Disruption of interareal control during propofol anesthesia

Anesthetic-induced unconsciousness may arise partly from a change in how brain areas can manipulate each others activity. To quantify this change, we use control-theoretic tools that can precisely characterize how easily subsystems in complex networks can control each other. These tools rely on the Jacobian of the dynamics, an object that fully specifies how inputs to a function affect the outputs. We built on JacobianODE, a method for data-driven Jacobian learning, to enable its application to neural recordings. We developed a deep learning framework that recovers directional, nonlinear control from partially observed multi-area recordings by combining delay-coordinate embedding, a volume-preserving invertible encoder, and latent dynamics via JacobianODE. We validated our framework on the Lorenz system and a partially observed working-memory recurrent neural network. We then applied it to local field potential recordings from posterior parietal (PPC), superior temporal gyrus (STG), frontal eye fields (FEF), and ventrolateral prefrontal cortex (vlPFC) in two non-human primates, comparing wakefulness with propofol anesthesia. Anesthesia pervasively reduced the ability of areas to control each other, both in terms of driving towards novel states and stabilizing along existing trajectories. This change in ease of control was driven by a decrease in the magnitude of interareal coupling. Directional ease of driving control from PPC to vlPFC and FEF to vlPFC was increased under anesthesia, providing a potential mechanism for paradoxical excitation observed during propofol infusion. Together, these results recast anesthetic unconsciousness as a directional breakdown of cortical control.