Rethinking the residual approach: leveraging statistical learning to operationalize cognitive resilience in Alzheimer’s disease

Rethinking the residual approach: leveraging statistical learning to operationalize cognitive resilience in Alzheimer’s disease

Abstract Cognitive resilience (CR) describes the phenomenon of individuals evading cognitive decline despite prominent Alzheimer’s disease neuropathology. Operationalization and measurement of this latent construct is non-trivial as it cannot be directly observed. The residual approach has been wide...

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Main Authors: Colin Birkenbihl, Madison Cuppels, Rory T. Boyle, Hannah M. Klinger, Oliver Langford, Gillian T. Coughlan, Michael J. Properzi, Jasmeer Chhatwal, Julie C. Price, Aaron P. Schultz, Dorene M. Rentz, Rebecca E. Amariglio, Keith A. Johnson, Rebecca F. Gottesman, Shubhabrata Mukherjee, Paul Maruff, Yen Ying Lim, Colin L. Masters, Alexa Beiser, Susan M. Resnick, Timothy M. Hughes, Samantha Burnham, Ilke Tunali, Susan Landau, Ann D. Cohen, Sterling C. Johnson, Tobey J. Betthauser, Sudha Seshadri, Samuel N. Lockhart, Sid E. O’Bryant, Prashanthi Vemuri, Reisa A. Sperling, Timothy J. Hohman, Michael C. Donohue, Rachel F. Buckley
Format: Article
Language:English
Published: SpringerOpen 2025-01-01
Series:Brain Informatics
Subjects:
Cognitive resilience
Alzheimer’s disease
Dementia
Machine learning
Artificial intelligence
Cognitive reserve
Online Access:https://doi.org/10.1186/s40708-024-00249-4
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https://doi.org/10.1186/s40708-024-00249-4

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