Graph Spectral Characterization of Brain Cortical Morphology

The human brain cortical layer has a convoluted morphology that is unique to each individual. Characterization of the cortical morphology is necessary in longitudinal studies of structural brain change, as well as in discriminating individuals in health and disease. A method for encoding the cortical morphology in the form of a graph is presented. The design of graphs that encode the global cerebral hemisphere cortices as well as localized cortical regions is proposed. Spectral metrics derived from these graphs are then studied and proposed as descriptors of cortical morphology. As proof-of-concept of their applicability in characterizing cortical morphology, the metrics are studied in the context of hemispheric asymmetry as well as gender dependent discrimination of cortical morphology.

[1]  Paul M. Thompson,et al.  Submission PDF Mapping Cortical Brain Asymmetry in 17 , 141 Healthy Individuals Worldwide via the ENIGMA Consortium , 2018 .

[2]  A M Dale,et al.  Measuring the thickness of the human cerebral cortex from magnetic resonance images. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[3]  A. Simmons,et al.  Multivariate data analysis and machine learning in Alzheimer's disease with a focus on structural magnetic resonance imaging. , 2014, Journal of Alzheimer's disease : JAD.

[4]  Pierre Vandergheynst,et al.  Spectrum-Adapted Tight Graph Wavelet and Vertex-Frequency Frames , 2013, IEEE Transactions on Signal Processing.

[5]  Miloš Daković,et al.  Reduced Interference Vertex-Frequency Distributions , 2018, IEEE Signal Processing Letters.

[6]  Stanley Durrleman,et al.  Spatiotemporal analysis for detection of pre-symptomatic shape changes in neurodegenerative diseases: Initial application to the GENFI cohort , 2019, NeuroImage.

[7]  Dimitri Van De Ville,et al.  Canonical cerebellar graph wavelets and their application to FMRI activation mapping , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[8]  Dimitri Van De Ville,et al.  Signal-Adapted Tight Frames on Graphs , 2016, IEEE Transactions on Signal Processing.

[9]  Mark E Bastin,et al.  Sex Differences in the Adult Human Brain: Evidence from 5216 UK Biobank Participants , 2017, bioRxiv.

[10]  Ulrike von Luxburg,et al.  A tutorial on spectral clustering , 2007, Stat. Comput..

[11]  Olivier Salvado,et al.  Detecting global and local hippocampal shape changes in Alzheimer's disease using statistical shape models , 2012, NeuroImage.

[12]  B. Parlett.  The Symmetric Eigenvalue Problem , 1981 .

[13]  Niklas Peinecke,et al.  Laplace-Beltrami spectra as 'Shape-DNA' of surfaces and solids , 2006, Comput. Aided Des..

[14]  Dimitri Van De Ville,et al.  Statistical parametric mapping of functional MRI data using wavelets adapted to the cerebral cortex , 2013, 2013 IEEE 10th International Symposium on Biomedical Imaging.

[15]  Essa Yacoub,et al.  The WU-Minn Human Connectome Project: An overview , 2013, NeuroImage.

[16]  Marie Chupin,et al.  Multidimensional classification of hippocampal shape features discriminates Alzheimer's disease and mild cognitive impairment from normal aging , 2009, NeuroImage.

[17]  Polina Golland,et al.  BrainPrint: A discriminative characterization of brain morphology , 2015, NeuroImage.

[18]  D. Harvey,et al.  Measures of brain morphology and infarction in the framingham heart study: establishing what is normal , 2005, Neurobiology of Aging.

[19]  Karolinska Schizophrenia.  Mapping cortical brain asymmetry in 17,141 healthy individuals worldwide via the ENIGMA consortium , 2018 .

[20]  Dimitri Van De Ville,et al.  Anatomically-adapted graph wavelets for improved group-level fMRI activation mapping , 2015, NeuroImage.

[21]  Bruce Fischl,et al.  FreeSurfer , 2012, NeuroImage.