sGraSP: A graph-based method for the derivation of subject-specific functional parcellations of the brain

- We describe sGraSP, a novel subject-specific functional parcellation method.
- sGraSP is tested using a large neurodevelopmental cohort (859 scans total).
- Additional tests are generated by mixing subject and population signal.
- sGraSP outperforms simpler approaches based on Voronoi tessellations.
- Neurodevelopmental trends are robustly captured by sGraSP for all the signal mixing.

BackgroundResting-state fMRI (rs-fMRI) has emerged as a prominent tool for the study of functional connectivity. The identification of the regions associated with the different brain functions has received significant interest. However, most of the studies conducted so far have focused on the definition of a common set of regions, valid for an entire population. The variation of the functional regions within a population has rarely been accounted for.New methodIn this paper, we propose sGraSP, a graph-based approach for the derivation of subject-specific functional parcellations. Our method generates first a common parcellation for an entire population, which is then adapted to each subject individually.ResultsSeveral cortical parcellations were generated for 859 children being part of the Philadelphia Neurodevelopmental Cohort. The stability of the parcellations generated by sGraSP was tested by mixing population and subject rs-fMRI signals, to generate subject-specific parcels increasingly closer to the population parcellation. We also checked if the parcels generated by our method were better capturing a development trend underlying our data than the original parcels, defined for the entire population.Comparison with existing methodsWe compared sGraSP with a simpler and faster approach based on a Voronoi tessellation, by measuring their ability to produce functionally coherent parcels adapted to the subject data.ConclusionsOur parcellations outperformed the Voronoi tessellations. The parcels generated by sGraSP vary consistently with respect to signal mixing, the results are highly reproducible and the neurodevelopmental trend is better captured with the subject-specific parcellation, under all the signal mixing conditions.