Bayesian recurrent state space model for rs-fMRI
This work addresses the challenge of identifying altered neural circuits in individuals with Mild Cognitive Impairment, which is incremental as it builds on existing methods for fMRI analysis.
The authors tackled the problem of modeling switching network connectivity in resting-state fMRI data to uncover shared and disease-specific neural patterns, achieving results that outperform current state-of-the-art deep learning methods on the ADNI2 dataset.
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.