Jorge Bosch‐Bayard

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

This study identifies two ASD subgroups with distinct brain morphology using cross-cultural datasets, highlighting key brain regions associated with subgroup characteristics and autistic traits.

The paper introduces a longitudinal dataset of school-age children and adolescents in China, detailing its design, sampling, and neuroimaging measurements.

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

The paper introduces brain charts as a standardized resource for benchmarking brain morphology across the human lifespan using MRI data from a large and diverse cohort.

This paper introduces a framework for linear causal filtering using multivariate autoregressive modeling to estimate directional causal information flow from non-directional association measures.

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

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

The paper introduces a method to measure Granger-causal effects in multivariate time series by comparing system dynamics before and after node deletion.

The paper identifies five mechanisms of negative BOLD responses and develops a windkessel model to detect, classify, and estimate these mechanisms from fMRI data.

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.

This paper introduces new coupling measures for analyzing cortical brain signals by generalizing weighted multiple regression models and defining inhibitory coupling to capture inverse relationships in neural activity.