A whole brain fMRI atlas generated via spatially constrained spectral clustering
Open accessAbstract
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/.
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Use of inappropriate node definition, such as a gross structural atlas-based parcellation that corresponds poorly to functional boundaries, results in network matrices whose neurobiological interpretability is limited.
“One specific risk is the use of inappropriate node definition [56, 66], such as a gross structural atlas-based parcellation that corresponds poorly to functional boundaries and results in network matrices whose neurobiological interpretability is limited”
From Functional connectomics from resting-state fMRI (2013) · cites this paper as [66]
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- Authors
- Craddock, R. C., James, G. A., Holtzheimer, P. E., Hu, X. P., & Mayberg, H. S.
- Affiliations
- Baylor College of Medicine (US)
- University of Arkansas for Medical Sciences (US)
- Emory University (US)
- Georgia Institute of Technology (US)
- Corresponding author
- Craddock, R. Cameron
- Hu, Xiaoping P.
- Venue
- Human Brain Mapping
- Publisher
- Wiley
- Published
- 2011-07-18
- Volume
- 33
- Issue
- 8
- Pages
- 1914-1928
- ISSN
- 1065-9471 (ISSN-L)
- 1097-0193
- PMC
- PMC3838923
- OpenAlex
- W2122457251
- Cited by
- 1,636
- References
- 55
- Citations by year
- 2026: 98 · 2025: 130 · 2024: 125 · 2023: 115 · 2022: 124 · 2021: 119 · 2020: 123 · 2019: 143 · 2018: 135 · 2017: 153 · 2016: 115 · 2015: 103 · 2014: 74 · 2013: 46 · 2012: 27
- FWCI
- 24.51
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Human Brain Mapping (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute of Mental Health (R01MH073719, P50MH077083, K23MH077869)
- National Institute of Biomedical Imaging and Bioengineering (R01EB002009)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- whole-brain parcellation, resting-state functional connectivity, spectral clustering, functional brain regions, spatially constrained clustering
- MeSH terms
- Adolescent, Adult, Anatomy, Artistic, Atlases as Topic, Brain, Brain Mapping, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Middle Aged, Software, Cluster Analysis, Young Adult
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