Dynamic patterns of correlated activity in the prefrontal cortex encode information about social behavior

Area of Science:

• Neuroscience
• Computational Neuroscience
• Behavioral Neuroscience

Background:

• Simultaneous neural recordings allow for the study of neuronal ensembles.
• Traditional ensemble analysis often overlooks collective neuronal activity and information transmission.
• Neurons can encode and transmit information through coordinated activity patterns, not just individual firing rates.

Purpose of the Study:

• To investigate how neurons synergistically transmit information about social behavior.
• To develop and apply a novel computational approach for analyzing neural network states based on coactivity patterns.
• To explore the role of correlated neural activity in social information processing and its disruption in genetic models of autism.

Main Methods:

• Utilized microendoscopic GCaMP imaging to record prefrontal cortex activity in mice during social interaction and isolation.
• Developed a neural network classifier combined with surrogate datasets to differentiate network states based on coactivity patterns.
• Compared the performance of surrogate datasets preserving correlated activity versus those preserving individual activity levels.

Main Results:

• Neural network classifiers can distinguish network states based on coactivity patterns, even without changes in individual neuron activity.
• Behaviorally specific patterns of correlated neural activity (coactivity) are crucial for transmitting social behavior information.
• Social interaction significantly increases correlated neural activity in the prefrontal cortex.
• The enhancement of information transmission via correlated activity is reduced in mice lacking the Shank3 gene, a model for autism spectrum disorder.

Conclusions:

• Synergy, driven by correlated neural activity, is a key mechanism for encoding social behavior.
• Prefrontal neuronal ensembles act collectively to transmit social information through coordinated firing patterns.
• Disruptions in correlated activity and neural synergy may underlie social deficits in neurodevelopmental disorders like autism.
• The developed computational methods provide a framework for studying collective neural coding and its alterations in disease states.