Machine learning based classification of major depressive disorder using clinical symptom scales and ultrahigh field MRI features
Gaurav Verma1, Xin Xing2, Yael Jacob3, Bradley N Delman4, James Murrough3, Ai-Ling Lin5, and Priti Balchandani1
1Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, United States, 2Computer Science, University of Kentucky, Lexington, KY, United States, 3Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, United States, 4Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States, 5Radiology, University of Missouri, Columbia, MO, United States
Major depression is highly-prevalent disorder with frustratingly-high rates of treatment resistance. Ultrahigh field imaging may provide objective quantitative biomarkers for characterizing depression, generating insight into clinical phenotypes of this heterogeneous disease. Forty-two major depressive disorder patients currently off anti-depressant treatment were recruited for scanning at ultrahigh field, and given batteries of clinical symptom measures. Machine-learning clustering analysis was performed to group patients by clinical symptoms and differences in imaging features observed. A separate analysis was performed in the reverse direction clustering on quantified imaging features and identifying clinical differences between clusters, including differences in ruminative response between the patient clusters.
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