Investigation on biological subtypes of depression based on diffusion tensor imaging
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...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Sichuan Mental Health
2023-08-01
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| Series: | Sichuan jingshen weisheng |
| Subjects: | |
| Online Access: | http://www.psychjm.net.cn/scjswszzen/ch/reader/view_abstract.aspx?file_no=202304002&flag=1 |
| _version_ | 1797627561029664768 |
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| author | Chen Xiongying Zhu Hua Wu Hang Cheng Jian Zhou Jingjing Feng Yuan Liu Rui Wang Yun Zhang Zhifang Feng Lei Zhou Yuan Wang Gang |
| author_facet | Chen Xiongying Zhu Hua Wu Hang Cheng Jian Zhou Jingjing Feng Yuan Liu Rui Wang Yun Zhang Zhifang Feng Lei Zhou Yuan Wang Gang |
| author_sort | Chen Xiongying |
| collection | DOAJ |
| description | 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］ |
| first_indexed | 2024-03-11T10:27:00Z |
| format | Article |
| id | doaj.art-c57a42548d964af186dc8c4142984396 |
| institution | Directory Open Access Journal |
| issn | 1007-3256 |
| language | zho |
| last_indexed | 2024-03-11T10:27:00Z |
| publishDate | 2023-08-01 |
| publisher | Editorial Office of Sichuan Mental Health |
| record_format | Article |
| series | Sichuan jingshen weisheng |
| spelling | doaj.art-c57a42548d964af186dc8c41429843962023-11-15T12:30:10ZzhoEditorial Office of Sichuan Mental HealthSichuan jingshen weisheng1007-32562023-08-0136429430010.11886/scjsws202305310011007-3256（2023）04-0294-07Investigation on biological subtypes of depression based on diffusion tensor imagingChen Xiongying0Zhu Hua1Wu Hang2Cheng Jian3Zhou Jingjing4Feng Yuan5Liu Rui6Wang Yun7Zhang Zhifang8Feng Lei9Zhou Yuan10Wang Gang11The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders， Beijing Anding Hospital， Capital Medical University， Beijing 100088， ChinaSchool of Biological Science and Medical Engineering， Beihang University， Beijing 100191， ChinaThe National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders， Beijing Anding Hospital， Capital Medical University， Beijing 100088， ChinaBeijing Advanced Innovation Center for Big Data-Based Precision Medicine， Beijing 100191， ChinaThe National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders， Beijing Anding Hospital， Capital Medical University， Beijing 100088， ChinaThe National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders， Beijing Anding Hospital， Capital Medical University， Beijing 100088， ChinaThe National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders， Beijing Anding Hospital， Capital Medical University， Beijing 100088， ChinaThe National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders， Beijing Anding Hospital， Capital Medical University， Beijing 100088， ChinaThe National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders， Beijing Anding Hospital， Capital Medical University， Beijing 100088， ChinaThe National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders， Beijing Anding Hospital， Capital Medical University， Beijing 100088， ChinaInstitute of Psychology， Chinese Academy of Sciences， Beijing 100101， ChinaThe National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders， Beijing Anding Hospital， Capital Medical University， Beijing 100088， ChinaBackgroundBeing 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］http://www.psychjm.net.cn/scjswszzen/ch/reader/view_abstract.aspx?file_no=202304002&flag=1depressiondiffusion tensor imagingbiological subtypesmachine learning |
| spellingShingle | 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 Sichuan jingshen weisheng depression diffusion tensor imaging biological subtypes machine learning |
| title | Investigation on biological subtypes of depression based on diffusion tensor imaging |
| title_full | Investigation on biological subtypes of depression based on diffusion tensor imaging |
| title_fullStr | Investigation on biological subtypes of depression based on diffusion tensor imaging |
| title_full_unstemmed | Investigation on biological subtypes of depression based on diffusion tensor imaging |
| title_short | Investigation on biological subtypes of depression based on diffusion tensor imaging |
| title_sort | investigation on biological subtypes of depression based on diffusion tensor imaging |
| topic | depression diffusion tensor imaging biological subtypes machine learning |
| url | http://www.psychjm.net.cn/scjswszzen/ch/reader/view_abstract.aspx?file_no=202304002&flag=1 |
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