Microscale multicircuit brain stimulation: Achieving real-time brain state control for novel applications

Abstract

Neurological and psychiatric disorders typically result from dysfunction across multiple neural circuits. Most of these disorders lack a satisfactory neuromodulation treatment. However, deep brain stimulation (DBS) has been successful in a limited number of disorders; DBS typically targets one or two brain areas with single contacts on relatively large electrodes, allowing for only coarse modulation of circuit function. Because of the dysfunction in distributed neural circuits - each requiring fine, tailored modulation - that characterizes most neuropsychiatric disorders, this approach holds limited promise. To develop the next generation of neuromodulation therapies, we will have to achieve fine-grained, closed-loop control over multiple neural circuits. Recent work has demonstrated spatial and frequency selectivity using microstimulation with many small, closely-spaced contacts, mimicking endogenous neural dynamics. Using custom electrode design and stimulation parameters, it should be possible to achieve bidirectional control over behavioral outcomes, such as increasing or decreasing arousal during central thalamic stimulation. Here, we discuss one possible approach, which we term microscale multicircuit brain stimulation (MMBS). We discuss how machine learning leverages behavioral and neural data to find optimal stimulation parameters across multiple contacts, to drive the brain towards desired states associated with behavioral goals. We expound a mathematical framework for MMBS, where behavioral and neural responses adjust the model in real-time, allowing us to adjust stimulation in real-time. These technologies will be critical to the development of the next generation of neurostimulation therapies, which will allow us to treat problems like disorders of consciousness and cognition.

Keywords: Cognitive control; Consciousness; Deep brain stimulation; Machine learning; Neuromodulation.

Graphical abstract

Fig. 1

Schematic of closed-loop stimulation for recovery of consciousness. A) high-dimensional observations Y(t) includes deep neural recordings from distributed contacts and vital signs (heart rate, pupil size). B) Non-parametric state-space modeling enables us to estimate the underlying latent state of these observations at baseline and with different sets of stimulation parameters. C) Reinforcement learning will determine which set of stimulation parameters will minimize our cost function, that is the distance of the predicted X(t, u(t)) from the healthy, desired X′(t).