Andrea I. Luppi

This study investigates how psychoactive drugs alter the global workspace of consciousness by analyzing resting-state fMRI data, finding distinct changes in synergistic and redundant interactions across drug types.

This paper benchmarks 16 brain network similarity measures, finding DeltaCon is most accurate for parameter recovery but no measure excels across all criteria.

The paper introduces netneurotools, a Python toolkit developed by trainees to streamline brain imaging workflows by integrating diverse data types and addressing interoperability challenges.

The paper presents a game-theoretic model showing human cortical networks prioritize computational reliability over communication efficiency and wiring cost.

The paper examines how preserved cortical hierarchy in blindsight-positive patients explains their ability to detect stimuli without awareness after V1 damage.

This paper introduces a homeostatic inhibitory plasticity rule into a biophysical model to study how local adaptive processes influence global brain dynamics.

This paper introduces hierarchical modular networks as reservoirs to study computational advantages of brain-like modularity, showing enhanced memory, multitasking, and temporal dynamics.

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.

This study shows that anaesthesia in marmosets alters large-scale brain organisation, similar to findings in humans and macaques, by constraining activity with structural connectivity and collapsing functional gradients.

This paper introduces a generative model of brain maps based on homophily and shows it can accurately reconstruct maps using biological and functional similarity.

This paper adapts network control theory to model spatially diffuse input signals in the human brain, showing reduced energy for state transitions and matching existing neurobiological maps.

This study investigates thalamic nuclei involvement in consciousness loss during anaesthesia and disorders of consciousness using functional neuroimaging, identifying distinct connectivity patterns in specific nuclei.

This paper uses AI to analyze neuroscientific literature and discovers conserved cognitive circuits across species.

The paper uses a whole-brain model of an anesthetized macaque to simulate and restore consciousness through thalamic stimulation, showing how brain states transition during loss and recovery of consciousness.

This paper introduces graphTRIP, a geometric deep learning model that predicts antidepressant treatment response using neuroimaging and clinical data, identifying biomarkers and causal mechanisms.

This study shows that intrinsic cortical hierarchies constrain experience-dependent plasticity in sensory-deprived humans, preserving functional gradients while allowing adaptive reorganization.

The paper examines how dimensionality reduction techniques preserve synergistic interactions in brain dynamics using resting-state fMRI data from 100 healthy participants.

This study benchmarks 239 pairwise interaction statistics for functional connectivity mapping, finding substantial variation in their topological, neurobiological, and behavioral properties.

This study uses network control theory to show DMT reduces brain control energy, correlates with EEG entropy and subjective experience, and links these effects to serotonin 2a receptor distribution.

The paper introduces a computational engine to systematically discover cause-effect relationships between brain structure and function using in-silico brain models.

This study profiles neural dynamics across species during anaesthesia, finding conserved dynamical changes and linking them to molecular mechanisms.

This study benchmarks macaque brain gene expression for horizontal and vertical translation by comparing it to human gene expression and protein density.

The paper shows that synapse diversity in the mouse brain influences regional dynamics, with specific synapse types linked to distinct patterns of brain activity and connectivity.

This study shows LSD reduces directed information hierarchy in the brain by balancing signal sending and receiving, as measured by MEG data.

This paper quantifies the control costs of human brain dynamics using network control theory and finds that brain state transitions are more efficient within hierarchical groups.

This study uses computational whole-brain modeling to show that competitive interactions in mammalian connectomes enhance computational capacity and fit brain dynamics better than cooperative-only models.

This study identifies dynamic structure-function correlations as robust markers of consciousness in humans across different unconscious states.

This paper proposes a synergistic global workspace model of consciousness using network science and information theory, showing that loss of consciousness correlates with reduced information integration in specific brain networks.

This study uses connectome harmonic decomposition to show that ketamine-induced unresponsiveness increases localised harmonics, similar to psychedelic states, unlike traditional sedation which increases global harmonics.

This study shows that DMT alters connectome harmonics, increasing their repertoire entropy and correlating with subjective experience intensity over time.