21 research outputs found

    Associations Between Performance on an Abbreviated CogState Battery, Other Measures of Cognitive Function, and Biomarkers in People at Risk for Alzheimer\u27s Disease

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    It is not known whether computerized cognitive assessments, like the CogState battery, are sensitive to preclinical cognitive changes or pathology in people at risk for Alzheimer\u27s disease(AD). In 469 late middle-aged participants from the Wisconsin Registry for Alzheimer\u27s Prevention(mean age 63.8±7 years at testing; 67% female; 39% APOE4+), we examined relationships between a CogState abbreviated battery(CAB) of seven tests and demographic characteristics, traditional paper-based neuropsychological tests as well as a composite cognitive impairment index, cognitive impairment status(determined by consensus review), and biomarkers for amyloid and tau(CSF phosphorylated-tau/Aβ42 and global PET-PiB burden) and neural injury(CSF neurofilament light protein). CSF and PET-PiB were collected in n = 71 and n = 91 participants, respectively, approximately four years prior to CAB testing. For comparison, we examined three traditional tests of delayed memory in parallel. Similar to studies in older samples, the CAB was less influenced by demographic factors than traditional tests. CAB tests were generally correlated with most paper-based cognitive tests examined and mapped onto the same cognitive domains. Greater composite cognitive impairment index was associated with worse performance on all CAB tests. Cognitively impaired participants performed significantly worse compared to normal controls on all but one CAB test. Poorer One Card Learning test performance was associated with higher levels of CSF phosphorylated-tau/Aβ42. These results support the use of the CogState battery as measures of early cognitive impairment in studies of people at risk for AD

    Positive Affect Predicts Cerebral Glucose Metabolism in Late Middle-aged Adults.

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    Positive affect is associated with a number of health benefits; however, few studies have examined the relationship between positive affect and cerebral glucose metabolism, a key energy source for neuronal function and a possible index of brain health. We sought to determine if positive affect was associated with cerebral glucose metabolism in late middle-aged adults (n = 133). Participants completed the positive affect subscale of the Center for Epidemiological Studies Depression Scale at two time points over a two-year period and underwent 18F-fluorodeoxyglucose-positron emission tomography scanning. After controlling for age, sex, perceived health status, depressive symptoms, anti-depressant use, family history of Alzheimer’s disease, APOE ε4 status and interval between visits, positive affect was associated with greater cerebral glucose metabolism across para-/limbic, frontal, temporal and parietal regions. Our findings provide evidence that positive affect in late midlife is associated with greater brain health in regions involved in affective processing and also known to be susceptible to early neuropathological processes. The current findings may have implications for interventions aimed at increasing positive affect to attenuate early neuropathological changes in at-risk individuals

    Correction for retest effects across repeated measures of cognitive functioning: a longitudinal cohort study of postoperative delirium

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    Abstract Background Few studies have compared methods to correct for retest effects or practice effects in settings where an acute event could influence test performance, such as major surgery. Our goal in this study was to evaluate the use of different methods to correct for the effects of practice or retest on repeated test administration in the context of an observational study of older adults undergoing elective surgery. Methods In a cohort of older surgical patients (N = 560) and a non-surgical comparison group (N = 118), we compared changes on repeated cognitive testing using a summary measure of general cognitive performance (GCP) between patients who developed post-operative delirium and those who did not. Surgical patients were evaluated pre-operatively and at 1, 2, 6, 12, and 18 months following surgery. Inferences from linear mixed effects models using four approaches were compared: 1) no retest correction, 2) mean-difference correction, 3) predicted-difference correction, and 4) model-based correction. Results Using Approaches 1 or 4, which use uncorrected data, both surgical groups appeared to improve or remain stable after surgery. In contrast, Approaches 2 and 3, which dissociate retest and surgery effects by using retest-adjusted GCP scores, revealed an acute decline in performance in both surgical groups followed by a recovery to baseline. Relative differences between delirium groups were generally consistent across all approaches: the delirium group showed greater short- and longer-term decline compared to the group without delirium, although differences were attenuated after 2 months. Standard errors and model fit were also highly consistent across approaches. Conclusion All four approaches would lead to nearly identical inferences regarding relative mean differences between groups experiencing a key post-operative outcome (delirium) but produced qualitatively different impressions of absolute performance differences following surgery. Each of the four retest correction approaches analyzed in this study has strengths and weakness that should be evaluated in the context of future studies. Retest correction is critical for interpretation of absolute cognitive performance measured over time and, consequently, for advancing our understanding of the effects of exposures such as surgery, hospitalization, acute illness, and delirium

    Cerebrospinal fluid biomarkers of neurofibrillary tangles and synaptic dysfunction are associated with longitudinal decline in white matter connectivity: A multi-resolution graph analysis

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    In addition to the development of beta amyloid plaques and neurofibrillary tangles, Alzheimer's disease (AD) involves the loss of connecting structures including degeneration of myelinated axons and synaptic connections. However, the extent to which white matter tracts change longitudinally, particularly in the asymptomatic, preclinical stage of AD, remains poorly characterized. In this study we used a novel graph wavelet algorithm to determine the extent to which microstructural brain changes evolve in concert with the development of AD neuropathology as observed using CSF biomarkers. A total of 118 participants with at least two diffusion tensor imaging (DTI) scans and one lumbar puncture for CSF were selected from two observational and longitudinally followed cohorts. CSF was assayed for pathology specific to AD (Aβ42 and phosphorylated-tau), neurodegeneration (total-tau), axonal degeneration (neurofilament light chain protein; NFL), and synaptic degeneration (neurogranin). Tractography was performed on DTI scans to obtain structural connectivity networks with 160 nodes where the nodes correspond to specific brain regions of interest (ROIs) and their connections were defined by DTI metrics (i.e., fractional anisotropy (FA) and mean diffusivity (MD)). For the analysis, we adopted a multi-resolution graph wavelet technique called Wavelet Connectivity Signature (WaCS) which derives higher order representations from DTI metrics at each brain connection. Our statistical analysis showed interactions between the CSF measures and the MRI time interval, such that elevated CSF biomarkers and longer time were associated with greater longitudinal changes in white matter microstructure (decreasing FA and increasing MD). Specifically, we detected a total of 17 fiber tracts whose WaCS representations showed an association between longitudinal decline in white matter microstructure and both CSF p-tau and neurogranin. While development of neurofibrillary tangles and synaptic degeneration are cortical phenomena, the results show that they are also associated with degeneration of underlying white matter tracts, a process which may eventually play a role in the development of cognitive decline and dementia. Keywords: DTI tractography, CSF biomarker, Longitudinal brain connectivity, Alzheimer's disease pathology, Multi-resolution analysi

    MCI Status, Amyloid and Tau Biomarkers, and Composite Cognitive Impairment Scores are Associated with Cogstate Performance in the Wisconsin Registry for Alzheimer\u27s Prevention

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    Background: CogState is a computerized cognitive battery spanning domains of memory, executive function, and speed and processing. CogState, designed to be robust to education level and efficient for repeated administration with minimal practice effects, holds potential for detecting early cognitive deficits that may prove to be due to preclinical Alzheimer’s disease (AD). This project aimed to provide convergent and construct validity for CogState in detecting preclinical AD during late-middle-age. Methods: 279 late-middle-aged participants from the Wisconsin Registry for Alzheimer’s Prevention (mean age 63±7 years; 69% female; 37% APOE4+) completed a traditional paper-based neuropsychological battery and a CogState battery consisting of seven tests approximately six years post-baseline. A composite cognitive impairment score (CCI) was calculated using eight neuropsychological tests acquired longitudinally and was estimated at age 50 to remove confounding age effects; higher CCI indicates lower cognitive performance. Mild Cognitive Impairment (MCI) status (n=36) was determined by consensus using clinical and/or pyschometric criteria. A subset underwent cerebrospinal fluid (CSF) collection (n=36) and PET-PiB imaging (n=46). To determine clinical relevance of CogState, MCI and normal controls were compared by ANCOVA on select CogState variables controlling for age, literacy, gender, APOE4, AD family history, self-rated computer familiarity, and depression. To determine whether biomarkers (CSF Aβ42/Aβ40, CSF total-tau/Aβ42, global PiB burden) or CCI predict CogState performance, we ran multiple regression models controlling for age, sex, literacy, and computer familiarity. Results: MCI participants performed significantly worse (p Conclusions: MCI status, biomarkers for amyloid and tau, and CCI all predict performance on the CogState variables assessed in this study of late-middle-aged adults. CogState performance at a single time point may be an important indicator of preclinical AD processes
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