Rethinking the residual approach: Leveraging machine learning to operationalize cognitive resilience in Alzheimer's disease
Birkenbihl, C.; Cuppels, M.; Boyle, R. T.; Klinger, H. M.; Langford, O.; Coughlan, G. T.; Properzi, M. J.; Chhatwal, J. P.; Price, J. T.; Schultz, A. P.; Rentz, D. M.; Amariglio, R. E.; Johnson, K. A.; Mukherjee, S.; Gottesman, R. F.; Maruff, P.; Lim, Y. Y.; Masters, C. L.; Beiser, A.; Resnick, S. M.; Hughes, T. M.; Burnham, S.; Tunali, I.; Landau, S.; Cohen, A. D.; Johnson, S. C.; Betthauser, T. J.; Seshadri, S.; Lockhart, S. N.; O'Bryant, S. E.; Vemuri, P.; Sperling, R. A.; Hohman, T. J.; Donohue, M. C.; Buckley, R. F.
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Cognitive resilience describes the phenomenon of individuals evading cognitive decline despite prominent Alzheimers 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 models 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 approachs 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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