Influence of depression severity on interhemispheric functional integration: An analysis from the REST-meta-MDD Database

Influence of depression severity on interhemispheric functional integration: An analysis from the REST-meta-MDD Database | 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 Influence of depression severity on interhemispheric functional integration: An analysis from the REST-meta-MDD Database Jie Ding, Junfeng Peng, Qian Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4541402/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Nov, 2024 Read the published version in Brain Imaging and Behavior → Version 1 posted 7 You are reading this latest preprint version Abstract Major depression disorder (MDD) is a pervasive mental health disorder that significantly impairs functionality, underscoring the need for precise stratification of its severity to tailor effective treatment. This study investigates the utility of voxel-mirrored homotopic connectivity (VMHC) in resting-state fMRI data as a neuroimaging biomarker to distinguish between different severities of patients with MDD. The results revealed significant reductions in VMHC within the fusiform gyrus in cased of mild to moderate depression, and more extensive reductions across the insula, postcentral gyrus, and angular gyrus in severe depression. Interestingly, increased VMHC in the middle cingulate cortex was observed in the severe MDD patients compared to those with mild to moderate cases, and this increase showed a significant positive correlation with the symptom scores. Additionally, receiver operating characteristic (ROC) curve analysis indicated that VMHC values in these regions effectively differentiate patients from healthy controls and across severities of MDD. These findings suggest that VMHC could serve as a valuable tool for clinical diagnosis and the stratification of depression severity, providing insights into the underlying neurobiological mechanisms of the disorder. Major depression disorder Voxel-mirrored homotopic connectivity Neuroimaging biomarkers Psychiatric diagnosis. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Major depressive disorder (MDD) is a severe mental disorder that characterized by persistent and pervasive sense of sadness, a lack of interest, and various emotional and physical problems (Jia et al., 2010) that significantly impairs the social functioning and quality of life of patients (Yuan et al., 2024), leading to a substantial socioeconomical burden (Sliz and Hayley, 2012). Despite considerable research efforts, the neurophysiological mechanisms underlying MDD remain elusive. Resting-state functional magnetic resonance imaging (fMRI) is commonly employed to investigate the neurological underpinnings of mental disorders (Keilholz et al., 2017). Accumulating evidence from numerous fMRI studies suggests that MDD is associated with widespread local alternations in several brain regions, including the prefrontal cortex (Frodl et al., 2009), posterior cingulate cortex (Mah et al., 2007), hippocampus (Lui et al., 2009), and cerebellum (Wei et al., 2015) . Importantly, the brain comprises two hemispheres that communicate via commissural regions, with interhemispheric functional connectivity playing a crucial role in integrating brain functions related to cognition, emotion, and behavior (Amann et al., 2009, Wyczesany et al., 2018, Zhao et al., 2020). Voxel-mirrored homotopic connectivity (VMHC) provides a robust method for assessing interhemispheric functional connectivity (Zuo et al., 2010), reflecting the brain's functional symmetry and coordination (Wei et al., 2014). This approach has been extensively utilized to investigate altered interhemispheric coordination in various psychiatric conditions, including depression (Liu et al., 2018, Lu et al., 2022, Wang et al., 2015), thereby enhancing our understanding of the underlying pathophysiology. Previous studies have reported abnormal VMHC patterns in MDD. For instance, decrease VMHC values in the medial prefrontal cortex, posterior cingulate cortex, and cerebellar posterior lobe has been observed in first-episode, drug-naïve MDD (Guo et al., 2013a, Lai and Wu, 2014), which correlate with cognitive impairment and clinical severity. Additionally, decreased VMHC in medial orbitofrontal gyrus, fusiform gyrus, cuneus, middle occipital gyrus, and parahippocampal gyrus has been noted in patients with first-episode MDD (Hermesdorf et al., 2016, Wang et al., 2013). Moreover, patients with treatment-resistant MDD have shown diminished VMHC in regions such as the fusiform gyrus, calcarine cortex, hippocampus, middle cingulate cortex, superior temporal gyrus, and precentral gyrus compared to those with treatment-sensitive MDD (Guo et al., 2013b, Takahashi et al., 2010). These findings indicate that interhemispheric interaction dysfunctions are linked to disruptions in emotional regulation and cognitive processes, forming a basis for the pathogenesis of MDD (Chen et al., 2024). The severity of depression, which can range from mild to severe, significantly influences the course and prognosis of the illness (Altamura et al., 2015, Kuehner and Huffziger, 2013). The severity of depression, which can range from mild to severe, significantly influences the course and prognosis of the illness (Adams et al., 2017, Fried and Nesse, 2014), and the symptom severity of MDD is associated with functional and structural brain abnormalities (Ramasubbu et al., 2016), which emphasizing the need for precise diagnosis and effective management. This study aims to explore the spatial heterogeneity of interhemispheric connectivity alterations across varying severity levels of MDD (from mild to moderate and severe). By comparing the VMHC profiles among patients with differing severity of depression, we aim to identify unique patterns of hemispheric connectivity disruptions. Based on previous findings, we hypothesize that patients with MDD of different severity will exhibit reduced VMHC compared to normal controls. We also explore the discrimination of VMHC values in critical brain regions among these MDD patients using receiver operating characteristic (ROC) curve analysis and their correlations with clinical symptoms. 2. Methods 2.1 Participants The study utilized resting-state fMRI data from the REST-meta-MDD project (http://rfmri.org/REST-meta-MDD), which comprised a total of 2,428 individuals (1,300 MDD patients and 1,128 normal controls) collected from 25 sites across 17 hospitals. Participants were included based on diagnoses confirmed via the Structured Clinical Interview of the Diagnostic and Statistical Manual of Mental Disorders 4th edition (DSM-IV) or International Classification of Diseases 10th edition (ICD-10) criteria. Exclusion criteria included patients with axis I disorders such as schizophrenia, bipolar disorder, anxiety disorders, substance abuse or dependence, those with acute physical diseases, or a history of brain injury with loss of consciousness. Additionally, controls with any mental disorder in first-degree relatives were excluded. The final sample consisted of 392 first-episode MDD patients and 440 controls from 9 sites, excluding participants aged under 18 years or over 65 years, those with excessive head movement and poor quality of structural scans. All participant data were deidentified and analyzed anonymously. Approval was obtained from local Institutional Review Boards, and informed consent was signed by each participant. 2.2 Clinical assessment The severity of depressive symptoms among patients was quantitatively assessed using the 17-item Hamilton Depression Scale (HAMD) (Hamilton, 1960). This scale evaluates various depression aspects, including mood, insomnia, anxiety, and weight loss, with each item scored from 0 (not present) to 4 (severe). Patients with HAMD scores from 7 to 23 were categorized as having mild to moderate depression, while scores of 24 or higher indicated severe depression (Zimmerman et al., 2013). 2.3 Data preprocessing Resting-state functional magnetic resonance imaging (fMRI) and T1-weighted MRI data processing were performed using the Data Processing Assistant for Resting-State fMRI (DPARSF) protocol (Chao-Gan and Yu-Feng, 2010) as previously described (Yan et al., 2019). Initially, the first 10 volumes of each session were discarded to allow for signal stabilization. Subsequent images were slice-time corrected and realigned to the mean function image. T1-weighted images were segmented into gray matter, white matter, and cerebrospinal fluid and transformed into Montreal Neurological Institute (MNI) space. Functional images were normalized to MNI space and were resample to 3 x 3 x mm 3 resolution, smoothed with a 4 mm full-width half-maximum Gaussian kernel, then linearly detrended and bandpass filtered (0.01 – 0.1 Hz). Spurious covariates such as head motion, white matter, cerebrospinal fluid, and whole-brain average signals were removed using linear regression. Each subject's images were registered to a symmetric MNI template for subsequent homotopic functional connectivity computation, ensuring no more than 2-mm maximum displacement in x, y, or z and 2° of angular motion were present. 2.4 VMHC calculation VMHC was calculated with DPABI toolbox (http://rfmri.org/DPABI). Individual VMHC maps were generated by computing the Pearson correlation (Fisher z-transformed) between a given voxel and a mirrored voxel in the opposite hemisphere. The resultant VMHC values were Fisher-Z transformed. Group-level voxelwise t-tests determined regional differences in VMHC. 2.5 Statistical analyses Demographic variables such as age and years of education were compared using one-way ANOVA, and gender distributions were compared using the chi-square test. Two-sample t-tests compared illness duration and HAMD total scores between patient subgroups. VMHC maps were analyzed using two-sample t-tests, controlling for age, sex, education, and site with covariates, with Gaussian random field (GRF) correction (voxel-level p < 0.05, cluster-level p 50). Significant group differences prompted further analysis of VMHC values’ correlation with clinical symptom scores and ROC curve analysis of brain regions with significant differences, using identified masks. Statistical significance was set at p < 0.05. 3. Results 3.1 Demographic and clinical characteristics The demographic and clinical characteristics of the groups are detailed in Table 1. There were no significant differences in age between the patient and control groups (F = 0.869, p = 0.420). However, notable differences were observed in gender distribution (F = 23.18, p < 0.001) and years of education (F = 20.17, p < 0.001) between the MDD subgroups and controls. The duration of illness did not significantly differ between the two MDD subgroups (t = 0.403, p = 0.687). As expected, significant differences in HAMD scores were noted, with higher scores observed in the severe MDD subgroup (t = 23.52, p < 0.001). Table 1 Demographics and clinical characteristics of participants Variates Patients Controls (n=440) F(t) p Mild to moderation (n=256) Severe (n=136) Age (years) 35.42±11.82 37.09±11.53 35.89±13.42 0.869 0.420 Sex (male/female) 87/169 42/94 180/260 23.18 <0.001 Education(years) 11.58±3.66 11.12±3.90 13.12±4.04 20.17 <0.001 Illness duration (month) 21.34±35.57 19.76±36.73 - 0.403 0.687 HAMD 19.27±3.23 27.56±3.48 - 23.52 <0.001 Abbreviations: HAMD, Hamilton Depression Scale. 3.2 VMHC comparisons between groups Figure 1 and Table 2 illustrate the comparisons of VMHC values between patients with mild to moderate symptoms and normal controls. This patient subgroup exhibited lower VMHC in the fusiform gyrus compared to normal controls. No regions displayed higher VMHC in this subgroup relative to the control group. -- Figure 1 -- Table 2 Regions showing VMHC differences between patients with mild to moderation depression and normal controls Anatomical label BA MNI coordinate size t x y z Left Fusiform Gyrus 18 -21 -63 -33 199 -5.951 Right Fusiform Gyrus 18 21 -63 -33 204 -5.951 Moreover, in patients with severe symptoms relative to normal controls, the reduction in VMHC values extended beyond the fusiform gyrus to include additional brain regions such as the insula, postcentral gyrus, and angular gyrus (Figure 2 and Table 3). -- Figure 2 -- Table 3 Regions showing VMHC differences between patients with severe depression and normal controls Anatomical label BA MNI coordinate size t x y z Left Fusiform Gyrus 37 -37 -42 -37 160 -4.700 Right Fusiform Gyrus 37 37 -42 -37 154 -4.700 Left Insula 48 -38 17 3 76 -4.919 Right Insula 48 38 17 3 82 -4.919 Left Postcentral Gyrus 3 -48 -27 48 375 -5.197 Right Postcentral Gyrus 3 48 -27 48 401 -5.197 Left Angular Gyrus 39 -42 -57 48 56 -4.429 Right Angular Gyrus 39 42 -57 48 110 -4.429 Additionally, when comparing VMHC values between the two patient subgroups, a significant increase in VMHC was observed in the superior frontal orbital region among patients with severe depression compared to those with mild to moderate depression (Figure 3 and Table 4). -- Figure 3 — Table 4 Regions showing VMHC differences between severe and mild to moderate depression patients Anatomical label BA MNI coordinate size t x y z Left Middle Cingulate Cortex 32 -15 12 39 55 4.298 Right Middle Cingulate Cortex 32 15 12 39 73 4.298 3.3 ROC analysis between groups As demonstrated, significant VMHC differences between patient subgroups and control groups suggest that VMHC measurements in these brain regions could be utilized to differentiate patients from controls or to distinguish between degrees of depression severity. ROC analysis confirmed this utility by examining the extracted mean VMHC values from these regions. The results revealed that the areas under the curves could effectively differentiate the patients (subgroups) from controls (Figure 4 and Table 5). -- Figure 4 — Table 5 ROC curve analysis for differentiating patients with depression from normal controls Brain regions AUC Sensitivity Specificity p value FFG 0.634/0.612* 0.566/0.511* 0.645/0.691* <0.001/<0.001* INS 0.566 0.605 0.500 0.020 POCG 0.602 0.607 0.574 <0.001 ANG 0.599 0.638 0.551 <0.001 MCC 0.589 0.522 0.617 <0.001 Abbreviations: FFG, fusiform gyrus; INS, insula; POCG, postcentral gyrus; ANG, angular gyrus; MCC, middle cingulate cortex; AUC, area under curve. * represents the comparison between mild to moderate depression and normal controls. 3.4 Correlation analysis The correlation analysis aimed to explore the relationship between significant VMHC values extracted from brain regions and clinical symptom severity as measured by the HAMD, controlling for age, sex, and educational level as covariates. A significantly positive correlation was observed between VMHC in the middle cingulate cortex and HAMD scores (r = 0.139, p = 0.006) in the patient group (Figure 5). -- Figure 5 -- 4. Discussion In this study, VMHC was applied for the first time to investigate interhemispheric functional connectivity in MDD across different level of symptom severity. The results demonstrated distinct patterns of VMHC reductions correlating with the severity of depression. Notably, there were significant reductions in the fusiform gyrus for patients with mild to moderate depression, with additional reductions observed in the insula, postcentral gyrus, and angular gyrus for severe cases. Moreover, patients with severe symptoms exhibited reduced VMHC in the middle cingulate cortex compared to those with mild to moderate symptoms. ROC analyses confirmed that VMHC measurements in these regions could effectively discriminate between patients with varying severity of MDD and healthy controls. Importantly, VMHC in the middle cingulate cortex correlated positively with HAMD scores, suggesting a direct relationship between VMHC and symptom severity. VMHC likely reflects the importance of interhemispheric communication in integrating brain functions underpinning coherent cognition and behavior (Kelly et al., 2011). Clinical studies have shown altered homotopic functional connectivity, measuring by VMHC, in both first-episode (Yang et al., 2019) and recurrent MDD patients (Guo et al., 2023). T The observed reductions in VMHC indicate disruptions in functional symmetry between corresponding hemispheric regions, which may underlie the neural basis of emotional and cognitive dysfunctions in depression (Gonda et al., 2015, Rottenberg, 2017). Specifically, the fusiform gyrus, critical for face perception and social cognition (Stuhrmann et al., 2011), showed reduced VMHC in patients with mild to moderate depression compared to controls. Impairments in this region could disrupt the ability to interpret facial expressions and social cues, key determinants of the interpersonal problems frequently observed in depression (Krause et al., 2021, Yoon et al., 2009), such as social withdrawal and feelings of interpersonal rejection. In severe depression, reductions in VMHC extended to include the insula, postcentral gyrus, and angular gyrus, alongside the fusiform gyrus. The insula, with extensive connections to the frontal and limbic areas (Guo et al., 2015b), plays a pivotal role in integrating emotional and sensory experience (Manoliu et al., 2013, Sprengelmeyer et al., 2011). Reduced connectivity in this region in MDD may relate to impaired emotion regulation, a hallmark of severe depression (Guo et al., 2015a, Joormann and Stanton, 2016). The postcentral gyrus, the site of the primary somatosensory cortex, receives projections from the thalamocortical circuit and is involved in sensory input (Enatsu et al., 2013). Decreased VMHC in this region may be linked to hypoesthesia in MDD patients (Liang et al., 2020). Decreased VMHC in this region may be linked to hypoesthesia in MDD patients (Seghier, 2012), belongs to the default mode network (Menon, 2011). Impairments in this region have been associated with negative self-referential thoughts (Zhang et al., 2018). The angular gyrus' reduced connectivity might contribute to depression and may contribute to the symptomatology of depression by reducing the brain's capacity for positive self-reflection and adaptive social interactions. The middle cingulate cortex showed significant VMHC reductions in severe depression compared to mild to moderate cases. This region, crucial for processing emotional information (Cohen Kadosh et al., 2016), may mediate depression symptoms due to its strategic position influencing subcortical structures related to affect generation (Kohn et al., 2014). The observed decrease in VMHC suggests a potential disruption in bilateral cortical connectivity, which may underlie the increased symptom severity seen in severe depression. The positive correlation with HAMD scores further emphasizes the involvement of the MCC in the clinical manifestations of depression, where greater connectivity deficits are linked with more severe symptoms. ROC curve analysis is widely recognized as a reliable method for comparing and evaluating the accuracy of radiologic imaging (Metz, 1986), serving as a distinct statistical technique to assess abnormal changes in a clinical setting (Obuchowski et al., 2004). In this study, ROC analysis provided compelling evidence that VMHC values from the identified regions could effectively distinguish between control subjects and individuals with depression. The ability of VMHC to differentiate between mild to moderate and severe depression adds considerable diagnostic utility. The clear distinction in VMHC patterns between control subjects and patients, and across levels of depression severity, suggests that VMHC could significantly enhance the accuracy of depression diagnostics, improve treatment efficacy, and aid in stratifying patients according to the severity of their symptoms (Jorgensen et al., 2024). These findings across various severities of depression significantly advance our understanding of the neurobiological underpinnings of this complex disorder. Recognizing these distinct neural connectivity patterns could lead to more tailored therapeutic approaches that address specific neural disruptions associated with different levels of depression severity. Moreover, could enhance our comprehension of the progression from mild forms of depression to more severe conditions, facilitating earlier and more precise interventions that could prevent this progression by stabilizing key neural circuits before extensive network dysregulation occurs. 5. Conclusion This study investigate interhemispheric functional connectivity in MDD across different levels of symptom severity. Our findings reveal significant reductions in VMHC in key brain regions associated with emotional regulation functions in depression, particularly in patients with severe symptoms. These insights These insights contribute to a deeper understanding of the neurobiological mechanisms underlying MDD. Declarations Author contributions Jie Ding: Writing – review & editing, Writing – original draft, Formal analysis, Conceptualization. Junfeng Peng: Writing – review & editing, Formal analysis. Qian Zhang: Writing – review & editing, Formal analysis. Funding This study did not receive any financial support or grants. Data availability The datasets generated during the current study are available from the REST-meta-MDD consortium. Ethics approval This work was approved by the Ethical Committee for Medicine of the REST-meta-MDD consortium. 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Proceedings of the National Academy of Sciences 2019;116(18):9078-9083; doi: 10.1073/pnas.1900390116. Yang H, Wang C, Ji G, et al. Aberrant interhemispheric functional connectivity in first-episode, drug-naïve major depressive disorder. Brain Imaging and Behavior 2019;13(5):1302-1310; doi: 10.1007/s11682-018-9917-x. Yoon KL, Joormann J, Gotlib IH. Judging the intensity of facial expressions of emotion: Depression-related biases in the processing of positive affect. Journal of Abnormal Psychology 2009;118(1):223-228; doi: 10.1037/a0014658. Yuan X, Chen M, Ding P, et al. Cross-Domain Identification of Multisite Major Depressive Disorder Using End-to-End Brain Dynamic Attention Network. IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society 2024;32:33-42; doi: 10.1109/tnsre.2023.3341923. Zhang F-F, Peng W, Sweeney JA, et al. Brain structure alterations in depression: Psychoradiological evidence. CNS Neuroscience and Therapeutics 2018;24(11):994-1003; doi: https://doi.org/10.1111/cns.12835. Zhao J, Manza P, Wiers C, et al. Age-related decreases in interhemispheric resting-state functional connectivity and their relationship with executive function. Frontiers in Aging Neuroscience 2020;12(2):20; doi: 10.3389/fnagi.2020.00020. Zimmerman M, Martinez JH, Young D, et al. Severity classification on the Hamilton depression rating scale. Journal of Affective Disorders 2013;150(2):384-388; doi: https://doi.org/10.1016/j.jad.2013.04.028. Zuo X-N, Kelly C, Di Martino A, et al. Growing together and growing apart: Regional and sex differences in the lifespan developmental trajectories of functional homotopy. The Journal of Neuroscience 2010;30(45):15034; doi: 10.1523/JNEUROSCI.2612-10.2010. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 30 Nov, 2024 Read the published version in Brain Imaging and Behavior → Version 1 posted Editorial decision: Revision requested 05 Sep, 2024 Reviews received at journal 01 Aug, 2024 Reviewers agreed at journal 12 Jul, 2024 Reviewers invited by journal 10 Jul, 2024 Editor assigned by journal 10 Jul, 2024 Submission checks completed at journal 10 Jun, 2024 First submitted to journal 06 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-4541402","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":312579799,"identity":"c31518a5-19c3-4cfb-b264-989bdfd70a5c","order_by":0,"name":"Jie Ding","email":"","orcid":"","institution":"Xinyang Vocational and Technical College","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Ding","suffix":""},{"id":312579800,"identity":"9ba51ef0-10c3-4bc8-826d-e29273b1a7e4","order_by":1,"name":"Junfeng Peng","email":"","orcid":"","institution":"Xinyang Vocational and Technical College","correspondingAuthor":false,"prefix":"","firstName":"Junfeng","middleName":"","lastName":"Peng","suffix":""},{"id":312579801,"identity":"7181fdaf-fcf8-4136-90f4-02a3a1034994","order_by":2,"name":"Qian Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYDACCRjJ3nzgwAcDGzk2IINILTzHEh/OqEgz5uM5lkCMFhAjx9iY48yhxHkSOQp4dcjPbn728EuZhbw5zxkzaca2A+ltDDkMDD8qtuHUwjjnmLmxzDkJw53tbWXShW13ctsYzh5g7DlzG6cWZokEM2nJNgnGDWcOb5Oe2fYst42xL4GZsQ23FjaJ9G8gLfYbbgD18rYdTmdj5jHAq4VHIsdM8mObROKGGynGxjxnDiewsRHQIiGRUybNcE4iecMZSCAbtvGwJRzE5xf5GenbJH+U1dluOA6JSnn5+Y8PPvhRgVsLOAh42NBEDuBVDwSMP9C1jIJRMApGwShABgDbuFwExj5SUAAAAABJRU5ErkJggg==","orcid":"","institution":"Henan Provincial People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Qian","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-06-06 15:26:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4541402/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4541402/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11682-024-00960-0","type":"published","date":"2024-11-30T15:58:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":59434704,"identity":"a6cd2af5-e624-465f-aec8-b2791aa063e4","added_by":"auto","created_at":"2024-07-01 19:04:55","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":59527,"visible":true,"origin":"","legend":"\u003cp\u003eStatistical maps showing voxel-mirrored homotopic connectivity (VMHC) differences between patients with mild to moderate depression and normal controls. Blue denotes lower VMHC, with the color bars representing the t value from t-test between groups.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4541402/v1/9a00a5a7de84c070826abcd2.jpg"},{"id":59434702,"identity":"40f8f0de-8a93-4de7-8bd0-ddad4d9606ec","added_by":"auto","created_at":"2024-07-01 19:04:55","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":74032,"visible":true,"origin":"","legend":"\u003cp\u003eStatistical maps showing voxel-mirrored homotopic connectivity (VMHC) differences between patients with severe depression and normal controls. Blue denotes lower VMHC, with color bars showing the t value from t-test between groups.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4541402/v1/8816683958a83c986caf8ca0.jpg"},{"id":59434703,"identity":"a6b6233a-3e74-44ef-ba8a-0444ee5bba64","added_by":"auto","created_at":"2024-07-01 19:04:55","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":59427,"visible":true,"origin":"","legend":"\u003cp\u003eStatistical maps showing voxel-mirrored homotopic connectivity (VMHC) differences between patients with severe and those who with mild to moderate depression. Red denotes higher VMHC, with color bars representing the t value from t-test between groups.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4541402/v1/bb872be2cb026117cb55e557.jpg"},{"id":59434700,"identity":"ef05f8d7-3f37-4ba6-9f23-15c4f947bfe6","added_by":"auto","created_at":"2024-07-01 19:04:55","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":65705,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic (ROC) curves for voxel-mirrored homotopic connectivity (VMHC) to differentiate between patients (mild to moderate and severe) and normal controls.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4541402/v1/9119828a0b173de23a758ccf.jpg"},{"id":59434701,"identity":"2bfbf249-227c-4839-9c0b-42690e8de49a","added_by":"auto","created_at":"2024-07-01 19:04:55","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":58647,"visible":true,"origin":"","legend":"\u003cp\u003eSignificant positive correlation between voxel-mirrored homotopic connectivity (VMHC) in the middle cingulate cortex (MCC) and Hamilton Depression Scale (HAMD) scores within the patient group.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4541402/v1/72e5aa2d5ec3ca70f599f968.jpg"},{"id":70388857,"identity":"f86e3759-d5ef-43ca-98da-778af411fdce","added_by":"auto","created_at":"2024-12-02 17:27:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":797249,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4541402/v1/67e114de-9c02-42cf-b02c-40f6645e0f05.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Influence of depression severity on interhemispheric functional integration: An analysis from the REST-meta-MDD Database","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMajor depressive disorder (MDD) is a severe mental disorder that characterized by persistent and pervasive sense of sadness, a lack of interest, and various emotional and physical problems\u0026nbsp;(Jia et al., 2010)\u0026nbsp;that significantly impairs the social functioning and quality of life of patients\u0026nbsp;(Yuan et al., 2024), leading to a substantial socioeconomical burden\u0026nbsp;(Sliz and Hayley, 2012). Despite considerable research efforts, the neurophysiological mechanisms underlying MDD remain elusive.\u003c/p\u003e\n\u003cp\u003eResting-state functional magnetic resonance imaging (fMRI) is commonly employed to investigate the neurological underpinnings of mental disorders\u0026nbsp;(Keilholz et al., 2017). Accumulating evidence from numerous fMRI studies suggests that MDD is associated with widespread local alternations in several brain regions, including the prefrontal cortex\u0026nbsp;(Frodl et al., 2009), posterior cingulate cortex\u0026nbsp;(Mah et al., 2007), hippocampus\u0026nbsp;(Lui et al., 2009), and cerebellum\u0026nbsp;(Wei et al., 2015)\u0026nbsp;. Importantly, the brain comprises two hemispheres that communicate via commissural regions, with interhemispheric functional connectivity playing a crucial role in integrating brain functions related to cognition, emotion, and behavior\u0026nbsp;(Amann et al., 2009, Wyczesany et al., 2018, Zhao et al., 2020). Voxel-mirrored homotopic connectivity (VMHC) provides a robust method for assessing interhemispheric functional connectivity\u0026nbsp;(Zuo et al., 2010), reflecting the brain's functional symmetry and coordination\u0026nbsp;(Wei et al., 2014). This approach has been extensively utilized to investigate altered interhemispheric coordination in various psychiatric conditions, including depression\u0026nbsp;(Liu et al., 2018, Lu et al., 2022, Wang et al., 2015), thereby enhancing our understanding of the underlying pathophysiology.\u003c/p\u003e\n\u003cp\u003ePrevious studies have reported abnormal VMHC patterns in MDD. For instance, decrease VMHC values in the medial prefrontal cortex, posterior cingulate cortex, and cerebellar posterior lobe has been observed in first-episode, drug-naïve MDD\u0026nbsp;(Guo et al., 2013a, Lai and Wu, 2014), which correlate with cognitive impairment and clinical severity. Additionally, decreased VMHC in medial orbitofrontal gyrus, fusiform gyrus, cuneus, middle occipital gyrus, and parahippocampal gyrus has been noted in patients with first-episode MDD\u0026nbsp;(Hermesdorf et al., 2016, Wang et al., 2013). Moreover, patients with treatment-resistant MDD have shown diminished VMHC in regions such as the fusiform gyrus, calcarine cortex, hippocampus, middle cingulate cortex, superior temporal gyrus, and precentral gyrus compared to those with treatment-sensitive MDD\u0026nbsp;(Guo et al., 2013b, Takahashi et al., 2010). These findings indicate that interhemispheric interaction dysfunctions are linked to disruptions in emotional regulation and cognitive processes, forming a basis for the pathogenesis of MDD\u0026nbsp;(Chen et al., 2024).\u003c/p\u003e\n\u003cp\u003eThe severity of depression, which can range from mild to severe, significantly influences the course and prognosis of the illness (Altamura et al., 2015, Kuehner and Huffziger, 2013). The severity of depression, which can range from mild to severe, significantly influences the course and prognosis of the illness (Adams et al., 2017, Fried and Nesse, 2014), and the symptom severity of MDD is associated with functional and structural brain abnormalities (Ramasubbu et al., 2016), which emphasizing the need for precise diagnosis and effective management. This study aims to explore the spatial heterogeneity of interhemispheric connectivity alterations across varying severity levels of MDD (from mild to moderate and severe). By comparing the VMHC profiles among patients with differing severity of depression, we aim to identify unique patterns of hemispheric connectivity disruptions. Based on previous findings, we hypothesize that patients with MDD of different severity will exhibit reduced VMHC compared to normal controls. We also explore the discrimination of VMHC values in critical brain regions among these MDD patients using receiver operating characteristic (ROC) curve analysis and their correlations with clinical symptoms.\u0026nbsp;\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study utilized resting-state fMRI data from the REST-meta-MDD project (http://rfmri.org/REST-meta-MDD), which comprised a total of 2,428 individuals (1,300 MDD patients and 1,128 normal controls) collected from 25 sites across 17 hospitals. Participants were included based on diagnoses confirmed via \u0026nbsp;the Structured Clinical Interview of the Diagnostic and Statistical Manual of Mental Disorders 4th edition (DSM-IV) or International Classification of Diseases 10th edition (ICD-10) criteria. Exclusion criteria included patients with axis I disorders such as schizophrenia, bipolar disorder, anxiety disorders, substance abuse or dependence, those with acute physical diseases, or a history of brain injury with loss of consciousness. Additionally, controls with any mental disorder in first-degree relatives were excluded. The final sample consisted of 392 first-episode MDD patients and 440 controls from 9 sites, excluding participants aged under 18 years or over 65 years, those with excessive head movement and poor quality of structural scans. All participant data were deidentified and analyzed anonymously. Approval was obtained from local Institutional Review Boards, and informed consent was signed by each participant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Clinical assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe severity of depressive symptoms among patients was quantitatively assessed using the 17-item Hamilton Depression Scale (HAMD)\u0026nbsp;(Hamilton, 1960). This scale evaluates various depression aspects, including mood, insomnia, anxiety, and weight loss, with each item scored from 0 (not present) to 4 (severe). Patients with HAMD scores from 7 to 23 were categorized as having mild to moderate depression, while scores of 24 or higher indicated severe depression\u0026nbsp;(Zimmerman et al., 2013).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Data preprocessing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResting-state functional magnetic resonance imaging (fMRI) and T1-weighted MRI data processing were performed using the Data Processing Assistant for Resting-State fMRI (DPARSF) protocol\u0026nbsp;(Chao-Gan and Yu-Feng, 2010)\u0026nbsp;as previously described\u0026nbsp;(Yan et al., 2019). Initially, the first 10 volumes of each session were discarded to allow for signal stabilization. Subsequent images were slice-time corrected and realigned to the mean function image. T1-weighted images were segmented into gray matter, white matter, and cerebrospinal fluid and transformed into Montreal Neurological Institute (MNI) space. Functional images were normalized to MNI space and were resample to 3 x 3 x mm\u003csup\u003e3\u003c/sup\u003e resolution, smoothed with a 4 mm full-width half-maximum Gaussian kernel, then linearly detrended and bandpass filtered (0.01 \u0026ndash; 0.1 Hz). Spurious covariates such as head motion, white matter, cerebrospinal fluid, and whole-brain average signals were removed using linear regression. Each subject\u0026apos;s images were registered to a symmetric MNI template for subsequent homotopic functional connectivity computation, ensuring no more than 2-mm maximum displacement in x, y, or z and 2\u0026deg; of angular motion were present.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 VMHC calculation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVMHC was calculated with DPABI toolbox (http://rfmri.org/DPABI). Individual VMHC maps were generated by computing the Pearson correlation (Fisher z-transformed) between a given voxel and a mirrored voxel in the opposite hemisphere. The resultant VMHC values were Fisher-Z transformed. Group-level voxelwise t-tests determined regional differences in VMHC.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Statistical analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDemographic variables such as age and years of education were compared using one-way ANOVA, and gender distributions were compared using the chi-square test. Two-sample t-tests compared illness duration and HAMD total scores between patient subgroups. VMHC maps were analyzed using two-sample t-tests, controlling for age, sex, education, and site with covariates, with Gaussian random field (GRF) correction (voxel-level \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05, cluster-level \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, cluster size \u0026gt; 50). Significant group differences prompted further analysis of VMHC values\u0026rsquo; correlation with clinical symptom scores and ROC curve analysis of brain regions with significant differences, using identified masks. Statistical significance was set at \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Demographic and clinical characteristics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe demographic and clinical characteristics of the groups are detailed in Table 1. There were no significant differences in age between the patient and control groups (F = 0.869, \u003cem\u003ep\u003c/em\u003e = 0.420). However, notable differences were observed in gender distribution (F = 23.18, p \u0026lt; 0.001) and years of education (F = 20.17, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) between the MDD subgroups and controls. The duration of illness did not significantly differ between the two MDD subgroups (t = 0.403, \u003cem\u003ep\u003c/em\u003e = 0.687). As expected, significant differences in HAMD scores were noted, with higher scores observed in the severe MDD subgroup (t = 23.52, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Demographics and clinical characteristics of participants\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"724\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.26832641770401%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eVariates\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.398340248962654%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003ePatients\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.627939142461964%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.001383125864454%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eControls\u003c/p\u003e\n \u003cp\u003e(n=440)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.12863070539419%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eF(t)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.575380359612724%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"64.797507788162%\" valign=\"top\"\u003e\n \u003cp\u003eMild to moderation\u003c/p\u003e\n \u003cp\u003e(n=256)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.202492211838006%\" valign=\"top\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003cp\u003e(n=136)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.26832641770401%\" valign=\"top\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.76901798063624%\" valign=\"top\"\u003e\n \u003cp\u003e35.42\u0026plusmn;11.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.629322268326417%\" valign=\"top\"\u003e\n \u003cp\u003e37.09\u0026plusmn;11.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.627939142461964%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.001383125864454%\" valign=\"top\"\u003e\n \u003cp\u003e35.89\u0026plusmn;13.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.12863070539419%\" valign=\"top\"\u003e\n \u003cp\u003e0.869\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.575380359612724%\" valign=\"top\"\u003e\n \u003cp\u003e0.420\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.26832641770401%\" valign=\"top\"\u003e\n \u003cp\u003eSex (male/female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.76901798063624%\" valign=\"top\"\u003e\n \u003cp\u003e87/169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.629322268326417%\" valign=\"top\"\u003e\n \u003cp\u003e42/94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.627939142461964%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.001383125864454%\" valign=\"top\"\u003e\n \u003cp\u003e180/260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.12863070539419%\" valign=\"top\"\u003e\n \u003cp\u003e23.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.575380359612724%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.26832641770401%\" valign=\"top\"\u003e\n \u003cp\u003eEducation(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.76901798063624%\" valign=\"top\"\u003e\n \u003cp\u003e11.58\u0026plusmn;3.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.629322268326417%\" valign=\"top\"\u003e\n \u003cp\u003e11.12\u0026plusmn;3.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.627939142461964%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.001383125864454%\" valign=\"top\"\u003e\n \u003cp\u003e13.12\u0026plusmn;4.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.12863070539419%\" valign=\"top\"\u003e\n \u003cp\u003e20.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.575380359612724%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.26832641770401%\" valign=\"top\"\u003e\n \u003cp\u003eIllness duration (month)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.76901798063624%\" valign=\"top\"\u003e\n \u003cp\u003e21.34\u0026plusmn;35.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.629322268326417%\" valign=\"top\"\u003e\n \u003cp\u003e19.76\u0026plusmn;36.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.627939142461964%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.001383125864454%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.12863070539419%\" valign=\"top\"\u003e\n \u003cp\u003e0.403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.575380359612724%\" valign=\"top\"\u003e\n \u003cp\u003e0.687\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.26832641770401%\" valign=\"top\"\u003e\n \u003cp\u003eHAMD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.76901798063624%\" valign=\"top\"\u003e\n \u003cp\u003e19.27\u0026plusmn;3.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.629322268326417%\" valign=\"top\"\u003e\n \u003cp\u003e27.56\u0026plusmn;3.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"2.627939142461964%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.001383125864454%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.12863070539419%\" valign=\"top\"\u003e\n \u003cp\u003e23.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.575380359612724%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: HAMD, Hamilton Depression Scale.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 VMHC comparisons between groups\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 1 and Table 2 illustrate the comparisons of VMHC values between patients with mild to moderate symptoms and normal controls. This patient subgroup exhibited lower VMHC in the fusiform gyrus compared to normal controls. No regions displayed higher VMHC in this subgroup relative to the control group.\u003c/p\u003e\n\u003cp\u003e--\u003cstrong\u003eFigure 1\u003c/strong\u003e--\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Regions showing VMHC differences between patients with mild to moderation depression and normal controls\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"558\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.85304659498208%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eAnatomical label\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.232974910394265%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eBA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.483870967741936%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eMNI coordinate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003esize\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.21505376344086%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.19095477386934%\" valign=\"top\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.155778894472363%\" valign=\"top\"\u003e\n \u003cp\u003ey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.65326633165829%\" valign=\"top\"\u003e\n \u003cp\u003ez\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.801431127012524%\" valign=\"top\"\u003e\n \u003cp\u003eLeft Fusiform Gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.20572450805009%\" valign=\"top\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.595706618962433%\" valign=\"top\"\u003e\n \u003cp\u003e-21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091234347048301%\" valign=\"top\"\u003e\n \u003cp\u003e-63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912343470483005%\" valign=\"top\"\u003e\n \u003cp\u003e-33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.196779964221825%\" valign=\"top\"\u003e\n \u003cp\u003e199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.196779964221825%\" valign=\"top\"\u003e\n \u003cp\u003e-5.951\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"28.801431127012524%\" valign=\"top\"\u003e\n \u003cp\u003eRight Fusiform Gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.20572450805009%\" valign=\"top\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.595706618962433%\" valign=\"top\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.091234347048301%\" valign=\"top\"\u003e\n \u003cp\u003e-63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.912343470483005%\" valign=\"top\"\u003e\n \u003cp\u003e-33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.196779964221825%\" valign=\"top\"\u003e\n \u003cp\u003e204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.196779964221825%\" valign=\"top\"\u003e\n \u003cp\u003e-5.951\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eMoreover, in patients with severe symptoms relative to normal controls, the reduction in VMHC values extended beyond the fusiform gyrus to include additional brain regions such as the insula, postcentral gyrus, and angular gyrus (Figure 2 and Table 3).\u003c/p\u003e\n\u003cp\u003e--\u003cstrong\u003eFigure 2\u003c/strong\u003e--\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e Regions showing VMHC differences between patients with severe depression and normal controls\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.810375670840784%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eAnatomical label\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.595706618962433%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eBA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.801431127012524%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eMNI coordinate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.196779964221825%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003esize\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.595706618962433%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.64596273291925%\" valign=\"top\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.950310559006212%\" valign=\"top\"\u003e\n \u003cp\u003ey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"35.40372670807454%\" valign=\"top\"\u003e\n \u003cp\u003ez\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.810375670840784%\" valign=\"top\"\u003e\n \u003cp\u003eLeft Fusiform Gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.595706618962433%\" valign=\"top\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.554561717352415%\" valign=\"top\"\u003e\n \u003cp\u003e-37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.050089445438283%\" valign=\"top\"\u003e\n \u003cp\u003e-42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.196779964221825%\" valign=\"top\"\u003e\n \u003cp\u003e-37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.196779964221825%\" valign=\"top\"\u003e\n \u003cp\u003e160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.595706618962433%\" valign=\"top\"\u003e\n \u003cp\u003e-4.700\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.810375670840784%\" valign=\"top\"\u003e\n \u003cp\u003eRight Fusiform Gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.595706618962433%\" valign=\"top\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.554561717352415%\" valign=\"top\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.050089445438283%\" valign=\"top\"\u003e\n \u003cp\u003e-42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.196779964221825%\" valign=\"top\"\u003e\n \u003cp\u003e-37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.196779964221825%\" valign=\"top\"\u003e\n \u003cp\u003e154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.595706618962433%\" valign=\"top\"\u003e\n \u003cp\u003e-4.700\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.810375670840784%\" valign=\"top\"\u003e\n \u003cp\u003eLeft Insula\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.595706618962433%\" valign=\"top\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.554561717352415%\" valign=\"top\"\u003e\n \u003cp\u003e-38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.050089445438283%\" valign=\"top\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.196779964221825%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.196779964221825%\" valign=\"top\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.595706618962433%\" valign=\"top\"\u003e\n \u003cp\u003e-4.919\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.810375670840784%\" valign=\"top\"\u003e\n \u003cp\u003eRight Insula\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.595706618962433%\" valign=\"top\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.554561717352415%\" valign=\"top\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.050089445438283%\" valign=\"top\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.196779964221825%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.196779964221825%\" valign=\"top\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.595706618962433%\" valign=\"top\"\u003e\n \u003cp\u003e-4.919\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.810375670840784%\" valign=\"top\"\u003e\n \u003cp\u003eLeft Postcentral Gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.595706618962433%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.554561717352415%\" valign=\"top\"\u003e\n \u003cp\u003e-48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.050089445438283%\" valign=\"top\"\u003e\n \u003cp\u003e-27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.196779964221825%\" valign=\"top\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.196779964221825%\" valign=\"top\"\u003e\n \u003cp\u003e375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.595706618962433%\" valign=\"top\"\u003e\n \u003cp\u003e-5.197\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.810375670840784%\" valign=\"top\"\u003e\n \u003cp\u003eRight Postcentral Gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.595706618962433%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.554561717352415%\" valign=\"top\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.050089445438283%\" valign=\"top\"\u003e\n \u003cp\u003e-27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.196779964221825%\" valign=\"top\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.196779964221825%\" valign=\"top\"\u003e\n \u003cp\u003e401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.595706618962433%\" valign=\"top\"\u003e\n \u003cp\u003e-5.197\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.810375670840784%\" valign=\"top\"\u003e\n \u003cp\u003eLeft Angular Gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.595706618962433%\" valign=\"top\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.554561717352415%\" valign=\"top\"\u003e\n \u003cp\u003e-42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.050089445438283%\" valign=\"top\"\u003e\n \u003cp\u003e-57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.196779964221825%\" valign=\"top\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.196779964221825%\" valign=\"top\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.595706618962433%\" valign=\"top\"\u003e\n \u003cp\u003e-4.429\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.810375670840784%\" valign=\"top\"\u003e\n \u003cp\u003eRight Angular Gyrus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.595706618962433%\" valign=\"top\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.554561717352415%\" valign=\"top\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.050089445438283%\" valign=\"top\"\u003e\n \u003cp\u003e-57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.196779964221825%\" valign=\"top\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.196779964221825%\" valign=\"top\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.595706618962433%\" valign=\"top\"\u003e\n \u003cp\u003e-4.429\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAdditionally, when comparing VMHC values between the two patient subgroups, a significant increase in VMHC was observed in the superior frontal orbital region among patients with severe depression compared to those with mild to moderate depression (Figure 3 and Table 4).\u003c/p\u003e\n\u003cp\u003e--\u003cstrong\u003eFigure 3\u003c/strong\u003e\u0026mdash;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e Regions showing VMHC differences between severe and mild to moderate depression patients\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"605\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.943894389438945%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eAnatomical label\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.541254125412541%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eBA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.702970297029704%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eMNI coordinate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.910891089108912%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003esize\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.900990099009901%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.666666666666664%\" valign=\"top\"\u003e\n \u003cp\u003ex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003ey\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.666666666666668%\" valign=\"top\"\u003e\n \u003cp\u003ez\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.943894389438945%\" valign=\"top\"\u003e\n \u003cp\u003eLeft Middle Cingulate Cortex\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.541254125412541%\" valign=\"top\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.891089108910892%\" valign=\"top\"\u003e\n \u003cp\u003e-15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.405940594059405%\" valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.405940594059405%\" valign=\"top\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.910891089108912%\" valign=\"top\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.900990099009901%\" valign=\"top\"\u003e\n \u003cp\u003e4.298\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.943894389438945%\" valign=\"top\"\u003e\n \u003cp\u003eRight Middle Cingulate Cortex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.541254125412541%\" valign=\"top\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.891089108910892%\" valign=\"top\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.405940594059405%\" valign=\"top\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.405940594059405%\" valign=\"top\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.910891089108912%\" valign=\"top\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.900990099009901%\" valign=\"top\"\u003e\n \u003cp\u003e4.298\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 ROC analysis between groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs demonstrated, significant VMHC differences between patient subgroups and control groups suggest that VMHC measurements in these brain regions could be utilized to differentiate patients from controls or to distinguish between degrees of depression severity. ROC analysis confirmed this utility by examining the extracted mean VMHC values from these regions. The results revealed that the areas under the curves could effectively differentiate the patients (subgroups) from controls (Figure 4 and Table 5).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e--\u003cstrong\u003eFigure 4\u003c/strong\u003e\u0026mdash;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u003c/strong\u003e ROC curve analysis for differentiating patients with depression from normal controls\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"599\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.896321070234112%\" valign=\"top\"\u003e\n \u003cp\u003eBrain regions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.568561872909697%\" valign=\"top\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.906354515050168%\" valign=\"top\"\u003e\n \u003cp\u003eSensitivity\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.558528428093645%\" valign=\"top\"\u003e\n \u003cp\u003eSpecificity\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.070234113712374%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.896321070234112%\" valign=\"top\"\u003e\n \u003cp\u003eFFG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.568561872909697%\" valign=\"top\"\u003e\n \u003cp\u003e0.634/0.612*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.906354515050168%\" valign=\"top\"\u003e\n \u003cp\u003e0.566/0.511*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.558528428093645%\" valign=\"top\"\u003e\n \u003cp\u003e0.645/0.691*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.070234113712374%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001/\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.896321070234112%\" valign=\"top\"\u003e\n \u003cp\u003eINS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.568561872909697%\" valign=\"top\"\u003e\n \u003cp\u003e0.566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.906354515050168%\" valign=\"top\"\u003e\n \u003cp\u003e0.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.558528428093645%\" valign=\"top\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.070234113712374%\" valign=\"top\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.896321070234112%\" valign=\"top\"\u003e\n \u003cp\u003ePOCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.568561872909697%\" valign=\"top\"\u003e\n \u003cp\u003e0.602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.906354515050168%\" valign=\"top\"\u003e\n \u003cp\u003e0.607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.558528428093645%\" valign=\"top\"\u003e\n \u003cp\u003e0.574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.070234113712374%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.896321070234112%\" valign=\"top\"\u003e\n \u003cp\u003eANG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.568561872909697%\" valign=\"top\"\u003e\n \u003cp\u003e0.599\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.906354515050168%\" valign=\"top\"\u003e\n \u003cp\u003e0.638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.558528428093645%\" valign=\"top\"\u003e\n \u003cp\u003e0.551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.070234113712374%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.896321070234112%\" valign=\"top\"\u003e\n \u003cp\u003eMCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.568561872909697%\" valign=\"top\"\u003e\n \u003cp\u003e0.589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.906354515050168%\" valign=\"top\"\u003e\n \u003cp\u003e0.522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.558528428093645%\" valign=\"top\"\u003e\n \u003cp\u003e0.617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.070234113712374%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAbbreviations: FFG, fusiform gyrus; INS, insula; POCG, postcentral gyrus; ANG, angular gyrus; MCC, middle cingulate cortex; AUC, area under curve. * represents the comparison between mild to moderate depression and normal controls.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Correlation analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe correlation analysis aimed to explore the relationship between significant VMHC values extracted from brain regions and clinical symptom severity as measured by the HAMD, controlling for age, sex, and educational level as covariates. A significantly positive correlation was observed between VMHC in the middle cingulate cortex and HAMD scores (r = 0.139, \u003cem\u003ep\u003c/em\u003e = 0.006) in the patient group (Figure 5).\u003c/p\u003e\n\u003cp\u003e--\u003cstrong\u003eFigure 5\u003c/strong\u003e--\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, VMHC was applied for the first time to investigate interhemispheric functional connectivity in MDD across different level of symptom severity. The results demonstrated distinct patterns of VMHC reductions correlating with the severity of depression. Notably, there were significant reductions in the fusiform gyrus for patients with mild to moderate depression, with additional reductions observed in the insula, postcentral gyrus, and angular gyrus for severe cases. Moreover, patients with severe symptoms exhibited reduced VMHC in the middle cingulate cortex compared to those with mild to moderate symptoms. ROC analyses confirmed that VMHC measurements in these regions could effectively discriminate between patients with varying severity of MDD and healthy controls. Importantly, VMHC in the middle cingulate cortex correlated positively with HAMD scores, suggesting a direct relationship between VMHC and symptom severity.\u003c/p\u003e\n\u003cp\u003eVMHC likely reflects the importance of interhemispheric communication in integrating brain functions underpinning coherent cognition and behavior\u0026nbsp;(Kelly et al., 2011). Clinical studies have shown altered homotopic functional connectivity, measuring by VMHC, in both first-episode\u0026nbsp;(Yang et al., 2019)\u0026nbsp;and recurrent MDD patients\u0026nbsp;(Guo et al., 2023). T\u0026nbsp;The observed reductions in VMHC indicate disruptions in functional symmetry between corresponding hemispheric regions, which may underlie the neural basis of emotional and cognitive dysfunctions in depression\u0026nbsp;(Gonda et al., 2015, Rottenberg, 2017).\u0026nbsp;Specifically, the fusiform gyrus, critical for face perception and social cognition\u0026nbsp;(Stuhrmann et al., 2011), showed reduced VMHC in patients with mild to moderate depression compared to controls. Impairments in this region could disrupt the ability to interpret facial expressions and social cues, key determinants of the interpersonal problems frequently observed in depression\u0026nbsp;(Krause et al., 2021, Yoon et al., 2009), such as social withdrawal and feelings of interpersonal rejection.\u003c/p\u003e\n\u003cp\u003eIn severe depression, reductions in VMHC extended to include the insula, postcentral gyrus, and angular gyrus, alongside the fusiform gyrus. The insula, with extensive connections to the frontal and limbic areas\u0026nbsp;(Guo et al., 2015b), plays a pivotal role in integrating emotional and sensory experience\u0026nbsp;(Manoliu et al., 2013, Sprengelmeyer et al., 2011). Reduced connectivity in this region in MDD may relate to impaired emotion regulation, a hallmark of severe depression\u0026nbsp;(Guo et al., 2015a, Joormann and Stanton, 2016). The postcentral gyrus, the site of the primary somatosensory cortex, receives projections from the thalamocortical circuit and is involved in sensory input\u0026nbsp;(Enatsu et al., 2013). Decreased VMHC in this region may be linked to hypoesthesia in MDD patients\u0026nbsp;(Liang et al., 2020).\u0026nbsp;Decreased VMHC in this region may be linked to hypoesthesia in MDD patients\u0026nbsp;(Seghier, 2012), belongs to the default mode network\u0026nbsp;(Menon, 2011). Impairments in this region have been associated with negative self-referential thoughts\u0026nbsp;(Zhang et al., 2018). The angular gyrus' reduced connectivity might contribute to depression\u0026nbsp;and may contribute to the symptomatology of depression by reducing the brain's capacity for positive self-reflection and adaptive social interactions.\u003c/p\u003e\n\u003cp\u003eThe middle cingulate cortex showed significant VMHC reductions in severe depression compared to mild to moderate cases. This region, crucial for processing emotional information\u0026nbsp;(Cohen Kadosh et al., 2016),\u0026nbsp;may mediate depression symptoms due to its strategic position influencing subcortical structures related to affect generation\u0026nbsp;(Kohn et al., 2014). The observed decrease in VMHC suggests a potential disruption in bilateral cortical connectivity, which may underlie the increased symptom severity seen in severe depression. The positive correlation with HAMD scores further emphasizes the involvement of the MCC in the clinical manifestations of depression, where greater connectivity deficits are linked with more severe symptoms.\u003c/p\u003e\n\u003cp\u003eROC curve analysis is widely recognized as a reliable method for comparing and evaluating the accuracy of radiologic imaging\u0026nbsp;(Metz, 1986), serving as a distinct statistical technique to assess abnormal changes in a clinical setting\u0026nbsp;(Obuchowski et al., 2004). In this study, ROC analysis provided compelling evidence that VMHC values from the identified regions could effectively distinguish between control subjects and individuals with depression. The ability of VMHC to differentiate between mild to moderate and severe depression adds considerable diagnostic utility. The clear distinction in VMHC patterns between control subjects and patients, and across levels of depression severity, suggests that VMHC could significantly enhance the accuracy of depression diagnostics, improve treatment efficacy, and aid in stratifying patients according to the severity of their symptoms\u0026nbsp;(Jorgensen et al., 2024).\u003c/p\u003e\n\u003cp\u003eThese findings across various severities of depression significantly advance our understanding of the neurobiological underpinnings of this complex disorder. Recognizing these distinct neural connectivity patterns could lead to more tailored therapeutic approaches that address specific neural disruptions associated with different levels of depression severity. Moreover, could enhance our comprehension of the progression from mild forms of depression to more severe conditions, facilitating earlier and more precise interventions that could prevent this progression by stabilizing key neural circuits before extensive network dysregulation occurs.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study investigate interhemispheric functional connectivity in MDD across different levels of symptom severity. Our findings reveal significant reductions in VMHC in key brain regions associated with emotional regulation functions in depression, particularly in patients with severe symptoms. These insights These insights contribute to a deeper understanding of the neurobiological mechanisms underlying MDD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJie Ding: Writing \u0026ndash; review \u0026amp; editing, Writing \u0026ndash; original draft, Formal analysis, Conceptualization. Junfeng Peng: Writing \u0026ndash; review \u0026amp; editing, Formal analysis. Qian Zhang: Writing \u0026ndash; review \u0026amp; editing, Formal analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not receive any financial support or grants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during the current study are available from the REST-meta-MDD consortium.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThis work was approved by the Ethical Committee for Medicine of the REST-meta-MDD consortium.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e Participants provided written informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAdams H, Thibault P, Ellis T, et al. 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The Journal of Neuroscience 2010;30(45):15034; doi: 10.1523/JNEUROSCI.2612-10.2010.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"brain-imaging-and-behavior","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bior","sideBox":"Learn more about [Brain Imaging and Behavior](https://www.springer.com/journal/11682)","snPcode":"11682","submissionUrl":"https://submission.nature.com/new-submission/11682/3","title":"Brain Imaging and Behavior","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Major depression disorder, Voxel-mirrored homotopic connectivity, Neuroimaging biomarkers, Psychiatric diagnosis. ","lastPublishedDoi":"10.21203/rs.3.rs-4541402/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4541402/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Major depression disorder (MDD) is a pervasive mental health disorder that significantly impairs functionality, underscoring the need for precise stratification of its severity to tailor effective treatment. This study investigates the utility of voxel-mirrored homotopic connectivity (VMHC) in resting-state fMRI data as a neuroimaging biomarker to distinguish between different severities of patients with MDD. The results revealed significant reductions in VMHC within the fusiform gyrus in cased of mild to moderate depression, and more extensive reductions across the insula, postcentral gyrus, and angular gyrus in severe depression. Interestingly, increased VMHC in the middle cingulate cortex was observed in the severe MDD patients compared to those with mild to moderate cases, and this increase showed a significant positive correlation with the symptom scores. Additionally, receiver operating characteristic (ROC) curve analysis indicated that VMHC values in these regions effectively differentiate patients from healthy controls and across severities of MDD. These findings suggest that VMHC could serve as a valuable tool for clinical diagnosis and the stratification of depression severity, providing insights into the underlying neurobiological mechanisms of the disorder.","manuscriptTitle":"Influence of depression severity on interhemispheric functional integration: An analysis from the REST-meta-MDD Database","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-01 19:04:50","doi":"10.21203/rs.3.rs-4541402/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-05T14:25:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-02T02:41:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"167262230235916042692884262575482766064","date":"2024-07-12T18:57:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-07-10T20:37:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-10T20:02:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-10T10:06:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"Brain Imaging and Behavior","date":"2024-06-06T15:24:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"brain-imaging-and-behavior","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bior","sideBox":"Learn more about [Brain Imaging and Behavior](https://www.springer.com/journal/11682)","snPcode":"11682","submissionUrl":"https://submission.nature.com/new-submission/11682/3","title":"Brain Imaging and Behavior","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"36b93a10-5141-46ac-8df4-3d8e561518ce","owner":[],"postedDate":"July 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-02T17:23:03+00:00","versionOfRecord":{"articleIdentity":"rs-4541402","link":"https://doi.org/10.1007/s11682-024-00960-0","journal":{"identity":"brain-imaging-and-behavior","isVorOnly":false,"title":"Brain Imaging and Behavior"},"publishedOn":"2024-11-30 15:58:00","publishedOnDateReadable":"November 30th, 2024"},"versionCreatedAt":"2024-07-01 19:04:50","video":"","vorDoi":"10.1007/s11682-024-00960-0","vorDoiUrl":"https://doi.org/10.1007/s11682-024-00960-0","workflowStages":[]},"version":"v1","identity":"rs-4541402","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4541402","identity":"rs-4541402","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}