Chen Xiongying,Zhu Hua,Wu Hang,Cheng Jian,Zhou Jingjing,Feng Yuan,Liu Rui,Wang Yun,Zhang Zhifang,Feng Lei,Zhou Yuan,Wang Gang,Investigation on biological subtypes of depression based on diffusion tensor imaging[J].SICHUAN MENTAL HEALTH,2023,36(4):294-300

Investigation on biological subtypes of depression based on diffusion tensor imaging

DOI：10.11886/scjsws20230531001

English keywords:Depression Diffusion tensor imaging Biological subtypes Machine learning

Fund projects:国家重点研发计划（项目名称：基于客观指标和量化评价的抑郁障碍诊疗适宜技术研究，项目编号：2016YFC1307200）；北京市属医院科研培育计划（项目名称：结合认知任务和功能磁共振精准引导抑郁症rTMS治疗靶点及相关脑机制研究，项目编号：PX2023066）；首都医科大学附属北京安定医院院级课题（项目名称：伴忧郁特征抑郁症患者静息态脑功能连接研究，项目编号：YJ201904；项目名称：抑郁症视觉工作记忆容量的机制研究，项目编号：YJ201911）

Author Name	Affiliation	Postcode
Chen Xiongying	The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders， Beijing Anding Hospital， Capital Medical University， Beijing 100088， China	100088
Zhu Hua	School of Biological Science and Medical Engineering， Beihang University， Beijing 100191， China	100191
Wu Hang	The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders， Beijing Anding Hospital， Capital Medical University， Beijing 100088， China	100088
Cheng Jian	Beijing Advanced Innovation Center for Big Data-Based Precision Medicine， Beijing 100191， China	100191
Zhou Jingjing	The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders， Beijing Anding Hospital， Capital Medical University， Beijing 100088， China	100088
Feng Yuan	The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders， Beijing Anding Hospital， Capital Medical University， Beijing 100088， China	100088
Liu Rui	The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders， Beijing Anding Hospital， Capital Medical University， Beijing 100088， China	100088
Wang Yun	The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders， Beijing Anding Hospital， Capital Medical University， Beijing 100088， China	100088
Zhang Zhifang	The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders， Beijing Anding Hospital， Capital Medical University， Beijing 100088， China	100088
Feng Lei	The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders， Beijing Anding Hospital， Capital Medical University， Beijing 100088， China	100088
Zhou Yuan	Institute of Psychology， Chinese Academy of Sciences， Beijing 100101， China Department of Psychology， University of Chinese Academy of Sciences， Beijing 100049， China	100049
Wang Gang^*	The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders， Beijing Anding Hospital， Capital Medical University， Beijing 100088， China	100088

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English abstract:

Background Being complex and highly heterogeneous with regard to the etiology and clinical manifestations of depression， neuroimaging studies make a breakthrough for exploring the biological subtypes of depression， while the current data-driven approach for the identification of subtyping depression using structural magnetic resonance imaging （MRI） data is insufficient.Objective To explore the biological subtypes of depression using diffusion tensor imaging （DTI） and machine learning methods.Methods A total of 127 patients with depression who attended Beijing Anding Hospital from September 2017 to August 2021 and met the Diagnostic and Statistical Manual of Mental Disorders， fourth edition （DSM-IV） diagnostic criteria were included， and another 80 healthy individuals matched for gender and age were recruited through advertisements in surrounding communities during the same period. DTI findings， demographic characteristics and clinical data were collected from all participants. Tract-based spatial statistics （TBSS） and the Johns Hopkins University （JHU） white matter probability maps were used to extract fractional anisotropy （FA） values of white matter tracts. A semi-supervised machine learning technique was used to identify the subtypes， and the FA values for whole brain white matter of patients and controls were compared.Results Patients with depression were classified into two biological subtypes. FA values in multiple tracts including corpus callosum and corona radiata of subtype I patients were smaller than those of healthy controls （P<0.01， FDR corrected）， and FA values in middle cerebellar peduncle， left superior cerebellar peduncle and left cerebral peduncle of subtype II patients were larger than those of healthy controls （P<0.01， FDR-corrected）. Baseline Hamilton Depression Scale-17 item （HAMD-17） score yielded no statistical difference between subtype I and subtype II patients （P>0.05）， while subtype I patients scored lower on HAMD-17 than subtype II patients after 12 weeks of treatment （t=2.410， P<0.05）.Conclusion Depression patients exhibit two biological subtypes with distinct patterns of white matter damage. Furthermore， the subtypes respond differently to the medication treatment. ［Funded by the National Key Research and Development Program of China （number， 2016YFC1307200）， the Scientific Research and Cultivation Program of Beijing Municipal Hospitals （number，PX2023066）， Beijing Anding Hospital， Capital Medical University （number，YJ201904， YJ201911）； www.chictr.org.cn number： ChiCTR-OOC-17012566］

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