Stimulation Artifacts Research Articles

Optimal artifact suppression in simultaneous electrocorticography stimulation and recording for bi-directional brain-computer interface applications

Objective. Electrocorticogram (ECoG)-based brain-computer interfaces (BCIs) are a promising platform for the restoration of motor and sensory functions to those with neurological deficits. Such bi-directional BCI operation necessitates simultaneous ECoG recording and stimulation, which is challenging given the presence of strong stimulation artifacts. This problem is exacerbated if the BCI’s analog front-end operates in an ultra-low power regime, which is a basic requirement for fully implantable medical devices. In this study, we developed a novel method for the suppression of stimulation artifacts before they reach the analog front-end. Approach. Using elementary biophysical considerations, we devised an artifact suppression method that employs a weak auxiliary stimulation delivered between the primary stimulator and the recording grid. The exact location and amplitude of this auxiliary stimulating dipole were then found through a constrained optimization procedure. The performance of our method was tested in both simulations and phantom brain tissue experiments. Main results. The solution found through the optimization procedure matched the optimal canceling dipole in both simulations and experiments. Artifact suppression as large as 28.7 dB and 22.9 dB were achieved in simulations and brain phantom experiments, respectively. Significance. We developed a simple constrained optimization-based method for finding the parameters of an auxiliary stimulating dipole that yields optimal artifact suppression. Our method suppresses stimulation artifacts before they reach the analog front-end and may prevent the front-end amplifiers from saturation. Additionally, it can be used along with other artifact mitigation techniques to further reduce stimulation artifacts.

Signal recovery from stimulation artifacts in intracranial recordings with dictionary learning

Objective. Electrical stimulation of the human brain is commonly used for eliciting and inhibiting neural activity for clinical diagnostics, modifying abnormal neural circuit function for therapeutics, and interrogating cortical connectivity. However, recording electrical signals with concurrent stimulation results in dominant electrical artifacts that mask the neural signals of interest. Here we develop a method to reproducibly and robustly recover neural activity during concurrent stimulation. We concentrate on signal recovery across an array of electrodes without channel-wise fine-tuning of the algorithm. Our goal includes signal recovery with trains of stimulation pulses, since repeated, high-frequency pulses are often required to induce desired effects in both therapeutic and research domains. We have made all of our code and data publicly available. Approach. We developed an algorithm that automatically detects templates of artifacts across many channels of recording, creating a dictionary of learned templates using unsupervised clustering. The artifact template that best matches each individual artifact pulse is subtracted to recover the underlying activity. To assess the success of our method, we focus on whether it extracts physiologically interpretable signals from real recordings. Main results. We demonstrate our signal recovery approach on invasive electrophysiologic recordings from human subjects during stimulation. We show the recovery of meaningful neural signatures in both electrocorticographic (ECoG) arrays and deep brain stimulation (DBS) recordings. In addition, we compared cortical responses induced by the stimulation of primary somatosensory (S1) by natural peripheral touch, as well as motor cortex activity with and without concurrent S1 stimulation. Significance. Our work will enable future advances in neural engineering with simultaneous stimulation and recording.

Quantifying volume conducted potential using stimulation artefact in cortico-cortical evoked potentials

BackgroundCortico-cortical evoked potentials (CCEP) are a technique using low frequency stimulation to infer regions of cortical connectivity in patients undergoing Stereo-electroencephalographic (SEEG) monitoring for refractory epilepsy. Little attention has been given to volume conducted components of CCEP responses, and how they may inflate CCEP connectivity. New MethodUsing data from 37 SEEG-CCEPs patients, a novel method was developed to quantify stimulation artefact by measuring the peak-to-peak voltage difference in the first 10 ms after CCEP stimulation. Early responses to CCEP stimulation were also quantified by calculating the root mean square of the 10−100 ms period after each stimulation pulse. Both the early CCEP responses and amplitude of stimulation artefact were regressed by physical distance, stimulation waveform, stimulation intensity and tissue type to identify conduction related properties. ResultsBoth stimulation artefact and early responses were correlated strongly with the inverse square of the distance from the stimulating electrode. Once corrected for the inverse square distance from the electrode, stimulation artefact and CCEP responses showed a linear relationship, indicating a volume conducted component. Comparison with existing methodsThis is the first study to use stimulation artefact to quantify volume conducted potentials, and is the first to quantify volume conducted potentials in SEEG. A single prior study utilizing electrocorticography has shown that parts of early CCEP responses are due to volume conduction. ConclusionsThe linear relationship between stimulation artefact amplitude and CCEP early responses, once corrected for distance, suggests that stimulation artefact can be used as a measure to quantify the volume conducted components.

Sleep disruption is not observed with brain-responsive neurostimulation for epilepsy.

ObjectiveNeurostimulation devices that deliver electrical impulses to the nervous system are widely used to treat seizures in patients with medically refractory epilepsy, but the effects of these therapies on sleep are incompletely understood. Vagus nerve stimulation can contribute to obstructive sleep apnea, and thalamic deep brain stimulation can cause sleep disruption. A device for brain‐responsive neurostimulation (RNS® System, NeuroPace, Inc) is well tolerated in clinical trials, but potential effects on sleep are unknown.MethodsSix adults with medically refractory focal epilepsy treated for at least six months with the RNS System underwent a single night of polysomnography (PSG). RNS System lead locations included mesial temporal and neocortical targets. Sleep stages and arousals were scored according to standard guidelines. Stimulations delivered by the RNS System in response to detections of epileptiform activity were identified by artifacts on scalp electroencephalography.ResultsOne subject was excluded for technical reasons related to unreliable identification of stimulation artifact on EEG during PSG. In the remaining five subjects, PSG showed fragmented sleep with frequent arousals. Arousal histograms aligned to stimulations revealed a significant peak in arousals just before stimulation. In one of these subjects, the arousal peak began before stimulation and extended ~1 seconds after stimulation. A peak in arousals occurring only after stimulation was not observed.SignificanceIn this small cohort of patients, brain‐responsive neurostimulation does not appear to disrupt sleep. If confirmed in larger studies, this could represent a potential clinical advantage of brain‐responsive neurostimulation over other neurostimulation modalities.

Machine learning validation of EEG+tACS artefact removal

Objective. Electroencephalography (EEG) recorded during transcranial alternating current simulation (tACS) is highly desirable in order to investigate brain dynamics during stimulation, but is corrupted by large amplitude stimulation artefacts. Artefact removal algorithms have been presented previously, but with substantial debates on their performance, utility, and the presence of any residual artefacts. This paper investigates whether machine learning can be used to validate artefact removal algorithms. The postulation is that residual artefacts in the EEG after cleaning would be independent of the experiment performed, making it impossible to differentiate between different parts of an EEG+tACS experiment, or between different behavioural tasks performed. Approach. Ten participates undertook two tasks (nBack and backwards digital recall) during simultaneous EEG+tACS, exercising different aspects of working memory. Stimulations during no task and sham conditions were also performed. A previously reported tACS artefact removal algorithm from our group was used to clean the EEG and a linear discriminant analysis was trained on the cleaned EEG to differentiate different parts of the experiment. Main results. Baseline, baseline during tACS, working memory task without tACS, and working memory task with tACS data segments could be differentiated with accuracies ranging from 65%–94%, far exceeding chance levels. EEG from the nBack and backwards digital recall tasks could be separated during stimulation, with an accuracy exceeding 72%. If residual tACS artefacts remained after the EEG cleaning these did not dominate the classification process. Significance. This helps in building confidence that true EEG information is present after artefact removal. Our methodology presents a new approach to validating tACS artefact removal approaches.

Prediction of Ankle Dorsiflexion Moment by Combined Ultrasound Sonography and Electromyography.

To provide an effective and safe therapy to persons with neurological impairments, accurate determination of their residual volitional ability is required. However, accurate measurement of the volitional ability, through non-invasive means (e.g., electromyography), is challenging due to signal interference from neighboring muscles or stimulation artifacts caused by functional electrical stimulation (FES). In this work, a new model-based intention detection method that combines signals from both surface electromyography (sEMG) and ultrasound (US) sonography to predict isometric volitional ankle dorsiflexion moment is proposed. The work is motivated by the fact that the US-derived signals, unlike sEMG, provide direct visualization of the muscle activity, and hence may enhance the prediction accuracy of the volitional ability, when combined with sEMG. The weighted summation of sEMG and US imaging signals, measured on the tibialis anterior muscle, is utilized as an input to a modified Hill-type neuromusculoskeletal model that predicts the ankle dorsiflexion moment. The effectiveness of the proposed model-based moment prediction method is validated by comparing the predicted and the measured ankle joint moments. The new modeling method has a better prediction accuracy compared to a prediction model that uses sole sEMG or sole US sonography. This finding provides a more accurate approach to detect movement intent in the lower limbs. The approach can be potentially beneficial for the development of US sonography-based robotic or FES-assisted rehabilitation devices.

Evaluation of algorithms for correction of transcranial magnetic stimulation-induced artifacts in electroencephalograms.

Transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) is widely used to study the reactivity and connectivity of brain regions for clinical or research purposes. The electromagnetic pulse of the TMS device generates at the instant of administration an artifact of large amplitude and a duration up to tens of milliseconds that overlaps with brain activity. Methods for TMS artifact correction have been developed to remove the artifact and recover the underlying, immediate response of the cerebral cortex to the magnetic stimulus. In this study, four such algorithms are evaluated. Since there is no ground truth for the masked brain activity, pilot data formed from the superposition of the isolated TMS artifact on EEG brain activity are used to evaluate the performance of the algorithms. Different scenarios of TMS-EEG experiments are considered for the evaluation: TMS at resting state, TMS inducing epileptiform discharges, and TMS administered during epileptiform discharges. We show that a proposed gap filling method is able to reproduce qualitative characteristics and, in many cases, closely resemble the hidden EEG signal. Finally, shortcomings of the TMS correction algorithms as well as the pilot data approach are discussed. Graphical abstract The transcranial magnetic stimulation (TMS) artifact on the electroencephalogram (EEG) and its correction.

An impedance matching algorithm for common-mode interference removal in vagus nerve recordings

BackgroundThe peripheral nervous system is involved in a multitude of physiological functions. Recording neural signals provides information that can be used by diagnostic bioelectronic medicine devices, closed-loop neuromodulation therapies and other neuroprosthetic applications. The ability to accurately record these signals is challenging, due to the presence of various biological and instrument-related interference sources. New methodWe developed a common-mode interference rejection algorithm based on an impedance matching approach for bipolar cuff electrodes. Two unipolar channels were recorded from the two electrode contacts of a bipolar cuff. The impedance mismatch was estimated and used to correct one of the two channels. ResultsWhen applied to electrocardiographic (ECG) artifacts collected from three mice using CorTec electrodes, the algorithm reduced the interference to noise ratio (INR) over simple subtraction by 12 dB on average. The algorithm also reduced the INR of stimulation artifacts in recordings from three rats collected using flexible electrodes by an additional 2.4 dB. In the same experiments evoked electromyographic (EMG) interference was suppressed by 1.3 dB. Comparison with existing methodsSimple subtraction is the common approach for reducing common-mode interference in bipolar recordings, however impedance mismatches that exist or emerge compromise its efficiency. ConclusionsThe algorithm significantly reduced the common-mode interference from ECG artifacts, stimulation artifacts, and evoked EMG interference, while retaining neural signals, in two animal models and two recording setups. This approach can be used in a variety of different neurophysiological setups to remove common-mode interference from a variety of sources.

A high-performance 4 nV (√Hz)−1 analog front-end architecture for artefact suppression in local field potential recordings during deep brain stimulation

Objective. Recording of local field potentials (LFPs) during deep brain stimulation (DBS) is necessary to investigate the instantaneous brain response to stimulation, minimize time delays for closed-loop neurostimulation and maximise the available neural data. To our knowledge, existing recording systems lack the ability to provide artefact-free high-frequency (>100 Hz) LFP recordings during DBS in real time primarily because of the contamination of the neural signals of interest by the stimulation artefacts. Approach. To solve this problem, we designed and developed a novel, low-noise and versatile analog front-end (AFE) that uses a high-order (8th) analog Chebyshev notch filter to suppress the artefacts originating from the stimulation frequency. After defining the system requirements for concurrent LFP recording and DBS artefact suppression, we assessed the performance of the realised AFE by conducting both in vitro and in vivo experiments using unipolar and bipolar DBS (monophasic pulses, amplitude ranging from 3 to 6 V peak-to-peak, frequency 140 Hz and pulse width 100 µs). A full performance comparison between the proposed AFE and an identical AFE, equipped with an 8th order analog Bessel notch filter, was also conducted. Main results. A high-performance, 4 nV ()−1 AFE that is capable of recording nV-scale signals was designed in accordance with the imposed specifications. Under both in vitro and in vivo experimental conditions, the proposed AFE provided real-time, low-noise and artefact-free LFP recordings (in the frequency range 0.5–250 Hz) during stimulation. Its sensing and stimulation artefact suppression capabilities outperformed the capabilities of the AFE equipped with the Bessel notch filter. Significance. The designed AFE can precisely record LFP signals, in and without the presence of either unipolar or bipolar DBS, which renders it as a functional and practical AFE architecture to be utilised in a wide range of applications and environments. This work paves the way for the development of externalized research tools for closed-loop neuromodulation that use low- and higher-frequency LFPs as control signals.

A High DR, DC-Coupled, Time-Based Neural-Recording IC With Degeneration R-DAC for Bidirectional Neural Interface

The bidirectional neural interface is essential to realize the closed-loop neuromodulation, which is the core of next-generation neurological devices. For the bidirectional neural interface, a recording circuit with a high dynamic range (DR) is required to record the neural signal while stimulating the neuronal cells simultaneously. This article presents a voltage-controlled oscillator (VCO)-based neural-recording IC, which directly quantizes the input signal and achieves a large DR to process the small-amplitude neural signal in the presence of the large-amplitude stimulation artifact (SA). A feedback-controlled source degeneration is applied to the input transconductor circuit ( $G_{\mathrm {m, in}}$ ) by using a resistor digital-to-analog converter (R-DAC). It mitigates the circuit nonlinearity, resulting in a large signal-to-noise-and-distortion ratio (SNDR) and a high input impedance ( $Z_{\mathrm {in}}$ ). The implemented neural-recording IC achieves 81.3-dB SNDR over 200-Hz signal bandwidth and 200-mVpp maximum allowable input range with consuming 3.9 $\mu \text{W}$ per channel. The in-vitro measurement with prerecorded neural signal demonstrates that the original neural signal is well preserved in the presence of the large-amplitude artifact without any saturation or significant distortion.

Blind Localization of Heating in Neural Tissues Induced by a Train of the Infrared Pulse Laser.

Introduction: Recently, infrared lasers (wavelengths larger than 1100 nm) have been applied to stimulate neural tissues. Infrared neural stimulation (INS) has some advantages over conventional electric stimulation, including contact-free delivery, spatial precision, and lack of stimulation artifacts. In this study and based on a photothermal mechanism, we applied the heat diffusion equation to study temperature variation of a biological phantom during INS. In addition, the impact of laser parameters on spatially localized heating induced by 2 different infrared wavelengths were studied. Methods: We studied the localization of INS inside a phantom similar to cortical neural tissue. First, we analytically solved the heat diffusion equation to study the distribution of temperature inside this phantom. Then, the accuracy of analytical results was verified by heating the phantom using amplitude-modulated infrared lasers (lambda= 1450 and 1500 nm, the energy between 2 and 5 mJ and pulse duration up to 20 ms). The laser light was directed to sample by a multimode optical fiber (NA=0.22, core size= 200 microns). Finally, the impacts of laser properties on the spatial resolution of infrared heating were discerned. Results: In order to verify analytical results, we measured the maximum temperatures of the phantom during illumination of lasers and compared them with analytical results. The analytical results were in agreement with the experimental results. The effects of laser beam properties such as pulse duration, energy and repetition rate frequency on the spatial resolution were investigated. The results indicated that the spatial resolution of INS can be smaller than one millimeter. Conclusion: Here, the influences of laser properties on the localization of INS inside a biological phantom were studied. These results can be applied to improve the spatial selectivity of the peripheral nerve interface.

On the neural basis of deep brain stimulation evoked resonant activity

Objective: Deep brain stimulation can be a remarkably effective treatment for Parkinson’s disease and other conditions; however, an electrophysiological feedback signal is needed to improve surgical accuracy and for optimising therapy according to patient needs. Evoked responses may provide such a signal, although it is crucial to determine that recorded potentials are of neural origin and not a consequence of stimulation artefacts. Here, we use several in vitro and in vivo methods to establish the neural basis of resonant deep brain stimulation evoked activity. Approach: Recordings were obtained from deep brain stimulation electrodes in saline, in feline brain regions not expected to produce resonant neural responses, and in fourteen subthalamic nuclei in people with Parkinson’s disease following stimulation with 60 μs per phase biphasic current pulses with different polarities. Main results: Electrodes in saline did not exhibit stimulation artefacts beyond 1 ms. Changing the pulse polarity reversed the stimulation artefact. Electrodes in feline brain elicited early latency activity (<5 ms); however, the activity did not resemble a decaying oscillation. Electrodes in human subthalamic nuclei evoked resonant neural activity that was not reversed by changing the pulse polarity. The latency of resonant peaks from stimuli with opposing polarities differed by about the expected amount and were strongly correlated (ρ = 0.998, p < 0.001). Resonant peak amplitudes were also strongly correlated (ρ = 0.945, p < 0.001). Significance: The absence of resonant activity in recordings from electrodes in saline and feline brain, in addition to findings that resonant activity occurs in the subthalamic nucleus but not neighbouring white matter regions, demonstrates that such activity is not an artefact of the stimulation and recording system. Furthermore, non-reversal of resonant activity with changing pulse polarity in human subthalamic nuclei indicates that it is independent from stimulation artefact. Thus, these methods provide strong evidence of the neural basis of deep brain stimulation evoked resonant activity.

A Method for Suppressing Electrical Stimulation Artifacts from Electromyography.

When surface electromyography (EMG) signal is used in a real-time functional electrical stimulation (FES) system for feedback control, the artifact from electrical stimulation is a key challenge for EMG signal processing. To address this challenge, this study proposes a novel method to suppress stimulation artifacts in the EMG-driven closed-loop FES system. The proposed method is inspired by an experimental study that compares artifacts generated by electrical stimulations with different current intensities. It is found that (1) spikes of stimulation artifacts are susceptible to the current intensity and (2) tailing components are similar under different current intensities. Based on these observations, the proposed method combines the blanking and template subtracting strategies for suppressing stimulation artifact. The length of blanking window for suppressing the stimulation spike is adaptively determined by a spike detection algorithm and the first-order derivative analysis of signal. An autoregressive model is used to estimate the tailing part of stimulation artifact, which is an adaptive template for subtracting the artifact. The proposed method is evaluated on both semi-synthetic and experimental datasets. Verified on the semi-synthetic dataset, the proposed method achieves better performance than the classic blanking method. Validated on the experimental dataset, the proposed method substantially decreases the power of stimulation artifact in the EMG. These results indicate that the proposed method can effectively suppress the stimulation artifact while retains the useful EMG signal for an EMG-driven FES system.

Towards Safe Infrared Nerve Stimulation: A Systematic Experimental Approach.

Neural activation by infrared nerve stimulation (INS) gains growing interest as a potential alternative to conventional electric nerve stimulation, since unambiguous advantages like contact-free operation, enhanced spatial selectivity and lack of (electrical) stimulation artifacts are promising for both future electrophysiological research and clinical application. For the systematic investigation of laser nerve activation, we recently introduced a novel experimental approach. Comprising a defined focused beam profile, it enables remote controlled, contact-free pulsed laser stimulation of the rat sciatic nerve, simultaneous to high-speed temperature measurement in vivo. Up to now, successful neural activation with single laser pulses (2 - 6 mJ) was observed in all performed experiments, however, it strongly depended on the particular nerve location. Hence, we depict the investigation of spatial dependency of the nerve response and identify `regions of excitability' on the nerve surface, that are highly susceptible to INS. By means of thermal imaging, we simultaneously monitored the nerve surface temperature, where we observed progressing temperature build-up during single pulse stimulation with repetition rates above 4 Hz. In this work, we present current results of our ongoing research.

Measuring Sharp Waves and Oscillatory Population Activity With the Genetically Encoded Calcium Indicator GCaMP6f.

GCaMP6f is among the most widely used genetically encoded calcium indicators for monitoring neuronal activity. Applications are at both the cellular and population levels. Here, we explore two important and under-explored issues. First, we have tested if GCaMP6f is sensitive enough for the detection of population activity with sparse firing, similar to the sensitivity of the local field potential (LFP). Second, we have tested if GCaMP6f is fast enough for the detection of fast network oscillations critical for the encoding and consolidation of memory. We have focused this study on the activity of the hippocampal network including sharp waves (SWs), carbachol-induced theta oscillations, and interictal-like spikes. We compare simultaneous LFP and optical GCaMP6f fluorescent recordings in Thy1-GCaMP6f mouse hippocampal slices. We observe that SWs produce a clear population GCaMP6f signal above noise with an average magnitude of 0.3% ΔF/F. This population signal is highly correlated with the LFP, albeit with a delay of 40.3 ms (SD 10.8 ms). The population GCaMP6f signal follows the LFP evoked by 20 Hz stimulation with high fidelity, while electrically evoked oscillations up to 40 Hz were detectable with reduced amplitude. GCaMP6f and LFP signals showed a large amplitude discrepancy. The amplitude of GCaMP6f fluorescence increased by a factor of 28.9 (SD 13.5) between spontaneous SWs and carbachol-induced theta bursts, while the LFP amplitude increased by a factor of 2.4 (SD 1.0). Our results suggest that GCaMP6f is a useful tool for applications commonly considered beyond the scope of genetically encoded calcium indicators. In particular, population GCaMP6f signals are sensitive enough for detecting synchronous network events with sparse firing and sub-threshold activity, as well as asynchronous events with only a nominal LFP. In addition, population GCaMP6f signals are fast enough for monitoring theta and beta oscillations (<25 Hz). Faster calcium indicators (e.g., GCaMP7) will further improve the frequency response for the detection of gamma band oscillations. The advantage of population optical over LFP recordings are that they are non-contact and free from stimulation artifacts. These features may be particularly useful for high-throughput recordings and applications sensitive to stimulus artifact, such as monitoring responses during continuous stimulation.

Device-device interaction

With a continuous increase in the approval of cardiac implantable electronic devices (CIED), not only pacemakers (PM) and implantable cardioverter defibrillators (ICD) but especially devices for treating chronic heart failure, more and more possibilities of device-device interactions arise, which in isolated cases can lead to death of the patient. Because of the still low numbers of patients overall, there are very few scientific studies and only isolated case reports on this topic. Devices which are at risk of interaction with a previously implanted PM are wearable cardioverter defibrillators (WCD) and subcutaneous ICDs (S-ICD). These two devices both use the surface electrocardiogram (ECG) in their algorithm for detecting ventricular arrhythmia. These surface ECGs seem to be prone to unipolar pacemaker stimulation artefacts. By correct programming of implanted pacemakers in the bipolar stimulation mode it is possible to avoid ECG artefacts and inadequate treatment. In baroreceptor activation therapy (BAT) there seem to be no device interactions so far, even though this device shows substantial highly frequent artefacts in the ECG. The cardiac contractility modulation (CCM) system has also until now not shown interactions with transvenous or subcutaneous ICD devices, even though randomized trials are missing.

Diffusion geometry approach to efficiently remove electrical stimulation artifacts in intracranial electroencephalography

Objective. Cortical oscillations, electrophysiological activity patterns, associated with cognitive functions and impaired in many psychiatric disorders can be observed in intracranial electroencephalography (iEEG). Direct cortical stimulation (DCS) may directly target these oscillations and may serve as therapeutic approaches to restore functional impairments. However, the presence of electrical stimulation artifacts in neurophysiological data limits the analysis of the effects of stimulation. Currently available methods suffer in performance in the presence of nonstationarity inherent in biological data. Approach. Our algorithm, shape adaptive nonlocal artifact removal (SANAR) is based on unsupervised manifold learning. By estimating the Euclidean median of k-nearest neighbors of each artifact in a nonlocal fashion, we obtain a faithful representation of the artifact which is then subtracted. This approach overcomes the challenges presented by nonstationarity. Main results. SANAR is effective in removing stimulation artifacts in the time domain while preserving the spectral content of the endogenous neurophysiological signal. We demonstrate the performance in a simulated dataset as well as in human iEEG data. Using two quantitative measures, that capture how much of information from endogenous activity is retained, we demonstrate that SANAR’s performance exceeds that of one of the widely used approaches, independent component analysis, in the time domain as well as the frequency domain. Significance. This approach allows for the analysis of iEEG data, single channel or multiple channels, during DCS, a crucial step in advancing our understanding of the effects of periodic stimulation and developing new therapies.

Causally Investigating Cortical Dynamics and Signal Processing by Targeting Natural System Attractors With Precisely Timed (Electrical) Stimulation.

Electrical stimulation is a promising tool for interacting with neuronal dynamics to identify neural mechanisms that underlie cognitive function. Since effects of a single short stimulation pulse typically vary greatly and depend on the current network state, many experimental paradigms have rather resorted to continuous or periodic stimulation in order to establish and maintain a desired effect. However, such an approach explicitly leads to forced and “unnatural” brain activity. Further, continuous stimulation can make it hard to parse the recorded activity and separate neural signal from stimulation artifacts. In this study we propose an alternate strategy: by monitoring a system in realtime, we use the existing preferred states or attractors of the network and apply short and precise pulses in order to switch between those states. When pushed into one of its attractors, one can use the natural tendency of the system to remain in such a state to prolong the effect of a stimulation pulse, opening a larger window of opportunity to observe the consequences on cognitive processing. To elaborate on this idea, we consider flexible information routing in the visual cortex as a prototypical example. When processing a stimulus, neural populations in the visual cortex have been found to engage in synchronized gamma activity. In this context, selective signal routing is achieved by changing the relative phase between oscillatory activity in sending and receiving populations (communication through coherence, CTC). In order to explore how perturbations interact with CTC, we investigate a network of interneuronal gamma (ING) oscillators composed of integrate-and-fire neurons exhibiting similar synchronization and signal routing phenomena. We develop a closed-loop stimulation paradigm based on the phase-response characteristics of the network and demonstrate its ability to establish desired synchronization states. By measuring information content throughout the model, we evaluate the effect of signal contamination caused by the stimulation in relation to the magnitude of the injected pulses and intrinsic noise in the system. Finally, we demonstrate that, up to a critical noise level, precisely timed perturbations can be used to artificially induce the effect of attention by selectively routing visual signals to higher cortical areas.

Development of a Closed-Loop Stimulator for Laryngeal Reanimation: Part 2. Device Testing in the Canine Model of Laryngeal Paralysis.

Laryngeal paralysis of central or peripheral origin can potentially be treated using functional electrical stimulation (FES) of laryngeal muscles. Experiments in canines (dogs) were performed using implant prototypes capable of closed-loop FES to refine engineering designs and specifications, test surgical approaches for implantation, and better understand the in vivo effects of laryngeal muscle stimulation on short- and long-term glottic function. Prospective, laboratory. We designed and tested a series of microprocessor-based implantable devices that can stimulate glottic opening or closing based on input from physiological control signals (real-time processing of electromyographic [EMG] signals). After acute device testing experiments, 2 dogs were implanted for 8 and 24 months, with periodic testing of closed-loop laryngeal muscle stimulation triggered from EMG signals. In total, 5 dogs were tested for the effects of laryngeal muscle stimulation on vocal fold (VF) posturing in larynges with nerve supplies that were intact (7 VFs), synkinetically reinnervated (2 VFs), or chronically denervated (1 VF). In 3 cases, the stimulation was combined with airflow-driven phonation to study the consequent modulation of phonatory parameters. Initial device prototypes used inductive coupling for power and communication, while later iterations used battery power and infrared light communication (detailed descriptions are provided in the Part 1 companion paper). Two animals were successfully implanted with the inductively powered units, which operated until removed at 8 months in 1 animal or for more than 16 months in the second animal. Surgically, the encapsulated implants were well tolerated, and procedures for placing, attaching, and connecting the devices were developed. To simulate EMG control signals in anesthetized animals, we created 2 types of nerve/muscle signal sources. In one approach, a neck muscle had a cuff electrode placed on its motor nerve that was connected to transdermal electrical connection ports for periodic testing. In the second approach, the recurrent laryngeal nerve on one side of the larynx was stimulated to generate a VF EMG signal, which was then used to trigger FES of the paralyzed contralateral side (eg, restoring VF movement symmetry). Implant testing identified effective stimulation parameters and closed-loop stimulation artifact rejection techniques for FES of both healthy and paralyzed VFs. Stimulation levels effective for VF adduction did not cause signs of discomfort during awake testing. Our inductive and battery-powered prototypes performed effectively during in vivo testing, and the 2 units that were implanted for long-term evaluation held up well. As a proof of concept, we demonstrated that elicited neck strap muscle or laryngeal EMG potentials could be used as a control signal for closed-loop stimulation of laryngeal adduction and vocal pitch modulation, depending on electrode positioning, and that VFs were stimulable in the presence of synkinetic reinnervation or chronic denervation.

Dual responsive neurostimulation implants for epilepsy.

Closed-loop brain-responsive neurostimulation via the RNS System is a treatment option for adults with medically refractory focal epilepsy. Using a novel technique, 2 RNS Systems (2 neurostimulators and 4 leads) were successfully implanted in a single patient with bilateral parietal epileptogenic zones. In patients with multiple epileptogenic zones, this technique allows for additional treatment options. Implantation can be done successfully, without telemetry interference, using proper surgical planning and neurostimulator positioning.Trajectories for the depth leads were planned using neuronavigation with CT and MR imaging. Stereotactic frames were used for coordinate targeting. Each neurostimulator was positioned with maximal spacing to avoid telemetry interference while minimizing patient discomfort. A separate J-shaped incision was used for each neurostimulator to allow for compartmentalization in case of infection. In order to minimize surgical time and risk of infection, the neurostimulators were implanted in 2 separate surgeries, approximately 3 weeks apart.The neurostimulators and leads were successfully implanted without adverse surgical outcomes. The patient recovered uneventfully, and the early therapy settings over several months resulted in preliminary decreases in aura and seizure frequency. Stimulation by one of the neurostimulators did not result in stimulation artifacts detected by the contralateral neurostimulator.