Fidel Vila‐Rodriguez

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 study evaluates the relationship between proximity to individualized functional targets (iFTs) and clinical outcomes in rTMS trials for treatment-resistant depression, finding no association between proximity and improved outcomes.

This study identifies neurophysiological signatures predicting response to TMS in treatment-resistant depression using combined EEG and computational modeling.

The paper develops and evaluates dimensional and categorical approaches to identify depression subtypes using brain-behavior dimensions and clustering in a large single-site sample of MDD patients and controls.

This study examines the combined effects of yoga and tDCS on ERP components during a working memory task, finding significant changes in P200 and P300 amplitudes.

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 develops a real-time closed-loop method for estimating neural activation dynamics and input-output curves using transcranial magnetic stimulation, showing improved accuracy over conventional methods.

This paper develops a formalism for real-time sequential estimation of neural membrane time constant and input-output curve using transcranial magnetic stimulation, showing improved accuracy with optimal sampling.

This paper introduces a tool for automatically tuning pulse amplitude and width in EMG-guided cTMS to estimate membrane time constants and input-output curves using Fisher information matrix and normalized depolarization factor maximization.

This paper investigates the impact of TMS pulses on fMRI image quality in a TMS-fMRI system and finds minimal noise artifacts in the setup.

SS-Detect improves source-based morphometry in multi-scanner studies by detecting both common and scanner-specific brain patterns with higher accuracy and sensitivity than conventional methods.