Learning Signal Subgraphs from Longitudinal Brain Networks with Symmetric Bilinear Logistic Regression

TL;DR

This paper introduces a symmetric bilinear logistic regression method to identify small, interpretable brain subgraphs associated with cognitive traits from longitudinal brain network data, capturing how these associations change over time.

Contribution

The proposed method uniquely enforces clique-structured subgraphs and models their time effects, advancing the analysis of longitudinal brain connectomics for cognitive prediction.

Findings

Identified relevant brain region interconnections in frontal and temporal lobes.

Achieved better predictive performance than existing methods.

Revealed how brain subgraph effects evolve over time.

Abstract

Modern neuroimaging technologies, combined with state-of-the-art data processing pipelines, have made it possible to collect longitudinal observations of an individual's brain connectome at different ages. It is of substantial scientific interest to study how brain connectivity varies over time in relation to human cognitive traits. In brain connectomics, the structural brain network for an individual corresponds to a set of interconnections among brain regions. We propose a symmetric bilinear logistic regression to learn a set of small subgraphs relevant to a binary outcome from longitudinal brain networks as well as estimating the time effects of the subgraphs. We enforce the extracted signal subgraphs to have clique structure which has appealing interpretations as they can be related to neurological circuits. The time effect of each signal subgraph reflects how its predictive effect on the outcome varies over time, which may improve our understanding of interactions between the aging of brain structure and neurological disorders. Application of this method on longitudinal brain connectomics and cognitive capacity data shows interesting discovery of relevant interconnections among a small set of brain regions in frontal and temporal lobes with better predictive performance than competitors.