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"Brain Aging"
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Successful aging : a neuroscientist explores the power and potential of our lives
\"Author of the iconic bestsellers This Is Your Brain on Music and The Organized Mind, Daniel Levitin turns his keen insights to what happens in our brains as we age; why we should think about health span, not life span; and, based on a rigorous analysis of neuroscientific evidence, how you can make the most of your seventies, eighties, and nineties today, no matter how old you are now\"-- Provided by publisher.
Microglial cathepsin B as a key driver of inflammatory brain diseases and brain aging
Interleukin-1β is a potent proinflammatory cytokine that plays a key role in the pathogenesis of the brain aging and diverse range of neurological diseases including Alzheimer's disease, Parkinson's disease, stroke and persistent pain. Activated microglia are the main cellular source of interleukin-1β in the brain. Cathepsin B is associated with the production and secretion of interleukin-1β through pyrin domain-containing protein 3 inflammasome-independent processing of procaspase-3 in the phagolysosomes. The leakage of cathepsin B from the endosomal-lysosomal system during aging is associated with the proteolytic degradation of mitochondrial transcription factor A, which can stabilize mitochondrial DNA. Therefore, microglial cathepsin B could function as a major driver for inflammatory brain diseases and brain aging. Orally active and blood-brain barrier-permeable specific inhibitors for cathepsin B can be potentially effective new pharmaceutical interventions against inflammatory brain diseases and brain aging.
Journal Article
Fast three‐dimensional image generation for healthy brain aging using diffeomorphic registration
Predicting brain aging can help in the early detection and prognosis of neurodegenerative diseases. Longitudinal cohorts of healthy subjects scanned through magnetic resonance imaging (MRI) have been essential to understand the structural brain changes due to aging. However, these cohorts suffer from missing data due to logistic issues in the recruitment of subjects. This paper proposes a methodology for filling up missing data in longitudinal cohorts with anatomically plausible images that capture the subject‐specific aging process. The proposed methodology is developed within the framework of diffeomorphic registration. First, two novel modules are introduced within Synthmorph, a fast, state‐of‐the‐art deep learning‐based diffeomorphic registration method, to simulate the aging process between the first and last available MRI scan for each subject in three‐dimensional (3D). The use of image registration also makes the generated images plausible by construction. Second, we used six image similarity measurements to rearrange the generated images to the specific age range. Finally, we estimated the age of every generated image by using the assumption of linear brain decay in healthy subjects. The methodology was evaluated on 2662 T1‐weighted MRI scans from 796 healthy participants from 3 different longitudinal cohorts: Alzheimer's Disease Neuroimaging Initiative, Open Access Series of Imaging Studies‐3, and Group of Neuropsychological Studies of the Canary Islands (GENIC). In total, we generated 7548 images to simulate the access of a scan per subject every 6 months in these cohorts. We evaluated the quality of the synthetic images using six quantitative measurements and a qualitative assessment by an experienced neuroradiologist with state‐of‐the‐art results. The assumption of linear brain decay was accurate in these cohorts (R2 ∈ [.924, .940]). The experimental results show that the proposed methodology can produce anatomically plausible aging predictions that can be used to enhance longitudinal datasets. Compared to deep learning‐based generative methods, diffeomorphic registration is more likely to preserve the anatomy of the different structures of the brain, which makes it more appropriate for its use in clinical applications. The proposed methodology is able to efficiently simulate anatomically plausible 3D MRI scans of brain aging of healthy subjects from two images scanned at two different time points. In this work, we proposed a methodology with the aim of simulating subject‐specific aging in brain magnetic resonance imaging (MRI) given two three‐dimensional images acquired at different time points. Deep learning‐based diffeomorphic registration was used as a backbone to generate deformation fields at different integration points. Similarity measurements were used for controlling the age estimation of the generated images by using a linear assumption.
Journal Article
The aging brain : functional adaptation across adulthood
\"Brain aging has long been seen as a process of deterioration and decline. Today, this view been challenged with research showing that not all cognitive processes decline with age, that some improve over the course of adulthood, and those that improve can often compensate for those that decline. Chapters in this multidisciplinary volume examine the neural mechanisms underlying changes in the aging brain, changes in learning and memory, risk and protective factors, and the assessment and prevention of cognitive decline\"--Provided by publisher.
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Transcriptomic Changes Highly Similar to Alzheimer’s Disease Are Observed in a Subpopulation of Individuals During Normal Brain Aging
Aging is a major risk factor for late-onset Alzheimer’s disease (LOAD). How aging contributes to the development of LOAD remains elusive. In this study, we examined multiple large-scale transcriptomic datasets from both normal aging and LOAD brains to understand the molecular interconnection between aging and LOAD. We found that shared gene expression changes between aging and LOAD are mostly seen in the hippocampal and several cortical regions. In the hippocampus, the expression of phosphoprotein, alternative splicing and cytoskeleton genes are commonly changed in both aging and AD, while synapse, ion transport, and synaptic vesicle genes are commonly down-regulated. Aging-specific changes are associated with acetylation and methylation, while LOAD-specific changes are more related to glycoprotein (both up- and down-regulations), inflammatory response (up-regulation), myelin sheath and lipoprotein (down-regulation). We also found that normal aging brain transcriptomes from relatively young donors (45–70 years old) clustered into several subgroups and some subgroups showed gene expression changes highly similar to those seen in LOAD brains. Using brain transcriptomic datasets from another cohort of older individuals (>70 years), we found that samples from cognitively normal older individuals clustered with the “healthy aging” subgroup while AD samples mainly clustered with the “AD similar” subgroups. This may imply that individuals in the healthy aging subgroup will likely remain cognitively normal when they become older and vice versa. In summary, our results suggest that on the transcriptome level, aging and LOAD have strong interconnections in some brain regions in a subpopulation of cognitively normal aging individuals. This supports the theory that the initiation of LOAD occurs decades earlier than the manifestation of clinical phenotype and it may be essential to closely study the “normal brain aging” to identify the very early molecular events that may lead to LOAD development.
Journal Article
Age Prediction Using Resting-State Functional MRI
The increasing lifespan and large individual differences in cognitive capability highlight the importance of comprehending the aging process of the brain. Contrary to visible signs of bodily ageing, like greying of hair and loss of muscle mass, the internal changes that occur within our brains remain less apparent until they impair function. Brain age, distinct from chronological age, reflects our brain’s health status and may deviate from our actual chronological age. Notably, brain age has been associated with mortality and depression. The brain is plastic and can compensate even for severe structural damage by rewiring. Functional characterization offers insights that structural cannot provide. Contrary to the multitude of studies relying on structural magnetic resonance imaging (MRI), we utilize resting-state functional MRI (rsfMRI). We also address the issue of inclusion of subjects with abnormal brain ageing through outlier removal. In this study, we employ the Least Absolute Shrinkage and Selection Operator (LASSO) to identify the 39 most predictive correlations derived from the rsfMRI data. The data is from a cohort of 176 healthy right-handed volunteers, aged 18-78 years (95/81 male/female, mean age 48, SD 17) collected at the Mind Research Imaging Center at the National Cheng Kung University. We establish a normal reference model by excluding 68 outliers, which achieves a leave-one-out mean absolute error of 2.48 years. By asking which additional features that are needed to predict the chronological age of the outliers with a smaller error, we identify correlations predictive of abnormal aging. These are associated with the Default Mode Network (DMN). Our normal reference model has the lowest prediction error among published models evaluated on adult subjects of almost all ages and is thus a candidate for screening for abnormal brain aging that has not yet manifested in cognitive decline. This study advances our ability to predict brain aging and provides insights into potential biomarkers for assessing brain age, suggesting that the role of DMN in brain aging should be studied further.
Journal Article
The Health Equity Scholars Program: Fostering Culturally Competent and Successful Independent Investigators in Alzheimer's Disease and Related Dementia Research
INTRODUCTION The Health Equity Scholars Program (HESP) addresses the critical need for a diverse, culturally competent workforce to study and treat older adults from underrepresented populations (URPs) with Alzheimer's disease and related dementias (AD/ADRD). The HESP offers tailored mentored training in AD/ADRD research concepts, aiming to develop successful independent researchers. It recruits Scholars from underrepresented backgrounds as well as those passionate about AD/ADRD health equity research. METHODS We (1) describe the fundamental elements of the HESP, and (2) present preliminary data from the HESP program evaluation results performed by an outside agency, pre–post participation surveys, and Scholar accomplishments. RESULTS The HESP Scholars reported high rates of proficiency, satisfaction, and competency in nearly all evaluated areas, and have been successful in obtaining grants, promotions, and publications. DISCUSSION These initial outcomes data suggest that the HESP is meeting its objective of diversifying the workforce in the field of AD/ADRD research and care. Highlights The Health Equity Scholars Program aims to cultivate a diverse and culturally competent workforce, who are well‐prepared to study and treat underrepresented older adults with Alzheimer's disease and related dementias (AD/ADRD). The program provides tailored mentored training in AD/ADRD research concepts, with the goal of nurturing successful independent researchers. Rigorous evaluation processes for applications ensure the selection of highly qualified Scholars. The program includes tailored training activities such as seminars and grant writing workshops, and tracks Scholar achievements while undergoing annual external evaluation to enhance its training program iteratively.
Journal Article