Drift Diffusion Model of Animacy Categorization Task Can Detect Patients with Mild Cognitive Impairment and Mild Alzheimer's Disease
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Abstract
Purpose: The process of neurodegeneration in Alzheimer's Disease (AD) is irreversible using current therapeutics. An earlier diagnosis of the disease can lead to earlier interventions, which will help patients sustain their cognitive abilities for longer. Individuals within the early stages of AD, shown to have trouble making confident and sounds decisions. Here we proposed a computational approach to quantify the decision-making ability in patients with mild cognitive impairment and mild AD.
Materials and Methods: To study the quantified decision-making abilities at the early stages of the disease, we took advantage of a 2-Alternative Forced-Choice (2AFC) task. We applied the Drift Diffusion Model to determine whether the information accumulation process in a categorization task is altered in patients with mild cognitive impairment and mild AD. We implemented a classification model to detect cognitive impairment based on the Drift Diffusion Model's estimated parameters.
Results: The results show a significant correlation of the classification score with the standard pen-and-paper tests, suggesting that the quantified decision-making parameters are undergoing significant change in patients with cognitive impairment.
Conclusion: We confirmed that the decision-making ability deteriorates at the early stages of AD. We introduced a computational approach for measuring the decline in decision-making and used that measurement to distinguish patients from healthy individuals.
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30- R. Ratcliff and G. McKoon, "The Diffusion Decision Model: Theory and Data for Two-Choice Decision Tasks," Neural computation, vol. 20, pp. 873-922, 2008/04// 2008.
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2- R. J. Bateman, C. Xiong, T. L. S. Benzinger, A. M. Fagan, A. Goate, N. C. Fox, et al., "Clinical and Biomarker Changes in Dominantly Inherited Alzheimer's Disease," New England Journal of Medicine, vol. 367, pp. 795-804, 2012/08/30/ 2012.
3- E. M. Reiman, Y. T. Quiroz, A. S. Fleisher, K. Chen, C. Velez-Pardo, M. Jimenez-Del-Rio, et al., "Brain imaging and fluid biomarker analysis in young adults at genetic risk for autosomal dominant Alzheimer's disease in the presenilin 1 E280A kindred: a case-control study," The Lancet. Neurology, vol. 11, pp. 1048-1056, 2012/12// 2012.
4- C. R. Jack, D. S. Knopman, W. J. Jagust, R. C. Petersen, M. W. Weiner, P. S. Aisen, et al., "Tracking pathophysiological processes in Alzheimer's disease: an updated hypothetical model of dynamic biomarkers," The Lancet. Neurology, vol. 12, pp. 207-216, 2013/02// 2013.
5- V. L. Villemagne, S. Burnham, P. Bourgeat, B. Brown, K. A. Ellis, O. Salvado, et al., "Amyloid β deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer's disease: a prospective cohort study," The Lancet. Neurology, vol. 12, pp. 357-367, 2013/04// 2013.
6- B. W. Rovner, R. J. Casten, M. T. Hegel, and B. Leiby, "Preventing Cognitive Decline in Black Individuals With Mild Cognitive Impairment: A Randomized Clinical Trial," JAMA neurology, vol. 75, pp. 1487-1493, 2018/12/01/ 2018.
7- T. Ngandu, J. Lehtisalo, A. Solomon, E. Levälahti, S. Ahtiluoto, R. Antikainen, et al., "A 2 year multidomain intervention of diet, exercise, cognitive training, and vascular risk monitoring versus control to prevent cognitive decline in at-risk elderly people (FINGER): a randomised controlled trial," Lancet (London, England), vol. 385, pp. 2255-2263, 2015/06/06/ 2015.
8- "2018 Alzheimer's disease facts and figures," Alzheimer's & Dementia: The Journal of the Alzheimer's Association, vol. 14, pp. 367-429, 2018/03/01/ 2018.
9- A. Ward, S. Tardiff, C. Dye, and H. M. Arrighi, "Rate of conversion from prodromal Alzheimer's disease to Alzheimer's dementia: a systematic review of the literature," Dementia and Geriatric Cognitive Disorders Extra, vol. 3, pp. 320-332, 2013 2013.
10- H. Jahn, "Memory loss in Alzheimer's disease," Dialogues in Clinical Neuroscience, vol. 15, p. 445, 2013/12// 2013.
11- C. Ga and O.-B. M. (1992). “Memory deficits in Alzheimer's patients: a comprehensive review”. Available: https://pubmed.ncbi.nlm.nih.gov/1300219/
12- K. G. White and A. C. Ruske, "Memory deficits in Alzheimer's disease: The encoding hypothesis and cholinergic function," Psychonomic Bulletin & Review, vol. 9, pp. 426-437, 2002/09/01/ 2002.
13- A. Guarino, F. Favieri, I. Boncompagni, F. Agostini, M. Cantone, and M. Casagrande, "Executive Functions in Alzheimer Disease: A Systematic Review," Frontiers in Aging Neuroscience, vol. 10, 2019 2019.
14- R. J. Perry and J. R. Hodges, "Attention and executive deficits in Alzheimer's diseaseA critical review," Brain, vol. 122, pp. 383-404, 1999/03/01/ 1999.
15- G. R. Jackson and C. Owsley, "Visual dysfunction, neurodegenerative diseases, and aging," Neurologic Clinics, vol. 21, pp. 709-728, 2003/08/01/ 2003.
16- E. Aretouli and J. Brandt, "Everyday functioning in mild cognitive impairment and its relationship with executive cognition," International Journal of Geriatric Psychiatry, vol. 25, pp. 224-233, 2010/03/01/ 2010.
17- S. M. Albert, M. H. Tabert, A. Dienstag, G. Pelton, and D. Devanand, "The impact of mild cognitive impairment on functional abilities in the elderly," Current Psychiatry Reports, vol. 4, p. 64, 2002/01/01/ 2002.
18- J. C. Allaire, A. Gamaldo, B. J. Ayotte, R. Sims, and K. Whitfield, "Mild Cognitive Impairment and Objective Instrumental Everyday Functioning: The Everyday Cognition Battery Memory Test," Journal of the American Geriatrics Society, vol. 57, p. 120, 2009/01// 2009.
19- P. R, P. C, S. C, H. J, T. N, G. T, et al. (2006). “Impairment of activities of daily living requiring memory or complex reasoning as part of the MCI syndrome”. Available: https://pubmed.ncbi.nlm.nih.gov/16416470/
20- J. H. T. Karlawish, D. Casarett, K. J. Propert, B. D. James, M. Bioethics, and C. M. Clark, "Relationship between Alzheimer's Disease Severity and Patient Participation in Decisions about Their Medical Care," Journal of Geriatric Psychiatry and Neurology, vol. 15, pp. 68-72, 2002/06/01/ 2002.
21- S.-M. Khaligh-Razavi, S. Habibi, M. Sadeghi, H. Marefat, M. Khanbagi, S. M. Nabavi, et al., "Integrated Cognitive Assessment: Speed and Accuracy of Visual Processing as a Reliable Proxy to Cognitive Performance," Scientific Reports, vol. 9, 2019/01/31/ 2019.
22- H. Karimi, H. Marefat, M. Khanbagi, C. Kalafatis, Z. Vahabi, and S.-M. K. Razavi, "Electroencephalography (EEG) reveals a decrease in speed of animacy processing in mild cognitive impairment and an alteration in neural response patterns," in 2020 Alzheimer's Association International Conference, 2020.
23- C. Kalafatis, M. H. Modarres, H. Marefat, M. Khanbagi, H. Karimi, Z. Vahabi, et al., "P4‐207: EMPLOYING ARTIFICIAL INTELLIGENCE IN THE DEVELOPMENT OF A SELF‐ADMINISTERED, COMPUTERISED COGNITIVE ASSESSMENT FOR THE ASSESSMENT OF NEURODEGENERATION," Alzheimer's & Dementia, vol. 15, pp. P1355-P1356, 2019/07/01/ 2019.
24- R. Ratcliff, "A theory of memory retrieval," Psychological Review, vol. 85, pp. 59-108, 1978 1978.
25- G. McKhann, D. Drachman, M. Folstein, R. Katzman, D. Price, and E. M. Stadlan, "Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease," Neurology, vol. 34, pp. 939-944, 1984/07// 1984.
26- C. R. Jack, D. A. Bennett, K. Blennow, M. C. Carrillo, B. Dunn, S. B. Haeberlein, et al., "NIA-AA Research Framework: Toward a biological definition of Alzheimer's disease," Alzheimer's & Dementia: The Journal of the Alzheimer's Association, vol. 14, pp. 535-562, 2018//04/ 2018.
27- Z. S. Nasreddine, N. A. Phillips, V. Bédirian, S. Charbonneau, V. Whitehead, I. Collin, et al., "The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment," Journal of the American Geriatrics Society, vol. 53, pp. 695-699, 2005/04// 2005.
28- E. Mioshi, K. Dawson, J. Mitchell, R. Arnold, and J. R. Hodges, "The Addenbrooke's Cognitive Examination Revised (ACE-R): a brief cognitive test battery for dementia screening," International Journal of Geriatric Psychiatry, vol. 21, pp. 1078-1085, 2006/11// 2006.
29- J. A. Yesavage, T. L. Brink, T. L. Rose, O. Lum, V. Huang, M. Adey, et al., "Development and validation of a geriatric depression screening scale: A preliminary report," Journal of Psychiatric Research, vol. 17, pp. 37-49, 1982/01// 1982.
30- R. Ratcliff and G. McKoon, "The Diffusion Decision Model: Theory and Data for Two-Choice Decision Tasks," Neural computation, vol. 20, pp. 873-922, 2008/04// 2008.
31- A. Voss and J. Voss, "Fast-dm: A free program for efficient diffusion model analysis," Behavior Research Methods, vol. 39, pp. 767-775, 2007/11/01/ 2007.
32- F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, et al., "Scikit-learn: Machine Learning in Python," Journal of Machine Learning Research, vol. 12, pp. 2825-2830, 2011 2011.
33- Student, PROBABLE ERROR OF A CORRELATION COEFFICIENT vol. 6, 1908.
34- C. J. Kowalski, "On the Effects of Non-Normality on the Distribution of the Sample Product-Moment Correlation Coefficient," Journal of the Royal Statistical Society. Series C (Applied Statistics), vol. 21, pp. 1-12, 1972.
35- H. Braak, I. Alafuzoff, T. Arzberger, H. Kretzschmar, and K. Del Tredici, "Staging of Alzheimer disease-associated neurofibrillary pathology using paraffin sections and immunocytochemistry," Acta Neuropathol, vol. 112, pp. 389-404, Oct 2006.
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| Issue | Vol 8 No 1 (2021) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/fbt.v8i1.5857 | |
| Keywords | ||
| Alzheimer's Disease Mild Cognitive Impairment Drift Diffusion Model Machine Learning Decision Making | ||
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How to Cite
1.
Karimi H, Marefat H, Khanbagi M, Karami A, Vahabi Z. Drift Diffusion Model of Animacy Categorization Task Can Detect Patients with Mild Cognitive Impairment and Mild Alzheimer’s Disease. Frontiers Biomed Technol. 2021;8(1):42-49.
