Sparse Low-dimensional Causal Modeling for the Analysis of Brain Function

摘要

Resting-state fMRI (rs-fMRI) provides a means to study how the information is processed in the brain. Thismodality has been increasingly used to estimate dynamical interactions between brain regions. However, the noiseand the limited temporal resolution obtained from typical rs-fMRI scans make the extraction of reliable dynamicalinteractions challenging. In this work, we propose a new approach to tackle these issues. We estimate GrangerCausality in full resolution rs-fMRI data by fitting sparse low-dimensional multivariate autoregressive models.We elaborate an efficient optimization strategy by combining spatial and temporal dimensionality reduction,extrapolation and stochastic gradient descent. We demonstrate by processing the rs-fMRI scans of the hundredunrelated Human Connectome Project subjects that our method captures interpretable brain interactions, inparticular when a differentiable sparsity-inducing regularization is introduced in our framework.