Browsing by Subject "Neural ageing"

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    Accelerated functional brain aging in pre-clinical familial Alzheimer’s disease
    (Springer Nature, 2021-09-09) Gonneaud, Julie; Baria, Alex T.; Binette, Alexa Pichet; Gordon, Brian A.; Chhatwal, Jasmeer P.; Cruchaga, Carlos; Jucker, Mathias; Levin, Johannes; Salloway, Stephen; Farlow, Martin; Gauthier, Serge; Benzinger, Tammie L.S.; Morris, John C.; Bateman, Randall J.; Breitner, John C.S.; Poirier, Judes; Vachon-Presseau, Etienne; Villeneuve, Sylvia; Neurology, School of Medicine
    Resting state functional connectivity (rs-fMRI) is impaired early in persons who subsequently develop Alzheimer’s disease (AD) dementia. This impairment may be leveraged to aid investigation of the pre-clinical phase of AD. We developed a model that predicts brain age from resting state (rs)-fMRI data, and assessed whether genetic determinants of AD, as well as beta-amyloid (Aβ) pathology, can accelerate brain aging. Using data from 1340 cognitively unimpaired participants between 18–94 years of age from multiple sites, we showed that topological properties of graphs constructed from rs-fMRI can predict chronological age across the lifespan. Application of our predictive model to the context of pre-clinical AD revealed that the pre-symptomatic phase of autosomal dominant AD includes acceleration of functional brain aging. This association was stronger in individuals having significant Aβ pathology.
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    Spatial cell type composition in normal and Alzheimers human brains is revealed using integrated mouse and human single cell RNA sequencing
    (Nature Publishing Group, 2020-10-22) Johnson, Travis S.; Xiang, Shunian; Helm, Bryan R.; Abrams, Zachary B.; Neidecker, Peter; Machiraju, Raghu; Zhang, Yan; Huang, Kun; Zhang, Jie; Medicine, School of Medicine
    Single-cell RNA sequencing (scRNA-seq) resolves heterogenous cell populations in tissues and helps to reveal single-cell level function and dynamics. In neuroscience, the rarity of brain tissue is the bottleneck for such study. Evidence shows that, mouse and human share similar cell type gene markers. We hypothesized that the scRNA-seq data of mouse brain tissue can be used to complete human data to infer cell type composition in human samples. Here, we supplement cell type information of human scRNA-seq data, with mouse. The resulted data were used to infer the spatial cellular composition of 3702 human brain samples from Allen Human Brain Atlas. We then mapped the cell types back to corresponding brain regions. Most cell types were localized to the correct regions. We also compare the mapping results to those derived from neuronal nuclei locations. They were consistent after accounting for changes in neural connectivity between regions. Furthermore, we applied this approach on Alzheimer’s brain data and successfully captured cell pattern changes in AD brains. We believe this integrative approach can solve the sample rarity issue in the neuroscience.