TY - JOUR
T1 - sGraSP
T2 - A graph-based method for the derivation of subject-specific functional parcellations of the brain
AU - Honnorat, N.
AU - Satterthwaite, T. D.
AU - Gur, R. E.
AU - Gur, R. C.
AU - Davatzikos, C.
N1 - Funding Information:
This work was supported by the grant NIH R01AG14971 and by RC2 grants from the National Institute of Mental Health MH089983 and MH089924 and P50MH096891 . Support for developing statistical analyses (TDS) was provided by a seed grant from the Center for Biomedical Computing and Image Analysis (CBICA) at Penn. Additional support was provided by K23MH098130 to TDS and the Dowshen Program for Neuroscience. Raquel E. Gur served on Otsuka Advisory Board. All other study authors report no competing interests.
Publisher Copyright:
© 2016 Elsevier B.V.
PY - 2017/2/1
Y1 - 2017/2/1
N2 - Background Resting-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 method In 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. Results Several 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 methods We 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. Conclusions Our 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.
AB - Background Resting-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 method In 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. Results Several 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 methods We 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. Conclusions Our 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.
KW - Parcellation
KW - Rs-fMRI
KW - Tessellation
UR - http://www.scopus.com/inward/record.url?scp=85004125701&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85004125701&partnerID=8YFLogxK
U2 - 10.1016/j.jneumeth.2016.11.014
DO - 10.1016/j.jneumeth.2016.11.014
M3 - Article
C2 - 27913211
AN - SCOPUS:85004125701
SN - 0165-0270
VL - 277
SP - 1
EP - 20
JO - Journal of Neuroscience Methods
JF - Journal of Neuroscience Methods
ER -