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Prediction of Aphasia Severity in Patients with Stroke Using Diffusion Tensor Imaging
Jin-Kook Lee1,2, Myoung-Hwan Ko1,2,3, Sung-Hee Park1,3
1Department of Physical Medicine & Rehabilitation, Jeonbuk National University Medical School, Jeonju 54907, Korea.
Brain Sciences
|March 6, 2021
Summary
This study used diffusion tensor imaging to identify key white matter tracts related to language in aphasia patients. Specific fiber bundles accurately predict aphasia severity and correlate with language function scores.
Area of Science:
- Neuroimaging
- Neurology
- Speech-Language Pathology
Background:
- Aphasia, a language disorder post-stroke, significantly impacts communication.
- Accurate classification of aphasia severity is crucial for treatment planning.
- Understanding the neural correlates of aphasia severity is an ongoing research area.
Purpose of the Study:
- To classify aphasia severity using the Western Aphasia Battery (WAB).
- To determine optimal fractional anisotropy (FA) cut-off values for language-related white matter (WM) fibers.
- To examine correlations between WM integrity and WAB subscores.
Main Methods:
- Retrospective analysis of 64 aphasia patients.
- Diffusion tensor imaging (DTI) to reconstruct language-related WM fasciculi.
- Receiver operating characteristic (ROC) curve analysis to determine optimal FA cut-off values for predicting aphasia severity.
Main Results:
- The arcuate fasciculus (AF) and superior longitudinal fasciculus (SLF) demonstrated fair accuracy in predicting aphasia severity.
- The inferior frontal occipital fasciculus (IFOF) showed poor predictive accuracy.
- Combinations of these WM tracts also showed fair accuracy in predicting severity.
- Significant correlations were found between AF, SLF, IFOF FA values and WAB subscores (spontaneous speech, auditory verbal comprehension, repetition, naming).
Conclusions:
- DTI-based analysis of language-related WM can help predict aphasia severity.
- Specific WM tracts, particularly AF and SLF, are important indicators of language impairment.
- This approach may aid in objective assessment and management of post-stroke aphasia.
