Biology subjects

Valdes-Sosa, P. A.

Publications and source records attributed to Valdes-Sosa, P. A..

At least 19 recordsLinked to original sources

bioRxiv2026

Neuronal loss reshapes survivor dynamics and limits mechanism inference in excitatory inhibitory neural fields

Does neuronal loss simply reduce measured activity, or also change how the surviving network behaves? We separate these effects in a next-generation excitatory-inhibitory neural field by writing the viable population measure as q_a = lambda_a f_a, where lambda_a is viable population mass and f_a is the normalized survivor distribution. Under state-independent thinning with fixed Cauchy heterogeneity, normalization commutes with the Ott-Antonsen/Montbrio-Pazo-Roxin reduction on the specified analytic invariant manifold. The mortality term disappears from conditional transport, but viable mass remains in recurrent coupling: loss can reshape survivor dynamics, not merely scale their contribution to tissue activity. Conversely, for otherwise identical constant homogeneous parameters, viability, pathway integrity and compensation give exactly conjugate conditional deterministic dynamics whenever c_ab lambda_b^(1-nu_ab) is preserved. Identical conditional activity therefore need not imply an identical biological mechanism. Equilibrium and oscillatory bifurcations, finite-population escape, and delayed propagation reveal consequences of these two principles. In particular, matched field simulations show that localized loss can increase whole-sheet firing through recurrent reorganization, while coherent-wave continuation quantifies viability-dependent propagation and phase relaxation. The framework distinguishes neuronal abundance from survivor state and places an exact limit on mechanism inference. Attributing activity changes to neuronal loss therefore requires information beyond conditional neural dynamics, such as tissue-level measurements or independent structural constraints, interpreted through an appropriate observation model.

neuroscience↗

bioRxiv2026

Spatial transcriptomic programs relate to spectrolaminar rhythms across macaque cortex

The laminar organization of cortical dynamics is thought to reflect underlying cell-type architecture, but this relationship has not been resolved across primate cortex. Here we developed a spectro-omic framework that allows laminar local field potentials and single-cell spatial transcriptomics to be compared within a common layer-4-referenced coordinate system. Instead of relying on raw band-power analysis, we used Local Spectral Expansion (LSE), a spectrolaminar component model that resolves frequency-by-depth LFP power maps into distinct{delta} , {theta}, , {beta}, low-{gamma} and high-{gamma} components. We then introduced layer-4 projection (L4P) to unfold nonlinear spatial transcriptomic cortical ribbons into flat layer-4-referenced manifolds compatible with the electrophysiological depth profiles. Across twelve matched macaque cortical regions, transcriptomic predictors improved held-out prediction of LSE-derived six-anatomical-layer spectral composition beyond a hierarchy-plus-layer baseline in a partwise logit model: R2 = 0.621 versus 0.384; {Delta}R2 = +0.237; r = 0.809 versus 0.692. This gain was supported by hierarchy-preserving shift/reflect nulls [Formula] and by a Freedman-Lane region-block residual-permutation test [Formula]. Full-depth PLS1 decoding linked /{beta} processes to deep-layer, especially L4/5/6 and L6, glutamatergic programs enriched for axonal, synaptic and myelin-associated biology, whereas low-{gamma} and high-{gamma} processes were linked to superficial-to-middle-layer GABAergic/PVALB-enriched inhibitory programs together with excitability, ion-homeostasis, non-neuronal and energy-metabolism signatures. Together, these observed spectro-omic relationships suggest that laminar molecular and cellular architecture forms a plausible substrate for the spectrolaminar motif across macaque cortex.

neuroscience↗

bioRxiv2026

Source-space precision charts for lifespan EEG connectomics

Source-space electroencephalography (EEG) connectomics aims to estimate interactions among cortical generators from sensor cross-spectra that are mixed by the head and lead field. This task is difficult because marginal source covariance or coherence can retain leakage, common drive and indirect mediation, whereas developmental mapping requires conditional interactions that can be estimated repeatedly across large cohorts. We developed JSPACE (Joint Source-space Precision And Cross-spectral Estimation), a frequency-domain inverse framework for estimating multi-frequency source precision matrices from scalp cross-spectra. JSPACE couples posterior source cross-spectral estimation with standardized precision fitting, sparse frequency-smooth anatomical regularization, stochastic active-set optimization and post-selection refitting. In simulations, its advantage was target-specific: JSPACE reduced coherence inflation and achieved the lowest imaginary-coherence and peak-frequency errors in a forward neural-mass benchmark. When the ground-truth precision matrix was known, it achieved the highest exact, edge-collapsed and leakage-aware support recovery. We applied JSPACE to HarMNqEEG cross-spectral data from 1,935 participants aged 5.17-97.00 years, spanning 47 frequency bins and 360 cortical parcels. Affine-invariant Karcher tangent harmonization reconstructed subject-level estimates into a lifespan atlas of 360 diagonal and 64,620 source-pair age-frequency surfaces. The atlas revealed a continuous off-diagonal morphology landscape, in which age direction, frequency preference and interaction strength varied as overlapping axes rather than discrete edge classes. In contrast, diagonal precision surfaces shared a conserved alpha-trough morphology across parcels. Representative real-precision pathways captured posterior parietal, sensorimotor-parietal, frontopolar and visual-parietal motifs. Delta-band gradients were moderately aligned with the sensorimotor-association (S-A) organization of cortex from childhood through late adulthood, with a candidate oldest-old deviation in the sparsest age range. JSPACE provides a scalable framework for frequency-resolved source-precision charting in lifespan EEG.

neuroscience↗

bioRxiv2026

Quilting the Brain: Whole-Brain iEEG Reconstruction via Incomplete Observation Linear Mixed Models

Mapping human brain function at high spatiotemporal resolution is constrained by the physical limitations of non-invasive imaging and the sparse sampling of invasive electrophysiology. While intracranial electroencephalography (iEEG) captures local eld potentials with millimeter precision, clinical implantation strategies result in a "coverage paradox" : observations are restricted to disjoint, patient-specific patches, leaving most of the cortex unobserved. This study introduces the Incomplete Observation Linear Mixed-Effect Model (IOLMM), a statistical framework that resolves this paradox by "quilting" fragmented observations into continuous, whole-brain source activity maps. Our approach integrates two innovations: (1) Sure Independence Screening (SIS) adapted from ultra-high-dimensional statistics to distinguish true physiological signals from volume-conducted "ghost sources"; (2) a hierarchical IOLMM that decouples group-level physiological fixed effects from subject-specific instrumental random effects, solving the scaling ambiguities that plague iEEG group analyses. Applied to the MNI Open iEEG Atlas, the framework is validated through sleep stage-dependent cortical source power reconstruction across Wake, N2, N3, and REM states, recovering the frontal predominance of NREM slow-wave activity and the graded electrophysiological hierarchy from fragmented recordings of 106 patients. This work establishes the first cortical surface-level normative electrophysiological atlas derived from iEEG, providing a quantitative reference for detecting and predicting epileptogenic lesions and bridging the gap between the microscopic precision of electrophysiology and the macroscopic scope of systems neuroscience. HighlightsO_LISolving the Coverage Paradox: A novel IOLMM statistical framework quilts sparse, non-overlapping iEEG data into a unified whole-brain probabilistic map. C_LIO_LIGeometric Screening: Adapts high-dimensional Sure Independence Screening (SIS) using cortical geometric eigenmodes to effectively filter out spurious ghost sources in inverse solutions. C_LIO_LIRobust Harmonization: Decouples group-level physiological fixed effects from subject-specific random effects, resolving scaling ambiguities inherent in heterogeneous multi-center recordings. C_LIO_LIBiological Validation: Validates the framework on real multi-center iEEG data by reconstructing sleep stage-dependent cortical source power maps, recovering known electrophysiological signatures of NREM and REM sleep from fragmented recordings. C_LI

neuroscience↗

bioRxiv2026

Generalized Normative Modeling: A One-Step Hierarchical Kernel Framework for Multi-Site Brain Charts with Self-Correcting Z-Scores

Normative modeling expresses individual brain phenotypes as z-scores relative to a population norm, but in multi-site studies batch effects contaminate these z-scores and undermine their biomarker value. Existing approaches either harmonize data before fitting a normative model (ComBat+Normative), letting residual site effects leak into z-scores, or use parametric one-step methods (GAMLSS, HBR) that cannot flexibly model multivariate covariate interactions. We propose Generalized Normative Modeling (GNM), a onestep hierarchical framework that jointly estimates the global trajectory and site-specific effects via NUFFT-accelerated kernel regression with GCV bandwidth selection. Because the z-score is the ratio of batch-corrected residual to batch-corrected scale, residual site variance cancels algebraically -- a property we term self-correction. On ABIDE I cortical thickness (387 HC, 11 sites, 68 ROIs) and HarMNqEEG log-power spectra (1,564 subjects, 14 sites, 18 channels x 235 frequency bins), GNM produced the most site-invariant z-scores and best age-signal preservation among four methods. This work provides an open-source MATLAB toolbox with a declarative formula interface (https://github.com/LMNonlinear/Generalized-Normative-Modeling), enabling reliable individual-level inference in pooled multi-site cohorts and advancing the use of normative deviations as clinical biomarkers in precision psychiatry and neurology.

bioengineering↗

bioRxiv2025

Aperiodic and Periodic EEG Component Lifespan Trajectories: Monotonic Decrease versus Growth-then-Decline

1.1Unraveling the lifespan trajectories of human brain development is critical for understanding brain health and disease. Recent research demonstrates that electroencephalography signals are composed of periodic and aperiodic components reflecting distinct physiological substrates. This dissociation raises the possibility that they follow different developmental tendencies. Here, we delineate the lifespan trajectories of aperiodic and periodic neural oscillations using a large international cohort (N=1,563, ages 5-95, resting state, eyes closed). We reveal two fundamental developmental patterns: a Monotonic decrease in aperiodic activity and a Growth-and-Decline pattern for periodic activity. Both components have inflections around age 20 and transition to a stable senescent phase around age 40. Spatially, anterior regions mainly exhibit aperiodic activity, while periodic activity concentrate on posterior regions and these patterns remain stable throughout life. Crucially, multimodal analysis shows these trajectories map onto distinct biological substrates. The periodic components Growth and Decline trajectory aligns with GABAergic function and myelination. In contrast, the monotonically decreasing trajectory of aperiodic activity mirrors fundamental biomarkers of biological aging, such as DNA methylation and telomere length. Transforming age to a logarithmic scale simplifies these nonlinear trajectories into a linear decreasing and a piecewise concave linear model for aperiodic and periodic components. This form provides a robust and parsimonious framework for quantifying maturation and identifying neurological deviations. HighlightsO_LIWe delineate distinct lifespan trajectories of aperiodic and periodic neural activity in a large-scale international cohort (N=1,563, ages 5-95). Aperiodic activity undergoes a Monotonic Decrease with age. In contrast, periodic activity follows a Growth-then-Decline trajectory, peaking in early adulthood. C_LIO_LIBoth trajectories feature a critical transition around age 20 and stabilize into a protracted senescent phase from approximately 40 onward. C_LIO_LIThese neural trajectories map onto distinct biological substrates: periodic activity tracks integrative functions (myelination, GABAergic, and aperiodic decline mirrors fundamental aging processes (DNA methylation). C_LIO_LIA stable pattern observed throughout the lifespan is the spatial segregation of neural activity, where aperiodic signals are dominant in anterior regions and periodic signals are concentrated in posterior ones. C_LIO_LILogarithmically transforming age linearized the developmental trajectories, yielding a monotonic decline for the aperiodic component and a concave piecewise for the periodic one. This process establishes robust linear norms for the personalized assessment of brain dysfunction. C_LI

neuroscience↗

bioRxiv2025

Whole brain resting-state EEG dynamic: A mixture of linear aperiodic and nonlinear resonant stochastic processes

A longstanding debate in neuroscience concerns whether macroscale brain signals can be described as purely linear Gaussian processes or they harbor the more complex statistics of nonlinear dynamics. We introduce BiSpectral EEG Component Analysis (BiSCA), a framework unifying power spectral and bispectral analysis to test for nonlinearity by identifying inter-frequency harmonic relationships and disambiguating them from superimposed components. In particular, simulations confirm that it is able to separate valid nonlinearity from non-Gaussianity, which is a common confound. Applying this test to two large human brain recordings datasets (1,771 intracranial channels and 960 individuals scalp EEG), we find the brains broadband, aperiodic background behaves as a linear, Gaussian process, while narrowband Rho oscillatory --including the Alpha and Mu rhythms --are the primary source of cortical nonlinearity, exhibiting significant quadratic cross-frequency coupling. Both recordings show significant departures from linear Gaussian behavior. We observe a clear dissociation between signal power and nonlinearity. While the occipital Alpha rhythm dominates the power spectrum, the strongest nonlinear signatures arise from the less dominant parietal Mu rhythm. These findings suggest that nonlinear resonance is pervasive in cortical signals, primarily expressed through resonant oscillations rather than aperiodic activity. Without accounting for these intrinsic nonlinear interactions, a principled understanding of neural activity will be incomplete.

neuroscience↗

bioRxiv2025

Lifespan Mapping of EEG Source Spectral Dynamics with Xi-AlphaNET

We introduce{xi} - Net, a mesoscale generative model of cortical activity that models the EEG aperiodic ({xi}) and -rhythm () components via Hida-Matern processes constrained by anatomical connectivity and interareal delays. This framework integrates fractional differential equations (modeling damping/resonance), decomposition of Spectral Granger Causality, and lifespan quantification. Bayesian inversion of the model, on cross-spectral rsEEG data from 1,965 participants aged 5-100 years (HarMNqEEG dataset), allows us to estimate cortical activity and effective connectivity patterns at 8,003-voxel resolution.{xi} processes showed extended cortical networks, with occurrence probability increasing with age but amplitude peaking midlife (inverted-U trajectory). processes exhibited a localization in the posterior areas of the cortex and two specialized networks (parieto-occipital, sensorimotor), with increasing localization probability and peak amplitude/frequency showing age-linked inverted-U trajectory. The model uniquely estimates global conduction delays, which are negatively correlated with frequency and independent myelin concentration profiles, mechanistically linking neural propagation times to regulation.

neuroscience↗

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↗

bioRxiv2023

{xi}-π: a nonparametric model for neural powerspectra decomposition

The power spectra estimated from the brain recordings are the mixed representation of aperiodic transient activity and periodic oscillations, i.e., aperiodic component (AC) and periodic component (PC). Quantitative neurophysiology requires precise decomposition preceding parameterizing each component. However, the shape, statistical distribution, scale, and mixing mechanism of AC and PCs are unclear, challenging the effectiveness of current popular parametric models such as FOOOF, IRASA, BOSC, etc. Here,{xi} -{pi} was proposed to decompose the neural spectra by embedding the nonparametric spectra estimation with penalized Whittle likelihood and the shape language modeling into the expectation maximization frame-work.{xi} -{pi} was validated on the synthesized spectra with loss statistics and on the sleep EEG and the large sample iEEG with evaluation metrics and neurophysiological evidence. Compared to FOOOF, both the simulation presenting shape irregularities and the batch simulation with multiple isolated peaks indicated that{xi} -{pi} improved the fit of AC and PCs with less loss and higher F1-score in recognizing the centering frequencies and the number of peaks; the sleep EEG revealed that{xi} -{pi} produced more distinguishable AC exponents and improved the sleep state classification accuracy; the iEEG showed that{xi} -{pi} approached the clinical findings in peak discovery. Overall,{xi} -{pi} offered good performance in the spectra decomposition, which allows flexible parameterization using descriptive statistics or kernel functions.{xi} -{pi} may be a promising tool for brain signal decoding in fields such as cognitive neuroscience, brain-computer interface, neurofeedback, and brain diseases.

bioinformatics↗

bioRxiv2022

Co-fluctuations of neural activity define intra-V1 networks related to perceptual organization

Using functional resonance imaging (fMRI), we studied the relationship between perceptual organization and network topology within the primary visual cortex (V1). Twenty-six humans (male and female) were recorded during active observation of two Global and two Local Navon letters. Correlations between fMRI fluctuations from different V1 sites were measured (after removing stimulus-evoked signals) in windows specific to each condition. Intra-V1, like brain-wide networks, presented an overall decrease of correlations during stimulation compared to baseline and increased statistical dimensionality. Massive edgewise testing and network based-statistics (both corrected by FDR) identified differences between conditions of connection strengths that were mapped to the visual field. Global letters elicited long links mainly connecting V1 sites mapping the lower left/right visual quadrants. Shorter links were associated with Local letters, primarily mapped within the lower-left visual quadrant. Frequently link lengths exceeded V1 population receptive field sizes. These connections were not observed in the time-locked (feedforward) responses shared across participants. Thus, these networks reflect activity idiosyncratic to each participant, possibly generated by interactions within or feedback to V1. Perception would sculpt V1 connectivity, with specific increases in link strengths (in a background of decreases). These findings could help shed light on V1 as a "cognitive blackboard".

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↗

bioRxiv2019

Multinational qEEG developmental surfaces

The quantitative electroencephalogram (qEEG) is a diagnostic method based on the spectral features of the resting state EEG. The departure of spectral features from normality is gauged by the z transform with respect to the age-adjusted mean and deviation of normative databases - known as the developmental equations/surfaces. However, the extent to which the data collected from different countries with various equipment require separate developmental equations remains unanswered. Here, we analyzed the EEG of 535 subjects from 3 countries, Switzerland, the USA and Cuba. The EEG power spectra of all samples were log transformed and their relations to the covariables ( age, frequency, country and individual) were analyzed using the linear mixed effects model. We found that the origin country of the subjects did not play a significant effect on the log spectra, even without interactions with other independent variables, whereas, age and frequency were highly significant. To estimate the developmental surfaces in greater detail, we carried out kernel regression (lowess) in two dimensions of log-age and frequency. We found two main phenomena: 1) slow rhythms ({delta}, {theta}) predominated in the lower ages and then decreased with a tendency to disappear at higher ages; 2) rhythm was absent at lower ages, but gradually appeared more relevant in occipital and parietal regions, and increased with aging with an increasing centering frequency of rhythm. We consider both phenomena as an expression of healthy neurodevelopmental and maturation related to age. It is the first study of multinational qEEG developmental surfaces accounting for country. The results demonstrate the possibility of creating international qEEG norms since the individual and age variability are much larger than the specific factors like country, or the technology employed device.

neuroscience↗

bioRxiv2019

Xi rhythms: decoding neural oscillations to create full brain high resolution spectra parametric mapping

Neural oscillations excitability shape sensory, motor, and cognitive processes of the brain operation. To quantify the spectrum of brain magnetic and electrical recordings has become the main methodology to study neural oscillations. However, there is still lacking a valid approach, although the literatures on the spectrum decomposition is vast. In this work, we fit the neural spectrum by means of the Expectation Maximization algorithm, where the E step turns into a Winner filtering to separate multiple components and the M step is to fit each component by minimizing the smoothness penalized Whittle likelihood and shape-restricted regression, say, monotonicity, that is able to fit the diverse shape of each component. The decomposition allows characterizing the oscillation amplitude, resonance frequency, bandwidth, skewness, kurtosis, and slope of each component. This approach is termed as Xi rhythms, with Xi and rhythms standing for the background activity and multiple peaks. We apply it to: 1) multinational EEG database consisting of 535 subjects to create the quantitative spectrum norms (QSN); 2) large sample intracranial EEG (iEEG) dataset to infer the oscillations from recorded region to unrecorded areas within one subject and over inter-individuals and create the full brain high resolution statistical spectra parametric mapping. The statistical spectrum parameter mapping of iEEG promisingly provides an atlas and creates a norm for neural oscillations and quantitative electrophysiology study which can gain us more insightful understanding to brain dynamics, cognitive process and mental disorders.

neuroscience↗

bioRxiv2019

ERP sources in middle cingulate and precuneus differentiate Parkinson's patients from healthy controls and lingual gyri sources reflect human recombinant EPO effects in a Flanker task

We used EEG source analysis to identify which cortical areas were involved in the automatic and controlled processes of inhibitory control on a flanker task and compared the potential efficacy of recombinant-human erythropoietin (rHuEPO) on the performance of Parkinson s Disease patients. The samples were 18 medicated PD patients (nine of them received rHuEPO in addition to their usual anti-PD medication through random allocation and the other nine patients were on their regular anti-PD medication only) and 9 age and education-matched healthy controls (HCs) who completed the flanker task with simultaneous EEG recordings. N1 and N2 event-related potential (ERP) components were identified and a low-resolution tomography (LORETA) inverse solution was employed to localize the neural generators. Reaction times and errors were increased for the incongruent flankers for PD patients compared to controls. EEG source analysis identified an effect of rHuEPO on the lingual gyri for the early N1 component. N2-related sources in middle cingulate and precuneus were associated with the inhibition of automatic responses evoked by incongruent stimuli differentiated PD and HCs. From our results rHuEPO, seems to mediate an effect on N1 sources in lingual gyri but not on behavioural performance. N2-related sources in middle cingulate and precuneus evoked by incongruent stimuli differentiated PD and HCs.

neuroscience↗

bioRxiv2019

Impact of early childhood malnutrition on the adult brain function: an ERP study

According to the World Health Organization, 45% of deaths among children under five years of age are caused by malnutrition, which impacts more than 224 million children globally. The Barbados Nutrition Study (BNS) is a 50+ year longitudinal study on a Barbadian cohort with histories of moderate to severe protein-energy malnutrition (PEM) limited to the first year of life and a healthy comparison group. We have previously used quantitative electroencephalography (EEG) to highlight differences in brain function during childhood (lower alpha1 activity and higher theta, alpha2 and beta activity) between participants who suffered from early PEM and controls. In order to determine whether similar differences between the PEM and control groups persisted into adulthood, our current study used recordings obtained during a Go-No-Go task in a subsample of the original BNS cohort (N=53) at ages 45-51 years. We found that previously malnourished adults (n=24) had a higher rate of omission errors on the task relative to controls (n=29). Evoked-Related Potentials (ERP) were significantly different in participants with histories of early PEM, who presented with lower N2 amplitudes (p<0.05). These findings are typically associated with impaired conflict monitoring and/or attention deficits and may therefore be linked to the attentional and executive function deficits that have been previously reported in this cohort in childhood and again in middle-adulthood. HighlightsO_LIChildhood malnutrition increases risk of brain function alterations. C_LIO_LIThere is a need to investigate the evolution of those outcomes later in life. C_LIO_LIAdults who suffered childhood malnutrition undertook a Go-No-Go task during EEG. C_LIO_LITask performance and N2 amplitude were reduced in malnutrition group (vs control). C_LIO_LIFirst evidence of adult brain function alteration following childhood malnutrition. C_LI

neuroscience↗

bioRxiv2019

Are Sources of EEG and MEG rhythmic activity the same? An analysis based on BC-VARETA

In the resting state (closed or open eyes) the electroencephalogram (EEG) and the magnetoencephalogram (MEG) exhibit rhythmic brain activity is typically the 10 Hz alpha rhythm. It has a topographic frequency spectral distribution that is, quite similar for both modalities--something not surprising since both EEG and MEG are generated by the same basic oscillations in thalamocortical circuitry. However, different physical aspects underpin the two types of signals. Does this difference lead to a different distribution of reconstructed sources for EEG and MEG rhythms? This question is important for the transferal of results from one modality to the other but has surprisingly received scant attention till now. We address this issue by comparing eyes open EEG source spectra recorded from 70 subjects from the Cuban Human Brain Mapping project with the MEG of 70 subjects from the Human Connectome Project. Source spectra for each voxel and frequencies between 0-50Hz with 100 frequency points were obtained via a novel sparse-covariance inverse method (BC-VARETA) based on individualized BEM head models with subject-specific regularization parameters (noise to signal ratio). We performed a univariate permutation-based rank test among sources of both modalities and found out no differences. To carry out an unbiased comparison we computed sources from eLORETA and LCMV, performed the same permutation-based comparison, and found the same results we got with BC-VARETA.

neuroscience↗