Abstract
The application of deep learning techniques to analyze brain functional magnetic resonance imaging (fMRI) data has led to significant advancements in identifying prospective biomarkers associated with various clinical phenotypes and neurological conditions. Despite these achievements, the aspect of prediction uncertainty has been relatively underexplored in brain fMRI data analysis. Accurate uncertainty estimation is essential for trustworthy learning, given the challenges associated with brain fMRI data acquisition and the potential diagnostic implications for patients. To address this gap, we introduce a novel posterior evidential network, named the Brain Posterior Evidential Network (BPEN), designed to capture both aleatoric and epistemic uncertainty in the analysis of brain fMRI data. We conducted comprehensive experiments using data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and ADNI-depression (ADNI-D) cohorts, focusing on predictions for mild cognitive impairment (MCI) and depression across various diagnostic groups. Our experiments not only unequivocally demonstrate the superior predictive performance of our BPEN model compared to existing state-of-the-art methods but also underscore the importance of uncertainty estimation in predictive models.