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Complexity in mood disorder diagnosis: fMRI connectivity networks predicted medication‐class of response in complex patients
Author(s) -
Osuch E.,
Gao S.,
Wammes M.,
Théberge J.,
Williamson P.,
Neufeld R. J.,
Du Y.,
Sui J.,
Calhoun V.
Publication year - 2018
Publication title -
acta psychiatrica scandinavica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.849
H-Index - 146
eISSN - 1600-0447
pISSN - 0001-690X
DOI - 10.1111/acps.12945
Subject(s) - psychology , mood , major depressive disorder , psychiatry , functional connectivity , clinical psychology , neuroscience , cognitive psychology
Objective This study determined the clinical utility of an fMRI classification algorithm predicting medication‐class of response in patients with challenging mood diagnoses. Methods Ninety‐nine 16–27‐year‐olds underwent resting state fMRI scans in three groups—BD, 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. Results Classification 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). Conclusion 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.