Jesús M. Cortés

This study identifies six consistent couplings between systemic and kinematic properties of Amoeba proteus migration across different environments.

The paper quantifies the information capacity of DNA methylation using Shannon information theory to estimate its role as an epigenetic identity code.

The paper quantifies the information capacity of post-translational modifications (PTMs) on human RNA polymerase II, distinguishing theoretical maximum from physiologically achievable regulatory capacity.

The paper develops a model to predict brain age using digital cognitive metrics and finds it detects accelerated aging in neurological conditions.

This paper introduces a voxel-wise O-information framework to analyze high-order functional interactions in neuroimaging data, revealing age-related changes in default mode network redundancy.

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

This paper uses a physics-based model of brain networks to show that frequency-selective stimulation entrains specific sub-networks, with lower frequencies promoting broader synchrony and higher frequencies producing localized effects, determined by network structure and oscillatory dynamics.

This study investigates the coupling between structural connectivity and high-order functional interactions in the human brain, finding that structural networks are associated with both redundant and synergistic functional interactions.

AgeML is an open-source software for age prediction from clinical data, enabling standardization, reproducibility, and analysis of age-related relationships in health and disease.

This study uses partial correlation on fMRI data to better align functional connectivity with structural connectivity, showing improved matches with regularization and thresholding.

This paper compares deep learning models for predicting cognitive performance trajectories using real-world data and finds CNNs to be the most robust for pattern recognition and overfitting resistance.

This study investigates the migratory behavior of enucleated and non-enucleated cells, showing that locomotion is regulated by systemic, self-organized dynamics.

The paper quantitatively analyzes cell migration trajectories across species and stimuli, finding that directional movement results from systemic, integrative cellular processes rather than individual molecular components.

The paper provides open datasets and code to study multi-scale relationships between structural, functional, and neuro-genetic aspects of the human brain.

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

This paper extends lesion network mapping to a multimodal approach using functional and structural connectivity maps to predict sensorimotor behavior in stroke patients.

The paper analyzes anticorrelated networks in the human brain using neuroimaging data, finding increased cerebellar anticorrelation in older adults.

This paper builds a whole-brain computational model to show that age-related changes in high-order functional interactions are explained by variations in the connectome.

This study shows that inhibiting STAT3 signaling during epileptogenesis prevents epileptic activity, cell loss, and inflammatory glial states in an in vitro model.

The paper links hubness in brain networks to embryonic neurogenesis, showing that earlier-born nodes become stronger hubs and are associated with genes related to neurological diseases.

The paper analyzes structural and dynamical functional connectivity in cerebellar networks and finds lower variability in dynamical functional connectivity in the posterior cerebellum compared to other brain regions.

The paper identifies transcriptional signatures of synaptic vesicle genes associated with neurodegeneration in Myotonic Dystrophy Type I using transcriptome and brain imaging data.

This study uses O-Information to analyze how high-order statistical interdependencies in fMRI data change with age, finding increased redundancy in older adults across brain regions.

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 estimates brain-connectome age using structural and functional connectivity data and finds physical activity reduces this estimated age.

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.