Multimodality neuroimaging brain-age in UK biobank: relationship to biomedical, lifestyle, and cognitive factors
James H. Cole · 2020 · Neurobiology of Aging
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
The brain-age paradigm is proving increasingly useful for exploring aging-related disease and can predict important future health outcomes. Most brain-age research uses structural neuroimaging to index brain volume. However, aging affects multiple aspects of brain structure and function, which can be examined using multimodality neuroimaging. Using UK Biobank, brain-age was modeled in n = 2205 healthy people with T1-weighted MRI, T2-FLAIR, T2∗, diffusion-MRI, task fMRI, and resting-state fMRI. In a held-out healthy validation set (n = 520), chronological age was accurately predicted (r = 0.78,
Abstract by James H. Cole, Neurobiology of Aging (2020) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1016/j.neurobiolaging.2020.03.014