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Aims and objectives Methods and materials Results Conclusion Personal information References, Aims and objectives: Essential tremor (ET) represents the most common neurologic disorder that causes postural or action tremor, with an estimated prevalence worldwide of up to 5% of the population [1]. The pathophysiology and mechanisms of ET as well as the possible...

Resting-state networks (RSNs) and functional connectivity (FC) have been increasingly exploited for mapping brain activity and identifying abnormalities in pathologies, including epilepsy. The majority of studies currently available are based on blood-oxygenation-level-dependent (BOLD) contrast in combination with either independent component analysis (ICA) or pairwise region of interest (ROI) correlations. Despite its success, this approach has several shortcomings as BOLD is only an indirect and non-quantitative measure of brain activity. Conversely, promising results have recently been achieved by arterial spin labeling (ASL) MRI, primarily developed to quantify brain perfusion. However, the wide application of ASL-based FC has been hampered by its complexity and relatively low robustness to noise, leaving several aspects of this approach still largely unexplored. In this study, we firstly aimed at evaluating the effect of noise reduction on spatio-temporal ASL analyses and quantifying the impact of two ad-hoc processing pipelines (basic and advanced) on connectivity measures. Once the optimal strategy had been defined, we investigated the applicability of ASL for connectivity mapping in patients with drug-resistant temporal epilepsy vs. controls (10 per group), aiming at revealing between-group voxel-wise differences in each RSN and ROI-wise FC changes. We first found ASL was able to identify the main network (DMN) along with all the others generally detected with BOLD but never previously reported from ASL. For all RSNs, ICA-based denoising (advanced pipeline) allowed to increase their similarity with the corresponding BOLD template. ASL-based RSNs were visibly consistent with literature findings; however, group differences could be identified in the structure of some networks. Indeed, statistics revealed areas of significant FC decrease in patients within different RSNs, such as DMN and cerebellum (CER), while significant increases were found in some cases, such as the visual networks. Finally, the ROI-based analyses identified several inter-hemispheric dysfunctional links (controls > patients) mainly between areas belonging to the DMN, right-left thalamus and right-left temporal lobe. Conversely, fewer connections, predominantly intra-hemispheric, showed the opposite pattern (controls < patients). All these elements provide novel insights into the pathological modulations characterizing a “network disease” as epilepsy, shading light on the importance of perfusion-based approaches for identifying the disrupted areas and communications between brain regions.

PURPOSE: To assess the applicability of arterial spin labeling (ASL) in comparison to blood-oxygenation-level-dependent (BOLD) contrast fMRI in detecting brain activations elicited by active and passive hand movements. MATERIALS AND METHODS: A block design for ASL and BOLD fMRI was applied in 8 healthy subjects using active and passive hand tasks. Data analyses were performed at individual and group level, comparing both the different movements and the performance of the two techniques. RESULTS: Group analyses showed involvement of the same areas during both tasks, as the contralateral sensorimotor cortex, supplementary motor area, cerebellum, inferior parietal lobes, thalamus. ASL detected smaller activation volumes than BOLD, but the areas had a high degree of colocalization. Few significant differences (P < 0.05) were found when the two tasks were compared for the number of activated voxels, coordinates of center of mass, and CBF estimates. Considering together all the areas, the mean %BOLD change was 0.79 ± 0.27 and 0.73 ± 0.24 for the active and passive movements respectively, while the mean %CBF changes were 34.1 ± 8.9 and 27.1 ± 14.8. CONCLUSION: Our findings confirm passive and active tasks are strongly coupled, supporting the importance of passive tasks as a diagnostic tool in the clinical setting. ASL fMRI proved suitable for functional mapping and quantifying CBF changes, making it a promising technique for patient cohort applications.