
==== Front
medRxiv
MEDRXIV
medRxiv
Cold Spring Harbor Laboratory

10.1101/2024.08.19.24312256
preprint
1
Article
Rethinking the residual approach: Leveraging machine learning to operationalize cognitive resilience in Alzheimer’s disease
Birkenbihl Colin http://orcid.org/0000-0002-7212-7700

Cuppels Madison
Boyle Rory T. http://orcid.org/0000-0003-0787-6892

Klinger Hannah M.
Langford Oliver
Coughlan Gillian T. http://orcid.org/0000-0003-1806-702X

Properzi Michael J.
Chhatwal Jasmeer
Price Julie T.
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.
Hughes Timothy M.
Burnham Samantha
Tunali Ilke
Landau Susan
Cohen Ann D.
Johnson Sterling C.
Betthauser Tobey J. http://orcid.org/0000-0001-8856-1352

Seshadri Sudha
Lockhart Samuel N.
O’Bryant Sid E.
Vemuri Prashanthi
Sperling Reisa A.
Hohman Timothy J.
Donohue Michael C.
Buckley Rachel F. http://orcid.org/0000-0002-5356-5537

20 8 2024
2024.08.19.24312256https://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License, which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.
http://medrxiv.org/lookup/doi/10.1101/2024.08.19.24312256
nihpp-2024.08.19.24312256.pdf
Abstract

Cognitive resilience 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. 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 construct to be measured and achieves better estimation accuracy on simulated ground-truth data.
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