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Poor sleep may age the brain faster, according to a major imaging study from Karolinska Institutet. People who struggle with poor sleep tend to have brains that look older than their actual age, according...

Poor sleep may accelerate brain ageing, with disrupted rest linked to inflammation and cognitive decline. Healthy sleep habits can help protect brain health.

A new study reports that individuals with chronic insomnia may face a more rapid decline in memory and thinking abilities as they age. Published in Neurology, the medical journal of the American Academy...

The green-Mediterranean diet may protect against brain aging by reducing protein markers linked to cognitive decline. Adopting a green-Mediterranean diet, which incorporates green tea and the aquatic...

Researchers utilized longitudinal neuroimaging data to examine the effect of the COVID-19 pandemic on brain age.

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The authors identify ferritin light chain 1 (FTL1), an iron-associated protein, as a pro-aging neuronal factor that increases with age and promotes cognitive decline. Targeting FTL1 in the brain impro...

ine. What is unclear, however, is the extent to which antioxidant nutrients may exert neuroprotective effects through their influence on established indicators of age-related changes in brain tissue. ...

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Background: Precise assessment of brain aging is crucial for early detection of neurodegenerative disorders and aiding clinical practice. Existing magnetic resonance imaging (MRI)–based methods excel in this task, but they still have room for improvement in capturing local morphological variations across brain regions and preserving the inherent neurobiological topological structures. Objective: To develop and validate a deep learning framework incorporating both connectivity and complexity for accurate brain aging estimation, facilitating early identification of neurodegenerative diseases. Methods: We used 5889 T1-weighted MRI scans from the Alzheimer’s Disease Neuroimaging Initiative dataset. We proposed a novel brain vision graph neural network (BVGN), incorporating neurobiologically informed feature extraction modules and global association mechanisms to provide a sensitive deep learning–based imaging biomarker. Model performance was evaluated using mean absolute error (MAE) against benchmark models, while generalization capability was further validated on an external UK Biobank dataset. We calculated the brain age gap across distinct cognitive states and conducted multiple logistic regressions to compare its discriminative capacity against conventional cognitive-related variables in distinguishing cognitively normal (CN) and mild cognitive impairment (MCI) states. Longitudinal track, Cox regression, and Kaplan-Meier plots were used to investigate the longitudinal performance of the brain age gap. Results: The BVGN model achieved an MAE of 2.39 years, surpassing current state-of-the-art approaches while obtaining an interpretable saliency map and graph theory supported by medical evidence. Furthermore, its performance was validated on the UK Biobank cohort (N=34,352) with an MAE of 2.49 years. The brain age gap derived from BVGN exhibited significant difference across cognitive states (CN vs MCI vs Alzheimer disease; P

Even when people didn't get sick.