Multivariate Approaches to Understanding Aphasia and its Neural Substrates

Stephen M Wilson, William D. Hula

Research output: Contribution to journalReview article

Abstract

Purpose of Review: Aphasia is often characterized in terms of subtype and severity, yet these constructs have limited explanatory power, because aphasia is inherently multifactorial both in its neural substrates and in its symptomatology. The purpose of this review is to survey current and emerging multivariate approaches to understanding aphasia. Recent Findings: Techniques such as factor analysis and principal component analysis have been used to define latent underlying factors that can account for performance on batteries of speech and language tests, and for characteristics of spontaneous speech production. Multivariate lesion-symptom mapping has been shown to outperform univariate approaches to lesion-symptom mapping for identifying brain regions where damage is associated with specific speech and language deficits. It is increasingly clear that structural damage results in functional changes in wider neural networks, which mediate speech and language outcomes. Summary: Multivariate statistical approaches are essential for understanding the complex relationships between the neural substrates of aphasia, and resultant profiles of speech and language function.

Original languageEnglish (US)
Article number53
JournalCurrent Neurology and Neuroscience Reports
Volume19
Issue number8
DOIs
StatePublished - Aug 1 2019

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Aphasia
Language
Brain Mapping
Language Tests
Principal Component Analysis
Statistical Factor Analysis

Keywords

  • Aphasia
  • Factor analysis
  • Multivariate
  • Multivariate lesion-symptom mapping
  • Neural substrates
  • Principal components analysis

ASJC Scopus subject areas

  • Neuroscience(all)
  • Clinical Neurology

Cite this

Multivariate Approaches to Understanding Aphasia and its Neural Substrates. / Wilson, Stephen M; Hula, William D.

In: Current Neurology and Neuroscience Reports, Vol. 19, No. 8, 53, 01.08.2019.

Research output: Contribution to journalReview article

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