Background: Cognitive reserve (CR) contributes to inter-individual variability of cognitive performance and to preserve cognitive functioning facing aging and brain damage. However, brain anatomical and functional substrates of CR still need to be fully explored in young healthy subjects (HS). By evaluating a relatively large cohort of young HS, we investigated the associations between CR and structural and functional magnetic resonance imaging (MRI) measures in early adulthood.
Methods: A global Cognitive Reserve Index (CRI), combining intelligence quotient, leisure activities and education, was measured from 77 HS and its brain anatomical and functional substrates were evaluated through a multiparametric MRI approach. Substrates of the three subdomains (cognitive/social/physical) of leisure activities were also explored.
Results: Higher global and subdomain CRIs were associated with higher gray matter volume of brain regions involved in motor and cognitive functions, such as the right (R) supplementary motor area, left (L) middle frontal gyrus and L cerebellum. No correlation with measures of white matter (WM) integrity was found. Higher global and subdomains CRIs were associated with lower resting-state functional connectivity (RS FC) of L postcentral gyrus and R insula in sensorimotor network, L postcentral gyrus in salience network and R cerebellum in the executive-control network. Moreover, several CRIs were also associated with higher RS FC of R cuneus in default-mode network.
Conclusions: CR modulates structure and function of several brain motor and cognitive networks responsible for complex cognitive functioning already in young HS. CR could promote optimization of the recruitment of brain networks.
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http://dx.doi.org/10.1007/s00415-020-10331-6 | DOI Listing |
Alzheimers Dement
December 2024
Boston University Alzheimer's Disease Research Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.
Background: Alzheimer's disease (AD) has both genetic and environmental risk factors. Gene-environment interaction may help explain some missing heritability. There is strong evidence for cigarette smoking as a risk factor for AD.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
Background: The Apolipoprotein E ε4 (APOE-ε4) allele is common in the population, but acts as the strongest genetic risk factor for late-onset Alzheimer's disease (AD). Despite the strength of the association, there is notable heterogeneity in the population including a strong modifying effect of genetic ancestry, with the APOE-ε4 allele showing a stronger association among individuals of European ancestry (EUR) compared to individuals of African ancestry (AFR). Given this heterogeneity, we sought to identify genetic modifiers of APOE-ε4 related to cognitive decline leveraging APOE-ε4 stratified and interaction genome-wide association analyses (GWAS).
View Article and Find Full Text PDFAlzheimers Dement
December 2024
University of Pittsburgh School of Public Health, Pittsburgh, PA, USA.
Background: Many complex traits and diseases show sex-specific biases in clinical presentation and prevalence. For instance, two-thirds of AD cases are female. Studies suggest that women might have higher cognitive reserve but steeper cognitive decline in older age.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Institute of Transformative Molecular Medicine, Case western Reserve University, Cleveland, OH, USA.
Background: Alzheimer's disease (AD) is a severe neurodegenerative condition that affects millions of people worldwide. The TgF344 AD rat model, which exhibits early depression-like behavior followed by later cognitive impairment, is widely used to evaluate putative biomarkers and potential treatments for AD. The P7C3 neuroprotective compounds have shown protective efficacy for both brain pathology and neuropsychiatric impairment in this model.
View Article and Find Full Text PDFAlzheimers Dement
December 2024
Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas, Las Vegas, NV, USA.
Background: Although high-throughput DNA/RNA sequencing technologies have generated massive genetic and genomic data in human disease, translation of these findings into new patient treatment has not materialized by lack of effective approaches, such as Artificial Intelligence (AL) and Machine Learning (ML) tools.
Method: To address this problem, we have used AI/ML approaches, Mendelian randomization (MR), and large patient's genetic and functional genomic data to evaluate druggable targets using Alzheimer's disease (AD) as a prototypical example. We utilized the genomic instruments from 9 expression quantitative trait loci (eQTL) and 3 protein quantitative trait loci (pQTL) datasets across five human brain regions from three biobanks.
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