Complexity in mood disorder diagnosis: fMRI connectivity networks predicted medication-class of response in complex patients

doi:10.1111/acps.12945

	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
刊名	ACTA PSYCHIATRICA SCANDINAVICA
	2018-11-01
卷号	138 期号:5 页码:472-482
关键词	mood disorders bipolar disorder functional neuroimaging machine learning differential diagnosis
ISSN号	0001-690X
DOI	10.1111/acps.12945
通讯作者	Osuch, E.(Elizabeth.osuch@lhsc.on.ca) ; Sui, J.(jing.sui@nlpr.ia.ac.cn)
英文摘要	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.
资助项目	National Institutes of Health[P20GM103472] ; National Institutes of Health[1R01EB006841] ; National Institutes of Health[R01EB005846] ; 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关键词	MAJOR DEPRESSIVE-DISORDERS ; SCALE BRAIN NETWORKS ; UNIPOLAR DEPRESSION ; BIPOLAR DISORDER ; UNMEDICATED PATIENTS ; GROUP ICA ; ANTIDEPRESSANTS ; VALIDATION ; FRAMEWORK
WOS研究方向	Psychiatry
语种	英语
出版者	WILEY
WOS记录号	WOS:000448780800011
资助机构	National Institutes of Health ; China National High-Tech Development Plan (863 plan) ; Strategic Priority Research Program of the Chinese Academy of Sciences ; China National Natural Science Foundation ; Natural Science Foundation of Shanxi Province ; Lawson Health Research Institute ; Pfizer Independent Investigator Award ; University of Western Ontario ; Schulich School of Medicine and Dentistry ; London Health Sciences Centre ; St. Joseph's Health Care ; Lawson Health Research Institute
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/22791]
专题	自动化研究所_脑网络组研究中心
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
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.