Complexity in mood disorder diagnosis: fMRI connectivity networks predicted medication-class of response in complex patients | |
Osuch, E.1,2,3; Gao, S.4,5,6; Wammes, M.2; Theberge, J.1,2,3; Willimason, P.2,3; Neufeld, R. J.7; Du, Y.8,9; Sui, J.4,5,6,8,10; Calhoun, V.8,11 | |
发表期刊 | ACTA PSYCHIATRICA SCANDINAVICA |
ISSN | 0001-690X |
2018-11-01 | |
卷号 | 138期号:5页码:472-482 |
摘要 | ObjectiveMethodsThis study determined the clinical utility of an fMRI classification algorithm predicting medication-class of response in patients with challenging mood diagnoses. Ninety-nine 16-27-year-olds underwent resting state fMRI scans in three groupsBD, MDD and healthy controls. A predictive algorithm was trained and cross-validated on the known-diagnosis patients using maximally spatially independent components (ICs), constructing a similarity matrix among subjects, partitioning the matrix in kernel space and optimizing support vector machine classifiers and IC combinations. This classifier was also applied to each of 12 new individual patients with unclear mood disorder diagnoses. ResultsConclusionClassification within the known-diagnosis group was approximately 92.4% accurate. The five maximally contributory ICs were identified. Applied to the complicated patients, the algorithm diagnosis was consistent with optimal medication-class of response to sustained recovery in 11 of 12 cases (i.e., almost 92% accuracy). This classification algorithm performed well for the know-diagnosis but also predicted medication-class of response in difficult-to-diagnose patients. Further research can enhance this approach and extend these findings to be more clinically accessible. |
关键词 | mood disorders bipolar disorder functional neuroimaging machine learning differential diagnosis |
DOI | 10.1111/acps.12945 |
关键词[WOS] | MAJOR DEPRESSIVE-DISORDERS ; SCALE BRAIN NETWORKS ; UNIPOLAR DEPRESSION ; BIPOLAR DISORDER ; UNMEDICATED PATIENTS ; GROUP ICA ; ANTIDEPRESSANTS ; VALIDATION ; FRAMEWORK |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Lawson Health Research Institute ; St. Joseph's Health Care ; London Health Sciences Centre ; Schulich School of Medicine and Dentistry ; University of Western Ontario ; Pfizer Independent Investigator Award[WS2249136] ; Lawson Health Research Institute[LHR D1374] ; Natural Science Foundation of Shanxi Province[2016021077] ; China National Natural Science Foundation[61703253] ; China National Natural Science Foundation[61773380] ; China National Natural Science Foundation[81471367] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB02060005] ; China National High-Tech Development Plan (863 plan)[2015AA020513] ; National Institutes of Health[1R01EB006841] ; National Institutes of Health[R01EB005846] ; National Institutes of Health[P20GM103472] ; National Institutes of Health[P20GM103472] ; National Institutes of Health[R01EB005846] ; National Institutes of Health[1R01EB006841] ; China National High-Tech Development Plan (863 plan)[2015AA020513] ; Strategic Priority Research Program of the Chinese Academy of Sciences[XDB02060005] ; China National Natural Science Foundation[81471367] ; China National Natural Science Foundation[61773380] ; China National Natural Science Foundation[61703253] ; Natural Science Foundation of Shanxi Province[2016021077] ; Lawson Health Research Institute[LHR D1374] ; Pfizer Independent Investigator Award[WS2249136] ; University of Western Ontario ; Schulich School of Medicine and Dentistry ; London Health Sciences Centre ; St. Joseph's Health Care ; Lawson Health Research Institute |
WOS研究方向 | Psychiatry |
WOS类目 | Psychiatry |
WOS记录号 | WOS:000448780800011 |
出版者 | WILEY |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/22792 |
专题 | 脑网络组研究 |
通讯作者 | Osuch, E.; Sui, J. |
作者单位 | 1.Univ Western Ontario, Schulich Sch Med & Dent, London Hlth Sci Ctr, Lawson Hlth Res Inst, London, ON, Canada 2.Univ Western Ontario, Schulich Sch Med & Dent, Dept Psychiat, London, ON, Canada 3.Univ Western Ontario, Dept Med Biophys, London, ON, Canada 4.Chinese Acad Sci, Inst Automat, Brainnetome Ctr, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China 5.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China 6.Univ Chinese Acad Sci, Beijing, Peoples R China 7.Univ Western Ontario, Dept Psychol, London, ON, Canada 8.Mind Res Network, Albuquerque, NM USA 9.Shanxi Univ, Sch Comp & Informat Technol, Taiyuan, Shanxi, Peoples R China 10.Chinese Acad Sci, Inst Automat, CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing, Peoples R China 11.Univ New Mexico, Dept Elect & Comp Engn, Albuquerque, NM 87131 USA |
通讯作者单位 | 中国科学院自动化研究所; 模式识别国家重点实验室; 中国科学院分子影像重点实验室 |
推荐引用方式 GB/T 7714 | Osuch, E.,Gao, S.,Wammes, M.,et al. Complexity in mood disorder diagnosis: fMRI connectivity networks predicted medication-class of response in complex patients[J]. ACTA PSYCHIATRICA SCANDINAVICA,2018,138(5):472-482. |
APA | Osuch, E..,Gao, S..,Wammes, M..,Theberge, J..,Willimason, P..,...&Calhoun, V..(2018).Complexity in mood disorder diagnosis: fMRI connectivity networks predicted medication-class of response in complex patients.ACTA PSYCHIATRICA SCANDINAVICA,138(5),472-482. |
MLA | Osuch, E.,et al."Complexity in mood disorder diagnosis: fMRI connectivity networks predicted medication-class of response in complex patients".ACTA PSYCHIATRICA SCANDINAVICA 138.5(2018):472-482. |
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