Biology subjects

Eisen, A. J.

Publications and source records attributed to Eisen, A. J..

4 recordsLinked to original sources

bioRxiv2026

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.

neuroscience↗

bioRxiv2026

A metric for comparing complex systems by their dynamics

Comparisons are fundamental to science: experiment against model, one organism against another, a system against itself across time. Because many systems, from brains to climate, are characterized by how they evolve in time, it is a natural goal to compare their dynamics. Dynamical systems comparison is well defined, but has been intractable for nonlinear, high-dimensional, noisy, and partially observed data. As a result, standard comparison methods have focused on the geometry or topology of data. Here we present Dynamical Similarity Analysis (DSA), a class of metrics to compare systems by their temporal evolution. Its foundation is Koopman Operator theory, which recasts nonlinear systems as linear operators. We estimate these operators from data, then compare the operators across systems. The computation is fast, scalable, and robust to noise and partial observation. It is also differentiable. DSA identifies dynamical structure that geometric and topological methods miss. It matches recordings from the head direction circuit to ring attractor models. It shows that macaque motor cortex dynamics for two reaching tasks drift apart across years despite preserved behavior, and that primary motor cortex breaks from premotor cortex as movement begins. As an optimization objective, it induces neural networks to learn never-before hypothesized solutions that run counter to their inductive biases. Thus, DSA transforms the dynamics of a system into an object that can be measured, compared, and optimized.

neuroscience↗

bioRxiv2025

Similar destabilization of neural dynamics under different general anesthetics

Different classes of anesthetics can induce unconsciousness despite acting through distinct biological mechanisms. This raises the possibility that they produce a convergent effect on the dynamics or temporal evolution of neural population activity. To explore this, we analyzed intracortical electrophysiological recordings during infusions of propofol, ketamine, and dexmedetomidine, using a rigorous method to estimate dynamical stability. We found that all three anesthetics, despite their molecular differences, similarly affect cortical states by destabilizing their dynamics. This destabilization matched the slower recovery from sensory perturbations and longer stimulus-induced autocorrelation times observed during the anesthetic infusions. The destabilization was also reflected predominantly in lower-frequency ranges, linking it to the well-known increase in low-frequency power during anesthesia. Finally, destabilization closely tracked real-time fluctuations in consciousness. Together, these findings suggest that cortical destabilization may be a shared neural correlate of anesthetic-induced unconsciousness, offering a mechanistic explanation for low-frequency oscillations observed during anesthesia.

neuroscience↗

bioRxiv2023

Propofol anesthesia destabilizes neural dynamics across cortex

Every day, hundreds of thousands of people undergo general anesthesia. One hypothesis is that anesthesia disrupts dynamic stability, the ability of the brain to balance excitability with the need to be stable and thus controllable. We tested this hypothesis using a new method for quantifying population-level dynamic stability in complex systems, Delayed Linear Analysis for Stability Estimation (DeLASE). Propofol was used to transition animals between the awake state and anesthetized unconsciousness. DeLASE was applied to macaque cortex local field potentials (LFPs). We found that neural dynamics were more unstable in unconsciousness compared to the awake state. Cortical trajectories mirrored predictions from destabilized linear systems. We mimicked the effect of propofol in simulated neural networks by increasing inhibitory tone. Paradoxically, increased inhibition also destabilized the networks. Our results suggest that anesthesia disrupts dynamical stability that is required for consciousness.

neuroscience↗