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

Increased structural white and grey matter network connectivity compensates for functional decline in early multiple sclerosis

Background The pathology of multiple sclerosis (MS) consists of demyelination and neuronal injury, which occur early in the disease; yet, remission phases indicate repair. Whether and how the central nervous system (CNS) maintains homeostasis to counteract clinical impairment is not known. Objective We analyse the structural connectivity of white matter (WM) and grey matter (GM) networks to understand the absence of clinical decline as the disease progresses. Methods A total of 138 relapsing–remitting MS patients (classified into six groups by disease duration) and 32 healthy controls were investigated using 3-Tesla magnetic resonance imaging (MRI). Networks were analysed using graph theoretical approaches based on connectivity patterns derived from diffusion-tensor imaging with probabilistic tractography for WM and voxel-based morphometry and regional-volume-correlation matrix for GM. Results In the first year after disease onset, WM networks evolved to a structure of increased modularity, strengthened local connectivity and increased local clustering while no clinical decline occurred. GM networks showed a similar dynamic of increasing modularity. This modified connectivity pattern mainly involved the cerebellum, cingulum and temporo-parietal regions. Clinical impairment was associated at later disease stages with a divergence of the network patterns. Conclusion Our findings suggest that network functionality in MS is maintained through structural adaptation towards increased local and modular connectivity, patterns linked to adaptability and homeostasis

Treadmill training in Parkinson's disease is underpinned by the interregional connectivity in cortical-subcortical network

Treadmill training (TT) has been extensively used as an intervention to improve gait and mobility in patients with Parkinson’s disease (PD). Regional and global effects on brain activity could be induced through TT. Training effects can lead to a beneficial shift of interregional connectivity towards a physiological range. The current work investigates the effects of TT on brain activity and connectivity during walking and at rest by using both functional near-infrared spectroscopy and functional magnetic resonance imaging. Nineteen PD patients (74.0 ± 6.59 years, 13 males, disease duration 10.45 ± 6.83 years) before and after 6 weeks of TT, along with 19 age-matched healthy controls were assessed. Interregional effective connectivity (EC) between cortical and subcortical regions were assessed and its interrelation to prefrontal cortex (PFC) activity. Support vector regression (SVR) on the resting-state ECs was used to predict prefrontal connectivity. In response to TT, EC analysis indicated modifications in the patients with PD towards the level of healthy controls during walking and at rest. SVR revealed cerebellum related connectivity patterns that were associated with the training effect on PFC. These findings suggest that the potential therapeutic effect of training on brain activity may be facilitated via changes in compensatory modulation of the cerebellar interregional connectivity

A "kissing lesion": In-vivo 7T evidence of meningeal inflammation in early multiple sclerosis

BACKGROUND: The role of cortical lesions (CLs) in disease progression and clinical deficits is increasingly recognized in multiple sclerosis (MS); however the origin of CLs in MS still remains unclear. OBJECTIVE: Here, we report a para-sulcal CL detected two years after diagnosis in a relapsing-remitting MS (RRMS) patient without manifestation of clinical deficit. METHODS: Ultra-high field (7T) MR imaging using magnetization-prepared 2 rapid acquisition gradient echoes (MP2RAGE) sequence was performed. RESULTS: A para-sulcal CL was detected which showed hypointense rim and iso- to hyperintense core. This was detected in the proximity of the leptomeninges in the left precentral gyrus extending to the adjacent postcentral gyrus. CONCLUSION: This finding indicates that inflammatory infiltration into the cortex through the meninges underlies cortical pathology already in the early stage of disease and in mild disease course

EEG-Based Mapping of Resting-State Functional Brain Networks in Patients with Parkinson’s Disease

(1) Background: Directed functional connectivity (DFC) alterations within brain networks are described using fMRI. EEG has been scarcely used. We aimed to explore changes in DFC in the sensory-motor network (SMN), ventral-attention network (VAN), dorsal-attention network (DAN), and central-executive network (CEN) using an EEG-based mapping between PD patients and healthy controls (HCs). (2) Methods: Four-minutes resting EEG was recorded from 29 PD patients and 28 HCs. Network’s hubs were defined using fMRI-based binary masks and their electrical activity was calculated using the LORETA. DFC between each network’s hub-pairs was calculated for theta, alpha and beta bands using temporal partial directed coherence (tPDC). (3) Results: tPDCs percent was lower in the CEN and DAN in PD patients compared to HCs, while no differences were observed in the SMN and VAN (group*network: F = 5.943, p < 0.001) in all bands (group*band: F = 0.914, p = 0.401). However, in the VAN, PD patients showed greater tPDCs strength compared to HCs (p < 0.001). (4) Conclusions: Our results demonstrated reduced connectivity in the CEN and DAN, and increased connectivity in the VAN in PD patients. These results indicate a complex pattern of DFC alteration within major brain networks, reflecting the co-occurrence of impairment and compensatory mechanisms processes taking place in PD