Leonardo L. Gollo

The paper maps intrinsic neural timescales across rest and tasks, showing task-induced changes while preserving temporal hierarchy and structural constraints.

This paper introduces AMAge-Net, a multimodal brain age prediction model combining fMRI and sMRI, achieving high accuracy and interpretability in estimating brain aging.

This paper develops a multiscale computational framework to study how brain criticality at local and global scales interacts through structural connectivity.

This study shows that stroke alters intrinsic neural timescales and long-range temporal correlations, indicating a shift toward criticality and potential as biomarkers for recovery.

The paper maps age-related changes in intrinsic neural timescales and links them to gray matter volume and cognitive performance in young and elderly adults.

This study uses neuronal avalanches to improve brain-computer interfaces by showing their task-specific differences can be decoded better than current methods.

This paper introduces a method to detect cross-frequency coupling between brain areas using phase-linearity measurements, demonstrating its effectiveness on both simulated and real MEG data.

This paper shows that fMRI preprocessing choices significantly affect dynamical models of brain activity, highlighting the importance of data quality in model accuracy.

This study investigates stochastic synchronization in the human connectome, showing that noise can enhance synchronization in neural networks under specific conditions.

The paper simulates aging effects on neuronal morphology through dendritic pruning, showing reduced firing rates and energy efficiency, and proposes soma centrality as a measure of neuronal reserve.

The paper classifies neurons based on dendritic structure and analyzes how morphology affects their dynamic range and energy consumption.

This study investigates reduced brain flexibility in Parkinson's disease using magnetoencephalography, finding stereotyped dynamics and beta-band hypersynchronization linked to symptom severity.

This paper identifies metastable wave patterns in human brain activity, showing they emerge from dynamic nonlinear dynamics and align with multiple imaging modalities.

The paper uses network analysis to show that anatomical constraints shape functional connectivity between muscles during postural tasks.

The paper shows that neural decoding of visual stimuli is higher during periods of high global network efficiency compared to low efficiency.