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Combining Surface and Fiber Geometry: An Integrated Approach to Brain Morphology

Institution:
1Laboratory for Mathematics in Imaging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
2Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
3Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.
4Children’s Hospital of Philadelphia, Philadelphia, PA, USA.
Publisher:
Int Conf Med Image Comput Comput Assist Interv. MICCAI 2013
Publication Date:
Sep-2013
Volume Number:
16
Issue Number:
Pt 1
Pages:
50-57
Citation:
Int Conf Med Image Comput Comput Assist Interv. 2013 Sep;16(Pt 1):50-7.
PubMed ID:
24505648
PMCID:
PMC4041908
Appears in Collections:
NAC, LMI, NA-MIC, SPL
Sponsors:
R01 MH092862/MH/NIMH NIH HHS/United States
R01 MH074794/MH/NIMH NIH HHS/United States
R01 MH082918/MH/NIMH NIH HHS/United States
R01 MH097979/MH/NIMH NIH HHS/United States
P41 EB015902/EB/NIBIB NIH HHS/United States
P41 RR013218/RR/NCRR NIH HHS/United States
Pennsylvania Department of Health grants SAP 4100042728, SAP 4100047863
Swedish Research Council (VR) grant 2012-3682
Generated Citation:
Savadjiev P., Rathi Y., Bouix S., Smith A.R., Schultz R.T., Verma R., Westin C-F. Combining Surface and Fiber Geometry: An Integrated Approach to Brain Morphology. Int Conf Med Image Comput Comput Assist Interv. 2013 Sep;16(Pt 1):50-7. PMID: 24505648. PMCID: PMC4041908.
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Despite the fact that several theories link cortical development and function to the development of white matter and its geometrical structure, the relationship between gray and white matter morphology has not been widely researched. In this paper, we propose a novel framework for investigating this relationship. Given a set of fiber tracts which connect to a particular cortical region, the key idea is to compute two scalar fields that represent geometrical characteristics of the white matter and of the surface of the cortical region. The distributions of these scalar values are then linked via Mutual Information, which results in a quantitative marker that can be used in the study of normal and pathological brain structure and development. We apply this framework to a population study on autism spectrum disorder in children.

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