Javier Rasero

This study evaluates ten excitability metrics from MEG data to show cortical excitability is multidimensional with distinct spatial and functional patterns.

This study finds that adolescents with autism have higher biological aging rates in brain structures compared to neurotypical controls, as measured by DunedinPACNI.

This study identifies sex-specific morphological network alterations in adolescents with ASD using structural MRI, revealing distinct connectivity patterns between males and females.

The paper predicts instantaneous heart period from whole brain fMRI data using machine learning, identifying cortical and subcortical brain networks linked to psychological stress as reliable predictors.

This paper evaluates ROI extraction methods for MEG connectivity analysis and recommends strategies based on sensitivity and specificity trade-offs.

The paper shows how dopaminergic plasticity shifts evidence accumulation in humans by altering competition between action representations during decision-making.

The paper identifies two subtypes of autism based on functional connectivity, linking hyperconnectivity to excitation-inhibition imbalance in 42% of patients.

The paper compares task-related network profiles and activation patterns in two executive control tasks, finding greater differences in functional correlations than in evoked activity.

The paper improves prediction of cognitive abilities by integrating multiple neuroimaging modalities using machine learning, achieving a 1-4% increase in variance explained compared to single-modality approaches.

The paper was withdrawn due to methodological concerns that altered the main conclusion about gene-cholesterol cascades and brain connectivity in autistic patients.

This study identifies hyperconnectivity of the default mode network in MODS patients without apparent brain damage, suggesting functional brain network alterations post-ICU discharge.

The paper proposes using consensus clustering on brain connectivity matrices to improve subgroup separation in neuroimaging studies by reducing intra-group variability.

The paper uses machine learning to compare brain network configurations in task-based and resting-state fMRI data, finding that task-related areas show consistent connectivity patterns while others differ.

The paper calculates interaction information from fMRI data to examine redundancy and synergy in brain networks across lifespan, finding high redundancy in the default mode network and balanced redundancy-synergy patterns.

The paper uses diffusion-tensor brain networks from ADNI data to compare connectivity patterns across Alzheimer’s disease severity stages, finding progressive disconnections in memory-related and other brain networks.