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Abstract #0228

A DTI connectome and machine learning approach to predict symptom improvement in depressed adolescents with cognitive-behavioral therapy (CBT)

Olga Tymofiyeva1, Justin Yuan1, Colm G Connolly2, Eva Henje Blom3, Duan Xu1, and Tony Yang1

1University of California, San Francisco, San Francisco, CA, United States, 2Florida State University, Tallahassee, FL, United States, 3Umea University, Umea, Sweden

We applied machine learning to DTI-based structural connectome data in order to predict improvement of symptoms in 30 depressed adolescents with cognitive-behavioral therapy (CBT). The J48 pruned tree classifier was applied with a 10-fold cross-validation, resulting in an 83% accuracy. The resulting tree highlights the role of the thalamus, a region known to be directly involved in anticipatory anhedonia and generation of goal-directed behavior, the lack of which can make subsequent CBT ineffective. The gained knowledge can significantly improve treatment planning in cases of adolescent depression and help optimize and develop new preventive and therapeutic interventions for this devastating disorder.

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