Bridging big data in the ENIGMA consortium to combine non-equivalent cognitive measures
Abstract Investigators in neuroscience have turned to Big Data to address replication and reliability issues by increasing sample size. These efforts unveil new questions about how to integrate data across distinct sources and instruments. The goal of this study was to link scores across common audi...
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Nature Portfolio
2024-10-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-72968-x |
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| author | Eamonn Kennedy Shashank Vadlamani Hannah M. Lindsey Pui-Wa Lei Mary Jo-Pugh Paul M. Thompson David F. Tate Frank G. Hillary Emily L. Dennis Elisabeth A. Wilde for the ENIGMA Clinical Endpoints Working Group |
| author_facet | Eamonn Kennedy Shashank Vadlamani Hannah M. Lindsey Pui-Wa Lei Mary Jo-Pugh Paul M. Thompson David F. Tate Frank G. Hillary Emily L. Dennis Elisabeth A. Wilde for the ENIGMA Clinical Endpoints Working Group |
| author_sort | Eamonn Kennedy |
| collection | DOAJ |
| description | Abstract Investigators in neuroscience have turned to Big Data to address replication and reliability issues by increasing sample size. These efforts unveil new questions about how to integrate data across distinct sources and instruments. The goal of this study was to link scores across common auditory verbal learning tasks (AVLTs). This international secondary analysis aggregated multisite raw data for AVLTs across 53 studies totaling 10,505 individuals. Using the ComBat-GAM algorithm, we isolated and removed the component of memory scores associated with site effects while preserving instrumental effects. After adjustment, a continuous item response theory model used multiple memory items of varying difficulty to estimate each individual’s latent verbal learning ability on a single scale. Equivalent raw scores across AVLTs were then found by linking individuals through the ability scale. Harmonization reduced total cross-site score variance by 37% while preserving meaningful memory effects. Age had the largest impact on scores overall (− 11.4%), while race/ethnicity variable was not significant (p > 0.05). The resulting tools were validated on dually administered tests. The conversion tool is available online so researchers and clinicians can convert memory scores across instruments. This work demonstrates that global harmonization initiatives can address reproducibility challenges across the behavioral sciences. |
| format | Article |
| id | doaj-art-fb501d249bbd4b50bbbbb5ced8d8df78 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-fb501d249bbd4b50bbbbb5ced8d8df782025-08-20T02:33:00ZengNature PortfolioScientific Reports2045-23222024-10-0114111510.1038/s41598-024-72968-xBridging big data in the ENIGMA consortium to combine non-equivalent cognitive measuresEamonn Kennedy0Shashank Vadlamani1Hannah M. Lindsey2Pui-Wa Lei3Mary Jo-Pugh4Paul M. Thompson5David F. Tate6Frank G. Hillary7Emily L. Dennis8Elisabeth A. Wilde9for the ENIGMA Clinical Endpoints Working GroupDepartment of Neurology, University of Utah School of MedicineDepartment of Neurology, University of Utah School of MedicineDepartment of Neurology, University of Utah School of MedicineDepartment of Educational Psychology, Counseling, and Special Education, Pennsylvania State UniversityDepartment of Neurology, University of Utah School of MedicineImaging Genetics Center, Stevens Neuroimaging & Informatics Institute, Keck School of Medicine of USCDepartment of Neurology, University of Utah School of MedicineDepartment of Psychology, Penn State UniversityDepartment of Neurology, University of Utah School of MedicineDepartment of Neurology, University of Utah School of MedicineAbstract Investigators in neuroscience have turned to Big Data to address replication and reliability issues by increasing sample size. These efforts unveil new questions about how to integrate data across distinct sources and instruments. The goal of this study was to link scores across common auditory verbal learning tasks (AVLTs). This international secondary analysis aggregated multisite raw data for AVLTs across 53 studies totaling 10,505 individuals. Using the ComBat-GAM algorithm, we isolated and removed the component of memory scores associated with site effects while preserving instrumental effects. After adjustment, a continuous item response theory model used multiple memory items of varying difficulty to estimate each individual’s latent verbal learning ability on a single scale. Equivalent raw scores across AVLTs were then found by linking individuals through the ability scale. Harmonization reduced total cross-site score variance by 37% while preserving meaningful memory effects. Age had the largest impact on scores overall (− 11.4%), while race/ethnicity variable was not significant (p > 0.05). The resulting tools were validated on dually administered tests. The conversion tool is available online so researchers and clinicians can convert memory scores across instruments. This work demonstrates that global harmonization initiatives can address reproducibility challenges across the behavioral sciences.https://doi.org/10.1038/s41598-024-72968-xHarmonizationVerbal learningMega analysisTraumatic brain injuryItem response theory |
| spellingShingle | Eamonn Kennedy Shashank Vadlamani Hannah M. Lindsey Pui-Wa Lei Mary Jo-Pugh Paul M. Thompson David F. Tate Frank G. Hillary Emily L. Dennis Elisabeth A. Wilde for the ENIGMA Clinical Endpoints Working Group Bridging big data in the ENIGMA consortium to combine non-equivalent cognitive measures Scientific Reports Harmonization Verbal learning Mega analysis Traumatic brain injury Item response theory |
| title | Bridging big data in the ENIGMA consortium to combine non-equivalent cognitive measures |
| title_full | Bridging big data in the ENIGMA consortium to combine non-equivalent cognitive measures |
| title_fullStr | Bridging big data in the ENIGMA consortium to combine non-equivalent cognitive measures |
| title_full_unstemmed | Bridging big data in the ENIGMA consortium to combine non-equivalent cognitive measures |
| title_short | Bridging big data in the ENIGMA consortium to combine non-equivalent cognitive measures |
| title_sort | bridging big data in the enigma consortium to combine non equivalent cognitive measures |
| topic | Harmonization Verbal learning Mega analysis Traumatic brain injury Item response theory |
| url | https://doi.org/10.1038/s41598-024-72968-x |
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