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Updated: Apr 28, 2026

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

Phase-dependent stimulation response is shaped by the brain's dynamic functional connectivity.

Sophie Benitez Stulz¹, Samy Castro^2,3, Boris Gutkin⁴

¹Aix-Marseille Université, INSERM, INS, Institut de Neurosciences des Systèmes, Marseille, France.

Network Neuroscience (Cambridge, Mass.)

|April 27, 2026

Summary

Brain stimulation effects depend on brain activity dynamics. Understanding functional connectivity improves stimulation predictability by up to 40%, crucial for reliable cognitive restoration.

Keywords:

Brain stimulation Dynamic functional connectivity Oscillations Phase response curve Whole-brain modelling

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Brain State-dependent Brain Stimulation with Real-time Electroencephalography-Triggered Transcranial Magnetic Stimulation

Brain State-dependent Brain Stimulation with Real-time Electroencephalography-Triggered Transcranial Magnetic Stimulation

Published on: August 20, 2019

Area of Science:

Computational neuroscience
Cognitive neuroscience
Neuroimaging

Background:

External brain stimulation is a key tool for studying and modifying cognitive functions.
Its clinical potential lies in restoring dysfunctional neural dynamics.
Stimulation effects are known to depend on the brain's ongoing activity.

Purpose of the Study:

To investigate how brain stimulation effects are influenced by local and global brain dynamics.
To explore the role of oscillatory phase and functional connectivity in stimulation outcomes.
To assess the impact of functional network awareness on predicting stimulation effects.

Main Methods:

Connectome-based whole-brain computational modeling.
Simulation of single-pulse stimulation across different brain regions.
Analysis of stimulation effects based on regional oscillatory phase and transient functional connectivity.
Machine learning models to predict stimulation outcomes.

Main Results:

Stimulation effects are highly dependent on the phase of regional neural oscillations.
The transient network of functional connectivity at the time of stimulation significantly modulates its impact.
External brain stimulation can induce global state switching in brain dynamics.
Functional network-aware measures improved prediction of stimulation effects by up to 40%.

Conclusions:

The efficacy and outcome of external brain stimulation are critically dependent on the brain's intrinsic functional connectivity dynamics.
Characterizing these dynamics is essential for enhancing the reliability and precision of brain stimulation therapies.
This work highlights the importance of dynamic network states in understanding brain function and therapeutic interventions.