Predicting Rehabilitation Responses in Persons with Aphasia Undergoing Language Therapy Using Baseline Whole-Brain Task fMRI in a Multivariate Method Restricted; Files & ToC

Abstract

Background: With the diversity in aphasia profiles as well as a small window of plasticity, it is imperative to deliver the most effective rehabilitation treatment to patients soon after their incidence of stroke. Voxel-based lesion-symptom mapping (VLSM) is a widely used approach for identifying predictors of treatment outcomes, but the lack of network-level information in VLSM may omit the holistic details needed to create valid language-network prediction models. As task-fMRI offers neurobiological inspection of lesion impact on the whole language network, this study aims to develop a multivariate analysis inclusive of whole-brain task-fMRI data to more holistically predict treatment outcomes in persons with aphasia (PWA) undergoing language therapy.

 

Methods: This thesis will be a correlational study focused on analyzing the relationship between patterns of pretreatment brain activity across multiple PWA and treatment-induced language improvements over time to predict post-treatment outcomes. Initially, multivariate methods will be optimized to handle whole-brain task fMRI data by inserting a customized mask representative of the whole-brain as well as testing a series of k-fold cross-validations in order to find the optimal sparseness value. This will then be tested systematically for reliability, extended utility, and overall sensitivity.

 

Results: Multivariate optimizations included the modification of k-fold CVs, brain masks, and lesion size correction. With these changes, the multivariate analysis was able to feasibly predict post-treatment outcomes at both whole-level and group-level analyses. Additionally, predictive biomarkers such as the right extrastriate area and right fusiform gyrus were discovered during brain-behavior analysis of semantic fluency. When analyzing the extended utility of multivariate methods, the left medial frontal gyrus was found to predict post-treatment responses to more sentence-level language functions such as grammar.

 

Conclusion: Ultimately, the goal of this study is to create an optimized prediction model that is better able to capture network-level information for functions such as language. The use of such information would help clinicians predict rehabilitation responses in PWA to develop a more effective rehabilitation regimen for aphasia patients. 

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About this Honors Thesis

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School	Emory College
Department	Neuroscience and Behavioral Biology
Degree	B.S.
Submission	Honors Thesis
Language	English
Research Field	Health Sciences, Rehabilitation and Therapy Biology, Neuroscience Health Sciences, Speech Pathology
Keyword	Neuroimaging Aphasia Predictive Biomarkers
Committee Chair / Thesis Advisor	Krishnamurthy, Venkatagiri, Emory University
Committee Members	Rodriguez, Amy, Emory University Murugan, Malavika, Emory University

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