Caio Seguin

This study examines polysynaptic signal propagation in neural mass models across different dynamical regimes and benchmarks results against empirical data.

The paper analyzes the Drosophila connectome to reveal hierarchical community structures and identifies functional circuit archetypes.

The paper shows that trait-relevant tasks improve personality prediction from structural-functional brain network coupling during resting state and specific tasks.

The paper simulates sensory signal propagation in the Drosophila connectome to study modality-specific cascades and neuron classification based on dynamic response profiles.

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.

The paper evaluates neural oscillatory mechanisms for communication in the human brain using MEG data, finding that power and phase coherence in specific frequency bands correlate with communication patterns.

The paper analyzes high-amplitude events in edge time series across the human lifespan, finding age-related changes in their patterns and associations with cognitive phenotypes.

The paper introduces a deep learning model to generate individual connectomes from personal attributes and shows it can accurately replicate empirical connectomes from the UK Biobank.

This paper introduces EWC, a method to infer discrete neural communication from continuous time series data, showing it is faster and effective at capturing directional signaling.

The paper develops a framework using optimal control theory to estimate brain structure-function interactions from MRI data, showing correlations between self-inhibition parameters and neural timescales across species.

This study introduces a framework using game theory to characterize optimal communication in brain networks, finding that broadcasting models best describe communication efficiency, with rich-club regions playing a key role.

The paper investigates how connectome architecture supports dual communication modes, showing that modular networks enable within-module diffusion and between-module routing across multiple organisms.

The paper predicts general intelligence using structural-functional brain network coupling during task performance and resting state.

The paper analyzes a larval Drosophila connectome to identify hierarchical communities linked to cell type and function, revealing assortative and non-assortative interactions and rich-club structures.

The paper investigates how structural-functional brain network coupling relates to individual differences in general cognitive ability using data from the Human Connectome Project.

This paper introduces a generative model of the connectome that simulates dynamic axon growth, producing brain-like network structures and revealing how axonal guidance principles shape macroscopic connectomes.

The paper provides a large-scale connectome resource for 40,000 UK Biobank participants, including structural and functional brain networks with multiple parcellations and preprocessing techniques.

This paper introduces a coupled oscillator model that dynamically limits brain network communication, showing it better fits empirical functional connectivity than standard models.

The paper uses intracranial EEG data to show that structural connectivity models can predict the propagation of electrical brain stimulation across the cortex.

This paper introduces a method to weight anatomical brain connectivity data using multi-modal MRI data and evaluates its performance on the Human Connectome Project dataset.

The paper shows that adding models of indirect neural communication to structural brain networks better aligns their modular organization with functional systems.

The paper introduces a multi-policy communication framework for the human connectome that outperforms existing models and explains variance in time-varying functional connectivity.

This paper introduces Connectome Spatial Smoothing (CSS) to improve the reliability and sensitivity of structural connectomes by applying spatial smoothing to tractography data.

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.

This study shows that network communication models enhance the prediction of behavior and strengthen structure-function coupling in the human structural connectome.

The paper infers neural signalling directionality from undirected structural connectomes using send-receive communication asymmetry and shows it aligns with effective connectivity directionality.