CNNP Lab (https://www.cnnp-lab.com), School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom; Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom; UCL Institute of Neurology, Queen Square, London, United Kingdom
Karoline Leiberg
CNNP Lab (https://www.cnnp-lab.com), School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom
Nathan Kindred
Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom; Department of Neurosurgery, University of Iowa, Des Moines, United States
Peter Neal Taylor
CNNP Lab (https://www.cnnp-lab.com), School of Computing, Newcastle University, Newcastle upon Tyne, United Kingdom; Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom; UCL Institute of Neurology, Queen Square, London, United Kingdom
The cerebral cortex displays a bewildering diversity of shapes and sizes across and within species. Despite this diversity, we present a universal multi-scale description of primate cortices. We show that all cortical shapes can be described as a set of nested folds of different sizes. As neighbouring folds are gradually merged, the cortices of 11 primate species follow a common scale-free morphometric trajectory, that also overlaps with over 70 other mammalian species. Our results indicate that all cerebral cortices are approximations of the same archetypal fractal shape with a fractal dimension of df = 2.5. Importantly, this new understanding enables a more precise quantification of brain morphology as a function of scale. To demonstrate the importance of this new understanding, we show a scale-dependent effect of ageing on brain morphology. We observe a more than fourfold increase in effect size (from two standard deviations to eight standard deviations) at a spatial scale of approximately 2 mm compared to standard morphological analyses. Our new understanding may, therefore, generate superior biomarkers for a range of conditions in the future.