Journal
GRAPHICAL MODELS
Volume 73, Issue -, Pages 313-322Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.gmod.2010.11.002
Keywords
MRI; fMRI; Neuroimaging; Brain; Laminae; Segmentation; Surface models; Deformable surface; Isosurfaces
Categories
Funding
- NIBIB NIH HHS [R01 EB000487, R01 EB004873-04, R01 EB004873] Funding Source: Medline
- NIGMS NIH HHS [R01 GM073087-03, T32 GM007308, R01 GM074258-03, R01 GM074258-03S1, R01 GM073087, R01 GM074258] Funding Source: Medline
- Direct For Social, Behav & Economic Scie
- Division Of Behavioral and Cognitive Sci [1446377] Funding Source: National Science Foundation
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Functional magnetic resonance imaging (fMRI) has become a popular technique for studies of human brain activity. Typically, fMRI is performed with >3-mm sampling, so that the imaging data can be regarded as two-dimensional samples that average through the 1.5-4-mm thickness of cerebral cortex. The increasing use of higher spatial resolutions, <1.5-mm sampling, complicates the analysis of fMRI, as one must now consider activity variations within the depth of the brain tissue. We present a set of surface-based methods to exploit the use of high-resolution fMRI for depth analysis. These methods utilize white-matter segmentations coupled with deformable-surface algorithms to create a smooth surface representation at the gray-white interface and pial membrane. These surfaces provide vertex positions and normals for depth calculations, enabling averaging schemes that can increase contrast-to-noise ratio, as well as permitting the direct analysis of depth profiles of functional activity in the human brain. (C) 2010 Elsevier Inc. All rights reserved.
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