Multivariate lesion symptom mapping for predicting trajectories of recovery from aphasia Article Swipe
Deborah F. Levy
,
Jillian L. Entrup
,
Sarah M. Schneck
,
Caitlin F Onuscheck
,
Maysaa Rahman
,
Anna Kasdan
,
Marianne Casilio
,
Emma Willey
,
L. Taylor Davis
,
Michael de Riesthal
,
Howard S. Kirshner
,
Stephen M. Wilson
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1093/braincomms/fcae024
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1093/braincomms/fcae024
Individuals with post-stroke aphasia tend to recover their language to some extent; however, it remains challenging to reliably predict the nature and extent of recovery that will occur in the long term. The aim of this study was to quantitatively predict language outcomes in the first year of recovery from aphasia across multiple domains of language and at multiple timepoints post-stroke. We recruited 217 patients with aphasia following acute left hemisphere ischaemic or haemorrhagic stroke and evaluated their speech and language function using the Quick Aphasia Battery acutely and then acquired longitudinal follow-up data at up to three timepoints post-stroke: 1 month (n = 102), 3 months (n = 98) and 1 year (n = 74). We used support vector regression to predict language outcomes at each timepoint using acute clinical imaging data, demographic variables and initial aphasia severity as input. We found that ∼60% of the variance in long-term (1 year) aphasia severity could be predicted using these models, with detailed information about lesion location importantly contributing to these predictions. Predictions at the 1- and 3-month timepoints were somewhat less accurate based on lesion location alone, but reached comparable accuracy to predictions at the 1-year timepoint when initial aphasia severity was included in the models. Specific subdomains of language besides overall severity were predicted with varying but often similar degrees of accuracy. Our findings demonstrate the feasibility of using support vector regression models with leave-one-out cross-validation to make personalized predictions about long-term recovery from aphasia and provide a valuable neuroanatomical baseline upon which to build future models incorporating information beyond neuroanatomical and demographic predictors.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/braincomms/fcae024
- OA Status
- gold
- Cited By
- 7
- References
- 80
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391463594
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4391463594Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1093/braincomms/fcae024Digital Object Identifier
- Title
-
Multivariate lesion symptom mapping for predicting trajectories of recovery from aphasiaWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-12-28Full publication date if available
- Authors
-
Deborah F. Levy, Jillian L. Entrup, Sarah M. Schneck, Caitlin F Onuscheck, Maysaa Rahman, Anna Kasdan, Marianne Casilio, Emma Willey, L. Taylor Davis, Michael de Riesthal, Howard S. Kirshner, Stephen M. WilsonList of authors in order
- Landing page
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https://doi.org/10.1093/braincomms/fcae024Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1093/braincomms/fcae024Direct OA link when available
- Concepts
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Aphasia, Stroke (engine), Lesion, Regression, Audiology, Regression analysis, Medicine, Lateralization of brain function, Psychology, Physical medicine and rehabilitation, Cognitive psychology, Computer science, Machine learning, Surgery, Psychotherapist, Engineering, Mechanical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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7Total citation count in OpenAlex
- Citations by year (recent)
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2025: 7Per-year citation counts (last 5 years)
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80Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.quantitatively | 40 |
| abstract_inverted_index.neuroanatomical | 251, 262 |
| abstract_inverted_index.cross-validation | 237 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| corresponding_author_ids | https://openalex.org/A5038410609 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 12 |
| corresponding_institution_ids | https://openalex.org/I901861585 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.8299999833106995 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile.value | 0.82622101 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |