Literature DB >> 21635955

Population-wide principal component-based quantification of blood-brain-barrier dynamics in multiple sclerosis.

Russell T Shinohara1, Ciprian M Crainiceanu, Brian S Caffo, María Inés Gaitán, Daniel S Reich.   

Abstract

The processes by which new white matter lesions in multiple sclerosis (MS) develop are only partially understood. Much of this understanding has come through magnetic resonance imaging (MRI) of the human brain. One of the hallmarks of new lesion development in MS is enhancement on T(1)-weighted MRI scans following the intravenous administration of a gadolinium-based contrast agent that shortens the longitudinal relaxation time of the tissue. Visible enhancement on the MRI results from the opening of the blood-brain barrier and reveals areas of active inflammation. The incidence and number of existing enhancing lesions are common outcome measures used in MS treatment clinical trials. Dynamic-contrast-enhanced MRI (DCE-MRI) can estimate the rate at which contrast agents pass from the plasma to MS lesions. In this paper, we develop a principal component-based framework for the analysis of these data that provides biologically meaningful quantification of blood-brain-barrier opening in new MS lesions. To accomplish this, we use functional principal components analysis to study directions of variation in the voxel-level time series of intensities both within and across subjects. The analysis reveals and allows quantification of typical spatiotemporal enhancement patterns in acute MS lesions, providing measures of magnitude, rate, shape (ring-like vs. nodular), and dynamics (centrifugal vs. centripetal). Across 10 subjects with relapsing-remitting and primary progressive MS, we found subjects to have between 0 and 12 gadolinium-enhancing lesions, the majority of which enhanced centripetally. We quantified the spatiotemporal behavior within each of these lesions using novel measures. Further application of these techniques will determine the extent to which these lesion measures can predict or track response to therapy or long-term prognosis in this disorder.
Copyright © 2011 Elsevier Inc. All rights reserved.

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Year:  2011        PMID: 21635955      PMCID: PMC3138825          DOI: 10.1016/j.neuroimage.2011.05.038

Source DB:  PubMed          Journal:  Neuroimage        ISSN: 1053-8119            Impact factor:   6.556


  10 in total

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Review 2.  Cortical lesions in multiple sclerosis.

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Journal:  Nat Rev Neurol       Date:  2010-07-13       Impact factor: 42.937

Review 3.  Therapy of MS.

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Journal:  Clin Neurol Neurosurg       Date:  2010-04-01       Impact factor: 1.876

4.  Longitudinal functional principal component analysis.

Authors:  Sonja Greven; Ciprian Crainiceanu; Brian Caffo; Daniel Reich
Journal:  Electron J Stat       Date:  2010       Impact factor: 1.125

5.  MULTILEVEL FUNCTIONAL PRINCIPAL COMPONENT ANALYSIS.

Authors:  Chong-Zhi Di; Ciprian M Crainiceanu; Brian S Caffo; Naresh M Punjabi
Journal:  Ann Appl Stat       Date:  2009-03-01       Impact factor: 2.083

6.  A topology-preserving approach to the segmentation of brain images with multiple sclerosis lesions.

Authors:  Navid Shiee; Pierre-Louis Bazin; Arzu Ozturk; Daniel S Reich; Peter A Calabresi; Dzung L Pham
Journal:  Neuroimage       Date:  2009-09-17       Impact factor: 6.556

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Journal:  Neurology       Date:  1983-11       Impact factor: 9.910

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Journal:  Radiology       Date:  1988-10       Impact factor: 11.105

9.  Heterogeneity of blood-brain barrier changes in multiple sclerosis: an MRI study with gadolinium-DTPA enhancement.

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Journal:  Neurology       Date:  1990-02       Impact factor: 9.910

10.  Gadolinium-pentetic acid magnetic resonance imaging in patients with relapsing remitting multiple sclerosis.

Authors:  R Capra; N Marcianò; L A Vignolo; A Chiesa; R Gasparotti
Journal:  Arch Neurol       Date:  1992-07
  10 in total
  23 in total

1.  Sample-size calculations for short-term proof-of-concept studies of tissue protection and repair in multiple sclerosis lesions via conventional clinical imaging.

Authors:  Daniel S Reich; Richard White; Irene Cm Cortese; Luisa Vuolo; Colin D Shea; Tassie L Collins; John Petkau
Journal:  Mult Scler       Date:  2015-02-06       Impact factor: 6.312

2.  Longitudinal High-Dimensional Principal Components Analysis with Application to Diffusion Tensor Imaging of Multiple Sclerosis.

Authors:  Vadim Zipunnikov; Sonja Greven; Haochang Shou; Brian Caffo; Daniel S Reich; Ciprian Crainiceanu
Journal:  Ann Appl Stat       Date:  2014       Impact factor: 2.083

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

Authors:  Gina-Maria Pomann; Elizabeth M Sweeney; Daniel S Reich; Ana-Maria Staicu; Russell T Shinohara
Journal:  Stat Med       Date:  2015-05-04       Impact factor: 2.373

4.  Experimental design and sample size considerations in longitudinal magnetic resonance imaging-based biomarker detection for multiple sclerosis.

Authors:  Menghan Hu; Matthew K Schindler; Blake E Dewey; Daniel S Reich; Russell T Shinohara; Ani Eloyan
Journal:  Stat Methods Med Res       Date:  2020-02-19       Impact factor: 3.021

5.  A Spatio-Temporal Model for Longitudinal Image-on-Image Regression.

Authors:  Arnab Hazra; Brian J Reich; Daniel S Reich; Russell T Shinohara; Ana-Maria Staicu
Journal:  Stat Biosci       Date:  2017-10-23

6.  Removing inter-subject technical variability in magnetic resonance imaging studies.

Authors:  Jean-Philippe Fortin; Elizabeth M Sweeney; John Muschelli; Ciprian M Crainiceanu; Russell T Shinohara
Journal:  Neuroimage       Date:  2016-02-23       Impact factor: 6.556

7.  MIMoSA: An Automated Method for Intermodal Segmentation Analysis of Multiple Sclerosis Brain Lesions.

Authors:  Alessandra M Valcarcel; Kristin A Linn; Simon N Vandekar; Theodore D Satterthwaite; John Muschelli; Peter A Calabresi; Dzung L Pham; Melissa Lynne Martin; Russell T Shinohara
Journal:  J Neuroimaging       Date:  2018-03-08       Impact factor: 2.486

8.  The Predictive Performance of Objective Measures of Physical Activity Derived From Accelerometry Data for 5-Year All-Cause Mortality in Older Adults: National Health and Nutritional Examination Survey 2003-2006.

Authors:  Ekaterina Smirnova; Andrew Leroux; Quy Cao; Lucia Tabacu; Vadim Zipunnikov; Ciprian Crainiceanu; Jacek K Urbanek
Journal:  J Gerontol A Biol Sci Med Sci       Date:  2020-09-16       Impact factor: 6.053

9.  Automatic lesion incidence estimation and detection in multiple sclerosis using multisequence longitudinal MRI.

Authors:  E M Sweeney; R T Shinohara; C D Shea; D S Reich; C M Crainiceanu
Journal:  AJNR Am J Neuroradiol       Date:  2012-07-05       Impact factor: 3.825

10.  Statistical estimation of T1 relaxation times using conventional magnetic resonance imaging.

Authors:  Amanda F Mejia; Elizabeth M Sweeney; Blake Dewey; Govind Nair; Pascal Sati; Colin Shea; Daniel S Reich; Russell T Shinohara
Journal:  Neuroimage       Date:  2015-12-28       Impact factor: 6.556

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