Groupwise structural parcellation of the whole cortex: A logistic random effects model based approach

ABSTRACT Current theories hold that brain function is highly related to long‐range physical connections through axonal bundles, namely extrinsic connectivity. However, obtaining a groupwise cortical parcellation based on extrinsic connectivity remains challenging. Current parcellation methods are computationally expensive; need tuning of several parameters or rely on ad‐hoc constraints. Furthermore, none of these methods present a model for the cortical extrinsic connectivity of the cortex. To tackle these problems, we propose a parsimonious model for the extrinsic connectivity and an efficient parceling technique based on clustering of tractograms. Our technique allows the creation of single subject and groupwise parcellations of the whole cortex. The parcellations obtained with our technique are in agreement with structural and functional parcellations in the literature. In particular, the motor and sensory cortex are subdivided in agreement with the human homunculus of Penfield. We illustrate this by comparing our resulting parcels with the motor strip mapping included in the Human Connectome Project data. HIGHLIGHTSWe propose a random effects model for the extrinsic connectivity.Our model allows to manipulate tractograms in a Euclidean space.We propose a whole‐cortex groupwise parceling technique.Our results are consistent across similar groups.Our results are consistent with extant parcellations.

[1]  K. Brodmann Vergleichende Lokalisationslehre der Großhirnrinde : in ihren Prinzipien dargestellt auf Grund des Zellenbaues , 1985 .

[2]  Alfred Anwander,et al.  A hierarchical method for whole‐brain connectivity‐based parcellation , 2014, Human brain mapping.

[3]  Steen Moeller,et al.  The Human Connectome Project: A data acquisition perspective , 2012, NeuroImage.

[4]  M. Lindstrom,et al.  A survey of methods for analyzing clustered binary response data , 1996 .

[5]  Alan C. Evans,et al.  Enhancement of MR Images Using Registration for Signal Averaging , 1998, Journal of Computer Assisted Tomography.

[6]  Daniel S. Margulies,et al.  Prioritizing spatial accuracy in high-resolution fMRI data using multivariate feature weight mapping , 2014, Front. Neurosci..

[7]  Fionn Murtagh,et al.  Methods of Hierarchical Clustering , 2011, ArXiv.

[8]  Jean-Francois Mangin,et al.  Tractography-Based Parcellation of the Cortex Using a Spatially-Informed Dimension Reduction of the Connectivity Matrix , 2009, MICCAI.

[9]  Daniel S. Margulies,et al.  NeuroVault.org: A repository for sharing unthresholded statistical maps, parcellations, and atlases of the human brain , 2016, NeuroImage.

[10]  Daniel Rueckert,et al.  Tractography-Driven Groupwise Multi-scale Parcellation of the Cortex , 2015, IPMI.

[11]  Klaas E. Stephan,et al.  The anatomical basis of functional localization in the cortex , 2002, Nature Reviews Neuroscience.

[12]  Rachid Deriche,et al.  Groupwise Structural Parcellation of the Cortex: A Sound Approach Based on Logistic Models , 2016, MICCAI 2016.

[13]  B. Dubois,et al.  Rostro-caudal Architecture of the Frontal Lobes in Humans , 2016, Cerebral cortex.

[14]  P. Schaeffer,et al.  Analysis of county employment and income growth in Appalachia: a spatial simultaneous-equations approach , 2007 .

[15]  P. McCullagh,et al.  Generalized Linear Models , 1984 .

[16]  Marc Modat,et al.  A Framework for Using Diffusion Weighted Imaging to Improve Cortical Parcellation , 2010, MICCAI.

[17]  Anders M. Dale,et al.  An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest , 2006, NeuroImage.

[18]  Jean-Francois Mangin,et al.  Groupwise connectivity-based parcellation of the whole human cortical surface using watershed-driven dimension reduction , 2016, Medical Image Anal..

[19]  Jonathan Goldstein,et al.  When Is ''Nearest Neighbor'' Meaningful? , 1999, ICDT.

[20]  Timothy Edward John Behrens,et al.  Characterization and propagation of uncertainty in diffusion‐weighted MR imaging , 2003, Magnetic resonance in medicine.

[21]  Saad Jbabdi,et al.  Long-range connectomics , 2013, Annals of the New York Academy of Sciences.

[22]  Mark Jenkinson,et al.  The minimal preprocessing pipelines for the Human Connectome Project , 2013, NeuroImage.

[23]  Jean-Baptiste Poline,et al.  Which fMRI clustering gives good brain parcellations? , 2014, Front. Neurosci..

[24]  Stamatios N. Sotiropoulos,et al.  Mapping Connections in Humans and Non-Human Primates , 2014 .

[25]  W. Eric L. Grimson,et al.  Using the logarithm of odds to define a vector space on probabilistic atlases , 2007, Medical Image Anal..

[26]  Fionn Murtagh,et al.  Multidimensional clustering algorithms , 1985 .

[27]  Romain Valabregue,et al.  Subdivision of the occipital lobes: An anatomical and functional MRI connectivity study , 2014, Cortex.

[28]  Alan Connelly,et al.  Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution , 2004, NeuroImage.

[29]  Jesper Andersson,et al.  A multi-modal parcellation of human cerebral cortex , 2016, Nature.

[30]  Abraham Z. Snyder,et al.  Function in the human connectome: Task-fMRI and individual differences in behavior , 2013, NeuroImage.

[31]  G. N. Lance,et al.  A General Theory of Classificatory Sorting Strategies: 1. Hierarchical Systems , 1967, Comput. J..

[32]  D. Pandya,et al.  Fiber Pathways of the Brain , 2006 .

[33]  B. Wandell,et al.  The vertical occipital fasciculus: A century of controversy resolved by in vivo measurements , 2014, Proceedings of the National Academy of Sciences.

[34]  Maxime Descoteaux,et al.  Dipy, a library for the analysis of diffusion MRI data , 2014, Front. Neuroinform..

[35]  A. Tucholka,et al.  The Corticocortical Structural Connectivity of the Human Insula , 2017, Cerebral cortex.

[36]  N. Kanwisher,et al.  The fusiform face area: a cortical region specialized for the perception of faces , 2006, Philosophical Transactions of the Royal Society B: Biological Sciences.

[37]  David K. Yu,et al.  Superficial white matter fiber systems impede detection of long-range cortical connections in diffusion MR tractography , 2015, Proceedings of the National Academy of Sciences.

[38]  Jean-Francois Mangin,et al.  Inter-subject Connectivity-Based Parcellation of a Patch of Cerebral Cortex , 2010, MICCAI.

[39]  M. Cugmas,et al.  On comparing partitions , 2015 .

[40]  J. H. Ward Hierarchical Grouping to Optimize an Objective Function , 1963 .

[41]  Alan C. Evans,et al.  Enhancement of T1 MR images using registration for signal averaging , 1996, NeuroImage.

[42]  Russell A. Poldrack,et al.  Large-scale automated synthesis of human functional neuroimaging data , 2011, Nature Methods.