Andrea Brovelli

Functional and Structural Brain Age Gaps as Complementary Predictive Markers for Cognitive Performance

The paper predicts structural and functional brain age gaps from MRI data to assess their predictive power for cognitive performance in healthy adults.

A spectral partial information decomposition framework for quantifying information about cognitive variables in oscillatory brain networks

This paper introduces NeOPID, a framework to quantify information in brain oscillations, showing phase and amplitude co-modulations act as complementary coding channels with redundant and synergistic components.

Higher-order interdependencies of the epileptogenic network in presurgical resting-state fMRI

The paper uses multivariate information theory to analyze higher-order network interactions in drug-resistant focal epilepsy patients using resting-state fMRI, finding that redundant information-sharing dominates epileptogenic networks and correlates with surgical outcomes.

Reciprocal Fronto-Parietal Interactions Support Distinct Motor Strategies during Sequential Reaching

This study investigates how fronto-parietal interactions support different motor strategies during sequential reaching in rhesus macaques, finding bidirectional information transfer between parietal and motor areas.

Learning regularities in noise engages both neural predictive activity and representational changes

This study shows that learning regularities in noise involves both rapid neural predictive activity and slower representational changes, with the former preceding behavioral improvements.

Structural connectivity shapes a cross-modal gradient of brain flexibility

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.

Sensorimotor encoding of epistemic value during goal-directed causal learning

The paper investigates how the brain encodes epistemic value during goal-directed causal learning, showing that expected information gain is represented in sensorimotor regions and challenges traditional views of reward-based valuation.

Higher-order and distributed synergistic functional interactions encode information gain in goal-directed learning

This study shows that information gain in goal-directed learning is encoded through synergistic and higher-order functional interactions across cortical circuits.

The Topological Architecture of Brain Identity

This paper introduces a topological fingerprinting method using homological scaffolds from resting-state fMRI data, achieving near-perfect individual identification accuracy.

Directed neural interactions in fMRI: a comparison between Granger Causality and Effective Connectivity

The paper compares Granger Causality and Effective Connectivity methods for inferring directed neural interactions in fMRI data, showing their mathematical relationship and limitations.

Decomposing neural circuit function into information processing primitives

The paper proposes that cognitive functions emerge from combinations of low-level information processing primitives and tests this using computational simulations and information theory metrics.

Neural interactions in the human frontal cortex dissociate reward and punishment learning

The paper identifies distinct neural interaction subsystems in the human frontal cortex for reward and punishment learning using intracranial recordings.

Neural shape mediation analysis

This study introduces a model to analyze neural signal shapes and identifies brain regions mediating behavioural processes, finding that depletion periods, not amplitude, correlate with behavioural speed.

An information-theoretic quantification of the content of communication between brain regions

This paper introduces a new information-theoretic measure, FIT, to quantify feature-specific information transfer between brain regions using simulated and empirical neural data.

Group-level inference of information-based measures for the analyses of cognitive brain networks from neurophysiological data

The paper introduces a non-parametric permutation-based framework for group-level analysis of cognitive brain networks using neurophysiological data and presents an open-source Python toolbox called Frites.

Stroke-related alterations ininter-areal communication

This study investigates how stroke alters long-range brain communication by analyzing functional connectivity and information transfer using resting-state fMRI and Granger causality, revealing inter-hemispheric asymmetries and reduced information transfer.

Tensorpac : an open-source Python toolbox for tensor-based Phase-Amplitude Couplingmeasurement in electrophysiological brain signals

Tensorpac provides an open-source Python toolbox for efficient and comprehensive phase-amplitude coupling analysis in electrophysiological brain signals.

A Non-negative Measure Of Feature-specific Information Transfer Between Neural Signals

This paper introduces a new measure, Feature-specific Information Transfer (FIT), to quantify feature-specific information flow between neuronal signals and validates it using simulated and real data.