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Cross-Modal Multivariate Pattern Analysis

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Published on: November 9, 2011

MULTI-OBJECT DATA INTEGRATION IN THE STUDY OF PRIMARY PROGRESSIVE APHASIA.

Rene Gutierrez¹, Aaron Scheffler², Rajarshi Guhaniyogi¹

¹Department of Statistics, Texas A & M.

The Annals of Applied Statistics

|December 18, 2025

Summary

This study identifies brain regions linked to speech rate in primary progressive aphasia (PPA) using integrated neuroimaging data. The findings enhance understanding of neurodegenerative patterns in PPA.

Keywords:

Bayesian inference brain connectome functional magnetic resonance imaging gray matter multi-modal imaging primary progressive aphasia spike and slab prior

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Translational Brain Mapping at the University of Rochester Medical Center: Preserving the Mind Through Personalized Brain Mapping

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Published on: August 12, 2019

Area of Science:

Neuroimaging
Neuroscience
Medical Imaging

Background:

Primary progressive aphasia (PPA) is a neurodegenerative disorder affecting speech.
Understanding the neural correlates of PPA is crucial for diagnosis and treatment.
Multi-modal imaging data, including gray matter structure and brain connectivity, offer rich information.

Purpose of the Study:

To identify specific brain regions associated with speech rate deficits in PPA.
To develop an integrated framework for analyzing structural and functional brain data.
To gain insights into the neurodegenerative patterns underlying PPA.

Main Methods:

Developed an integrated object response regression framework for network and gray matter (GM) images.
Utilized functional magnetic resonance imaging (fMRI) for brain connectome networks and structural MRI for GM.
Applied a novel integrated prior formulation within a Bayesian framework to analyze imaging coefficients.

Main Results:

The integrated framework successfully identified brain regions significantly related to the speech rate measure.
The approach leveraged both brain connectome and GM information for improved analysis.
Characterized uncertainty in identifying brain regions associated with PPA-related speech motor loss.

Conclusions:

The study provides new insights into the relationship between specific brain regions and PPA.
The developed framework offers a deeper understanding of neurodegenerative patterns in PPA.
This multi-modal approach advances the analysis of complex neurological disorders.