Modeling the interaction of navigational systems in a reward-based virtual navigation task

Area of Science:

• Neuroscience
• Cognitive Science
• Computational Neuroscience

Background:

• Human navigation relies on allocentric (spatial) and egocentric (response) learning systems.
• The interaction (parallel, competitive, or cooperative) between these systems during navigation is debated.
• Understanding these interactions is key to deciphering brain network dynamics in goal-directed behavior.

Purpose of the Study:

• To investigate the functional and effective connectivity patterns of neural networks during navigation.
• To characterize how competition and cooperation between spatial and response learning strategies modulate brain networks.
• To explore the causal interactions within the brain's navigational network.

Main Methods:

• Utilized a single-subject virtual reality environment to simulate a large-scale navigation task.
• Employed functional analyses to map brain activation and statistical inference.
• Applied dynamic causal modeling to estimate effective connectivities and model network interactions.

Main Results:

• Cooperative navigational strategies showed significant functional and effective connectivity between the hippocampus and striatum.
• Competitive strategies led to weakened hippocampus-striatum connections and strengthened connections with the prefrontal cortex.
• Experimental conditions modulated the underlying brain network based on strategy (competition vs. cooperation).

Conclusions:

• Navigational strategy interaction (cooperation vs. competition) dynamically reconfigures brain networks.
• Cooperation strengthens hippocampus-striatum links, while competition involves the prefrontal cortex.
• This adaptive network reconfiguration supports goal-directed behavior and brain organization.