A whole brain fMRI atlas generated via spatially constrained spectral clustering

• Type of Material: Article
• Authors: R. Cameron Craddock, Baylor College of Medicine; G. Andrew James, University of Arkansas; Paul Holtzheimer, Emory University; Xiaoping Hu, Emory University; Helen S Mayberg, Emory University
• Language: English
• Date: 2012-08-01
• Publisher: Wiley: 12 months
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2011 Wiley Periodicals, Inc.
• Final Published Version (URL): http://dx.doi.org/10.1002/hbm.21333
• Title of Journal or Parent Work: Human Brain Mapping
• ISSN: 1065-9471
• Volume: 33
• Issue: 8
• Start Page: 1914
• End Page: 1928
• Grant/Funding Information: Data collection and salary support was provided: by P50 MH077083 (HSM), R01 MH073719 (HSM), NIH R01 EB002009 (XPH), K23 MH077869 (PEH), and a NARSAD Young Investigator Award (PEH).
• Keywords: Life Sciences & Biomedicine; atlas; Neurosciences; functional connectivity; Neurosciences & Neurology; MRI; regions of interest; TIME-SERIES; Science & Technology; IMAGING DATA; Radiology, Nuclear Medicine & Medical Imaging; Neuroimaging; resting state; SEGMENTATION; NETWORKS; PARCELLATION; ARCHITECTURE; CEREBRAL-CORTEX; clustering; STATE FUNCTIONAL CONNECTIVITY; CINGULATE CORTEX
• Research Categories: Biology, Neuroscience; Health Sciences, Radiology

Abstract

Connectivity analyses and computational modeling of human brain function from fMRI data frequently require the specification of regions of interests (ROIs). Several analyses have relied on atlases derived from anatomical or cyto-architectonic boundaries to specify these ROIs, yet the suitability of atlases for resting state functional connectivity (FC) studies has yet to be established. This article introduces a data-driven method for generating an ROI atlas by parcellating whole brain resting-state fMRI data into spatially coherent regions of homogeneous FC. Several clustering statistics are used to compare methodological trade-offs as well as determine an adequate number of clusters. Additionally, we evaluate the suitability of the parcellation atlas against four ROI atlases (Talairach and Tournoux, Harvard-Oxford, Eickoff-Zilles, and Automatic Anatomical Labeling) and a random parcellation approach. The evaluated anatomical atlases exhibit poor ROI homogeneity and do not accurately reproduce FC patterns present at the voxel scale. In general, the proposed functional and random parcellations perform equivalently for most of the metrics evaluated. ROI size and hence the number of ROIs in a parcellation had the greatest impact on their suitability for FC analysis. With 200 or fewer ROIs, the resulting parcellations consist of ROIs with anatomic homology, and thus offer increased interpretability. Parcellation results containing higher numbers of ROIs (600 or 1,000) most accurately represent FC patterns present at the voxel scale and are preferable when interpretability can be sacrificed for accuracy. The resulting atlases and clustering software have been made publicly available at: http://www.nitrc.org/projects/cluster_roi/.