Atlas-based analysis of resting-state functional connectivity: Evaluation for reproducibility and multi-modal anatomy-function correlation studies

Andreia V. Faria, Suresh E. Joel, Yajing Zhang, Kenichi Oishi, Peter C.M. van Zjil, Michael I. Miller, James J. Pekar, Susumu Mori

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)613-621
Number of pages9
JournalNeuroImage
Volume61
Issue number3
DOIs
StatePublished - Jul 2 2012

Keywords

  • Atlas
  • Connectivity
  • FMRI
  • Resting state
  • Rsfc-fMRI

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

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