Decoding hidden goal-directed navigational states and their neuronal representations using a novel labyrinth paradigm...

Area of Science:

• Neuroscience
• Cognitive Science
• Computational Biology

Background:

• Goal-directed navigation is crucial for survival and involves complex planning.
• Identifying hidden cognitive states and their neural basis in navigation is challenging.

Purpose of the Study:

• To develop a novel framework for inferring hidden states in goal-directed navigation.
• To investigate the neural substrates of these states using a complex labyrinth task.
• To examine navigational behavior in a mouse model of Alzheimer's disease (AD).

Main Methods:

• Developed a complex labyrinth task simulating naturalistic foraging.
• Implemented a hierarchical probabilistic model, Cognitive Mapping of Planned Actions with State Spaces (CoMPASS).
• Analyzed behavioral data from wild-type and humanized APP_SAA mice, correlating with posterior parietal cortex (PPC) gamma oscillations.

Main Results:

• CoMPASS successfully inferred nested states (surveillance-ambulation and goal-oriented navigational).
• Successful navigation correlated with increased recruitment of both state types at decision nodes.
• Alzheimer's disease model mice showed impaired navigation, reduced goal states, and increased stochasticity.
• PPC gamma oscillations encode CoMPASS states and link spatial locations to long-term goals.

Conclusions:

• CoMPASS provides a novel paradigm for assessing hidden goal-directed navigational states.
• Gamma oscillations in the PPC are identified as the neural substrates for these navigational states.
• This work offers insights into the neural mechanisms of navigation and AD-related impairments.