重度抑郁症感觉运动浅表白质系统的网络拓扑结构异常

王鹏; 白艳玲; 肖杨; 郑宇宏; 孙励; The DIRECT Consortium; 王金辉; 薛绍伟

doi:10.1631/jzus.B2300880

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重度抑郁症感觉运动浅表白质系统的网络拓扑结构异常

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Research Articles | Updated：2025-01-10

重度抑郁症感觉运动浅表白质系统的网络拓扑结构异常
Aberrant network topological structure of sensorimotor superficial white-matter system in major depressive disorder
浙江大学学报（英文版）（B辑：生物医学和生物技术） 2025年26卷第1期页码：39-51
Affiliations：

1.Center for Cognition and Brain Disorders / Department of Neurology, The Affiliated Hospital of Hangzhou Normal University, Hangzhou311121, China
2.Institute of Psychological Science, Hangzhou Normal University, Hangzhou311121, China
3.Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou311121, China
4.Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou311121, China
5.Institute of Mental Health, Peking University Sixth Hospital, Peking University, Beijing100191, China
6.Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou510631, China
Funds：

the Zhejiang Medical and Health Science and Technology Project(2022KY1055);the Zhejiang Provincial Natural Science Foundation of China(LY17H180007);the Key Clinical Specialty Construction Project of Zhejiang Province, and the Key Medical Disciplines of Hangzhou, China
DOI：10.1631/jzus.B2300880
中图分类号：
CSTR：
纸质出版日期： 2025-01-15 ，

网络出版日期： 2024-12-06 ，

收稿日期： 2023-12-06 ，

修回日期： 2024-04-19 ，
Accepted：

王鹏,白艳玲,肖杨等.重度抑郁症感觉运动浅表白质系统的网络拓扑结构异常[J].浙江大学学报（英文版）（B辑：生物医学和生物技术）,2025,26(01):39-51.

PENG WANG, YANLING BAI, YANG XIAO, et al. Aberrant network topological structure of sensorimotor superficial white-matter system in major depressive disorder. [J]. Journal of zhejiang university-science b (biomedicine & biotechnology), 2025, 26(1): 39-51.
王鹏,白艳玲,肖杨等.重度抑郁症感觉运动浅表白质系统的网络拓扑结构异常[J].浙江大学学报（英文版）（B辑：生物医学和生物技术）,2025,26(01):39-51. DOI： 10.1631/jzus.B2300880.

PENG WANG, YANLING BAI, YANG XIAO, et al. Aberrant network topological structure of sensorimotor superficial white-matter system in major depressive disorder. [J]. Journal of zhejiang university-science b (biomedicine & biotechnology), 2025, 26(1): 39-51. DOI： 10.1631/jzus.B2300880.

摘要

白质纤维在传递感觉和运动信息、促进两侧大脑间的通讯及整合不同脑区方面发挥着至关重要的作用。与此同时，感觉运动功能异常是重度抑郁症（MDD）患者的常见症状之一。然而，MDD中异常的感觉运动白质系统的作用大部分仍是未知的。本研究调查了来自DIRECT联盟的233名MDD患者与257名匹配的健康对照（HC）的白质形态脑网络的拓扑结构变化。白质网络是通过结合基于体素的形态学测量（VBM）和三维离散小波变换（3D-DWT）方法，从磁共振成像（MRI）数据中构建出来，通过使用支持向量机（SVM）分析区分MDD和HC。结果表明，在MDD中，节点度、节点效率和节点介数的网络拓扑异常主要位于感觉运动浅表白质系统中。利用网络节点拓扑特性作为分类特征，SVM模型能有效区分MDD和HC。上述发现从白质形态脑网络的视角出发，强调了感觉运动系统在MDD脑机制中的重要性。

Abstract

White-matter tracts play a pivotal role in transmitting sensory and motor information

facilitating interhemispheric communication and integrating different brain regions. Meanwhile

sensorimotor disturbance is a common symptom in patients with major depressive disorder (MDD). However

the role of aberrant sensorimotor white-matter system in MDD remains largely unknown. Herein

we investigated the topological structure alterations of white-matter morphological brain networks in 233 MDD patients versus 257 matched healthy controls (HCs) from the DIRECT consortium. White-matter networks were derived from magnetic resonance imaging (MRI) data by combining voxel-based morphometry (VBM) and three-dimensional discrete wavelet transform (3D-DWT) approaches. Support vector machine (SVM) analysis was performed to discriminate MDD patients from HCs. The results indicated that the network topological changes in node degree

node efficiency

and node betweenness were mainly located in the sensorimotor superficial white-matter system in MDD. Using network nodal topological properties as classification features

the SVM model could effectively distinguish MDD patients from HCs. These findings provide new evidence to highlight the importance of the sensorimotor system in brain mechanisms underlying MDD from a new perspective of white-matter morphological network.

关键词

重度抑郁症磁共振成像白质脑网络

Keywords

Major depressive disorder (MDD)Magnetic resonance imaging (MRI)White matterBrain network

references

Alexander-Bloch A, Clasen L, Stockman M, et al., 2016. Subtle in-scanner motion biases automated measurement of brain anatomy from in vivo MRI. Hum Brain Mapp, 37(7):2385-2397. https://doi.org/10.1002/hbm.23180https://doi.org/10.1002/hbm.23180

Ashburner J, 2007. A fast diffeomorphic image registration algorithm. NeuroImage, 38(1):95-113. https://doi.org/10.1016/j.neuroimage.2007.07.007https://doi.org/10.1016/j.neuroimage.2007.07.007

Aydogan DB, Jacobs R, Dulawa S, et al., 2018. When tractography meets tracer injections: a systematic study of trends and variation sources of diffusion-based connectivity. Brain Struct Funct, 223(6):2841-2858. https://doi.org/10.1007/s00429-018-1663-8https://doi.org/10.1007/s00429-018-1663-8

Bassett DS, Sporns O, 2017. Network neuroscience. Nat Neurosci, 20(3):353-364. https://doi.org/10.1038/nn.4502https://doi.org/10.1038/nn.4502

Baum GL, Roalf DR, Cook PA, et al., 2018. The impact of in-scanner head motion on structural connectivity derived from diffusion MRI. NeuroImage, 173:275-286. https://doi.org/10.1016/j.neuroimage.2018.02.041https://doi.org/10.1016/j.neuroimage.2018.02.041

Bullmore E, Sporns O, 2012. The economy of brain network organization. Nat Rev Neurosci, 13(5):336-349. https://doi.org/10.1038/nrn3214https://doi.org/10.1038/nrn3214

Canbeyli R, 2013. Sensorimotor modulation of mood and depression: in search of an optimal mode of stimulation. Front Hum Neurosci, 7:428. https://doi.org/10.3389/fnhum.2013.00428https://doi.org/10.3389/fnhum.2013.00428

Chen X, Lu B, Li HX, et al., 2022. The direct consortium and the REST-meta-MDD project: towards neuroimaging biomarkers of major depressive disorder. Psychoradiology, 2(1):32-42. https://doi.org/10.1093/psyrad/kkac005https://doi.org/10.1093/psyrad/kkac005

Fan YS, Li ZH, Duan XJ, et al., 2020. Impaired interactions among white-matter functional networks in antipsychotic-naive first-episode schizophrenia. Hum Brain Mapp, 41(1):230-240. https://doi.org/10.1002/hbm.24801https://doi.org/10.1002/hbm.24801

Fields RD, 2010. Change in the brain’s white matter. Science, 330(6005):768-769. https://doi.org/10.1126/science.1199139https://doi.org/10.1126/science.1199139

Filley CM, 1998. The behavioral neurology of cerebral white matter. Neurology, 50(6):1535-1540. https://doi.org/10.1212/WNL.50.6.1535https://doi.org/10.1212/WNL.50.6.1535

Fortin JP, Cullen N, Sheline YI, et al., 2018. Harmonization of cortical thickness measurements across scanners and sites. NeuroImage, 167:104-120. https://doi.org/10.1016/j.neuroimage.2017.11.024https://doi.org/10.1016/j.neuroimage.2017.11.024

Georgiadis F, Larivière S, Glahn D, et al., 2024. Connectome architecture shapes large-scale cortical alterations in schizophrenia: a worldwide ENIGMA study. Mol Psychiatry, 29:1869-1881. https://doi.org/10.1038/s41380-024-02442-7https://doi.org/10.1038/s41380-024-02442-7

Honey CJ, Thivierge JP, Sporns O, 2010. Can structure predict function in the human brain?NeuroImage, 52(3):766-776. https://doi.org/10.1016/j.neuroimage.2010.01.071https://doi.org/10.1016/j.neuroimage.2010.01.071

Iwabuchi SJ, Krishnadas R, Li CB, et al., 2015. Localized connectivity in depression: a meta-analysis of resting state functional imaging studies. Neurosci Biobehav Rev, 51:77-86. https://doi.org/10.1016/j.neubiorev.2015.01.006https://doi.org/10.1016/j.neubiorev.2015.01.006

Korgaonkar MS, Fornito A, Williams LM, et al., 2014. Abnormal structural networks characterize major depressive disorder: a connectome analysis. Biol Psychiatry, 76(7):567-574. https://doi.org/10.1016/j.biopsych.2014.02.018https://doi.org/10.1016/j.biopsych.2014.02.018

Li HR, Yang J, Yin L, et al., 2021. Alteration of single-subject gray matter networks in major depressed patients with suicidality. J Magn Reson Imaging, 54(1):215-224. https://doi.org/10.1002/jmri.27499https://doi.org/10.1002/jmri.27499

Li J, Chen H, Fan FY, et al., 2020. White-matter functional topology: a neuromarker for classification and prediction in unmedicated depression. Transl Psychiat, 10:365. https://doi.org/10.1038/s41398-020-01053-4https://doi.org/10.1038/s41398-020-01053-4

Li JL, Li Z, Yang YP, et al., 2023. Morphological brain networks of white matter: mapping, evaluation, characterization, and application. Adv Sci, 11(35):e2400061. https://doi.org/10.1002/advs.202400061https://doi.org/10.1002/advs.202400061

Liao XH, Vasilakos AV, He Y, 2017. Small-world human brain networks: perspectives and challenges. Neurosci Biobehav Rev, 77:286-300. https://doi.org/10.1016/j.neubiorev.2017.03.018https://doi.org/10.1016/j.neubiorev.2017.03.018

Lu FM, Cui Q, Huang XJ, et al., 2020. Anomalous intrinsic connectivity within and between visual and auditory networks in major depressive disorder. Prog Neuro-Psychopharmacol Biol Psychiatry, 100:109889. https://doi.org/10.1016/j.pnpbp.2020.109889https://doi.org/10.1016/j.pnpbp.2020.109889

Luscher B, Shen Q, Sahir N, 2011. The GABaergic deficit hypothesis of major depressive disorder. Mol Psychiatry, 16(4):383-406. https://doi.org/10.1038/mp.2010.120https://doi.org/10.1038/mp.2010.120

Ma N, Li LJ, Shu N, et al., 2007. White matter abnormalities in first-episode, treatment-naive young adults with major depressive disorder. Am J Psychiatry, 164(5):823-826. https://doi.org/10.1176/ajp.2007.164.5.823https://doi.org/10.1176/ajp.2007.164.5.823

Malhi GS, Mann JJ, 2018. Depression. Lancet, 392(10161):2299-2312. https://doi.org/10.1016/s0140-6736(18)31948-2https://doi.org/10.1016/s0140-6736(18)31948-2

Maslov S, Sneppen K, 2002. Specificity and stability in topology of protein networks. Science, 296(5569):910-913. https://doi.org/10.1126/science.1065103https://doi.org/10.1126/science.1065103

Meinertzhagen IA, 2018. Of what use is connectomics?A personal perspective on the Drosophila connectome. J Exp Biol, 221(10):jeb164954. https://doi.org/10.1242/jeb.164954https://doi.org/10.1242/jeb.164954

Nakano T, Takamura M, Ichikawa N, et al., 2020. Enhancing multi-center generalization of machine learning-based depression diagnosis from resting-state fMRI. Front Psychiatry, 11:400. https://doi.org/10.3389/fpsyt.2020.00400https://doi.org/10.3389/fpsyt.2020.00400

Pardoe HR, Martin SP, 2022. In-scanner head motion and structural covariance networks. Hum Brain Mapp, 43(14):4335-4346. https://doi.org/10.1002/hbm.25957https://doi.org/10.1002/hbm.25957

Peer M, Nitzan M, Bick AS, et al., 2017. Evidence for functional networks within the human brain’s white matter. J Neurosci, 37(27):6394-6407. https://doi.org/10.1523/JNEUROSCI.3872-16.2017https://doi.org/10.1523/JNEUROSCI.3872-16.2017

Petersen SE, Sporns O, 2015. Brain networks and cognitive architectures. Neuron, 88(1):207-219. https://doi.org/10.1016/j.neuron.2015.09.027https://doi.org/10.1016/j.neuron.2015.09.027

Petrella JR, 2011. Use of graph theory to evaluate brain networks: a clinical tool for a small world?Radiology, 259(2):317-320. https://doi.org/10.1148/radiol.11110380https://doi.org/10.1148/radiol.11110380

Power JD, Cohen AL, Nelson SM, et al., 2011. Functional network organization of the human brain. Neuron, 72(4):665-678. https://doi.org/10.1016/j.neuron.2011.09.006https://doi.org/10.1016/j.neuron.2011.09.006

Ramasubbu R, Brown MRG, Cortese F, et al., 2016. Accuracy of automated classification of major depressive disorder as a function of symptom severity. NeuroImage Clin, 12:320-331. https://doi.org/10.1016/j.nicl.2016.07.012https://doi.org/10.1016/j.nicl.2016.07.012

Sampaio-Baptista C, Johansen-Berg H, 2017. White matter plasticity in the adult brain. Neuron, 96(6):1239-1251. https://doi.org/10.1016/j.neuron.2017.11.026https://doi.org/10.1016/j.neuron.2017.11.026

Schilling KG, Gao YR, Stepniewska I, et al., 2019. Anatomical accuracy of standard-practice tractography algorithms in the motor system‍ –‍ a histological validation in the squirrel monkey brain. Magn Reson Imaging, 55:7-25. https://doi.org/10.1016/j.mri.2018.09.004https://doi.org/10.1016/j.mri.2018.09.004

Seidlitz J, Váša F, Shinn M, et al., 2018. Morphometric similarity networks detect microscale cortical organization and predict inter-individual cognitive variation. Neuron, 97(1):231-247.e7. https://doi.org/10.1016/j.neuron.2017.11.039https://doi.org/10.1016/j.neuron.2017.11.039

Shi YC, Zhang LH, Wang Z, et al., 2021. Multivariate machine learning analyses in identification of major depressive disorder using resting-state functional connectivity: a multicentral study. ACS Chem Neurosci, 12(15):‍2878-2886. https://doi.org/10.1021/acschemneuro.1c00256https://doi.org/10.1021/acschemneuro.1c00256

Wang JH, Wang XD, Xia MR, et al., 2015. GRETNA: a graph theoretical network analysis toolbox for imaging connectomics. Front Hum Neurosci, 9:386. https://doi.org/10.3389/fnhum.2015.00386https://doi.org/10.3389/fnhum.2015.00386

Wang P, Wang JL, Michael A, et al., 2022. White matter functional connectivity in resting-state fMRI: robustness, reliability, and relationships to gray matter. Cereb Cortex, 32(8):1547-1559. https://doi.org/10.1093/cercor/bhab181https://doi.org/10.1093/cercor/bhab181

Wang XH, Zhao BH, Li LH, 2022. Mapping white matter structural covariance connectivity for single subject using wavelet transform with T1-weighted anatomical brain MRI. Front Neurosci, 16:1038514. https://doi.org/10.3389/fnins.2022.1038514https://doi.org/10.3389/fnins.2022.1038514

Wang Y, Liu G, Hong DD, et al., 2016. White matter injury in ischemic stroke. Prog Neurobiol, 141:45-60. https://doi.org/10.1016/j.pneurobio.2016.04.005https://doi.org/10.1016/j.pneurobio.2016.04.005

Whitwell JL, 2009. Voxel-based morphometry: an automated technique for assessing structural changes in the brain. J Neurosci, 29(31):9661-9664. https://doi.org/10.1523/jneurosci.2160-09.2009https://doi.org/10.1523/jneurosci.2160-09.2009

Xiao Y, Zhao L, Zang XL, et al., 2023. Compressed primary-to-transmodal gradient is accompanied with subcortical alterations and linked to neurotransmitters and cellular signatures in major depressive disorder. Hum Brain Mapp, 44(17):5919-5935. https://doi.org/10.1002/hbm.26485https://doi.org/10.1002/hbm.26485

Yan CG, Chen X, Li L, et al., 2019. Reduced default mode network functional connectivity in patients with recurrent major depressive disorder. Proc Natl Acad Sci USA, 116(18):9078-9083. https://doi.org/10.1073/pnas.1900390116https://doi.org/10.1073/pnas.1900390116

Yang H, Chen X, Chen ZB, et al., 2021. Disrupted intrinsic functional brain topology in patients with major depressive disorder. Mol Psychiatry, 26(12):7363-7371. https://doi.org/10.1038/s41380-021-01247-2https://doi.org/10.1038/s41380-021-01247-2

Yeo BTT, Krienen FM, Sepulcre J, et al., 2011. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol, 106(3):1125-1165. https://doi.org/10.1152/jn.00338.2011https://doi.org/10.1152/jn.00338.2011

Zalesky A, Fornito A, Harding IH, et al., 2010. Whole-brain anatomical networks: does the choice of nodes matter?NeuroImage, 50(3):970-983. https://doi.org/10.1016/j.neuroimage.2009.12.027https://doi.org/10.1016/j.neuroimage.2009.12.027

Zhang FF, Peng W, Sweeney JA, et al., 2018. Brain structure alterations in depression: psychoradiological evidence. CNS Neurosci Ther, 24(11):994-1003. https://doi.org/10.1111/cns.12835https://doi.org/10.1111/cns.12835

Zhang YR, Liu XY, Hou ZH, et al., 2021. Global topology alteration of the brain functional network affects the 8-week antidepressant response in major depressive disorder. J Affect Disord, 294:491-496. https://doi.org/10.1016/j.jad.2021.07.078https://doi.org/10.1016/j.jad.2021.07.078

Zhang ZQ, Liao W, Chen HF, et al., 2011. Altered functional–structural coupling of large-scale brain networks in idiopathic generalized epilepsy. Brain, 134(10):2912-2928. https://doi.org/10.1093/brain/awr223https://doi.org/10.1093/brain/awr223

Zhou Y, Zhu YH, Ye HT, et al., 2024. Abnormal changes of dynamic topological characteristics in patients with major depressive disorder. J Affect Disord, 345:349-357. https://doi.org/10.1016/j.jad.2023.10.143https://doi.org/10.1016/j.jad.2023.10.143

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