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How to Measure Cortical Folding from MR Images: a Step-by-Step Tutorial to Compute Local Gyrification Index
Published on: January 2, 2012
Simplified gyral pattern in severe developmental microcephalies? New insights from allometric modeling for spatial
D Germanaud1, J Lefèvre2, C Fischer3
1INSERM, UMR 1129, F-75015 Paris, France; CEA, NeuroSpin, UNIACT, UNIPEDIA, F-91191 Gif sur Yvette, France; AP-HP, Hôpital Robert Debré, Service de Neuropédiatrie et Pathologie Métabolique, F-75019 Paris, France; Univ Paris Diderot, Sorbonne Paris Cité, Faculté de Médecine Paris Diderot, F-75010 Paris, France.
Abstract:
The strong positive-allometric relationship between brain size, cortical extension and gyrification complexity, recently highlighted in the general population, could be modified by brain developmental disorders. Indeed, in case of brain growth insufficiency, the pathophysiological relevance of the "simplified gyral pattern" phenotype is strongly disputed since almost no genotype-phenotype correlations have been found in primary microcephalies. Using surface scaling analysis and newly-developed spectral analysis of gyrification (Spangy), we tested whether the gyral simplification in groups of severe microcephalies related to ASPM, PQBP1 or fetal-alcohol-syndrome could be fully explained by brain size reduction according to the allometric scaling law established in typically-developing control groups, or whether an additional disease effect was to be suspected. We found the surface area reductions to be fully explained by scaling effect, leading to predictable folding intensities measured by gyrification indices. As for folding pattern assessed by spectral analysis, scaling effect also accounted for the majority of the variations, but an additional negative or positive disease effect was found in the case of ASPM and PQBP1-linked microcephalies, respectively. Our results point out the necessity of taking allometric scaling into account when studying the gyrification variability in pathological conditions. They also show that the quantitative analysis of gyrification complexity through spectral analysis can enable distinguishing between even (predictable, non-specific) and uneven (unpredictable, maybe disease-specific) gyral simplifications.
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