Emahnuel Troisi Lopez

This study decomposes gait cycles using a dynamic system framework to identify coordination patterns in human locomotion.

The paper examines how structural brain connectivity influences the initiation and termination of neuronal avalanches using MEG and dMRI data from healthy individuals.

The paper analyzes neuronal avalanche dynamics in resting-state MEG data to show temporal persistence and structural organization in brain activity.

This study shows that changes in whole-brain microstate dynamics after levodopa administration correlate with clinical improvement in Parkinson's disease.

The paper introduces a new measure, flexibility gradient, to quantify regional contributions to brain flexibility and finds that structural properties correlate with regional flexibility contributions across multiple modalities.

This paper introduces a network-based approach for movement recognition using kinematic data, showing that the kinectome outperforms PCA in classification accuracy and interpretability.

This study shows that hormone levels across the menstrual cycle affect brain activity patterns and predict psychological well-being.

This paper introduces a framework linking oscillatory and broadband activities to transient salient events in resting brain data, showing how they emerge from spectral properties and interactions.

The paper investigates how structural brain connections enable diverse dynamic patterns by testing if neuronal activity near a critical regime explains structural-functional relationships.

This study uses PHATE to reduce the dimensionality of resting-state MEG data, revealing dynamic brain states and their transitions in a low-dimensional space.

This paper compares non-invasive and invasive methods to estimate brain connectivity and signal propagation delays, finding they yield similar results.

This paper identifies subject-specific brain features by analyzing non-linear neuronal avalanches in magnetoencephalographic data, showing improved identifiability compared to linear methods.

The paper shows that brain timescales dynamically shift between back-to-front and front-to-back gradients based on non-local interactions.

The paper investigates functional connectivity and brain network differences between Parkinson's disease patients with and without cognitive impairment, finding specific alterations linked to cognitive decline.

This study investigates how structural brain connections constrain fast neural dynamics using MEG and diffusion MRI data, finding a significant association between structural connectivity and neuronal avalanche spread.

The paper introduces a method to estimate functional transmission delays in the brain using in vivo measurements and structural connectome data, showing that larger bundles have faster velocities and delays are greater in multiple sclerosis patients.

The paper examines how sex hormones affect brain network topology during the menstrual cycle and links these changes to subjective well-being.

This study introduces a framework to detect connectome features from clinical populations and shows that these features predict cognitive decline using magnetoencephalography data.

This paper introduces a trunk displacement index (TDI) to quantify motor impairment in Parkinson's disease, showing it correlates with clinical scores and detects drug-induced state changes.

This study examines how different frequencies of rhythmic acoustic stimulation affect gait kinematics and trunk stability in healthy elderly individuals, finding that high frequencies and cadence-matched stimulation improve stability.

This study shows that sleep deprivation causes brain network disintegration and worsens attention but not switching abilities.