Biophysically constrained dynamical modelling of the brain using multimodal magnetic resonance imaging.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Biophysics

Background:

• Brain function involves complex, coupled processes like action potential propagation and regional cerebral blood flow.
• Modeling these dynamics requires integrating biophysical properties with computational approaches.

Purpose of the Study:

• To introduce a novel analog electrical network model, BRAINN, for simulating coupled brain dynamics.
• To validate BRAINN's ability to model neuronal integrity and functional connectivity.
• To assess BRAINN's controllability in pediatric leukemia patients.

Main Methods:

• BRAINN was constructed using analog electrical circuits (resistor, capacitor, inductor) representing action potential propagation and blood flow.
• Electrical components were derived from in vivo multimodal MRI and biophysical brain properties.
• The network was interconnected at Brodmann areas using diffusion tensor imaging data and simulated in MATLAB/Simulink.

Main Results:

• BRAINN successfully generated sustained activity when stimulated and demonstrated functional connectivity comparable to resting-state fMRI.
• Control system analyses confirmed BRAINN's stability for all participants.
• BRAINN revealed disrupted controllability in leukemia patients pre-treatment, which improved during therapy.

Conclusions:

• BRAINN provides a biophysically constrained model for studying brain dynamics.
• The model shows potential for assessing neurological conditions like leukemia and monitoring treatment effects.
• Scalable BRAINN models can aid in identifying therapeutic targets for brain stimulation.