Network dynamics with BrainX(3): a large-scale simulation of the human brain network with real-time interaction

Area of Science:

• Neuroscience
• Computational Biology
• Virtual Reality

Background:

• Complex dynamical networks in the human brain are challenging to explore.
• Existing methods lack real-time interaction and immersive visualization.

Purpose of the Study:

• To introduce BrainX(3), a 3D virtual reality simulation for real-time human brain activity analysis.
• To leverage computational power and human intuition for exploring brain network dynamics.

Main Methods:

• Structural connectivity data from diffusion spectrum imaging.
• Neuronal population dynamics modeling.
• Real-time user interaction via perturbations and a library of graph theoretic measures.

Main Results:

• A noisy network model favors a low firing attractor state.
• Noisy networks exhibit reduced resilience to simulated lesions.
• Transcranial magnetic stimulation (TMS) simulations show both widespread inhibition and sparse excitation.

Conclusions:

• BrainX(3) serves as an immersive platform for analyzing brain network dynamics at rest or during tasks.
• The simulation offers insights into signaling pathways and potential for virtual neurosurgery.
• Findings suggest that even lesioned networks can exhibit localized increased activity post-perturbation.