NSAIDs May Protect Against Age-Related Brain Atrophy

The use of non-steroidal anti-inflammatory drugs (NSAIDs) in humans is associated with brain differences including decreased number of activated microglia. In animals, NSAIDs are associated with reduced microglia, decreased amyloid burden, and neuronal preservation. Several studies suggest NSAIDs protect brain regions affected in the earliest stages of AD, including hippocampal and parahippocampal regions. In this cross-sectional study, we examined the protective effect of NSAID use on gray matter volume in a group of middle-aged and older NSAID users (n = 25) compared to non-user controls (n = 50). All participants underwent neuropsychological testing and T1-weighted magnetic resonance imaging. Non-user controls showed smaller volume in portions of the left hippocampus compared to NSAID users. Age-related loss of volume differed between groups, with controls showing greater medial temporal lobe volume loss with age compared to NSAID users. These results should be considered preliminary, but support previous reports that NSAIDs may modulate age-related loss of brain volume

Brain enlargement and increased behavioral and cytokine reactivity in infant monkeys following acute prenatal endotoxemia

Infections and inflammatory conditions during pregnancy can dysregulate neural development and increase the risk for developing autism and schizophrenia. The following research utilized a nonhuman primate model to investigate the potential impact of a mild endotoxemia during pregnancy on brain maturation and behavioral reactivity as well as the infants’ hormone and immune physiology. Nine pregnant female rhesus monkeys (Macaca mulatta) were administered nanogram concentrations of lipopolysaccharide (LPS) on two consecutive days, six weeks before term, and their offspring were compared to nine control animals. When tested under arousing challenge conditions, infants from the LPS pregnancies were more behaviorally disturbed, including a failure to show a normal attenuation of startle responses on tests of prepulse inhibition. Examination of their brains at one year of age with magnetic resonance imaging (MRI) revealed the unexpected finding of a significant 8.8% increase in global white matter volume distributed across many cortical regions compared to controls. More selective changes in regional gray matter volume and cortical thickness were noted in parietal, medial temporal, and frontal areas. While inhibited neural growth has been described previously after prenatal infection and LPS administration at higher doses in rodents, this low dose endotoxemia in the monkey is the first paradigm to produce a neural phenotype associated with augmented gray and white matter growth

CSF T-Tau/Aβ42 Predicts White Matter Microstructure in Healthy Adults at Risk for Alzheimer’s Disease

Cerebrospinal fluid (CSF) biomarkers T-Tau and Aβ42 are linked with Alzheimer’s disease (AD), yet little is known about the relationship between CSF biomarkers and structural brain alteration in healthy adults. In this study we examined the extent to which AD biomarkers measured in CSF predict brain microstructure indexed by diffusion tensor imaging (DTI) and volume indexed by T1-weighted imaging. Forty-three middle-aged adults with parental family history of AD received baseline lumbar puncture and MRI approximately 3.5 years later. Voxel-wise image analysis methods were used to test whether baseline CSF Aβ42, total tau (T-Tau), phosphorylated tau (P-Tau) and neurofilament light protein predicted brain microstructure as indexed by DTI and gray matter volume indexed by T1-weighted imaging. T-Tau and T-Tau/Aβ42 were widely correlated with indices of brain microstructure (mean, axial, and radial diffusivity), notably in white matter regions adjacent to gray matter structures affected in the earliest stages of AD. None of the CSF biomarkers were related to gray matter volume. Elevated P-Tau and P-Tau/Aβ42 levels were associated with lower recognition performance on the Rey Auditory Verbal Learning Test. Overall, the results suggest that CSF biomarkers are related to brain microstructure in healthy adults with elevated risk of developing AD. Furthermore, the results clearly suggest that early pathological changes in AD can be detected with DTI and occur not only in cortex, but also in white matter

Serum vitamin B12 and related 5-methyltetrahydrofolate-homocysteine methyltransferase reductase and cubilin genotypes predict neural outcomes across the Alzheimerʼs disease spectrum

Epidemiological studies show mixed findings for serum vitamin B12 and both cognitive and regional volume outcomes. No studies to date have comprehensively examined, in non-supplemented individuals, serum B12 level associations with neurodegeneration, hypometabolism, and cognition across the Alzheimer\u27s disease (AD) spectrum. Serum vitamin B12 was assayed from the Alzheimer\u27s Disease Neuroimaging Initiative (ADNI) and the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL). Voxel-wise analyses regressed B12 levels against regional gray matter (GM) volume and glucose metabolism (

Brain volumetric and microstructural correlates of executive and motor performance in aged rhesus monkeys

The aged rhesus macaque exhibits brain atrophy and behavioral deficits similar to normal aging in humans. Here we studied the association between cognitive and motor performance and anatomic and microstructural brain integrity measured with 3T magnetic resonance imaging in aged monkeys. About half of these animals were maintained on moderate calorie restriction, the only intervention shown to delay the aging process in lower animals. T1-weighted anatomic and diffusion tensor images were used to obtain gray matter volume, and fractional anisotropy and mean diffusivity, respectively. We tested the extent to which brain health indexed by gray matter volume, fractional anisotropy, and mean diffusivity were related to executive and motor function, and determined the effect of the dietary intervention on this relationship. We hypothesized that fewer errors on the executive function test and faster motor times would be correlated with higher volume, higher fractional anisotropy, and lower mean diffusivity in frontal areas that mediate executive function, and in motor, premotor, subcortical, and cerebellar areas underlying goal-directed motor behaviors. Higher error percentage on a cognitive conceptual shift task was significantly associated with lower gray matter volume in frontal and parietal cortices, and lower fractional anisotropy in major association fiber bundles. Similarly, slower performance time on the motor task was significantly correlated with lower volumetric measures in cortical, subcortical, and cerebellar areas and decreased fractional anisotropy in several major association fiber bundles. Notably, performance during the acquisition phase of the hardest level of the motor task was significantly associated with anterior mesial temporal lobe volume. Finally, these brain-behavior correlations for the motor task were attenuated in calorie restricted animals compared to controls, indicating a potential protective effect of the dietary intervention

Serum vitamin B12 and related 5-methyltetrahydrofolate-homocysteine methyltransferase reductase and cubilin genotypes predict neural outcomes across the Alzheimerʼs disease spectrum

Epidemiological studies show mixed findings for serum vitamin B12 and both cognitive and regional volume outcomes. No studies to date have comprehensively examined, in non-supplemented individuals, serum B12 level associations with neurodegeneration, hypometabolism, and cognition across the Alzheimer's disease (AD) spectrum. Serum vitamin B12 was assayed from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL). Voxel-wise analyses regressed B12 levels against regional gray matter (GM) volume and glucose metabolism (pThis article has been published in a revised form in The British Journal of Nutrition. DOI: 10.1017/S0007114520000951. This version is free to view and download for private research and study only. Not for re-distribution or re-use. © The Authors. Posted with permission.</p

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. The first is the need to identify the COVID-19 symptoms, genetic polymorphisms and treatment decisions associated with risk of cognitive impairment. The second is the absence of model systems in which to test hypotheses relating infection to cognition. The third is the need for consortia for studying both existing and new longitudinal cohorts in which to monitor long term consequences of COVID-19 infection. A final knowledge gap discussed is the impact of the isolation and lack of social services brought about by quarantine/lockdowns on people living with dementia and their caregivers. Research into these areas may lead to interventions that reduce the overall risk of cognitive decline for COVID-19 survivors

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. Among 1303 participants in UK Biobank, latent growth curve models classified participants into different cognitive groups based on longitudinal fluid intelligence (FI) scores over 7–9 years. Random Forest (RF) classification was then used to predict cognitive trajectory types using longitudinal predictors including demographic, vascular, bioenergetic, and immune factors. Feature ranking importance and performance metrics of the model were reported. Despite model complexity, we achieved a precision of 77% when determining who would be in the “positive-aging” group (n = 563) vs. cognitive decline group (n = 380). Among the top fifteen features, an equal number were related to either vascular health or cellular bioenergetics but not demographics like age, sex, or socioeconomic status. Sensitivity analyses showed worse model results when combining a cognitive maintainer group (n = 360) with the positive-aging or cognitive decline group. Our results suggest that optimal cognitive aging may not be related to age per se but biological factors that may be amenable to lifestyle or pharmacological changes.This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at DOI: 10.1007/s11357-022-00657-6. Copyright 2021 The Author(s). Posted with permission

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

Abstract 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. METHODS In this study, principal component analysis initially created latent cognitive trajectories groups. A hybrid algorithm of machine learning and optimization was then designed to predict latent groups using neuronal functional connectivity networks derived from resting state functional magnetic resonance imaging. Specifically, the Optimal Labeling with Bayesian Optimization (OLBO) algorithm used an unsupervised approach, iterating a logistic regression function with Bayesian posterior updating. This study encompassed 6369 adults from the UK Biobank cohort. RESULTS OLBO outperformed baseline models, achieving an area under the curve of 88% when distinguishing between Positive‐Agers and cognitive decliners. DISCUSSION OLBO may be a novel algorithm that distinguishes cognitive trajectories with a high degree of accuracy in cognitively unimpaired adults. Highlights Design an algorithm to distinguish between a Positive‐Ager and a Cognitive‐Decliner. Introduce a mathematical definition for cognitive classes based on cognitive tests. Accurate Positive‐Ager identification using rsfMRI and demographic data (AUC = 0.88). Posterior default mode network has the highest impact on Positive‐Aging odds ratio