ArticleJournal of neuroscience methods2017
sGraSP: A graph-based method for the derivation of subject-specific functional parcellations of the brain.
Article in Journal of neuroscience methods, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Mapping Functional Brain Organization Using Artificial Intelligence.Chemical & biomedical imaging · 2026Review
- Functional parcellation of the hippocampus by semi-supervised clustering of resting state fMRI data.Scientific reports · 2020Article
- Personalizing neuromodulation.International journal of psychophysiology : official journal of the International Organization of Psychophysiology · 2020Review
- Harnessing networks and machine learning in neuropsychiatric care.Current opinion in neurobiology · 2019Review
- A review and outlook on visual analytics for uncertainties in functional magnetic resonance imaging.Brain informatics · 2018Review
- Subject-Specific Structural Parcellations Based on Randomized AB-divergences.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2017Article
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Authors and funding
5 authors.
Funding
Abstract
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.
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