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

• Published in: NeuroImage (Orlando, Fla.) Vol. 170; pp. 307 - 320
• Main Authors: Gallardo, Guillermo, Wells, William, Deriche, Rachid, Wassermann, Demian
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.04.2018; Elsevier Limited; Elsevier
• Subjects: Adult; Applications; Bioinformatics; Cerebral Cortex - diagnostic imaging; Computer Science; Cortex (motor); Cortex (somatosensory); Diffusion Tensor Imaging - methods; Female; Homunculus; Humans; Male; Medical Imaging; Models, Theoretical; Neural networks; Neuroimaging - methods; Specialization; Statistical clustering models; Statistics; Structural connectivity; Structural parcellation; Structure-function relationships; Tractography; Washington DC; United States > US; Structural parcellation; Statistical clustering models; Structural connectivity; Tractography; Structural Connectivity; Statistical Clustering Models; Structural Parcellation
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2017.01.070

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. •We 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.