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Lookup NU author(s): Professor Yujiang WangORCiD, Dr Karoline LeibergORCiD, Dr Beth LittleORCiD, Dr Joe Necus, Professor Peter TaylorORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 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.
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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