Preserved brain functional plasticity after upper limb task-oriented rehabilitation in progressive multiple sclerosis.

BACKGROUND AND PURPOSE: Limited research has been dedicated to upper limb (UL) rehabilitation in progressive multiple sclerosis (PMS). The objective in this pilot study was to investigate the effect of task-oriented UL rehabilitation in PMS and to perform explorative analyses of the magnetic resonance imaging (MRI) correlates of changes in motor performance. METHODS: Twenty-six PMS patients with mild UL impairment were prospectively enrolled and randomized into two groups: an active treatment group (ATG, n = 13) and a passive treatment group (PTG, n = 13). At baseline and after training, patients underwent MRI scans with structural and functional imaging and were evaluated with the action research arm test, the nine-hole peg test, the ABILHAND scale and the modified fatigue impact scale (MFIS). Measures of motor finger performance were obtained by engineered glove measuring. RESULTS: After rehabilitation, the ATG improved in several finger motor tasks (0.001 ≤ P ≤ 0.03, 0.72 ≤ Cohen's d ≤ 1.22) and showed reduced MFIS scores compared with the PTG (P = 0.03). The ATG showed increased functional connectivity within the cerebellar and thalamic resting state networks compared with the PTG (P < 0.05). Correlations were found between several measures of motor improvement and thalamic and sensorimotor networks (0.87 ≤ r ≤ 0.93, 0.001 ≤ P ≤ 0.03). No changes in cerebral volumes and diffusion tensor imaging derived measures were detected. CONCLUSIONS: Progressive multiple sclerosis patients with mild UL dysfunction benefit from task-oriented UL rehabilitation, which seems to be more efficient than simple passive mobilization. Despite a high burden of disability and brain damage, functional adaptive capacities seem to be preserved, thus providing a rationale for the use of rehabilitative treatments in late PMS.

Functional connectivity analysis using whole brain and regional network metrics in MS patients.

In the present study we investigated brain network connectivity differences between patients with relapsing-remitting multiple sclerosis (RRMS) and healthy controls (HC) as derived from functional resonance magnetic imaging (fMRI) using graph theory. Resting state fMRI data of 18 RRMS patients (12 female, mean age ± SD: 42 ± 12.06 years) and 25 HC (8 female, 29.2 ± 5.38 years) were analyzed. In order to obtain information of differences in entire brain network, we focused on both, local and global network connectivity parameters. And the regional connectivity differences were assessed using regional network parameters. RRMS patients presented a significant increase of modularity in comparison to HC, pointing towards a network structure with densely interconnected nodes within one module, while the number of connections with other modules outside decreases. This higher decomposable network favours cost-efficient local information processing and promotes long-range disconnection. In addition, at the regional anatomical level, the network parameters clustering coefficient and local efficiency were increased in the insula, the superior parietal gyrus and the temporal pole. Our study indicates that modularity as derived from fMRI can be seen as a characteristic connectivity feature that is increased in MS patients compared to HC. Furthermore, specific anatomical regions linked to perception, motor function and cognition were mainly involved in the enhanced local information processing.

Testing different ICA algorithms and connectivity analyses on MS patients.

Multiple sclerosis (MS) is a progressive neurological disorder that affects the central nervous system. Functional magnetic resonance imaging (fMRI) has been employed to track the course and disease progression in patients with MS. The two main aims of this study were to apply in a data-driven approach the independent component analysis (ICA) in the spatial domain to depict the active sources and to look at the effective connectivity between the identified spatial sources. Several ICA algorithms have been proposed for fMRI data analysis. In this study, we aimed to test two well characterized algorithms, namely, the fast ICA and the complex infomax algorithms, followed by two effective connectivity algorithms, namely, Granger causality (GC) and generalized partial directed coherence (GPDC), to illustrate the connections between the spatial sources in patients with MS. The results obtained from the ICA analyses showed the involvement of the default mode network sources. The connectivity analyses depicted significant changes between the two applied algorithms. The significance of this study was to demonstrate the robustness of the analyzed algorithms in patients with MS and to validate them before applying them on larger datasets of patients with MS.