Dementia in the workplace: Identifying better ways of assessing cognitive functioning

This item is only available electronically.With a push towards self-sufficiency in retirement and staying at work for longer, there is a growing need to be able to anticipate the cognitive capacity of older workers. This review concerns the dangers of dementia associated with continued employment and considers two broader issues within the organisational context: (i) workers may exit the workforce prematurely due to concerns about cognition (ii) workers may stay at work for longer despite cognitive decline. Current measures used to screen for dementia, such as the MMSE, have limited diagnostic value. A new objective measure, the Cambridge Neuropsychological Test Automated Battery (CANTAB), is explored.Thesis (M.Psych(Organisational & Human Factors)) -- University of Adelaide, School of Psychology, 201

Detecting Cognitive, Functional and Behavioral Response to Donepezil in Alzheimer’s Disease: The Role of Attention Tasks

Introduction: Cholinesterase Inhibitors (ChEIs) used in Alzheimer’s Disease (AD) have modest effects, heterogeneous treatment response, and it has been difficult to detect treatment response. The standard research and clinical outcome measure, the Alzheimer’s Disease Assessment Scale-Cognitive subscale (ADAS-Cog) aggregates multiple cognitive domains, and has limited sensitivity. We propose that because acetylcholine is directly linked to the cognitive domain of attention, and ChEIs increase available acetylcholine, measures of attention under high-load conditions could predict long-term cognitive, functional and behavioral response, and thus, unlike global measures, could be sensitive to treatment efficacy. Method: We conducted a longitudinal, open label donepezil trial assessing twenty-three participants with AD at baseline (T1), 6 weeks (T2) and 6 months (T3). Outcome measures were: a) Computerized measures of attention: Foreperiod Effect, Covert Orienting, and Attentional Blink tasks; b) Cognitive: global (ADAS-Cog), memory (Hopkins Verbal Learning Test–Total Recall), executive function (Delis-Kaplan Executive Functioning Scale- Trail Making Test Condition 4); c) Functional: Lawton-Brody Instrumental Activities of Daily Living (IADL); d) Behavioral: Neuropsychiatric Inventory (NPI). Stepwise hierarchical regression analyses were conducted to assess the contribution of different domains, as well as attention change score T2-T1, on ADAS-Cog T3-T1 change score. Linear regressions assessed whether measures of attention at T2 predicted IADL and NPI scores. Results: Our findings show that attention measures at 6 weeks (T2) could predict 6-month (T3) global cognitive response to treatment better than any other memory or executive measure. Moreover, change in attention performance from baseline to 6 weeks (T2-T1) similarly predicted cognitive performance at 6 months (T3). Finally, performance on attention at 6 weeks (T2) also predicted instrumental activities of daily living and neuropsychiatric symptoms at six months (T3). Conclusion: Our findings support our hypothesis that measures of attention under high-load conditions are sensitive to donepezil. Performance on these measures predicted long-term cognitive, functional, and behavioral response to ChEIs

Protocol of the Cognitive Health in Ageing Register: Investigational, Observational and Trial Studies in Dementia Research (CHARIOT): Prospective Readiness cOhort (PRO) SubStudy

The Cognitive Health in Ageing Register: Investigational, Observational and Trial Studies in Dementia Research (CHARIOT): Prospective Readiness cOhort (PRO) SubStudy (CPSS), sponsored by Janssen Pharmaceutical Research & Development LLC, is an Alzheimer's disease (AD) biomarker enriched observational study that began 3 July 2015 CPSS aims to identify and validate determinants of AD, alongside cognitive, functional and biological changes in older adults with or without detectable evidence of AD pathology at baseline. CPSS is a dual-site longitudinal cohort (3.5 years) assessed quarterly. Cognitively normal participants (60-85 years) were recruited across Greater London and Edinburgh. Participants are classified as high, medium (amnestic or non-amnestic) or low risk for developing mild cognitive impairment-Alzheimer's disease based on their Repeatable Battery for the Assessment of Neuropsychological Status performance at screening. Additional AD-related assessments include: a novel cognitive composite, the Global Preclinical Alzheimer's Cognitive Composite, brain MRI and positron emission tomography and cerebrospinal fluid analysis. Lifestyle, other cognitive and functional data, as well as biosamples (blood, urine, and saliva) are collected. Primarily, study analyses will evaluate longitudinal change in cognitive and functional outcomes. Annual interim analyses for descriptive data occur throughout the course of the study, although inferential statistics are conducted as required. CPSS received ethical approvals from the London-Central Research Ethics Committee (15/LO/0711) and the Administration of Radioactive Substances Advisory Committee (RPC 630/3764/33110) The study is at the forefront of global AD prevention efforts, with frequent and robust sampling of the well-characterised cohort, allowing for detection of incipient pathophysiological, cognitive and functional changes that could inform therapeutic strategies to prevent and/or delay cognitive impairment and dementia. Dissemination of results will target the scientific community, research participants, volunteer community, public, industry, regulatory authorities and policymakers. On study completion, and following a predetermined embargo period, CPSS data are planned to be made accessible for analysis to facilitate further research into the determinants of AD pathology, onset of symptomatology and progression. The CHARIOT:PRO SubStudy is registered with clinicaltrials.gov (NCT02114372). Notices of protocol modifications will be made available through this trial registry. [Abstract copyright: © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Artificial intelligence approaches to predicting and detecting cognitive decline in older adults: A conceptual review.

Preserving cognition and mental capacity is critical to aging with autonomy. Early detection of pathological cognitive decline facilitates the greatest impact of restorative or preventative treatments. Artificial Intelligence (AI) in healthcare is the use of computational algorithms that mimic human cognitive functions to analyze complex medical data. AI technologies like machine learning (ML) support the integration of biological, psychological, and social factors when approaching diagnosis, prognosis, and treatment of disease. This paper serves to acquaint clinicians and other stakeholders with the use, benefits, and limitations of AI for predicting, diagnosing, and classifying mild and major neurocognitive impairments, by providing a conceptual overview of this topic with emphasis on the features explored and AI techniques employed. We present studies that fell into six categories of features used for these purposes: (1) sociodemographics; (2) clinical and psychometric assessments; (3) neuroimaging and neurophysiology; (4) electronic health records and claims; (5) novel assessments (e.g., sensors for digital data); and (6) genomics/other omics. For each category we provide examples of AI approaches, including supervised and unsupervised ML, deep learning, and natural language processing. AI technology, still nascent in healthcare, has great potential to transform the way we diagnose and treat patients with neurocognitive disorders

Enhancing Alzheimer Disease Segmentation through Adaptively Regularized Weighted Kernel-Based Clustering

Image segmentation is important in image analysis because it helps to locate objects and boundaries within a picture. This study offers Adaptively Regularized Weighted Kernel-Based Clustering (ARWKC), a unique segmentation technique built exclusively for recovering brain tissue from medical pictures. The proposed approach incorporates adaptive regularization and weighted kernel-based clustering techniques to increase the accuracy and resilience of brain tissue segmentation. The picture is initially preprocessed with the ARWKC method to improve its quality and eliminate any noise or artifacts. The adaptive regularization method is then utilized to effectively deal with the visual variation of brain tissue in clinical images. This adaptive regularization contributes to more accurate and consistent segmentation outcomes. The weighted kernel-based clustering method is then used to find and group pixels with comparable properties, with a focus on brain tissue areas. This clustering approach employs a weighted kernel function that takes into account both geographical closeness and pixel intensities, allowing the algorithm to capture local picture features and improve segmentation accuracy. Extensive experiments were conducted on a collection of medical images to evaluate the efficacy of the ARWKC algorithm. The well-known k-means clustering method, often used in image segmentation applications, was utilized as a benchmark for comparison. In terms of accuracy and resilience for brain tissue segmentation, the experimental findings showed that the ARWKC method surpasses the k-means clustering approach

AI and Non AI Assessments for Dementia

Current progress in the artificial intelligence domain has led to the development of various types of AI-powered dementia assessments, which can be employed to identify patients at the early stage of dementia. It can revolutionize the dementia care settings. It is essential that the medical community be aware of various AI assessments and choose them considering their degrees of validity, efficiency, practicality, reliability, and accuracy concerning the early identification of patients with dementia (PwD). On the other hand, AI developers should be informed about various non-AI assessments as well as recently developed AI assessments. Thus, this paper, which can be readable by both clinicians and AI engineers, fills the gap in the literature in explaining the existing solutions for the recognition of dementia to clinicians, as well as the techniques used and the most widespread dementia datasets to AI engineers. It follows a review of papers on AI and non-AI assessments for dementia to provide valuable information about various dementia assessments for both the AI and medical communities. The discussion and conclusion highlight the most prominent research directions and the maturity of existing solutions.Comment: 49 page

Do informal caregivers of people with dementia mirror the cognitive deficits of their demented patients?:A pilot study

Recent research suggests that informal caregivers of people with dementia (ICs) experience more cognitive deficits than noncaregivers. The reason for this is not yet clear. Objective: to test the hypothesis that ICs ‘mirror' the cognitive deficits of the demented people they care for. Participants and methods: 105 adult ICs were asked to complete three neuropsychological tests: letter fluency, category fluency, and the logical memory test from the WMS-III. The ICs were grouped according to the diagnosis of their demented patients. One-sample ttests were conducted to investigate if the standardized mean scores (t-scores) of the ICs were different from normative data. A Bonferroni correction was used to correct for multiple comparisons. Results: 82 ICs cared for people with Alzheimer's dementia and 23 ICs cared for people with vascular dementia. Mean letter fluency score of the ICs of people with Alzheimer's dementia was significantly lower than the normative mean letter fluency score, p = .002. The other tests yielded no significant results. Conclusion: our data shows that ICs of Alzheimer patients have cognitive deficits on the letter fluency test. This test primarily measures executive functioning and it has been found to be sensitive to mild cognitive impairment in recent research. Our data tentatively suggests that ICs who care for Alzheimer patients also show signs of cognitive impairment but that it is too early to tell if this is cause for concern or not

How Valuable are Clinical Neuropsychological Assessments? A Meta-analysis of Neuropsychological Tests with Comparison to Common Medical Tests and Treatments

There has been a general decrease in neuropsychological assessments at a time when medical diagnostic technology and treatments have expanded, leading to a faulty assumption that medical tests and healthcare treatments provide more reliable or valid data than psychological assessments. A landmark report from the American Psychological Association’s (APA) Psychological Assessment Work Group (PAWG) found that validity coefficients for many psychological tests were indistinguishable from those of medical tests (Meyer et al., 2001). An updated systematic review of the advancement in neuropsychological testing is essential to the continued advancement of the value of neuropsychological assessment in healthcare. This meta-analysis sought to (1) summarize effect sizes of neuroimaging to diagnose dementia, medications to treat chronic diseases, and neuropsychological tests to diagnose dementia and TBI, (2) determine the differences (if any) in effect sizes between medical domains, and (3) determine the differences (if any) in effect sizes between medical domains and neuropsychological tests. EBSCO networks were searched for original research examining the efficacy of neuroimaging for Alzheimer’s Disease (AD), xi neuropsychological tests for AD and traumatic brain injury (TBI), and medication to treat memory impairment and cardiovascular events between clinical and control samples. Studies were coded using a complex multi-comparison, outcome, and subgroup schema. Data were analyzed under random-effects modeling. Of 6,668 studies identified, 78 were retained for primary and ancillary meta-analyses (715 effect sizes extracted; 35,810 clinical and 42,964 control participants represented). Primary results indicated a significant difference between domains, such that neuroimaging (g = -1.603) and neuropsychological tests (g = -1.591) both yielded greater effect sizes than medication studies (g = -0.009]. Secondary results indicated the AD neuropsychological test effect size [g = -2.213) was significantly different than the TBI neuropsychological test efficacy [g = -0.649; Q(1) = 42.821, p = 0.000]. Additionally, results indicated nonsignificant effect sizes for both memory impairment medications (g = -.052) and aspirin for cardiovascular events (g = .017). CONCLUSIONS: The diagnostic efficacy of neuroimaging and neuropsychological tests were both substantial and non-significantly different from one another. These findings provide clinicians and consumers with convincing evidence that neuropsychological tests are a reliable diagnostic tool for people with acquired and neurodegenerative brain disorders