Multivariate Approaches to Understanding Aphasia and its Neural Substrates

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 m...

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Veröffentlicht in:Current neurology and neuroscience reports 2019-08, Vol.19 (8), p.53-9, Article 53
Hauptverfasser: Wilson, Stephen M., Hula, William D.
Format: Artikel
Sprache:eng
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Zusammenfassung: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.
ISSN:1528-4042
1534-6293
DOI:10.1007/s11910-019-0971-6