Prediction of estimated risk for bipolar disorder using machine learning and structural MRI features

This is the authors' abstract. We don't add key points for this paper.

Abstract Individuals with bipolar disorder are commonly correctly diagnosed a decade after the onset of first symptoms. Machine learning techniques may aid in early recognition and reduce the disease burden. As both individuals at risk and those with a manifest disease display structural brain markers, structural magnetic resonance imaging (MRI) may provide relevant classification features. Following a pre-registered protocol, we trained linear support vector machine (SVM) to classify individuals according to their estimated risk for bipolar disorder using regional cortical thickness values of help-seeking individuals from 7 study sites (N = 276). We estimated the risk using three state-of-the-art assessment instruments (BPSS-P, BARS and EPIbipolar). For BPSS-P, SVM achieved a fair performance of Cohen's kappa of 0.235 (95% CI 0.11–0.361) and a balanced accuracy of 63.1% (95% CI 55.9–70.3) in the 10-fold crossvalidation. In the leave-one-site-out crossvalidation, the model performed with a Cohen's kappa of 0.128 (95% CI -0.069-0.325) and a balanced accuracy of 56.2% (95% CI 44.6–67.8). BARS and EPIbipolar could not be predicted. In post hoc analyses, regional surface area, subcortical volumes as well as hyperparameter optimization did not improve the performance. Individuals at risk for bipolar disorder, as assessed by BPSS-P, display brain structural alterations that can be detected using machine learning. The achieved performance is comparable to previous studies which attempted to classify patients with manifest disease and healthy controls. Unlike previous studies of bipolar risk, our multicenter design permitted a leave-one-site-out crossvalidation. Whole brain cortical thickness seem to be superior to other structural brain features.