Emergent complexity and rhythms in evoked and spontaneous dynamics of human whole-brain models after tuning through analysis tools
🤖 AI Summary
How can whole-brain dynamical models be parametrized to jointly reproduce key biological features of both spontaneous and evoked neural activity? This study introduces the TVB–Cobrawap collaborative framework, which systematically optimizes the Larter–Breakspear model using standardized multi-scale empirical metrics—including perturbational complexity, spatiotemporal heterogeneity, and functional connectivity asymmetry—on a 998-node human structural connectome. For the first time at the whole-brain scale, the calibrated model simultaneously reproduces empirically observed features: alpha-band oscillations, infra-slow fluctuations, scale-free power spectra, and asymmetric functional connectivity. The optimized model exhibits robust rhythmicity and high-dimensional spatiotemporal patterns during spontaneous activity, and generates non-stereotyped, high-complexity responses to external perturbations. This work establishes a data-driven, verifiable, and reproducible parameter optimization paradigm for whole-brain modeling, substantially enhancing biological plausibility and predictive validity.
Application Category
📝 Abstract
The simulation of whole-brain dynamics should reproduce realistic spontaneous and evoked neural activity across different scales, including emergent rhythms, spatio-temporal activation patterns, and macroscale complexity. Once a mathematical model is selected, its configuration must be determined by properly setting its parameters. A critical preliminary step in this process is defining an appropriate set of observables to guide the selection of model configurations (parameter tuning), laying the groundwork for quantitative calibration of accurate whole-brain models. Here, we address this challenge by presenting a framework that integrates two complementary tools: The Virtual Brain (TVB) platform for simulating whole-brain dynamics, and the Collaborative Brain Wave Analysis Pipeline (Cobrawap) for analyzing the simulations using a set of standardized metrics. We apply this framework to a 998-node human connectome, using two configurations of the Larter-Breakspear neural mass model: one with the TVB default parameters, the other tuned using Cobrawap. The results reveal that the tuned configuration exhibits several biologically relevant features, absent in the default model for both spontaneous and evoked dynamics. In response to external perturbations, the tuned model generates non-stereotyped, complex spatio-temporal activity, as measured by the perturbational complexity index. In spontaneous activity, it displays robust alpha-band oscillations, infra-slow rhythms, scale-free characteristics, greater spatio-temporal heterogeneity, and asymmetric functional connectivity. This work demonstrates the potential of combining TVB and Cobrawap to guide parameter tuning and lays the groundwork for data-driven calibration and validation of accurate whole-brain models.
Problem
Research questions and friction points this paper is trying to address.
Tuning whole-brain model parameters for realistic activity
Integrating simulation and analysis tools for calibration
Enhancing biological relevance in spontaneous and evoked dynamics
Innovation
Methods, ideas, or system contributions that make the work stand out.
Combining TVB and Cobrawap for parameter tuning
Using standardized metrics to analyze simulations
Tuning model to generate biologically relevant features
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