A whole brain fMRI atlas generated via spatially constrained spectral clustering

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/. Hum Brain Mapp, 2012. © 2011 Wiley Periodicals, Inc

[1]  R Cameron Craddock,et al.  Disease state prediction from resting state functional connectivity , 2009, Magnetic resonance in medicine.

[2]  M. Rushworth,et al.  Behavioral / Systems / Cognitive Connectivity-Based Parcellation of Human Cingulate Cortex and Its Relation to Functional Specialization , 2008 .

[3]  Nava Rubin,et al.  Cluster-based analysis of FMRI data , 2006, NeuroImage.

[4]  Timothy Edward John Behrens,et al.  Diffusion-Weighted Imaging Tractography-Based Parcellation of the Human Lateral Premotor Cortex Identifies Dorsal and Ventral Subregions with Anatomical and Functional Specializations , 2007, The Journal of Neuroscience.

[5]  Karl J. Friston,et al.  Unified segmentation , 2005, NeuroImage.

[6]  Edward T. Bullmore,et al.  Whole-brain anatomical networks: Does the choice of nodes matter? , 2010, NeuroImage.

[7]  Yingli Lu,et al.  Region growing method for the analysis of functional MRI data , 2003, NeuroImage.

[8]  V. Haughton,et al.  Frequencies contributing to functional connectivity in the cerebral cortex in "resting-state" data. , 2001, AJNR. American journal of neuroradiology.

[9]  Xenophon Papademetris,et al.  Graph-theory based parcellation of functional subunits in the brain from resting-state fMRI data , 2010, NeuroImage.

[10]  M. P. van den Heuvel,et al.  Normalized Cut Group Clustering of Resting-State fMRI Data , 2008, PloS one.

[11]  R Baumgartner,et al.  Fuzzy clustering of gradient‐echo functional MRI in the human visual cortex. Part I: Reproducibility , 1997, Journal of magnetic resonance imaging : JMRI.

[12]  Thomas E. Nichols,et al.  Non-white noise in fMRI: Does modelling have an impact? , 2006, NeuroImage.

[13]  Charles A. Micchelli,et al.  On Spectral Learning , 2010, J. Mach. Learn. Res..

[14]  Jianbo Shi,et al.  Multiclass spectral clustering , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[15]  Marie desJardins,et al.  Constrained Spectral Clustering under a Local Proximity Structure Assumption , 2005, FLAIRS.

[16]  Christian Windischberger,et al.  Toward discovery science of human brain function , 2010, Proceedings of the National Academy of Sciences.

[17]  Ulrike von Luxburg,et al.  A tutorial on spectral clustering , 2007, Stat. Comput..

[18]  Maurizio Corbetta,et al.  The human brain is intrinsically organized into dynamic, anticorrelated functional networks. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[19]  David Borsook,et al.  Modulation of Resting State Functional Connectivity of the Brain by Naloxone Infusion , 2008, Brain Imaging and Behavior.

[20]  H. Alkadhi,et al.  Localization of the motor hand area to a knob on the precentral gyrus. A new landmark. , 1997, Brain : a journal of neurology.

[21]  O. Sporns,et al.  Mapping the Structural Core of Human Cerebral Cortex , 2008, PLoS biology.

[22]  R W Cox,et al.  AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. , 1996, Computers and biomedical research, an international journal.

[23]  L. K. Hansen,et al.  On Clustering fMRI Time Series , 1999, NeuroImage.

[24]  K. Amunts,et al.  Receptor mapping: architecture of the human cerebral cortex , 2009, Current opinion in neurology.

[25]  P. Rousseeuw.  Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[26]  Yingli Lu,et al.  Regional homogeneity approach to fMRI data analysis , 2004, NeuroImage.

[27]  Habib Benali,et al.  Identification of large-scale networks in the brain using fMRI , 2006, NeuroImage.

[28]  X Hu,et al.  Analysis of functional magnetic resonance imaging data using self‐organizing mapping with spatial connectivity , 1999, Magnetic resonance in medicine.

[29]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[30]  Karl J. Friston,et al.  Statistical parametric maps in functional imaging: A general linear approach , 1994 .

[31]  S Makeig,et al.  Analysis of fMRI data by blind separation into independent spatial components , 1998, Human brain mapping.

[32]  R Baumgartner,et al.  Fuzzy clustering of gradient‐echo functional MRI in the human visual cortex. Part II: Quantification , 1997, Journal of magnetic resonance imaging : JMRI.

[33]  B. Biswal,et al.  The resting brain: unconstrained yet reliable. , 2009, Cerebral cortex.

[34]  Justin L. Vincent,et al.  Precuneus shares intrinsic functional architecture in humans and monkeys , 2009, Proceedings of the National Academy of Sciences.

[35]  Scott J Peltier,et al.  Detecting low‐frequency functional connectivity in fMRI using a self‐organizing map (SOM) algorithm , 2003, Human brain mapping.

[36]  Simon B. Eickhoff,et al.  A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data , 2005, NeuroImage.

[37]  Jean-Baptiste Poline,et al.  Dealing with the shortcomings of spatial normalization: Multi‐subject parcellation of fMRI datasets , 2006, Human brain mapping.

[38]  Damien A. Fair,et al.  Defining functional areas in individual human brains using resting functional connectivity MRI , 2008, NeuroImage.

[39]  Nicholas Ayache,et al.  Parcellation of brain images with anatomical and functional constraints for fMRI data analysis , 2002, Proceedings IEEE International Symposium on Biomedical Imaging.

[40]  Yaniv Assaf,et al.  Cluster analysis of resting-state fMRI time series , 2009, NeuroImage.

[41]  Liang Wang,et al.  Parcellation‐dependent small‐world brain functional networks: A resting‐state fMRI study , 2009, Human brain mapping.

[42]  M. Petrides,et al.  Broca’s region: linking human brain functional connectivity data and non‐human primate tracing anatomy studies , 2010, The European journal of neuroscience.

[43]  Nicholas Ayache,et al.  Improved Detection Sensitivity in Functional MRI Data Using a Brain Parcelling Technique , 2002, MICCAI.

[44]  N. Tzourio-Mazoyer,et al.  Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.

[45]  Karl J. Friston,et al.  A critique of functional localisers , 2006, NeuroImage.

[46]  Jonathan D. Power,et al.  Prediction of Individual Brain Maturity Using fMRI , 2010, Science.

[47]  M. Torrens.  Co-Planar Stereotaxic Atlas of the Human Brain—3-Dimensional Proportional System: An Approach to Cerebral Imaging, J. Talairach, P. Tournoux. Georg Thieme Verlag, New York (1988), 122 pp., 130 figs. DM 268 , 1990 .

[48]  J L Lancaster,et al.  Automated Talairach Atlas labels for functional brain mapping , 2000, Human brain mapping.

[49]  L. R. Dice.  Measures of the Amount of Ecologic Association Between Species , 1945 .

[50]  Daniel S. Margulies,et al.  Mapping the functional connectivity of anterior cingulate cortex , 2007, NeuroImage.

[51]  A. Schleicher,et al.  Receptor architecture of human cingulate cortex: Evaluation of the four‐region neurobiological model , 2009, Human brain mapping.

[52]  Thomas E. Nichols,et al.  Everything You Never Wanted to Know about Circular Analysis, but Were Afraid to Ask , 2010, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[53]  Mark W. Woolrich,et al.  Network modelling methods for FMRI , 2011, NeuroImage.

[54]  Stephen M Smith,et al.  Correspondence of the brain's functional architecture during activation and rest , 2009, Proceedings of the National Academy of Sciences.