Brain-Age delta is a non-invasive marker of biological brain aging that is sensitive to the presence of risk factors and altered biomarkers of Alzheimer's disease in non-demented individuals.

Machine learning-based models of brain age using distinct MRI modalities (structural MRI and functional connectivity) capture complementary signals related to healthy age differences and capture unique patterns related to Alzheimer disease, including stages of disease progression, biomarkers, and cognition.

When used to capture fluid cognition, Brain Age likely fails to add substantially more information over and above chronological age and could miss up to around one-third of the variation.

Protein changes in cerebral cortex following a mild traumatic brain injury (mTBI) identified herein will help in the development of monitoring and response biomarkers that may translate to clinical mTBI.

DNL343, a brain-penetrant integrated stress response inhibitor, reduces neurodegeneration in mouse models and identifies biomarker candidates for assessing treatment responses in clinical settings.

Multiscale Graph Correlation, an interpretable hypothesis test with strong theoretical guarantees for discerning relationships in complex data, requires about half the sample size as other methods, whilst maintaining computational tractability.

Patients with Type 2 diabetes show patterns of neurodegeneration consistent with accelerated brain aging, including impaired cognition and loss of brain volume, which are more severe in those with increased disease duration.

A functional connectivity gradient in striatum is obtained that maps onto DaT SPECT-derived dopaminergic projections and thereby likely provides a new biomarker for investigating dopaminergic (dys)function in the human brain.

A panel of serum biomarkers, including dietary and microbial-derived metabolites related to the gut-brain axis, were identified as potential for tracking children at risk of early childhood developmental delays.

Predicting age jointly from multimodal brain images and electrophysiology with machine learning enhances detecting health issues and facets of cognitive decline.