Thalamic-hippocampal-prefrontal disruption in relapsing-remitting multiple sclerosis

Background: Cortical, thalamic and hippocampal gray matter atrophy in relapsing-remitting MS (RRMS) is associated cognitive deficits. However, the role of interconnecting white matter pathways including the fornix, cingulum, and uncinate fasciculus (UF) is less well studied. Objective: To assess MS damage to a hippocampal-thalamic-prefrontal network and the relative contributions of its components to specific cognitive domains. Methods: We calculated diffusion tensor fractional anisotropy (FA) in the fornix, cingulum and UF as well as thalamic and hippocampal volumes in 27 RRMS patients and 20 healthy controls. A neuropsychological battery was administered and 4 core tests known to be sensitive to MS changes were used to assess cognitive impairment. To determine the relationships between structure and cognition, all tests were grouped into 4 domains: attention/executive function, processing speed, verbal memory, and spatial memory. Univariate correlations with structural measures and depressive symptoms identified potential contributors to cognitive performance and subsequent linear regression determined their relative effects on performance in each domain. For significant predictors, we also explored the effects of laterality and axial versus radial diffusivity. Results: RRMS patients had worse performance on the Symbol Digit Modalities Test, but no significant impairment in the 4 cognitive domains. RRMS had reduced mean FA of all 3 pathways and reduced thalamic and hippocampal volumes compared to controls. In RRMS we found that thalamic volume and BDI predicted attention/executive function, UF FA predicted processing speed, thalamic volume predicted verbal memory, and UF FA and BDI predicted spatial memory. Conclusions: Hippocampal-thalamic-prefrontal disruption affects cognitive performance in early RRMS with mild to minimal cognitive impairment, confirming both white and gray matter involvement in MS and demonstrating utility in assessing functional networks to monitor cognition. (C) 2014 Published by Elsevier