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Relating multi-sequence longitudinal intensity profiles and clinical covariates in incident multiple sclerosis lesions.

Sweeney EM, Shinohara RT, Dewey BE, Schindler MK, Muschelli J, Reich DS, Crainiceanu CM, Eloyan A - Neuroimage Clin (2015)

Bottom Line: The proposed biomarker's ability to identify such effects is validated by two experienced clinicians (a neuroradiologist and a neurologist).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.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics, The Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21205, United States ; Translational Neuroradiology Unit, Division of Neuroimmunology and Neurovirology, National Institute of Neurological Disease and Stroke, National Institute of Health, Bethesda, MD 20892, United States.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus

Coefficient functions from the function-on-scalar regression with the T1 profile as an outcome. Each dark line represents the coefficient function, and the shaded area represents a bootstrapped, point-wise 95% confidence interval. Along the x-axis of each plot is the time in days from lesion incidence. Along the y-axis is the value of the coefficient function at each time point. Only distance from the boundary and age were found to be different from 0 at any point along the profile.
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f0075: Coefficient functions from the function-on-scalar regression with the T1 profile as an outcome. Each dark line represents the coefficient function, and the shaded area represents a bootstrapped, point-wise 95% confidence interval. Along the x-axis of each plot is the time in days from lesion incidence. Along the y-axis is the value of the coefficient function at each time point. Only distance from the boundary and age were found to be different from 0 at any point along the profile.

Mentions: The coefficient functions from the function-on-scalar regression with bootstrapped 95% confidence intervals with the T2, PD, and T1 profile as the outcome are shown below. Similar to using the FLAIR profile as the outcome, only the distance to the boundary and age were found to be different from 0 at any point along the profile (Fig. 13, Fig. 14, Fig. 15Fig. 13


Relating multi-sequence longitudinal intensity profiles and clinical covariates in incident multiple sclerosis lesions.

Sweeney EM, Shinohara RT, Dewey BE, Schindler MK, Muschelli J, Reich DS, Crainiceanu CM, Eloyan A - Neuroimage Clin (2015)

Coefficient functions from the function-on-scalar regression with the T1 profile as an outcome. Each dark line represents the coefficient function, and the shaded area represents a bootstrapped, point-wise 95% confidence interval. Along the x-axis of each plot is the time in days from lesion incidence. Along the y-axis is the value of the coefficient function at each time point. Only distance from the boundary and age were found to be different from 0 at any point along the profile.
© Copyright Policy - CC BY-NC-ND
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4660378&req=5

f0075: Coefficient functions from the function-on-scalar regression with the T1 profile as an outcome. Each dark line represents the coefficient function, and the shaded area represents a bootstrapped, point-wise 95% confidence interval. Along the x-axis of each plot is the time in days from lesion incidence. Along the y-axis is the value of the coefficient function at each time point. Only distance from the boundary and age were found to be different from 0 at any point along the profile.
Mentions: The coefficient functions from the function-on-scalar regression with bootstrapped 95% confidence intervals with the T2, PD, and T1 profile as the outcome are shown below. Similar to using the FLAIR profile as the outcome, only the distance to the boundary and age were found to be different from 0 at any point along the profile (Fig. 13, Fig. 14, Fig. 15Fig. 13

Bottom Line: The proposed biomarker's ability to identify such effects is validated by two experienced clinicians (a neuroradiologist and a neurologist).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.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics, The Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21205, United States ; Translational Neuroradiology Unit, Division of Neuroimmunology and Neurovirology, National Institute of Neurological Disease and Stroke, National Institute of Health, Bethesda, MD 20892, United States.

ABSTRACT
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.

No MeSH data available.


Related in: MedlinePlus