Pedro Antonio Valdes-Sosa

The paper shows neuronal loss can reshape network dynamics and limit mechanism inference in excitatory-inhibitory neural fields by separating loss effects from activity changes.

The paper develops a spectro-omic framework to relate spatial transcriptomics and laminar LFP dynamics in macaque cortex, finding transcriptomic predictors improve spectral composition prediction.

The paper introduces JSPACE, a method for estimating source precision matrices from EEG data, and applies it to create a lifespan atlas of cortical interactions.

This paper introduces a statistical framework to reconstruct whole-brain iEEG activity from sparse, non-overlapping recordings, validated on multi-center data to recover known electrophysiological signatures.

This paper introduces a unified framework for batch harmonization in normative modeling, showing how existing methods can be expressed as special cases of a single location-scale equation.

This paper introduces GNM, a hierarchical framework for multi-site brain normative modeling that produces site-invariant z-scores and preserves age-related signals better than existing methods.

This paper introduces BiSCA to detect nonlinearity in EEG data and finds that narrowband oscillations like Alpha and Mu rhythms exhibit significant nonlinear behavior, while broadband activity is linear and Gaussian.

The paper introduces {xi}-Net, a generative model of cortical activity that estimates EEG components and connectivity across lifespan using rsEEG data.

The paper compares EEG patterns between COVID-19 survivors, close contacts, and a normative database to identify brain function impairments and abnormal activity patterns.

The paper analyzes EEG data from a large cohort to show aperiodic neural activity decreases monotonically with age, while periodic activity increases then decreases, mapping to distinct biological processes.

The paper uses fMRI data to predict Navon letter stimuli from intra-V1 functional networks, showing inter-individual stability but intra-individual variability in shape prediction.

This paper introduces {xi}-π, a nonparametric model for decomposing neural power spectra into aperiodic and periodic components, showing improved performance over existing parametric models.

The paper examines intra-V1 networks during perceptual tasks, finding condition-specific connection strengths linked to visual field regions.

This study examines the long-term effects of early childhood malnutrition on adult brain function using ERP data from a longitudinal cohort.

This paper develops multinational qEEG norms using Hermitian Riemannian methods, showing improved diagnostic accuracy for brain dysfunction and providing harmonization tools.

This study compares EEG connectivity in full-term and preterm infants during their first year, finding age-related trends and differences between groups.

The paper compares EEG and MEG source spectra using BC-VARETA and finds no differences in reconstructed sources between the two modalities.

The paper introduces a multimodal neuroimaging and cognitive dataset from 282 healthy Cuban participants, including EEG, MRI, and psychological tests.

This study examines how rHuEPO affects neural activity in Parkinson's patients during a flanker task, showing effects on N1 components in the lingual gyri but not on behavioral performance.

This paper introduces a method to control leakage in electrophysiological source imaging using structured sparse Bayesian learning and evaluates its performance against existing methods.

This study analyzes multinational qEEG data to determine if country-specific developmental equations are needed, finding that age and frequency are more significant than country.

The paper introduces a method to decompose neural oscillations into parameters and applies it to EEG and iEEG data to create high-resolution brain spectrum mappings.

The paper introduces EECoG-Comp, an open-source platform for analyzing EECoG data, and evaluates four ESI methods on a real dataset and simulations, highlighting their limitations.

This paper introduces BC-VARETA, a method for estimating brain sources and connectivity from M/EEG data using a frequency domain approach and sparse modeling.