Investigation on biological subtypes of depression based on diffusion tensor imaging
10.11886/scjsws20230531001
- VernacularTitle:基于弥散张量成像的抑郁症生物学亚型研究
- Author:
Xiongying CHEN
1
;
Hua ZHU
2
;
Hang WU
1
;
Jian CHENG
3
;
Jingjing ZHOU
1
;
Yuan FENG
1
;
Rui LIU
1
;
Yun WANG
1
;
Zhifang ZHANG
1
;
Lei FENG
1
;
Yuan ZHOU
4
;
Gang WANG
1
Author Information
1. The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders， Beijing Anding Hospital， Capital Medical University， Beijing 100088， China
2. School of Biological Science and Medical Engineering， Beihang University， Beijing 100191， China
3. Beijing Advanced Innovation Center for Big Data-Based Precision Medicine， Beijing 100191， China
4. Institute of Psychology， Chinese Academy of Sciences， Beijing 100101， China
- Publication Type:Journal Article
- Keywords:
Depression;
Diffusion tensor imaging;
Biological subtypes;
Machine learning
- From:
Sichuan Mental Health
2023;36(4):294-300
- CountryChina
- Language:Chinese
-
Abstract:
BackgroundBeing 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. ObjectiveTo explore the biological subtypes of depression using diffusion tensor imaging （DTI） and machine learning methods. MethodsA 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. ResultsPatients 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）. ConclusionDepression 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］
