Scan-stratified case-control sampling for modeling blood-brain barrier integrity in multiple sclerosis

Gina Maria Pomann, Elizabeth M. Sweeney, Daniel S. Reich, Ana Maria Staicu, Russell T. Shinohara

Research output: Contribution to journalArticlepeer-review


Multiple sclerosis (MS) is an immune-mediated neurological disease that causes morbidity and disability. In patients with MS, the accumulation of lesions in the white matter of the brain is associated with disease progression and worse clinical outcomes. Breakdown of the blood-brain barrier in newer lesions is indicative of more active disease-related processes and is a primary outcome considered in clinical trials of treatments for MS. Such abnormalities in active MS lesions are evaluated in vivo using contrast-enhanced structural MRI, during which patients receive an intravenous infusion of a costly magnetic contrast agent. In some instances, the contrast agents can have toxic effects. Recently, local image regression techniques have been shown to have modest performance for assessing the integrity of the blood-brain barrier based on imaging without contrast agents. These models have centered on the problem of cross-sectional classification in which patients are imaged at a single study visit and pre-contrast images are used to predict post-contrast imaging. In this paper, we extend these methods to incorporate historical imaging information, and we find the proposed model to exhibit improved performance. We further develop scan-stratified case-control sampling techniques that reduce the computational burden of local image regression models, while respecting the low proportion of the brain that exhibits abnormal vascular permeability.

Original languageEnglish (US)
Pages (from-to)2872-2880
Number of pages9
JournalStatistics in Medicine
Issue number20
StatePublished - Sep 10 2015


  • Case-control sampling
  • Logistic regression
  • Magnetic resonance imaging
  • Multiple sclerosis

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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