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Updated: May 12, 2026

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

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Exploring dynamical whole-brain models in high-dimensional parameter spaces.

Kevin J Wischnewski^1,2,3, Florian Jarre³, Simon B Eickhoff^1,2

¹Institute of Neuroscience and Medicine - Brain and Behaviour (INM-7), Forschungszentrum Jülich, Germany.

|May 12, 2025

Summary

This summary is machine-generated.

High-dimensional whole-brain modeling enhances personalized brain activity analysis. Despite parameter variability, this approach improves model fit and brain-behavior predictions, advancing dynamical brain modeling.

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Area of Science:

Computational neuroscience
Brain dynamics modeling
Systems neuroscience

Background:

Personalized modeling of resting-state brain activity requires complex dynamical whole-brain models.
High-dimensional parameter spaces in these models present undocumented practical benefits and mathematical challenges.
The utility of high-dimensional approaches for brain modeling remains an open question.

Purpose of the Study:

To investigate the benefits and challenges of high-dimensional parameter spaces in dynamical whole-brain models.
To evaluate the impact of high-dimensional parameter optimization on model fitting and predictive accuracy.
To explore the application of these models to inter-individual variability and brain-behavior relationships.

Main Methods:

Utilized a whole-brain model of coupled phase oscillators, progressing from low-dimensional to high-dimensional parameter spaces.
Employed Bayesian Optimization and Covariance Matrix Adaptation Evolution Strategy for optimizing up to 103 parameters simultaneously.
Optimized models to maximize correlation between simulated and empirical functional connectivity for 272 subjects.

Main Results:

High-dimensional parameter optimization led to increased parameter variability within subjects and reduced reliability across runs.
Model validation (goodness-of-fit) significantly improved and remained stable, alongside simulated functional connectivity.
Sex classification prediction accuracy increased when using high-dimensional optimized parameters as features.

Conclusions:

High-dimensional parameter spaces in whole-brain models offer improved goodness-of-fit and predictive power for brain-behavior relationships.
These findings support the utility of complex, high-dimensional models for understanding inter-individual differences in brain function.
The study provides insights into optimizing and applying dynamical brain models in high-dimensional parameter spaces.