From the Departments of Radiology (H.E.H., M.D.S., A.V., H.V., F.B.), Anatomy and Neurosciences (H.E.H., J.J.G.G.), Physics and Medical Technology (P.J.W.P.), Neurology (B.M.J.U., C.H.P.), and Epidemiology and Biostatistics (B.M.J.U.), VU University Medical Center, Amsterdam, the Netherlands.
To investigate whether extent and severity of white matter (WM) damage, as measured with diffusion tensor imaging (DTI), can distinguish cognitively preserved (CP) from cognitively impaired (CI) multiple sclerosis (MS) patients.
Conventional MRI and DTI data were acquired from 55 MS patients (35 CP, 20 CI) and 30 healthy controls (HC). Voxelwise analyses were used to investigate fractional anisotropy (FA), mean diffusivity, radial diffusivity, and axial diffusivity of a WM skeleton. Regional gray matter volume was quantified and lesion probability maps were generated.
Compared to HCs, decreased FA was found in 49% of the investigated WM skeleton in CP patients and in 76% of the investigated WM in CI patients. Several brain areas that showed reduced FA in both patient groups were significantly worse in CI patients, i.e, corpus callosum, superior and inferior longitudinal fasciculus, corticospinal tracts, forceps major, cingulum, and fornices. In CI patients, WM integrity damage was additionally seen in cortical brain areas, thalamus, uncinate fasciculus, brainstem, and cerebellum. These findings were independent of lesion location and regional gray matter volume, since no differences were found between the groups.
CI patients diverged from CP patients only on DTI metrics. WM integrity changes were found in areas that are highly relevant for cognition in the CI patients but not in the CP patients. These WM changes are therefore thought to be related to the cognitive deficits and suggest that DTI might be a powerful tool when monitoring cognitive impairment in MS.
"Cognitive impairment occurs in approximately 40–60% of patients with multiple sclerosis (MS), and is associated with structural and functional brain changes (Hulst et al., 2013; Lansley et al., 2013). Approximately 14–18% of MS patients use cannabis for symptomatic relief from pain, spasticity and insomnia (Chong et al., 2006; Page et al., 2003). "
[Show abstract][Hide abstract] ABSTRACT: A subset of patients with multiple sclerosis (MS) smoke cannabis to relieve symptoms including spasticity and pain. Recent evidence suggests that smoking cannabis further impairs cognition in people with MS and is linked to impaired functional brain changes. No such association, however, has been reported between cannabis use and structural brain changes, hence the focus of the present study.
Twenty patients with MS who smoke cannabis for symptom relief, and 19 matched non-cannabis-smoking MS patients were given the Brief Repeatable Neuropsychological Battery and structural MRI scans. Images were segmented into gray matter and white matter, and subsequently analysed with Partial Least Squares, a data-driven multivariate technique that explores brain-behaviour associations.
In both groups, the Partial Least Squares analysis yielded significant correlations between cognitive scores and both gray matter (33% variance, p < .0001) and white matter (17% variance, p < .05) volume. Gray matter volume in the thalamus, basal ganglia, medial temporal, and medial prefrontal regions, and white matter volume in the fornix correlated with cognitive deficits. Crucially, the analysis indicated that brain volume reductions were associated with more extensive cognitive impairment in the cannabis versus the non-cannabis MS group.
These results suggest that cannabis use in MS results in more widespread cognitive deficits, which correlate with tissue volume in subcortical, medial temporal, and prefrontal regions. These are the first findings demonstrating an association between cannabis use, cognitive impairment and structural brain changes in MS patients.
"The cerebellum is dually implicated in MS pathophysiology. First, it is a region where extensive cortical and subcortical demyelination occurs during late disease phases [Kutzelnigg et al., 2007], and where alterations of fiber bundles [Hulst et al., 2013] and atrophy [Calabrese et al., 2010] reportedly contribute to cognitive and motor dysfunction [Calabrese et al., 2010; Hulst et al., 2013]. Second, it is a structure implicated in functional motor recovery [Saini et al., 2004]. "
[Show abstract][Hide abstract] ABSTRACT: Background:
Cerebellar pathology occurs in late multiple sclerosis (MS) but little is known about cerebellar changes during early disease stages. In this study, we propose a new multicontrast "connectometry" approach to assess the structural and functional integrity of cerebellar networks and connectivity in early MS.
We used diffusion spectrum and resting-state functional MRI (rs-fMRI) to establish the structural and functional cerebellar connectomes in 28 early relapsing-remitting MS patients and 16 healthy controls (HC). We performed multicontrast "connectometry" by quantifying multiple MRI parameters along the structural tracts (generalized fractional anisotropy-GFA, T1/T2 relaxation times and magnetization transfer ratio) and functional connectivity measures. Subsequently, we assessed multivariate differences in local connections and network properties between MS and HC subjects; finally, we correlated detected alterations with lesion load, disease duration, and clinical scores.
In MS patients, a subset of structural connections showed quantitative MRI changes suggesting loss of axonal microstructure and integrity (increased T1 and decreased GFA, P < 0.05). These alterations highly correlated with motor, memory and attention in patients, but were independent of cerebellar lesion load and disease duration. Neither network organization nor rs-fMRI abnormalities were observed at this early stage.
Multicontrast cerebellar connectometry revealed subtle cerebellar alterations in MS patients, which were independent of conventional disease markers and highly correlated with patient function. Future work should assess the prognostic value of the observed damage.
Human Brain Mapping 04/2015; 36(4):n/a-n/a. DOI:10.1002/hbm.22698 · 5.97 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Background
Prevalence rates of cognitive impairment (CI) in multiple sclerosis (MS) vary between 40% and 80%. Differences in classification criteria for CI may explain this variance.
This study reviewed and compared classification criteria for CI in patients with early and late MS.
The paper consists of two parts: a systematic review of published classification criteria and the presentation of new data. Criteria were reviewed in respect to percentage of abnormal parameters and cut-offs concerning standard deviations. Thereafter, criteria were applied to cognitive data of 25 patients with early MS (duration ≤ 2y), 52 matched patients with late MS (≥ 12y), and 75 matched controls. The test battery assessed alertness, divided attention, mental flexibility, verbal and visual learning, memory, and visuospatial abilities.
Seventy classification criteria were revealed and grouped into 20 distinct approaches that can be subdivided into three basic classification strategies. Most commonly, CI was defined as performing 1.5 SD or 2 SD below the normative mean in 18-30% of test parameters (n = 42). Other criteria utilized cognitive domains (n = 6), composite indices (n = 8), or combinations of cut-offs and strategies. The stringency of the criteria was correlated with the prevalence rate of CI (r = -.43) and disease duration (r = .48). In the new data, a substantial effect of classification criteria was found with a prevalence rate ranging from 0 to 68% in early and 4 to 81% in late MS. Increased rates of CI in patients vs. controls were found following 18 out of 20 criteria in the sample of late MS. In early MS, an increased rate of CI was only found following a liberal 1.5 SD cut-off criterion. Inter-rater reliability between all criteria was moderate. However, between criteria of comparable stringency the inter-rater reliability was found to be strong.
Classification based on different published criteria is not fully comparable and criteria need to be better homogenized.
Journal of the Neurological Sciences 08/2014; 343(1-2). DOI:10.1016/j.jns.2014.05.042 · 2.47 Impact Factor
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