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Independent components of human brain morphology

Lookup NU author(s): Dr Yujiang Wang, Karoline Leiberg, Dr Joe Necus, Dr Peter Taylor

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

© 2020. Quantification of brain morphology has become an important cornerstone in understanding brain structure. Measures of cortical morphology such as thickness and surface area are frequently used to compare groups of subjects or characterise longitudinal changes. However, such measures are often treated as independent from each other. A recently described scaling law, derived from a statistical physics model of cortical folding, demonstrates that there is a tight covariance between three commonly used cortical morphology measures: cortical thickness, total surface area, and exposed surface area. We show that assuming the independence of cortical morphology measures can hide features and potentially lead to misinterpretations. Using the scaling law, we account for the covariance between cortical morphology measures and derive novel independent measures of cortical morphology. By applying these new measures, we show that new information can be gained; in our example we show that distinct morphological alterations underlie healthy ageing compared to temporal lobe epilepsy, even on the coarse level of a whole hemisphere. We thus provide a conceptual framework for characterising cortical morphology in a statistically valid and interpretable manner, based on theoretical reasoning about the shape of the cortex.


Publication metadata

Author(s): Wang Y, Leiberg K, Ludwig T, Little B, Necus JH, Winston G, Vos SB, Tisi JD, Duncan JS, Taylor PN, Mota B

Publication type: Article

Publication status: Published

Journal: NeuroImage

Year: 2021

Volume: 226

Print publication date: 01/02/2021

Online publication date: 10/11/2020

Acceptance date: 05/11/2020

Date deposited: 04/01/2021

ISSN (print): 1053-8119

ISSN (electronic): 1095-9572

Publisher: Academic Press Inc.

URL: https://doi.org/10.1016/j.neuroimage.2020.117546

DOI: 10.1016/j.neuroimage.2020.117546

PubMed id: 33186714


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