2020/11/14 by Arunesh Mittal, Scott W. Linderman, Scott Linderman +6
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · Neuroscience · #Blind Source Separation Techniques #FOS: Biological sciences #FOS: Computer and information sciences #Functional Brain Connectivity Studies #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural dynamics and brain function #Neurons and Cognition (q-bio.NC) #cs.LG #q-bio.NC #stat.ML
paper · pdf · doi:10.48550/arxiv.2011.07365
Machine Learning for Health (ML4H) at NeurIPS 2020 - Extended Abstract
arxiv created 2020/11/14 · openalex publication_date 2020/11/14 · arxiv updated 2020/11/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose a hierarchical Bayesian recurrent state space model for modeling switching network connectivity in resting state fMRI data. Our model allows us to uncover shared network patterns across disease conditions. We evaluate our method on the ADNI2 dataset by inferring latent state patterns corresponding to altered neural circuits in individuals with Mild Cognitive Impairment (MCI). In addition to states shared across healthy and individuals with MCI, we discover latent states that are predominantly observed in individuals with MCI. Our model outperforms current state of the art deep learning method on ADNI2 dataset.