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Quantitative Analysis of PiB-PET with FreeSurfer ROIs

Overview of attention for article published in PLOS ONE, November 2013
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Title
Quantitative Analysis of PiB-PET with FreeSurfer ROIs
Published in
PLOS ONE, November 2013
DOI 10.1371/journal.pone.0073377
Pubmed ID
Authors

Yi Su, Gina M. D'Angelo, Andrei G. Vlassenko, Gongfu Zhou, Abraham Z. Snyder, Daniel S. Marcus, Tyler M. Blazey, Jon J. Christensen, Shivangi Vora, John C. Morris, Mark A. Mintun, Tammie L. S. Benzinger

Abstract

In vivo quantification of β-amyloid deposition using positron emission tomography is emerging as an important procedure for the early diagnosis of the Alzheimer's disease and is likely to play an important role in upcoming clinical trials of disease modifying agents. However, many groups use manually defined regions, which are non-standard across imaging centers. Analyses often are limited to a handful of regions because of the labor-intensive nature of manual region drawing. In this study, we developed an automatic image quantification protocol based on FreeSurfer, an automated whole brain segmentation tool, for quantitative analysis of amyloid images. Standard manual tracing and FreeSurfer-based analyses were performed in 77 participants including 67 cognitively normal individuals and 10 individuals with early Alzheimer's disease. The manual and FreeSurfer approaches yielded nearly identical estimates of amyloid burden (intraclass correlation = 0.98) as assessed by the mean cortical binding potential. An MRI test-retest study demonstrated excellent reliability of FreeSurfer based regional amyloid burden measurements. The FreeSurfer-based analysis also revealed that the majority of cerebral cortical regions accumulate amyloid in parallel, with slope of accumulation being the primary difference between regions.

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Geographical breakdown

Country Count As %
United Kingdom 1 <1%
Spain 1 <1%
United States 1 <1%
Unknown 110 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 28 25%
Student > Ph. D. Student 26 23%
Student > Bachelor 9 8%
Student > Master 6 5%
Student > Doctoral Student 5 4%
Other 15 13%
Unknown 24 21%
Readers by discipline Count As %
Neuroscience 25 22%
Medicine and Dentistry 19 17%
Engineering 9 8%
Agricultural and Biological Sciences 8 7%
Computer Science 4 4%
Other 17 15%
Unknown 31 27%