Closed-loop Stimulation Research Articles

Closed-Loop Auditory Stimulation (CLAS) During Sleep Augments Language and Discovery Learning

Background/Objectives: Slow oscillation (SO) brainwaves observed during sleep have been shown to reflect the process of memory consolidation, that underlies the critical role of sleep in learning, memory, and other cognitive functions. Closed-loop auditory stimulation (CLAS) uses tones presented in phase with SOs to increase their amplitude and number, along with other brainwave signatures related to memory consolidation. Prior studies have found that CLAS maximizes the ability to perform rote memorization tasks, although this remains controversial. The present study examined whether CLAS affects a broader range of learning tasks than has been tested previously, including a rote language learning task requiring basic memorization and also two discovery learning tasks requiring insight, hypothesis testing, and integration of experience, all processes that benefit from memory consolidation. Methods: Twenty-eight healthy participants performed language and discovery learning tasks before sleeping in our laboratory for three continuous nights per week over two weeks, with verum or control CLAS using a prototype NeuroGevity system (NeuroGeneces, Inc., Santa Fe, NM, USA) in a crossed, randomized, double-blind manner. Results: Language learning showed a 35% better word recall (p = 0.048), and discovery learning showed a 26% better performance (p < 0.001) after three continuous nights of CLAS vs. control. EEG measures showed increased SO amplitude and entrainment, SO-spindle coupling, and other features that may underlie the learning benefits of CLAS. Conclusions: Taken together, the present results show that CLAS can alter brain dynamics and enhance learning, especially in complex discovery learning tasks that may benefit more from memory consolidation compared with rote word pair or language learning.

MCLAS adaptively rescues disease-specific sleep and wake phenotypes in neurodegeneration

Sleep alterations are hallmarks of prodromal Alzheimer’s (AD) and Parkinson’s disease (PD), with fundamental neuropathological processes of both diseases showing susceptibility of change upon deep sleep modulation. However, promising pharmacological deep sleep enhancement results are hindered by specificity and scalability issues, thus advocating for noninvasive slow-wave activity (SWA) boosting methods to investigate the links between deep sleep and neurodegeneration. Accordingly, we have recently introduced mouse closed-loop auditory stimulation (mCLAS), which is able to successfully boost SWA during deep sleep in neurodegeneration models. Here, we aim at further exploring mCLAS’ acute effect onto disease-specific sleep and wake alterations in AD (Tg2576) and PD (M83) mice. We found that mCLAS adaptively rescues pathological sleep and wake traits depending on the disease-specific impairments observed at baseline in each model. Notably, in AD mice mCLAS significantly increases NREM long/short bout ratio, decreases vigilance state distances by decreasing transition velocities and increases the percentage of cumulative time spent in NREM sleep in the last three hours of the dark period. Contrastingly, in PD mice mCLAS significantly decreases NREM sleep consolidation, by potentiating faster and more frequent transitions between vigilance states, decreases average EMG muscle tone during REM sleep and increases alpha power in WAKE and NREM sleep. Overall, our results indicate that mCLAS selectively prompts an acute alleviation of neurodegeneration-associated sleep and wake phenotypes, by either potentiating sleep consolidation and vigilance state stability in AD or by rescuing bradysomnia and decreasing cortical hyperexcitability in PD. Further experiments assessing the electrophysiological, neuropathological and behavioural long-term effects of mCLAS in neurodegeneration may majorly impact the clinical establishment of sleep-based therapies.

Thalamic Local Field Potentials and Closed-Loop Deep Brain Stimulation in Orthostatic Tremor.

Orthostatic tremor (OT) is a rare movement disorder characterized by a feeling of unsteadiness and a high-frequency tremor in the legs (13-18 Hz) relieved by sitting or walking. The aims were to study the brain electrophysiology captured chronically in a person with medication-refractory OT while standing and walking and in the semi-recumbent position using bilateral ventral intermedius nucleus deep brain stimulation (DBS) (Medtronic Percept PC) and to describe the clinical use of closed-loop DBS. A sensing survey was used to capture baseline local field potentials (LFPs) while standing. Livestreamed LFPs were synchronized with data collected from two accelerometers (legs) and gait analysis during OFF stimulation and continuous and closed-loop DBS. Strong oscillatory coupling between thalamic LFP and leg tremor with significant coherence at 14.65 Hz was found during weight-bearing. Single-threshold adaptive DBS (sensing at this frequency) was superior to continuous stimulation in reducing tremor and stimulation-related gait ataxia. This study provides new insights into both the pathophysiology and management of OT. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

An adaptive dynamic surface control strategy for suppressing pathological oscillations in a neural mass model

Pathological oscillations that have a frequency in the [Formula: see text] band are widely recognized to be involved in Parkinson’s disease. Now, we present an adaptive dynamic surface control strategy for suppressing pathological oscillations that exist in the Parkinsonian state. First, the interactions between the subthalamic nucleus and external globus pallidus are fully considered and establish a neural mass model of Parkinson’s disease. Next, by reasonable state transformation, suppressing pathological oscillations can be converted into a tracking control study of a pure-feedback nonlinear system. Moreover, the dynamic surface control technique adopted reduces the dimensionality of the neural network input and effectively eliminates the complexity explosion problem commonly associated with existing methods. By Lyapunov stability analysis, it can be obtained that all the signals of the resulting closed-loop system are bounded, and the tracking error converges to a small neighborhood of zero. Finally, the simulation results demonstrate the effectiveness of the designed control strategy. This work may provide an effective approach to closed-loop deep brain stimulation optimization for the alleviation of the Parkinsonian state.

Effect of closed-loop vibration stimulation on sleep quality for poor sleepers.

Recent studies have investigated the autonomic modulation method using closed-loop vibration stimulation (CLVS) as a novel strategy for enhancing sleep quality. This study aimed to explore the effects of CLVS on sleep quality, autonomic regulation, and brain activity in individuals with poor sleep quality. Twenty-seven participants with poor sleep quality (Pittsburgh sleep quality index >5) underwent two experimental sessions using polysomnography and a questionnaire, one with CLVS (STIM) and the other without (SHAM). Sleep macrostructure analysis first showed that CLVS significantly reduced the total time, proportion, and average duration of waking after sleep onset. These beneficial effects were paralleled by significantly increased self-reported sleep quality. Moreover, there was a significant increase in the normalized high-frequency (nHF) and electroencephalography relative powers of delta activity during N3 sleep under STIM. Additionally, coherence analysis between nHF and delta activity revealed strengthened coupling between cortical and cardiac oscillations. This study demonstrated that CLVS significantly improves sleep quality in individuals with poor sleep quality by enhancing both subjective and objective measures. These findings suggest that CLVS has the potential to be a practical, noninvasive tool for enhancing sleep quality in individuals with sleep disturbances, offering an effective alternative to pharmacological treatments.

Orbit symmetry breaking in MXene implements enhanced soft bioelectronic implants.

Bioelectronic implants featuring soft mechanics, excellent biocompatibility, and outstanding electrical performance hold promising potential to revolutionize implantable technology. These biomedical implants can record electrophysiological signals and execute direct therapeutic interventions within internal organs, offering transformative potential in the diagnosis, monitoring, and treatment of various pathological conditions. However, challenges remain in improving excessive impedance at the bioelectronic-tissue interface and thus the efficacy of electrophysiological signaling and intervention. Here, we devise orbit symmetry breaking in MXene (a low-cost scalability, biocompatible, and conductive two dimensionally layered material, which we refer to as OBXene), which exhibits low bioelectronic-tissue impedance, originating from the out-of-plane charge transfer. Furthermore, the Schottky-induced piezoelectricity stemming from the asymmetric orbital configuration of OBXene facilitates interlayered charge transport in the device. We report an OBXene-based cardiac patch applied on the left ventricular epicardium of both rodent and porcine models to enable spatiotemporal epicardium mapping and pacing while coupling the wireless and battery-free operation for long-term real-time recording and closed-loop stimulation.

Improving lung function capacity in patients with chronotropic incompetence using closed loop stimulation: A randomized crossover study

BackgroundChronotropic incompetence (CI) severely limits exercise tolerance due to impaired heart rate responses. This study investigated whether pacemaker with closed-loop stimulation (DDD-CLS) pacing, which provides rate acceleration in response to exertion, could enhance lung function and cardiopulmonary capacity compared pacemaker without CLS pacing in patients with CI. MethodsThis randomized crossover trial included 32 patients with CI who were compared to each CLS and DDD pacing over 2 months. Spirometry was used to assess the forced vital capacity (FVC). Cycling-based cardiopulmonary exercise testing (CPET) measured peak oxygen uptake (peak VO2). ResultsDDD-CLS pacing yielded significantly higher FVC (2.2 ± 0.8 L) versus DDD pacing (2.0 ± 0.7 L), p = 0.036, a 3.2% improvement. Moreover, those in the DDD-CLS mode showed a significantly higher FEV1 of 1.8 ± 0.6 L compared to the DDD mode of 1.7 ± 0.5 L (p = 0.03). Compared with DDD pacing, DDD-CLS pacing also achieved superior peak VO2 (14.9 ± 4.0 vs 12.2 ± 3.7 mlO2/kg/min, p < 0.001) and peak heart rate (106.9 ± 9.7 vs 98.3 ± 19.8 bpm, p = 0.013). DDD-CLS and DDD pacing modes showed significant correlations between FVC and peak VO2 and between peak VO2 and peak heart rate during CPET. ConclusionsCompared with DDD mode, DDD-CLS pacing provided physiological chronotropic support to improve cardiopulmonary function during exertion, which enhanced lung capacity in patients with CI.

Neuromodulation for Neuropathic Pain Syndromes

ABSTRACT OBJECTIVE This article reviews the principles, applications, and emerging trends of neuromodulation as a therapeutic approach for managing painful neuropathic diseases. By parsing evidence for possible mechanisms of action and clinical trial outcomes for various diseases, this article focuses on five common therapy modalities: cutaneous, peripheral nerve, spinal cord, and brain stimulation, and intrathecal drug delivery. LATEST DEVELOPMENTS Recent advances in both invasive and noninvasive neuromodulation for pain have introduced personalized and closed-loop techniques, integrating real-time feedback mechanisms and combining therapies to improve physical and psychosocial function. Novel stimulation waveforms may influence distinct neural tissues to rectify pathologic pain signaling. ESSENTIAL POINTS With appropriate patient selection, peripheral nerve stimulation or epidural stimulation of the spinal cord can provide enduring relief for a variety of chronic pain syndromes. Newer technology using high frequencies, unique waveforms, or closed-loop stimulation may have selective advantages, but our current understanding of therapy mechanisms is very poor. For certain diagnoses and patients who meet clinical criteria, neuromodulation can provide profound, long-lasting relief that significantly improves quality of life. While many therapies are supported by data from large clinical trials, there is a risk of bias as most clinical studies were funded by device manufacturers or insurance companies, which increases the importance of real-world data analysis. Emerging methods like invasive or noninvasive brain stimulation may help us dissect basic mechanisms of pain processing and hold promise for personalized therapies for refractory pain syndromes. Finally, intrathecal delivery of drugs directly to segments of the spinal cord can also modify pain signaling to provide therapy for severe pain syndromes.

Can Neuromodulation Improve Sleep and Psychiatric Symptoms?

In this review, we evaluate recent studies that employ neuromodulation, in the form of non-invasive brain stimulation, to improve sleep in both healthy participants, and patients with psychiatric disorders. We review studies using transcranial electrical stimulation, transcranial magnetic stimulation, and closed-loop auditory stimulation, and consider both subjective and objective measures of sleep improvement. Neuromodulation can alter neuronal activity underlying sleep. However, few studies utilizing neuromodulation report improvements in objective measures of sleep. Enhancements in subjective measures of sleep quality are replicable, however, many studies conducted in this field suffer from methodological limitations, and the placebo effect is robust. Currently, evidence that neuromodulation can effectively enhance sleep is lacking. For the field to advance, methodological issues must be resolved, and the full range of objective measures of sleep architecture, alongside subjective measures of sleep quality, must be reported. Additionally, validation of effective modulation of neuronal activity should be done with neuroimaging.

ID: 344930 Targeting the Conus Medullaris with Closed Loop Stimulation for Chronic Pelvic Pain: A Case Series

ID: 344930 Targeting the Conus Medullaris with Closed Loop Stimulation for Chronic Pelvic Pain: A Case Series

Current and future applications of local field potential-guided programming for Parkinson's disease with the Percept™ rechargeable neurostimulator.

Deep brain stimulation (DBS) has been established as an effective neuromodulatory treatment for Parkinson's disease (PD) with motor complications or refractory tremor. Various DBS devices with unique technology platforms are commercially available and deliver continuous, open-loop stimulation. The Percept™ family of neurostimulators use BrainSense™ technology with five key features to sense local field potentials while stimulating, enabling integration of physiologic data into the routine practice of DBS programming. The newly approved Percept™ rechargeable RC implantable pulse generator offers a smaller, thinner design and reduced recharge time with prolonged recharge interval. In this review, we describe the application of local field potential sensing-based programming in PD and highlight the potential future clinical implementation of closed-loop stimulation using the Percept™ RC implantable pulse generator.

Closed-loop Low-frequency stimulation: seizure reduction and more.

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Dye-Based Fluorescent Organic Nanoparticles, New Promising Tools for Optogenetics.

Dye-based fluorescent organic nanoparticles are a specific class of nanoparticles obtained by nanoprecipitation in water of pure dyes only. While the photophysical and colloidal properties of the nanoparticles strongly depend on the nature of the aggregated dyes, their excellent brightness in the visible and in the near infrared make these nanoparticles a unique and versatile platform for in vivo application. This article examines the promising utilization of these nanoparticles for in vivo optogenetics applications. Their photophysical properties as well as their biocompatibility and their capacity to activate Chrimson opsin in vivo through the fluorescence reabsorption process are demonstrated. Additionally, an illustrative example of employing these nanoparticles in fear reduction in mice through closed-loop stimulation is presented. Through an optogenetic methodology, the nanoparticles demonstrate an ability to selectively manipulate neurons implicated in the fear response and diminish the latter. Dye-based fluorescent organic nanoparticles represent a promising and innovative strategy for optogenetic applications, holding substantial potential in the domain of translational neuroscience. This work paves the way for novel therapeutic modalities for neurological and neuropsychiatricdisorders.

Predictive Modeling of Sleep Slow Oscillation Emergence on the electrode manifold: Toward Personalized Closed-Loop Brain Stimulation.

Sleep slow oscillations (SOs), characteristic of NREM sleep, are causally tied to cognitive outcomes and the health-promoting homeostatic functions of sleep. Due to these known benefits, brain stimulation techniques aiming to enhance SOs are being developed, with great potential to contribute to clinical interventions, as they hold promise for improving sleep functions in populations with identified SO deficits (e.g., mild cognitive impairment). SO-targeting closed-loop stimulation protocols currently strive to identify SO occurrences in real time, a computationally intensive step that can lead to reduced precision (compared to post-hoc detection). These approaches are also often limited to focusing on only one electrode location, thus inherently precluding targeting of SOs that is informed by the overall organization of SOs in space-time. Prediction of SO emergence across the electrode manifold would establish an alternative to online detection, thus greatly advancing the development of personalized and flexible brain stimulation paradigms. This study presents a computational model that predicts SO occurrences at multiple locations across a night of sleep. In combination with our previous study on optimizing brain stimulation protocols using the spatiotemporal properties of SOs, this model contributes to increasing the accuracy of SO targeting in brain stimulation applications. SOs were detected in a dataset of nighttime sleep of 22 subjects (9 females), acquired with polysomnography including 64 EEG channels. Modeling of SO occurrence was achieved for SOs in stage N3, or in a combination of stages N2 and N3 (N2&N3). We study SO emergence at progressively more refined time scales. First, the cumulative SO occurrences in successive sleep cycles were successfully fit with exponentials. Secondly, the SO timing in each individual was modeled with a renewal point process. Using an inverse Gaussian model, we estimated the probability density function of SO timing and its parameters μ (mean) and λ (shape, representing skewness) in successive cycles. We observed a declining trend in the SO count across sleep cycles, which we modeled using a power law relationship. The decay rate per cycle was 1.473 for N3 and 1.139 for N2&N3, with variances of the decay rates across participants being 1 and 0.53, respectively. This pattern mirrors the declining trend of slow wave activity (SWA) across sleep cycles, likely due to the inherent relationship between SWA and SO. Additionally, the SO timing model for N3 showed an increasing trend in the model parameters (μ, λ) across cycles. The increase rate per cycle followed a power law relationship with a rate of 0.83 and an exponential relationship with a rate of 4.59, respectively. The variances of the increase rates were 0.02 for μ and 0.44 for λ across participants. This study establishes a predictive model for SO occurrence during NREM sleep, providing insights into its organization in successive cycles and at different EEG channels, which is relevant to development of personalized stimulation paradigms. These findings imply that personalized model parameters can be estimated by incorporating SO information in the first sleep cycle, and hence SO timing can be predicted before its occurrence with a probability distribution, enabling more precise targeting of SOs.

Novel murine closed-loop auditory stimulation paradigm elicits macrostructural sleep benefits in neurodegeneration.

Boosting slow-wave activity (SWA) by modulating slow waves through closed-loop auditory stimulation (CLAS) might provide a powerful non-pharmacological tool to investigate the link between sleep and neurodegeneration. Here, we established mouse CLAS (mCLAS)-mediated SWA enhancement and explored its effects on sleep deficits in neurodegeneration, by targeting the up-phase of slow waves in mouse models of Alzheimer's disease (AD, Tg2576) and Parkinson's disease (PD, M83). We found that tracking a 2 Hz component of slow waves leads to highest precision of non-rapid eye movement (NREM) sleep detection in mice, and that its combination with a 30° up-phase target produces a significant 15-30% SWA increase from baseline in wild-type (WTAD)and transgenic (TGAD) mice versus a mock stimulation group. Conversely, combining 2 Hz with a 40° phase target yields a significant increase ranging 30-35% in WTPD and TGPD mice. Interestingly, these phase-target-triggered SWA increases are not genotype dependent but strain specific. Sleep alterations that may contribute to disease progression and burden were described in AD and PD lines. Notably, pathological sleep traits were rescued by mCLAS, which elicited a 14% decrease of pathologically heightened NREM sleep fragmentation in TGAD mice, accompanied by a steep decrease in microarousal events during both light and dark periods. Overall, our results indicate that model-tailored phase targeting is key to modulate SWA through mCLAS, prompting the acute alleviation of key neurodegeneration-associated sleep phenotypes and potentiating sleep regulation and consolidation. Further experiments assessing the long-term effect of mCLAS in neurodegeneration may majorly impact the establishment of sleep-based therapies.

Closed-loop auditory stimulation targeting alpha and theta oscillations during REM sleep induces phase-dependent power and frequency changes.

Alpha and theta oscillations characterize the waking human electroencephalogram (EEG) and can be modulated by closed-loop auditory stimulation (CLAS). These oscillations also occur during rapid eye movement (REM) sleep, but their function here remains elusive. CLAS represents a promising tool to pinpoint how these brain oscillations contribute to brain function in humans. Here we investigate whether CLAS can modulate alpha and theta oscillations during REM sleep in a phase-dependent manner. We recorded high-density EEG during an extended overnight sleep period in 18 healthy young adults. Auditory stimulation was delivered during both phasic and tonic REM sleep in alternating 6s ON and 6s OFF windows. During the ON windows, stimuli were phase-locked to four orthogonal phases of ongoing alpha or theta oscillations detected in a frontal electrode. The phases of ongoing alpha and theta oscillations were targeted with high accuracy during REM sleep. Alpha and theta CLAS induced phase-dependent changes in power and frequency at the target location. Frequency-specific effects were observed for alpha trough (speeding up) and rising (slowing down) and theta trough (speeding up) conditions. CLAS-induced phase-dependent changes were observed during both REM sleep substages, even though auditory evoked potentials were very much reduced in phasic compared to tonic REM sleep. This study provides evidence that faster REM sleep rhythms can be modulated by CLAS in a phase-dependent manner. This offers a new approach to investigate how modulation of REM sleep oscillations affects the contribution of this vigilance state to brain function.

Flexible modeling of large-scale neural network stimulation: electrical and optical extensions to The Virtual Electrode Recording Tool for EXtracellular Potentials (VERTEX).

Computational models that predict effects of neural stimulation can be used as a preliminary tool to inform in-vivo research, reducing the costs, time, and ethical considerations involved. However, current models do not support the diverse neural stimulation techniques used in-vivo, including the expanding selection of electrodes, stimulation modalities, and stimulation paradigms. To develop a more comprehensive software, we created several extensions to The Virtual Electrode Recording Tool for EXtracellular Potentials (VERTEX), the MATLAB-based neural stimulation tool from Newcastle University. VERTEX simulates input currents in a large population of multi-compartment neurons within a small cortical slice to model electric field stimulation, while recording local field potentials (LFPs) and spiking activity. Our extensions to its existing electric field stimulation framework include multiple pairs of parametrically defined electrodes and biphasic, bipolar stimulation delivered at programmable delays. To support the growing use of optogenetic approaches for targeted neural stimulation, we introduced a feature that models optogenetic stimulation through an additional VERTEX input function that converts irradiance to currents at optogenetically responsive neurons. Finally, we added extensions to allow complex stimulation protocols including paired-pulse, spatiotemporal patterned, and closed-loop stimulation. We demonstrated our novel features using VERTEX's built-in functionalities, illustrating how these extensions can be used to efficiently and systematically test diverse, targeted, and individualized stimulation patterns.

Using DeepLabCut-Live to probe state dependent neural circuits of behavior with closed-loop optogenetic stimulation.

Closed-loop behavior paradigms enable us to dissect the state-dependent neural circuits underlying behavior in real-time. However, studying context-dependent locomotor perturbations has been challenging due to limitations in molecular tools and techniques for real-time manipulation of spinal cord circuits. We developed a novel closed-loop optogenetic stimulation paradigm that utilizes DeepLabCut-Live pose estimation to manipulate primary sensory afferent activity at specific phases of the locomotor cycle in mice. A compact DeepLabCut model was trained to track hindlimb kinematics in real-time and integrated into the Bonsai visual programming framework. This allowed an LED to be triggered to photo-stimulate sensory neurons expressing channelrhodopsin at user-defined pose-based criteria, such as during the stance or swing phase. Optogenetic activation of nociceptive TRPV1 + sensory neurons during treadmill locomotion reliably evoked paw withdrawal responses. Photoactivation during stance generated a brief withdrawal, while stimulation during swing elicited a prolonged response likely engaging stumbling corrective reflexes. Comparison with Existing Methods: This new method allows for high spatiotemporal precision in manipulating spinal circuits based on the phase of the locomotor cycle. Unlike previous approaches, this closed-loop system can control for the state-dependent nature of sensorimotor responses during locomotion. Integrating DeepLabCut-Live with optogenetics provides a powerful new approach to dissect the context-dependent role of sensory feedback and spinal interneurons in modulating locomotion. This technique opens new avenues for uncovering the neural substrates of state-dependent behaviors and has broad applicability for studies of real-time closed-loop manipulation based on pose estimation. Highlights: Closed-loop system probes state-dependent behaviors at pose-modulated instancesBonsai integrates DeepLabCut models for real-time pose estimation during locomotion Phase-dependent TRPV1 + sensory afferent photostimulation elicits context-specific withdrawal responses.

Closed-loop transcranial electrical stimulation for inhibiting epileptic activity propagation: a whole-brain model study

Closed-loop transcranial electrical stimulation for inhibiting epileptic activity propagation: a whole-brain model study

Negative feedback control of hypothalamic feeding circuits by the taste of food

The rewarding taste of food is critical for motivating animals to eat, but whether taste has a parallel function in promoting meal termination is not well understood. Here, we show that hunger-promoting agouti-related peptide (AgRP) neurons are rapidly inhibited during each bout of ingestion by a signal linked to the taste of food. Blocking these transient dips in activity via closed-loop optogenetic stimulation increases food intake by selectively delaying the onset of satiety. We show that upstream leptin-receptor-expressing neurons in the dorsomedial hypothalamus (DMHLepR) are tuned to respond to sweet or fatty tastes and exhibit time-locked activation during feeding that is the mirror image of downstream AgRP cells. These findings reveal an unexpected role for taste in the negative feedback control of ingestion. They also reveal a mechanism by which AgRP neurons, which are the primary cells that drive hunger, are able to influence the moment-by-moment dynamics of food consumption.