| Scan-rescan reliability of subcortical brain volumes derived from automated segmentation. | |
| | |
MedLine Citation:
|
PMID: 20162602 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
|
Large-scale longitudinal studies of regional brain volume require reliable quantification using automated segmentation and labeling. However, repeated MR scanning of the same subject, even if using the same scanner and acquisition parameters, does not result in identical images due to small changes in image orientation, changes in prescan parameters, and magnetic field instability. These differences may lead to appreciable changes in estimates of volume for different structures. This study examined scan-rescan reliability of automated segmentation algorithms for measuring several subcortical regions, using both within-day and across-day comparison sessions in a group of 23 normal participants. We found that the reliability of volume measures including percent volume difference, percent volume overlap (Dice's coefficient), and intraclass correlation coefficient (ICC), varied substantially across brain regions. Low reliability was observed in some structures such as the amygdala (ICC = 0.6), with higher reliability (ICC = 0.9) for other structures such as the thalamus and caudate. Patterns of reliability across regions were similar for automated segmentation with FSL/FIRST and FreeSurfer (longitudinal stream). Reliability was associated with the volume of the structure, the ratio of volume to surface area for the structure, the magnitude of the interscan interval, and the method of segmentation. Sample size estimates for detecting changes in brain volume for a range of likely effect sizes also differed by region. Thus, longitudinal research requires a careful analysis of sample size and choice of segmentation method combined with a consideration of the brain structure(s) of interest and the magnitude of the anticipated effects. |
| | |
Authors:
|
Rajendra A Morey; Elizabeth S Selgrade; Henry Ryan Wagner; Scott A Huettel; Lihong Wang; Gregory McCarthy |
Publication Detail:
|
Type: Journal Article; Research Support, N.I.H., Extramural; Research Support, U.S. Gov't, Non-P.H.S. |
Journal Detail:
|
Title: Human brain mapping Volume: 31 ISSN: 1097-0193 ISO Abbreviation: Hum Brain Mapp Publication Date: 2010 Nov |
Date Detail:
|
Created Date: 2010-10-19 Completed Date: 2011-01-28 Revised Date: - |
Medline Journal Info:
|
Nlm Unique ID: 9419065 Medline TA: Hum Brain Mapp Country: United States |
Other Details:
|
Languages: eng Pagination: 1751-62 Citation Subset: IM |
Copyright Information:
|
© 2010 Wiley-Liss, Inc. |
Affiliation:
|
Duke-UNC Brain Imaging and Analysis Center, Duke University, Durham, North Carolina 27705, USA. rajendra.morey@duke.edu |
Export Citation:
|
APA/MLA Format Download EndNote Download BibTex |
| MeSH Terms | |
Descriptor/Qualifier:
|
Adult Analysis of Variance Brain / anatomy & histology* Female Humans Image Processing, Computer-Assisted / methods* Magnetic Resonance Imaging / methods* Male Organ Size Reproducibility of Results |
| Grant Support | |
ID/Acronym/Agency:
|
K23 MH073091/MH/NIMH NIH HHS; P50-MH60451/MH/NIMH NIH HHS |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
Previous Document: Assessment of white matter tract damage in mild cognitive impairment and Alzheimer's disease.
Next Document: Interest in politics modulates neural activity in the amygdala and ventral striatum.