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Mult Scler. 2016 Apr;22(5):608-19. doi: 10.1177/1352458515596598. Epub 2015 Jul 24.

Multi-parametric structural magnetic resonance imaging in relation to cognitive dysfunction in long-standing multiple sclerosis.

Author information

1
Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Centre, The Netherlands/Department of Anatomy and Neurosciences, Neuroscience Campus Amsterdam, VU University Medical Centre, The Netherlands m.daams@vumc.nl.
2
Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Centre, The Netherlands.
3
Department of Anatomy and Neurosciences, Neuroscience Campus Amsterdam, VU University Medical Centre, The Netherlands.
4
Department of Neurology, Neuroscience Campus Amsterdam, VU University Medical Centre, The Netherlands.

Abstract

BACKGROUND:

Cognitive deficits are common in multiple sclerosis. Most previous studies investigating the imaging substrate of cognitive deficits in multiple sclerosis included patients with relatively short disease durations and were limited to one modality/brain region.

OBJECTIVE:

To identify the strongest neuroimaging predictors for cognitive dysfunction in a large cohort of patients with long-standing multiple sclerosis.

METHODS:

Extensive neuropsychological testing and multimodal 3.0T MRI was performed in 202 patients with multiple sclerosis and 52 controls. Cognitive scores were compared between groups using Z-scores. Whole-brain, white matter, grey matter, deep grey matter and lesion volumes; cortical thickness, (juxta)cortical and cerebellar lesions; and extent and severity of diffuse white matter damage were measured. Stepwise linear regression was used to identify the strongest predictors for cognitive dysfunction.

RESULTS:

All cognitive domains were affected in patients. Patients showed extensive atrophy, focal pathology and damage in up to 75% of the investigated white matter. Associations between imaging markers and average cognition were two times stronger in cognitively impaired patients than in cognitively preserved patients. The final model for average cognition consisted of deep grey matter DGMV volume and fractional anisotropy severity (adjusted R²=0.490; p<0.001).

CONCLUSION:

From all imaging markers, deep grey matter atrophy and diffuse white matter damage emerged as the strongest predictors for cognitive dysfunction in long-standing multiple sclerosis.

KEYWORDS:

Multiple sclerosis; cognition; magnetic resonance imaging

PMID:
26209593
DOI:
10.1177/1352458515596598
[Indexed for MEDLINE]

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