Efficient hierarchical Bayesian inference for spatio-temporal regression models in neuroimaging

• URL: http://arxiv.org/abs/2111.01692v1
• Date: Tue, 2 Nov 2021 15:50:01 GMT
• Title: Efficient hierarchical Bayesian inference for spatio-temporal regression models in neuroimaging
• Authors: Ali Hashemi, Yijing Gao, Chang Cai, Sanjay Ghosh, Klaus-Robert M\"uller, Srikantan S. Nagarajan, Stefan Haufe
• Abstract summary: Examples include M/EEG inverse problems, encoding neural models for task-based fMRI analyses, and temperature monitoring schemes. We devise a novel hierarchical flexible Bayesian framework within which the intrinsic-temporal dynamics of model parameters and noise are modeled.
• Score: 6.512092052306553
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Several problems in neuroimaging and beyond require inference on the parameters of multi-task sparse hierarchical regression models. Examples include M/EEG inverse problems, neural encoding models for task-based fMRI analyses, and temperature monitoring of climate or CPU and GPU. In these domains, both the model parameters to be inferred and the measurement noise may exhibit a complex spatio-temporal structure. Existing work either neglects the temporal structure or leads to computationally demanding inference schemes. Overcoming these limitations, we devise a novel flexible hierarchical Bayesian framework within which the spatio-temporal dynamics of model parameters and noise are modeled to have Kronecker product covariance structure. Inference in our framework is based on majorization-minimization optimization and has guaranteed convergence properties. Our highly efficient algorithms exploit the intrinsic Riemannian geometry of temporal autocovariance matrices. For stationary dynamics described by Toeplitz matrices, the theory of circulant embeddings is employed. We prove convex bounding properties and derive update rules of the resulting algorithms. On both synthetic and real neural data from M/EEG, we demonstrate that our methods lead to improved performance.

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