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A Bayesian Multiplex Graph Classifier of Functional Brain Connectivity Across Cognitive Tasks

Abstract Number:

961 

Submission Type:

Contributed Abstract 

Contributed Abstract Type:

Poster 

Participants:

Jose Rodriguez-Acosta (1), Sharmistha Guha (1), Ivo Dinov (2)

Institutions:

(1) Texas A&M University, N/A, (2) Statistics Online Computational Resource, N/A

Co-Author(s):

Sharmistha Guha  
Texas A&M University
Ivo Dinov  
Statistics Online Computational Resource

First Author:

Jose Rodriguez-Acosta  
Texas A&M University

Presenting Author:

Jose Rodriguez-Acosta  
Texas A&M University

Abstract Text:

This work seeks to investigate the impact of aging on functional connectivity across different cognitive control scenarios, particularly emphasizing the identification of brain regions significantly associated with early aging. By conceptualizing functional connectivity within each cognitive control scenario as a graph, with brain regions as nodes, the statistical challenge revolves around devising a regression framework to predict a binary scalar outcome (aging or normal) using multiple graph predictors. To address this challenge, we propose the Bayesian Multiplex Graph Classifier (BMGC). Accounting for multiplex graph topology, our method models edge coefficients at each graph layer using bilinear interactions between the latent effects associated with the two nodes connected by the edge. This approach also employs a variable selection framework on node-specific latent effects from all graph layers to identify influential nodes linked to observed outcomes. Crucially, the proposed framework is computationally efficient and quantifies the uncertainty in node identification, coefficient estimation, and binary outcome prediction.

Keywords:

Bayesian statistics|Multiplex graph classification|Variable selection| Functional brain connectivity| |

Sponsors:

Section on Bayesian Statistical Science

Tracks:

Applications in Life Sciences and Medicine

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