TY - JOUR
T1 - Relating multi-sequence longitudinal intensity profiles and clinical covariates in incident multiple sclerosis lesions
AU - Sweeney, Elizabeth M.
AU - Shinohara, Russell T.
AU - Dewey, Blake E.
AU - Schindler, Matthew K.
AU - Muschelli, John
AU - Reich, Daniel S.
AU - Crainiceanu, Ciprian M.
AU - Eloyan, Ani
N1 - Funding Information:
The project described is supported in part by the NIH grants RO1 EB012547 from the National Institute of Biomedical Imaging and Bioengineering , RO1 NS060910 and RO1 NS085211 from the National Institute of Neurological Disorders and Stroke (NINDS) , T32 AG021334 from the National Institute of Aging , and RO1 MH095836 from the National Institute of Mental Health . The research is also supported by the Intramural Research Program of NINDS. The content is solely the responsibility of the authors and does not necessarily represent the official views of the funding agencies.
Publisher Copyright:
© 2015 The Authors. Published by Elsevier Inc.
PY - 2016
Y1 - 2016
N2 - The formation of multiple sclerosis (MS) lesions is a complex process involving inflammation, tissue damage, and tissue repair - all of which are visible on structural magnetic resonance imaging (MRI) and potentially modifiable by pharmacological therapy. In this paper, we introduce two statistical models for relating voxel-level, longitudinal, multi-sequence structural MRI intensities within MS lesions to clinical information and therapeutic interventions: (1) a principal component analysis (PCA) and regression model and (2) function-on-scalar regression models. To do so, we first characterize the post-lesion incidence repair process on longitudinal, multi-sequence structural MRI from 34 MS patients as voxel-level intensity profiles. For the PCA regression model, we perform PCA on the intensity profiles to develop a voxel-level biomarker for identifying slow and persistent, long-term intensity changes within lesion tissue voxels. The proposed biomarker's ability to identify such effects is validated by two experienced clinicians (a neuroradiologist and a neurologist). On a scale of 1 to 4, with 4 being the highest quality, the neuroradiologist gave the score on the first PC a median quality rating of 4 (95% CI: [4,4]), and the neurologist gave the score a median rating of 3 (95% CI: [3,3]). We then relate the biomarker to the clinical information in a mixed model framework. Treatment with disease-modifying therapies (p < 0.01), steroids (p < 0.01), and being closer to the boundary of abnormal signal intensity (p < 0.01) are all associated with return of a voxel to an intensity value closer to that of normal-appearing tissue. The function-on-scalar regression model allows for assessment of the post-incidence time points at which the covariates are associated with the profiles. In the function-on-scalar regression, both age and distance to the boundary were found to have a statistically significant association with the lesion intensities at some time point. The two models presented in this article show promise for understanding the mechanisms of tissue damage in MS and for evaluating the impact of treatments for the disease in clinical trials.
AB - The formation of multiple sclerosis (MS) lesions is a complex process involving inflammation, tissue damage, and tissue repair - all of which are visible on structural magnetic resonance imaging (MRI) and potentially modifiable by pharmacological therapy. In this paper, we introduce two statistical models for relating voxel-level, longitudinal, multi-sequence structural MRI intensities within MS lesions to clinical information and therapeutic interventions: (1) a principal component analysis (PCA) and regression model and (2) function-on-scalar regression models. To do so, we first characterize the post-lesion incidence repair process on longitudinal, multi-sequence structural MRI from 34 MS patients as voxel-level intensity profiles. For the PCA regression model, we perform PCA on the intensity profiles to develop a voxel-level biomarker for identifying slow and persistent, long-term intensity changes within lesion tissue voxels. The proposed biomarker's ability to identify such effects is validated by two experienced clinicians (a neuroradiologist and a neurologist). On a scale of 1 to 4, with 4 being the highest quality, the neuroradiologist gave the score on the first PC a median quality rating of 4 (95% CI: [4,4]), and the neurologist gave the score a median rating of 3 (95% CI: [3,3]). We then relate the biomarker to the clinical information in a mixed model framework. Treatment with disease-modifying therapies (p < 0.01), steroids (p < 0.01), and being closer to the boundary of abnormal signal intensity (p < 0.01) are all associated with return of a voxel to an intensity value closer to that of normal-appearing tissue. The function-on-scalar regression model allows for assessment of the post-incidence time points at which the covariates are associated with the profiles. In the function-on-scalar regression, both age and distance to the boundary were found to have a statistically significant association with the lesion intensities at some time point. The two models presented in this article show promise for understanding the mechanisms of tissue damage in MS and for evaluating the impact of treatments for the disease in clinical trials.
KW - Biomarker
KW - Expert rater trial
KW - Function-on-scalar regression
KW - Longitudinal lesion behavior
KW - Longitudinal study
KW - Multi-sequence imaging
KW - Multiple sclerosis
KW - Principal component analysis and regression
KW - Structural magnetic resonance imaging
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U2 - 10.1016/j.nicl.2015.10.013
DO - 10.1016/j.nicl.2015.10.013
M3 - Article
C2 - 26693397
AN - SCOPUS:84947284118
SN - 2213-1582
VL - 10
SP - 1
EP - 17
JO - NeuroImage: Clinical
JF - NeuroImage: Clinical
ER -