Enrico Amico

This study uses functional connectome fingerprinting to show that individual brain organization remains stable during social interactions, with connectivity patterns linked to social roles and autistic traits.

This paper uses higher-order topology to analyze brain activity during naturalistic emotion tasks, finding that it captures fine-grained affective structure while pairwise connectivity captures broad arousal.

The paper shows that using low-dimensional temporal subspaces from fMRI data reveals distinct functional connectivity regimes that depend on cognitive state and individual identity.

This study shows that cognitive co-activation dominates fMRI synchrony in the awake brain but is replaced by anatomical and molecular constraints during consciousness perturbations.

The paper uses connectome-based brain fingerprinting to identify early cognitive decline markers in Parkinson's patients with minor hallucinations using resting-state fMRI.

This study tracks how brain connectivity patterns change after stroke, showing early stabilization of functional fingerprints and long-term circuit remodeling that predicts recovery outcomes.

This study uses connICA to analyze resting-state fMRI data and identifies distinct functional connectivity traits associated with psilocybin's effects on brain networks and cognitive performance.

This study examines how different temporal resolutions in resting-state fMRI affect subject identifiability through functional connectivity fingerprinting.

This study compares higher-order and pairwise interaction models for fMRI data, showing that higher-order methods better decode tasks, identify individuals, and link brain activity to behavior.

This study shows that functional connectivity patterns remain unique across stages of Alzheimer's disease but change in their configuration, suggesting potential for individualized brain fingerprinting in clinical settings.

This study shows that the head model significantly affects the accuracy of subject identification and differentiation in MEG-derived functional connectivity fingerprinting.

The paper introduces a homological formalism to analyze higher-order properties of human brain functional sub-circuits and identifies task-specific homological differences in brain connectivity.

This study shows that general anaesthesia reduces the uniqueness of human brain connectivity both between individuals and across species.

This paper uses brain-fingerprinting to show that psilocybin increases idiosyncratic functional connectivity in the default-mode network, which predicts subjective drug experiences.

This paper introduces a method to estimate brain graphs using graph signal processing and demonstrates its superior performance over correlation-based functional connectomes in individual characterization.

The paper introduces an arrow-of-time sensitive metric to identify causal triggers of brain function in neuroimaging data, revealing more localized causal effects than traditional connectivity measures.

The paper embeds brain networks into high-dimensional graph spaces using nodal invariants and shows improved classification accuracy in higher dimensions.

This study shows that MEG signatures for individual identification improve during task-related behavior compared to resting-state, with controlled tasks showing the most significant improvements.

This study examines how ayahuasca use affects functional connectomes, finding shared functional spaces and predictive relationships between connectivity patterns and drug experiences.

The paper compares information transmission patterns in human, macaque, and mouse brains, finding that humans use parallel pathways while other mammals use single pathways.

This paper uses graph signal processing to decode brain tasks and fingerprint individuals by analyzing structure-function coupling in brain imaging data.

This paper investigates the temporal dynamics of human brain fingerprints, finding that longer time scales (~300s) yield the best identification, while shorter bursts correlate with neuronal activity and specific cognitive functions.

The paper demonstrates that regional homogeneity (ReHo) fMRI metrics can uniquely identify individuals and correlate with intelligence, showing high stability and potential for precision medicine.

This paper evaluates MEG brain fingerprinting using resting-state data, finding that performance depends on connectivity measures, frequency bands, and spatial correction, with higher scores in phase-coupling methods and specific brain networks.

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

The paper identifies spatial patterns in functional brain connectomes that distinguish acute from chronic cannabis use using whole-brain network analysis.

This paper identifies two spatially independent functional connectivity components across EEG and fMRI data, revealing linked but distinct connectivity dynamics across timescales.

This study maps TMS-induced EEG changes onto information flow and structural connectivity using multimodal data to show how stimulation site and brain rhythms influence functional-structural relationships.