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Utilizing Repetitive Transcranial Magnetic Stimulation to Improve Language Function in Stroke Patients with Chronic Non-fluent Aphasia
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Predicting language outcomes after stroke: Is structural disconnection a useful predictor?

Thomas M H Hope1, Alex P Leff2, Cathy J Price1

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|July 24, 2018
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Structural brain connectivity data does not improve predictions of aphasia recovery in stroke survivors. Lesion load alone is as effective as connectivity disruption for predicting language outcomes.

Keywords:
AphasiaConnectomicsLanguageMRIOutcomesStrokeWhite matter

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Area of Science:

  • Neuroscience
  • Neurology
  • Speech and Language Pathology

Background:

  • Predicting language recovery in stroke survivors with aphasia is crucial for rehabilitation.
  • Lesion location is a known predictor, but its optimal encoding remains debated.
  • The role of white matter connectivity in aphasia prognosis is an emerging area of research.

Purpose of the Study:

  • To investigate whether disrupted white matter connectivity provides unique prognostic information for aphasia recovery in stroke survivors.
  • To compare the predictive power of lesion load versus connectivity disruption for language outcomes.

Main Methods:

  • Utilized data from 818 stroke patients from the PLORAS database, including structural MRI and Comprehensive Aphasia Test (CAT) scores.
  • Encoded lesions using the Automatic Anatomical Labelling atlas and calculated lesion load.
  • Quantified white matter connection disruption using the Network Modification toolbox.

Main Results:

  • Connectivity disruption data did not consistently improve prognostic models for any CAT language score.
  • Connectivity disruption variables were strongly correlated with lesion load variables.
  • Both lesion load and connectivity disruption conveyed prognostic information, but largely overlapping variance.

Conclusions:

  • Disrupted white matter connectivity does not offer superior predictive accuracy for aphasia recovery compared to lesion load alone.
  • Lesion load remains a primary structural predictor of language outcomes post-stroke.
  • Further research may explore alternative methods for encoding connectivity data or focus on different aspects of brain structure.