At the interface of sensory and motor dysfunctions and Alzheimer's disease

Albers, Mark W.; Gilmore, Grover C.; Kaye, Jeffrey; Murphy, Claire; Wingfield, Arthur; Bennett, David A.; Boxer, Adam L.; Buchman, Aron S.; Cruickshanks, Karen J.; Devanand, Davangere P.; Duffy, Charles J.; Gall, Christine M.; Gates, George A.; Granholm, Ann Charlotte; Hensch, Takao K.; Holtzer, Roee; Hyman, Bradley T.; Lin, Frank R.; McKee, Ann C.; Morris, John C.; Petersen, Ronald C.; Silbert, Lisa C.; Struble, Robert G.; Trojanowski, John Q.; Verghese, Joe; Wilson, Donald A.; Xu, Shunbin; Zhang, Li I.

doi:10.1016/j.jalz.2014.04.514

Cited by 461 publications

(430 citation statements)

References 354 publications

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“…The large number of participants who had low performance on odor identification may speak to nonspecific, age-related changes in olfactory ability, or it may suggest a cohort of individuals at risk of progressing to MCI and AD dementia, in keeping with emerging models of the association between sensory/motor changes and AD dementia. 33 These findings support the hypothesis that thinner EC, in the setting of high amyloid burden, is associated with odor identification deficits in preclinical AD. This is in line with an emerging conceptual model of preclinical AD, which postulates an interaction between the presence of amyloid and presumed neurodegeneration associated with accelerated cognitive decline.…”

Section: Resultssupporting

confidence: 75%

Odor identification and Alzheimer disease biomarkers in clinically normal elderly

et al. 2015

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Objectives: Our objective was to investigate cross-sectional associations between odor identification ability and imaging biomarkers of neurodegeneration and amyloid deposition in clinically normal (CN) elderly individuals, specifically testing the hypothesis that there may be an interaction between amyloid deposition and neurodegeneration in predicting odor identification dysfunction.Methods: Data were collected on 215 CN participants from the Harvard Aging Brain Study. Measurements included the 40-item University of Pennsylvania Smell Identification Test and neuropsychological testing, hippocampal volume (HV) and entorhinal cortex (EC) thickness from MRI, and amyloid burden using Pittsburgh compound B (PiB) PET. A linear regression model with backward elimination (p , 0.05 retention) evaluated the cross-sectional association between the University of Pennsylvania Smell Identification Test and amyloid burden, HV, and EC thickness, assessing for effect modification by PiB status. Covariates included age, sex, premorbid intelligence, APOE e4 carrier status, and Boston Naming Test.Results: In unadjusted univariate analyses, worse olfaction was associated with decreased HV

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Section: Resultssupporting

confidence: 75%

Odor identification and Alzheimer disease biomarkers in clinically normal elderly

et al. 2015

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“…Awareness has also grown regarding the important links between sensory and cognitive aging (Lindenberger & Baltes 1994;Baltes & Lindenberger 1997;Wingfield & Tun 2001;Albers et al 2015). In this context, research in cognitive hearing science has flourished (Arlinger et al 2009).…”

Section: Clinical Relevance Of Auditory-cognitive Interactions and LImentioning

confidence: 99%

“…From a population health perspective, epidemiological research has provided evidence of a significant association between hearing loss and incident dementia (Albers et al 2015) and prompted questions regarding the potential advantages of adopting a more integrated approach to research on hearing health and cognitive health Pichora-Fuller et al 2015). Over the past decade, cognition has been introduced as a topic in practice guidelines for audiologists (Valente et al 2006), in tutorial reviews for audiologists (e.g., Pichora-Fuller & Singh 2006), and in audiology textbooks (e.g., Pichora-Fuller 2013).…”

Section: Clinical Relevance Of Auditory-cognitive Interactions and LImentioning

confidence: 99%

Hearing Impairment and Cognitive Energy: The Framework for Understanding Effortful Listening (FUEL)

et al. 2016

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5SThe Fifth Eriksholm Workshop on "Hearing Impairment and Cognitive Energy" was convened to develop a consensus among interdisciplinary experts about what is known on the topic, gaps in knowledge, the use of terminology, priorities for future research, and implications for practice. The general term cognitive energy was chosen to facilitate the broadest possible discussion of the topic. It goes back to Titchener (1908) who described the effects of attention on perception; he used the term psychic energy for the notion that limited mental resources can be flexibly allocated among perceptual and mental activities. The workshop focused on three main areas: (1) theories, models, concepts, definitions, and frameworks; (2) methods and measures; and (3) knowledge translation. We defined effort as the deliberate allocation of mental resources to overcome obstacles in goal pursuit when carrying out a task, with listening effort applying more specifically when tasks involve listening. We adapted Kahneman's seminal (1973) Capacity Model of Attention to listening and proposed a heuristically useful Framework for Understanding Effortful Listening (FUEL). Our FUEL incorporates the well-known relationship between cognitive demand and the supply of cognitive capacity that is the foundation of cognitive theories of attention. Our FUEL also incorporates a motivation dimension based on complementary theories of motivational intensity, adaptive gain control, and optimal performance, fatigue, and pleasure. Using a three-dimensional illustration, we highlight how listening effort depends not only on hearing difficulties and task demands but also on the listener's motivation to expend mental effort in the challenging situations of everyday life.

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“…See section under "Sporadic cases with mutations in APOE", for more details. The disease induces loss of memory and impairs cognitive function, but is also known to induce loss in olfaction, hearing and even some motor function [39]. Depression, agitation, apathy, social withdrawal, insomnia, delusion and emotional/physical outbursts are all typical symptoms associated with AD.…”

Section: Modelling Alzheimer's Disease Using Pluripotent Stem Cellsmentioning

confidence: 99%

Modelling Neurodegenerative Diseases Using Human Pluripotent Stem Cells

Hall¹

2016

Pluripotent Stem Cells - From the Bench to the Clinic

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Neurodegenerative diseases are being modelled in-vitro using human patient-specific, induced pluripotent stem cells and transgenic embryonic stem cells to determine more about disease mechanisms, as well as to discover new treatments for patients. Current research in modelling Alzheimer's disease, frontotemporal dementia and Parkinson's disease using pluripotent stem cells is described, along with the advent of gene-editing, which has been the complimentary tool for the field. Current methods used to model these diseases are predominantly dependent on 2D cell culture methods. Outcomes reveal that only some of the phenotype can be observed in-vitro, but these phenotypes, when compared to the patient, correlate extremely well. Many studies have found novel molecular mechanisms involved in the disease and therefore elucidate new potential targets for reversing the phenotype. Future research that includes studying more complex 3D cell cultures, as well as accelerating aging of the neurons, may help to yield stronger phenotypes in the cultured cells. Thus, the use and application of pluripotent stem cells for modelling disease have already shown to be a powerful approach for discovering more about these diseases, but will lead to even more findings in the future as gene and cell culture technology continues to develop.

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At the interface of sensory and motor dysfunctions and Alzheimer's disease

Cited by 461 publications

References 354 publications

Odor identification and Alzheimer disease biomarkers in clinically normal elderly

Odor identification and Alzheimer disease biomarkers in clinically normal elderly

Hearing Impairment and Cognitive Energy: The Framework for Understanding Effortful Listening (FUEL)

Modelling Neurodegenerative Diseases Using Human Pluripotent Stem Cells

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