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Circular Representation of Human Cortical Networks for Subject and Population-level Connectomic Visualization
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Institution: |
Laboratory of Neuro Imaging, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, 635 Charles E Young Drive South, Suite 225, Los Angeles, CA 90095, USA. andrei.irimia@loni.ucla.edu |
Publisher: |
Elsevier |
Publication Date: |
Apr-2012 |
Journal: |
Neuroimage |
Volume Number: |
60 |
Issue Number: |
2 |
Pages: |
1340-51 |
Citation: |
Neuroimage. 2012 Apr 2;60(2):1340-51. |
PubMed ID: |
22305988 |
PMCID: |
PMC3594415 |
Keywords: |
Connectomics, DTI, MRI, Neuroimaging, Cortical network |
Appears in Collections: |
NA-MIC |
Sponsors: |
U54 EB005149 (EB) funded by NIBIB NIH HHS |
Generated Citation: |
Irimia A., Chambers M.C., Torgerson C.M., Van Horn J.D. Circular Representation of Human Cortical Networks for Subject and Population-level Connectomic Visualization. Neuroimage. 2012 Apr 2;60(2):1340-51. PMID: 22305988. PMCID: PMC3594415. |
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Cortical network architecture has predominantly been investigated visually using graph theory representations. In the context of human connectomics, such representations are not however always satisfactory because canonical methods for vertex-edge relationship representation do not always offer optimal insight regarding functional and structural neural connectivity. This article introduces an innovative framework for the depiction of human connectomics by employing a circular visualization method which is highly suitable to the exploration of central nervous system architecture. This type of representation, which we name a 'connectogram', has the capability of classifying neuroconnectivity relationships intuitively and elegantly. A multimodal protocol for MRI/DTI neuroimaging data acquisition is here combined with automatic image segmentation to (1) extract cortical and non-cortical anatomical structures, (2) calculate associated volumetrics and morphometrics, and (3) determine patient-specific connectivity profiles to generate subject-level and population-level connectograms. The scalability of our approach is demonstrated for a population of 50 adults. Two essential advantages of the connectogram are (1) the enormous potential for mapping and analyzing the human connectome, and (2) the unconstrained ability to expand and extend this analysis framework to the investigation of clinical populations and animal models.
Additional Material
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