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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
Source PublicationACTA PSYCHIATRICA SCANDINAVICA
ISSN0001-690X
2018-11-01
Volume138Issue:5Pages:472-482
Corresponding AuthorOsuch, E.(Elizabeth.osuch@lhsc.on.ca) ; Sui, J.(jing.sui@nlpr.ia.ac.cn)
AbstractObjectiveMethodsThis 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.
Keywordmood disorders bipolar disorder functional neuroimaging machine learning differential diagnosis
DOI10.1111/acps.12945
WOS KeywordMAJOR DEPRESSIVE-DISORDERS ; SCALE BRAIN NETWORKS ; UNIPOLAR DEPRESSION ; BIPOLAR DISORDER ; UNMEDICATED PATIENTS ; GROUP ICA ; ANTIDEPRESSANTS ; VALIDATION ; FRAMEWORK
Indexed BySCI
Language英语
Funding ProjectNational 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
Funding OrganizationNational 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
WOS Research AreaPsychiatry
WOS SubjectPsychiatry
WOS IDWOS:000448780800011
PublisherWILEY
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/22792
Collection脑网络组研究中心
Corresponding AuthorOsuch, E.; Sui, J.
Affiliation1.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
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences;  Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China;  Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
Recommended Citation
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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