Relating multi-sequence longitudinal intensity profiles and clinical covariates in incident multiple sclerosis lesions
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...
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| Vydané v: | NeuroImage clinical Ročník 10; číslo C; s. 1 - 17 |
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| Hlavní autori: | , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Netherlands
Elsevier Inc
01.01.2016
Elsevier |
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| ISSN: | 2213-1582, 2213-1582 |
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| 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.
•Pipeline to extract voxel level longitudinal profiles from four MRI sequences within lesion tissue•Propose two statistical models to relate clinical covariates to the longitudinal profiles•Develop a biomarker that identifies areas of slow, long-term intensity changes at a voxel level•We validate the biomarker with ratings by two expert MS clinicians |
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| AbstractList | AbstractThe 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. 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. 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. •Pipeline to extract voxel level longitudinal profiles from four MRI sequences within lesion tissue•Propose two statistical models to relate clinical covariates to the longitudinal profiles•Develop a biomarker that identifies areas of slow, long-term intensity changes at a voxel level•We validate the biomarker with ratings by two expert MS clinicians 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. • Pipeline to extract voxel level longitudinal profiles from four MRI sequences within lesion tissue • Propose two statistical models to relate clinical covariates to the longitudinal profiles • Develop a biomarker that identifies areas of slow, long-term intensity changes at a voxel level • We validate the biomarker with ratings by two expert MS clinicians 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. |
| Author | Sweeney, Elizabeth M. Reich, Daniel S. Shinohara, Russell T. Dewey, Blake E. Muschelli, John Schindler, Matthew K. Crainiceanu, Ciprian M. Eloyan, Ani |
| AuthorAffiliation | c Department of Biostatistics and Epidemiology, Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States a Department of Biostatistics, The Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21205, United States d Department of Biostatistics, Brown University School of Public Health, RI 02912, United States b Translational Neuroradiology Unit, Division of Neuroimmunology and Neurovirology, National Institute of Neurological Disease and Stroke, National Institute of Health, Bethesda, MD 20892, United States |
| AuthorAffiliation_xml | – name: a Department of Biostatistics, The Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21205, United States – name: b Translational Neuroradiology Unit, Division of Neuroimmunology and Neurovirology, National Institute of Neurological Disease and Stroke, National Institute of Health, Bethesda, MD 20892, United States – name: d Department of Biostatistics, Brown University School of Public Health, RI 02912, United States – name: c Department of Biostatistics and Epidemiology, Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States |
| Author_xml | – sequence: 1 givenname: Elizabeth M. orcidid: 0000-0002-2555-5497 surname: Sweeney fullname: Sweeney, Elizabeth M. email: emsweene@jhsph.edu organization: Department of Biostatistics, The Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21205, United States – sequence: 2 givenname: Russell T. surname: Shinohara fullname: Shinohara, Russell T. organization: Department of Biostatistics and Epidemiology, Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, United States – sequence: 3 givenname: Blake E. surname: Dewey fullname: Dewey, Blake E. organization: Translational Neuroradiology Unit, Division of Neuroimmunology and Neurovirology, National Institute of Neurological Disease and Stroke, National Institute of Health, Bethesda, MD 20892, United States – sequence: 4 givenname: Matthew K. surname: Schindler fullname: Schindler, Matthew K. organization: Translational Neuroradiology Unit, Division of Neuroimmunology and Neurovirology, National Institute of Neurological Disease and Stroke, National Institute of Health, Bethesda, MD 20892, United States – sequence: 5 givenname: John surname: Muschelli fullname: Muschelli, John organization: Department of Biostatistics, The Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21205, United States – sequence: 6 givenname: Daniel S. surname: Reich fullname: Reich, Daniel S. organization: Department of Biostatistics, The Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21205, United States – sequence: 7 givenname: Ciprian M. surname: Crainiceanu fullname: Crainiceanu, Ciprian M. organization: Department of Biostatistics, The Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21205, United States – sequence: 8 givenname: Ani surname: Eloyan fullname: Eloyan, Ani organization: Department of Biostatistics, Brown University School of Public Health, RI 02912, United States |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/26693397$$D View this record in MEDLINE/PubMed |
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| Keywords | RRMS Multiple sclerosis Biomarker MRI SPMS CI MS Structural magnetic resonance imaging Multi-sequence imaging Longitudinal study Principal component analysis and regression Longitudinal lesion behavior NAWM NINDS PCA sd PC PD T FLAIR Expert rater trial T1 T2 Function-on-scalar regression normal-appearing white matter magnetic resonance imaging principal component T2-weighted relapsing remitting MS proton density-weighted fluid-attenuated inversion recovery multiple sclerosis National Institute of Neurological Disease and Stroke T1-weighted secondary progressive MS Tesla confidence interval principal component analysis standard deviation T1, T1-weighted CI, confidence interval SPMS, secondary progressive MS RRMS, relapsing remitting MS FLAIR, fluid-attenuated inversion recovery PC, principal component T2, T2-weighted NAWM, normal-appearing white matter PCA, principal component analysis MS, multiple sclerosis T, Tesla MRI, magnetic resonance imaging sd, standard deviation PD, proton density-weighted NINDS, National Institute of Neurological Disease and Stroke |
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| Snippet | The formation of multiple sclerosis (MS) lesions is a complex process involving inflammation, tissue damage, and tissue repair — all of which are visible on... AbstractThe formation of multiple sclerosis (MS) lesions is a complex process involving inflammation, tissue damage, and tissue repair — all of which are... The formation of multiple sclerosis (MS) lesions is a complex process involving inflammation, tissue damage, and tissue repair - all of which are visible on... |
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| SubjectTerms | Adolescent Adult Biomarker Biomarkers Brain - pathology Expert rater trial Female Function-on-scalar regression Humans Longitudinal lesion behavior Longitudinal Studies Longitudinal study Magnetic Resonance Imaging - methods Male Middle Aged Multi-sequence imaging Multiple sclerosis Multiple Sclerosis - diagnosis Multiple Sclerosis - pathology Multiple Sclerosis - therapy Principal Component Analysis Principal component analysis and regression Radiology Regression Analysis Regular Reproducibility of Results Steroids - therapeutic use Structural magnetic resonance imaging Young Adult |
| Title | Relating multi-sequence longitudinal intensity profiles and clinical covariates in incident multiple sclerosis lesions |
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