ArticleNeuroImage. Clinical2016
Relating multi-sequence longitudinal intensity profiles and clinical covariates in incident multiple sclerosis lesions.
Article in NeuroImage. Clinical, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Mesoscale Recovery of Microglial and Neuronal Dynamics After Craniotomy Across Wide Cortex in Transgenic Mice.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Matrix decomposition for modeling lesion development processes in multiple sclerosis.Biostatistics (Oxford, England) · 2022Article
- Experimental design and sample size considerations in longitudinal magnetic resonance imaging-based biomarker detection for multiple sclerosis.Statistical methods in medical research · 2020Article
- Bayesian modeling of dependence in brain connectivity data.Biostatistics (Oxford, England) · 2020Article
- A Spatio-Temporal Model for Longitudinal Image-on-Image Regression.Statistics in biosciences · 2019Article
- Imaging outcome measures of neuroprotection and repair in MS: A consensus statement from NAIMS.Neurology · 2019Review
- MIMoSA: An Automated Method for Intermodal Segmentation Analysis of Multiple Sclerosis Brain Lesions.Journal of neuroimaging : official journal of the American Society of Neuroimaging · 2018Article
- An Automated Statistical Technique for Counting Distinct Multiple Sclerosis Lesions.AJNR. American journal of neuroradiology · 2018Article
- Dice Overlap Measures for Objects of Unknown Number: Application to Lesion Segmentation.Brainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries. BrainLes (Workshop) · 2018Article
- A LAG FUNCTIONAL LINEAR MODEL FOR PREDICTION OF MAGNETIZATION TRANSFER RATIO IN MULTIPLE SCLEROSIS LESIONS.The annals of applied statistics · 2016Article
- Removing inter-subject technical variability in magnetic resonance imaging studies.NeuroImage · 2016Article
- PREVAIL: Predicting Recovery through Estimation and Visualization of Active and Incident Lesions.NeuroImage. Clinical · 2016Article
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Authors and funding
8 authors.
Funding
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.
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