Connectome-Based Prediction of Optimal Weight Loss Six Months After Bariatric Surgery

Abstract

Abstract Despite bariatric surgery being the most effective treatment for obesity, a proportion of subjects have suboptimal weight loss post-surgery. Therefore, it is necessary to understand the mechanisms behind the variance in weight loss and identify specific baseline biomarkers to predict optimal weight loss. Here, we employed functional magnetic resonance imaging (fMRI) with baseline whole-brain resting-state functional connectivity (RSFC) and a multivariate prediction framework integrating feature selection, feature transformation, and classification to prospectively identify obese patients that exhibited optimal weight loss at 6 months post-surgery. Siamese network, which is a multivariate machine learning method suitable for small sample analysis, and K-nearest neighbor (KNN) were cascaded as the classifier (Siamese-KNN). In the leave-one-out cross-validation, the Siamese-KNN achieved an accuracy of 83.78%, which was substantially higher than results from traditional classifiers. RSFC patterns contributing to the prediction consisted of brain networks related to salience, reward, self-referential, and cognitive processing. Further RSFC feature analysis indicated that the connection strength between frontal and parietal cortices was stronger in the optimal versus the suboptimal weight loss group. These findings show that specific RSFC patterns could be used as neuroimaging biomarkers to predict individual weight loss post-surgery and assist in personalized diagnosis for treatment of obesity.