TY - JOUR
T1 - Atlas-based analysis of resting-state functional connectivity
T2 - Evaluation for reproducibility and multi-modal anatomy-function correlation studies
AU - Faria, Andreia V.
AU - Joel, Suresh E.
AU - Zhang, Yajing
AU - Oishi, Kenichi
AU - van Zjil, Peter C.M.
AU - Miller, Michael I.
AU - Pekar, James J.
AU - Mori, Susumu
N1 - Funding Information:
This publication was made possible by NIH grants UL1 RR025005 from NCRR and NIH Roadmap for Medical Research (AVF); RO1AG20012 , and RO1NS058299 (SM), R21AG033774 , and P50AG005146 (KO); and P41 EB015909 from NCRR/NIBIB . Its contents are solely the responsibility of the authors and do not necessarily represent the official view of any of these institutes.
PY - 2012/7/2
Y1 - 2012/7/2
N2 - Resting state functional connectivity MRI (rsfc-MRI) reveals a wealth of information about the functional organization of the brain, but poses unique challenges for quantitative image analysis, mostly related to the large number of voxels with low signal-to-noise ratios. In this study, we tested the idea of using a prior spatial parcellation of the entire brain into various structural units, to perform an analysis on a structure-by-structure, rather than voxel-by-voxel, basis. This analysis, based upon atlas parcels, potentially offers enhanced SNR and reproducibility, and can be used as a common anatomical framework for cross-modality and cross-subject quantitative analysis. We used Large Deformation Diffeomorphic Metric Mapping (LDDMM) and a deformable brain atlas to parcel each brain into 185 regions. To investigate the precision of the cross-subject analysis, we computed inter-parcel correlations in 20 participants, each of whom was scanned twice, as well as the consistency of the connectivity patterns inter- and intra-subject, and the intersession reproducibility. We report significant inter-parcel correlations consistent with previous findings, and high test-retest reliability, an important consideration when the goal is to compare clinical populations. As an example of the cross-modality analysis, correlation with anatomical connectivity is also examined.
AB - Resting state functional connectivity MRI (rsfc-MRI) reveals a wealth of information about the functional organization of the brain, but poses unique challenges for quantitative image analysis, mostly related to the large number of voxels with low signal-to-noise ratios. In this study, we tested the idea of using a prior spatial parcellation of the entire brain into various structural units, to perform an analysis on a structure-by-structure, rather than voxel-by-voxel, basis. This analysis, based upon atlas parcels, potentially offers enhanced SNR and reproducibility, and can be used as a common anatomical framework for cross-modality and cross-subject quantitative analysis. We used Large Deformation Diffeomorphic Metric Mapping (LDDMM) and a deformable brain atlas to parcel each brain into 185 regions. To investigate the precision of the cross-subject analysis, we computed inter-parcel correlations in 20 participants, each of whom was scanned twice, as well as the consistency of the connectivity patterns inter- and intra-subject, and the intersession reproducibility. We report significant inter-parcel correlations consistent with previous findings, and high test-retest reliability, an important consideration when the goal is to compare clinical populations. As an example of the cross-modality analysis, correlation with anatomical connectivity is also examined.
KW - Atlas
KW - Connectivity
KW - FMRI
KW - Resting state
KW - Rsfc-fMRI
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U2 - 10.1016/j.neuroimage.2012.03.078
DO - 10.1016/j.neuroimage.2012.03.078
M3 - Article
C2 - 22498656
AN - SCOPUS:84861191169
SN - 1053-8119
VL - 61
SP - 613
EP - 621
JO - NeuroImage
JF - NeuroImage
IS - 3
ER -