The Aphasia Recovery Cohort, an open-source chronic stroke repository

Sharing neuroimaging datasets enables reproducibility, education, tool development, and new discoveries. Neuroimaging from many studies are publicly available, providing a glimpse into progressive disorders and human development. In contrast, few stroke studies are shared, and these datasets lack longitudinal sampling of functional imaging, diffusion imaging, as well as the behavioral and demographic data that encourage novel applications. This is surprising, as stroke is a leading cause of disability, and acquiring brain imaging is considered standard of care. The first release of the Aphasia Recovery Cohort includes imaging data, demographics and behavioral measures from 230 chronic stroke survivors who experienced aphasia. We also share scripts to illustrate how the imaging data can predict impairment. In conclusion, recent advances in machine learning thrive on large, diverse datasets. Clinical data sharing can contribute to improvements in automated detection of brain injury, identification of white matter hyperintensities, measures of brain health, and prognostic abilities to guide care.

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Publication Details

Journal
Scientific Data
Published
2024-09-09
DOI
https://doi.org/10.1038/s41597-024-03819-7
Citations
19
Primary Topic
Acute Ischemic Stroke Management
Type
article
Field-Weighted Citation Impact
6.96

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article

The Aphasia Recovery Cohort, an open-source chronic stroke repository

Dirk‐Bart den Ouden, Julius Fridriksson, Argye E. Hillis, Heather Shaw Bonilha et al.
19 citations
Scientific Data
Acute Ischemic Stroke Management
6.96
article

The Aphasia Recovery Cohort, an open-source chronic stroke repository

Dirk‐Bart den Ouden, Julius Fridriksson, Argye E. Hillis, Heather Shaw Bonilha, Gregory Hickok, Makayla Gibson, Chris Rorden, Roger Newman-Norlund
article en
19 citations

Abstract

Sharing neuroimaging datasets enables reproducibility, education, tool development, and new discoveries. Neuroimaging from many studies are publicly available, providing a glimpse into progressive disorders and human development. In contrast, few stroke studies are shared, and these datasets lack longitudinal sampling of functional imaging, diffusion imaging, as well as the behavioral and demographic data that encourage novel applications. This is surprising, as stroke is a leading cause of disability, and acquiring brain imaging is considered standard of care. The first release of the Aphasia Recovery Cohort includes imaging data, demographics and behavioral measures from 230 chronic stroke survivors who experienced aphasia. We also share scripts to illustrate how the imaging data can predict impairment. In conclusion, recent advances in machine learning thrive on large, diverse datasets. Clinical data sharing can contribute to improvements in automated detection of brain injury, identification of white matter hyperintensities, measures of brain health, and prognostic abilities to guide care.

Scientific DataVol. 11(1)
Johns Hopkins University (US), University of South Carolina (US), University of California, Irvine (US)
National Institute of Mental Health, National Institute on Deafness and Other Communication Disorders
Openalex Percentile: Top 2%
Acute Ischemic Stroke Management
6.96
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