Gray Matter Volume Alterations in Early-Adulthood Major Depressive Disorder Patients With Non-Suicidal Self-Injury and Suicide Attempts

Gray Matter Volume Alterations in Early-Adulthood Major Depressive Disorder Patients With Non-Suicidal Self-Injury and Suicide Attempts | 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 Gray Matter Volume Alterations in Early-Adulthood Major Depressive Disorder Patients With Non-Suicidal Self-Injury and Suicide Attempts mengzhi zhang, aihua zhou, meijun liu, yibo wang, jiabo shi, yu chen, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9178989/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 15 You are reading this latest preprint version Abstract BACKGROUND Major depressive disorder (MDD) is frequently accompanied by self-harm behaviors, including non-suicidal self-injury (NSSI) and suicide attempts (SA), which are important risk factors for suicide and poor clinical outcomes. Although neuroimaging studies have identified structural brain abnormalities related to suicidal behaviors in MDD, the neurobiological distinctions between NSSI and SA remain poorly understood. In particular, few studies have simultaneously compared MDD patients with NSSI, MDD patients with SA, and MDD patients without self-harm behaviors. Therefore, this study aimed to investigate gray matter volume (GMV) alterations and their associations with clinical symptoms in early-adulthood MDD patients using voxel-based morphometry (VBM). METHODS A total of 54 MDD patients with NSSI (MDD/NSSI), 68 MDD patients without NSSI (sMDD), 50 MDD patients with SA (MDD/SA), and 66 healthy controls (HC) were included. Voxel-based morphometry (VBM) was used to examine GMV differences using high-resolution T1-weighted MRI scans. Age, sex, education, and intracranial volume were included as covariates. One-way ANOVA with Gaussian random field (GRF) correction was performed, followed by post-hoc t-tests. Correlations between GMV and clinical measures of depression (HAMD), anxiety (HAMA), and suicide risk (NGASR) were assessed. RESULTS Significant GMV differences were found in the right superior/middle frontal gyrus, left superior frontal gyrus, bilateral caudate nuclei, right superior/middle temporal gyrus, and bilateral middle cingulate gyrus (voxel-level p < 0.001, GRF corrected p < 0.05). Compared to MDD/NSSI, the MDD/SA group showed reduced GMV in several regions. Correlations between GMV in the caudate nucleus and cingulate cortex with depression severity, anxiety, and suicide risk were observed, but did not survive FDR correction. CONCLUSIONS MDD patients with NSSI and SA show distinct GMV alterations. Findings suggest that MDD with SA involves unique neural impairments, contributing to emotional dysregulation and suicide risk. Major depressive disorder Non-suicidal self-injury Suicide attempts Gray matter volume Neuroimaging Voxel-based morphometry Figures Figure 1 Figure 2 Introduction Major depressive disorder (MDD) is a common and debilitating mental disorder characterized by persistent low mood, anhedonia, and cognitive impairment. With a lifetime prevalence of approximately 16%, MDD represents one of the leading contributors to global disease burden and years lived with disability worldwide [ 1 – 3 ]. Beyond its core affective and cognitive symptoms, MDD substantially impairs social functioning and quality of life and is strongly associated with a range of high-risk behaviors, particularly self-harm and suicide, making it a major global public health concern [ 4 – 6 ].Suicide is one of the leading causes of premature mortality worldwide. Epidemiological data indicate that approximately 800,000 individuals die by suicide each year globally, accounting for nearly 1.5% of all deaths, and this number is projected to exceed one million by 2040 [ 7 ]. MDD has consistently been identified as the most important psychiatric risk factor for suicidal behavior, with a substantial proportion of individuals who attempt or die by suicide having experienced depressive episodes prior to the event [ 5 , 6 ]. In addition to suicidal behavior, another form of self-harm that is highly prevalent yet often underrecognized among individuals with MDD is non-suicidal self-injury (NSSI). From a psychological and neurocognitive perspective, individuals with MDD may be particularly vulnerable to engaging in self-harm behaviors due to impairments in emotion regulation, reduced inhibitory control, and altered value-based decision-making. These processes are primarily supported by fronto–limbic–striatal circuits, which are consistently implicated in the pathophysiology of depression. Dysfunction within these circuits may compromise the ability to cope with intense negative affect, thereby increasing the likelihood of maladaptive behavioral responses such as self-harm [ 8 – 10 ].Self-harm behaviors are commonly categorized into NSSI and suicide attempt (SA). NSSI refers to the deliberate, repetitive destruction of one’s own body tissue without suicidal intent and without socially sanctioned purposes[ 11 ], whereas SA involves potentially lethal self-injurious behavior carried out with at least some intent to die, but which does not result in death due to rescue or other factors [ 12 ]. Although these two behaviors differ fundamentally in terms of suicidal intent and lethality, they frequently co-occur in clinical and community samples.Studies involving adolescents and young adults in non-clinical populations with NSSI have found that 69.2–83.3% reported having engaged in SA behaviors. In another study, 24.4% reported having attempted suicide, suggesting that NSSI may serve as a strong predictor or developmental precursor of suicidal behavior [ 13 , 14 ].Based on the behavioral overlap between NSSI and SA, some studies have suggested that these behaviors may lie along a continuum of self-harm severity [ 15 ]. However, accumulating evidence indicates that, despite behavioral similarities, NSSI and SA differ in their underlying motivations, functional roles, and developmental trajectories [ 10 , 16 ]. Suicidal behavior has been consistently associated with overwhelming psychological distress, hopelessness, and elevated suicide risk [ 16 ], whereas NSSI is more commonly conceptualized as a maladaptive emotion regulation strategy, serving functions such as alleviating negative affect, reducing emotional numbness, self-punishment, or eliciting interpersonal support [ 17 ]. These distinctions suggest that NSSI represents a clinically meaningful construct that is not merely a less severe form of suicidal behavior [ 10 , 16 , 17 ]. Neuroimaging research has provided important insights into the neural mechanisms underlying MDD and self-harm behaviors. Structural and functional MRI studies consistently demonstrate that MDD is associated with widespread abnormalities in fronto–limbic–striatal networks, which play central roles in emotion regulation, impulse control, and reward-based decision-making. These networks encompass prefrontal cortices, anterior cingulate cortex, and subcortical structures critical for regulating affective responses and cognitive control. Voxel-based morphometry (VBM) meta-analyses have revealed robust reductions in gray matter volume (GMV) in prefrontal and limbic regions in patients with MDD compared with healthy controls [ 18 – 20 ]. Importantly, studies in medication-free MDD samples confirm that these structural alterations represent core neurobiological features of depression rather than treatment effects [ 20 ]. Growing evidence suggests that structural MRI can differentiate MDD patients with and without suicidal behaviors.Large-scale ENIGMA consortium studies and meta-analytic evidence indicate that suicidal ideation and suicide attempts in MDD are associated with additional gray matter volume reductions in frontal, cingulate, insular, and parietal regions, beyond the core neuroanatomical alterations observed in major depressive disorder more generally [ 21 – 23 ]. A systematic review focusing on suicide attempts across different age groups identified widespread and age-dependent GMV alterations in medial, dorsolateral, and orbitofrontal cortices, highlighting shared and developmental aspects of suicidality-related brain changes in MDD [ 24 ]. More specifically, MDD patients with a history of suicide attempts show reduced GMV in dorsolateral and medial prefrontal cortices, angular gyrus, and parietal regions, which may underlie impairments in executive control, decision-making, and emotion regulation associated with elevated suicide risk [ 24 , 25 ]. Recent machine-learning studies further demonstrate that structural MRI features can distinguish MDD patients with suicide ideation or attempts from those without suicidal behaviors, supporting the presence of identifiable neuroanatomical signatures of suicidality [ 26 ].Structural MRI studies in adolescents with NSSI have reported significant gray matter volume reductions as well as surface-based cortical morphology alterations in fronto-limbic regions, including the insula, anterior cingulate cortex, putamen, and frontal gyri, compared with healthy controls. These alterations encompass changes in gray matter volume, cortical thickness, and cortical complexity across prefrontal, insular, and sensorimotor regions, providing convergent evidence for disrupted neural substrates underlying affect regulation and self-referential processing in NSSI [ 27 ].Together, these findings suggest both overlapping and distinct neural substrates for MDD, SA and NSSI. However, most previous studies have failed to clearly distinguish between NSSI and SA, often combining them into a single self-harm category. Moreover, few investigations have included MDD patients without self-harm behaviors as a clinical comparison group. Studies simultaneously comparing MDD with NSSI, MDD with SA, MDD without self-harm, and healthy controls remain scarce. Consequently, it remains unclear whether NSSI represents a neurobiological pattern independent of suicide attempts within MDD. To address these gaps, the present study employed VBM to compare gray matter volume among four groups: MDD with NSSI, MDD with suicide attempts, MDD without self-harm behaviors, and healthy controls. Methods 2.1 Participants Between July 2017 and December 2019, a total of 263 Han Chinese patients with MDD, aged 18–30 years, were recruited from Nanjing Brain Hospital. The cohort comprised patients with MDD categorized into three behavioral subgroups: those with non-suicidal self-injury (MDD/NSSI), those with suicide attempts (MDD/SA), and those without either behavior (sMDD). Additionally, 66 healthy controls (HCs), matched for age and sex, were recruited from the local community. The inclusion criteria for MDD patients were as follows:(1) Met DSM-5 criteria for MDD; (2) Had a 17-item Hamilton Depression Rating Scale (HAMD-17) [ 28 ] score > 17 on the day of MRI scanning; (3) Had a score < 14 on the 32-item Hypomania Checklist and < 10 on the Young Mania Rating Scale (YMRS) [ 29 ]. The inclusion criteria for HCs were as follows: (1) no major physical illnesses, psychiatric disorders, or neurological diseases; (2) no history of psychotropic medication use. Group-specific inclusion criteria: MDD/NSSI: (1) Met DSM-5 criteria for NSSI; (2) Had a Clinician-rated severity of non-suicidal self-injury (CRSNSSI) [ 30 ] score ≥ 2; (3) No suicidal intent during self-injury; (4) Suicide attempts during the current episode were excluded. MDD/SA:(1) A suicide attempt within the past month; (2) MRI scanning performed ≥ 7 days after overdose attempts; (3) Explicit intent to die and had suicidal ideation on the scanning day; (4) No lifetime history of NSSI. sMDD: (1) No lifetime history of suicide attempts or NSSI (CRSNSSI = 0).General exclusion criteria Exclusion criteria for all: (1) Any psychiatric disorder other than borderline personality disorder; (2) Substance abuse or dependence within the past year; (3) History of neurological disorders, systemic medical illnesses, head injury, or any other organic condition that may cause psychiatric symptoms; (4) Pregnancy or breastfeeding; (5) Contraindications to MRI scanning (e.g., metallic implants, claustrophobia). This study was approved by the Research Ethics Review Committee of Nanjing Brain Hospital, affiliated with Nanjing Medical University. After providing participants with a detailed explanation of the study, written informed consent was obtained from all participants. 2.2 Research Methods 2.2.1 Clinical Assessments Clinical data were collected using a self-designed questionnaire to gather general information about the patients, including age, gender, years of education, age of onset of depression, age of onset of NSSI, frequency of depressive episodes, presence of borderline personality disorder, family history, and childhood abuse history. The severity of depressive symptoms was assessed using the HAMD-17, while anxiety symptoms were assessed using the Hamilton Anxiety Rating Scale (HAMA) [ 31 ]. The severity of NSSI was evaluated using the CRSNSSI scale, and suicide risk was assessed using the Nurses’ global assessment of suicide risk (NGASR) [ 32 ]. Additionally, the Ottawa Self-Injury Inventory was used to assess NSSI behaviors. The number of self-injury incidents in the past week, month, six months, and twelve months was recorded. The presence of suicidal ideation during this episode and any history of suicide attempts during previous episodes (where the suicide attempt occurred more than one year before enrollment and there were no suicide attempts during the current episode) were also documented. 2.2.2 MRI Date Acquisition All MRI data were collected using a Siemens 3.0T Signal MRI scanner (Germany) to acquire blood oxygen level-dependent (BOLD) signals. Three-dimensional structural MRI data were collected, and cotton earplugs were provided to the patients during the scan to minimize noise and reduce anxiety, helping them adapt to the scanning environment more quickly. Participants were instructed to avoid conscious thought activities, to adopt a comfortable position, keep their eyes closed during the scan, and remain as still as possible to minimize head movement.The scanning parameters were as follows: Repetition time (TR) = 1900 ms, Echo time (TE) = 2.48 ms, Field of view (FOV) = 250 mm × 250 mm, Matrix = 256 × 256, Slice thickness = 1 mm, Scan time = 4 min 18 s. 2.2.3 Data Processing Preprocessing MRI data preprocessing and analysis were conducted on the MATLAB 2013 platform using SPM8 (Statistical Parametric Mapping) software ( https://www.fil.ion.ucl.ac.uk/spm/ ). Data with imaging abnormalities, anatomical results inconsistencies, or artifacts were excluded from further analysis. GMV Analysis Structural data for each participant were segmented using the New Segment method in VBM to obtain gray matter, white matter, and cerebrospinal fluid components[ 33 ]. The segmented gray matter images were iteratively processed using the DARTEL algorithm to create an average template. The original gray matter images were modulated, spatially normalized, and resampled into a 1.5×1.5×1.5 mm³ gray matter image. Finally, Gaussian smoothing was applied with an 8 mm full-width at half-maximum (FWHM) kernel, producing GMV values suitable for statistical analysis. 2.2.4 Statistical Analysis Demographic and clinical data were analyzed using SPSS version 19.0. Analysis of variance (ANOVA) was used to compare differences in age, years of education, age of onset of depression, age of onset of NSSI, frequency of depressive episodes, HAMD-17, HAMA, CRSNSSI, and NGASR among the four groups: MDD/NSSI, MDD/SA, sMDD, and HCs. A two-sample t-test was used to compare differences between the MDD/NSSI and MDD/SA groups. Chi-square tests or Fisher's exact tests were applied to examine gender, presence of borderline personality disorder, family history, and childhood abuse. DPABI software was used to analyze GM differences among the MDD/SA, MDD/NSSI, sMDD, and HCs, with age, gender, years of education, and total intracranial volume as covariates. ANOVA was used to compare the four groups, and the GRF method was applied for multiple comparison correction. Regions with voxel-level p < 0.001 and a cluster size (K) ≥ 693, after GRF cluster correction with p < 0.05, were considered to show statistically significant differences. Post-hoc t-tests were performed for MDD/SA compared with the other three groups, with further multiple comparison corrections using the GRF method. For this analysis, voxel-level p < 0.001 and a cluster size (K) ≥ 113 were applied. Results 3.1 Demographic and Clinical Data Of the 263 initially recruited MDD patients, 62 were excluded due to incomplete scale assessments, and a further 29 were excluded for failure to complete the MRI scan. Finally, 54 MDD/NSSI patients (mean age ± SD = 20.81 ± 3.56 years; mean education ± SD = 13.0 ± 2.2 years; 48 females), 68 sMDD patients (mean age = 20.3 ± 1.9 years; mean education = 13.0 ± 1.80 years; 56 females), and 50 MDD/SA patients (mean age ± SD = 24.3 ± 4.59 years; mean education ± SD = 13.6 ± 2.7 years; 30 females) were included in the final analysis.A total of 66 age- and gender-matched healthy controls (HCs) were recruited from the community (mean age = 21.1 ± 2.0 years; mean education = 13.6 ± 1.5 years; 58 females). There were significant differences among the four groups—MDD/SA, MDD/NSSI, sMDD, and HCs—in terms of gender, age, family history, presence of borderline personality disorder, family history of mental illness, suicidal ideation, total HAMD-17 score, total HAMA score, and types of antidepressant medication (p < 0.05). No significant differences were found between the MDD/SA and MDD/NSSI groups in terms of years of education, total illness duration, presence of borderline personality disorder, family history of mental illness, childhood abuse, or types of antidepressant medication (p > 0.05).Descriptive statistics for the study sample can be found in Table 1. 3.2 GMV Analysis Results Significant gray matter volume differences were observed between the four groups in the right superior frontal gyrus (SFG)/middle frontal gyrus (MFG), left SFG, bilateral caudate nucleus, right superior temporal gyrus (STG)/middle temporal gyrus (MTG), and bilateral cingulate gyrus (CG) (single voxel p < 0.001, cluster size K ≥ 693, GRF corrected p < 0.05), as shown in Table 2 and Figure 1a.Based on the one-way ANOVA, post-hoc independent sample t-tests were conducted between the MDD/SA group and the other three groups. Compared to the MDD/NSSI group, the MDD/SA group showed reduced gray matter volume in the right SFG/MFG, right caudate nucleus, left SFG, right STG/MTG, left caudate nucleus, and bilateral CG, as shown in Figure 1b. Compared to the sMDD group, the MDD/SA group showed a reduction in the gray matter volume of the right SFG, as shown in Figure 1c. Compared to the HC groups, the MDD/SA group exhibited decreased gray matter volume in the right STG/amygdala, right MTG, and right caudate nucleus, as shown in Figure 1d (single voxel p < 0.001, cluster size K ≥ 113, GRF corrected p < 0.05), as summarized in Table 2. 3.3 Correlation Analysis Between Gray Matter Volume in Differentiated Brain Regions and Clinical Scale Scores In this study, we conducted partial correlation analyses between the gray matter volume of seven differentiated brain regions—SFG/MFG, left SFG, right STG/MTG, bilateral caudate nucleus, and bilateral cingulate gyrus (CG)—identified in the MDD/SA and MDD/NSSI groups, and the scores on clinical scales. It is important to note that the results presented below were not corrected for multiple comparisons using FDR (False Discovery Rate), and should therefore be considered as preliminary exploratory findings.In the pure depression group (sMDD, n = 68), the gray matter volume of the right caudate nucleus was significantly negatively correlated with the HAMA score (r = -0.347, p = 0.004), suggesting that more severe anxiety symptoms were associated with smaller caudate volume. However, this correlation did not reach significance after FDR correction (pFDR > 0.05). No significant correlations were observed between the gray matter volume of other brain regions and scores on the HAMD, HAMA, or the NGASR.In the combined sample of the three groups (MDD/SA, MDD/NSSI, and sMDD, n = 172), the gray matter volume of the right caudate nucleus was negatively correlated with the HAMD (r = -0.197, p = 0.010) and HAMA (r = -0.161, p = 0.035) scores; the gray matter volume of the left anterior cingulate gyrus (ACG) was negatively correlated with the HAMD (r = -0.158, p = 0.038) and HAMA (r = -0.190, p = 0.012) scores; and the gray matter volume of the right CG was negatively correlated with NGASR scores (r = -0.165, p = 0.030). After FDR correction, none of these correlations reached significance (pFDR > 0.05), as shown in Figure 2. In summary, the reduced gray matter volume in the caudate nucleus and cingulate gyrus showed a trend of negative correlation with depression, anxiety, and suicide risk, suggesting that these brain regions may play a potential role in emotional regulation and suicidality. Although these results did not pass the FDR correction for significance, these findings provide important clues for future research and need further validation with larger sample sizes and more stringent statistical corrections. Discussion This study systematically explored the structural differences in GMV in early adulthood depression patients with NSSI and SA using VBM. The results revealed that, compared to MDD/NSSI patients, MDD/SA patients exhibited significant reductions in GMV in brain regions including the right SFG/MFG, right caudate nucleus, left SFG, right STG/MTG, left caudate nucleus, and bilateral CG. Further analysis of the patterns of brain structural damage in the two groups showed that MDD/NSSI patients mainly exhibited compensatory increases in GMV in prefrontal-limbic system-related regions, whereas MDD/SA patients displayed reductions in GMV in brain regions related to the prefrontal-limbic-striatal system. This result supports the hypothesis of different patterns of brain damage between MDD/NSSI and MDD/SA.Correlation analysis revealed a negative trend between the GMV of the right caudate nucleus, left ACG, and right CG with depression, anxiety, and suicide risk scores. Although these results did not reach statistical significance after multiple comparison corrections, the consistent direction of the correlations suggests that structural changes in these brain regions may be closely related to impairments in emotional regulation, cognitive control, and impulse inhibition. The structural changes in the ventral prefrontal cortex (VPFC) and orbital frontal cortex (OFC) are crucial for emotional regulation, decision-making, and self-control [ 34 ]. In adolescent patients with MDD and bipolar disorder (BD), those with a history of SA exhibit significantly smaller GMV in the VPFC and OFC compared to those without SA[ 35 , 36 ]. Furthermore, a prospective study found that reduced GMV in the baseline VPFC and rostral prefrontal cortex (PFC) may increase the risk of future suicide attempts in adolescents with mood disorders[ 6 ]. These findings suggest that reduced frontal lobe GMV may contribute to the occurrence of suicidal behavior by altering emotional regulation, decision-making, and self-control processes. Previous postmortem studies have shown that individuals who died by suicide exhibit atrophy in structures such as the frontal lobe, hippocampus, caudate nucleus, thalamus, and anterior cingulate[ 37 ].In studies of suicidal behavior in MDD, the ENIGMA-MDD working group’s meta-analysis reported reduced overall subcortical volume in MDD patients with suicide attempts, while no robust and consistent regional volume reductions were identified [ 22 ]. In non-clinical populations with NSSI, reduced gray matter volume was observed in the insula and anterior cingulate gyrus[ 38 ]. However, research on gray matter in MDD/NSSI is rare. This study adds to previous findings on gray matter changes in MDD/NSSI, revealing increased gray matter volume in the SFG in NSSI, and decreased volume in SA. The superior frontal gyrus is a crucial node in the fronto-parietal network, involved in decision-making, reward processing, emotional regulation, and cognitive control [ 39 ].Furthermore, our study found significant reductions in gray matter volume in key brain regions, including the cingulate gyrus, caudate nucleus, and left SFG, in MDD/SA patients compared to both MDD/NSSI patients and healthy controls. Notably, these regions are core structural nodes in the fronto-parietal executive network and reward circuitry[35 = 40]. Previous functional imaging studies have observed functional impairments in the fronto-parietal network in individuals with suicide attempts. Therefore, we speculate that the gray matter structural damage observed in this study reflects compromised neural integrity in these core brain regions, which in turn leads to a decline in the functional coherence of the fronto-parietal network. Activation in fronto-parietal network-related brain regions is involved in logical reasoning, attentional shifting, working memory, decision-making, and impulse control functions[ 41 ], and works in coordination with the default network to ensure proper cognitive function[ 42 ]. Functional damage to the fronto-parietal network is believed to be closely associated with cognitive impairment in MDD. Impaired fronto-parietal network function leads to dysfunction in the default network it coordinates with[ 42 ], resulting in long-term negative cognition that traps patients in a rumination state, ultimately leading them into the core damage of the suicidal neurocognitive model[ 43 , 44 ].According to the three-step neurocognitive model of suicide, individuals who engage in suicidal behavior first experience heightened social stress, accompanied by increased sensitivity to adverse environmental stimuli, rendering them less capable of coping with negative stress compared to their usual state [ 44 ]. The second step involves cognitive impairment, characterized by dysfunction within the reward circuitry, which leads to deficits in risk evaluation and decision-making. The reduced GMV in key brain regions observed in the MDD/SA group in this study—such as the cingulate gyrus, caudate nucleus, and SFG—may represent the structural basis of such cognitive impairment. Cognitive dysfunction within the fronto-parietal network disrupts its coordinated interaction with the default mode network, triggering negative emotional rumination and trapping individuals in overwhelming sadness, distress, and hopelessness [ 44 ]. Finally, impairment of impulse control within the fronto-parietal network prevents suppression of suicidal impulses triggered by suicidal ideation, ultimately leading to suicidal behavior[ 44 ]. In contrast, non-suicidal self-injury is widely conceptualized as a maladaptive coping strategy aimed at obtaining temporary relief from negative affect or interpersonal distress, rather than an intent to die. Such behaviors are thought to serve affect-regulation and interpersonal functions, distinguishing them from suicidal behavior [ 10 , 11 , 16 , 17 ].Based on our findings, we further hypothesize that patients with MDD/NSSI may exhibit compensatory structural enhancement, reflected by increased GMV in the left SFG. Given the established role of the SFG in cognitive control and emotion regulation [ 45 ], increased GMV in this region may facilitate the regulation of negative emotions and support interpersonal adjustment in individuals engaging in NSSI.Conversely, MDD patients with suicide attempts show frontal lobe–related structural and functional abnormalities, accompanied by impaired emotional regulation and reward processing, which are associated with cognitive dysfunction, reduced impulse control, and increased suicide risk [ 25 ] .In the clinical correlation analysis, this study found that in the pure depression group (sMDD), the gray matter volume (GMV) of the right caudate nucleus was significantly negatively correlated with the severity of anxiety (r = -0.347, p = 0.004), suggesting that more severe anxiety symptoms were associated with smaller caudate nucleus volume, although this result did not reach statistical significance after FDR correction. The caudate nucleus, as an integral part of the striatum, is involved in reward processing, motivational regulation, and emotional response control[ 46 , 47 ]. Its structural and functional abnormalities have been closely linked to depression and anxiety[ 48 ]. Previous studies have shown that reduced caudate volume or activity in depressed patients is significantly associated with decreased reward sensitivity, anhedonia, and a bias toward negative emotional processing[ 49 ], which may explain the trend of reduced caudate volume as anxiety or depression symptoms worsen.In the combined sample of the three groups, the GMV of the right caudate nucleus and left ACG were both negatively correlated with HAMD and HAMA scores, while the GMV of the right middle cingulate gyrus (MCC) was negatively correlated with NGASR scores. Although these results did not reach statistical significance after FDR correction, the direction of the correlations remained consistent. The anterior cingulate cortex (ACC) is a core node in the emotional regulation network, involved in emotional conflict monitoring, self-regulation, and error feedback processing. Its structural or functional impairments have been closely associated with the severity of depression and anxiety[ 50 , 51 ]. Reduced GMV in the ACC may reflect a weakening of emotional regulation and cognitive control functions, leading to a higher burden of emotional symptoms. Additionally, this study found that the GMV of the right MCC was negatively correlated with suicide risk. The MCC is primarily involved in behavioral inhibition and impulse control, and its reduced volume or function has been linked to increased impulsivity, self-harm, and suicide risk[ 52 , 53 ]. Therefore, the results of this study suggest that the caudate nucleus, anterior cingulate gyrus, and middle cingulate gyrus may collectively form key neural circuits for emotional regulation, reward processing, and impulse inhibition. The decline in the structural integrity of these brain regions may serve as a potential neurobiological foundation for exacerbated emotional symptoms and increased suicide risk. This study also found that MDD/SA patients exhibited reduced GMV in the right STG/MTG compared to MDD/NSSI patients. Previous research on adolescents with MDD and suicide attempts has found reduced volume in the right STG[ 53 ], and some studies have shown that temporal lobe volume can distinguish between suicidal and non-suicidal individuals[ 54 ]. The STG/MTG is involved in critical functions such as auditory cognition and language expression and is believed to be associated with anxiety and impaired social cognition in MDD[ 55 ]. Research has also demonstrated that reduced volume in the right STG is related to social cognition in individuals with suicide attempts[ 56 ]. In this study, structural abnormalities in the right STG/MTG were observed in both MDD/SA and MDD/NSSI patients, with the SA group showing reduced volume and the NSSI group showing increased volume. This suggests that the decreased volume in the right STG/MTG may impair social cognition, potentially leading to suicidal behavior, while the increased volume in the same regions may compensate by enhancing social cognition, thus allowing MDD/NSSI patients to alleviate stress through NSSI behavior, ultimately escaping painful experiences. This could represent a potential pathological mechanism underlying MDD with SA and NSSI, warranting further validation in larger samples. Several limitations of the present study should be acknowledged. First, the cross-sectional design limits causal inference regarding GMV alterations and self-harm behaviors. Longitudinal studies are needed to clarify whether observed GMV differences reflect pre-existing vulnerability or consequences of illness progression. Second, despite a moderate sample size, residual confounding cannot be excluded. The MDD/SA group was relatively older and had a different sex distribution than the MDD/NSSI group. Although age and sex were included as covariates, these differences may still influence group comparisons. Third, medication effects cannot be fully ruled out. Although antidepressant class differences were nonsignificant and patients with overdose attempts were scanned ≥ 1 week after discontinuation, cumulative medication exposure was not systematically controlled.Finally, larger-scale multimodal studies in more diverse populations are needed to improve generalizability and mechanistic insight. Conclusions In conclusion, this study identified group-related differences in gray matter volume patterns among early-adulthood patients with major depressive disorder who exhibited suicide attempts, non-suicidal self-injury, or no self-harm behaviors. Compared with patients with non-suicidal self-injury, those with suicide attempts showed more pronounced gray matter volume reductions in fronto–parietal, cingulate, striatal, and temporal regions.These findings suggest that suicide attempts and non-suicidal self-injury in MDD may be associated with partially distinct neurobiological characteristics, rather than reflecting solely different levels of self-harm severity. The involvement of brain regions related to cognitive control, emotional regulation, and reward processing in patients with suicide attempts highlights neural systems that may be relevant to suicidal behavior in MDD.By directly comparing multiple MDD subgroups within a single study framework, this work adds to the existing neuroimaging literature and underscores the potential value of considering NSSI and suicide attempts as related but distinct clinical phenomena. Further longitudinal and multimodal studies are warranted to clarify the mechanisms underlying these differences and their potential clinical implications. Abbreviations MDD Major depressive disorder NSSI Non-suicidal self-injury SA Suicide attempt VBM Voxel-based morphometry GM Gray matter GMV Gray matter volume MRI Magnetic resonance imaging MDD/NSSI Major depressive disorder with non-suicidal self-injury MDD/SA Major depressive disorder with suicide attempts sMDD Major depressive disorder without self-harm HCs Healthy controls HAMD-17 17-item Hamilton Depression Rating Scale YMRS Young Mania Rating Scale DSM-5 Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition CRSNSSI Clinician-Rated Severity of Non-Suicidal Self-Injury HAMA Hamilton Anxiety Rating Scale NGASR Nurses’ Global Assessment of Suicide Risk BOLD Blood oxygen level–dependent TR Repetition time TE Echo time FOV Field of view SPM Statistical Parametric Mapping FWHM Full width at half maximum ANOVA Analysis of variance GRF Gaussian random field SFG Superior frontal gyrus MFG Middle frontal gyrus STG Superior temporal gyrus MTG Middle temporal gyrus CG Cingulate gyrus FDR False discovery rate VPFC Ventral prefrontal cortex OFC Orbitofrontal cortex BD Bipolar disorder PFC Prefrontal cortex MCC Middle cingulate cortex ACC Anterior cingulate cortex Declarations Ethics approval and consent to participate This study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Nanjing Brain Hospital (Approval No. 2016-KY010). Informed consent was obtained from all participants. Clinical trial number Not applicable. Consent for publication Not applicable. Competing interests The authors declared that they have no competing interests. Authors’ information Zhijian Yao, Email: [email protected] . Rui Yan, Email: [email protected] Qing Lu, Email: [email protected] . SUPPLEMENTARY Supplementary material related to this article can be found, in the online version. Funding This project was supported by the National Natural Science Foundation of China (62571115, 82571749, 82271568, 82301718); the Jiangsu Medical Innovation Center for Mental Illness (CXZX202226); the Jiangsu Provincial Key Research and Development Program (BE2019675, BE2023667); the Key Project of Science and Technology Innovation for Social Development in Suzhou (2022SS04); Jiangsu Provincial Natural Science Youth Fund (BK20230154). Author Contribution Mengzhi Zhang: Analysis of data and interpretation of result. Conception of idea. Drafting of paper. Aihua Zhou: Analysis of data. Interpretation of data. Meijun Liu: Analysis of data. Drafting of paper. Yibo Wang: Analysis of data. Acquisition of data. Jiabo Shi: Revision of paper.Yu Chen : Analysis of data. Lingling Hua: Analysis of data. Rui Yan: Revision of paper. Zhijian Yao: Revision of paper and final approval of the submission. Design of the study. Revision of Paper. Qing Lu: Interpretation of data. Revision of Paper. Acknowledgements We sincerely appreciate all the participants for their cooperation and support throughout the course of this research. Data Availability The datasets used and analysed during the current study are available from the corresponding author on reasonable request. References Kessler RC, Berglund P, Demler O, Jin R, Koretz D, Merikangas KR, Rush AJ, Walters EE, Wang PS. The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication (NCS-R). JAMA. 2003;289(23):3095–105. 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Foreman KJ, Marquez N, Dolgert A, Fukutaki K, Fullman N, McGaughey M, Pletcher MA, Smith AE, Tang K, Yuan CW, et al. Forecasting life expectancy, years of life lost, and all-cause and cause-specific mortality for 250 causes of death: reference and alternative scenarios for 2016-40 for 195 countries and territories. Lancet. 2018;392(10159):2052–90. Phillips ML, Drevets WC, Rauch SL, Lane R. Neurobiology of emotion perception I: The neural basis of normal emotion perception. Biol Psychiatry. 2003;54(5):504–14. Bechara A. Decision making, impulse control and loss of willpower to resist drugs: a neurocognitive perspective. Nat Neurosci. 2005;8(11):1458–63. Nock MK. Self-injury. Annu Rev Clin Psychol. 2010;6:339–63. Brown RC, Plener PL. Non-suicidal Self-Injury in Adolescence. Curr Psychiatry Rep. 2017;19(3):20. Klonsky ED, May AM, Saffer BY. Suicide, Suicide Attempts, and Suicidal Ideation. Annu Rev Clin Psychol. 2016;12:307–30. In-Albon T, Ruf C, Schmid M. 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Rentería ME, Schmaal L, Hibar DP, Couvy-Duchesne B, Strike LT, Mills NT, de Zubicaray GI, McMahon KL, Medland SE, Gillespie NA, et al. Subcortical brain structure and suicidal behaviour in major depressive disorder: a meta-analysis from the ENIGMA-MDD working group. Transl Psychiatry. 2017;7(5):e1116. Vieira R, Faria AR, Ribeiro D, Picó-Pérez M, Bessa JM. Structural and functional brain correlates of suicidal ideation and behaviors in depression: A scoping review of MRI studies. Prog Neuropsychopharmacol Biol Psychiatry. 2023;126:110799. Chen Z, Xu T, Li Q, Shu Y, Zhou X, Guo T, Liang F. Grey matter abnormalities in major depressive disorder patients with suicide attempts: A systematic review of age-specific differences. Heliyon. 2024;10(3):e24894. Domínguez-Baleón C, Gutiérrez-Mondragón LF, Campos-González AI, Rentería ME. Neuroimaging Studies of Suicidal Behavior and Non-suicidal Self-Injury in Psychiatric Patients: A Systematic Review. Front Psychiatry. 2018;9:500. Hu J, Huang Y, Zhang X, Liao B, Hou G, Xu Z, Dong S, Li P. Identifying suicide attempts, ideation, and non-ideation in major depressive disorder from structural MRI data using deep learning. Asian J Psychiatr. 2023;82:103511. Pang X, Wu D, Wang H, Zhang J, Yu Y, Zhao Y, Li Q, Ni L, Wang K, Zhang D, et al. Cortical morphological alterations in adolescents with major depression and non-suicidal self-injury. Neuroimage Clin. 2024;44:103701. Hamilton M. A rating scale for depression. J Neurol Neurosurg Psychiatry. 1960;23(1):56–62. Young RC, Biggs JT, Ziegler VE, Meyer DA. A rating scale for mania: reliability, validity and sensitivity. Br J Psychiatry. 1978;133:429–35. American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 5th ed. Arlington (VA): American Psychiatric Publishing; 2013. Hamilton M. The assessment of anxiety states by rating. Br J Med Psychol. 1959;32(1):50–5. Cutcliffe JR, Barker P. The Nurses' Global Assessment of Suicide Risk (NGASR): developing a tool for clinical practice. J Psychiatr Ment Health Nurs. 2004;11(4):393–400. Groschwitz RC, Plener PL, Groen G, Bonenberger M, Abler B. Differential neural processing of social exclusion in adolescents with non-suicidal self-injury: An fMRI study. Psychiatry Res Neuroimaging. 2016;255:43–9. Fettes P, Schulze L, Downar J. Cortico-Striatal-Thalamic Loop Circuits of the Orbitofrontal Cortex: Promising Therapeutic Targets in Psychiatric Illness. Front Syst Neurosci. 2017;11:25. Fan S, Lippard ETC, Sankar A, Wallace A, Johnston JAY, Wang F, Pittman B, Spencer L, Oquendo MA, Blumberg HP. Gray and white matter differences in adolescents and young adults with prior suicide attempts across bipolar and major depressive disorders. J Affect Disord. 2019;245:1089–97. Lippard ETC, Johnston JAY, Spencer L, Quatrano S, Fan S, Sankar A, Weathers J, Pittman B, Oquendo MA, Blumberg HP. Preliminary examination of gray and white matter structure and longitudinal structural changes in frontal systems associated with future suicide attempts in adolescents and young adults with mood disorders. J Affect Disord. 2019;245:1139–48. Bani-Fatemi A, Tasmim S, Graff-Guerrero A, Gerretsen P, Strauss J, Kolla N, Spalletta G, De Luca V. Structural and functional alterations of the suicidal brain: An updated review of neuroimaging studies. Psychiatry Res Neuroimaging. 2018;278:77–91. Ando A, Reichl C, Scheu F, Bykova A, Parzer P, Resch F, Brunner R, Kaess M. Regional grey matter volume reduction in adolescents engaging in non-suicidal self-injury. Psychiatry Res Neuroimaging. 2018;280:48–55. Kuusinen V, Cesnaite E, Peräkylä J, Ogawa KH, Hartikainen KM. Orbitofrontal Lesion Alters Brain Dynamics of Emotion-Attention and Emotion-Cognitive Control Interaction in Humans. Front Hum Neurosci. 2018;12:437. Vincent JL, Kahn I, Snyder AZ, Raichle ME, Buckner RL. Evidence for a frontoparietal control system revealed by intrinsic functional connectivity. J Neurophysiol. 2008;100(6):3328–42. Dixon ML, De La Vega A, Mills C, Andrews-Hanna J, Spreng RN, Cole MW, Christoff K. Heterogeneity within the frontoparietal control network and its relationship to the default and dorsal attention networks. Proc Natl Acad Sci U S A. 2018;115(7):E1598–607. Jia W, Zhu H, Ni Y, Su J, Xu R, Jia H, Wan X. Disruptions of frontoparietal control network and default mode network linking the metacognitive deficits with clinical symptoms in schizophrenia. Hum Brain Mapp. 2020;41(6):1445–58. Kaiser RH, Andrews-Hanna JR, Wager TD, Pizzagalli DA. Large-Scale Network Dysfunction in Major Depressive Disorder: A Meta-analysis of Resting-State Functional Connectivity. JAMA Psychiatry. 2015;72(6):603–11. Jollant F, Lawrence NL, Olié E, Guillaume S, Courtet P. The suicidal mind and brain: a review of neuropsychological and neuroimaging studies. World J Biol Psychiatry. 2011;12(5):319–39. Lu F, Yang W, Wei D, Sun J, Zhang Q, Qiu J. Superior frontal gyrus and middle temporal gyrus connectivity mediates the relationship between neuroticism and thought suppression. Brain Imaging Behav. 2022;16(3):1400–9. Haber SN, Knutson B. The reward circuit: linking primate anatomy and human imaging. Neuropsychopharmacology. 2010;35(1):4–26. Grahn JA, Parkinson JA, Owen AM. The cognitive functions of the caudate nucleus. Prog Neurobiol. 2008;86(3):141–55. Kim MJ, Hamilton JP, Gotlib IH. Reduced caudate gray matter volume in women with major depressive disorder. Psychiatry Res. 2008;164(2):114–22. Hamilton JP, Etkin A, Furman DJ, Lemus MG, Johnson RF, Gotlib IH. Functional neuroimaging of major depressive disorder: a meta-analysis and new integration of base line activation and neural response data. Am J Psychiatry. 2012;169(7):693–703. Pizzagalli DA. Frontocingulate dysfunction in depression: toward biomarkers of treatment response. Neuropsychopharmacology. 2011;36(1):183–206. Etkin A, Egner T, Kalisch R. Emotional processing in anterior cingulate and medial prefrontal cortex. Trends Cogn Sci. 2011;15(2):85–93. van Heeringen K, Mann JJ. The neurobiology of suicide. Lancet Psychiatry. 2014;1(1):63–72. Schmaal L, van Harmelen AL, Chatzi V, Lippard ETC, Toenders YJ, Averill LA, Mazure CM, Blumberg HP. Imaging suicidal thoughts and behaviors: a comprehensive review of 2 decades of neuroimaging studies. Mol Psychiatry. 2020;25(2):408–27. Gosnell SN, Fowler JC, Salas R. Classifying suicidal behavior with resting-state functional connectivity and structural neuroimaging. Acta Psychiatr Scand. 2019;140(1):20–9. Atmaca M, Koc M, Aslan S, Mermi O, Korkmaz S, Gurok MG, Yildirim H. Superior Temporal Gyrus Volumes in Patients With Social Anxiety Disorder. Prim Care Companion CNS Disord 2021, 23(5). Pan LA, Ramos L, Segreti A, Brent DA, Phillips ML. Right superior temporal gyrus volume in adolescents with a history of suicide attempt. Br J Psychiatry. 2015;206(4):339–40. Tables Table 1 and 2 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.docx Table2.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 20 Apr, 2026 Reviews received at journal 18 Apr, 2026 Reviews received at journal 09 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviews received at journal 07 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviews received at journal 07 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers invited by journal 07 Apr, 2026 Editor invited by journal 02 Apr, 2026 Editor assigned by journal 01 Apr, 2026 Submission checks completed at journal 01 Apr, 2026 First submitted to journal 20 Mar, 2026 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. 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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-9178989","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":620696195,"identity":"7901f2b4-efe6-4d5d-ae9d-a1f735bb4f30","order_by":0,"name":"mengzhi zhang","email":"","orcid":"","institution":"Nanjing University","correspondingAuthor":false,"prefix":"","firstName":"mengzhi","middleName":"","lastName":"zhang","suffix":""},{"id":620696196,"identity":"db5a29d9-d0fe-475d-b754-1e95eef0d391","order_by":1,"name":"aihua zhou","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"aihua","middleName":"","lastName":"zhou","suffix":""},{"id":620696197,"identity":"1c396ecc-9d45-4957-85c9-e2c48e41dfaa","order_by":2,"name":"meijun liu","email":"","orcid":"","institution":"Nanjing University","correspondingAuthor":false,"prefix":"","firstName":"meijun","middleName":"","lastName":"liu","suffix":""},{"id":620696198,"identity":"e9a5e3cf-4a4c-45e2-97e8-4ee0f3dc56be","order_by":3,"name":"yibo wang","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"yibo","middleName":"","lastName":"wang","suffix":""},{"id":620696199,"identity":"7fe4587a-29b2-4be3-83f3-8e1ca0b57ff4","order_by":4,"name":"jiabo shi","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"jiabo","middleName":"","lastName":"shi","suffix":""},{"id":620696200,"identity":"d49a5d50-93d8-48b8-ab8b-efa1a5f19551","order_by":5,"name":"yu chen","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"yu","middleName":"","lastName":"chen","suffix":""},{"id":620696201,"identity":"320045b0-fd3f-4cba-a33c-c1881cb25513","order_by":6,"name":"lingling hua","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"lingling","middleName":"","lastName":"hua","suffix":""},{"id":620696202,"identity":"607b38e5-d6e9-4bff-b0de-6dddc1e50de8","order_by":7,"name":"rui yan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtUlEQVRIiWNgGAWjYNACA4l6fmbmww9I0WKRINnOlmZAijUVCQbneRQkiDP/+Nljn3kKJPKMD/MwGDDU2EQT1nImL3k2j4FEsdlh3gMPGI6l5TYQ1HIgx5gZqIVx22G+BAPGhsNEaDn/BqJlczOIJErLDYgtiRuYidUieeONMeMcAwljicPAQE4gxi9853OMGd78qZPj7z98+MGHGhvCWhQOIPMSCCkHAXmCho6CUTAKRsEoAACl4TnYZOabqAAAAABJRU5ErkJggg==","orcid":"","institution":"Nanjing University","correspondingAuthor":true,"prefix":"","firstName":"rui","middleName":"","lastName":"yan","suffix":""},{"id":620696203,"identity":"7e60a7ba-936e-4cd9-ab61-a93bbd9048cd","order_by":8,"name":"zhijian yao","email":"","orcid":"","institution":"Nanjing University","correspondingAuthor":false,"prefix":"","firstName":"zhijian","middleName":"","lastName":"yao","suffix":""},{"id":620696204,"identity":"be84b1e9-ce19-41ac-b100-70a2584e1cbb","order_by":9,"name":"qing lu","email":"","orcid":"","institution":"Southeast University","correspondingAuthor":false,"prefix":"","firstName":"qing","middleName":"","lastName":"lu","suffix":""}],"badges":[],"createdAt":"2026-03-20 12:39:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9178989/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9178989/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106874577,"identity":"4ef6a017-5df0-4adb-9a52-2eb7b78bea1f","added_by":"auto","created_at":"2026-04-14 10:19:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":581799,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGray Matter Volume Differences Between Early-Onset Depression Subgroups and Healthy Controls\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(a) Results of one-way analysis of variance (ANOVA) showing gray matter volume differences across the four groups. Significant clusters were observed in the right superior frontal gyrus/middle frontal gyrus, left superior frontal gyrus, bilateral caudate nucleus, right superior temporal gyrus/middle temporal gyrus, and bilateral middle cingulate gyrus. The color bar indicates F values. (b) Two-sample t-test results comparing the MDD/SA and MDD/NSSI groups, showing reduced gray matter volume in the right superior frontal gyrus/middle frontal gyrus, bilateral caudate nucleus, right superior temporal gyrus/middle temporal gyrus, and bilateral middle cingulate gyrus in the MDD/SA group. (c) Two-sample t-test results comparing the MDD/SA and pure MDD (sMDD) groups, revealing reduced gray matter volume in the right superior frontal gyrus in the MDD/SA group. (d) Two-sample t-test results comparing the MDD/SA and healthy control (HC) groups, showing reduced gray matter volume in the right superior temporal gyrus/amygdala, right middle temporal gyrus, and right caudate nucleus in the MDD/SA group. All results are displayed at a voxel-wise threshold of p \u0026lt; 0.001 with cluster-level Gaussian random field–corrected p \u0026lt; 0.05. Warm and cool colors indicate relative increases and decreases in gray matter volume, respectively.\u003c/p\u003e","description":"","filename":"GMv.png","url":"https://assets-eu.researchsquare.com/files/rs-9178989/v1/ff729d842d547b7eed8a0def.png"},{"id":106874579,"identity":"d938c21a-0764-4926-87ee-52fe0384a641","added_by":"auto","created_at":"2026-04-14 10:19:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":681950,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation Analysis Between Gray Matter Volume in Differential Brain Regions and Clinical Scale Scores\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eScatter plots illustrate the correlations between gray matter volume (GMV) of selected brain regions and clinical scale scores. (a) Correlation between right caudate nucleus GMV and HAMA scores in the pure major depressive disorder group (sMDD, n = 68). (b–c) Correlations between right caudate nucleus GMV and HAMD (b) and HAMA (c) scores in all participants (n = 172). (d–e) Correlations between left anterior cingulate gyrus GMV and HAMD (d) and HAMA (e) scores in all participants (n = 172). (f) Correlation between right middle cingulate gyrus GMV and NGASR scores in all participants (n = 172). Solid lines represent linear regression fits, and shaded areas indicate 95% confidence intervals. Correlation coefficients (r) and uncorrected p values are shown in each panel. None of the correlations survived false discovery rate (FDR) correction (pFDR \u0026gt; 0.05).\u003c/p\u003e","description":"","filename":"file.png","url":"https://assets-eu.researchsquare.com/files/rs-9178989/v1/7066b687281f45ce92ec929a.png"},{"id":107481918,"identity":"8126dc91-194b-4cf9-be6b-5e76f8e8df39","added_by":"auto","created_at":"2026-04-22 02:20:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1802007,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9178989/v1/b498f750-4df0-424a-986e-9106169ffe31.pdf"},{"id":107480072,"identity":"f392d17a-2053-48a1-a9ce-46613d4dbd98","added_by":"auto","created_at":"2026-04-22 02:04:23","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":14597,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9178989/v1/25d3f4835612edcbd2db0d1a.docx"},{"id":106960750,"identity":"5a82fd16-d0fb-41bd-9dcc-6a4285c97800","added_by":"auto","created_at":"2026-04-15 09:22:56","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":13647,"visible":true,"origin":"","legend":"","description":"","filename":"Table2.docx","url":"https://assets-eu.researchsquare.com/files/rs-9178989/v1/8f2306ed083150a53c03e0ca.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Gray Matter Volume Alterations in Early-Adulthood Major Depressive Disorder Patients With Non-Suicidal Self-Injury and Suicide Attempts","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMajor depressive disorder (MDD) is a common and debilitating mental disorder characterized by persistent low mood, anhedonia, and cognitive impairment. With a lifetime prevalence of approximately 16%, MDD represents one of the leading contributors to global disease burden and years lived with disability worldwide [\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Beyond its core affective and cognitive symptoms, MDD substantially impairs social functioning and quality of life and is strongly associated with a range of high-risk behaviors, particularly self-harm and suicide, making it a major global public health concern [\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].Suicide is one of the leading causes of premature mortality worldwide. Epidemiological data indicate that approximately 800,000 individuals die by suicide each year globally, accounting for nearly 1.5% of all deaths, and this number is projected to exceed one million by 2040 [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. MDD has consistently been identified as the most important psychiatric risk factor for suicidal behavior, with a substantial proportion of individuals who attempt or die by suicide having experienced depressive episodes prior to the event [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In addition to suicidal behavior, another form of self-harm that is highly prevalent yet often underrecognized among individuals with MDD is non-suicidal self-injury (NSSI).\u003c/p\u003e\n\u003cp\u003eFrom a psychological and neurocognitive perspective, individuals with MDD may be particularly vulnerable to engaging in self-harm behaviors due to impairments in emotion regulation, reduced inhibitory control, and altered value-based decision-making. These processes are primarily supported by fronto\u0026ndash;limbic\u0026ndash;striatal circuits, which are consistently implicated in the pathophysiology of depression. Dysfunction within these circuits may compromise the ability to cope with intense negative affect, thereby increasing the likelihood of maladaptive behavioral responses such as self-harm [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].Self-harm behaviors are commonly categorized into NSSI and suicide attempt (SA). NSSI refers to the deliberate, repetitive destruction of one\u0026rsquo;s own body tissue without suicidal intent and without socially sanctioned purposes[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], whereas SA involves potentially lethal self-injurious behavior carried out with at least some intent to die, but which does not result in death due to rescue or other factors [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Although these two behaviors differ fundamentally in terms of suicidal intent and lethality, they frequently co-occur in clinical and community samples.Studies involving adolescents and young adults in non-clinical populations with NSSI have found that 69.2\u0026ndash;83.3% reported having engaged in SA behaviors. In another study, 24.4% reported having attempted suicide, suggesting that NSSI may serve as a strong predictor or developmental precursor of suicidal behavior [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].Based on the behavioral overlap between NSSI and SA, some studies have suggested that these behaviors may lie along a continuum of self-harm severity [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, accumulating evidence indicates that, despite behavioral similarities, NSSI and SA differ in their underlying motivations, functional roles, and developmental trajectories [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Suicidal behavior has been consistently associated with overwhelming psychological distress, hopelessness, and elevated suicide risk [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], whereas NSSI is more commonly conceptualized as a maladaptive emotion regulation strategy, serving functions such as alleviating negative affect, reducing emotional numbness, self-punishment, or eliciting interpersonal support [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. These distinctions suggest that NSSI represents a clinically meaningful construct that is not merely a less severe form of suicidal behavior [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eNeuroimaging research has provided important insights into the neural mechanisms underlying MDD and self-harm behaviors. Structural and functional MRI studies consistently demonstrate that MDD is associated with widespread abnormalities in fronto\u0026ndash;limbic\u0026ndash;striatal networks, which play central roles in emotion regulation, impulse control, and reward-based decision-making. These networks encompass prefrontal cortices, anterior cingulate cortex, and subcortical structures critical for regulating affective responses and cognitive control. Voxel-based morphometry (VBM) meta-analyses have revealed robust reductions in gray matter volume (GMV) in prefrontal and limbic regions in patients with MDD compared with healthy controls [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Importantly, studies in medication-free MDD samples confirm that these structural alterations represent core neurobiological features of depression rather than treatment effects [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Growing evidence suggests that structural MRI can differentiate MDD patients with and without suicidal behaviors.Large-scale ENIGMA consortium studies and meta-analytic evidence indicate that suicidal ideation and suicide attempts in MDD are associated with additional gray matter volume reductions in frontal, cingulate, insular, and parietal regions, beyond the core neuroanatomical alterations observed in major depressive disorder more generally [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. A systematic review focusing on suicide attempts across different age groups identified widespread and age-dependent GMV alterations in medial, dorsolateral, and orbitofrontal cortices, highlighting shared and developmental aspects of suicidality-related brain changes in MDD [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. More specifically, MDD patients with a history of suicide attempts show reduced GMV in dorsolateral and medial prefrontal cortices, angular gyrus, and parietal regions, which may underlie impairments in executive control, decision-making, and emotion regulation associated with elevated suicide risk [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Recent machine-learning studies further demonstrate that structural MRI features can distinguish MDD patients with suicide ideation or attempts from those without suicidal behaviors, supporting the presence of identifiable neuroanatomical signatures of suicidality [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].Structural MRI studies in adolescents with NSSI have reported significant gray matter volume reductions as well as surface-based cortical morphology alterations in fronto-limbic regions, including the insula, anterior cingulate cortex, putamen, and frontal gyri, compared with healthy controls. These alterations encompass changes in gray matter volume, cortical thickness, and cortical complexity across prefrontal, insular, and sensorimotor regions, providing convergent evidence for disrupted neural substrates underlying affect regulation and self-referential processing in NSSI [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].Together, these findings suggest both overlapping and distinct neural substrates for MDD, SA and NSSI. However, most previous studies have failed to clearly distinguish between NSSI and SA, often combining them into a single self-harm category. Moreover, few investigations have included MDD patients without self-harm behaviors as a clinical comparison group. Studies simultaneously comparing MDD with NSSI, MDD with SA, MDD without self-harm, and healthy controls remain scarce. Consequently, it remains unclear whether NSSI represents a neurobiological pattern independent of suicide attempts within MDD. To address these gaps, the present study employed VBM to compare gray matter volume among four groups: MDD with NSSI, MDD with suicide attempts, MDD without self-harm behaviors, and healthy controls.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Participants\u003c/h2\u003e \u003cp\u003eBetween July 2017 and December 2019, a total of 263 Han Chinese patients with MDD, aged 18\u0026ndash;30 years, were recruited from Nanjing Brain Hospital. The cohort comprised patients with MDD categorized into three behavioral subgroups: those with non-suicidal self-injury (MDD/NSSI), those with suicide attempts (MDD/SA), and those without either behavior (sMDD). Additionally, 66 healthy controls (HCs), matched for age and sex, were recruited from the local community.\u003c/p\u003e \u003cp\u003eThe inclusion criteria for MDD patients were as follows:(1) Met DSM-5 criteria for MDD; (2) Had a 17-item Hamilton Depression Rating Scale (HAMD-17) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] score\u0026thinsp;\u0026gt;\u0026thinsp;17 on the day of MRI scanning; (3) Had a score\u0026thinsp;\u0026lt;\u0026thinsp;14 on the 32-item Hypomania Checklist and \u0026lt;\u0026thinsp;10 on the Young Mania Rating Scale (YMRS) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The inclusion criteria for HCs were as follows: (1) no major physical illnesses, psychiatric disorders, or neurological diseases; (2) no history of psychotropic medication use. Group-specific inclusion criteria: MDD/NSSI: (1) Met DSM-5 criteria for NSSI; (2) Had a Clinician-rated severity of non-suicidal self-injury (CRSNSSI) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] score\u0026thinsp;\u0026ge;\u0026thinsp;2; (3) No suicidal intent during self-injury; (4) Suicide attempts during the current episode were excluded. MDD/SA:(1) A suicide attempt within the past month; (2) MRI scanning performed\u0026thinsp;\u0026ge;\u0026thinsp;7 days after overdose attempts; (3) Explicit intent to die and had suicidal ideation on the scanning day; (4) No lifetime history of NSSI. sMDD: (1) No lifetime history of suicide attempts or NSSI (CRSNSSI\u0026thinsp;=\u0026thinsp;0).General exclusion criteria Exclusion criteria for all: (1) Any psychiatric disorder other than borderline personality disorder; (2) Substance abuse or dependence within the past year; (3) History of neurological disorders, systemic medical illnesses, head injury, or any other organic condition that may cause psychiatric symptoms; (4) Pregnancy or breastfeeding; (5) Contraindications to MRI scanning (e.g., metallic implants, claustrophobia).\u003c/p\u003e \u003cp\u003e This study was approved by the Research Ethics Review Committee of Nanjing Brain Hospital, affiliated with Nanjing Medical University. After providing participants with a detailed explanation of the study, written informed consent was obtained from all participants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Research Methods\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Clinical Assessments\u003c/h2\u003e \u003cp\u003eClinical data were collected using a self-designed questionnaire to gather general information about the patients, including age, gender, years of education, age of onset of depression, age of onset of NSSI, frequency of depressive episodes, presence of borderline personality disorder, family history, and childhood abuse history. The severity of depressive symptoms was assessed using the HAMD-17, while anxiety symptoms were assessed using the Hamilton Anxiety Rating Scale (HAMA) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The severity of NSSI was evaluated using the CRSNSSI scale, and suicide risk was assessed using the Nurses\u0026rsquo; global assessment of suicide risk (NGASR) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Additionally, the Ottawa Self-Injury Inventory was used to assess NSSI behaviors. The number of self-injury incidents in the past week, month, six months, and twelve months was recorded. The presence of suicidal ideation during this episode and any history of suicide attempts during previous episodes (where the suicide attempt occurred more than one year before enrollment and there were no suicide attempts during the current episode) were also documented.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 MRI Date Acquisition\u003c/h2\u003e \u003cp\u003eAll MRI data were collected using a Siemens 3.0T Signal MRI scanner (Germany) to acquire blood oxygen level-dependent (BOLD) signals. Three-dimensional structural MRI data were collected, and cotton earplugs were provided to the patients during the scan to minimize noise and reduce anxiety, helping them adapt to the scanning environment more quickly. Participants were instructed to avoid conscious thought activities, to adopt a comfortable position, keep their eyes closed during the scan, and remain as still as possible to minimize head movement.The scanning parameters were as follows: Repetition time (TR)\u0026thinsp;=\u0026thinsp;1900 ms, Echo time (TE)\u0026thinsp;=\u0026thinsp;2.48 ms, Field of view (FOV)\u0026thinsp;=\u0026thinsp;250 mm \u0026times; 250 mm, Matrix\u0026thinsp;=\u0026thinsp;256 \u0026times; 256, Slice thickness\u0026thinsp;=\u0026thinsp;1 mm, Scan time\u0026thinsp;=\u0026thinsp;4 min 18 s.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Data Processing\u003c/h2\u003e \u003cp\u003e \u003cb\u003ePreprocessing\u003c/b\u003e \u003c/p\u003e \u003cp\u003eMRI data preprocessing and analysis were conducted on the MATLAB 2013 platform using SPM8 (Statistical Parametric Mapping) software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fil.ion.ucl.ac.uk/spm/\u003c/span\u003e\u003cspan address=\"https://www.fil.ion.ucl.ac.uk/spm/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Data with imaging abnormalities, anatomical results inconsistencies, or artifacts were excluded from further analysis.\u003c/p\u003e \u003cp\u003e \u003cb\u003eGMV Analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eStructural data for each participant were segmented using the New Segment method in VBM to obtain gray matter, white matter, and cerebrospinal fluid components[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The segmented gray matter images were iteratively processed using the DARTEL algorithm to create an average template. The original gray matter images were modulated, spatially normalized, and resampled into a 1.5\u0026times;1.5\u0026times;1.5 mm\u0026sup3; gray matter image. Finally, Gaussian smoothing was applied with an 8 mm full-width at half-maximum (FWHM) kernel, producing GMV values suitable for statistical analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4 Statistical Analysis\u003c/h2\u003e \u003cp\u003eDemographic and clinical data were analyzed using SPSS version 19.0. Analysis of variance (ANOVA) was used to compare differences in age, years of education, age of onset of depression, age of onset of NSSI, frequency of depressive episodes, HAMD-17, HAMA, CRSNSSI, and NGASR among the four groups: MDD/NSSI, MDD/SA, sMDD, and HCs. A two-sample t-test was used to compare differences between the MDD/NSSI and MDD/SA groups. Chi-square tests or Fisher's exact tests were applied to examine gender, presence of borderline personality disorder, family history, and childhood abuse.\u003c/p\u003e \u003cp\u003eDPABI software was used to analyze GM differences among the MDD/SA, MDD/NSSI, sMDD, and HCs, with age, gender, years of education, and total intracranial volume as covariates. ANOVA was used to compare the four groups, and the GRF method was applied for multiple comparison correction. Regions with voxel-level p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and a cluster size (K)\u0026thinsp;\u0026ge;\u0026thinsp;693, after GRF cluster correction with p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, were considered to show statistically significant differences. Post-hoc t-tests were performed for MDD/SA compared with the other three groups, with further multiple comparison corrections using the GRF method. For this analysis, voxel-level p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and a cluster size (K)\u0026thinsp;\u0026ge;\u0026thinsp;113 were applied.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Demographic and Clinical Data\u003c/strong\u003e\u003cbr\u003eOf the 263 initially recruited MDD patients, 62 were excluded due to incomplete scale assessments, and a further 29 were excluded for failure to complete the MRI scan. Finally, 54 MDD/NSSI patients (mean age \u0026plusmn; SD = 20.81 \u0026plusmn; 3.56 years; mean education \u0026plusmn; SD = 13.0 \u0026plusmn; 2.2 years; 48 females), 68 sMDD patients (mean age = 20.3 \u0026plusmn; 1.9 years; mean education = 13.0 \u0026plusmn; 1.80 years; 56 females), and 50 MDD/SA patients (mean age \u0026plusmn; SD = 24.3 \u0026plusmn; 4.59 years; mean education \u0026plusmn; SD = 13.6 \u0026plusmn; 2.7 years; 30 females) were included in the final analysis.A total of 66 age- and gender-matched healthy controls (HCs) were recruited from the community (mean age = 21.1 \u0026plusmn; 2.0 years; mean education = 13.6 \u0026plusmn; 1.5 years; 58 females). There were significant differences among the four groups\u0026mdash;MDD/SA, MDD/NSSI, sMDD, and HCs\u0026mdash;in terms of gender, age, family history, presence of borderline personality disorder, family history of mental illness, suicidal ideation, total HAMD-17 score, total HAMA score, and types of antidepressant medication (p \u0026lt; 0.05). No significant differences were found between the MDD/SA and MDD/NSSI groups in terms of years of education, total illness duration, presence of borderline personality disorder, family history of mental illness, childhood abuse, or types of antidepressant medication (p \u0026gt; 0.05).Descriptive statistics for the study sample can be found in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 GMV Analysis Results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSignificant gray matter volume differences were observed between the four groups in the right superior frontal gyrus (SFG)/middle frontal gyrus (MFG), left SFG, bilateral caudate nucleus, right superior temporal gyrus (STG)/middle temporal gyrus (MTG), and bilateral cingulate gyrus (CG) (single voxel p \u0026lt; 0.001, cluster size K \u0026ge; 693, GRF corrected p \u0026lt; 0.05), as shown in Table 2 and Figure 1a.Based on the one-way ANOVA, post-hoc independent sample t-tests were conducted between the MDD/SA group and the other three groups. \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCompared to the MDD/NSSI group, the MDD/SA group showed reduced gray matter volume in the right SFG/MFG, right caudate nucleus, left SFG, right STG/MTG, left caudate nucleus, and bilateral CG, as shown in Figure 1b. Compared to the sMDD group, the MDD/SA group showed a reduction in the gray matter volume of the right SFG, as shown in Figure 1c. Compared to the HC groups, the MDD/SA group exhibited decreased gray matter volume in the right STG/amygdala, right MTG, and right caudate nucleus, as shown in Figure 1d (single voxel p \u0026lt; 0.001, cluster size K \u0026ge; 113, GRF corrected p \u0026lt; 0.05), as summarized in Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Correlation Analysis Between Gray Matter Volume in Differentiated Brain Regions and Clinical Scale Scores\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, we conducted partial correlation analyses between the gray matter volume of seven differentiated brain regions\u0026mdash;SFG/MFG, left SFG, right STG/MTG, bilateral caudate nucleus, and bilateral cingulate gyrus (CG)\u0026mdash;identified in the MDD/SA and MDD/NSSI groups, and the scores on clinical scales. It is important to note that the results presented below were not corrected for multiple comparisons using FDR (False Discovery Rate), and should therefore be considered as preliminary exploratory findings.In the pure depression group (sMDD, n = 68), the gray matter volume of the right caudate nucleus was significantly negatively correlated with the HAMA score (r = -0.347, p = 0.004), suggesting that more severe anxiety symptoms were associated with smaller caudate volume. However, this correlation did not reach significance after FDR correction (pFDR \u0026gt; 0.05). No significant correlations were observed between the gray matter volume of other brain regions and scores on the HAMD, HAMA, or the NGASR.In the combined sample of the three groups (MDD/SA, MDD/NSSI, and sMDD, n = 172), the gray matter volume of the right caudate nucleus was negatively correlated with the HAMD (r = -0.197, p = 0.010) and HAMA (r = -0.161, p = 0.035) scores; the gray matter volume of the left anterior cingulate gyrus (ACG) was negatively correlated with the HAMD (r = -0.158, p = 0.038) and HAMA (r = -0.190, p = 0.012) scores; and the gray matter volume of the right CG was negatively correlated with NGASR scores (r = -0.165, p = 0.030). After FDR correction, none of these correlations reached significance (pFDR \u0026gt; 0.05), as shown in Figure 2.\u003c/p\u003e\n\u003cp\u003eIn summary, the reduced gray matter volume in the caudate nucleus and cingulate gyrus showed a trend of negative correlation with depression, anxiety, and suicide risk, suggesting that these brain regions may play a potential role in emotional regulation and suicidality. Although these results did not pass the FDR correction for significance, these findings provide important clues for future research and need further validation with larger sample sizes and more stringent statistical corrections.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study systematically explored the structural differences in GMV in early adulthood depression patients with NSSI and SA using VBM. The results revealed that, compared to MDD/NSSI patients, MDD/SA patients exhibited significant reductions in GMV in brain regions including the right SFG/MFG, right caudate nucleus, left SFG, right STG/MTG, left caudate nucleus, and bilateral CG. Further analysis of the patterns of brain structural damage in the two groups showed that MDD/NSSI patients mainly exhibited compensatory increases in GMV in prefrontal-limbic system-related regions, whereas MDD/SA patients displayed reductions in GMV in brain regions related to the prefrontal-limbic-striatal system. This result supports the hypothesis of different patterns of brain damage between MDD/NSSI and MDD/SA.Correlation analysis revealed a negative trend between the GMV of the right caudate nucleus, left ACG, and right CG with depression, anxiety, and suicide risk scores. Although these results did not reach statistical significance after multiple comparison corrections, the consistent direction of the correlations suggests that structural changes in these brain regions may be closely related to impairments in emotional regulation, cognitive control, and impulse inhibition.\u003c/p\u003e \u003cp\u003eThe structural changes in the ventral prefrontal cortex (VPFC) and orbital frontal cortex (OFC) are crucial for emotional regulation, decision-making, and self-control [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In adolescent patients with MDD and bipolar disorder (BD), those with a history of SA exhibit significantly smaller GMV in the VPFC and OFC compared to those without SA[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Furthermore, a prospective study found that reduced GMV in the baseline VPFC and rostral prefrontal cortex (PFC) may increase the risk of future suicide attempts in adolescents with mood disorders[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These findings suggest that reduced frontal lobe GMV may contribute to the occurrence of suicidal behavior by altering emotional regulation, decision-making, and self-control processes. Previous postmortem studies have shown that individuals who died by suicide exhibit atrophy in structures such as the frontal lobe, hippocampus, caudate nucleus, thalamus, and anterior cingulate[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].In studies of suicidal behavior in MDD, the ENIGMA-MDD working group\u0026rsquo;s meta-analysis reported reduced overall subcortical volume in MDD patients with suicide attempts, while no robust and consistent regional volume reductions were identified [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In non-clinical populations with NSSI, reduced gray matter volume was observed in the insula and anterior cingulate gyrus[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. However, research on gray matter in MDD/NSSI is rare. This study adds to previous findings on gray matter changes in MDD/NSSI, revealing increased gray matter volume in the SFG in NSSI, and decreased volume in SA. The superior frontal gyrus is a crucial node in the fronto-parietal network, involved in decision-making, reward processing, emotional regulation, and cognitive control [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].Furthermore, our study found significant reductions in gray matter volume in key brain regions, including the cingulate gyrus, caudate nucleus, and left SFG, in MDD/SA patients compared to both MDD/NSSI patients and healthy controls. Notably, these regions are core structural nodes in the fronto-parietal executive network and reward circuitry[35\u0026thinsp;=\u0026thinsp;40]. Previous functional imaging studies have observed functional impairments in the fronto-parietal network in individuals with suicide attempts. Therefore, we speculate that the gray matter structural damage observed in this study reflects compromised neural integrity in these core brain regions, which in turn leads to a decline in the functional coherence of the fronto-parietal network. Activation in fronto-parietal network-related brain regions is involved in logical reasoning, attentional shifting, working memory, decision-making, and impulse control functions[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], and works in coordination with the default network to ensure proper cognitive function[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Functional damage to the fronto-parietal network is believed to be closely associated with cognitive impairment in MDD. Impaired fronto-parietal network function leads to dysfunction in the default network it coordinates with[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], resulting in long-term negative cognition that traps patients in a rumination state, ultimately leading them into the core damage of the suicidal neurocognitive model[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].According to the three-step neurocognitive model of suicide, individuals who engage in suicidal behavior first experience heightened social stress, accompanied by increased sensitivity to adverse environmental stimuli, rendering them less capable of coping with negative stress compared to their usual state [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The second step involves cognitive impairment, characterized by dysfunction within the reward circuitry, which leads to deficits in risk evaluation and decision-making. The reduced GMV in key brain regions observed in the MDD/SA group in this study\u0026mdash;such as the cingulate gyrus, caudate nucleus, and SFG\u0026mdash;may represent the structural basis of such cognitive impairment. Cognitive dysfunction within the fronto-parietal network disrupts its coordinated interaction with the default mode network, triggering negative emotional rumination and trapping individuals in overwhelming sadness, distress, and hopelessness [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Finally, impairment of impulse control within the fronto-parietal network prevents suppression of suicidal impulses triggered by suicidal ideation, ultimately leading to suicidal behavior[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn contrast, non-suicidal self-injury is widely conceptualized as a maladaptive coping strategy aimed at obtaining temporary relief from negative affect or interpersonal distress, rather than an intent to die. Such behaviors are thought to serve affect-regulation and interpersonal functions, distinguishing them from suicidal behavior [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].Based on our findings, we further hypothesize that patients with MDD/NSSI may exhibit compensatory structural enhancement, reflected by increased GMV in the left SFG. Given the established role of the SFG in cognitive control and emotion regulation [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], increased GMV in this region may facilitate the regulation of negative emotions and support interpersonal adjustment in individuals engaging in NSSI.Conversely, MDD patients with suicide attempts show frontal lobe\u0026ndash;related structural and functional abnormalities, accompanied by impaired emotional regulation and reward processing, which are associated with cognitive dysfunction, reduced impulse control, and increased suicide risk [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] .In the clinical correlation analysis, this study found that in the pure depression group (sMDD), the gray matter volume (GMV) of the right caudate nucleus was significantly negatively correlated with the severity of anxiety (r = -0.347, p\u0026thinsp;=\u0026thinsp;0.004), suggesting that more severe anxiety symptoms were associated with smaller caudate nucleus volume, although this result did not reach statistical significance after FDR correction. The caudate nucleus, as an integral part of the striatum, is involved in reward processing, motivational regulation, and emotional response control[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Its structural and functional abnormalities have been closely linked to depression and anxiety[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Previous studies have shown that reduced caudate volume or activity in depressed patients is significantly associated with decreased reward sensitivity, anhedonia, and a bias toward negative emotional processing[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], which may explain the trend of reduced caudate volume as anxiety or depression symptoms worsen.In the combined sample of the three groups, the GMV of the right caudate nucleus and left ACG were both negatively correlated with HAMD and HAMA scores, while the GMV of the right middle cingulate gyrus (MCC) was negatively correlated with NGASR scores. Although these results did not reach statistical significance after FDR correction, the direction of the correlations remained consistent. The anterior cingulate cortex (ACC) is a core node in the emotional regulation network, involved in emotional conflict monitoring, self-regulation, and error feedback processing. Its structural or functional impairments have been closely associated with the severity of depression and anxiety[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Reduced GMV in the ACC may reflect a weakening of emotional regulation and cognitive control functions, leading to a higher burden of emotional symptoms. Additionally, this study found that the GMV of the right MCC was negatively correlated with suicide risk. The MCC is primarily involved in behavioral inhibition and impulse control, and its reduced volume or function has been linked to increased impulsivity, self-harm, and suicide risk[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Therefore, the results of this study suggest that the caudate nucleus, anterior cingulate gyrus, and middle cingulate gyrus may collectively form key neural circuits for emotional regulation, reward processing, and impulse inhibition. The decline in the structural integrity of these brain regions may serve as a potential neurobiological foundation for exacerbated emotional symptoms and increased suicide risk.\u003c/p\u003e \u003cp\u003eThis study also found that MDD/SA patients exhibited reduced GMV in the right STG/MTG compared to MDD/NSSI patients. Previous research on adolescents with MDD and suicide attempts has found reduced volume in the right STG[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], and some studies have shown that temporal lobe volume can distinguish between suicidal and non-suicidal individuals[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. The STG/MTG is involved in critical functions such as auditory cognition and language expression and is believed to be associated with anxiety and impaired social cognition in MDD[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Research has also demonstrated that reduced volume in the right STG is related to social cognition in individuals with suicide attempts[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. In this study, structural abnormalities in the right STG/MTG were observed in both MDD/SA and MDD/NSSI patients, with the SA group showing reduced volume and the NSSI group showing increased volume. This suggests that the decreased volume in the right STG/MTG may impair social cognition, potentially leading to suicidal behavior, while the increased volume in the same regions may compensate by enhancing social cognition, thus allowing MDD/NSSI patients to alleviate stress through NSSI behavior, ultimately escaping painful experiences. This could represent a potential pathological mechanism underlying MDD with SA and NSSI, warranting further validation in larger samples.\u003c/p\u003e \u003cp\u003eSeveral limitations of the present study should be acknowledged. First, the cross-sectional design limits causal inference regarding GMV alterations and self-harm behaviors. Longitudinal studies are needed to clarify whether observed GMV differences reflect pre-existing vulnerability or consequences of illness progression. Second, despite a moderate sample size, residual confounding cannot be excluded. The MDD/SA group was relatively older and had a different sex distribution than the MDD/NSSI group. Although age and sex were included as covariates, these differences may still influence group comparisons. Third, medication effects cannot be fully ruled out. Although antidepressant class differences were nonsignificant and patients with overdose attempts were scanned\u0026thinsp;\u0026ge;\u0026thinsp;1 week after discontinuation, cumulative medication exposure was not systematically controlled.Finally, larger-scale multimodal studies in more diverse populations are needed to improve generalizability and mechanistic insight.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, this study identified group-related differences in gray matter volume patterns among early-adulthood patients with major depressive disorder who exhibited suicide attempts, non-suicidal self-injury, or no self-harm behaviors. Compared with patients with non-suicidal self-injury, those with suicide attempts showed more pronounced gray matter volume reductions in fronto\u0026ndash;parietal, cingulate, striatal, and temporal regions.These findings suggest that suicide attempts and non-suicidal self-injury in MDD may be associated with partially distinct neurobiological characteristics, rather than reflecting solely different levels of self-harm severity. The involvement of brain regions related to cognitive control, emotional regulation, and reward processing in patients with suicide attempts highlights neural systems that may be relevant to suicidal behavior in MDD.By directly comparing multiple MDD subgroups within a single study framework, this work adds to the existing neuroimaging literature and underscores the potential value of considering NSSI and suicide attempts as related but distinct clinical phenomena. Further longitudinal and multimodal studies are warranted to clarify the mechanisms underlying these differences and their potential clinical implications.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\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\"\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\"\u003eSA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSuicide attempt\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eVBM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVoxel-based morphometry\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGray matter\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\"\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\"\u003eMDD/NSSI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMajor depressive disorder with non-suicidal self-injury\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMDD/SA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMajor depressive disorder with suicide attempts\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003esMDD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMajor depressive disorder without self-harm\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHCs\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHealthy controls\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHAMD-17\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e17-item Hamilton Depression Rating Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eYMRS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eYoung Mania Rating Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDSM-5\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDiagnostic and Statistical Manual of Mental Disorders, Fifth Edition\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCRSNSSI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eClinician-Rated Severity of Non-Suicidal Self-Injury\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eHAMA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHamilton Anxiety Rating Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNGASR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNurses\u0026rsquo; Global Assessment of Suicide Risk\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBOLD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBlood oxygen level\u0026ndash;dependent\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRepetition time\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTE\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEcho time\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFOV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eField of view\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSPM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStatistical Parametric Mapping\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFWHM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFull width at half maximum\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eANOVA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnalysis of variance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGRF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGaussian random field\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSFG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSuperior frontal gyrus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMFG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMiddle frontal gyrus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSTG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSuperior temporal gyrus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMTG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMiddle temporal gyrus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCG\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eCingulate gyrus\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\"\u003eVPFC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVentral prefrontal cortex\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOFC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOrbitofrontal cortex\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eBD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBipolar disorder\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePFC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePrefrontal cortex\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMCC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMiddle cingulate cortex\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eACC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnterior cingulate cortex\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 \u003cp\u003e This study was conducted in accordance with the Declaration of Helsinki and was approved by the Ethics Committee of Nanjing Brain Hospital (Approval No. 2016-KY010). Informed consent was obtained from all participants.\u003c/p\u003e \u003ch2\u003eClinical trial number\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003ch2\u003eConsent for publication\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declared that they have no competing interests.\u003c/p\u003e \u003ch2\u003eAuthors\u0026rsquo; information\u003c/h2\u003e \u003cp\u003eZhijian Yao, Email: [email protected].\u003c/p\u003e \u003cp\u003eRui Yan, Email: [email protected]\u003c/p\u003e \u003cp\u003eQing Lu, Email: [email protected].\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSUPPLEMENTARY\u003c/strong\u003e \u003cp\u003eSupplementary material related to this article can be found, in the online version.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis project was supported by the National Natural Science Foundation of China (62571115, 82571749, 82271568, 82301718); the Jiangsu Medical Innovation Center for Mental Illness (CXZX202226); the Jiangsu Provincial Key Research and Development Program (BE2019675, BE2023667); the Key Project of Science and Technology Innovation for Social Development in Suzhou (2022SS04); Jiangsu Provincial Natural Science Youth Fund (BK20230154).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMengzhi Zhang: Analysis of data and interpretation of result. Conception of idea. Drafting of paper. Aihua Zhou: Analysis of data. Interpretation of data. Meijun Liu: Analysis of data. Drafting of paper. Yibo Wang: Analysis of data. Acquisition of data. Jiabo Shi: Revision of paper.Yu Chen : Analysis of data. Lingling Hua: Analysis of data. Rui Yan: Revision of paper. Zhijian Yao: Revision of paper and final approval of the submission. Design of the study. Revision of Paper. Qing Lu: Interpretation of data. Revision of Paper.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe sincerely appreciate all the participants for their cooperation and support throughout the course of this research.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKessler RC, Berglund P, Demler O, Jin R, Koretz D, Merikangas KR, Rush AJ, Walters EE, Wang PS. The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication (NCS-R). JAMA. 2003;289(23):3095\u0026ndash;105.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerrari AJ, Charlson FJ, Norman RE, Flaxman AD, Patten SB, Vos T, Whiteford HA. The epidemiological modelling of major depressive disorder: application for the Global Burden of Disease Study 2010. PLoS ONE. 2013;8(7):e69637.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlobal burden. of 369 diseases and injuries in 204 countries and territories, 1990\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1204\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoussavi S, Chatterji S, Verdes E, Tandon A, Patel V, Ustun B. Depression, chronic diseases, and decrements in health: results from the World Health Surveys. Lancet. 2007;370(9590):851\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMann JJ, Apter A, Bertolote J, Beautrais A, Currier D, Haas A, Hegerl U, Lonnqvist J, Malone K, Marusic A, et al. Suicide prevention strategies: a systematic review. 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Proc Natl Acad Sci U S A. 2018;115(7):E1598\u0026ndash;607.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJia W, Zhu H, Ni Y, Su J, Xu R, Jia H, Wan X. Disruptions of frontoparietal control network and default mode network linking the metacognitive deficits with clinical symptoms in schizophrenia. Hum Brain Mapp. 2020;41(6):1445\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaiser RH, Andrews-Hanna JR, Wager TD, Pizzagalli DA. Large-Scale Network Dysfunction in Major Depressive Disorder: A Meta-analysis of Resting-State Functional Connectivity. JAMA Psychiatry. 2015;72(6):603\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJollant F, Lawrence NL, Oli\u0026eacute; E, Guillaume S, Courtet P. The suicidal mind and brain: a review of neuropsychological and neuroimaging studies. 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Lancet Psychiatry. 2014;1(1):63\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchmaal L, van Harmelen AL, Chatzi V, Lippard ETC, Toenders YJ, Averill LA, Mazure CM, Blumberg HP. Imaging suicidal thoughts and behaviors: a comprehensive review of 2 decades of neuroimaging studies. Mol Psychiatry. 2020;25(2):408\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGosnell SN, Fowler JC, Salas R. Classifying suicidal behavior with resting-state functional connectivity and structural neuroimaging. Acta Psychiatr Scand. 2019;140(1):20\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAtmaca M, Koc M, Aslan S, Mermi O, Korkmaz S, Gurok MG, Yildirim H. Superior Temporal Gyrus Volumes in Patients With Social Anxiety Disorder. Prim Care Companion CNS Disord 2021, 23(5).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePan LA, Ramos L, Segreti A, Brent DA, Phillips ML. 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Br J Psychiatry. 2015;206(4):339\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 and 2 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Major depressive disorder, Non-suicidal self-injury, Suicide attempts, Gray matter volume, Neuroimaging, Voxel-based morphometry","lastPublishedDoi":"10.21203/rs.3.rs-9178989/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9178989/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBACKGROUND\u003c/h2\u003e \u003cp\u003eMajor depressive disorder (MDD) is frequently accompanied by self-harm behaviors, including non-suicidal self-injury (NSSI) and suicide attempts (SA), which are important risk factors for suicide and poor clinical outcomes. Although neuroimaging studies have identified structural brain abnormalities related to suicidal behaviors in MDD, the neurobiological distinctions between NSSI and SA remain poorly understood. In particular, few studies have simultaneously compared MDD patients with NSSI, MDD patients with SA, and MDD patients without self-harm behaviors. Therefore, this study aimed to investigate gray matter volume (GMV) alterations and their associations with clinical symptoms in early-adulthood MDD patients using voxel-based morphometry (VBM).\u003c/p\u003e\u003ch2\u003eMETHODS\u003c/h2\u003e \u003cp\u003eA total of 54 MDD patients with NSSI (MDD/NSSI), 68 MDD patients without NSSI (sMDD), 50 MDD patients with SA (MDD/SA), and 66 healthy controls (HC) were included. Voxel-based morphometry (VBM) was used to examine GMV differences using high-resolution T1-weighted MRI scans. Age, sex, education, and intracranial volume were included as covariates. One-way ANOVA with Gaussian random field (GRF) correction was performed, followed by post-hoc t-tests. Correlations between GMV and clinical measures of depression (HAMD), anxiety (HAMA), and suicide risk (NGASR) were assessed.\u003c/p\u003e\u003ch2\u003eRESULTS\u003c/h2\u003e \u003cp\u003eSignificant GMV differences were found in the right superior/middle frontal gyrus, left superior frontal gyrus, bilateral caudate nuclei, right superior/middle temporal gyrus, and bilateral middle cingulate gyrus (voxel-level p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, GRF corrected p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Compared to MDD/NSSI, the MDD/SA group showed reduced GMV in several regions. Correlations between GMV in the caudate nucleus and cingulate cortex with depression severity, anxiety, and suicide risk were observed, but did not survive FDR correction.\u003c/p\u003e\u003ch2\u003eCONCLUSIONS\u003c/h2\u003e \u003cp\u003eMDD patients with NSSI and SA show distinct GMV alterations. Findings suggest that MDD with SA involves unique neural impairments, contributing to emotional dysregulation and suicide risk.\u003c/p\u003e","manuscriptTitle":"Gray Matter Volume Alterations in Early-Adulthood Major Depressive Disorder Patients With Non-Suicidal Self-Injury and Suicide Attempts","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-14 10:19:41","doi":"10.21203/rs.3.rs-9178989/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-20T11:07:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-19T03:23:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-09T12:21:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"61498348351543718506950567524733656627","date":"2026-04-09T03:13:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"96011029194531647201900133630170993176","date":"2026-04-08T19:40:58+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-08T02:26:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"281747321245591393325253986150703735466","date":"2026-04-07T23:51:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"272729147218022984344890773870727523267","date":"2026-04-07T22:19:37+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-07T16:12:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"314756293012167333895019826051077968388","date":"2026-04-07T14:59:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-07T11:13:26+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-02T08:46:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-01T05:59:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-01T05:58:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychiatry","date":"2026-03-20T12:29:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8957b228-d446-4281-8c90-66c368b91272","owner":[],"postedDate":"April 14th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T11:23:46+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-14 10:19:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9178989","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9178989","identity":"rs-9178989","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}