César Caballero‐Gaudes

This paper quantifies how spatial degradation and temporal acquisition affect flow biomarkers in a CFD benchmark, showing that spatial degradation increases nRMSE while temporal acquisition impacts PI error and timing.

This paper evaluates if biomarker-aware super-resolution improves hemodynamic endpoint fidelity in 4D flow MRI using a synthetic pulmonary-artery benchmark.

The paper introduces NSI, an objective measure to assess data quality for precision functional MRI, aligning with expert evaluations and enabling principled decisions on data sufficiency.

This paper introduces pBOLD, a new fMRI quality metric that quantifies BOLD dominance in multi-echo scans, validates it on datasets, and shows its utility in predicting phenotypes.

This study investigates how young children process degraded speech using eye-tracking and fNIRS, finding increased visual attention to the mouth with degraded speech and age-related differences in brain activity.

NEUROLINGUA provides a database of MRI data from multilingual individuals to study how sociolinguistic factors shape language comprehension networks.

The paper investigates disrupted salience network dynamics in migraine patients during pain imagery, finding shorter lifetime in the network compared to healthy controls during this task.

This study uses a longitudinal case-control design to analyze functional connectome changes in migraine patients across their cycle phases using resting-state fMRI and a novel multilevel clinical fingerprinting approach.

This study identifies brain regions involved in urge-to-blink using multi-echo fMRI and a timing-free approach in healthy volunteers.

This study compares signal models for mapping cerebrovascular reactivity using BH BOLD-fMRI, finding PetCO2 regressors with single gamma HRF provide the best results.

This study examines how bilingualism affects neural processing of speech in 4-month-old infants, finding distinct brain activity patterns in bilingual infants compared to monolingual infants.

The paper examines individual variability in the relationship between resting-state fMRI metrics and cerebrovascular reactivity (CVR) using longitudinal data from 7 subjects with 10 sessions each.

This paper introduces a variational Bayesian approach for cerebrovascular reactivity mapping using fMRI data and compares it to a standard GLM method, showing improved computational efficiency and handling of noisy voxels.

This study evaluates ME-ICA's effectiveness in reducing task-correlated head motion artifacts in motor-task fMRI data, showing improved signal clarity and stability in both subject and group analyses.

The paper identifies a two-fold expansion of the frontostriatal salience network in individuals with depression using longitudinal neuroimaging data and shows its stability over time and predictive value for future symptoms.

This paper introduces a Bayesian segmentation algorithm for thalamic nuclei using diffusion MRI and an improved histological atlas, showing improved accuracy in identifying nuclei and detecting Alzheimer's disease involvement.

This paper compares alternative reference signals for mapping cerebrovascular reactivity using breath-hold task BOLD fMRI, showing that RVT and GM-BOLD can produce comparable CVR maps when PETCO2 quality is insufficient.

This paper introduces a novel hemodynamic deconvolution algorithm for multi-echo fMRI that improves reliability and provides statistical certainty without requiring prior knowledge of experimental paradigms.

This study identifies enhanced top-down effective connectivity in sensorimotor cortices as a marker of trait somatic anxiety using resting-state fMRI and DCM.

The paper investigates how data acquisition and modelling choices affect the relationship between baseline cerebral blood flow and BOLD cerebrovascular reactivity, finding that breathing task data and lag-optimization improve correlations.

This paper modifies a resting-state fMRI protocol by adding breathing tasks to improve cerebrovascular reactivity (CVR) characterization and hemodynamic lag estimation.

This study compares six multi-echo fMRI combination methods for their impact on BOLD sensitivity in offline and real-time settings, finding T2*FIT-weighted provides the best temporal signal-to-noise ratio.

This study evaluates ICA-based denoising strategies for improving CVR estimates in multi-echo BOLD fMRI during breath-hold tasks, finding that a conservative ICA model reduces motion effects best.

The paper introduces a multi-echo fMRI dataset with task and resting state runs for developing and validating real-time analysis methods in neuroimaging.

This study finds no differences in the intrinsic functional organization of 4-month-old monolingual and bilingual infants' brains using fNIRS.

The paper investigates how linguistic input influences brain network configuration during language comprehension using fMRI and graph theory.

The paper analyzes dynamic functional connectivity during rest and finds that distinct cognitive periods influence resting-state brain activity, which can be inferred from data without behavioral input.

This paper introduces ME-SPFM, a deconvolution algorithm for multiecho fMRI that estimates transverse relaxation changes without prior event timing knowledge, showing high concordance with standard analysis.

This study shows human V1 can learn multiple concurrent complex visual sequences, including goal-irrelevant ones, using fMRI.