TY - JOUR
T1 - Penalized functional regression analysis of white-matter tract profiles in multiple sclerosis
AU - Goldsmith, Jeff
AU - Crainiceanu, Ciprian M.
AU - Caffo, Brian S.
AU - Reich, Daniel S.
N1 - Funding Information:
The authors thank Peter Calabresi, Peter van Zijl, Seth Smith, Eliza Gordon-Lipkin, Sheena Farrell, Terri Brawner, Kathleen Kahl, and Ivana Kusevic, all of whom were instrumental in collecting the data for this study. Jonathan Farrell and Bennett Landman provided assistance with image processing. The study was supported by the National Institutes of Health (the Intramural Research Program of the National Institute of Neurological Disorders and Stroke as well as grants K99NS064098 , R01NS060910 , R01EB012547 , and P41RR015241 ); the National Multiple Sclerosis Society (grant TR3760A3 ); and an unrestricted grant from EMD Serono to support data collection. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.
PY - 2011/7/15
Y1 - 2011/7/15
N2 - Diffusion tensor imaging (DTI) enables noninvasive parcellation of cerebral white matter into its component fiber bundles or tracts. These tracts often subserve specific functions, and damage to the tracts can therefore result in characteristic forms of disability. Attempts to quantify the extent of tract-specific damage have been limited in part by substantial spatial variation of imaging properties from one end of a tract to the other, variation that can be compounded by the effects of disease. Here, we develop a "penalized functional regression" procedure to analyze spatially normalized tract profiles, which powerfully characterize such spatial variation. The central idea is to identify and emphasize portions of a tract that are more relevant to a clinical outcome score, such as case status or degree of disability. The procedure also yields a "tract abnormality score" for each tract and MRI index studied. Importantly, the weighting function used in this procedure is constrained to be smooth, and the statistical associations are estimated using generalized linear models. We test the method on data from a cross-sectional MRI and functional study of 115 multiple-sclerosis cases and 42 healthy volunteers, considering a range of quantitative MRI indices, white-matter tracts, and clinical outcome scores, and using training and testing sets to validate the results. We show that attention to spatial variation yields up to 15% (mean across all tracts and MRI indices: 6.4%) improvement in the ability to discriminate multiple sclerosis cases from healthy volunteers. Our results confirm that comprehensive analysis of white-matter tract-specific imaging data improves with knowledge and characterization of the normal spatial variation.
AB - Diffusion tensor imaging (DTI) enables noninvasive parcellation of cerebral white matter into its component fiber bundles or tracts. These tracts often subserve specific functions, and damage to the tracts can therefore result in characteristic forms of disability. Attempts to quantify the extent of tract-specific damage have been limited in part by substantial spatial variation of imaging properties from one end of a tract to the other, variation that can be compounded by the effects of disease. Here, we develop a "penalized functional regression" procedure to analyze spatially normalized tract profiles, which powerfully characterize such spatial variation. The central idea is to identify and emphasize portions of a tract that are more relevant to a clinical outcome score, such as case status or degree of disability. The procedure also yields a "tract abnormality score" for each tract and MRI index studied. Importantly, the weighting function used in this procedure is constrained to be smooth, and the statistical associations are estimated using generalized linear models. We test the method on data from a cross-sectional MRI and functional study of 115 multiple-sclerosis cases and 42 healthy volunteers, considering a range of quantitative MRI indices, white-matter tracts, and clinical outcome scores, and using training and testing sets to validate the results. We show that attention to spatial variation yields up to 15% (mean across all tracts and MRI indices: 6.4%) improvement in the ability to discriminate multiple sclerosis cases from healthy volunteers. Our results confirm that comprehensive analysis of white-matter tract-specific imaging data improves with knowledge and characterization of the normal spatial variation.
KW - Diffusion tensor imaging
KW - Functional data analysis
KW - Magnetization transfer ratio
KW - Multiple sclerosis
KW - Regression methods
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U2 - 10.1016/j.neuroimage.2011.04.044
DO - 10.1016/j.neuroimage.2011.04.044
M3 - Article
C2 - 21554962
AN - SCOPUS:79958755327
SN - 1053-8119
VL - 57
SP - 431
EP - 439
JO - NeuroImage
JF - NeuroImage
IS - 2
ER -