Findings about LORETA Applied to High-Density EEG—A Review

Electroencephalography (EEG) is a non-invasive diagnostic technique for recording brain electric activity. The EEG source localization has been an area of research widely explored during the last decades because it provides helpful information about brain physiology and abnormalities. Source localization consists in solving the so-called EEG inverse problem. Over the years, one of the most employed method for solving it has been LORETA (Low Resolution Electromagnetic Tomography). In particular, in this review, we focused on the findings about the LORETA family algorithms applied to high-density EEGs (HD-EEGs), used for improving the low spatial resolution deriving from the traditional EEG systems. The results were classified according to their clinical application and some aspects arisen from the analyzed papers were discussed. Finally, suggestions were provided for future improvement. In this way, the combination of LORETA with HD-EEGs could become an even more valuable tool for noninvasive clinical evaluation in the field of applied neuroscience

COVID-19 Severity in Multiple Sclerosis: Putting Data Into Context

Background and objectives: It is unclear how multiple sclerosis (MS) affects the severity of COVID-19. The aim of this study is to compare COVID-19-related outcomes collected in an Italian cohort of patients with MS with the outcomes expected in the age- and sex-matched Italian population. Methods: Hospitalization, intensive care unit (ICU) admission, and death after COVID-19 diagnosis of 1,362 patients with MS were compared with the age- and sex-matched Italian population in a retrospective observational case-cohort study with population-based control. The observed vs the expected events were compared in the whole MS cohort and in different subgroups (higher risk: Expanded Disability Status Scale [EDSS] score > 3 or at least 1 comorbidity, lower risk: EDSS score ≤ 3 and no comorbidities) by the χ2 test, and the risk excess was quantified by risk ratios (RRs). Results: The risk of severe events was about twice the risk in the age- and sex-matched Italian population: RR = 2.12 for hospitalization (p < 0.001), RR = 2.19 for ICU admission (p < 0.001), and RR = 2.43 for death (p < 0.001). The excess of risk was confined to the higher-risk group (n = 553). In lower-risk patients (n = 809), the rate of events was close to that of the Italian age- and sex-matched population (RR = 1.12 for hospitalization, RR = 1.52 for ICU admission, and RR = 1.19 for death). In the lower-risk group, an increased hospitalization risk was detected in patients on anti-CD20 (RR = 3.03, p = 0.005), whereas a decrease was detected in patients on interferon (0 observed vs 4 expected events, p = 0.04). Discussion: Overall, the MS cohort had a risk of severe events that is twice the risk than the age- and sex-matched Italian population. This excess of risk is mainly explained by the EDSS score and comorbidities, whereas a residual increase of hospitalization risk was observed in patients on anti-CD20 therapies and a decrease in people on interferon

DMTs and Covid-19 severity in MS: a pooled analysis from Italy and France

We evaluated the effect of DMTs on Covid-19 severity in patients with MS, with a pooled-analysis of two large cohorts from Italy and France. The association of baseline characteristics and DMTs with Covid-19 severity was assessed by multivariate ordinal-logistic models and pooled by a fixed-effect meta-analysis. 1066 patients with MS from Italy and 721 from France were included. In the multivariate model, anti-CD20 therapies were significantly associated (OR = 2.05, 95%CI = 1.39–3.02, p < 0.001) with Covid-19 severity, whereas interferon indicated a decreased risk (OR = 0.42, 95%CI = 0.18–0.99, p = 0.047). This pooled-analysis confirms an increased risk of severe Covid-19 in patients on anti-CD20 therapies and supports the protective role of interferon

An eLORETA Longitudinal Analysis of Resting State EEG Rhythms in Alzheimer’s Disease

Alzheimer’s disease (AD) is a degenerative brain disorder which is the most common cause of dementia. As there is no cure for AD, an early diagnosis is essential to slow down the progression of the disease with a proper pharmacological treatment. Electroencephalography (EEG) represents a valid tool for studying AD. EEG signals of AD patients are characterized by a “slowing”, meaning the power increases in low frequencies (delta and theta) and decreases in higher frequency (alpha and beta), compared to normal elderly. The purpose of our study is the computation of the power current density in eight patients, who were diagnosed with MCI at time T0 and mild AD at time T1 (four months later), starting from the brain active source reconstruction. The novelty is that we employed the eLORETA algorithm, unlike the previous studies which used the old version of the algorithm named LORETA. It is also the first longitudinal study which considers such a short time period to explore the evolution of the disease. Five patients out of eight showed an increasing power in delta and theta bands. Seven patients exhibited a lower activation in alpha 1 and beta 2 bands. Finally, six patients revealed a decreased power in alpha 2 and beta 1 bands. These findings are consistent with those reported in literature. On the other hand, the discrepancy of some outcome could be related to a not yet severe stage of the disease. In our opinion, this study could represent a good starting point for more detailed future investigation

Effect of Rehabilitation on Brain Functional Connectivity in a Stroke Patient Affected by Conduction Aphasia

Stroke is a medical condition that affects the brain and represents a leading cause of death and disability. Associated with drug therapy, rehabilitative treatment is essential for promoting recovery. In the present work, we report an EEG-based study concerning a left ischemic stroke patient affected by conduction aphasia. Specifically, the objective is to compare the brain functional connectivity before and after an intensive rehabilitative treatment. The analysis was performed by means of local and global efficiency measures related to the execution of three tasks: naming, repetition and reading. As expected, the results showed that the treatment led to a balancing of the values of both parameters between the two hemispheres since the rehabilitation contributed to the creation of new neural patterns to compensate for the disrupted ones. Moreover, we observed that for both name and repetition tasks, shortly after the stroke, the global and local connectivity are lower in the affected lobe (left hemisphere) than in the unaffected one (right hemisphere). Conversely, for the reading task, global and local connectivity are higher in the impaired lobe. This apparently contrasting trend can be due to the effects of stroke, which affect not only the site of structural damage but also brain regions belonging to a functional network. Moreover, changes in network connectivity can be task-dependent. This work can be considered a first step for future EEG-based studies to establish the most suitable connectivity measures for supporting the treatment of stroke and monitoring the recovery process

A Multivariate Time Series Analysis of Ground Deformation Using Persistent Scatterer Interferometry

Ground deformations in urban areas can be the result of a combination of multiple factors and pose several hazards to infrastructures and human lives. In order to monitor these phenomena, Interferometric Synthetic Aperture Radar (InSAR) techniques are applied. The obtained signals record the overlapping of the phenomena, and their separation is a relevant issue. In this framework, we explored a new multi-method approach based on the combination of Principal Component Analysis (PCA), Independent Component Analysis (ICA) and Hierarchal Clustering (HC) on the standardized results to distinguish the main trends and seasonal signals embedded in the time series of ground displacements, to understand spatial-temporal patterns, to correlate ground deformation phenomena with geological and anthropogenic factors, and to recognize the specific footprints of different ground deformation phenomena. This method allows us to classify the ground deformations at the site scale in the metropolitan area of Naples, which is affected by uplift cycles, subsidence, cavity instabilities and sinkholes. At the local scale, the results allow a kinematic classification using the extracted components and considering the effect of the radius of influence generated by each cavity, as it is performed from a theoretical point of view when the draw angle is considered. According to the results, among the classified cavities, 2% were assigned to subsidence and 11% to uplift kinematics, while the remaining were found to be stable. Furthermore, our results show that the centering of the Spatial-PCA (S-PCA) is representative of the region’s main trend, whereas Temporal-PCA (T-PCA) gives information about the displacement rates identified by each component

A multivariate time series analysis of underground gas storage deformations using InSAR data

Underground gas storage (UGS) is of strategic importance both in terms of security of supply and to ensure the operational continuity of primary industrial basins. UGS reservoirs make it possible to guarantee the country a continuous and reliable supply of natural gas. It is well known that UGS activities can induce ground deformations, in response to gas injection and extraction cycles. The Lombardy region (Italy) has a predominant part in the Italian national policy of UGS in depleted reservoirs. In this work, five UGS reservoirs located in Lombardy and three additional ones in Italy, which differ in geometric and geo-lithological features, were considered.In this context, the InSAR (Interferometric Synthetic Aperture Radar) technique plays a key role in monitoring ground deformations induced by UGS activities, providing precise measurements of ground displacement.In this contribution, we present (i) an application of a multi-method approach for the analysis of trends and seasonal signals in the EGMS InSAR time series of ground displacements in the proximity of UGS reservoirs to recognise specific footprints and spatial-temporal patterns of ground deformation. For this purpose, large datasets of ground displacements covering the UGS area in Lombardy (25 km2) from 2015 to 2022 were analysed; and (ii) an interpretation of the possible causal relationship between displacement and gas injection and extraction time series using cross-correlation approach and wavelet tools in the time-frequency domain.The multi-method approach involves the application and optimization of Principal Component (PCA) and Independent Component Analyses (ICA) in temporal (T-) and spatial (S-) modes on both ascending and descending InSAR time series, as well as on the vertical and horizontal ones, allowing for a spatial-temporal separation of the original data into a set of limited components. Among them, it is possible to isolate those related to the USG deformations, from other signals typical of the region. Subsequently, clustering analysis is performed to group the InSAR time series and identify characteristic ground deformation patterns, which could also be related to differences in grain size properties.As a result, it was possible to recognize and separate a limited number of signal components, describing long-term displacement and seasonal fluctuations, and the derived maps allowed the characterization of the area of influence relative to each UGS reservoir. Finally, cross-correlation approach and wavelet tools made it possible to identify and interpret the time lag between the peaks and, consequently to improve the correlation between displacements and anthropogenic triggers.To validate the deformation patterns resulting from the approach, numerical analyses were performed in which the gas injection and extraction time series were considered as input variables

Cortical Reorganization after Rehabilitation in a Patient with Conduction Aphasia Using High-Density EEG

Conduction aphasia is a language disorder occurred after a left-brain injury. It is characterized by fluent speech production, reading, writing and normal comprehension, while speech repetition is impaired. The aim of this study is to investigate the cortical responses, induced by language activities, in a sub-acute stroke patient affected by conduction aphasia before and after an intensive speech therapy training. The patient was examined by using High-Density Electroencephalogram (HD-EEG) examination, while was performing language tasks. the patient was evaluated at baseline and after two months after rehabilitative treatment. Our results showed that an intensive rehabilitative process, in sub-acute stroke, could be useful for a good outcome of language deficits. HD-EEG results showed that left parieto-temporol-frontal areas were more activated after 2 months of rehabilitation training compared with baseline. Our results provided evidence that an intensive rehabilitation process could contribute to an inter- and intra-hemispheric reorganization

SARS-CoV-2 serology after COVID-19 in multiple sclerosis: An international cohort study

Background: The MuSC-19 project is an Italian cohort study open to international partners that collects data on multiple sclerosis (MS) patients with COVID-19. During the second wave of the pandemic, serological tests became routinely available. Objective: To evaluate the seroprevalence of anti-SARS-CoV-2 antibodies according to the use of disease-modifying therapy (DMT) in a subset of patients included in the MuSC-19 data set who had undergone a serological test. Methods: We evaluated the association between positive serological test results and time elapsed since infection onset, age, sex, Expanded Disability Status Scale score, comorbidities and DMT exposure using a multivariable logistic model. Results: Data were collected from 423 patients (345 from Italy, 61 from Turkey and 17 from Brazil) with a serological test performed during follow-up. Overall, 325 out of 423 tested patients (76.8%) had a positive serological test. At multivariate analysis, therapy with anti-CD20 was significantly associated with a reduced probability of developing antibodies after COVID-19 (odds ratio (OR) = 0.20, p = 0.002). Conclusion: Patients with MS maintain the capacity to develop humoral immune response against SARS-COV-2, although to a lesser extent when treated with anti-CD20 drugs. Overall, our results are reassuring with respect to the possibility to achieve sufficient immunization with vaccination