Impaired growth of the cerebellum in pediatric-onset acquired CNS demyelinating disease

Multiple Sclerosis

Volumn 22, Issue 10, 2016, Pages 1266-1278

  Weier, Katrin ^a   Fonov, Vladimir ^a   Aubert Broche, Bérengère ^a   Arnold, Douglas L ^a   Banwell, Brenda ^b   Collins, D Louis ^a  

^a MCGILL UNIVERSITY   (Canada)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

acute disseminated encephalomyelitis; Cerebellum; multiple sclerosis; pediatric; volumetric MRI

Indexed keywords

ACQUIRED DEMYELINATING DISEASE; ACUTE DISSEMINATED ENCEPHALOMYELITIS; ADOLESCENT; ADULT; ARTICLE; BRAIN GROWTH; BRAIN SIZE; CEREBELLUM; CHILD; CONTROLLED STUDY; DEMYELINATING DISEASE; FEMALE; FOLLOW UP; HUMAN; MAJOR CLINICAL STUDY; MALE; MULTIPLE SCLEROSIS; NUCLEAR MAGNETIC RESONANCE IMAGING; WHITE MATTER; CASE CONTROL STUDY; DIAGNOSTIC IMAGING; GROWTH, DEVELOPMENT AND AGING; ORGAN SIZE; PRESCHOOL CHILD; ATROPHY; BRAIN DEVELOPMENT; EXPANDED DISABILITY STATUS SCALE; GRAY MATTER; IMAGE PROCESSING; LONGITUDINAL STUDY; OPTIC NEURITIS; PHYSICAL DISABILITY;

ADOLESCENT; CASE-CONTROL STUDIES; CEREBELLUM; CHILD; CHILD, PRESCHOOL; DEMYELINATING AUTOIMMUNE DISEASES, CNS; ENCEPHALOMYELITIS, ACUTE DISSEMINATED; FEMALE; FOLLOW-UP STUDIES; HUMANS; MALE; MULTIPLE SCLEROSIS; ORGAN SIZE;

EID: 84986625364     PISSN: 13524585     EISSN: 14770970     Source Type: Journal    
DOI: 10.1177/1352458515615224     Document Type: Article

References (39)

1. Aubert-Broche B, Fonov V, Narayanan S, et al. Onset of multiple sclerosis before adulthood leads to failure of age-expected brain growth. Neurology 2014; 83: 2140-2146.
2. Aubert-Broche B, Fonov V, Ghassemi R, et al. Regional brain atrophy in children with multiple sclerosis. Neuroimage 2011; 58: 409-415.
3. 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 2012; 78: 194-201.
4. Mesaros S, Rocca MA, Absinta M, et al. Evidence of thalamic gray matter loss in pediatric multiple sclerosis. Neurology 2008; 70: 1107-1112.
5. Dobbing J, Sands J., Quantitative growth and development of human brain. Arch Dis Child 1973; 48: 757-767.
6. Barkovich AJ, Kjos BO, Jackson DE Jr, et al. Normal maturation of the neonatal and infant brain: MR imaging at 1.5 T. Radiology 1988; 166: 173-180.
7. Huttenlocher PR., Morphometric study of human cerebral cortex development. Neuropsychologia 1990; 28: 517-527.
8. Tam EW, Chau V, Ferriero DM, et al. Preterm cerebellar growth impairment after postnatal exposure to glucocorticoids. Sci Transl Med 2011; 3: 105ra105.
9. O'Hare ED, Kan E, Yoshii J, et al. Mapping cerebellar vermal morphology and cognitive correlates in prenatal alcohol exposure. Neuroreport 2005; 16: 1285-1290.
10. Dobbing J, Hopewell JW, Lynch A, et al. Vulnerability of developing brain. I. Some lasting effects of x-irradiation. Exp Neurol 1970; 28: 442-449.
11. Compston A, Coles A., Multiple sclerosis. Lancet 2008; 372: 1502-1517.
12. Renoux C, Vukusic S, Mikaeloff Y, et al. Natural history of multiple sclerosis with childhood onset. N Engl J Med 2007; 356: 2603-2613.
13. Verhey LH, Shroff M, Banwell B., Pediatric multiple sclerosis: pathobiological, clinical, and magnetic resonance imaging features. Neuroimag Clin N Am 2013; 23: 227-243.
14. Waubant E, Chabas D, Okuda DT, et al. Difference in disease burden and activity in pediatric patients on brain magnetic resonance imaging at time of multiple sclerosis onset vs adults. Arch Neurol 2009; 66: 967-971.
15. Ghassemi R, Narayanan S, Banwell B, et al. Quantitative determination of regional lesion volume and distribution in children and adults with relapsing-remitting multiple sclerosis. PLoS ONE 2014; 9: e85741.
16. Krupp LB, Banwell B, Tenembaum S, and International Pediatric MS Study Group. Consensus definitions proposed for pediatric multiple sclerosis and related disorders. Neurology 2007; 68: S7-S12.
17. Krupp LB, Tardieu M, Amato MP, et al. International Pediatric Multiple Sclerosis Study Group criteria for pediatric multiple sclerosis and immune-mediated central nervous system demyelinating disorders: revisions to the 2007 definitions. Mult Scler 2013; 19: 1261-1267.
18. Kurtzke JF., Rating neurologic impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology 1983; 33: 1444-1452.
19. Poser CM, Paty DW, Scheinberg L, et al. New diagnostic criteria for multiple sclerosis: guidelines for research protocols. Ann Neurol 1983; 13: 227-231.
20. Polman CH, Reingold SC, Edan G, et al. Diagnostic criteria for multiple sclerosis: 2005 revisions to the "McDonald Criteria." Ann Neurol 2005; 58: 840-846.
21. Banwell B, Bar-Or A, Arnold DL, et al. Clinical, environmental, and genetic determinants of multiple sclerosis in children with acute demyelination: a prospective national cohort study. Lancet Neurol 2011; 10: 436-445.
22. Evans AC, and Brain Development Cooperative G. The NIH MRI study of normal brain development. Neuroimage 2006; 30: 184-202.
23. Weier K, Fonov V, Lavoie K, et al. Rapid automatic segmentation of the human cerebellum and its lobules (RASCAL)-implementation and application of the patch-based label-fusion technique with a template library to segment the human cerebellum. Hum Brain Mapp 2014; 35: 5026-5039.
24. Eskildsen SF, Coupe P, Fonov V, et al. BEaST: brain extraction based on nonlocal segmentation technique. Neuroimage 2012; 59: 2362-2373.
25. Collins DL, Evans A., ANIMAL: validation and applications of non-linear registration-based segmentations. Int J Pattern Recogn 1997; 11: 1271.
26. Zijdenbos A, Evans A, Riahi F, et al. Automatic quantification of multiple sclerosis lesion volume using stereotaxic space. In: Höhne K, Kikinis R, (eds) Visualization in biomedical computing. Berlin, Heidelberg: Springer, 1996, pp. 439-448.
27. Bernal-Rusiel JL, Greve DN, Reuter M, et al.; Alzheimer's Disease Neuroimaging Initiative. Statistical analysis of longitudinal neuroimage data with Linear Mixed Effects models. Neuroimage 2012; 66: 249-260.
28. Pinheiro J, Bates D, DebRoy S, et al. nlme: linear and nonlinear mixed effects models. R package version 3.1-111. Available at: http://CRAN.R-project.org/package=nlme, 2013.
29. Hothorn T, Bretz F, Westfall P., Simultaneous inference in general parametric models. Biom J 2008; 50: 346-363.
30. Aubert-Broche B, Fonov VS, 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 2013; 82: 393-402.
31. Dunn OJ., Multiple comparisons among means. J Am Stat Assoc 1961; 56: 52-64.
32. Hawkes R, Leclerc N., Antigenic map of the rat cerebellar cortex: the distribution of parasagittal bands as revealed by monoclonal anti-Purkinje cell antibody mabQ113. J Comp Neurol 1987; 256: 29-41.
33. Zhou H, Lin Z, Voges K, et al. Cerebellar modules operate at different frequencies. eLife 2014; 3: e02536.
34. Voogd J., The human cerebellum. J Chem Neuroanat 2003; 26: 243-252.
35. Simmonds DJ, Hallquist MN, Asato M, et al. Developmental stages and sex differences of white matter and behavioral development through adolescence: a longitudinal diffusion tensor imaging (DTI) study. Neuroimage 2014; 92: 356-368.
36. Mabbott DJ, Noseworthy MD, Bouffet E, et al. Diffusion tensor imaging of white matter after cranial radiation in children for medulloblastoma: correlation with IQ. Neuro-oncol 2006; 8: 244-252.
37. Stoodley CJ, Schmahmann JD., Evidence for topographic organization in the cerebellum of motor control versus cognitive and affective processing. Cortex 2010; 46: 831-844.
38. Amato MP, Goretti B, Ghezzi A, et al. Cognitive and psychosocial features of childhood and juvenile MS. Neurology 2008; 70: 1891-1897.
39. Gorman MP, Healy BC, Polgar-Turcsanyi M, et al. Increased relapse rate in pediatric-onset compared with adult-onset multiple sclerosis. Arch Neurol 2009; 66: 54-59.