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  • Title: MRI correlates of cognitive impairment in childhood-onset multiple sclerosis.
    Author: Till C, Ghassemi R, Aubert-Broche B, Kerbrat A, Collins DL, Narayanan S, Arnold DL, Desrocher M, Sled JG, Banwell BL.
    Journal: Neuropsychology; 2011 May; 25(3):319-32. PubMed ID: 21534686.
    Abstract:
    OBJECTIVE: Brain MRI measures were correlated with neuropsychological function in 35 pediatric-onset multiple sclerosis (MS) patients and 33 age- and sex-matched healthy controls. METHOD: Mean age of MS patients was 16.3 ± 2.3 years with average disease duration of 4.3 ± 3.1 years. Cortical gray matter, thalamic, and global brain volumes were calculated for all participants using a scaling factor computed using normalization of atrophy method to normalize total and regional brain volumes for head size. T1- and T2-weighted lesion volumes were calculated for MS patients. RESULTS: Cognitive impairment (CI) was identified in 29% of the MS cohort. Cognitive deficits predominantly involved attention and processing speed, expressive language, and visuomotor integration. Relative to controls, the MS group showed significantly lower thalamic volume (p < .001), total brain volume (p < .008), and gray matter volume (p < .015). Corpus callosum area and thalamic volume differentiated patients identified as having CI from those without CI (p < .05). Regression models controlling for disease duration and age indicated that thalamic volume accounted for significant incremental variance in predicting global IQ, processing speed, and expressive vocabulary (ΔR2 ranging from .43 to .60) and was the most robust MRI predictor of cognition relative to other MRI metrics. CONCLUSIONS: The robust association between cognitive function and reduced size of thalamus and global brain volume in pediatric-onset MS patients implicate neurodegenerative processes early in the disease course, and suggest that plasticity of an immature central nervous system is not sufficient to protect patients from the deleterious consequences of MS on cognitive neural networks. (PsycINFO Database Record (c) 2011 APA, all rights reserved).
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