinsp;18 required for inclusion. All patients were of Han ethnicity and showed no clear lesions or developmental abnormalities on MRI scans (see Additional file 2: Sheet S1 for details). As this study is a retrospective analysis and all data were obtained from an imaging database, informed consent was waived. The study was reviewed and approved by the Ethics Committee of the Shandong Mental Health Center (Approval Number: KYSJWLL2024-1-075).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Imaging acquisition and preprocessing\u003c/h2\u003e \u003cp\u003eImaging data were collected using a 3.0 Tesla uMR 790 MRI scanner (United Imaging Healthcare, China). High-resolution T1-weighted anatomical images were acquired with a 3D gradient-recalled echo sequence (GRE-FSP). Specific imaging parameters are provided in Additional file 1: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Surface-based preprocessing of 3D T1 images was performed using FreeSurfer software (v7.4.1, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://surfer.nmr.mgh.harvard.edu/\u003c/span\u003e\u003cspan address=\"http://surfer.nmr.mgh.harvard.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The preprocessing steps included skull stripping, tissue segmentation, division of hemispheres and subcortical structures, and generation of gray-white matter and pial surfaces. The preprocessed images were initially reviewed by two experienced radiologists with over 10 years of experience. Euler numbers were calculated for each segmented T1-weighted image, and images with Euler numbers less than \u0026minus;\u0026thinsp;270 were excluded (see Additional file 1: Text). Total intracranial volume (TIV) was also calculated [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Construction of the MIND-Gd\u003c/h2\u003e \u003cp\u003eThe cortical surface was divided into 308 spatially continuous regions (D-K 308 atlas) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] by segmenting 68 cortical regions from the Desikan-Killiany (D-K) atlas. A recursive algorithm was used to ensure that each region had a similar surface area (approximately 500 mm\u0026sup2;). The segmentation results of the D-K atlas were then individually mapped onto the cortical surface of each participant. Five morphological features were extracted: gray matter volume (GMV), cortical thickness (CT), surface area (SA), mean curvature (MC), and sulcal depth (SD) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. These features were standardized using z-score normalization to account for distributional differences.Next, the transformed Kullback-Leibler (KL) divergence was used to calculate the MIND similarity metrics between any two cortical regions. This resulted in a 308\u0026times;308 MIND similarity matrix for each participant (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Additional file 1: Text and Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The BrainSpace toolbox was then used to preprocess the MIND statistical gradients [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSince the principal gradient is closely related to basic cortical properties and functional mapping [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], this study focused on the principal morphological statistical gradient (n_components\u0026thinsp;=\u0026thinsp;1) and its changes associated with NSSI in MDD. Finally, the principal gradient of the group-level MIND similarity matrix was generated (see Additional file 1: Text and Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Analysis of Regional MIND-Gd\u003c/h2\u003e \u003cp\u003eTo explore differences in the principal MIND gradient across 308 cortical nodes between groups, we performed regression analysis using a general linear model (GLM). Age, sex, and the interaction term age\u0026times;sex were included as covariates to control for their effects. Two-tailed t-tests were then conducted. To preserve spatial continuity, multiple comparison corrections were performed using a cluster-level false discovery rate (Cluster-FDR) method, with a significance threshold set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (see Additional file 1: Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). To further clarify changes in the principal MIND gradient within a broader cortical classification framework, all 308 cortical regions were mapped onto two classic cortical classification schemes: the Yeo-7 atlas, which is based on resting-state networks [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], and the von Economo atlas, which is based on cytoarchitectonic features [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. For this purpose, the mean principal MIND gradient was calculated for all regions within the Yeo-7 networks and von Economo classifications. Subsequently, GLM models were applied to analyze MIND gradient differences between the NSSI and non-NSSI groups while controlling for the same covariates. A significance threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was set at each level of analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Association of neurotransmitter receptors with MIND-Gd\u003c/h2\u003e \u003cp\u003eTo elucidate the molecular mechanisms underlying NSSI-related changes in regional MIND-Gd, we conducted cross-modal association analyses by examining the spatial correlations between case-control t-maps and neurotransmitter receptor distribution maps. The neurotransmitter receptor data were derived from PET imaging-based datasets provided by the JuSpace toolbox [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].Specifically, neurotransmitter maps were first mapped onto the DK-308 brain atlas. Spatial correlations were then calculated between the case-control t-maps and each neurotransmitter receptor map. Significance levels were assessed through 10,000 spin tests. Multiple comparisons were corrected using the Bonferroni method, with a significance threshold set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05(see Additional file 1: Text and Table S3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Gene expressional profiles preprocessing\u003c/h2\u003e \u003cp\u003eGene expression profile data were obtained from postmortem brain tissue samples of six individuals, covering 3,702 spatially distributed gene loci. The data were sourced from the AHBA database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://human.brain-map.org\u003c/span\u003e\u003cspan address=\"http://human.brain-map.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Since only two samples in the AHBA dataset included data from the right hemisphere, this study focused exclusively on the left hemisphere. The AHBA dataset was preprocessed using the Python-based abagen toolbox (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/rmarkello/abagen\u003c/span\u003e\u003cspan address=\"https://github.com/rmarkello/abagen\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Gene expression profile data were mapped onto 152 brain regions of the left hemisphere in the DK-308 atlas. This resulted in a transcriptional expression matrix containing 152 brain regions and 15,632 corresponding genes [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Transcription-neuroimaging association region PLS analysis\u003c/h2\u003e \u003cp\u003e To establish the association between gene expression profiles and regional MIND-Gd changes in NSSI patients, partial least squares (PLS) regression analysis was performed. The expression levels of 15,632 genes across 152 brain regions were used as predictor variables, while the case-control t-value differences of 152 MIND-Gd regions served as response variables [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. This analysis identified the linear combination of gene expressions that best predicted regional MIND-Gd changes.To assess whether the covariance explained by the PLS components between the MIND-Gdt-statistical map and the transcriptome exceeded chance levels, spatial autocorrelation analysis with 10,000 permutation iterations was conducted. Given the lack of consensus on the most predictive PLS components, the first or second components (PLS1 or PLS2) are commonly selected as optimal low-dimensional explanations of high-dimensional covariance. In this study, we selected PLS components with a variance explanation rate\u0026thinsp;\u0026gt;\u0026thinsp;30% and statistical significance above the threshold (p\u003csub\u003espin\u003c/sub\u003e\u0026lt;0.0001) [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Additionally, the significance of each gene's contribution to the PLS components was assessed using 10,000 bootstrap iterations. The z-values were calculated by dividing the expression weights by their bootstrap standard errors, and all genes were ranked based on their weights [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. For subsequent analyses, only genes with statistical significance after false discovery rate (FDR) correction (p_fdr\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) were retained.Finally, Spearman correlation analysis was used to examine the spatial relationship between the MIND-Gdt-statistical map and PLS scores, with statistical significance validated through 10,000 permutation iterations.\u003c/p\u003e \u003cp\u003eTo further explore the relationship between NSSI-related gene expression and changes in the principal MIND gradient, genes closely associated with NSSI based on previous literature [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] were screened. Genes related to neurotransmitter function that overlapped with the significant genes identified by the PLS+ method were analyzed for their spatial relationship with the case-control t-value differences of the principal MIND gradient in the left hemisphere.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Enrichment analysis\u003c/h2\u003e \u003cp\u003eTo further analyze the disease-related pathways associated with significant PLS+/\u0026minus; genes, we selected statistically significant genes from the first PLS component (PLS1+/\u0026minus;) and performed functional annotation using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway databases available on the Metascape platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://metascape.org/gp/index.html#/main/step1\u003c/span\u003e\u003cspan address=\"https://metascape.org/gp/index.html#/main/step1\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].Next, to localize NSSI-related genes identified through PLS regression to specific cortical regions, we conducted cortical structure enrichment analysis. Marker genes for six cortical regions were obtained from published transcriptomic studies [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].To further validate the cell-specificity of genes associated with regional MIND-Gd changes, we performed enrichment analysis by comparing the significant PLS1+/\u0026minus; gene lists with gene sets from seven types of cortical cells, including microglia, oligodendrocytes, endothelial cells, astrocytes, oligodendrocyte precursor cells (OPCs), excitatory neurons, and inhibitory neurons [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].All enrichment analyses were corrected for significance using the Bonferroni method, with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 set as the threshold for significance. Statistical significance was further evaluated using 10,000 spin tests.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Null model\u003c/h2\u003e \u003cp\u003eTo address potential confounding effects caused by spatial autocorrelation [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], we used a spin test technique based on null models. This method generates a set of Pearson correlation coefficients under the null hypothesis by randomly rotating the spherical projection of spatial maps while maintaining their spatial topology. Specifically, 10,000 spin permutations were applied to cortical regions to construct a null distribution model. The p\u003csub\u003espin\u003c/sub\u003e value was then calculated, defined as the proportion of null correlation coefficients that exceeded the observed value.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Robustness analysis\u003c/h2\u003e \u003cp\u003eTo ensure the reliability and robustness of our findings, we conducted five additional analyses: (a) We verified the robustness of the case-control principal MIND gradient by regressing out the effect of total intracranial volume (TIV); (b) We validated the robustness of MIND-Gd network construction using Spearman correlation analysis; (c) We assessed the impact of cluster size thresholds (n\u0026thinsp;=\u0026thinsp;5, 10, 15, 20) on network topology features. This step further confirmed the reliability of the case-control MIND-Gd difference statistics; (d) We randomly divided NSSI patients into two groups and compared MIND-Gd differences between the two NSSI subgroups and the control group without NSSI; (e) We evaluated the robustness of enrichment results using gene category enrichment analysis (GCEA) based on integrated null models [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Data samples and characteristics\u003c/h2\u003e \u003cp\u003eThis study included 241 adolescent participants (mean age: 15.91\u0026thinsp;\u0026plusmn;\u0026thinsp;2.15) who passed data quality control. Among them, 134 patients had MDD with NSSI (mean age: 15.41\u0026thinsp;\u0026plusmn;\u0026thinsp;1.98), and 107 patients had MDD without NSSI (mean age: 16.53\u0026thinsp;\u0026plusmn;\u0026thinsp;2.21). Details about image quality control, demographic information, clinical data, and global data residual analysis are provided in Additional file 1: Fig. S3, Table S4 and Table S5. The mean residuals were close to zero, which confirmed that the model was unbiased.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Case control differences in MIND-Gd\u003c/h2\u003e \u003cp\u003eIn the regional principal MIND gradient, areas with similar connectivity patterns are shown in the same color. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, the variance explained by the principal MIND gradient was generally similar between the NSSI and non-NSSI groups. Both groups exhibited higher values in the occipital cortex and lower values in the frontal and temporoparietal cortices. This pattern reflects the hierarchical organization of brain tissues. We then used a general linear model (GLM) with age, sex, and age \u0026times; sex as covariates to compare the regional principal MIND gradient between the two groups (details in Additional file 1: Table S6). The case-control t-map showed that positive t-values indicate an increase in the principal MIND gradient in MDD patients, while negative t-values indicate a decrease. After controlling for covariates, the average distribution of the principal MIND gradient differed significantly between the two groups (two-sample Kolmogorov-Smirnov test, p\u0026thinsp;=\u0026thinsp;1.11\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;4\u003c/sup\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The regional comparison revealed that NSSI patients showed an increase in the principal MIND gradient in the left caudal middle frontal (part4), left inferior temporal (part2), left middle temporal (part2), and right inferior temporal (part2) regions. In contrast, a decrease was observed in the left lingual (part3) and right superior parietal (part1) regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec and Additional file 1: Table S7). Additionally, after adjusting for the same covariates, we found a significant negative correlation between the average regional MIND gradient in non-NSSI patients and the case-control t-map (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.708, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed). This result suggests that highly connected regions tend to show larger case-control differences. Moreover, the case-control differences were more pronounced when the MIND gradient scores were at extreme values.\u003c/p\u003e \u003cp\u003eTo further interpret the case-control differences in regional MIND gradients, we applied two existing cortical parcellation methods: the Yeo 7 functional network atlas and the von Economo cytoarchitectonic atlas. In the Yeo functional network, MDD patients with NSSI showed a marginally significant decrease in the principal MIND gradient within the dorsal attention network compared to those without NSSI (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee, Additional file 1: Fig. S4 and Table S8). In the von Economo cytoarchitectonic atlas, no significant changes in the principal MIND gradient were observed in cortical cell types between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef, Additional file 1: Fig. S4 and Table S9).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Association of neurotransmitter receptor systems with MIND-Gd alterations\u003c/h2\u003e \u003cp\u003eWe examined the association between regional MIND-Gd changes and whole-brain cortical neurotransmitter receptor maps. Among the 30 neurotransmitter maps provided by JuSpace, we observed that the case-control t-map showed significant spatial correlations with 9 neurotransmitters (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Additional file 2: Sheet S2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Transcription-neuroimaging associations\u003c/h2\u003e \u003cp\u003eWe used the principal MIND gradient transcription matrix (152 brain regions \u0026times; 15,632 genes) to identify gene expression patterns. Partial least squares regression (PLS) revealed that PLS component 1 explained 39.53% of the macroscopic structural variance. This value was significantly higher than random expectations (p\u003csub\u003espin\u003c/sub\u003e\u0026lt;0.0001). Bootstrapping (n\u0026thinsp;=\u0026thinsp;10,000) was used to assess the contributions of PLS1 and PLS2 to the case-control t-values. PLS1 contributed 73.12% (Additional file 2: Sheet S3 and Additional file 1: Fig. S5). Further analysis showed that the weighted gene expression map of PLS1 increased progressively from the precentral gyrus to the inferior temporal gyrus (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). The PLS1 score showed a significant positive spatial correlation with the case-control t-map. (r\u0026thinsp;=\u0026thinsp;0.557, p\u003csub\u003espin\u003c/sub\u003e\u0026lt;0.001, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). This indicates that genes contributing to PLS1 were overexpressed in regions where the principal MIND gradient increased in MDD patients with NSSI. Using z-scores, we identified 2,209 PLS1\u0026thinsp;+\u0026thinsp;genes (Z\u0026thinsp;\u0026gt;\u0026thinsp;4.19, BH-FDR corrected p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and 2,110 PLS1\u0026thinsp;\u0026minus;\u0026thinsp;genes (Z\u0026thinsp;\u0026lt;\u0026thinsp;\u0026minus;\u0026thinsp;4.19, BH-FDR corrected p\u003csub\u003espin\u003c/sub\u003e\u0026lt;0.0001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec, Additional file 2: Sheet S4). Since NSSI-related gene databases are limited, we combined findings from previous studies [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. From the PLS1\u0026thinsp;+\u0026thinsp;gene list, we identified two genes closely related to NSSI in MDD: GABRA5 and MSGT1. Both genes showed significant positive spatial correlations with the case-control t-map (p\u003csub\u003espin\u003c/sub\u003e\u0026lt;0.0001, BH-FDR corrected, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed-e). This suggests that these genes were overexpressed in the non-NSSI group compared to the NSSI group.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Annotation of PLS-weighted genes for regional MIND-Gd changes\u003c/h2\u003e \u003cp\u003e To further elucidate the functional characteristics of genes associated with the regional principal MIND gradient changes, we used Metascape software to compared the functional enrichment results with the PLS1+/-gene list (Additional file 2: Sheet S4). The PLS1\u0026thinsp;+\u0026thinsp;genes were significantly enriched in synaptic signaling and neurodevelopment-related pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea-b), whereas the PLS1- genes were predominantly associated with chromatin regulation, cytoskeletal organization and nucleic acid metabolism processes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec-d). Aligned the human diseases with the PLS1 genes, We found that the PLS1\u0026thinsp;+\u0026thinsp;genes were significantly enriched in neurological and psychiatric disorders such as depression and epilepsy, whereas the PLS1- genes were enriched in neurodevelopmental disorders including autism and developmental delay (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee-f). The information aboved indicated that neurodevelopmental abnormalities, synaptic signaling abnormalities, and metabolic abnormalities may collectively participate in the process of principal MIND gradient changes in adolescent MDD patients with NSSI.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Cortical layer enrichment related to regional MIND-Gd changes\u003c/h2\u003e \u003cp\u003eUsing cortical gene markers from previous studies, we established the relationship between PLS1+/- genes and different cortical layers (L1-L6) through laminar gene marker analysis (Additional file 2: Sheet S5). Notably, PLS1\u0026thinsp;+\u0026thinsp;genes were significantly enriched in cortical layer I (gene overlap number\u0026thinsp;=\u0026thinsp;87, BH-FDR corrected p\u0026thinsp;=\u0026thinsp;0.015), layer II (gene overlap number\u0026thinsp;=\u0026thinsp;140, BH-FDR corrected p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and layer V (gene overlap number\u0026thinsp;=\u0026thinsp;30, BH-FDR corrected p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, PLS1\u0026thinsp;\u0026minus;\u0026thinsp;genes were significantly enriched in layer III (gene overlap number\u0026thinsp;=\u0026thinsp;63, BH-FDR corrected p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and layer IV (gene overlap number\u0026thinsp;=\u0026thinsp;83, BH-FDR corrected p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). There was no overlap in the enrichment of PLS1+/\u0026minus; genes across different cortical layers (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea-b).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Specific cell types enrichment related to regional MIND-Gd changes\u003c/h2\u003e \u003cp\u003eGiven the complex interactions among different cell types in the brains of adolescents with MDD and NSSI, we further explored the cell-specific changes in the principal MIND gradient. To refine our analysis, we included seven types of central nervous system cells (Additional file 2: Sheet S6).The cell-type-specific expression analysis revealed significant associations between PLS1\u0026thinsp;+\u0026thinsp;genes and excitatory neurons (gene overlap number\u0026thinsp;=\u0026thinsp;238, BH-FDR corrected p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), astrocytes (gene overlap number\u0026thinsp;=\u0026thinsp;212, BH-FDR corrected p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and inhibitory neurons (gene overlap number\u0026thinsp;=\u0026thinsp;173, BH-FDR corrected p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, PLS1\u0026thinsp;\u0026minus;\u0026thinsp;genes were significantly enriched in excitatory neurons (gene overlap number\u0026thinsp;=\u0026thinsp;185, BH-FDR corrected p\u0026thinsp;=\u0026thinsp;0.009) and inhibitory neurons (gene overlap number\u0026thinsp;=\u0026thinsp;163, BH-FDR corrected ) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec-d). Notably, both PLS1+/\u0026minus;genes were enriched in excitatory and inhibitory neurons. However, only PLS1\u0026thinsp;+\u0026thinsp;genes showed a significant association with astrocytes. This suggests that the enrichment of PLS1\u0026thinsp;\u0026minus;\u0026thinsp;genes may reflect a compensatory response in specific cell types during the disease process.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Sensitivity and robustness analyses\u003c/h2\u003e \u003cp\u003eWe verified the reliability of our findings using various sensitivity and robustness analysis strategies, including: (a) Examining MIND case-control differences that were not sensitive to TIV (r\u0026thinsp;=\u0026thinsp;1.000, p\u003csub\u003espin\u003c/sub\u003e\u0026lt;0.001, Additional file 1: Fig. S6); (b) Using Spearman correlation instead of Pearson correlation to construct the MIND-Gd network and further validate the reliability of case-control differences. The results showed that the case-control differences in the principal MIND gradient based on Spearman correlation were highly reproducible (r\u0026thinsp;=\u0026thinsp;0.999, p\u003csub\u003espin\u003c/sub\u003e\u0026lt;0.001, Additional file 1: Fig. S7) ; (c) Evaluating network topological features under different cluster size thresholds (n\u0026thinsp;=\u0026thinsp;5,10,15,20). The results indicated that the statistical differences in MIND-Gd between cases and controls remained highly consistent across thresholds. No significant differences were observed in clustering distribution patterns, node connectivity, or global topological metrics across thresholds (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Additional file 1: Table S10 and Fig. S8) ; (d) Dividing patients with NSSI into two random subgroups (subgroup 1 and subgroup 2) and assessing their MIND-Gd differences compared to the non-NSSI group. The case-control t-maps showed positive spatial correlations with subgroup 1 (r\u0026thinsp;=\u0026thinsp;0.912, p\u003csub\u003espin\u003c/sub\u003e=0.002) and subgroup 2 (r\u0026thinsp;=\u0026thinsp;0.933, p\u003csub\u003espin\u003c/sub\u003e\u0026lt;0.001, Additional file 1: Fig. S9) ; (e) Relatively reliable gene enrichment results (Additional file 1: Text and Additional file 2: Sheet S7).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eTo date, no studies have specifically investigated brain structural and network changes in adolescent MDD patients with or without NSSI. This study is the first to systematically explore brain structural changes and potential molecular mechanisms in adolescent MDD patients with and without NSSI. We achieved this using an integrated analytical approach. By constructing cortical structural similarity gradients based on the principal MIND gradient network, we identified clear case-control differences between the two groups. The results identified distinct alterations in neurotransmitter activity and structural connectivity among NSSI patients, highlighting the unique neurobiological features associated with this high-risk population. Using partial least squares (PLS) analysis, we found that the differences in MIND-Gd maps between cases and controls were significantly correlated with cortical gene expression maps.Through neurotransmitter-related analyses, we identified two key genes that may play a pivotal role in the pathophysiology of NSSI. Gene enrichment analysis showed that PLS1\u0026thinsp;+\u0026thinsp;genes were significantly enriched in metabolic pathways. In contrast, PLS1\u0026thinsp;\u0026minus;\u0026thinsp;genes were enriched in synaptic signaling pathways. Additionally, cortical laminar analysis revealed that PLS1\u0026thinsp;+\u0026thinsp;genes were enriched in cortical layers I, II, and V. Meanwhile, PLS1\u0026thinsp;\u0026minus;\u0026thinsp;genes were enriched in layers III and IV. Cell-type analysis further showed that PLS1\u0026thinsp;+\u0026thinsp;genes were significantly enriched in astrocytes.These findings highlight the abnormal MIND-Gd phenotypes in adolescent MDD patients with NSSI. They also provide new insights into the relationship between large-scale structural abnormalities and microscopic transcriptional patterns during disease progression.\u003c/p\u003e \u003cp\u003eThe MIND network uses large-scale, multidimensional, vertex-level structural MRI data to construct a unified network for assessing cortical structural similarity. Compared with the MSN phenotype, the MIND phenotype is more reliable, better aligned with cellular structural characteristics, and more sensitive in detecting individual differences in human brain structural connectivity [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Furthermore, it isclosely linked to fundamental cortical properties, such as gene expression, cellular structure, myelin architecture, and evolutionary expansion [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Adolescent MDD, whether accompanied by NSSI or not, is a disorder that disrupts neuroplasticity through multiple factors. These include genetics, gene-environment interactions, neuroendocrine dysfunction, and inflammation [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. To explore the differences between MDD patients with and without NSSI, we applied a neuroimaging model called the principal MIND gradient. Our results revealed region-specific alterations in the principal MIND gradient.\u0026zwnj; Specifically, our results showed that the principal MIND gradient \u0026zwnj;increased in frontal and temporal regions but decreased in parietal and occipital regions\u0026zwnj;. Increased gradients suggest reduced structural differentiation, while decreased gradients indicate greater structural segregation in typically connected areas. In these regions, the left occipital area is part of the principal visual cortex, which processes visual information at its initial stage. The bilateral temporal pathways are involved in visual memory and object recognition. The right parietal region integrates spatial attention, visual input, and somatosensory information. Meanwhile, the left frontal region is responsible for attention regulation, decision-making, and executive functions [60, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, 62].We observed that the visual functional system plays a key role in these regions. This supports the hypothesis that visual dysfunction may be an important clinical feature of MDD [63]. Additionally, when analyzing the functions of these regions, we found that NSSI might be linked to the immaturity of rational decision-making during adolescence. This immaturity, combined with hyperactivity in the limbic system, could lead to emotional dysregulation triggered by visual perception and memory [64, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, 66]. We also identified attention-related mechanisms in NSSI [67]. Specifically, the right superior parietal lobule, a core node of the dorsal attention network (DAN), works with the left caudal middle frontal gyrus and the left middle temporal gyrus to regulate goal-directed attention. In our study, the marginal significance of DAN network edges further supported this finding. Notably, no cortical expression was detected in these regions.These findings suggest that adolescents with NSSI may exhibit compensatory responses in cognitive task processing and information transmission before adulthood. This could be closely related to the physiological, psychological, and social changes unique to adolescence [68, 69].Finally, our reproducibility analysis confirmed that the case-control differences in t-values were not influenced by factors such as total intracranial volume (TIV), correlation analysis methods, or cluster size thresholds. This demonstrates the robustness of our results.\u003c/p\u003e \u003cp\u003eWe analyzed how neurotransmitter receptors are distributed along the principal MIND gradient. The results showed that the spatial pattern of case-control t-maps was linked to the cortical density of nine receptors. These receptors primarily act through G-protein-coupled receptor (GPCR) signaling pathways. GPCR signaling plays a pivotal role in regulating neuronal communication, synaptic plasticity, and emotional processing, all of which are critical for understanding the pathophysiology of mood disorders and self-injurious behaviors. This highlights the importance of examining receptor-mediated signaling in the context of structural and functional brain alterations, suggesting that dysregulation of GPCR pathways may contribute to the neural mechanisms underlying psychiatric conditions. Among these receptors, studies on 5-HT neurotransmitters are the most extensive in self-injury research. Dysfunction of 5-HT1A and 5-HT4 receptors is closely related to emotional regulation disorders. Low 5-HT levels may increase the risk of self-injury [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Impaired GABA signaling disrupts the balance between excitation and inhibition in neural circuits. This imbalance can lead to anxiety and impulsive self-injurious behavior [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. Dysfunction of the metabotropic glutamate receptor (mGluR5) may reduce emotional regulation and impulse control, increasing the risk of self-injury and suicide [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. It may also worsen emotional and behavioral disorders by interfering with GABAergic neuron function. Reduced D2 receptor density may weaken an individual\u0026rsquo;s ability to experience pleasure from healthy activities. In contrast, the \u0026ldquo;release\u0026rdquo; after self-injury activates dopamine release in the striatum. This creates a pain-reward reinforcement loop, driving individuals to seek temporary emotional relief through self-injury [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. Similarly, dysfunction of the \u0026micro;-opioid receptor (MU) may link to pain-related pleasure and addiction [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e]. The findings of this study also highlighted the role of dopamine and opioid systems in the development of NSSI. Specifically, we observed \u0026zwnj;reduced D2 receptor density\u0026zwnj;, which may weaken the ability to experience pleasure from healthy activities, and \u0026zwnj;dysfunction of the MU\u0026zwnj;, which could link to pain-related pleasure and addiction\u0026mdash;both aligning with and extending previous findings [76, 77].\u003c/p\u003e \u003cp\u003eWe performed pathway enrichment analysis using partial least squares regression (PLS). This analysis revealed key transcriptional features of the related genes. In the PLS1\u0026thinsp;+\u0026thinsp;gene set, synapse-related functions were significantly enriched. These include \"glutamatergic synapse\" (GO:0060079), \"chemical synaptic transmission\" (GO:0007268), and \"regulation of trans-synaptic signaling\" (GO:0099177). Key genes, such as GABRA5, SYT17, KCNMB4, EFNB3, and SSTR1, are involved in synaptic receptor function, vesicle release, and maintaining signal balance [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. The PLS1\u0026thinsp;+\u0026thinsp;gene set also plays a role in neural development. For example, DPYSL3 is involved in axon guidance. It works together with cytoskeletal regulation genes like TMSB10 and IFT22 to regulate the growth and direction of neuronal projections [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e]. This gene set is also linked to several disease phenotypes. These include mood disorders, epilepsy, and cognitive impairments. This suggests that the PLS1\u0026thinsp;+\u0026thinsp;gene set may influence NSSI mechanisms in adolescent MDD patients through a \"synapse-development-behavior\" network. The PLS1- gene set showed strong associations with transcriptional and epigenetic regulation. It was enriched in pathways such as \"transcription coregulator activity\" (GO:0003712), \"chromatin remodeling\" (GO:0006338), and \"chromatin binding\" (GO:0003682). Core genes in this set include CUX1, NCOA3, and RORA. These genes regulate transcription and chromatin accessibility [\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]. Additionally, the PLS1- gene set was enriched in pathways related to \"centrosome\" (GO:0005813), \"microtubule binding\" (GO:0015631), and \"DNA metabolic process\" (GO:0006259). Genes such as TUBD1, CEP95, EEPD1, and AGO1 are involved in structural maintenance and metabolic regulation [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e]. Phenotypic analysis revealed that PLS1- genes are closely linked to neurodevelopmental disorders. These include intellectual and language delays, autism-related behaviors, and morphological abnormalities. Neural connectivity and metabolic abnormalities may play key roles in the development and progression of MDD with NSSI. The PLS1\u0026thinsp;+\u0026thinsp;and PLS1- gene sets appear to have distinct roles. The former is mainly involved in synaptic and developmental regulation, while the latter focuses on basic cellular processes. These findings provide a framework for understanding their mechanisms and identifying potential pathways. We also identified two genes, GABRA5 and MSGT1, that overlap with neurotransmitter-related analysis. Studies show that variations in GABRA5 significantly increase the genetic risk of NSSI. These variations may promote self-injury tendencies by altering gray matter volume in the temporal lobe and disrupting the social-emotional network [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e]. Clinically, polygenic risk scores based on GABRA5 could help identify adolescents at high risk for NSSI. However, environmental factors, such as childhood trauma, should also be considered. MSGT1 regulates B-cell activation and cytokine release, such as IL-6. This is closely related to the inflammatory hypothesis of MDD [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThrough the combined analysis of cortical layer markers and single-cell expression data, we found that PLS1+/- genes are differentially enriched across cortical layers. Further analysis of gene enrichment in different brain cell types showed that both PLS1+/- are enriched in excitatory and inhibitory neurons. This finding implies that these genes may contribute to the fine-tuning of synaptic transmission and network oscillations, which are critical for maintaining cognitive flexibility and emotional regulation - processes frequently disrupted in mood disorders. However, PLS1\u0026thinsp;+\u0026thinsp;also shows significant expression in astrocytes. This result aligns with the neurotransmitter analysis, which identified shared cell types enriched in both PLS1+/-. It also highlights the regulatory role of astrocytes in maintaining neuronal and synaptic physiology during brain homeostasis. Combined with the findings on the MSGT1 gene, this supports the inflammation hypothesis of MDD. This hypothesis suggests that cytokines released by astrocytes mediate neuroinflammation [\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOverall, our results suggest that neurons, astrocytes, and their unique connectivity patterns within affected brain regions might offer critical neurobiological insights into the pathophysiological mechanisms underlying adolescent MDD with NSSI. This underscores the vital importance of early intervention strategies during this vulnerable developmental period. Given that early intervention during this vulnerable phase may mitigate long-term disease progression and reduce the severity of psychiatric outcomes, targeted therapeutic approaches based on identified neurobiological markers could offer more precise and effective treatment strategies. The ultimate goal of this study is to identify robust neurobiological signatures that can inform both diagnostic classification and intervention timing, with the potential to develop personalized treatment protocols that address the specific neural circuit abnormalities associated with adolescent MDD and NSSI. Such neurobiological markers may serve as objective measures for monitoring treatment response and predicting clinical outcomes, thereby advancing our understanding of the neural mechanisms underlying these conditions and facilitating the development of more targeted therapeutic interventions.\u003c/p\u003e \u003cp\u003eWhile this study makes significant contributions to elucidating the neural correlates of mood disorders through its innovative application of the principal MIND gradient in investigating brain structural changes, several limitations warrant consideration.\u0026zwnj; First, we investigated brain structural changes by a new MIND gradient, but the MIND network was built using T1 image and 5 morphological features [28,94]. Future studies should take into account larger scale microstructural indices. The combination of these with multimodal imaging information would help to investigate the hierarchical organization of morphological feature.Second, a relatively small sample size limits the generality of our results.More samples are needed to assess individual differences in MIND gradients.Third, we did not discuss some clinical variables that are known to be playing a significant role in MDD from NSSIs, such as impulsive personality traits, childhood experience, and social conditions. The study should consider them for their role in the disease dynamics.Finally, we used gene expression profiles from the AHBA database. They are affected by age, sex, and ethnicity [95]. Moreover, only left hemisphere data were used, which may have affected our results in this study.Future studies could be used with whole-brain transcriptomic datasets matching age,sex, and the ethnicity. Replication by similar studies could also be made.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, this study's findings strongly support our hypothesis that significant alterations in the principal MIND gradient exist between adolescent MDD patients with and without NSSI. \u0026zwnj;Our results delineate a clear regional pattern: the gradient is markedly increased in frontal and temporal regions, while it is decreased in parietal and occipital regions.\u0026zwnj; Crucially, these alterations not only exhibit striking spatial correlations with specific neuroreceptor maps but are also highly enriched in neurobiologically relevant pathways and preferentially expressed across distinct cortical layers and cell types. Taken together, our findings offer a groundbreaking perspective on structural coordination changes in adolescent MDD patients with NSSI. This may introduce a novel endophenotype, opening a valuable research avenue for deeper exploration of the disorder's complex mechanisms.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAHBA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAllen Human Brain Atlas\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCluster-FDR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCluster-level False Discovery Rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCortical Thickness\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDAN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDorsal Attention Network\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDWI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiffusion Weighted Imaging\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFDR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFalse Discovery Rate\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGLM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGeneral Linear Model\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGMV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGray Matter Volume\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMean Curvature\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMDD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMajor Depressive Disorder\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMIND\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMorphometric Inverse Divergence\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMIND-Gd\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMorphometric Inverse Divergence Gradient\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMSN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMorphometric Similarity Network\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMRI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMagnetic Resonance Imaging\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNSSI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNon-Suicidal Self-Injury\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePLS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePartial Least Squares Regression\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePLS1+/PLS1- Genes\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePLS component 1 positive/negative weight genes\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eICD-10\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInternational Classification of Diseases, 10th Revision\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSurface Area\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSulcal Depth\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSCN\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStructural Covariance Networks\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki. Ethical approval was obtained from the Ethics Committee of the Shandong Mental Health Center (ref: KYSJWLL2024-1-075). As this was a retrospective analysis utilizing data from an imaging database, the requirement for informed consent was waived.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe code for calculating the MIND could be found on the github: https://github.com/isebenius/MIND\u003c/p\u003e\n\u003cp\u003eThe toolbox for calculating the Gradient could be found on the github: https://github.com/MICA-MNI/BrainSpace\u003c/p\u003e\n\u003cp\u003eThe code for PLS analysis could be available at the github: https://github.com/SarahMorgan/Morphometric_Similarity_SZ\u003c/p\u003e\n\u003cp\u003eThe code for the computation of spatial permutation testing could be available at the github: https://github.com/frantisekvasa/rotate_parcellation\u003c/p\u003e\n\u003cp\u003eHuman gene expression data that support the findings of this study are available in the Allen Brain Atlas: http://human.brain-map.org/static/download\u003c/p\u003e\n\u003cp\u003eThe codes for gene expression analysis can be found at https://github.com/rmarkello/abagen \u003c/p\u003e\n\u003cp\u003eThe Metascape of gene enrichment analysis is available at http://metascape.org/\u003c/p\u003e\n\u003cp\u003eOther data will be made available on request. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1.National Natural Science Foundation of China (grant 62406128)\u003c/p\u003e\n\u003cp\u003e2.Shandong Provincial Medical and Health Science and Technology Project (2024), No. 202403091056\u003c/p\u003e\n\u003cp\u003e3.Sichuan VS Mental Health Survey and Early Intervention, SCDVA (2023), No. 05\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYan Zhang: Writing – original draft, Writing – review \u0026amp; editing, Data curation, Methodology, Visualization Formal analysis, Conceptualization. Xinyuan Hu: Writing – original draft, Data curation, Methodology, Formal analysis, Visualization. Wei Wang: Writing – review \u0026amp; editing, Formal analysis, Visualization, Investigation. Jing Wang: Writing – review \u0026amp; editing, Funding acquisition, Software, Visualization, Methodology, Resources. Hongying Li: Visualization, Funding acquisition, Methodology, Resources. Jianlei Zhu: Project administration, Methodology, Investigation. Yumei Wan: Writing – review \u0026amp; editing, Investigation. Fei Liu: Data curation, Validation. Shiyue Tao: Data curation, Formal analysis. Duanwei Wang: Investigation, Resources. Xiangtao Lin: Supervision, Investigation, Conceptualization. Yingying Zhang: Investigation, Resources. Hongmei Zhang: Writing – review \u0026amp; editing, Methodology, Resources, Methodology, Investigation. Yuandong Gong: Writing – review \u0026amp; editing, Funding acquisition, Supervision, Formal analysis, Resources, Investigation, Conceptualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eFilatova EV, Shadrina MI, Slominsky PA. Major depression: one brain, one disease, one set of intertwined processes. Cells. 2021; doi:10.3390/cells10061283.\u003c/li\u003e\n \u003cli\u003eGore FM, Bloem PJ, Patton GC, Ferguson J, Joseph V, Coffey C, et al. Global burden of disease in young people aged 10\u0026ndash;24 years: a systematic analysis. The Lancet. 2011; doi:10.1016/S0140-6736(11)60512-6.\u003c/li\u003e\n \u003cli\u003eLi Y, Wan Z, Gong X, Wen L, Sun T, Liu J, et al. The association between child maltreatment, cognitive reappraisal, negative coping styles, and non-suicidal self-injury in adolescents with major depressive disorder. BMC Psychiatry. 2024; doi:10.1186/s12888-024-06041-2.\u003c/li\u003e\n \u003cli\u003eHu C, Jiang W, Wu Y, Wang M, Lin J, Chen S, et al. Microstructural abnormalities of white matter in the cingulum bundle of adolescents with major depression and non-suicidal self-injury. Psychol Med. 2024; doi:10.1017/S003329172300291X.\u003c/li\u003e\n \u003cli\u003eHooley JM, Fox KR, Boccagno C. Nonsuicidal self-injury: diagnostic challenges and current perspectives. Neuropsychiatr Dis Treat. 2020; doi:10.2147/NDT.S198806.\u003c/li\u003e\n \u003cli\u003eVoss C, Hoyer J, Venz J, Pieper L, Beesdo‐Baum K. Non‐suicidal self‐injury and its co‐occurrence with suicidal behavior: an epidemiological study among adolescents and young adults. Acta Psychiatr Scand. 2020; doi:10.1111/acps.13237.\u003c/li\u003e\n \u003cli\u003ePlener PL, Libal G, Keller F, Fegert JM, Muehlenkamp JJ. An international comparison of adolescent non-suicidal self-injury (NSSI) and suicide attempts: Germany and the USA. Psychol Med. 2009; doi:10.1017/S0033291708005114.\u003c/li\u003e\n \u003cli\u003eBrown RC, Plener PL. Non-suicidal self-injury in adolescence. Curr Psychiatry Rep. 2017; doi:10.1007/s11920-017-0767-9.\u003c/li\u003e\n \u003cli\u003eMars B, Heron J, Klonsky ED, Moran P, O\u0026rsquo;Connor RC, Tilling K, et al. Predictors of future suicide attempt among adolescents with suicidal thoughts or non-suicidal self-harm: a population-based birth cohort study. Lancet Psychiatry. 2019; doi:10.1016/S2215-0366(19)30030-6.\u003c/li\u003e\n \u003cli\u003eMannekote Thippaiah S, Shankarapura Nanjappa M, Gude JG, Voyiaziakis E, Patwa S, Birur B, et al. Non-suicidal self-injury in developing countries: a review. Int J Soc Psychiatry. 2021; doi:10.1177/0020764020943627.\u003c/li\u003e\n \u003cli\u003eLawrence HR, Balkind EG, Ji JL, Burke TA, Liu RT. Mental imagery of suicide and non-suicidal self-injury: a meta-analysis and systematic review. Clin Psychol Rev. 2023; doi:10.1016/j.cpr.2023.102302.\u003c/li\u003e\n \u003cli\u003ePang X, Wu D, Wang H, Zhang J, Yu Y, Zhao Y, et al. Cortical morphological alterations in adolescents with major depression and non-suicidal self-injury. NeuroImage Clin. 2024; doi:10.1016/j.nicl.2024.103701.\u003c/li\u003e\n \u003cli\u003eLiang S, Xue K, Wang W, Yu W, Ma X, Luo S, et al. Altered brain function and clinical features in patients with first-episode, drug na\u0026iuml;ve major depressive disorder: a resting-state fMRI study. Psychiatry Res Neuroimaging. 2020; doi:10.1016/j.pscychresns.2020.111134.\u003c/li\u003e\n \u003cli\u003eZhang J, Wu D, Wang H, Yu Y, Zhao Y, Zheng H, et al. Large-scale functional network connectivity alterations in adolescents with major depression and non-suicidal self-injury. Behav Brain Res. 2025; doi:10.1016/j.bbr.2025.115443.\u003c/li\u003e\n \u003cli\u003eYang H, Chen X, Chen ZB, Li L, Li XY, Castellanos FX, et al. Disrupted intrinsic functional brain topology in patients with major depressive disorder. Mol Psychiatry. 2021; doi:10.1038/s41380-021-01247-2.\u003c/li\u003e\n \u003cli\u003eXue K, Guo L, Zhu W, Liang S, Xu Q, Ma L, et al. Transcriptional signatures of the cortical morphometric similarity network gradient in first-episode, treatment-naive major depressive disorder. Neuropsychopharmacology. 2023; doi:10.1038/s41386-022-01474-3.\u003c/li\u003e\n \u003cli\u003eXue K, Liu F, Liang S, Guo L, Shan Y, Xu H, et al. Brain connectivity and transcriptomic similarity inform abnormal morphometric similarity patterns in first-episode, treatment-na\u0026iuml;ve major depressive disorder. J Affect Disord. 2025; doi:10.1016/j.jad.2024.11.021.\u003c/li\u003e\n \u003cli\u003eZielinski BA, Gennatas ED, Zhou J, Seeley WW. Network-level structural covariance in the developing brain. Proc Natl Acad Sci. 2010; doi:10.1073/pnas.1003109107.\u003c/li\u003e\n \u003cli\u003eSingh MK, Kesler SR, Hadi Hosseini SM, Kelley RG, Amatya D, Hamilton JP, et al. Anomalous gray matter structural networks in major depressive disorder. Biol Psychiatry. 2013; doi:10.1016/j.biopsych.2013.03.005.\u003c/li\u003e\n \u003cli\u003eRepple J, Mauritz M, Meinert S, De Lange SC, Grotegerd D, Opel N, et al. Severity of current depression and remission status are associated with structural connectome alterations in major depressive disorder. Mol Psychiatry. 2020; doi:10.1038/s41380-019-0603-1.\u003c/li\u003e\n \u003cli\u003eLi J, Seidlitz J, Suckling J, Fan F, Ji GJ, Meng Y, et al. Cortical structural differences in major depressive disorder correlate with cell type-specific transcriptional signatures. Nat Commun. 2021; doi:10.1038/s41467-021-21943-5.\u003c/li\u003e\n \u003cli\u003eSebenius I, Seidlitz J, Warrier V, Bethlehem RAI, Alexander-Bloch A, Mallard TT, et al. Robust estimation of cortical similarity networks from brain MRI. Nat Neurosci. 2023; doi:10.1038/s41593-023-01376-7.\u003c/li\u003e\n \u003cli\u003eDonahue CJ, Sotiropoulos SN, Jbabdi S, Hernandez-Fernandez M, Behrens TE, Dyrby TB, et al. Using diffusion tractography to predict cortical connection strength and distance: a quantitative comparison with tracers in the monkey. J Neurosci. 2016; doi:10.1523/JNEUROSCI.0493-16.2016.\u003c/li\u003e\n \u003cli\u003eAlexander-Bloch A, Giedd JN, Bullmore E. Imaging structural co-variance between human brain regions. Nat Rev Neurosci. 2013; doi:10.1038/nrn3465.\u003c/li\u003e\n \u003cli\u003eWang J, He Y. Toward individualized connectomes of brain morphology. Trends Neurosci. 2024; doi:10.1016/j.tins.2023.11.011.\u003c/li\u003e\n \u003cli\u003eSun H, Sun Q, Li Y, Zhang J, Xing H, Wang J. Mapping individual structural covariance network in development brain with dynamic time warping. Cereb Cortex. 2024; doi:10.1093/cercor/bhae039.\u003c/li\u003e\n \u003cli\u003eHuntenburg JM, Bazin PL, Margulies DS. Large-scale gradients in human cortical organization. Trends Cogn Sci. 2018; doi:10.1016/j.tics.2017.11.002.\u003c/li\u003e\n \u003cli\u003eYang S, Wagstyl K, Meng Y, Zhao X, Li J, Zhong P, et al. Cortical patterning of morphometric similarity gradient reveals diverged hierarchical organization in sensory-motor cortices. Cell Rep. 2021; doi:10.1016/j.celrep.2021.109582.\u003c/li\u003e\n \u003cli\u003eHavinga PJ, Boschloo L, Bloemen AJP, Nauta MH, De Vries SO, Penninx BWJH, et al. Doomed for disorder? High incidence of mood and anxiety disorders in offspring of depressed and anxious patients: a prospective cohort study. J Clin Psychiatry. 2017; doi:10.4088/JCP.15m09936.\u003c/li\u003e\n \u003cli\u003eMeng X, Navoly G, Giannakopoulou O, Levey DF, Koller D, Pathak GA, et al. Multi-ancestry genome-wide association study of major depression aids locus discovery, fine mapping, gene prioritization and causal inference. Nat Genet. 2024; doi:10.1038/s41588-023-01596-4.\u003c/li\u003e\n \u003cli\u003eLiu M, Wang L, Zhang Y, Dong H, Wang C, Chen Y, et al. Investigating the shared genetic architecture between depression and subcortical volumes. Nat Commun. 2024; doi:10.1038/s41467-024-52121-y.\u003c/li\u003e\n \u003cli\u003eAnderson KM, Collins MA, Kong R, Fang K, Li J, He T, et al. Convergent molecular, cellular, and cortical neuroimaging signatures of major depressive disorder. Proc Natl Acad Sci. 2020; doi:10.1073/pnas.2008004117.\u003c/li\u003e\n \u003cli\u003eWang H, Zhao Q, Zhang Y, Ma J, Lei M, Zhang Z, et al. Shared genetic architecture of cortical thickness alterations in major depressive disorder and schizophrenia. Prog Neuropsychopharmacol Biol Psychiatry. 2024; doi:10.1016/j.pnpbp.2024.111121.\u003c/li\u003e\n \u003cli\u003eFischl B. FreeSurfer. NeuroImage. 2012; doi:10.1016/j.neuroimage.2012.01.021.\u003c/li\u003e\n \u003cli\u003eRosen AFG, Roalf DR, Ruparel K, Blake J, Seelaus K, Villa LP, et al. Quantitative assessment of structural image quality. NeuroImage. 2018; doi:10.1016/j.neuroimage.2017.12.059.\u003c/li\u003e\n \u003cli\u003eMonereo-S\u0026aacute;nchez J, De Jong JJA, Drenthen GS, Beran M, Backes WH, Stehouwer CDA, et al. Quality control strategies for brain MRI segmentation and parcellation: practical approaches and recommendations - insights from the Maastricht study. NeuroImage. 2021; doi:10.1016/j.neuroimage.2021.118174.\u003c/li\u003e\n \u003cli\u003eDesikan RS, S\u0026eacute;gonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage. 2006; doi:10.1016/j.neuroimage.2006.01.021.\u003c/li\u003e\n \u003cli\u003eRomero-Garcia R, Atienza M, Clemmensen LH, Cantero JL. Effects of network resolution on topological properties of human neocortex. NeuroImage. 2012; doi:10.1016/j.neuroimage.2011.10.086.\u003c/li\u003e\n \u003cli\u003eVos De Wael R, Benkarim O, Paquola C, Lariviere S, Royer J, Tavakol S, et al. BrainSpace: a toolbox for the analysis of macroscale gradients in neuroimaging and connectomics datasets. Commun Biol. 2020; doi:10.1038/s42003-020-0794-7.\u003c/li\u003e\n \u003cli\u003eWagstyl K, Larocque S, Cucurull G, Lepage C, Cohen JP, Bludau S, et al. BigBrain 3D atlas of cortical layers: cortical and laminar thickness gradients diverge in sensory and motor cortices. PLOS Biol. 2020; doi:10.1371/journal.pbio.3000678.\u003c/li\u003e\n \u003cli\u003eThomas Yeo BT, Krienen FM, Sepulcre J, Sabuncu MR, Lashkari D, Hollinshead M, et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol. 2011; doi:10.1152/jn.00338.2011.\u003c/li\u003e\n \u003cli\u003eVon Economo C, Koskinas GN. Atlas of cytoarchitectonics of the adult human cerebral cortex. Basel, Switzerland: Karger; 2008.\u003c/li\u003e\n \u003cli\u003eDukart J, Holiga S, Rullmann M, Lanzenberger R, Hawkins PCT, Mehta MA, et al. JuSpace: a tool for spatial correlation analyses of magnetic resonance imaging data with nuclear imaging derived neurotransmitter maps. Hum Brain Mapp. 2021; doi:10.1002/hbm.25244.\u003c/li\u003e\n \u003cli\u003eYang C, Zhang L, Liu J, Li K, Li S, Yang Z, et al. More severe brain network hierarchy disorganization in treatment-naive deficit compared to non-deficit schizophrenia and underlying neurotransmitter associations. Schizophr Bull. 2026; doi:10.1093/schbul/sbae231.\u003c/li\u003e\n \u003cli\u003eHawrylycz MJ, Lein ES, Guillozet-Bongaarts AL, Shen EH, Ng L, Miller JA, et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature. 2012; doi:10.1038/nature11405.\u003c/li\u003e\n \u003cli\u003eMarkello RD, Arnatkeviciute A, Poline JB, Fulcher BD, Fornito A, Misic B. Standardizing workflows in imaging transcriptomics with the abagen toolbox. eLife. 2021; doi:10.7554/eLife.72129.\u003c/li\u003e\n \u003cli\u003eKrishnan A, Williams LJ, McIntosh AR, Abdi H. Partial least squares (PLS) methods for neuroimaging: a tutorial and review. NeuroImage. 2011; doi:10.1016/j.neuroimage.2010.07.034.\u003c/li\u003e\n \u003cli\u003eAbdi H, Williams LJ. Partial least squares methods: partial least squares correlation and partial least square regression. In: Reisfeld B, Mayeno AN, editors. Computational Toxicology. Totowa, NJ, USA: Humana Press; 2013. p. 549\u0026ndash;79. doi:10.1007/978-1-62703-059-5_23.\u003c/li\u003e\n \u003cli\u003eMao H, Xu M, Wang H, Liu Y, Wang F, Gao Q, et al. Transcriptional patterns of brain structural abnormalities in CSVD-related cognitive impairment. Front Aging Neurosci. 2024; doi:10.3389/fnagi.2024.1503806.\u003c/li\u003e\n \u003cli\u003eV\u0026aacute;\u0026scaron;a F, Seidlitz J, Romero-Garcia R, Whitaker KJ, Rosenthal G, V\u0026eacute;rtes PE, et al. Adolescent tuning of association cortex in human structural brain networks. Cereb Cortex. 2018; doi:10.1093/cercor/bhx249.\u003c/li\u003e\n \u003cli\u003eDrevets WC, Wittenberg GM, Bullmore ET, Manji HK. Immune targets for therapeutic development in depression: towards precision medicine. Nat Rev Drug Discov. 2022; doi:10.1038/s41573-021-00368-1.\u003c/li\u003e\n \u003cli\u003eChawla A, Cakmakci D, Fiori LM, Zang W, Maitra M, Yang J, et al. Single-nucleus chromatin accessibility profiling identifies cell types and functional variants contributing to major depression. Nat Genet. 2025; doi:10.1038/s41588-025-02249-4.\u003c/li\u003e\n \u003cli\u003eZhou Y, Zhou B, Pache L, Chang M, Khodabakhshi AH, Tanaseichuk O, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 2019; doi:10.1038/s41467-019-09234-6.\u003c/li\u003e\n \u003cli\u003eHe Z, Han D, Efimova O, Guijarro P, Yu Q, Oleksiak A, et al. Comprehensive transcriptome analysis of neocortical layers in humans, chimpanzees and macaques. Nat Neurosci. 2017; doi:10.1038/nn.4548.\u003c/li\u003e\n \u003cli\u003eAlexander-Bloch AF, Shou H, Liu S, Satterthwaite TD, Glahn DC, Shinohara RT, et al. On testing for spatial correspondence between maps of human brain structure and function. NeuroImage. 2018; doi:10.1016/j.neuroimage.2018.05.070.\u003c/li\u003e\n \u003cli\u003eLiu S, Zhao W, Li Y, Li X, Li J, Cao H, et al. Improve cognition of depressive patients through the regulation of basal ganglia connectivity: combined medication using Shuganjieyu capsule. J Psychiatr Res. 2020; doi:10.1016/j.jpsychires.2020.01.013.\u003c/li\u003e\n \u003cli\u003eFulcher BD, Arnatkeviciute A, Fornito A. Overcoming false-positive gene-category enrichment in the analysis of spatially resolved transcriptomic brain atlas data. Nat Commun. 2021; doi:10.1038/s41467-021-22862-1.\u003c/li\u003e\n \u003cli\u003eOtte C, Gold SM, Penninx BW, Pariante CM, Etkin A, Fava M, et al. Major depressive disorder. Nat Rev Dis Primer. 2016; doi:10.1038/nrdp.2016.65.\u003c/li\u003e\n \u003cli\u003eLiu J, Guan J, Xiong J, Wang F. Effects of transcranial magnetic stimulation combined with sertraline on cognitive level, inflammatory response and neurological function in depressive disorder patients with non-suicidal self-injury behavior. Actas Esp Psiquiatr. 2024; doi:10.62641/aep.v52i1.1542.\u003c/li\u003e\n \u003cli\u003eKang DW, Wang SM, Na HR, Park SY, Kim NY, Lee CU, et al. Differences in cortical structure between cognitively normal East Asian and Caucasian older adults: a surface-based morphometry study. Sci Rep. 2020; doi:10.1038/s41598-020-77848-8.\u003c/li\u003e\n \u003cli\u003eYomogida Y, Matsumoto M, Aoki R, Sugiura A, Phillips AN, Matsumoto K. The neural basis of changing social norms through persuasion. Sci Rep. 2017; doi:10.1038/s41598-017-16572-2.\u003c/li\u003e\n \u003cli\u003ePatil AU, Ghate S, Madathil D, Tzeng OJL, Huang HW, Huang CM. Static and dynamic functional connectivity supports the configuration of brain networks associated with creative cognition. Sci Rep. 2021; doi:10.1038/s41598-020-80293-2.\u003c/li\u003e\n \u003cli\u003eLu F, Cui Q, Huang X, Li L, Duan X, Chen H, et al. Anomalous intrinsic connectivity within and between visual and auditory networks in major depressive disorder. Prog Neuropsychopharmacol Biol Psychiatry. 2020; doi:10.1016/j.pnpbp.2020.109889.\u003c/li\u003e\n \u003cli\u003ePlener PL, Bubalo N, Fladung AK, Ludolph AG, Lul\u0026eacute; D. Prone to excitement: adolescent females with non-suicidal self-injury (NSSI) show altered cortical pattern to emotional and NSS-related material. Psychiatry Res Neuroimaging. 2012; doi:10.1016/j.pscychresns.2011.12.012.\u003c/li\u003e\n \u003cli\u003eHuang Q, Xiao M, Ai M, Chen J, Wang W, Hu L, et al. Disruption of neural activity and functional connectivity in adolescents with major depressive disorder who engage in non-suicidal self-injury: a resting-state fMRI study. Front Psychiatry. 2021; doi:10.3389/fpsyt.2021.571532.\u003c/li\u003e\n \u003cli\u003eZhou Y, Yu R, Ai M, Cao J, Li X, Hong S, et al. A resting state functional magnetic resonance imaging study of unmedicated adolescents with non-suicidal self-injury behaviors: evidence from the amplitude of low-frequency fluctuation and regional homogeneity indicator. Front Psychiatry. 2022; doi:10.3389/fpsyt.2022.925672.\u003c/li\u003e\n \u003cli\u003eLu X, Zhang Y, Zhong S, Lai S, Yan S, Song X, et al. Cognitive impairment in major depressive disorder with non-suicidal self-injury: association with the functional connectivity of frontotemporal cortex. J Psychiatr Res. 2024; doi:10.1016/j.jpsychires.2024.07.008.\u003c/li\u003e\n \u003cli\u003eWang H, Wen S, Wang Y, Zhou Y, Niu B. Rumination, loneliness, and non-suicidal self-injury among adolescents with major depressive disorder: the moderating role of resilience. Soc Sci Med. 2025; doi:10.1016/j.socscimed.2024.117512.\u003c/li\u003e\n \u003cli\u003eNiu HM, Zhang ZM, Mu XM, Zhao HJ. The characteristics and influencing factors of nonsuicidal self-injury of adolescents with depressive disorder in China: a meta-analysis. J Nerv Ment Dis. 2023; doi:10.1097/NMD.0000000000001641.\u003c/li\u003e\n \u003cli\u003eWood EK, Kruger R, Day JP, Day SM, Hunter JN, Neville L, et al. A nonhuman primate model of human non-suicidal self-injury: serotonin-transporter genotype-mediated typologies. Neuropsychopharmacology. 2022; doi:10.1038/s41386-021-00994-8.\u003c/li\u003e\n \u003cli\u003eDeligiannidis KM, Clayton AH. Patient-specific considerations, the GABA pathway, and new clinical trial data on neuroactive steroids in MDD and PPD. J Clin Psychiatry. 2023; doi:10.4088/JCP.SG22045SU1C.\u003c/li\u003e\n \u003cli\u003eDavis MT, Hillmer A, Holmes SE, Pietrzak RH, DellaGioia N, Nabulsi N, et al. In vivo evidence for dysregulation of mGluR5 as a biomarker of suicidal ideation. Proc Natl Acad Sci. 2019; doi:10.1073/pnas.1818871116.\u003c/li\u003e\n \u003cli\u003eQing Xu, Haoming Xu, Shuhan Li. Alpha-2-macroglobulin (A2M) and dopamine receptor D2 (DRD2) expression analysis and influence of separation from parents in childhood on the suicide and self-injury behavior and psychological adjustment in adolescence. Cell Mol Biol. 2023; doi:10.14715/cmb/2022.69.1.12.\u003c/li\u003e\n \u003cli\u003eSt\u0026ouml;rkel LM, Karabatsiakis A, Hepp J, Kolassa IT, Schmahl C, Niedtfeld I. Salivary beta-endorphin in nonsuicidal self-injury: an ambulatory assessment study. Neuropsychopharmacology. 2021; doi:10.1038/s41386-020-00914-2.\u003c/li\u003e\n \u003cli\u003eBresin K, Gordon KH. Endogenous opioids and nonsuicidal self-injury: a mechanism of affect regulation. Neurosci Biobehav Rev. 2013; doi:10.1016/j.neubiorev.2013.01.020.\u003c/li\u003e\n \u003cli\u003eMantas I, Saarinen M, Xu ZQD, Svenningsson P. Update on GPCR-based targets for the development of novel antidepressants. Mol Psychiatry. 2022; doi:10.1038/s41380-021-01040-1.\u003c/li\u003e\n \u003cli\u003eMonfared RV, Alhassen W, Truong TM, Gonzales MAM, Vachirakorntong V, Chen S, et al. Transcriptome profiling of dysregulated GPCRs reveals overlapping patterns across psychiatric disorders and age-disease interactions. Cells. 2021; doi:10.3390/cells10112967.\u003c/li\u003e\n \u003cli\u003eRomero-Garcia R, Warrier V, Bullmore ET, Baron-Cohen S, Bethlehem RAI. Synaptic and transcriptionally downregulated genes are associated with cortical thickness differences in autism. Mol Psychiatry. 2019; doi:10.1038/s41380-018-0023-7.\u003c/li\u003e\n \u003cli\u003eZhu XN, Liu XD, Sun S, Zhuang H, Yang JY, Henkemeyer M, et al. Ephrin-B3 coordinates timed axon targeting and amygdala spinogenesis for innate fear behaviour. Nat Commun. 2016; doi:10.1038/ncomms11096.\u003c/li\u003e\n \u003cli\u003eBrenner R, Chen QH, Vilaythong A, Toney GM, Noebels JL, Aldrich RW. BK channel \u0026beta;4 subunit reduces dentate gyrus excitability and protects against temporal lobe seizures. Nat Neurosci. 2005; doi:10.1038/nn1573.\u003c/li\u003e\n \u003cli\u003eVan Der Ende EL, In \u0026lsquo;T Veld SGJG, Hanskamp I, Van Der Lee S, Dijkstra JIR, Hok-A-Hin YS, et al. CSF proteomics in autosomal dominant Alzheimer\u0026rsquo;s disease highlights parallels with sporadic disease. Brain. 2023; doi:10.1093/brain/awad213.\u003c/li\u003e\n \u003cli\u003eZhao H, Chen P, Gao X, Huang Z, Yang P, Shen H. Spatiotemporal proteomic and transcriptomic landscape of DAT+ dopaminergic neurons development and function. iScience. 2025; doi:10.1016/j.isci.2025.112115.\u003c/li\u003e\n \u003cli\u003eSasayama D, Hiraishi A, Tatsumi M, Kamijima K, Ikeda M, Umene-Nakano W, et al. Possible association of CUX1 gene polymorphisms with antidepressant response in major depressive disorder. Pharmacogenomics J. 2013; doi:10.1038/tpj.2012.18.\u003c/li\u003e\n \u003cli\u003eRybczyński P, Cacala R, Cepil Z, Fic E, Romanska W, Marczak L, et al. Contrasting effects of clozapine and risperidone on cholesterol metabolism, synaptic proteins, and transcriptional regulation in human LUHMES neurons. Mol Neurobiol. 2026; doi:10.1007/s12035-025-05515-y.\u003c/li\u003e\n \u003cli\u003eLogue MW, Baldwin C, Guffanti G, Melista E, Wolf EJ, Reardon AF, et al. A genome-wide association study of post-traumatic stress disorder identifies the retinoid-related orphan receptor alpha (RORA) gene as a significant risk locus. Mol Psychiatry. 2013; doi:10.1038/mp.2012.113.\u003c/li\u003e\n \u003cli\u003eSzklarczyk D, Gable AL, Nastou KC, Lyon D, Kirsch R, Pyysalo S, et al. The STRING database in 2021: customizable protein\u0026ndash;protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021; doi:10.1093/nar/gkaa1074.\u003c/li\u003e\n \u003cli\u003eLiu S, Jia L, Quan B, Rong G, Li M, Xie R, et al. Coiled-coil domain-containing protein 45 is a potential prognostic biomarker and is associated with immune cell enrichment of hepatocellular carcinoma. Dis Markers. 2022; doi:10.1155/2022/7745315.\u003c/li\u003e\n \u003cli\u003eNickoloff JA, Sharma N, Taylor L, Allen SJ, Lee SH, Hromas R. Metnase and EEPD1: DNA repair functions and potential targets in cancer therapy. Front Oncol. 2022; doi:10.3389/fonc.2022.808757.\u003c/li\u003e\n \u003cli\u003eNiu Y, Qian Q, Li J, Gong P, Jiao X, Mao X, et al. De novo variants in AGO1 recapitulate a heterogeneous neurodevelopmental disorder phenotype. Clin Genet. 2022; doi:10.1111/cge.14114.\u003c/li\u003e\n \u003cli\u003eSun Y, Zhao G, Zhang Y, Lu Z, Kang Z, Sun J, et al. Multitrait GWAS of non-suicidal self-injury and the polygenetic effects on child psychopathology and brain structures. Cell Rep Med. 2025; doi:10.1016/j.xcrm.2025.102119.\u003c/li\u003e\n \u003cli\u003eMorabito S, Miyoshi E, Michael N, Shahin S, Martini AC, Head E, et al. Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer\u0026rsquo;s disease. Nat Genet. 2021; doi:10.1038/s41588-021-00894-z.\u003c/li\u003e\n \u003cli\u003eGonz\u0026aacute;lez-Arias C, S\u0026aacute;nchez-Ruiz A, Esparza J, S\u0026aacute;nchez-Puelles C, Arancibia L, Ram\u0026iacute;rez-Franco J, et al. Dysfunctional serotonergic neuron-astrocyte signaling in depressive-like states. Mol Psychiatry. 2023; doi:10.1038/s41380-023-02269-8.\u003c/li\u003e\n \u003cli\u003eZhao YF, Verkhratsky A, Tang Y, Illes P. Astrocytes and major depression: the purinergic avenue. Neuropharmacology. 2022; doi:10.1016/j.neuropharm.2022.109252.\u003c/li\u003e\n \u003cli\u003eKing DJ, Wood AG. Clinically feasible brain morphometric similarity network construction approaches with restricted magnetic resonance imaging acquisitions. Netw Neurosci. 2020; doi:10.1162/netn_a_00123.\u003c/li\u003e\n \u003cli\u003eMogil LS, Andaleon A, Badalamenti A, Dickinson SP, Guo X, Rotter JI, et al. Genetic architecture of gene expression traits across diverse populations. PLOS Genet. 2018; doi:10.1371/journal.pgen.1007586.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Major depressive disorder, Non-suicidal self-injury, Adolescent, Morphometric Inverse Divergence Gradient network, Gene expression, Neuroreceptors","lastPublishedDoi":"10.21203/rs.3.rs-8994845/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8994845/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAdolescents with major depressive disorder (MDD) and non-suicidal self-injury (NSSI) face serious challenges to their mental and physical health. The development of this condition is influenced by genetic, environmental, and brain development factors. This study aimed to explore brain structural network abnormalities and their transcriptional mechanisms in adolescents with MDD and NSSI using the Morphometric Inverse Divergence Gradient Network for the first time.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA total of 241 adolescents with MDD participated in this study, including 134 individuals with a history of non-suicidal self-injury and 107 without. Using high-resolution T1-weighted magnetic resonance imaging, we constructed the principal morphometric gradient network, which reflects the similarity of cortical morphology between different brain regions. Partial least squares regression analysis was conducted to link imaging findings with neurotransmitter receptor distributions and gene expression patterns.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAdolescents with NSSI showed higher morphometric gradient values in the left caudal middle frontal gyrus and bilateral inferior temporal gyri, and lower values in the left lingual gyrus and right superior parietal lobule. These structural changes were slightly associated with functional alterations in the dorsal attention network. Further analysis revealed associations with the distribution of nine neurotransmitter receptor types, including serotonin 5-HT1A, GABAa, glutamate, and dopamine receptors. The first component of partial least squares regression analysis explained 39.53% of the spatial variance in the morphometric gradient and identified key genes related to NSSI, such as GABRA5 and MGST1. Enrichment analysis showed that positively correlated genes were involved in synaptic signaling, neurodevelopment, and mood disorder pathways, and were enriched in astrocytes and cortical layers I, II, and V. Negatively correlated genes were linked to chromatin regulation and cytoskeletal metabolism, and were mainly expressed in cortical layers III and IV.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAdolescents with MDD and non-suicidal self-injury display specific abnormalities in brain structural gradients that are closely associated with neurotransmitter receptor distributions and gene expression profiles. These findings provide multi-level evidence for understanding the neurobiological mechanisms underlying non-suicidal self-injury.\u003c/p\u003e","manuscriptTitle":"Major Depressive Disorder with Non-Suicidal Self-Injury in Adolescents: Altered Morphological Inverse Divergence Gradient in Brain Structure and Gene Expression Patterns","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-13 13:00:49","doi":"10.21203/rs.3.rs-8994845/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"72c92e66-ead9-4622-905c-02164772b2f6","owner":[],"postedDate":"March 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-27T05:41:08+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-13 13:00:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8994845","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8994845","identity":"rs-8994845","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}