Network States Classification based on Local Field Potential Recordings in the Awake Mouse Neocortex

Area of Science:

• Neuroscience
• Computational Neuroscience
• Systems Neuroscience

Background:

• Cortical network states influence sensory responses and perception in awake mice.
• Intracellular recordings, while informative, are challenging and yield limited data.
• Identifying behaviorally relevant network states from population-level signals like LFPs remains an open question.

Purpose of the Study:

• To develop a method for classifying brain network states using LFP signals.
• To investigate the relationship between single-cell (intracellular) and population (LFP) activity across different network states.
• To provide a tool for analyzing how network dynamics affect sensory processing.

Main Methods:

• Simultaneous intracellular and LFP recordings in the somatosensory cortex of awake mice.
• Development of the Network State Index (NSI) for LFP-based network state classification.
• Analysis of single-cell and population signal relationships under varying network states, including nonrhythmic and delta-oscillatory regimes.

Main Results:

• The NSI effectively classifies network states from LFP signals.
• In nonrhythmic states, LFP signals predict single-cell depolarization levels.
• In delta-oscillatory states, LFP rhythmicity correlates with stereotypical membrane potential oscillations.
• Network state variability, beyond oscillations, impacts single-cell and population signal correlations.
• NSI application in visual cortex showed correlations with pupil size, locomotion, and firing rates.

Conclusions:

• The NSI is a valuable tool for inferring network states from LFP recordings.
• Understanding network dynamics through LFP analysis is crucial for comprehending sensory processing flexibility during behavior.
• This LFP-based approach offers a scalable method to study brain states.