Independent components of human brain morphology

NeuroImage, Vol. 226 (2021)

Mots clés
Authors
  • Yujiang Wang
  • Corresponding author at: Interdisciplinary Complex Systems Group, School of Computing, Newcastle University, Newcastle upon Tyne, UK.; CNNP Lab (www.cnnp-lab.com), Interdisciplinary Complex Systems Group, School of Computing, Newcastle University, Newcastle upon Tyne, UK; Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK; UCL Queen Square Institute of Neurology, London, UK
  • Karoline Leiberg
  • CNNP Lab (www.cnnp-lab.com), Interdisciplinary Complex Systems Group, School of Computing, Newcastle University, Newcastle upon Tyne, UK
  • Tobias Ludwig
  • Graduate Training Center of Neuroscience, University of Tübingen, Tübingen, Germany
  • Bethany Little
  • CNNP Lab (www.cnnp-lab.com), Interdisciplinary Complex Systems Group, School of Computing, Newcastle University, Newcastle upon Tyne, UK; Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
  • Joe H Necus
  • CNNP Lab (www.cnnp-lab.com), Interdisciplinary Complex Systems Group, School of Computing, Newcastle University, Newcastle upon Tyne, UK; Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
  • Gavin Winston
  • UCL Queen Square Institute of Neurology, London, UK; Department of Medicine, Division of Neurology, Queen’s University, Kingston, Canada; Epilepsy Society MRI Unit, Chalfont St Peter, UK
  • Sjoerd B Vos
  • UCL Queen Square Institute of Neurology, London, UK; Centre for Medical Image Computing (CMIC), University College London, London, UK; Epilepsy Society MRI Unit, Chalfont St Peter, UK
  • Jane de Tisi
  • UCL Queen Square Institute of Neurology, London, UK
  • John S Duncan
  • UCL Queen Square Institute of Neurology, London, UK; Epilepsy Society MRI Unit, Chalfont St Peter, UK
  • Peter N Taylor
  • CNNP Lab (www.cnnp-lab.com), Interdisciplinary Complex Systems Group, School of Computing, Newcastle University, Newcastle upon Tyne, UK; Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK; UCL Queen Square Institute of Neurology, London, UK
  • Bruno Mota
  • Institute of Physics, Federal University of Rio de Janeiro, Brazil

Résumé

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.

Read more: fulltext