Joint estimation and regularized aggregation of brain network in FMRI data

Abstract

BackgroundIn the Gaussian graphical model framework, precision matrices reveal conditional dependence structure among random variables. In functional magnetic resonance imaging (fMRI) data, estimating such precision matrices of multi-subjects and aggregating them to a group-level is an essential step for constructing a group brain network. New methodIn this article, we considered joint estimation of multiple precision matrices with regularized aggregation. Also, in the construction of a group precision matrix, we integrated robust aggregation to the estimation. In the estimation of individual precision matrices, we took a regularization approach to induce sparsity, which made brain network estimation more realistic. ResultsWe demonstrated the effectiveness of the proposed method through simulated examples, and analyses on real fMRI data acquired during eye movement tasks assessing cognitive control. For the fMRI data, the joint estimation of multiple precision matrices (JEMP) with regularized aggregation (RA) captured more robust associations between task-relevant neural regions of interest (ROIs), compared to the analyses using JEMP alone. The JEMP with RA also was sensitive to increased neural efficiency after task practice. Comparison with existing method(s)The simple average of individual precision matrices may be affected by outliers and provide inconsistent outcomes between subject-level and group-level networks. In contrast, the proposed method yielded a robust group graph that could identify and ease the effect of outliers. ConclusionsThe proposed method identified regions of practice-induced attenuation associated with reduced cognitive demand after repeat task exposure. Through simulated and real data, we demonstrated that this method does not require any distribution assumption, can identify outliers, and provides robust, representative group brain networks. This method can be applied to datasets that have extensive variability and/or multiple outliers, including applications to specific, and general, cognitive processes, as well as for studies that may require longitudinal data, such as pharmaceutical trials.