Cortical surface parcellation based on intra-subject white matter fiber clustering

• Published in: arXiv.org
• Main Authors: López-López, Narciso, Vázquez, Andrea, Poupon, Cyril, Mangin, Jean-François, Guevara, Pamela
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 16.02.2020
• Subjects: Apexes; Brain; Cerebral cortex; Clustering; Computer Science - Computer Vision and Pattern Recognition; Functional integration; Outliers (statistics); Post-processing; Quantitative Biology - Neurons and Cognition
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2002.09034

We present a hybrid method that performs the complete parcellation of the cerebral cortex of an individual, based on the connectivity information of the white matter fibers from a whole-brain tractography dataset. The method consists of five steps, first intra-subject clustering is performed on the brain tractography. The fibers that make up each cluster are then intersected with the cortical mesh and then filtered to discard outliers. In addition, the method resolves the overlapping between the different intersection regions (sub-parcels) throughout the cortex efficiently. Finally, a post-processing is done to achieve more uniform sub-parcels. The output is the complete labeling of cortical mesh vertices, representing the different cortex sub-parcels, with strong connections to other sub-parcels. We evaluated our method with measures of brain connectivity such as functional segregation (clustering coefficient), functional integration (characteristic path length) and small-world. Results in five subjects from ARCHI database show a good individual cortical parcellation for each one, composed of about 200 subparcels per hemisphere and complying with these connectivity measures.