Predicting ‘Brainage’ in late childhood to adolescence (6-17yrs) using structural MRI, morphometric similarity, and machine learning
Daniel Griffiths-King; Amanda Wood; Jan Novák · 2023 · Scientific Reports
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
Brain development is regularly studied using structural MRI. Recently, studies have used a combination of statistical learning and large-scale imaging databases of healthy children to predict an individual's age from structural MRI. This data-driven, predicted 'Brainage' typically differs from the subjects chronological age, with this difference a potential measure of individual difference. Few studies have leveraged higher-order or connectomic representations of structural MRI data for this Brainage approach. We leveraged morphometric similarity as a network-level approach to structural MRI t
Abstract by Daniel Griffiths-King; Amanda Wood; Jan Novák, Scientific Reports (2023) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1038/s41598-023-42414-5