Abstract
A first episode of acute demyelination of the central nervous system may be a monophasic transient illness or represent the first attack of multiple sclerosis (MS). This study investigates if it is possible to distinguish these two groups of patients retrospectively at the time of the first episode, in a pediatric population. For each patient, the method consists in fitting an individual brain growth curve using multiple follow-up time-points, and using this curve to predict 4 metrics at the first attack: brain volume, brain growth rate, thalamus volume normalized by the brain volume (called normalized thalamus) and normalized thalamus growth rate. These metrics were compared to age-and-sex matched healthy controls by computing z-scores.
In this study, 85 patients were scanned up to 8 years after the first attack. During this follow-up period, 23 patients were subsequently diagnosed with MS (MS group). Among the 62 patients with a transient illness, 9 suffered from monophasic acute disseminated encephalomyelitis (ADEM group). The 53 remaining formed the non-ADEM monophasic (MONO) group.
The normalized thalamus growth rate was the only metric that distinguished patient groups: the z-scores were significantly smaller for MS than for the MONO group (p<0.01). Whereas 93% of monophasic subjects were correctly classified with a linear discriminant analysis, only 13% of the MS subjects were correctly classified, due to a large inter-individual variability in this group.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Kerbrat, A., Aubert-Broche, B., Fonov, V., et al.: Reduced head and brain size for age and disproportionately smaller thalami in child-onset MS. Neurology 78, 194–201 (2012)
Aubert-Broche, B., Fonov, V., Ghassemi, R., et al.: Regional brain atrophy in children with multiple sclerosis. NeuroImage 58, 409–415 (2011)
Aubert-Broche, B., Fonov, V., Guizard, N., Banwell, B., Narayanan, S., Arnold, D.L., Collins, D.L.: Brain growth rate is reduced in paediatric-onset multiple sclerosis. In: European Committees for Treatment and Research in Multiple Sclerosis 2012, Lyon, France (2012)
Mesaros, S., Rocca, M.A., Absinta, M., et al.: Evidence of thalamic gray matter loss in pediatric multiple sclerosis. Neurology 70, 1107–1112 (2008)
Polman, C.H., Reingold, S.C., Edan, G., et al.: Diagnostic criteria for multiple sclerosis: 2005 revisions to the “McDonald Criteria”. Ann. Neurol. 58, 840–846 (2005)
Evans, A.C.: Brain Development Cooperative G. The NIH MRI study of normal brain development. NeuroImage 30, 184–202 (2006)
Aubert-Broche, B., Fonov, V.S., Garcia-Lorenzo, D., et al.: A new method for structural volume analysis of longitudinal brain MRI data and its application in studying the growth trajectories of anatomical brain structures in childhood. NeuroImage 82C, 393–402 (2013)
Coupe, P., Manjon, J.V., Gedamu, E., Arnold, D., Robles, M., Collins, D.L.: Robust Rician noise estimation for MR images. Medical Image Analysis 14, 483–493 (2010)
Sled, J.G., Zijdenbos, A.P., Evans, A.C.: A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Transactions on Medical Imaging 17, 87–97 (1998)
Fonov, V., Evans, A.C., Botteron, K., et al.: Unbiased average age-appropriate atlases for pediatric studies. NeuroImage 54, 313–327 (2011)
Francis, S.: Automatic lesion identification in MRI of multiple sclerosis patients. McGill University (2004)
Collins, D.L., Neelin, P., Peters, T.M., Evans, A.C.: Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. Journal of Computer Assisted Tomography 18, 192–205 (1994)
Eskildsen, S.F., Coupe, P., Fonov, V., et al.: BEaST: brain extraction based on nonlocal segmentation technique. NeuroImage 59, 2362–2373 (2012)
Garcia-Lorenzo, D., Prima, S., Arnold, D.L., Collins, D.L., Barillot, C.: Trimmed-likelihood estimation for focal lesions and tissue segmentation in multisequence MRI for multiple sclerosis. IEEE Transactions on Medical Imaging 30, 1455–1467 (2011)
Bernal-Rusiel, J.L., Greve, D.N., Reuter, M., Fischl, B., Sabuncu, M.R.: For the Alzheimer’s Disease Neuroimaging I. Statistical analysis of longitudinal neuroimage data with Linear Mixed Effects models. NeuroImage 66C, 249–260 (2012)
Cheng, J., Edwards, L.J., Maldonado-Molina, M.M., Komro, K.A., Muller, K.E.: Real longitudinal data analysis for real people: building a good enough mixed model. Statistics in Medicine 29, 504–520 (2010)
Package ‘nlme’ Linear and nonlinear mixed effects models [computer program]. Version 3.1-104 (2009)
Venables, W.N., Ripley, B.D.: Modern Applied Statistics with S. Springer (2002)
Giedd, J.N., Blumenthal, J., Jeffries, N.O., et al.: Brain development during childhood and adolescence: a longitudinal MRI study. Nature Neuroscience 2, 861–863 (1999)
Zivadinov, R., Havrdova, E., Bergsland, N., et al.: Thalamic atrophy is associated with development of clinically definite multiple sclerosis. Radiology 268, 831–841 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Aubert-Broche, B. et al. (2015). Is It Possible to Differentiate the Impact of Pediatric Monophasic Demyelinating Disorders and Multiple Sclerosis After a First Episode of Demyelination?. In: Durrleman, S., Fletcher, T., Gerig, G., Niethammer, M., Pennec, X. (eds) Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data. STIA 2014. Lecture Notes in Computer Science(), vol 8682. Springer, Cham. https://doi.org/10.1007/978-3-319-14905-9_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-14905-9_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14904-2
Online ISBN: 978-3-319-14905-9
eBook Packages: Computer ScienceComputer Science (R0)