GraSP: Geodesic Graph-based Segmentation with Shape Priors for the functional parcellation of the cortex

• Published in: NeuroImage (Orlando, Fla.) Vol. 106; pp. 207 - 221
• Main Authors: Honnorat, N., Eavani, H., Satterthwaite, T.D., Gur, R.E., Gur, R.C., Davatzikos, C.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.02.2015; Elsevier Limited
• Subjects: Age; Algorithms; Cerebral Cortex - anatomy & histology; Cerebral Cortex - physiology; Clustering comparison; Connectome - methods; Grants; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Magnetic Resonance Imaging - methods; Methods; MRF; Parcellation; Pattern Recognition, Automated - methods; Resting-state fMRI; Sensitivity and Specificity; Software; Subtraction Technique; MRF; Resting-state fMRI; Parcellation; Clustering comparison
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2014.11.008

Resting-state functional MRI is a powerful technique for mapping the functional organization of the human brain. However, for many types of connectivity analysis, high-resolution voxelwise analyses are computationally infeasible and dimensionality reduction is typically used to limit the number of network nodes. Most commonly, network nodes are defined using standard anatomic atlases that do not align well with functional neuroanatomy or regions of interest covering a small portion of the cortex. Data-driven parcellation methods seek to overcome such limitations, but existing approaches are highly dependent on initialization procedures and produce spatially fragmented parcels or overly isotropic parcels that are unlikely to be biologically grounded. In this paper, we propose a novel graph-based parcellation method that relies on a discrete Markov Random Field framework. The spatial connectedness of the parcels is explicitly enforced by shape priors. The shape of the parcels is adapted to underlying data through the use of functional geodesic distances. Our method is initialization-free and rapidly segments the cortex in a single optimization. The performance of the method was assessed using a large developmental cohort of more than 850 subjects. Compared to two prevalent parcellation methods, our approach provides superior reproducibility for a similar data fit. Furthermore, compared to other methods, it avoids incoherent parcels. Finally, the method's utility is demonstrated through its ability to detect strong brain developmental effects that are only weakly observed using other methods. •We present a novel graph-based method for the parcellation of rs-fMRI data.•A validation on a large neuro-developmental database (>850 scans) was done.•It suggests that our method is more reproducible than two alternate approaches.•The modularity of our model opens the way to multiple applications.