title: Improved longitudinal gray and white matter atrophy assessment via application of a 4-dimensional hidden Markov random field model.
abstract: SIENA and similar techniques have demonstrated the utility of performing "direct" measurements as opposed to post-hoc comparison of cross-sectional data for the measurement of whole brain (WB) atrophy over time. However, gray matter (GM) and white matter (WM) atrophy are now widely recognized as important components of neurological disease progression, and are being actively evaluated as secondary endpoints in clinical trials. Direct measures of GM/WM change with advantages similar to SIENA have been lacking. We created a robust and easily-implemented method for direct longitudinal analysis of GM/WM atrophy, SIENAX multi-time-point (SIENAX-MTP). We built on the basic halfway-registration and mask composition components of SIENA to improve the raw output of FMRIB's FAST tissue segmentation tool. In addition, we created LFAST, a modified version of FAST incorporating a 4th dimension in its hidden Markov random field model in order to directly represent time. The method was validated by scan-rescan, simulation, comparison with SIENA, and two clinical effect size comparisons. All validation approaches demonstrated improved longitudinal precision with the proposed SIENAX-MTP method compared to SIENAX. For GM, simulation showed better correlation with experimental volume changes (r=0.992 vs. 0.941), scan-rescan showed lower standard deviations (3.8% vs. 8.4%), correlation with SIENA was more robust (r=0.70 vs. 0.53), and effect sizes were improved by up to 68%. Statistical power estimates indicated a potential drop of 55% in the number of subjects required to detect the same treatment effect with SIENAX-MTP vs. SIENAX. The proposed direct GM/WM method significantly improves on the standard SIENAX technique by trading a small amount of bias for a large reduction in variance, and may provide more precise data and additional statistical power in longitudinal studies.
authors: Buffalo Neuroimaging Analysis Center, Department of Neurology, School of Medicine and Biomedical Science, University at Buffalo, State University of New York, Buffalo, NY, USA. Electronic address: mgdwyer@bnac.net.
authors: Michael G
authors: MG
authors: Dwyer
authors: Buffalo Neuroimaging Analysis Center, Department of Neurology, School of Medicine and Biomedical Science, University at Buffalo, State University of New York, Buffalo, NY, USA.
authors: Niels
authors: N
authors: Bergsland
authors: Buffalo Neuroimaging Analysis Center, Department of Neurology, School of Medicine and Biomedical Science, University at Buffalo, State University of New York, Buffalo, NY, USA.
authors: Robert
authors: R
authors: Zivadinov
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D001284:Atrophy / Q000473:pathology; D001921:Brain / Q000473:pathology*; D001931:Brain Mapping / Q000379:methods*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male; D008390:Markov Chains; D008875:Middle Aged; D008959:Models, Neurological; D020529:Multiple Sclerosis, Relapsing-Remitting / Q000473:pathology*; D009412:Nerve Fibers / Q000473:pathology; D009413:Nerve Fibers, Myelinated / Q000473:pathology; D055815:Young Adult
keywords: 4-Dimensional HMRF; Atrophy; Direct method; Gray matter; Longitudinal; Random field; Segmentation; Tissue-specific; White matter