Closed-loop control of anesthetic state in nonhuman primates.

Research in human volunteers and surgical patients has shown that unconsciousness under general anesthesia can be reliably tracked using real-time electroencephalogram processing. Hence, a closed-loop anesthesia delivery (CLAD) system that maintains precisely specified levels of unconsciousness is feasible and would greatly aid intraoperative patient management. The US Federal Drug Administration has approved no CLAD system for human use due partly to a lack of testing in appropriate animal models. To address this key roadblock, we implement a nonhuman primate (NHP) CLAD system that controls the level of unconsciousness using the anesthetic propofol. The key system components are a local field potential (LFP) recording system; propofol pharmacokinetics and pharmacodynamic models; the control variable (LFP power between 20 and 30 Hz), a programmable infusion system and a linear quadratic integral controller. Our CLAD system accurately controlled the level of unconsciousness along two different 125-min dynamic target trajectories for 18 h and 45 min in nine experiments in two NHPs. System performance measures were comparable or superior to those in previous CLAD reports. We demonstrate that an NHP CLAD system can reliably and accurately control in real-time unconsciousness maintained by anesthesia. Our findings establish critical steps for CLAD systems' design and testing prior to human testing.

Multifunctional fibers enable modulation of cortical and deep brain activity during cognitive behavior in macaques.

Recording and modulating neural activity in vivo enables investigations of the neurophysiology underlying behavior and disease. However, there is a dearth of translational tools for simultaneous recording and localized receptor-specific modulation. We address this limitation by translating multifunctional fiber neurotechnology previously only available for rodent studies to enable cortical and subcortical neural recording and modulation in macaques. We record single-neuron and broader oscillatory activity during intracranial GABA infusions in the premotor cortex and putamen. By applying state-space models to characterize changes in electrophysiology, we uncover that neural activity evoked by a working memory task is reshaped by even a modest local inhibition. The recordings provide detailed insight into the electrophysiological effect of neurotransmitter receptor modulation in both cortical and subcortical structures in an awake macaque. Our results demonstrate a first-time application of multifunctional fibers for causal studies of neuronal activity in behaving nonhuman primates and pave the way for clinical translation of fiber-based neurotechnology.

Multifunctional microelectronic fibers enable wireless modulation of gut and brain neural circuits.

Progress in understanding brain-viscera interoceptive signaling is hindered by a dearth of implantable devices suitable for probing both brain and peripheral organ neurophysiology during behavior. Here we describe multifunctional neural interfaces that combine the scalability and mechanical versatility of thermally drawn polymer-based fibers with the sophistication of microelectronic chips for organs as diverse as the brain and the gut. Our approach uses meters-long continuous fibers that can integrate light sources, electrodes, thermal sensors and microfluidic channels in a miniature footprint. Paired with custom-fabricated control modules, the fibers wirelessly deliver light for optogenetics and transfer data for physiological recording. We validate this technology by modulating the mesolimbic reward pathway in the mouse brain. We then apply the fibers in the anatomically challenging intestinal lumen and demonstrate wireless control of sensory epithelial cells that guide feeding behaviors. Finally, we show that optogenetic stimulation of vagal afferents from the intestinal lumen is sufficient to evoke a reward phenotype in untethered mice.

Magnetically Actuated Fiber-Based Soft Robots.

Broad adoption of magnetic soft robotics is hampered by the sophisticated field paradigms for their manipulation and the complexities in controlling multiple devices. Furthermore, high-throughput fabrication of such devices across spatial scales remains challenging. Here, advances in fiber-based actuators and magnetic elastomer composites are leveraged to create 3D magnetic soft robots controlled by unidirectional fields. Thermally drawn elastomeric fibers are instrumented with a magnetic composite synthesized to withstand strains exceeding 600%. A combination of strain and magnetization engineering in these fibers enables programming of 3D robots capable of crawling or walking in magnetic fields orthogonal to the plane of motion. Magnetic robots act as cargo carriers, and multiple robots can be controlled simultaneously and in opposing directions using a single stationary electromagnet. The scalable approach to fabrication and control of magnetic soft robots invites their future applications in constrained environments where complex fields cannot be readily deployed.

Implantable brain-computer interface for neuroprosthetic-enabled volitional hand grasp restoration in spinal cord injury.

Loss of hand function after cervical spinal cord injury severely impairs functional independence. We describe a method for restoring volitional control of hand grasp in one 21-year-old male subject with complete cervical quadriplegia (C5 American Spinal Injury Association Impairment Scale A) using a portable fully implanted brain-computer interface within the home environment. The brain-computer interface consists of subdural surface electrodes placed over the dominant-hand motor cortex and connects to a transmitter implanted subcutaneously below the clavicle, which allows continuous reading of the electrocorticographic activity. Movement-intent was used to trigger functional electrical stimulation of the dominant hand during an initial 29-weeks laboratory study and subsequently via a mechanical hand orthosis during in-home use. Movement-intent information could be decoded consistently throughout the 29-weeks in-laboratory study with a mean accuracy of 89.0% (range 78-93.3%). Improvements were observed in both the speed and accuracy of various upper extremity tasks, including lifting small objects and transferring objects to specific targets. At-home decoding accuracy during open-loop trials reached an accuracy of 91.3% (range 80-98.95%) and an accuracy of 88.3% (range 77.6-95.5%) during closed-loop trials. Importantly, the temporal stability of both the functional outcomes and decoder metrics were not explored in this study. A fully implanted brain-computer interface can be safely used to reliably decode movement-intent from motor cortex, allowing for accurate volitional control of hand grasp.

A hidden Markov model reliably characterizes ketamine-induced spectral dynamics in macaque local field potentials and human electroencephalograms.

Ketamine is an NMDA receptor antagonist commonly used to maintain general anesthesia. At anesthetic doses, ketamine causes high power gamma (25-50 Hz) oscillations alternating with slow-delta (0.1-4 Hz) oscillations. These dynamics are readily observed in local field potentials (LFPs) of non-human primates (NHPs) and electroencephalogram (EEG) recordings from human subjects. However, a detailed statistical analysis of these dynamics has not been reported. We characterize ketamine's neural dynamics using a hidden Markov model (HMM). The HMM observations are sequences of spectral power in seven canonical frequency bands between 0 to 50 Hz, where power is averaged within each band and scaled between 0 and 1. We model the observations as realizations of multivariate beta probability distributions that depend on a discrete-valued latent state process whose state transitions obey Markov dynamics. Using an expectation-maximization algorithm, we fit this beta-HMM to LFP recordings from 2 NHPs, and separately, to EEG recordings from 9 human subjects who received anesthetic doses of ketamine. Our beta-HMM framework provides a useful tool for experimental data analysis. Together, the estimated beta-HMM parameters and optimal state trajectory revealed an alternating pattern of states characterized primarily by gamma and slow-delta activities. The mean duration of the gamma activity was 2.2s([1.7,2.8]s) and 1.2s([0.9,1.5]s) for the two NHPs, and 2.5s([1.7,3.6]s) for the human subjects. The mean duration of the slow-delta activity was 1.6s([1.2,2.0]s) and 1.0s([0.8,1.2]s) for the two NHPs, and 1.8s([1.3,2.4]s) for the human subjects. Our characterizations of the alternating gamma slow-delta activities revealed five sub-states that show regular sequential transitions. These quantitative insights can inform the development of rhythm-generating neuronal circuit models that give mechanistic insights into this phenomenon and how ketamine produces altered states of arousal.

Machine learning of EEG spectra classifies unconsciousness during GABAergic anesthesia.

In current anesthesiology practice, anesthesiologists infer the state of unconsciousness without directly monitoring the brain. Drug- and patient-specific electroencephalographic (EEG) signatures of anesthesia-induced unconsciousness have been identified previously. We applied machine learning approaches to construct classification models for real-time tracking of unconscious state during anesthesia-induced unconsciousness. We used cross-validation to select and train the best performing models using 33,159 2s segments of EEG data recorded from 7 healthy volunteers who received increasing infusions of propofol while responding to stimuli to directly assess unconsciousness. Cross-validated models of unconsciousness performed very well when tested on 13,929 2s EEG segments from 3 left-out volunteers collected under the same conditions (median volunteer AUCs 0.99-0.99). Models showed strong generalization when tested on a cohort of 27 surgical patients receiving solely propofol collected in a separate clinical dataset under different circumstances and using different hardware (median patient AUCs 0.95-0.98), with model predictions corresponding with actions taken by the anesthesiologist during the cases. Performance was also strong for 17 patients receiving sevoflurane (alone or in addition to propofol) (median AUCs 0.88-0.92). These results indicate that EEG spectral features can predict unconsciousness, even when tested on a different anesthetic that acts with a similar neural mechanism. With high performance predictions of unconsciousness, we can accurately monitor anesthetic state, and this approach may be used to engineer infusion pumps to intelligibly respond to patients' neural activity.

In Vivo Photopharmacology Enabled by Multifunctional Fibers.

Photoswitchable ligands can add an optical switch to a target receptor or signaling cascade and enable reversible control of neural circuits. The application of this approach, termed photopharmacology, to behavioral experiments has been impeded by a lack of integrated hardware capable of delivering both light and compounds to deep brain regions in moving subjects. Here, we devise a hybrid photochemical genetic approach to target neurons using a photoswitchable agonist of the capsaicin receptor TRPV1, red-AzCA-4. Using multifunctional fibers with optical and microfluidic capabilities, we delivered a transgene coding for TRPV1 into the ventral tegmental area (VTA). This sensitized excitatory VTA neurons to red-AzCA-4, allowing us to optically control conditioned place preference in mice, thus extending applications of photopharmacology to behavioral experiments. Applied to endogenous receptors, our approach may accelerate future studies of molecular mechanisms underlying animal behavior.