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

N. Honnorat, H. Eavani, T. D. Satterthwaite, R. E. Gur, R. C. Gur, C. Davatzikos

Research output: Contribution to journalArticlepeer-review

48 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)207-221
Number of pages15
JournalNeuroImage
Volume106
DOIs
StatePublished - Feb 1 2015
Externally publishedYes

Keywords

  • Clustering comparison
  • MRF
  • Parcellation
  • Resting-state fMRI

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

Fingerprint

Dive into the research topics of 'GraSP: Geodesic Graph-based Segmentation with Shape Priors for the functional parcellation of the cortex'. Together they form a unique fingerprint.

Cite this