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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

The mean profile and first PC for each of the four sequences. Panel A of the figure shows the mean profiles for each of the imaging sequences over the registered 200 day period, and panel B shows the first PC for each of the imaging sequences. The first PC explains 75% of the variation in the concatenated longitudinal profiles. Along the x-axis for both plots is plotted the time in days since lesion detection. The 95% confidence intervals in both panels are obtained using 1000 nonparametric bootstrapped samples.
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f0020: The mean profile and first PC for each of the four sequences. Panel A of the figure shows the mean profiles for each of the imaging sequences over the registered 200 day period, and panel B shows the first PC for each of the imaging sequences. The first PC explains 75% of the variation in the concatenated longitudinal profiles. Along the x-axis for both plots is plotted the time in days since lesion detection. The 95% confidence intervals in both panels are obtained using 1000 nonparametric bootstrapped samples.

Mentions: In Fig. 4A we show the mean profiles for each sequence over the registered 200 day period, and in Fig. 4B we show the first PC, ϕ1, for each sequence over the registered 200 day period, where the first PC is divided into different sequences for purposes of presentation. The subfigures for both the mean and the first PC show the bootstrapped 95% confidence intervals. The first PC explains 75% (95% CI: [72%, 76%]) of the variation in the concatenated longitudinal profiles.


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)

The mean profile and first PC for each of the four sequences. Panel A of the figure shows the mean profiles for each of the imaging sequences over the registered 200 day period, and panel B shows the first PC for each of the imaging sequences. The first PC explains 75% of the variation in the concatenated longitudinal profiles. Along the x-axis for both plots is plotted the time in days since lesion detection. The 95% confidence intervals in both panels are obtained using 1000 nonparametric bootstrapped samples.
© Copyright Policy - CC BY-NC-ND
Related In: Results  -  Collection

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

f0020: The mean profile and first PC for each of the four sequences. Panel A of the figure shows the mean profiles for each of the imaging sequences over the registered 200 day period, and panel B shows the first PC for each of the imaging sequences. The first PC explains 75% of the variation in the concatenated longitudinal profiles. Along the x-axis for both plots is plotted the time in days since lesion detection. The 95% confidence intervals in both panels are obtained using 1000 nonparametric bootstrapped samples.
Mentions: In Fig. 4A we show the mean profiles for each sequence over the registered 200 day period, and in Fig. 4B we show the first PC, ϕ1, for each sequence over the registered 200 day period, where the first PC is divided into different sequences for purposes of presentation. The subfigures for both the mean and the first PC show the bootstrapped 95% confidence intervals. The first PC explains 75% (95% CI: [72%, 76%]) of the variation in the concatenated longitudinal profiles.

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