Biology subjects

Paz-Linares, D.

Publications and source records attributed to Paz-Linares, D..

5 recordsLinked to original sources

bioRxiv2024

EEG Signatures of COVID-19 Survival compared to close contacts and the Cuban EEG normative database

BackgroundThe EEG constitutes a powerful neuroimaging technique for assessing functional brain impairment in COVID-19 patients. ObjectiveThe 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. MethodsThe 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). ResultsThe 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. ConclusionsBrain 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. HighlightsO_LIBackground 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. C_LIO_LIPatients with COVID-19 show more alpha and beta EEG activities related to normative EEG database. C_LIO_LIPatients with COVID-19 and close contacts show high cortical activation in temporo-parietal areas in alpha and beta bands compared to normative EEG database. C_LIO_LIPatients with COVID-19 (positive PCR) have high activation in the right middle frontal gyrus for alpha band related to close contacts. C_LI

neuroscience↗

bioRxiv2022

Harmonized-Multinational qEEG Norms (HarMNqEEG)

This paper extends our frequency domain quantitative electroencephalography (qEEG) methods pursuing higher sensitivity to detect Brain Developmental Disorders. Prior qEEG work lacked integration of cross-spectral information omitting important functional connectivity descriptors. Lack of geographical diversity precluded accounting for site-specific variance, increasing qEEG nuisance variance. We ameliorate these weaknesses. i) Create lifespan Hermitian Riemannian multinational qEEG norms for cross-spectral tensors. These norms result from the HarMNqEEG project fostered by the Global Brain Consortium. We calculate the norms with data from 9 countries, 12 devices, and 14 studies, including 1564 subjects. Instead of raw data, only anonymized metadata and EEG cross-spectral tensors were shared. After visual and automatic quality control developmental equations for the mean and standard deviation of qEEG traditional and Hermitian Riemannian descriptive parameters were calculated using additive mixed-effects models. We demonstrate qEEG "batch effects" and provide methods to calculate harmonized z-scores. ii) We also show that the multinational harmonized Hermitian Riemannian norms produce z-scores with increased diagnostic accuracy to predict brain dysfunction at school-age produced by malnutrition only in the first year of life. We provide data and software for constructing norms. iii) We offer open code and data to calculate different individual z-scores from the HarMNqEEG dataset. These results contribute to developing bias-free, low-cost neuroimaging technologies applicable in various health settings. HighlightsO_LIWe create lifespan Hermitian Riemannian qEEG norms for cross-spectral tensors. C_LIO_LIThe norms are based on 9 countries, 12 devices, and 14 studies, with 1564 subjects. C_LIO_LIWe demonstrate qEEG "batch effects", providing harmonization methods to remove them. C_LIO_LIMultinational harmonized z-scores increase diagnostic accuracy of brain dysfunction. C_LIO_LIData and software are available for norm and individual z-scores calculation. C_LI

neuroscience↗

bioRxiv2021

An exploratory study of EEG connectivity during the first year of life in preterm and full-term infants

AimTo evaluate electroencephalography (EEG) connectivity during the first year of age in healthy full-term infants and preterm infants with prenatal and perinatal risk factors for perinatal brain damage. MethodsThree groups of infants were studied: healthy full-term infants (n = 71), moderate/late-preterm infants (n = 54), and very preterm infants (n = 56). All preterm infants had perinatal and/or perinatal risk factors for brain damage. EEG was obtained during phase II of natural nonrapid eye movement (NREM) sleep. EEG analysis was performed in 24 2.56-s artifact-free segments. For the calculation of EEG sources, spectral structured sparse Bayesian learning was used. Connectivity was computed by the phase-lag index. ResultsIn healthy full-term infants, EEG interhemispheric connectivity in the different frequency bands followed similar trends with age to those reported in each frequency band: delta connectivity decreased, theta increased at the end of the year, the alpha band showed different trends based on the region studied, and beta interhemispheric connectivity decreased with age. EEG connectivity in preterm infants showed differences from the results of the term group. DiscussionImportant structural findings may explain the differences observed in EEG connectivity between the term and preterm groups. ConclusionThe study of EEG connectivity during the first year of age provides essential information on normal and abnormal brain development.

neuroscience↗

bioRxiv2020

Bottom-up control of leakage in spectral electrophysiological source imaging via structured sparse bayesian learning

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.

neuroscience↗

bioRxiv2018

Populational Super-Resolution Sparse M/EEG Sources and Connectivity Estimation

In this paper, we describe a novel methodology, BC-VARETA, for estimating the Inverse Solution (sources activity) and its Precision Matrix (connectivity parameters) in the frequency domain representation of Stationary Time Series. The aims of this method are three. First: Joint estimation of Source Activity and Connectivity as a frequency domain linear dynamical system identification approach. Second: Achieve super high resolution in the connectivity estimation through Sparse Hermitian Sources Graphical Model. Third: To be a populational approach, preventing the Inverse Solution and Connectivity statistical analysis across subjects as a postprocessing, by modeling population features of Source Activity and Connectivity. Our claims are supported by a wide simulation framework using realistic head models, realistic Sources Setup, and Inverse Crime effects evaluation. Also, a fair quantitative analysis is performed, based on a diversification of quality measures on which state of the art Inverse Solvers were tested.

neuroscience↗