Discourse- and Lesion-based Aphasia Quotient Estimation Using Machine Learning

Authors

Nicholas Riccardi, University of South Carolina
Satvik Nelakuditi, Spring Valley High School
Dirk B. den Ouden, Chapman UniversityFollow
Chris Rorden, University of South Carolina
Julius Fridriksson, University of South Carolina
Rutvik H. Desai, University of South Carolina

Document Type

Article

Publication Date

4-6-2024

Abstract

Discourse is a fundamentally important aspect of communication, and discourse production provides a wealth of information about linguistic ability. Aphasia commonly affects, in multiple ways, the ability to produce discourse. Comprehensive aphasia assessments such as the Western Aphasia Battery-Revised (WAB-R) are time- and resource-intensive. We examined whether discourse measures can be used to estimate WAB-R Aphasia Quotient (AQ), and whether this can serve as an ecologically valid, less resource-intensive measure. We used features extracted from discourse tasks using three AphasiaBank prompts involving expositional (picture description), story narrative, and procedural discourse. These features were used to train a machine learning model to predict the WAB-R AQ. We also compared and supplemented the model with lesion location information from structural neuroimaging. We found that discourse-based models could estimate AQ well, and that they outperformed models based on lesion features. Addition of lesion features to the discourse features did not improve the performance of the discourse model substantially. Inspection of the most informative discourse features revealed that different prompt types taxed different aspects of language. These findings suggest that discourse can be used to estimate aphasia severity, and provide insight into the linguistic content elicited by different types of discourse prompts.

Comments

This article was originally published in NeuroImage: Clinical, volume 42, in 2024. https://doi.org/10.1016/j.nicl.2024.103602

Recommended Citation

Riccardi, N., Nelakuditi, S., Den Ouden, D. B., Rorden, C., Fridriksson, J., & Desai, R. H. (2024). Discourse- and lesion-based aphasia quotient estimation using machine learning. NeuroImage: Clinical, 42, 103602. https://doi.org/10.1016/j.nicl.2024.103602

Peer Reviewed

1

Copyright

The authors

Creative Commons License

This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.