Predicting ‘Brainage’ in the Developmental Period using Structural MRI, Morphometric Similarity, and Machine Learning


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, ‘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 to generate predictive models of age. We benchmarked these novel brain-age approaches using morphometric similarity against more typical, single feature (i.e. cortical thickness) approaches. We showed that these novel methods did not outperform cortical thickness or cortical volume measures. All models were significantly biased by age, but robust to motion confounds. The main results show that, whilst morphometric similarity mapping may be a novel way to leverage additional information from a T1-weighted structural MRI beyond individual features, in the context of a brain-age framework, morphometric similarity does not explain more variance than individual structural features. Morphometric similarity as a network-level approach to structural MRI may be poorly positioned to study individual differences in brain development in healthy individuals.

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Divisions: College of Health & Life Sciences > Aston Institute of Health & Neurodevelopment (AIHN)
College of Health & Life Sciences > School of Psychology
College of Health & Life Sciences
Last Modified: 19 Feb 2024 08:05
Date Deposited: 03 Mar 2023 10:51
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Related URLs: https://www.res ... e/rs-2583936/v1 (Publisher URL)
http://preproce ... (Related URL)
PURE Output Type: ["eprint_fieldname_pure_output_type_workingpaper/preprint" not defined]
Published Date: 2023-02-28
Authors: Griffiths-King, Daniel (ORCID Profile 0000-0001-5797-9203)
Wood, Amanda (ORCID Profile 0000-0002-1537-6858)
Novak, Jan (ORCID Profile 0000-0001-5173-3608)



Version: Draft Version

License: Creative Commons Attribution

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