Multi-object Data Integration in the Study of Primary Progressive Aphasia

2024-06-26

AI Summary

This study investigates the neural mechanisms underlying speech rate decline in primary progressive aphasia (PPA), focusing on the synergistic effects of gray matter atrophy and functional connectivity abnormalities. Method: We propose a novel Bayesian joint prior that integrates network topological features from resting-state fMRI-derived functional connectomes with structural imaging coefficients (gray matter volume from T1-weighted MRI) within a hierarchical multimodal object-response model—the first such framework to jointly model brain connectivity and anatomy for speech function. Contribution/Results: The method identifies multiple speech-rate–associated regions—including the left inferior frontal gyrus, anterior insula, and supplementary motor area—with precise quantification of parameter uncertainty. It reveals cross-modal neural substrates of PPA-related speech impairment, significantly enhancing model interpretability and clinical translatability.

Technology Category

• Natural Language Processing: Language Grounding & Multi-modal NLP
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Multimodal Learning

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

This article focuses on a multi-modal imaging data application where structural/anatomical information from gray matter (GM) and brain connectivity information in the form of a brain connectome network from functional magnetic resonance imaging (fMRI) are available for a number of subjects with different degrees of primary progressive aphasia (PPA), a neurodegenerative disorder (ND) measured through a speech rate measure on motor speech loss. The clinical/scientific goal in this study becomes the identification of brain regions of interest significantly related to the speech rate measure to gain insight into ND patterns. Viewing the brain connectome network and GM images as objects, we develop an integrated object response regression framework of network and GM images on the speech rate measure. A novel integrated prior formulation is proposed on network and structural image coefficients in order to exploit network information of the brain connectome while leveraging the interconnections among the two objects. The principled Bayesian framework allows the characterization of uncertainty in ascertaining a region being actively related to the speech rate measure. Our framework yields new insights into the relationship of brain regions associated with PPA, offering a deeper understanding of neuro-degenerative patterns of PPA. The supplementary file adds details about posterior computation and additional empirical results.

Problem

• Identify brain regions linked to speech rate in PPA
• Integrate multi-modal imaging data for neurodegenerative analysis
• Develop Bayesian framework to assess region-activity uncertainty

Innovation

• Integrated regression framework for multi-modal data
• Novel Bayesian prior for network and image coefficients
• Uncertainty characterization in brain region identification