Bibliographic Details
| Title: |
Rethinking the residual approach: leveraging statistical learning to operationalize cognitive resilience in Alzheimer's disease. |
| Authors: |
Birkenbihl, Colin, Cuppels, Madison, Boyle, Rory T., Klinger, Hannah M., Langford, Oliver, Coughlan, Gillian T., Properzi, Michael J., Chhatwal, Jasmeer, Price, Julie C., Schultz, Aaron P., Rentz, Dorene M., Amariglio, Rebecca E., Johnson, Keith A., Gottesman, Rebecca F., Mukherjee, Shubhabrata, Maruff, Paul, Lim, Yen Ying, Masters, Colin L., Beiser, Alexa, Resnick, Susan M. |
| Source: |
Brain Informatics; 1/27/2025, Vol. 12 Issue 1, p1-11, 11p |
| Subject Terms: |
ALZHEIMER'S disease, STATISTICAL learning, ARTIFICIAL intelligence, COGNITION disorders, MACHINE learning |
| 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 widely applied to estimate CR, where the degree of resilience is estimated through a linear model's residuals. We demonstrate that this approach makes specific, uncontrollable assumptions and likely leads to biased and erroneous resilience estimates. This is especially true when information about CR is contained in the data the linear model was fitted to, either through inclusion of CR-associated variables or due to correlation. We propose an alternative strategy which overcomes the standard approach's limitations using machine learning principles. Our proposed approach makes fewer assumptions about the data and CR and achieves better estimation accuracy on simulated ground-truth data. [ABSTRACT FROM AUTHOR] |
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| Database: |
Complementary Index |
