Composite MRI measures and short-term disability in patients with clinically isolated syndrome suggestive of MS

Background: The use of composite magnetic resonance imaging (MRI) measures has been suggested to better explain disability in patients with multiple sclerosis (MS). However, little is known about the utility of composite scores at the earliest stages of the disease. Objective: To investigate whether, in patients with clinically isolated syndrome (CIS), a composite MRI measure, rather than the single metrics, would explain conversion to MS and would better correlate with disability at baseline and at 1 year of follow-up. Methods: Corticospinal tract (CST), corpus callosum (CC) and optic radiation (OR) volume, fractional anisotropy (FA), and mean diffusivity (MD) values were measured in 27 CIS patients and 24 healthy controls (HCs). Z-scores of FA, MD, and tract volume measures were calculated in patients, based on the corresponding measures obtained from HCs, and then combined in a composite score for each tract. Correlations between Z-scores at baseline and both the Expanded Disability Status Scale (EDSS) at baseline and at follow-up (FU-EDSS) were investigated. Results: Only CST, CC, and OR composite scores as well as the CST volume were significantly associated with FU-EDSS (p = 0.005, p = 0.007, p = 0.020, and p = 0.010, respectively). Conclusion: The combination of MRI measures rather than the individual metrics better captured the association between tissue damage in both the CC, OR and CST and short-term follow-up disability.

[1]  O. Ciccarelli,et al.  Periventricular lesions and MS diagnostic criteria in young adults with typical clinically isolated syndromes , 2017, Multiple sclerosis.

[2]  Matteo Pardini,et al.  Motor network efficiency and disability in multiple sclerosis , 2015, Neurology.

[3]  D. Ramasamy,et al.  Early magnetic resonance imaging predictors of clinical progression after 48 months in clinically isolated syndrome patients treated with intramuscular interferon β‐1a , 2015, European journal of neurology.

[4]  À. Rovira,et al.  Defining high, medium and low impact prognostic factors for developing multiple sclerosis. , 2015, Brain : a journal of neurology.

[5]  S. Rauch,et al.  Conversion from clinically isolated syndrome to multiple sclerosis: A large multicentre study , 2015, Multiple sclerosis.

[6]  O. Ciccarelli,et al.  Earlier and more frequent diagnosis of multiple sclerosis using the McDonald criteria , 2014, Journal of Neurology, Neurosurgery & Psychiatry.

[7]  K. Hoffmann,et al.  Whole-Brain Diffusion Tensor Imaging in Correlation to Visual-Evoked Potentials in Multiple Sclerosis: A Tract-Based Spatial Statistics Analysis , 2014, American Journal of Neuroradiology.

[8]  Sara Llufriu,et al.  Trans‐synaptic axonal degeneration in the visual pathway in multiple sclerosis , 2014, Annals of neurology.

[9]  C. Pfueller,et al.  Optic radiation damage in multiple sclerosis is associated with visual dysfunction and retinal thinning – an ultrahigh-field MR pilot study , 2014, European Radiology.

[10]  Dana Horakova,et al.  Volumetric MRI Markers and Predictors of Disease Activity in Early Multiple Sclerosis: A Longitudinal Cohort Study , 2012, PloS one.

[11]  M. Battaglini,et al.  Evaluating and reducing the impact of white matter lesions on brain volume measurements , 2012, Human brain mapping.

[12]  Jeffrey A Cohen,et al.  Disability outcome measures in multiple sclerosis clinical trials: current status and future prospects , 2012, The Lancet Neurology.

[13]  Massimo Filippi,et al.  Association between pathological and MRI findings in multiple sclerosis , 2012, The Lancet Neurology.

[14]  AJ Thompson,et al.  Brain lesion location and clinical status 20 years after a diagnosis of clinically isolated syndrome suggestive of multiple sclerosis , 2012, Multiple sclerosis.

[15]  David H. Miller,et al.  Clinically isolated syndromes , 2012, The Lancet Neurology.

[16]  Jonathan M. Mudge,et al.  Evidence for Transcript Networks Composed of Chimeric RNAs in Human Cells , 2012, PloS one.

[17]  H. Lei,et al.  Diffusion Tensor Group Tractography of the Corpus Callosum in Clinically Isolated Syndrome , 2010, American Journal of Neuroradiology.

[18]  Marco Bozzali,et al.  Clinically isolated syndrome suggestive of multiple sclerosis: voxelwise regional investigation of white and gray matter. , 2010, Radiology.

[19]  A. Janssens,et al.  Callosal lesion predicts future attacks after clinically isolated syndrome , 2009, Neurology.

[20]  M. Filippo,et al.  Brain atrophy and lesion load measures over 1 year relate to clinical status after 6 years in patients with clinically isolated syndromes , 2009, Journal of Neurology, Neurosurgery & Psychiatry.

[21]  S. Schippling,et al.  Early anisotropy changes in the corpus callosum of patients with optic neuritis , 2008, Neuroradiology.

[22]  A. Thompson,et al.  Disability and T2 MRI lesions: a 20-year follow-up of patients with relapse onset of multiple sclerosis. , 2008, Brain : a journal of neurology.

[23]  Peter A. Calabresi,et al.  Tract probability maps in stereotaxic spaces: Analyses of white matter anatomy and tract-specific quantification , 2008, NeuroImage.

[24]  Gareth J. Barker,et al.  Diffusion tensor imaging of post mortem multiple sclerosis brain , 2007, NeuroImage.

[25]  M. Jenkinson Non-linear registration aka Spatial normalisation , 2007 .

[26]  Katrin Amunts,et al.  White matter fiber tracts of the human brain: Three-dimensional mapping at microscopic resolution, topography and intersubject variability , 2006, NeuroImage.

[27]  Maria Assunta Rocca,et al.  A method for obtaining tract-specific diffusion tensor MRI measurements in the presence of disease: application to patients with clinically isolated syndromes suggestive of multiple sclerosis , 2005, NeuroImage.

[28]  J Pelletier,et al.  MRI/MRS of corpus callosum in patients with clinically isolated syndrome suggestive of multiple sclerosis , 2003, Multiple sclerosis.

[29]  Gareth J. Barker,et al.  A study of the mechanisms of normal-appearing white matter damage in multiple sclerosis using diffusion tensor imaging , 2003, Journal of Neurology.

[30]  Wolfgang Brück,et al.  Acute axonal damage in multiple sclerosis is most extensive in early disease stages and decreases over time. , 2002, Brain : a journal of neurology.

[31]  Michael Brady,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[32]  Stephen M. Smith,et al.  Accurate, Robust, and Automated Longitudinal and Cross-Sectional Brain Change Analysis , 2002, NeuroImage.

[33]  P. Matthews,et al.  Regional axonal loss in the corpus callosum correlates with cerebral white matter lesion volume and distribution in multiple sclerosis. , 2000, Brain : a journal of neurology.

[34]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .