Deirel Paz-Linares

Background The EEG constitutes a powerful neuroimaging technique for assessing functional brain impairment in COVID-19 patients. Objective The current investigation compared the EEG among COVID-19 survivors, close contacts and the Cuban EEG normative database, using semi-quantitative visual EEG inspection, quantitative and the current source density measures EEG analysis. Methods The resting-state EEG activity, quantitative QEEG, and VARETA inverse solution, were evaluated in 173 subjects: 87 patients confirmed cases by the positive reverse transcription polymerase chain reaction (RT-PCR), 86 close contacts (negative PCR) and the Cuban EEG normative database. All patients were physical, neurological, and clinically assessed using neurological retrospective survey and version 2.1 of the Schedules for Clinical Assessment in Neuropsychiatry (SCAN). Results The GTE score showed significant differences in terms of frequency scores of backgrounds rhythmic activity, diffuse slow activity, and focal abnormality. The QEEG analysis showed a pattern of abnormality with respect to the Cuban EEG normative values, displaying an excess of alpha and beta activities in the fronto-central-parietal areas in both groups. The anomalies, of COVID-19 patients and close contacts, differs in the right fronto-centro parietal area. The COVID 19 group differed-s from the close control group in theta band of the right parieto-central. The symptomatic group of COVID-19 patients differs from asymptomatic patients in delta and theta activities of the parieto-central region. The sources of activation using VARETA showed a difference in cortical activation patterns at alpha and beta frequencies in the groups studied with respect to the normative EEG database. In beta frequency were localized in right middle temporal gyrus in both groups and right angular gyrus in Covid 19 group only. In alpha band, the regions were the left supramarginal gyrus for Covid 19 group and the left superior temporal gyrus for Control group. Greater activation was found in the right middle temporal gyrus at alpha frequency in COVID-19 patients than in their close contacts. Conclusions Brain functions are impaired in long COVID-19 patients. QEEG and VARETA permit us to comprehend the susceptibility of particular brain regions exposed to viral illness. Highlights Background frequency abnormalities diffuse slow activity and focal abnormality associated with a pattern of excess oftheta, alpha and beta energies in in the right fronto-centro-parietal regions in QEEG analysis characterizedCOVID-19 patients. Patients with COVID-19 show more alpha and beta EEG activities related to normative EEG database. Patients with COVID-19 and close contacts show high cortical activation in temporo-parietal areas in alpha and beta bands compared to normative EEG database. Patients with COVID-19 (positive PCR) have high activation in the right middle frontal gyrus for alpha band related to close contacts.

The paper introduces a novel non-parametric Riemannian regression method using Isometric Riemannian Manifolds (IRMs). The proposed technique, Intrinsic Local Polynomial Regression on IRMs (ILPR-IRMs), enables global data mapping between Riemannian manifolds while preserving underlying geometries. The ILPR method is generalized to handle multivariate covariates on any Riemannian manifold and isometry. Specifically, for manifolds equipped with Euclidean Pullback Metrics (EPMs), a closed analytical formula is derived for the multivariate ILPR (ILPR-EPM). Asymptotic statistical properties of the ILPR-EPM for the multivariate local linear case are established, including a formula for the asymptotic bias, establishing estimator consistency. The paper showcases possible applications of the method by focusing on a group of Riemannian metrics on the Symmetric Positive Definite (SPD) manifold, which arises in machine learning and neuroscience. It is demonstrated that several metrics on the SPD manifold are EPMs, resulting in a closed analytical expression for the multivariate ILPR estimator on the SPD manifold. The paper evaluates the ILPR estimator's performance under two specific EPMs, Log-Cholesky and Log-Euclidean, on simulated data on the SPD manifold and compares it with extrinsic LPR using the Affine-Invariant when scaling the manifold and covariate dimension. The results show that the ILPR using the Log-Cholesky metric is computationally faster and provides a better trade-off between error and time than other metrics. Finally, the Log-Cholesky metric on the SPD manifold is employed to implement an efficient and intrinsic version of Rie-SNE for visualizing high-dimensional SPD data. The code for implementing ILPR-EPMs and other relevant calculations is available on the GitHub page.

Brain electrical activity in different spectral bands has been associated with diverse mechanisms underlying Brain function. Deeper reconnoitering of these mechanisms entails mapping in grayordinates (Gray Matter coordinates), the spectral features of electrophysiological Brain signals. Such mapping is possible through MEG/EEG signals, due to their wide Brain coverage and excellent temporal resolution in reflecting neural-electrical-activity. This process-coined Electrophysiological Source Imaging (ESI)-can only produce approximated images of Brain activity, which are severely distorted by leakage: a pervasive effect in almost any imaging technique. It has been proposed that leakage control to tolerable levels can be achived through using priors or regularization within ESI, but their implementation commonly yields meager statistical guaranties. We introduce bottom-up control of leakage: defined as maximum Bayesian evidence search braced with priors precisely on the spectral responses. This is feasible due to an instance of Bayesian learning of complex valued data: spectral Structured Sparse Bayesian Learning (sSSBL). “Spectral” refers to specific spatial topologies that are reflected by the MEG/EEG spectra. We also present a new validation benchmark based on the concurrency between high density MEG and its associated pseudo-EEG of lower density. This reveals that prevealing methods like eLORETA and LCMV can fall short of expectations whereas sSSBL exibits an exellent performance. A final qualitative assesment reveals that sSSBL can outline brain lessions using just low density EEG, according to the T2 MRI shine through of the affected areas.

Simplistic estimation of neural connectivity in MEEG sensor space is impossible due to volume conduction. The only viable alternative is to carry out connectivity estimation in source space. Among the neuroscience community this is claimed to be impossible or misleading due to Leakage: linear mixing of the reconstructed sources. To address this problematic we propose a novel solution method that caulks the Leakage in MEEG source activity and connectivity estimates: BC-VARETA. It is based on a joint estimation of source activity and connectivity in the frequency domain representation of MEEG time series. To achieve this, we go beyond current methods that assume a fixed gaussian graphical model for source connectivity. In contrast we estimate this graphical model in a Bayesian framework by placing priors on it, which allows for highly optimized computations of the connectivity, via a new procedure based on the local quadratic approximation under quite general prior models. A further contribution of this paper is the rigorous definition of leakage via the Spatial Dispersion Measure and Earth Movers Distance based on the geodesic distances over the cortical manifold. Both measures are extended for the first time to quantify Connectivity Leakage by defining them on the cartesian product of cortical manifolds. Using these measures, we show that BC-VARETA outperforms most state of the art inverse solvers by several orders of magnitude.