Parcellating Cortical Functional Networks in Individuals

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Abstract

The capacity to identify the unique functional architecture of an individual’s brain is a critical step towards personalized medicine and understanding the neural basis of variations in human cognition and behavior. Here, we developed a novel cortical parcellation approach to accurately map functional organization at the individual level using resting-state fMRI. A population-based functional atlas and a map of inter-individual variability were employed to guide the iterative search for functional networks in individual subjects. Functional networks mapped by this approach were highly reproducible within subjects and effectively captured the variability across subjects, including individual differences in brain lateralization. The algorithm performed well across different subject populations and data types including task fMRI data. The approach was then validated by invasive cortical stimulation mapping in surgical patients, suggesting great potential for use in clinical applications.

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Most cited references 36

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A whole brain fMRI atlas generated via spatially constrained spectral clustering.

R Cameron Craddock, G. Andrew James, Paul E. Holtzheimer … (2012)

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/. Copyright © 2011 Wiley Periodicals, Inc.

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Individual variability in functional connectivity architecture of the human brain.

Sophia Mueller, Danhong Wang, Michael D. Fox … (2013)

The fact that people think or behave differently from one another is rooted in individual differences in brain anatomy and connectivity. Here, we used repeated-measurement resting-state functional MRI to explore intersubject variability in connectivity. Individual differences in functional connectivity were heterogeneous across the cortex, with significantly higher variability in heteromodal association cortex and lower variability in unimodal cortices. Intersubject variability in connectivity was significantly correlated with the degree of evolutionary cortical expansion, suggesting a potential evolutionary root of functional variability. The connectivity variability was also related to variability in sulcal depth but not cortical thickness, positively correlated with the degree of long-range connectivity but negatively correlated with local connectivity. A meta-analysis further revealed that regions predicting individual differences in cognitive domains are predominantly located in regions of high connectivity variability. Our findings have potential implications for understanding brain evolution and development, guiding intervention, and interpreting statistical maps in neuroimaging. Copyright © 2013 Elsevier Inc. All rights reserved.

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Groupwise whole-brain parcellation from resting-state fMRI data for network node identification.

X. Shen, F Tokoglu, X Papademetris … (2013)

In this paper, we present a groupwise graph-theory-based parcellation approach to define nodes for network analysis. The application of network-theory-based analysis to extend the utility of functional MRI has recently received increased attention. Such analyses require first and foremost a reasonable definition of a set of nodes as input to the network analysis. To date many applications have used existing atlases based on cytoarchitecture, task-based fMRI activations, or anatomic delineations. A potential pitfall in using such atlases is that the mean timecourse of a node may not represent any of the constituent timecourses if different functional areas are included within a single node. The proposed approach involves a groupwise optimization that ensures functional homogeneity within each subunit and that these definitions are consistent at the group level. Parcellation reproducibility of each subunit is computed across multiple groups of healthy volunteers and is demonstrated to be high. Issues related to the selection of appropriate number of nodes in the brain are considered. Within typical parameters of fMRI resolution, parcellation results are shown for a total of 100, 200, and 300 subunits. Such parcellations may ultimately serve as a functional atlas for fMRI and as such three atlases at the 100-, 200- and 300-parcellation levels derived from 79 healthy normal volunteers are made freely available online along with tools to interface this atlas with SPM, BioImage Suite and other analysis packages. Copyright © 2013 Elsevier Inc. All rights reserved.

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Journal

Journal ID (nlm-journal-id): 9809671

Journal ID (pubmed-jr-id): 21092

Journal ID (nlm-ta): Nat Neurosci

Journal ID (iso-abbrev): Nat. Neurosci.

Title: Nature neuroscience

ISSN (Print): 1097-6256

ISSN (Electronic): 1546-1726

Publication date Nihms-submitted: 23 October 2015

Publication date (Electronic): 09 November 2015

Publication date (Print): December 2015

Publication date PMC-release: 01 June 2016

Volume: 18

Issue: 12

Pages: 1853-1860

Affiliations

[1 ]Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA

[2 ]Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

[3 ]Department of Psychology, Center for Brain Science, Harvard University, Cambridge, MA, USA

[4 ]Berenson-Allen Center for Noninvasive Brain Stimulation, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA

[5 ]Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

[6 ]Department of Psychology, Yale University, New Haven, CT, USA

[7 ]Ludwig Maximilians University Munich, Institute of Clinical Radiology, Munich, Germany

[8 ]Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Austria

[9 ]Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, USA

[10 ]Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China

[11 ]Siemens Healthcare, MR Collaboration NE Asia, Beijing, China

[12 ]Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China

[13 ]Psychotic Disorders Division, McLean Hospital, Belmont, MA, USA

[14 ]Harvard-MIT Health Sciences and Technology, Institute for Medical Engineering and Science, Cambridge, MA, USA

[15 ]Department of Neurology, the First Affiliated Hospital of Anhui Medical University, Hefei, China

[16 ]Beijing Institute for Brain Disorders, Capital Medical University, Beijing, China

Author notes

Address Correspondence To: Dr. Hesheng Liu, Suite 2301, 149 13 ^th St., Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 02129, hesheng@ 123456nmr.mgh.harvard.edu Or Dr. Bo Hong, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, China, hongbo@ 123456tsinghua.edu.cn

Article

Manuscript ID: NIHMS730867

DOI: 10.1038/nn.4164

PMC ID: 4661084

PubMed ID: 26551545

SO-VID: e1142c6f-0088-4538-aa9e-767cf2fab760

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Parcellating Cortical Functional Networks in Individuals

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Most cited references 36

A whole brain fMRI atlas generated via spatially constrained spectral clustering.

Individual variability in functional connectivity architecture of the human brain.

Groupwise whole-brain parcellation from resting-state fMRI data for network node identification.

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