Learning a Widespread Electrical Brain Network for Anxiety Across Contexts

In rodents, anxiety is characterized by heightened vigilance during low-threat and uncertain situations. Though activity in the frontal cortex and limbic system is fundamental to supporting this internal state, the underlying network architecture that integrates activity across brain regions to encode anxiety across animals and paradigms remains unclear.  In this talk, I will describe how parallel electrical recordings from distributed brain regions in freely behaving mice, combined with modern machine-learning methods, can be used to identify a multi-region electrical network that encodes anxiety that is robust and reliable to data perturbations. The network is composed of circuits widely implicated in anxiety behavior, it generalizes across many behavioral contexts that induce anxiety, and it fails to encode multiple behavioral contexts that do not. Strikingly, the activity of this network is also principally altered in two mouse models of depression. Thus, we establish a network-level process whereby the brain encodes anxiety in health and disease. Together, these results illustrate how data-driven modeling of large-scale neural dynamics can uncover interpretable brain-state representations that bridge circuits, behavior, and disease.

Learning Objectives:

1. Describe how machine-learning models can be used to identify latent, distributed brain networks from multiregion electrophysiological recordings.

2. Discuss how network-level representations of internal state can inform translational models of mood and anxiety disorders.

3. Explain how generalization across behavioral paradigms can be tested and validated for neural state representations.