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

Song, Serena (Spring 2022)

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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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