Supporting data for "Cerebellocerebral Connectivity Predicts Body Mass Index: a New Open-Source Python-based Framework for Connectome-based Predictive Modeling"

The cerebellum is one of the major central-nervous structures consistently altered in obesity. Its role in higher-cognitive function, parts of which are affected by obesity, is mediated through projections to and from the cerebral cortex. We therefore investigated the relationship between body mass index (BMI) and cerebellocerebral connectivity. <br>We utilized the Human Connectome Projects Young Adults dataset including functional MRI (fMRI) and behavioral data, to perform connectome-based predictive modeling (CPM) restricted to cerebellocerebral connectivity of resting-state fMRI and task-based fMRI. We developed a Python-based open-source framework to perform CPM, a data-driven technique with built-in cross validation to establish brain-behavior relationships. Significance was assessed with permutation analysis. <br>We found that task-general cerebellocerebral connectivity predicted BMI more reliably than resting-state fMRI and individual task-based fMRI separetely. Predictive networks derived this way overlapped with established functional brain networks (namely frontoparietal networks, the somatomotor network, the salience network, and the default mode network). We found there was an inverse overlap between networks predictive of BMI and networks predictive of cognitive measures aversely affected by overweight/obesity. <br>Our results suggest obesity-specific alterations in cerebellocerebral connectivity, specifically with regard to task execution. With brain areas and brain networks relevant to task performance implicated, these alterations seem to reflect a neurobiological substrate for task performance aversely affected by obesity.