In this episode, we discuss aging and brain health with Dr. Rhoda Au. Specifically, we cover the Framingham Heart Study and cardiovascular risk factors, the Boston Process Approach, Alzheimer’s disease, and digital biomarkers.
A pdf of the transcript for this episode is available here.
About Rhoda
Rhoda Au, PhD, MBA, is Professor of Anatomy & Neurobiology, Neurology and Epidemiology at Boston University Schools of Medicine and Public Health. She serves as one of PIs of the Framingham Heart Study Brain Aging Program and is also Director of Neuropsychology. She is also Director of Global Cohort Development for the Davos Alzheimer’s Collaborative. Her work includes using technologies to promote equal opportunity science and to develop and validate multi-sensor digital biomarkers. Her long-term research objective is to enable global solutions that move the primary focus of health technologies from precision medicine to a broader emphasis on precision brain health.
References
Amini, S., Zhang, L., Hao, B., Gupta, A., Song, M., Karjadi, C., Lin, H., Kolachalama, V. B., Au, R., & Paschalidis, I. C. (2021). An AI-assisted method for dementia detection using images from the clock drawing test. Journal of Alzheimer’s Disease,83(2), 581–589. https://doi.org/10.3233/jad-210299
Andersen, S. L., Sweigart, B., Glynn, N. W., Wojczynski, M. K., Thyagarajan, B., Mengel-From, J., Thielke, S., Perls, T. T., Libon, D. J., Au, R., Cosentino, S., & Sebastiani, P. (2021). Digital technology differentiates graphomotor and information processing speed patterns of behavior. Journal of Alzheimer’s Disease, 82(1), 17–32. https://doi.org/10.3233/jad-201119
Armstrong, N. M., Bangen, K. J., Au, R., & Gross, A. L. (2019). Associations between midlife (but not late-life) elevated coronary heart disease risk and lower cognitive performance: Results from the Framingham Offspring Study. American Journal of Epidemiology, 188(12), 2175–2187. https://doi.org/10.1093/aje/kwz210
Au, R. (2019). Heterogeneity in Alzheimer’s disease and related dementias. Advances in Geriatric Medicine and Research, 1. https://doi.org/10.20900/agmr20190010
Au, R. Ritchie, M., Hardy, S., Ang, T. F. A., & Lin, H. (2019). Aging well: Using precision to drive down costs and increase health quality. Advances in Geriatric Medicine and Research, 1. https://doi.org/10.20900/agmr20190003
Au, R., Kolachalama, V. B., & Paschalidis, I. C. (2022). Redefining and validating digital biomarkers as fluid, dynamic multi-dimensional digital signal patterns. Frontiers in Digital Health, 3. https://doi.org/10.3389/fdgth.2021.751629
Au, R., Piers, R. J., & Devine, S. (2017). How technology is reshaping cognitive assessment: Lessons from the Framingham Heart Study. Neuropsychology, 31(8), 846–861. https://doi.org/10.1037/neu0000411
Bangen, K. J., Armstrong, N. M., Au, R., & Gross, A. L. (2019). Metabolic syndrome and cognitive trajectories in the Framingham Offspring Study. Journal of Alzheimer’s Disease, 71(3), 931–943. https://doi.org/10.3233/jad-190261
Davoudi, A., Dion, C., Formanski, E., Frank, B. E., Amini, S., Matusz, E. F., Wasserman, V., Penney, D., Davis, R., Rashidi, P., Tighe, P. J., Heilman, K. M., Au, R., Libon, D. J., & Price, C. C. (2021). Normative references for graphomotor and latency Digital Clock Drawing metrics for adults age 55 and older: Operationalizing the production of a normal appearing clock. Journal of Alzheimer’s Disease, 82(1), 59–70. https://doi.org/10.3233/jad-201249
Ding, H., An, N., Au, R., Devine, S., Auerbach, S. H., Massaro, J., Joshi, P., Liu, X., Liu, Y., Mahon, E., Ang, T. F. A., & Lin, H. (2020). Exploring the hierarchical influence of cognitive functions for Alzheimer disease: The Framingham Heart Study. Journal of Medical Internet Research, 22(4). https://doi.org/10.2196/15376
Frank, B., Ally, M., Tripodis, Y., Puzo, C., Labriolo, C., Hurley, L., Martin, B., Palmisano, J., Chan, L., Steinberg, E., Turk, K., Budson, A., O’Connor, M., Au, R., Qiu, W. Q., Goldstein, L., Kukull, W., Kowall, N., Killiany, R., … Alosco, M. (2022). Trajectories of cognitive decline in brain donors with autopsy-confirmed Alzheimer disease and cerebrovascular disease. Neurology, 98(24). https://doi.org/10.1212/wnl.0000000000200304
Joshi, P. S., Heydari, M., Kannan, S., Alvin Ang, T. F., Qin, Q., Liu, X., Mez, J., Devine, S., Au, R., & Kolachalama, V. B. (2019). Temporal association of neuropsychological test performance using unsupervised learning reveals a distinct signature of Alzheimer’s disease status. Alzheimer’s & Dementia: Translational Research & Clinical Interventions, 5(1), 964–973. https://doi.org/10.1016/j.trci.2019.11.006
Li, J., Joshi, P., Ang, T. F., Liu, C., Auerbach, S., Devine, S., & Au, R. (2021). Mid- to late-life body mass index and dementia risk: 38 years of follow-up of the Framingham Study. American Journal of Epidemiology, 190(12), 2503–2510. https://doi.org/10.1093/aje/kwab096
Li, J., Ogrodnik, M., Kolachalama, V. B., Lin, H., & Au, R. (2018). Assessment of the mid-life demographic and lifestyle risk factors of dementia using data from the Framingham Heart Study Offspring Cohort. Journal of Alzheimer’s Disease, 63(3), 1119–1127. https://doi.org/10.3233/jad-170917
Libon, D. J., Baliga, G., Swenson, R., & Au, R. (2021). Digital neuropsychological assessment: New technology for measuring subtle neuropsychological behavior. Journal of Alzheimer’s Disease, 82(1), 1–4. https://doi.org/10.3233/jad-210513
Libon, D. J., Swenson, R., Lamar, M., Price, C. C., Baliga, G., Pascual-Leone, A., Au, R., Cosentino, S., & Andersen, S. L. (2022). The Boston Process Approach and digital neuropsychological assessment: Past research and future directions. Journal of Alzheimer’s Disease, 87(4), 1419–1432. https://doi.org/10.3233/jad-220096
Lin, H., Karjadi, C., Ang, T. F., Prajakta, J., McManus, C., Alhanai, T. W., Glass, J., & Au, R. (2020). Identification of digital voice biomarkers for cognitive health. Exploration of Medicine, 1(6), 406–417. https://doi.org/10.37349/emed.2020.00028
Matusz, E. F., Price, C. C., Lamar, M., Swenson, R., Au, R., Emrani, S., Wasserman, V., Libon, D. J., & Thompson, L. I. (2022). Dissociating statistically determined normal cognitive abilities and mild cognitive impairment subtypes with DCT clock. Journal of the International Neuropsychological Society, 1–11. https://doi.org/10.1017/s1355617722000091
Nishtala, A., Piers, R. J., Himali, J. J., Beiser, A. S., Davis-Plourde, K. L., Saczynski, J. S., McManus, D. D., Benjamin, E. J., & Au, R. (2018). Atrial fibrillation and cognitive decline in the Framingham Heart Study. Heart Rhythm, 15(2), 166–172. https://doi.org/10.1016/j.hrthm.2017.09.036
Nori, V. S., Hane, C. A., Crown, W. H., Au, R., Burke, W. J., Sanghavi, D. M., & Bleicher, P. (2019). Machine learning models to predict onset of dementia: A label learning approach. Alzheimer’s & Dementia: Translational Research & Clinical Interventions, 5(1), 918–925. https://doi.org/10.1016/j.trci.2019.10.006
Piers, R. J., Liu, Y., Ang, T. F. A., Tao, Q., Au, R., & Qiu, W. Q. (2021). Association between elevated depressive symptoms and cognitive function moderated by ApoE4 status: Framingham Offspring Study. Journal of Alzheimer’s Disease, 80(3), 1269–1279. https://doi.org/10.3233/jad-200998
Souillard-Mandar, W., Penney, D., Schaible, B., Pascual-Leone, A., Au, R., & Davis, R. (2021). DCT clock: Clinically-interpretable and automated artificial intelligence analysis of drawing behavior for capturing cognition. Frontiers in Digital Health, 3. https://doi.org/10.3389/fdgth.2021.750661
Thomas, J. A., Burkhardt, H. A., Chaudhry, S., Ngo, A. D., Sharma, S., Zhang, L., Au, R., & Hosseini Ghomi, R. (2020). Assessing the utility of language and voice biomarkers to predict cognitive impairment in the Framingham Heart Study cognitive aging cohort data. Journal of Alzheimer’s Disease, 76(3), 905–922. https://doi.org/10.3233/jad-190783
Thomas, R. J., Kim, H., Maillard, P., DeCarli, C. S., Heckman, E. J., Karjadi, C., Ang, T. F., & Au, R. (2021). Digital sleep measures and white matter health in the Framingham Heart Study. Exploration of Medicine, 2(3), 253–267. https://doi.org/10.37349/emed.2021.00045
Wong, C. G., Thomas, K. R., Edmonds, E. C., Weigand, A. J., Bangen, K. J., Eppig, J. S., Jak, A. J., Devine, S. A., Delano-Wood, L., Libon, D. J., Edland, S. D., Au, R., & Bondi, M. W. (2018). Neuropsychological criteria for mild cognitive impairment in the Framingham Heart Study’s old-old. Dementia and Geriatric Cognitive Disorders, 46(5-6), 253–265. https://doi.org/10.1159/000493541
Xue, C., Karjadi, C., Paschalidis, I. C., Au, R., & Kolachalama, V. B. (2021). Detection of dementia on voice recordings using deep learning: A Framingham Heart Study. Alzheimer’s Research & Therapy, 13(1). https://doi.org/10.1186/s13195-021-00888-3
Yaffe, K., Vittinghoff, E., Pletcher, M. J., Hoang, T. D., Launer, L. J., Whitmer, R. A., Coker, L. H., & Sidney, S. (2014). Early adult to midlife cardiovascular risk factors and cognitive function. Circulation, 129(15), 1560–1567. https://doi.org/10.1161/circulationaha.113.004798
Yuan, J., Au, R., Karjadi, C., Ang, T. F., Devine, S., Auerbach, S., DeCarli, C., Libon, D. J., Mez, J., & Lin, H. (2022). Associations between the Digital Clock Drawing Test and brain volume: Large community-based prospective cohort (Framingham Heart Study). Journal of Medical Internet Research, 24(4). https://doi.org/10.2196/34513
Zhang, L., Ngo, A., Thomas, J. A., Burkhardt, H. A., Parsey, C. M., Au, R., & Hosseini Ghomi, R. (2021). Neuropsychological test validation of speech markers of cognitive impairment in the Framingham Cognitive Aging Cohort. Exploration of Medicine, 2(3), 232–252. https://doi.org/10.37349/emed.2021.00044
Zhang, X., Tong, T., Chang, A., Ang, T. F., Tao, Q., Auerbach, S., Devine, S., Qiu, W. Q., Mez, J., Massaro, J., Lunetta, K. L., Au, R., & Farrer, L. A. (2022). Midlife lipid and glucose levels are associated with Alzheimer’s disease. Alzheimer’s & Dementia. https://doi.org/10.1002/alz.12641
