Policies Restricting Overlapping Surgeries Negatively Impact Access to Care, Clinical Efficiency, and Hospital Revenue: A Forecasting Model for Surgical Scheduling.

OBJECTIVE: To model the financial impact of policies governing the scheduling of overlapping surgeries, and to identify optimal solutions that maximize operating efficiency that satisfy the fiduciary duty to patients. BACKGROUND: Hospitals depend on procedural revenue to maintain financial health as the recent pandemic has revealed. Proposed policies governing the scheduling of overlapping surgeries may dramatically impact hospital revenue. To date, the potential financial impact has not been modeled. METHODS: A linear forecasting model based on a logic matrix decision tree enabled an analysis of surgeon productivity annualized over a fiscal year. The model applies procedural and operational variables to policy constraints limiting surgical scheduling. Model outputs included case and financial metrics modeled over 1000-surgeon-year simulations. case metrics included annual case volume, case mix, operating room (OR) utilization, surgeon utilization, idle time, and staff overtime hours. Financial outputs included annual revenue, expenses, and contribution margin. RESULTS: The model was validated against surgical data. case and financial metrics decreased as a function of increasingly restrictive scheduling scenarios, with the greatest contribution margin loses ($1,650,000 per surgeon-year) realized with the introduction of policies mandating that a second patient could not enter the OR until the critical portion of the first surgery was completed. We identify an optimal scheduling scenario that maximizes surgeon efficiency, minimizes OR idle time and revenue loses, and satisfies ethical obligations to patients. CONCLUSIONS: Hospitals may expect significant financial loses with the introduction of policies restricting OR scheduling. We identify an optimal solution that maximizes efficiency while satisfying ethical duty to patients. This forecast is immediately relevant to any hospital system that depends upon procedural revenue.

Learning active sensing strategies using a sensory brain-machine interface.

Diverse organisms, from insects to humans, actively seek out sensory information that best informs goal-directed actions. Efficient active sensing requires congruity between sensor properties and motor strategies, as typically honed through evolution. However, it has been difficult to study whether active sensing strategies are also modified with experience. Here, we used a sensory brain-machine interface paradigm, permitting both free behavior and experimental manipulation of sensory feedback, to study learning of active sensing strategies. Rats performed a searching task in a water maze in which the only task-relevant sensory feedback was provided by intracortical microstimulation (ICMS) encoding egocentric bearing to the hidden goal location. The rats learned to use the artificial goal direction sense to find the platform with the same proficiency as natural vision. Manipulation of the acuity of the ICMS feedback revealed distinct search strategy adaptations. Using an optimization model, the different strategies were found to minimize the effort required to extract the most salient task-relevant information. The results demonstrate that animals can adjust motor strategies to match novel sensor properties for efficient goal-directed behavior.

Primate somatosensory cortical neurons are entrained to both spontaneous and peripherally evoked spindle oscillations.

Recurrent thalamocortical circuits produce a number of rhythms critical to brain function. In slow-wave sleep, spindles (7-16 Hz) are a prominent spontaneous oscillation generated by thalamic circuits and triggered by cortical slow waves. In wakefulness and under anesthesia, brief peripheral sensory stimuli can evoke 10-Hz reverberations due potentially to similar thalamic mechanisms. Functionally, sleep spindles and peripherally evoked spindles may play a role in memory consolidation and perception, respectively. Yet, rarely have the circuits involved in these two rhythms been compared in the same animals and never in primates. Here, we investigated the entrainment of primary somatosensory cortex (S1) neurons to both rhythms in ketamine-sedated macaques. First, we compared spontaneous spindles in sedation and natural sleep to validate the model. Then, we quantified entrainment with spike-field coherence and phase-locking statistics. We found that S1 neurons entrained to spontaneous sleep spindles were also entrained to the evoked spindles, although entrainment strength and phase systematically differed. Our results indicate that the spindle oscillations triggered by top-down spontaneous cortical activity and bottom-up peripheral input share a common cortical substrate.NEW & NOTEWORTHY Brief sensory stimuli evoke 10-Hz oscillations in thalamocortical neuronal activity and in perceptual thresholds. The mechanisms underlying this evoked rhythm are not well understood but are thought to be similar to those generating sleep spindles. We directly compared the entrainment of cortical neurons to both spontaneous spindles and peripherally evoked oscillations in sedated monkeys. We found that the entrainment strengths to each rhythm were positively correlated, although with differing entrainment phases, implying involvement of similar networks.

Hippocampal gamma-slow oscillation coupling in macaques during sedation and sleep.

Behavioral and neurophysiological evidence suggests that the slow (≤1 Hz) oscillation (SO) during sleep plays a role in consolidating hippocampal (HIPP)-dependent memories. The effects of the SO on HIPP activity have been studied in rodents and cats both during natural sleep and during anesthetic administration titrated to mimic sleep-like slow rhythms. In this study, we sought to document these effects in primates. First, HIPP field potentials were recorded during ketamine-dexmedetomidine sedation and during natural sleep in three rhesus macaques. Sedation produced regionally-specific slow and gamma (∼40 Hz) oscillations with strong coupling between the SO phase and gamma amplitude. These same features were seen in slow-wave sleep (SWS), but the coupling was weaker and the coupled gamma oscillation had a higher frequency (∼70 Hz) during SWS. Second, electrical stimuli were delivered to HIPP afferents in the parahippocampal gyrus (PHG) during sedation to assess the effects of sleep-like SO on excitability. Gamma bursts after the peak of SO cycles corresponded to periods of increased gain of monosynaptic connections between the PHG and HIPP. However, the two PHG-HIPP connectivity gains during sedation were both substantially lower than when the animal was awake. We conclude that the SO is correlated with rhythmic excitation and inhibition of the PHG-HIPP network, modulating connectivity and gamma generators intrinsic to this network. Ketamine-dexmedetomidine sedation produces a similar effect, but with a decreased contribution of the PHG to HIPP activity and gamma generation.

Bioresorbable silicon electronics for transient spatiotemporal mapping of electrical activity from the cerebral cortex.

Bioresorbable silicon electronics technology offers unprecedented opportunities to deploy advanced implantable monitoring systems that eliminate risks, cost and discomfort associated with surgical extraction. Applications include postoperative monitoring and transient physiologic recording after percutaneous or minimally invasive placement of vascular, cardiac, orthopaedic, neural or other devices. We present an embodiment of these materials in both passive and actively addressed arrays of bioresorbable silicon electrodes with multiplexing capabilities, which record in vivo electrophysiological signals from the cortical surface and the subgaleal space. The devices detect normal physiologic and epileptiform activity, both in acute and chronic recordings. Comparative studies show sensor performance comparable to standard clinical systems and reduced tissue reactivity relative to conventional clinical electrocorticography (ECoG) electrodes. This technology offers general applicability in neural interfaces, with additional potential utility in treatment of disorders where transient monitoring and modulation of physiologic function, implant integrity and tissue recovery or regeneration are required.

A chronic neural interface to the macaque dorsal column nuclei.

The dorsal column nuclei (DCN) of the brain stem contain secondary afferent neurons, which process ascending somatosensory information. Most of the known physiology of the DCN in primates has been acquired in acute experiments with anesthetized animals. Here, we developed a technique to implant a multielectrode array (MEA) chronically in the DCN of macaque monkeys to enable experiments with the animals awake. Two monkeys were implanted with brain-stem MEAs for 2-5 mo with no major adverse effects. Responses of the cuneate and gracile nuclei were quantified at the level of both field potentials and single units. Tactile receptive fields (RFs) were identified for 315 single units. A subset of these units had very regular spiking patterns with spike frequencies predominantly in the alpha band (8-14 Hz). The stability of the neuronal recordings was assessed with a novel analysis that identified units by their mean spike waveform and by the spike-triggered average of activity on all other electrodes in the array. Fifty-six identified neurons were observed over two or more sessions and in a few cases for as long as 1 mo. RFs of stable neurons were largely consistent across days. The results demonstrate that a chronic DCN implant in a macaque can be safe and effective, yielding high-quality unit recording for several months. The unprecedented access to these nuclei in awake primates should lead to a better understanding of their role in sensorimotor behavior.

Generalized analog thresholding for spike acquisition at ultralow sampling rates.

Efficient spike acquisition techniques are needed to bridge the divide from creating large multielectrode arrays (MEA) to achieving whole-cortex electrophysiology. In this paper, we introduce generalized analog thresholding (gAT), which achieves millisecond temporal resolution with sampling rates as low as 10 Hz. Consider the torrent of data from a single 1,000-channel MEA, which would generate more than 3 GB/min using standard 30-kHz Nyquist sampling. Recent neural signal processing methods based on compressive sensing still require Nyquist sampling as a first step and use iterative methods to reconstruct spikes. Analog thresholding (AT) remains the best existing alternative, where spike waveforms are passed through an analog comparator and sampled at 1 kHz, with instant spike reconstruction. By generalizing AT, the new method reduces sampling rates another order of magnitude, detects more than one spike per interval, and reconstructs spike width. Unlike compressive sensing, the new method reveals a simple closed-form solution to achieve instant (noniterative) spike reconstruction. The base method is already robust to hardware nonidealities, including realistic quantization error and integration noise. Because it achieves these considerable specifications using hardware-friendly components like integrators and comparators, generalized AT could translate large-scale MEAs into implantable devices for scientific investigation and medical technology.

Lateral Transorbital Endoscopic Access to the Hippocampus, Amygdala, and Entorhinal Cortex: Initial Clinical Experience.

BACKGROUND/AIMS: Transorbital approaches traditionally have focused on skull base and cavernous sinus lesions medial to the globe. Lateral orbital approaches to the temporal lobe have not been widely explored despite several theoretical advantages compared to open craniotomy. Recently, we demonstrated the feasibility of the lateral transorbital technique in cadaveric specimens with endoscopic visualization. We describe our initial clinical experience with the endoscope-assisted lateral transorbital approach to lesions in the temporal lobe. METHODS: Two patients with mesial temporal lobe pathology presenting with seizures underwent surgery. The use of a transpalpebral or Stallard-Wright eyebrow incision enabled access to the intraorbital compartment, and a lateral orbital wall 'keyhole' opening permitted visualization of the anterior temporal pole. RESULTS: This approach afforded adequate access to the surgical target and surrounding structures and was well tolerated by the patients. To the best of our knowledge, this report constitutes the first case series describing the endoscope-assisted lateral transorbital approach to the temporal lobe. We discuss the limits of exposure, the nuances of opening and closing, and comparisons to open craniotomy. CONCLUSION: Further prospective investigation of this approach is warranted for comparison to traditional approaches to the mesial temporal lobe.

Dichotomous frequency-dependent phase synchrony in the sensorimotor network characterizes simplistic movement.

It is hypothesized that disparate brain regions interact via synchronous activity to control behavior. The nature of these interconnected ensembles remains an area of active investigation, and particularly the role of high frequency synchronous activity in simplistic behavior is not well known. Using intracranial electroencephalography, we explored the spectral dynamics and network connectivity of sensorimotor cortical activity during a simple motor task in seven epilepsy patients. Confirming prior work, we see a "spectral tilt" (increased high-frequency (HF, 70-100 Hz) and decreased low-frequency (LF, 3-33 Hz) broadband oscillatory activity) in motor regions during movement compared to rest, as well as an increase in LF synchrony between these regions using time-resolved phase-locking. We then explored this phenomenon in high frequency and found a robust but opposite effect, where time-resolved HF broadband phase-locking significantly decreased during movement. This "connectivity tilt" (increased LF synchrony and decreased HF synchrony) displayed a graded anatomical dependency, with the most robust pattern occurring in primary sensorimotor cortical interactions and less robust pattern occurring in associative cortical interactions. Connectivity in theta (3-7 Hz) and high beta (23-27 Hz) range had the most prominent low frequency contribution during movement, with theta synchrony building gradually while high beta having the most prominent effect immediately following the cue. There was a relatively sharp, opposite transition point in both the spectral and connectivity tilt at approximately 35 Hz. These findings support the hypothesis that task-relevant high-frequency spectral activity is stochastic and that the decrease in high-frequency synchrony may facilitate enhanced low frequency phase coupling and interregional communication. Thus, the "connectivity tilt" may characterize behaviorally meaningful cortical interactions.

A BIOCOMPATIBLE GLASS-ENCAPSULATED TRIAXIAL FORCE SENSOR FOR IMPLANTABLE TACTILE SENSING APPLICATIONS.

This paper reports a microfabricated triaxial capacitive force sensor. The sensor is fully encapsulated with inert and biocompatible glass (fused silica) material. The sensor comprises two glass plates, on which four capacitors are located. The sensor is intended for subdermal implantation in fingertips and palms and providing tactile sensing capabilities for patients with paralyzed hands. Additional electronic components, such as passives and IC chips, can also be integrated with the sensor in a hermetic glass package to achieve an implantable tactile sensing system. Through attachment to a human palm, the sensor has been shown to respond appropriately to typical hand actions, such as squeezing or picking up a bottle.

Design of a Sleep Modulation System with FPGA-Accelerated Deep Learning for Closed-loop Stage-Specific In-Phase Auditory Stimulation.

Closed-loop sleep modulation is an emerging research paradigm to treat sleep disorders and enhance sleep benefits. However, two major barriers hinder the widespread application of this research paradigm. First, subjects often need to be wire-connected to rack-mount instrumentation for data acquisition, which negatively affects sleep quality. Second, conventional real-time sleep stage classification algorithms give limited performance. In this work, we conquer these two limitations by developing a sleep modulation system that supports closed-loop operations on the device. Sleep stage classification is performed using a lightweight deep learning (DL) model accelerated by a low-power field-programmable gate array (FPGA) device. The DL model uses a single channel electroencephalogram (EEG) as input. Two convolutional neural networks (CNNs) are used to capture general and detailed features, and a bidirectional long-short-term memory (LSTM) network is used to capture time-variant sequence features. An 8-bit quantization is used to reduce the computational cost without compromising performance. The DL model has been validated using a public sleep database containing 81 subjects, achieving a state-of-the-art classification accuracy of 85.8% and a F1-score of 79%. The developed model has also shown the potential to be generalized to different channels and input data lengths. Closed-loop in-phase auditory stimulation has been demonstrated on the test bench.

An implantable wireless tactile sensing system.

The sense of touch is critical to dexterous use of the hands and thus an essential component to efforts to restore hand function after amputation or paralysis. Prosthetic systems have focused on wearable tactile sensors. But wearable sensors are suboptimal for neuroprosthetic systems designed to reanimate a patient's own paralyzed hand. Here, we developed an implantable tactile sensing system intended for subdermal placement. The system is composed of a microfabricated capacitive force sensor, a custom integrated circuit supporting wireless powering and data transmission, and a laser-fused hermetic silica package. The miniature device was validated through simulations, benchtop testing, and ex vivo testing in a primate hand. The sensor implanted in the fingertip accurately measured skin forces with a resolution of 4.3 mN. The output from this novel sensor could be encoded in the brain with microstimulation to provide tactile feedback. More broadly, the materials, system design, and fabrication approach establish new foundational capabilities for various applications of implantable sensing systems.

An implantable, wireless, battery-free system for tactile pressure sensing.

The sense of touch is critical to dexterous use of the hands and thus an essential component of efforts to restore hand function after amputation or paralysis. Prosthetic systems have addressed this goal with wearable tactile sensors. However, such wearable sensors are suboptimal for neuroprosthetic systems designed to reanimate a patient's own paralyzed hand. Here, we developed an implantable tactile sensing system intended for subdermal placement. The system is composed of a microfabricated capacitive pressure sensor, a custom integrated circuit supporting wireless powering and data transmission, and a laser-fused hermetic silica package. The miniature device was validated through simulations, benchtop assessment, and testing in a primate hand. The sensor implanted in the fingertip accurately measured applied skin forces with a resolution of 4.3 mN. The output from this novel sensor could be encoded in the brain with microstimulation to provide tactile feedback. More broadly, the materials, system design, and fabrication approach establish new foundational capabilities for various applications of implantable sensing systems.

Activity of the same motor cortex neurons during repeated experience with perturbed movement dynamics.

Neurons in the primary motor cortex (M1) have been shown to have persistent, memory-like activity following adaptation to altered movement dynamics. However, the techniques used to study these memory traces limited recordings to only single sessions lasting no more than a few hours. Here, chronically implanted microelectrode arrays were used to study the long-term neuronal responses to repeated experience with perturbing, velocity-dependent force fields. Force-field-related neuronal activity within each session was similar to that found previously. That is, the directional tuning curves of the M1 neurons shifted in a manner appropriate to compensate for the forces. Next, the across-session behavior was examined. Long-term learning was evident in the performance improvements across multiple force-field sessions. Correlated with this change, the neuronal population had smaller within-session spike rate changes as experience with the force field increased. The smaller within-session changes were a result of persistent across-session shifts in directional tuning. The results extend the observation of memory traces of newly learned dynamics and provide further evidence for the role of M1 in early motor memory formation.

Vitamin C-reduced graphene oxide improves the performance and stability of multimodal neural microelectrodes.

Nanocarbons are often employed as coatings for neural electrodes to enhance surface area. However, processing and integrating them into microfabrication flows requires complex and harmful chemical and heating conditions. This article presents a safe, scalable, cost-effective method to produce reduced graphene oxide (rGO) coatings using vitamin C (VC) as the reducing agent. We spray coat GO + VC mixtures onto target substrates, and then heat samples for 15 min at 150°C. The resulting rGO films have conductivities of ∼44 S cm-1, and are easily integrated into an ad hoc microfabrication flow. The rGO/Au microelectrodes show ∼8x lower impedance and ∼400x higher capacitance than bare Au, resulting in significantly enhanced charge storage and injection capacity. We subsequently use rGO/Au arrays to detect dopamine in vitro, and to map cortical activity intraoperatively over rat whisker barrel cortex, demonstrating that conductive VC-rGO coatings improve the performance and stability of multimodal microelectrodes for different applications.

Motor learning with unstable neural representations.

It is often assumed that learning takes place by changing an otherwise stable neural representation. To test this assumption, we studied changes in the directional tuning of primate motor cortical neurons during reaching movements performed in familiar and novel environments. During the familiar task, tuning curves exhibited slow random drift. During learning of the novel task, random drift was accompanied by systematic shifts of tuning curves. Our analysis suggests that motor learning is based on a surprisingly unstable neural representation. To explain these results, we propose that motor cortex is a redundant neural network, i.e., any single behavior can be realized by multiple configurations of synaptic strengths. We further hypothesize that synaptic modifications underlying learning contain a random component, which causes wandering among synaptic configurations with equivalent behaviors but different neural representations. We use a simple model to explore the implications of these assumptions.

A System-on-Chip for Closed-loop Optogenetic Sleep Modulation.

Stimulation of target neuronal populations using optogenetic techniques during specific sleep stages has begun to elucidate the mechanisms and effects of sleep. To conduct closed-loop optogenetic sleep studies in untethered animals, we designed a fully integrated, low-power system-on-chip (SoC) for real-time sleep stage classification and stage-specific optical stimulation. The SoC consists of a 4-channel analog front-end for recording polysomnography signals, a mixed-signal machine-learning (ML) core, and a 16-channel optical stimulation back-end. A novel ML algorithm and innovative circuit design techniques improved the online classification performance while minimizing power consumption. The SoC was designed and simulated in 180 nm CMOS technology. In an evaluation using an expert labeled sleep database with 20 subjects, the SoC achieves a high sensitivity of 0.806 and a specificity of 0.947 in discriminating 5 sleep stages. Overall power consumption in continuous operation is 97 µW.

Edge deep learning for neural implants: a case study of seizure detection and prediction.

Objective.Implanted devices providing real-time neural activity classification and control are increasingly used to treat neurological disorders, such as epilepsy and Parkinson's disease. Classification performance is critical to identifying brain states appropriate for the therapeutic action (e.g. neural stimulation). However, advanced algorithms that have shown promise in offline studies, in particular deep learning (DL) methods, have not been deployed on resource-restrained neural implants. Here, we designed and optimized three DL models or edge deployment and evaluated their inference performance in a case study of seizure detection.Approach.A deep neural network (DNN), a convolutional neural network (CNN), and a long short-term memory (LSTM) network were designed and trained with TensorFlow to classify ictal, preictal, and interictal phases from the CHB-MIT scalp EEG database. A sliding window based weighted majority voting algorithm was developed to detect seizure events based on each DL model's classification results. After iterative model compression and coefficient quantization, the algorithms were deployed on a general-purpose, off-the-shelf microcontroller for real-time testing. Inference sensitivity, false positive rate (FPR), execution time, memory size, and power consumption were quantified.Main results.For seizure event detection, the sensitivity and FPR for the DNN, CNN, and LSTM models were 87.36%/0.169 h-1, 96.70%/0.102 h-1, and 97.61%/0.071 h-1, respectively. Predicting seizures for early warnings was also feasible. The LSTM model achieved the best overall performance at the expense of the highest power. The DNN model achieved the shortest execution time. The CNN model showed advantages in balanced performance and power with minimum memory requirement. The implemented model compression and quantization achieved a significant saving of power and memory with an accuracy degradation of less than 0.5%.Significance.Inference with embedded DL models achieved performance comparable to many prior implementations that had no time or computational resource limitations. Generic microcontrollers can provide the required memory and computational resources, while model designs can be migrated to application-specific integrated circuits for further optimization and power saving. The results suggest that edge DL inference is a feasible option for future neural implants to improve classification performance and therapeutic outcomes.

Cortical Stimulation Paired With Volitional Unimanual Movement Affects Interhemispheric Communication.

Cortical stimulation (CS) of the motor cortex can cause excitability changes in both hemispheres, showing potential to be a technique for clinical rehabilitation of motor function. However, previous studies that have investigated the effects of delivering CS during movement typically focus on a single hemisphere. On the other hand, studies exploring interhemispheric interactions typically deliver CS at rest. We sought to bridge these two approaches by documenting the consequences of delivering CS to a single motor cortex during different phases of contralateral and ipsilateral limb movement, and simultaneously assessing changes in interactions within and between the hemispheres via local field potential (LFP) recordings. Three macaques were trained in a unimanual reaction time (RT) task and implanted with epidural or intracortical electrodes over bilateral motor cortices. During a given session CS was delivered to one hemisphere with respect to movements of either the contralateral or ipsilateral limb. Stimulation delivered before contralateral limb movement onset shortened the contralateral limb RT. In contrast, stimulation delivered after the end of contralateral movement increased contralateral RT but decreased ipsilateral RT. Stimulation delivered before ipsilateral limb movement decreased ipsilateral RT. All other stimulus conditions as well as random stimulation and periodic stimulation did not have consistently significant effects on either limb. Simultaneous LFP recordings from one animal revealed correlations between changes in interhemispheric alpha band coherence and changes in RT, suggesting that alpha activity may be indicative of interhemispheric communication. These results show that changes caused by CS to the functional coupling within and between precentral cortices is contingent on the timing of CS relative to movement.

Wireless, battery-free, and fully implantable electrical neurostimulation in freely moving rodents.

Implantable deep brain stimulation (DBS) systems are utilized for clinical treatment of diseases such as Parkinson's disease and chronic pain. However, long-term efficacy of DBS is limited, and chronic neuroplastic changes and associated therapeutic mechanisms are not well understood. Fundamental and mechanistic investigation, typically accomplished in small animal models, is difficult because of the need for chronic stimulators that currently require either frequent handling of test subjects to charge battery-powered systems or specialized setups to manage tethers that restrict experimental paradigms and compromise insight. To overcome these challenges, we demonstrate a fully implantable, wireless, battery-free platform that allows for chronic DBS in rodents with the capability to control stimulation parameters digitally in real time. The devices are able to provide stimulation over a wide range of frequencies with biphasic pulses and constant voltage control via low-impedance, surface-engineered platinum electrodes. The devices utilize off-the-shelf components and feature the ability to customize electrodes to enable broad utility and rapid dissemination. Efficacy of the system is demonstrated with a readout of stimulation-evoked neural activity in vivo and chronic stimulation of the medial forebrain bundle in freely moving rats to evoke characteristic head motion for over 36 days.