Machine learning classification of first-onset drug-naive MDD using structural MRI

Abstract

It is hard to differentiate adolescent Major Depressive Disorder (MDD) patients from healthy adolescent controls based on structural MRI research findings, as the clinical characteristics of the patient group are heterogeneous, and the neuroimaging study results are ambiguous. We aimed to determine whether it is possible to reliably train a highly accurate predictive classification algorithm, even with the first onset of drug-naive adolescent MDD, solely using structural magnetic resonance imaging and without using any other clinical data from the patients. We also estimated the probability of the subject belonging to the predicted class to quantify the confidence of the prediction. Medication-naive adolescent patients in their first episode of MDD and healthy volunteers, matched for age, sex, and years of education, were prospectively recruited. Twenty-seven patients and 27 controls participated in the study. The two most significant variables were the standard deviations of intensity of the right ventral diencephalon and thickness of the superior segment of the circular sulcus of the insula. A participant is diagnosed as having MDD when the variation of either intensity in the right ventral diencephalon region or thickness of the superior segment of the circular sulcus of the insula increases. Structural brain changes can be used to build an accurate classification model for machine learning, even when the duration of illness is relatively short and the influence of MDD on the brain structure is minimal.