Browsing by Subject "Cognitive aging"

Now showing 1 - 8 of 8
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    Application of Neuropsychological Criteria to Classify Mild Cognitive Impairment in the ACTIVE Study
    (American Psychological Association, 2020-11) Thomas, Kelsey R.; Cook, Sarah E.; Bondi, Mark W.; Unverzagt, Frederick W.; Gross, Alden L.; Willis, Sherry L.; Marsiske, Michael; Psychiatry, School of Medicine
    Objective: Comprehensive neuropsychological criteria (NP criteria) for mild cognitive impairment (MCI) has reduced diagnostic errors and better predicted progression to dementia than conventional MCI criteria that rely on a single impaired score and/or subjective report. This study aimed to implement an actuarial approach to classifying MCI in the Advanced Cognitive Training for Independent and Vital Elderly (ACTIVE) study. Method: ACTIVE study participants (N = 2,755) were classified as cognitively normal (CN) or as having MCI using NP criteria. Estimated proportion of MCI participants and reversion rates were examined as well as baseline characteristics by MCI subtype. Mixed effect models examined associations of MCI subtype with 10-year trajectories of self-reported independence and difficulty performing instrumental activities of daily living (IADLs). Results: The proportion of MCI participants was estimated to be 18.8%. Of those with MCI at baseline, 19.2% reverted to CN status for all subsequent visits. At baseline, the multidomain-amnestic MCI group generally had the greatest breadth and depth of cognitive impairment and reported the most IADL difficulty. Longitudinally, MCI participants showed faster IADL decline than CN participants (multidomain-amnestic MCI > single domain-amnestic MCI > nonamnestic MCI). Conclusion: NP criteria identified a proportion of MCI and reversion rate within ACTIVE that is consistent with prior studies involving community-dwelling samples. The pattern of everyday functioning change suggests that being classified as MCI, particularly amnestic MCI, is predictive of future loss of independence. Future work will apply these classifications in ACTIVE to better understand the relationships between MCI and health, social, and cognitive intervention-related factors.
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    The Association of Late Life Cognitive Activity with Healthcare and Financial Decision Making in Community-Dwelling, Non-Demented Older Adults
    (Elsevier, 2021) Glover, Crystal M.; Yu, Lei; Stewart, Christopher C.; Wilson, Robert S.; Bennett, David A.; Boyle, Patricia A.; Neurology, School of Medicine
    Objective: The purpose of this study was to test the hypothesis that late life cognitive activity is associated with decision-making in older adults and to examine whether this association varies by level of cognitive function. Design: This study employed a cross-sectional design. Setting: All data were collected in participants' community-based residences. Participants: Participants were 1,084 older adults (mean age = 81.05 years, standard deviation = 7.53) without dementia (median Mini-Mental State Examination score = 29, interquartile range = 27.86-30.00). Measurements: Participants completed assessments of late life cognitive activity, cognitive function, and decision-making. We used linear regression models to examine the associations of late life cognitive activity and cognitive function with decision-making. Results: In a regression model adjusted for age, gender, and education, more frequent late life cognitive activity was associated with better decision-making, as was higher cognitive function. Furthermore, in an additional model that included the interaction of late life cognitive activity and cognitive function, the interaction was significant, such that late life cognitive activity was most strongly associated with decision-making among participants with lower levels of cognitive function. Conclusion: Frequent engagement in late life cognitive activity may help maintain decision-making among older persons, particularly among those with lower levels of cognitive function.
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    Childhood Socioeconomic Status Interacts with Cognitive Function to Impact Scam Susceptibility Among Community-Dwelling Older Adults
    (Taylor & Francis, 2023) Glover, Crystal M.; Yu, Lei; Stewart, Christopher C.; Wilson, Robert S.; Bennett, David A.; Lamar, Melissa; Boyle, Patricia A.; Neurology, School of Medicine
    Objectives: We examined whether childhood socioeconomic status (SES) is related to scam susceptibility in old age and tested the hypothesis that childhood SES interacts with cognitive function to impact scam susceptibility. Methods: This study employed a cross-sectional design. All data were collected in participants' community-based residences. Participants were 1071 older adults (mean age = 81.05 years, SD = 7.53) without dementia (median MMSE score = 28.29, IQR = 27.86-30.00). Participants completed assessments of childhood SES, cognitive function, and scam susceptibility. We used linear regression models to examine the associations of childhood SES and cognitive function with scam susceptibility. Results: In a regression model adjusted for age, gender, and education, poorer cognitive function was associated with higher scam susceptibility, but childhood SES was not. However, in an additional model that included the interaction of childhood SES and cognitive function, the interaction was significant, such that lower childhood SES was associated with higher scam susceptibility among participants with lower cognitive function. Conclusion: Lower childhood SES is associated with higher scam susceptibility among older adults with lower levels of cognitive function. Thus, older adults who experienced limited resources in childhood and have lower cognitive function may represent a specific group for interventions to increase scam awareness and prevent financial exploitation.
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    Mayo Normative Studies: Amyloid and Neurodegeneration Negative Normative Data for the Auditory Verbal Learning Test and Sex-Specific Sensitivity to Mild Cognitive Impairment/Dementia
    (IOS Press, 2024) Stricker, Nikki H.; Christianson, Teresa J.; Pudumjee, Shehroo B.; Polsinelli, Angelina J.; Lundt, Emily S.; Frank, Ryan D.; Kremers, Walter K.; Machulda, Mary M.; Fields, Julie A.; Jack, Clifford R., Jr.; Knopman, David S.; Graff-Radford, Jonathan; Vemuri, Prashanthi; Mielke, Michelle M.; Petersen, Ronald C.; Neurology, School of Medicine
    Background: Conventional normative samples include individuals with undetected Alzheimer's disease neuropathology, lowering test sensitivity for cognitive impairment. Objective: We developed Mayo Normative Studies (MNS) norms limited to individuals without elevated amyloid or neurodegeneration (A-N-) for Rey's Auditory Verbal Learning Test (AVLT). We compared these MNS A-N- norms in female, male, and total samples to conventional MNS norms with varying levels of demographic adjustments. Methods: The A-N- sample included 1,059 Mayo Clinic Study of Aging cognitively unimpaired (CU) participants living in Olmsted County, MN, who are predominantly non-Hispanic White. Using a regression-based approach correcting for age, sex, and education, we derived fully-adjusted T-score formulas for AVLT variables. We validated these A-N- norms in two independent samples of CU (n = 261) and mild cognitive impairment (MCI)/dementia participants (n = 392) > 55 years of age. Results: Variability associated with age decreased by almost half in the A-N- norm sample relative to the conventional norm sample. Fully-adjusted MNS A-N- norms showed approximately 7- 9% higher sensitivity to MCI/dementia compared to fully-adjusted MNS conventional norms for trials 1- 5 total and sum of trials. Among women, sensitivity to MCI/dementia increased with each normative data refinement. In contrast, age-adjusted conventional MNS norms showed greatest sensitivity to MCI/dementia in men. Conclusions: A-N- norms show some benefits over conventional normative approaches to MCI/dementia sensitivity, especially for women. We recommend using these MNS A-N- norms alongside MNS conventional norms. Future work is needed to determine if normative samples that are not well characterized clinically show greater benefit from biomarker-refined approaches.
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    Predicting Working Memory in Healthy Older Adults Using Real-Life Language and Social Context Information: A Machine Learning Approach
    (JMIR, 2022-03-08) Ferrario, Andrea; Luo, Minxia; Polsinelli, Angelina J.; Moseley, Suzanne A.; Mehl, Matthias R.; Yordanova, Kristina; Martin, Mike; Demiray, Burcu; Neurology, School of Medicine
    Background: Language use and social interactions have demonstrated a close relationship with cognitive measures. It is important to improve the understanding of language use and behavioral indicators from social context to study the early prediction of cognitive decline among healthy populations of older adults. Objective: This study aimed at predicting an important cognitive ability, working memory, of 98 healthy older adults participating in a 4-day-long naturalistic observation study. We used linguistic measures, part-of-speech (POS) tags, and social context information extracted from 7450 real-life audio recordings of their everyday conversations. Methods: The methods in this study comprise (1) the generation of linguistic measures, representing idea density, vocabulary richness, and grammatical complexity, as well as POS tags with natural language processing (NLP) from the transcripts of real-life conversations and (2) the training of machine learning models to predict working memory using linguistic measures, POS tags, and social context information. We measured working memory using (1) the Keep Track test, (2) the Consonant Updating test, and (3) a composite score based on the Keep Track and Consonant Updating tests. We trained machine learning models using random forest, extreme gradient boosting, and light gradient boosting machine algorithms, implementing repeated cross-validation with different numbers of folds and repeats and recursive feature elimination to avoid overfitting. Results: For all three prediction routines, models comprising linguistic measures, POS tags, and social context information improved the baseline performance on the validation folds. The best model for the Keep Track prediction routine comprised linguistic measures, POS tags, and social context variables. The best models for prediction of the Consonant Updating score and the composite working memory score comprised POS tags only. Conclusions: The results suggest that machine learning and NLP may support the prediction of working memory using, in particular, linguistic measures and social context information extracted from the everyday conversations of healthy older adults. Our findings may support the design of an early warning system to be used in longitudinal studies that collects cognitive ability scores and records real-life conversations unobtrusively. This system may support the timely detection of early cognitive decline. In particular, the use of a privacy-sensitive passive monitoring technology would allow for the design of a program of interventions to enable strategies and treatments to decrease or avoid early cognitive decline.
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    Psychological Wellbeing Relates to Healthcare and Financial Decision Making in a Study of Predominantly White Older Adults
    (Sage, 2023) Glover, Crystal M.; Stewart, Christopher C.; Yu, Lei; Wilson, Robert S.; Lamar, Melissa; Bennett, David A.; Boyle, Patricia A.; Neurology, School of Medicine
    The purpose of this study was to test the hypotheses that psychological well-being is associated with healthcare and financial decision making in older adults and that this association varies by the level of cognitive function. Participants were 1082 older adults (97% non-Latino White; 76% women; mean age = 81.04 years; SD = 7.53) without dementia (median MMSE score = 29.00, IQR = 27.86-30.00). In a regression model adjusted for age, gender, and years of education, higher levels of psychological well-being were associated with better decision making (estimate = 0.39, standard error [SE] = 0.11, p < .001), as was better cognitive function (estimate = 2.37, SE = 0.14, p < .0001). In an additional model, an interaction of psychological well-being and cognitive function was significant (estimate = -0.68, SE = 0.20, p < .001), such that higher levels of psychological well-being were most beneficial for decision making among participants with lower levels of cognitive function. Higher levels of psychological well-being may help sustain decision making among older persons, particularly those with lower levels of cognitive function.
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    Social Enrichment on the Job: Complex Work with People Improves Episodic Memory, Promotes Brain Reserve, and Reduces the Risk of Dementia
    (Wiley, 2023) Coleman, Max E.; Roessler, Meghan E. H.; Peng, Siyun; Roth, Adam R.; Risacher, Shannon L.; Saykin, Andrew J.; Apostolova, Liana G.; Perry, Brea L.; Radiology and Imaging Sciences, School of Medicine
    Individuals with more complex jobs experience better cognitive function in old age and a lower risk of dementia, yet complexity has multiple dimensions. Drawing on the Social Networks in Alzheimer Disease study, we examine the association between occupational complexity and cognition in a sample of older adults (N = 355). A standard deviation (SD) increase in complex work with people is associated with a 9% to 12% reduction in the probability of mild cognitive impairment or dementia, a 0.14-0.19 SD increase in episodic memory, and a 0.18-0.25 SD increase in brain reserve, defined as the gap (residual) between global cognitive function and magnetic resonance imaging (MRI) indicators of brain atrophy. In contrast, complexity with data or things is rarely associated with cognitive outcomes. We discuss the clinical and methodological implications of these findings, including the need to complement data-centered activities (e.g., Sudoku puzzles) with person-centered interventions that increase social complexity.
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    Why the cognitive "fountain of youth" may be upstream: Pathways to dementia risk and resilience through social connectedness
    (Wiley, 2022) Perry, Brea L.; McConnell, Will R.; Coleman, Max E.; Roth, Adam R.; Peng, Siyun; Apostolova, Liana G.; Neurology, School of Medicine
    Research suggests social connectedness may help older adults with dementia maintain cognitive functionality and quality of life. However, little is known about its specific social and biological mechanisms. This paper proposes two pathways through social bridging (i.e., cognitive enrichment through expansive social networks) and bonding (i.e., neuroendocrine benefits of integration in cohesive social networks). We provide preliminary evidence for these pathways using neuroimaging, cognitive, and egocentric social network data from the Social Networks and Alzheimer's Disease (SNAD) study (N = 280). We found that network size, density, and presence of weak ties (i.e., social bridging) moderated the association between brain atrophy and cognitive function, while marriage/cohabitation (i.e., social bonding) moderated the association between perceived stress and cognitive function. We argue that social connectedness may have downstream implications for multiple pathophysiological processes in cognitive aging, even negating existing structural damage to the brain, making it a strong candidate for clinical or policy intervention.