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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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-01-01
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| Series: | Brain Informatics |
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| Online Access: | https://doi.org/10.1186/s40708-024-00249-4 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Colin Birkenbihl |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-aee7926ddbe0447c949ef263f7ee6a7c |
| institution | Kabale University |
| issn | 2198-4018 2198-4026 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Brain Informatics |
| spelling | doaj-art-aee7926ddbe0447c949ef263f7ee6a7c2025-02-02T12:48:32ZengSpringerOpenBrain Informatics2198-40182198-40262025-01-0112111110.1186/s40708-024-00249-4Rethinking the residual approach: leveraging statistical learning to operationalize cognitive resilience in Alzheimer’s diseaseColin Birkenbihl0Madison Cuppels1Rory T. Boyle2Hannah M. Klinger3Oliver Langford4Gillian T. Coughlan5Michael J. Properzi6Jasmeer Chhatwal7Julie C. Price8Aaron P. Schultz9Dorene M. Rentz10Rebecca E. Amariglio11Keith A. Johnson12Rebecca F. Gottesman13Shubhabrata Mukherjee14Paul Maruff15Yen Ying Lim16Colin L. Masters17Alexa Beiser18Susan M. Resnick19Timothy M. Hughes20Samantha Burnham21Ilke Tunali22Susan Landau23Ann D. Cohen24Sterling C. Johnson25Tobey J. Betthauser26Sudha Seshadri27Samuel N. Lockhart28Sid E. O’Bryant29Prashanthi Vemuri30Reisa A. Sperling31Timothy J. Hohman32Michael C. Donohue33Rachel F. Buckley34Department of Neurology, Massachusetts General Hospital, Harvard Medical SchoolDepartment of Neurology, Massachusetts General Hospital, Harvard Medical SchoolPenn Frontotemporal Degeneration Center, Department of Neurology, Perelman School of Medicine, University of PennsylvaniaDepartment of Neurology, Massachusetts General Hospital, Harvard Medical SchoolAlzheimer Therapeutic Research Institute, University of Southern CaliforniaDepartment of Neurology, Massachusetts General Hospital, Harvard Medical SchoolDepartment of Neurology, Massachusetts General Hospital, Harvard Medical SchoolDepartment of Neurology, Massachusetts General Hospital, Harvard Medical SchoolDepartment of Radiology, Massachusetts General Hospital, Harvard Medical SchoolDepartment of Neurology, Massachusetts General Hospital, Harvard Medical SchoolDepartment of Neurology, Center for Alzheimer Research and Treatment, Brigham and Women’s Hospital, Harvard Medical SchoolDepartment of Neurology, Center for Alzheimer Research and Treatment, Brigham and Women’s Hospital, Harvard Medical SchoolDepartment of Radiology, Massachusetts General Hospital, Harvard Medical SchoolNational Institute of Neurological Disorders and StrokeDivision of General Internal Medicine, Department of Medicine, University of WashingtonTurner Institute for Brain and Mental Health, School of Psychological Sciences, Monash UniversityTurner Institute for Brain and Mental Health, School of Psychological Sciences, Monash UniversityFlorey Institute, University of MelbourneDepartment of Neurology, Chobanian and Avedisian School of Medicine, Boston University School of MedicineLaboratory of Behavioral Neuroscience, National Institute on AgingDepartment of Internal Medicine, Wake Forest School of MedicineEli Lilly and CompanyEli Lilly and CompanyNeuroscience Department, University of California, BerkeleyDepartment of Psychiatry, School of Medicine, University of PittsburghDepartment of Medicine, University of Wisconsin-MadisonDepartment of Medicine, University of Wisconsin-MadisonDepartment of Neurology, Chobanian and Avedisian School of Medicine, Boston University School of MedicineDepartment of Internal Medicine, Wake Forest School of MedicineInstitute for Translational Research, Department of Family Medicine, University of North Texas Health Science CenterDepartment of Radiology, Mayo ClinicDepartment of Neurology, Massachusetts General Hospital, Harvard Medical SchoolVanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical CenterAlzheimer Therapeutic Research Institute, University of Southern CaliforniaDepartment of Neurology, Massachusetts General Hospital, Harvard Medical SchoolAbstract 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.https://doi.org/10.1186/s40708-024-00249-4Cognitive resilienceAlzheimer’s diseaseDementiaMachine learningArtificial intelligenceCognitive reserve |
| spellingShingle | 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 Rethinking the residual approach: leveraging statistical learning to operationalize cognitive resilience in Alzheimer’s disease Brain Informatics Cognitive resilience Alzheimer’s disease Dementia Machine learning Artificial intelligence Cognitive reserve |
| title | Rethinking the residual approach: leveraging statistical learning to operationalize cognitive resilience in Alzheimer’s disease |
| title_full | Rethinking the residual approach: leveraging statistical learning to operationalize cognitive resilience in Alzheimer’s disease |
| title_fullStr | Rethinking the residual approach: leveraging statistical learning to operationalize cognitive resilience in Alzheimer’s disease |
| title_full_unstemmed | Rethinking the residual approach: leveraging statistical learning to operationalize cognitive resilience in Alzheimer’s disease |
| title_short | Rethinking the residual approach: leveraging statistical learning to operationalize cognitive resilience in Alzheimer’s disease |
| title_sort | rethinking the residual approach leveraging statistical learning to operationalize cognitive resilience in alzheimer s disease |
| topic | Cognitive resilience Alzheimer’s disease Dementia Machine learning Artificial intelligence Cognitive reserve |
| url | https://doi.org/10.1186/s40708-024-00249-4 |
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