Login / Signup

Exploring the Complexity of Aphasia With Network Analysis.

Sameer A AshaieNichol Castro
Published in: Journal of speech, language, and hearing research : JSLHR (2021)
Purpose Aphasia is a complex, neurogenic language disorder, with different aphasia syndromes hallmarked by impairment in fluency, auditory comprehension, naming, and/or repetition. Broad, standardized assessments of language domains and specific language and cognitive assessments provide a holistic impairment profile of a person with aphasia. While many recognize the correlations between assessments, there remains a need to continue understanding the complexity of relationships between assessments for the purpose of better characterization of language impairment profiles of persons with aphasia. We explored the use of network analysis to identify the complex relationships between a variety of language assessments. Method We computed a regularized partial correlation network and a directed acyclic graph network to estimate the relations between different aphasia assessments in 128 persons with aphasia. Results Western Aphasia Battery-Revised Comprehension subtest was the most central assessment in the aphasia symptom network, whereas the Philadelphia Naming Test had the most putative causal influence on other assessments. Additionally, the language assessments segregated into three empirically derived communities denoting phonology, semantics, and syntax. Furthermore, several assessments, including the Philadelphia Naming Test, belonged to multiple communities, suggesting that certain assessments may capture multiple language impairments. Conclusion We discuss the implications of using a network analysis approach for clinical intervention and driving forward novel questions in the field of clinical aphasiology. Supplemental Material https://doi.org/10.23641/asha.16620229.
Keyphrases
  • network analysis
  • autism spectrum disorder
  • acute lymphoblastic leukemia
  • randomized controlled trial
  • computed tomography
  • spinal cord injury
  • magnetic resonance
  • machine learning
  • working memory
  • contrast enhanced