Probing the depths of unconsciousness with multifunctional neurotechnology

Innovation in the interrelated fields of anesthesia and psychiatry demands an improved understanding of the mechanisms behind altered states of consciousness. Studying the brain's functional equilibrium, and how it can be disrupted, has motivated increasingly high resolution and multifunctional neurotechnology. Inferring the dynamic structure of neuronal signaling from high dimensional data requires concomitant computational advances. This thesis focuses on how the intersection of neuroscience, engineering, and statistics can be leveraged to unravel the mechanisms behind altered consciousness induced by high-dose ketamine. Although ketamine has been indispensable to medical practice since 1970, the neurobiological mechanisms behind its unique behavioral effects are not fully understood. I hypothesized that ketamine’s inhibition of N-methyl-D-aspartate receptors (NMDARs) leads to a systemic restructuring of both chemical and electrical neuronal signaling which ultimately disrupts consciousness. Systematically testing this hypothesis required the ability to probe electrochemical signaling across the behavioral spectrum spanning cognition and unconsciousness. To enable this study, I first developed multifunctional fiber-based neurotechnology capable of simultaneously recording and modulating cortical and deep brain electrochemical signaling in non-human primates. Second, I developed a state-space model framework for characterizing the structure of neural activity and its dynamic response to neuromodulation. Using these developments, I found that ketamine's systemic alteration of electrochemical signaling results in rigidly structured neural activity that disrupts communication between brain areas, resulting in loss of consciousness. This work furthers our understanding of the neural dynamics that define unconsciousness, while also empowering systems neuroscience with an integrated, generalized toolbox for characterizing neuropharmacology.Ph.D

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.</jats:p

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

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

Abstract 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. Brain–computer interfaces, which translate internal electrical signals related to desired hand movement may be used to restore independence to patients with paralysis. In this work, Cajigas et al. demonstrate that a fully implanted interface can reliably decode motor commands using electrocorticography and can be deployed for home use. Such systems may provide means for self-driven portable rehabilitation and assistance. Graphical Abstract Graphical Abstract Video Abstract 10.1093/braincomms/fcab248_video6 Video Abstract fcab248media1 628207378700