Closed-Loop Brain Model of Neocortical Information-Based Exchange.

Area of Science:

• Computational neuroscience
• Systems neuroscience
• Neuroscience

Background:

• The brain's complex network functions involve intricate interactions between key structures like the neocortex, basal ganglia, and thalamus.
• Understanding system set points and transitions is crucial for deciphering brain dynamics under various conditions.

Purpose of the Study:

• To propose and analyze a novel "information-based exchange" model of brain function.
• To investigate the role of neuromodulation and plasticity in regulating brain system set points.
• To explore the potential link between information exchange dynamics and neurodegenerative disease risk.

Main Methods:

• Development of a closed-loop computational model grounded in neuroanatomy.
• Simulation of information-based exchange of action potentials between neocortical areas.
• Analysis of system set point measures, including transition rates and heterogeneity.

Main Results:

• The model successfully analyzes whole brain system set points, such as transition dynamics in the striatum and neocortex.
• Simulations demonstrated changes in these measures by implementing maximum information-based exchange.
• A novel "Grand Loop" through the neocortex is proposed, involving specific plasticity mechanisms.

Conclusions:

• The proposed model offers a framework for understanding brain function through information exchange.
• Dynamic set points and modulations in resting-state activity are hypothesized to exist.
• Altered information exchange may modify tissue risk profiles, potentially leading to neurodegenerative diseases.