A multi-stage feature selection method to improve classification of potential super-agers and cognitive decliners using structural brain MRI data-a UK biobank study.

Cognitive aging is described as the age-related decline in areas such as memory, executive function, reasoning, and processing speed. Super-Agers, adults over 80 years old, have cognitive function performance comparable to middle-aged adults. To improve cognitive reserve and potentially decrease Alzheimer's disease (AD) risk, it is essential to contrast changes in regional brain volumes between "Positive-Agers" who have superior cognitive performance compared to their age peers but are not 80 years old yet and aging adults who show cognitive decline (i.

A machine learning approach for potential Super-Agers identification using neuronal functional connectivity networks.

Introduction: Aging is often associated with cognitive decline. Understanding neural factors that distinguish adults in midlife with superior cognitive abilities (Positive-Agers) may offer insight into how the aging brain achieves resilience. The goals of this study are to (1) introduce an optimal labeling mechanism to distinguish between Positive-Agers and Cognitive Decliners, and (2) identify Positive-Agers using neuronal functional connectivity networks data and demographics.

Bioenergetic and vascular predictors of potential super-ager and cognitive decline trajectories-a UK Biobank Random Forest classification study.

Aging has often been characterized by progressive cognitive decline in memory and especially executive function. Yet some adults, aged 80 years or older, are "super-agers" that exhibit cognitive performance like younger adults. It is unknown if there are adults in mid-life with similar superior cognitive performance ("positive-aging") versus cognitive decline over time and if there are blood biomarkers that can distinguish between these groups.

Using machine learning to predict COVID-19 infection and severity risk among 4510 aged adults: a UK Biobank cohort study.

Many risk factors have emerged for novel 2019 coronavirus disease (COVID-19). It is relatively unknown how these factors collectively predict COVID-19 infection risk, as well as risk for a severe infection (i.e.

Impact of COVID-19 on the Onset and Progression of Alzheimer's Disease and Related Dementias: A Roadmap for Future Research.

COVID-19 causes lasting neurological symptoms in some survivors. Like other infections, COVID-19 may increase risk of cognitive impairment. This perspective highlights four knowledge gaps about COVID-19 that need to be filled to avoid this possible health issue.

Inflammation, negative affect, and amyloid burden in Alzheimer's disease: Insights from the kynurenine pathway.

Background: Depressive symptoms in Alzheimer's disease (AD) predict worse cognitive and functional outcomes. Both AD and major depression inflammatory processes are characterized by shunted tryptophan metabolism away from serotonin (5-HT) and toward the neuroinflammatory kynurenine (Kyn) pathway. The present study assessed associations between Kyn and behavioral, neuroanatomical, neuropathological, and physiological outcomes common to both AD and negative affect across the AD continuum.

Using machine learning to predict COVID-19 infection and severity risk among 4,510 aged adults: a UK Biobank cohort study.

Background: Many risk factors have emerged for novel 2019 coronavirus disease (COVID-19). It is relatively unknown how these factors collectively predict COVID-19 infection risk, as well as risk for a severe infection (i.e.