B0854
Title: Accurate estimation of individual functional brain connectivity and topology via ICA with empirical population priors
Authors: Amanda Mejia - Indiana University (United States) [presenting]
Daniel Spencer - Indiana University (United States)
Ani Eloyan - Brown University (United States)
Abstract: A primary objective in resting-state fMRI studies is the localization of functional areas (i.e., resting-state networks) and the functional connectivity (FC) between them. These spatial and temporal properties of brain organization have been shown to be related to disease progression, development, and aging. Independent component analysis (ICA) is a popular tool to estimate functional areas and their FC. However, due to the high noise levels and short scan duration of typical fMRI data, subject-level ICA results tend to be noisy. Thus, group-level functional areas are often used in lieu of subject-specific ones, ignoring inter-subject variability in functional topology. These group-average maps also often form the basis for estimating FC, leading to potential bias in FC estimates given the topological differences in underlying functional areas. An alternative to these two extremes (noisy subject-level ICA and one-size-fits-all group ICA) is Bayesian hierarchical ICA, wherein information shared across subjects is leveraged to improve the subject-level estimation of spatial maps and FC. Functional connectivity template ICA is a computationally convenient hierarchical ICA framework using empirical population priors on the spatial configuration and connectivity between functional brain networks. These priors can be derived from large fMRI databases or holdout data. The proposed approach is validated through simulations and functional MRI data from the Human Connectome Project.
