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Neuroimage. 2010 Jan 1;49(1):240-8. doi: 10.1016/j.neuroimage.2009.08.035. Epub 2009 Aug 22.

FDDNP binding using MR derived cortical surface maps.

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  • 1Department of Biomathematics, David Geffen School of Medicine at UCLA, University of California-Los Angeles, 10833 Le Conte Ave., Los Angeles, CA 90095, USA. hprotas@ucla.edu

Abstract

OBJECTIVES:

To assess quantitatively the cortical pattern profile of regional FDDNP binding to beta-amyloid and neurofibrillary tangles on MR derived cortical maps, FDDNP PET images were corrected for movement and partial volume (PV), and optimized for kernel size.

METHODS:

FDDNP DVR PET images from 23 subjects (7 with Alzheimer's disease (AD), 6 with mild cognitive impairment and 10 controls) were obtained from Logan analysis using cerebellum as reference. A hemispheric cortical surface model for each subject was extracted from the MRI. The same transformations were applied to the FDDNP DVR PET images to map them into the same space. The cortical map with PV correction was calculated as the ratio of the DVR cortical surface and that of the simulated map, created from the mask derived from MRI and smoothed to the PET resolution. Discriminant analysis was used to order the FDDNP DVR cortical surfaces based on subjects' disease state. Linear regression was used to assess the rate of change of DVR vs. MMSE for each hemispheric cortical surface point.

RESULTS:

The FDDNP DVR cortical surface corrected for movement and PV had less hemispheric asymmetry. Optimal kernel size was determined to be 9 mm. The corrected cortical surface map of FDDNP DVR showed clear spatial pattern that was consistent with the known pathological progression of AD.

CONCLUSION:

Correcting for movement, PV as well as optimizing kernel size provide sensitive statistical analysis of FDDNP distribution which confirms in the living brain known pathology patterns earlier observed with cognitive decline with brain specimens.

PMID:
19703569
[PubMed - indexed for MEDLINE]
PMCID:
PMC2764817
Free PMC Article

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