Closed-loop Stimulation Research Articles

Closed-Loop Auditory Stimulation to Guide Respiration: Preliminary Study to Evaluate the Effect on Time Spent in Sleep Initiation during a Nap.

Various stimulation systems to modulate sleep structure and function have been introduced. However, studies on the time spent in sleep initiation (TSSI) are limited. This study proposes a closed-loop auditory stimulation (CLAS) to gradually modulate respiratory rhythm linked to the autonomic nervous system (ANS) activity directly associated with sleep. CLAS is continuously updated to reflect the individual's current respiratory frequency and pattern. Six participants took naps on different days with and without CLAS. The average values of the TSSI are 14.00 ± 4.24 and 9.67 ± 5.31 min in the control and stimulation experiments (p < 0.03), respectively. Further, the values of respiratory instability and heart rate variability differ significantly between the control and stimulation experiments. Based on our findings, CLAS supports the individuals to gradually modulate their respiratory rhythms to have similar characteristics observed near sleep initiation, and the changed respiratory rhythms influence ANS activities, possibly influencing sleep initiation. Our approach aims to modulate the respiratory rhythm, which can be controlled intentionally. Therefore, this method can probably be used for sleep initiation and daytime applications.

Wireless Device with Energy Management for Closed-Loop Deep Brain Stimulation (CLDBS)

Deep brain stimulation (DBS) is an effective and safe medical treatment that improves the lives of patients with a wide range of neurological and psychiatric diseases, and has been consolidated as a first-line tool in the last two decades. Closed-loop deep brain stimulation (CLDBS) pushes this tool further by automatically adjusting the stimulation parameters to the brain response in real time. The main contribution of this paper is a low-size/power-controlled, compact and complete CLDBS system with two simultaneous acquisition channels, two simultaneous neurostimulation channels and wireless communication. Each channel has a low-noise amplifier (LNA) buffer in differential configuration to eliminate the DC signal component of the input. Energy management is efficiently done by the control and communication unit. The battery supports almost 9 h with both the acquisition and stimulation circuits active. If only the stimulation circuit is used as an Open Loop DBS, the battery can hold sufficient voltage for 24 h of operation. The whole system is low-cost and portable and therefore it could be used as a wearable device.

Quasi-LPV positive observer-based control of closed-loop deep brain stimulation systems

Parkinson’s disease is a neurodegenerative disorder in which dopamine (a specific type of neurotransmitter) production by neurons is destroyed in the brain. Therefore, there is less or no production of dopamine in the human brain and the patient has motor and non-motor complications. The main reason for Parkinson’s disease is the basal ganglia (BG) system imbalance. Therefore, it is imperative to find effective control methods to stabilize and regulate the BG system. To control the BG system within the normal oscillating performance, the opinion of closed-loop deep brain stimulation (DBS) system is used as the most innovative surgical treatment. In this paper, a quasi-linear parameter-varying (LPV) model is extracted from two known nonlinear models (the RT model and the modified RT model with 19 dynamics as the state variables of the BG system) as a comprehensive model to result in a more accurate control for the DBS system. The theoretical contributions in this paper are novel because this model is considered as a positive system and the related theories and properties are discussed. In view of this, quasi-LPV positive observer-based control method is designed for two situations; when all parameters are available for estimation (exact observer) and when all accurate parameters cannot be easily accessed (inexact observer). By designing such controllers, the provided algorithm does not need the availability of the scheduling parameters of the model, which is the novel challenge of this paper. The positivity and asymptotic stability of the proposed controllers have been studied based on linear matrix inequalities. The feasibility of the proposed closed-loop system and the effectiveness of the controllers are verified by numerical simulation. The designed controllers make the closed-loop DBS system stable and positive in both situations. It is while, without this controller, there is no such oscillating performance for Parkinsonian patients.

Closed-Loop Neurostimulation for Biomarker-Driven, Personalized Treatment of Major Depressive Disorder.

Deep brain stimulation involves the administration of electrical stimulation to targeted brain regions for therapeutic benefit. In the context of major depressive disorder (MDD), most studies to date have administered continuous or open-loop stimulation with promising but mixed results. One factor contributing to these mixed results may stem from when the stimulation is applied. Stimulation administration specific to high-symptom states in a personalized and responsive manner may be more effective at reducing symptoms compared to continuous stimulation and may avoid diminished therapeutic effects related to habituation. Additionally, a lower total duration of stimulation per day is advantageous for reducing device energy consumption. This protocol describes an experimental workflow using a chronically implanted neurostimulation device to achieve closed-loop stimulation for individuals with treatment-refractory MDD. This paradigm hinges on determining a patient-specific neural biomarker that is related to states of high symptoms and programming the device detectors, such that stimulation is triggered by this read-out of symptom state. The described procedures include how to obtain neural recordings concurrent with patient symptom reports, how to use these data in a state-space model approach to differentiate low- and high-symptom states and corresponding neural features, and how to subsequently program and tune the device to deliver closed-loop stimulation therapy.

Closed-loop brain stimulation augments fear extinction in male rats

Dysregulated fear reactions can result from maladaptive processing of trauma-related memories. In post-traumatic stress disorder (PTSD) and other psychiatric disorders, dysfunctional extinction learning prevents discretization of trauma-related memory engrams and generalizes fear responses. Although PTSD may be viewed as a memory-based disorder, no approved treatments target pathological fear memory processing. Hippocampal sharp wave-ripples (SWRs) and concurrent neocortical oscillations are scaffolds to consolidate contextual memory, but their role during fear processing remains poorly understood. Here, we show that closed-loop, SWR triggered neuromodulation of the medial forebrain bundle (MFB) can enhance fear extinction consolidation in male rats. The modified fear memories became resistant to induced recall (i.e., ‘renewal’ and ‘reinstatement’) and did not reemerge spontaneously. These effects were mediated by D2 receptor signaling-induced synaptic remodeling in the basolateral amygdala. Our results demonstrate that SWR-triggered closed-loop stimulation of the MFB reward system enhances extinction of fearful memories and reducing fear expression across different contexts and preventing excessive and persistent fear responses. These findings highlight the potential of neuromodulation to augment extinction learning and provide a new avenue to develop treatments for anxiety disorders.

Delayed closed-loop neurostimulation for the treatment of pathological brain rhythms in mental disorders: a computational study.

Mental disorders are among the top most demanding challenges in world-wide health. A large number of mental disorders exhibit pathological rhythms, which serve as the disorders characteristic biomarkers. These rhythms are the targets for neurostimulation techniques. Open-loop neurostimulation employs stimulation protocols, which are rather independent of the patients health and brain state in the moment of treatment. Most alternative closed-loop stimulation protocols consider real-time brain activity observations but appear as adaptive open-loop protocols, where e.g., pre-defined stimulation sets in if observations fulfil pre-defined criteria. The present theoretical work proposes a fully-adaptive closed-loop neurostimulation setup, that tunes the brain activities power spectral density (PSD) according to a user-defined PSD. The utilized brain model is non-parametric and estimated from the observations via magnitude fitting in a pre-stimulus setup phase. Moreover, the algorithm takes into account possible conduction delays in the feedback connection between observation and stimulation electrode. All involved features are illustrated on pathological α- and γ-rhythms known from psychosis. To this end, we simulate numerically a linear neural population brain model and a non-linear cortico-thalamic feedback loop model recently derived to explain brain activity in psychosis.

Biomarker-guided neuromodulation aids memory in traumatic brain injury

Traumatic brain injury (TBI) is a leading cause of cognitive disability in adults, often characterized by marked deficits in episodic memory and executive function. Prior studies have found that direct electrical stimulation of the temporal cortex yielded improved memory in epilepsy patients, but it is not clear if these results generalize to patients with a specific history of TBI. Here we asked whether applying closed-loop, direct electrical stimulation to lateral temporal cortex could reliably improve memory in a TBI cohort. Among a larger group of patients undergoing neurosurgical evaluation for refractory epilepsy, we recruited a subset of patients with a history of moderate-to-severe TBI. By analyzing neural data from indwelling electrodes as patients studied and recalled lists of words, we trained personalized machine-learning classifiers to predict momentary fluctuations in mnemonic function in each patient. We subsequently used these classifiers to trigger high-frequency stimulation of the lateral temporal cortex (LTC) at moments when memory was predicted to fail. This strategy yielded a 19% boost in recall performance on stimulated as compared with non-stimulated lists (P = 0.012). These results provide a proof-of-concept for using closed-loop stimulation of the brain in treatment of TBI-related memory impairment.

Closed-loop neurostimulation for the treatment of psychiatric disorders.

Despite increasing prevalence and huge personal and societal burden, psychiatric diseases still lack treatments which can control symptoms for a large fraction of patients. Increasing insight into the neurobiology underlying these diseases has demonstrated wide-ranging aberrant activity and functioning in multiple brain circuits and networks. Together with varied presentation and symptoms, this makes one-size-fits-all treatment a challenge. There has been a resurgence of interest in the use of neurostimulation as a treatment for psychiatric diseases. Initial studies using continuous open-loop stimulation, in which clinicians adjusted stimulation parameters during patient visits, showed promise but also mixed results. Given the periodic nature and fluctuations of symptoms often observed in psychiatric illnesses, the use of device-driven closed-loop stimulation may provide more effective therapy. The use of a biomarker, which is correlated with specific symptoms, to deliver stimulation only during symptomatic periods allows for the personalized therapy needed for such heterogeneous disorders. Here, we provide the reader with background motivating the use of closed-loop neurostimulation for the treatment of psychiatric disorders. We review foundational studies of open- and closed-loop neurostimulation for neuropsychiatric indications, focusing on deep brain stimulation, and discuss key considerations when designing and implementing closed-loop neurostimulation.

A feasibility study on AI-controlled closed-loop electrical stimulation implants

Miniaturized electrical stimulation (ES) implants show great promise in practice, but their real-time control by means of biophysical mechanistic algorithms is not feasible due to computational complexity. Here, we study the feasibility of more computationally efficient machine learning methods to control ES implants. For this, we estimate the normalized twitch force of the stimulated extensor digitorum longus muscle on n = 11 Wistar rats with intra- and cross-subject calibration. After 2000 training stimulations, we reach a mean absolute error of 0.03 in an intra-subject setting and 0.2 in a cross-subject setting with a random forest regressor. To the best of our knowledge, this work is the first experiment showing the feasibility of AI to simulate complex ES mechanistic models. However, the results of cross-subject training motivate more research on error reduction methods for this setting.

Advancements in the Clinical Outcomes of Functional Neurosurgery With Deep Brain Stimulation for Movement Disorders: A Literature Review.

This literature review explores recent advancements in deep brain stimulation (DBS) surgery for movement disorders. It highlights notable improvements, including closed-loop stimulation techniques, optogenetics, and improved surgical targeting. Positive clinical outcomes with low complication rates and improved motor symptoms are consistently reported. The review emphasizes the importance of minimizing risks through meticulous surgical practices and discussespotentialcomplications associated with DBS surgery. Future prospects focus on enhancing technology, refining surgical techniques, and conducting further research. Closed-loop stimulation optimizes DBS efficacy by tailoring stimulation parameters to individual patient needs. Optogenetics offers precise modulation of neural activity with light-sensitive proteins, enabling more targeted treatments. Cybersecurity measures are essential due to the integration of wireless and digital technologies in DBS systems. DBS surgery has significantly improved the management of movement disorders with its safety and effectiveness. Ongoing research in closed-loop stimulation, optogenetics, and cybersecurity is expected to further enhance DBS technology and outcomes, benefiting patients with treatment-resistant movement disorders.

Gait-combined closed-loop brain stimulation can improve walking dynamics in Parkinsonian gait disturbances: a randomised-control trial

ObjectiveGait disturbance lowers activities of daily living in patients with Parkinson’s disease (PD) and related disorders. However, the effectiveness of pharmacological, surgical and rehabilitative treatments is limited. We recently developed...

ID: 216933 A Review of Spinal Cord Stimulation Devices for Chronic Pain: Open-Loop Versus Closed-Loop

Spinal cord stimulation devices deliver electrical pulses to treat chronic pain. Previous studies looked at the efficacy of open-loop stimulation where pre-programmed parameters may lead to under-stimulation or over-stimulation while recent studies on closed-loop stimulation offer a system based on evoked-compound action potentials to adjust stimulation doses. This review is aimed at identifying clinical indications for open-loop and closed-loop stimulation devices.

Balance between pallidal neural oscillations correlated with dystonic activity and severity

Background and objectiveThe balance between neural oscillations provides valuable insights into the organisation of neural oscillations related to brain states, which may play important roles in dystonia. We aim to investigate the relationship of the balance in the globus pallidus internus (GPi) with the dystonic severity under different muscular contraction conditions. MethodsTwenty-one patients with dystonia were recruited. All of them underwent bilateral GPi implantation, and local field potentials (LFPs) from the GPi were recorded via simultaneous surface electromyography. The power spectral ratio between neural oscillations was computed as the measure of neural balance. This ratio was calculated under high and low dystonic muscular contraction conditions, and its correlation with the dystonic severity was assessed using clinical scores. ResultsThe power spectral of the pallidal LFPs peaked in the theta and alpha bands. Within participant comparison showed that the power spectral of the theta oscillations significantly increased during high muscle contraction compared with that during low contraction. The power spectral ratios between the theta and alpha, theta and low beta, and theta and high gamma oscillations were significantly higher during high contraction than during low contraction. The total score and motor score were associated with the power spectral ratio between the low and high beta oscillations, which was correlated with the dystonic severity both during high and low contractions. The power spectral ratios between the low beta and low gamma and between the low beta and high gamma oscillations showed a significantly positive correlation with the total score during both high and low contractions; a correlation with the motor scale score was found only during high contraction. Meanwhile, the power spectral ratio between the theta and alpha oscillations during low contraction showed a significantly negative correlation with the total score. The power spectral ratios between the alpha and high beta, alpha and low gamma, and alpha and high gamma oscillations were significantly correlated with the dystonic severity only during low contraction. ConclusionThe balance between neural oscillations, as quantified by the power ratio between specific frequency bands, differed between the high and low muscular contraction conditions and was correlated with the dystonic severity. The balance between the low and high beta oscillations was correlated with the dystonic severity during both conditions, making this parameter a new possible biomarker for closed-loop deep brain stimulation in patients with dystonia.

A CMOS Synchronized Sample-and-Hold Artifact Blanking Analog Front-End Local Field Potential Acquisition Unit With ±3.6-V Stimulation Artifact Tolerance and Monopolar Electrode-Tissue Impedance Measurement Circuit for Closed-Loop Deep Brain Stimulation SoCs

In this paper, an analog front-end (AFE) local-field potential (LFP) acquisition unit with real-time stimulation artifact removal is proposed and verified for closed-loop deep brain stimulation (DBS) applications. The proposed acquisition unit is called the synchronized sample-and-hold stimulation artifact blanking (SSAB) AFE LFP acquisition unit. Both right-leg-driven (RLD) circuit and monopolar electrode-tissue impedance (ETI) measurement circuit associated with the AFE amplifier are also proposed. During closed-loop stimulations, the artifact removal is realized through the SSAB-IPC by blanking the AR-CCIA with a clock synchronized to the stimulation-enable signal and holding the amplifier at its state before stimulation through a sample-and-hold operation. After stimulation, the acquisition unit can quickly recover from the holding state back to the LFP recording state to reduce the discontinuity in LFP recording. The proposed acquisition unit was fabricated in 0.18-um CMOS technology. With the RLD circuit, the measured CMRR is 124 – 145 dB in the signal bandwidth. The fabricated monopolar ETI measurement circuit has a measurement error less than 8.3% with an extra power consumption of 2.65 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $</tex-math> </inline-formula> W. The experimental results have shown that the proposed SSAB AFE LFP acquisition unit is feasible for the integration of SoCs in real-time closed-loop DBS systems for various applications.

Augmenting hippocampal–prefrontal neuronal synchrony during sleep enhances memory consolidation in humans

Memory consolidation during sleep is thought to depend on the coordinated interplay between cortical slow waves, thalamocortical sleep spindles and hippocampal ripples, but direct evidence is lacking. Here, we implemented real-time closed-loop deep brain stimulation in human prefrontal cortex during sleep and tested its effects on sleep electrophysiology and on overnight consolidation of declarative memory. Synchronizing the stimulation to the active phases of endogenous slow waves in the medial temporal lobe (MTL) enhanced sleep spindles, boosted locking of brain-wide neural spiking activity to MTL slow waves, and improved coupling between MTL ripples and thalamocortical oscillations. Furthermore, synchronized stimulation enhanced the accuracy of recognition memory. By contrast, identical stimulation without this precise time-locking was not associated with, and sometimes even degraded, these electrophysiological and behavioral effects. Notably, individual changes in memory accuracy were highly correlated with electrophysiological effects. Our results indicate that hippocampo–thalamocortical synchronization during sleep causally supports human memory consolidation.

ID: 215136 Inter-Patient Variability in the Evoked Compound Action Potentials Generated During Spinal Cord Stimulation

A paresthesia-centric closed-loop spinal cord stimulation (SCS) system described recently uses evoked compound action potentials (ECAPs) as a control signal to compensate for spinal cord movement [1]. While ECAP-based SCS has demonstrated clinical efficacy, significant knowledge gaps remain about how the main control variable (i.e., ECAP amplitude) and other metrics (e.g., ECAP threshold (ET), ECAP growth rate) vary across subjects, postures, and pulse widths. These knowledge gaps may complicate the appropriate application of ECAP-controlled, closed-loop SCS. Therefore, in this study, we utilized a combined experimental and computational modeling approach to answer these questions.

ID: 211562 Real-World Treatment Outcomes With ECAP-Controlled Closed-Loop Spinal Cord Stimulation: The Dutch Triangle Pain Clinic Experience

Evoked Compound Action Potential (ECAP)-controlled closed-loop spinal cord stimulation (SCS) measures and controls neural activation in real-time. Consistent and accurate neural activation resulted in superior pain relief in closed-loop compared to traditional, open-loop SCS patients [1],[2]. Here, interim results from 3 Dutch clinics (Triangle Painclinics) are presented (Netherlands Trial Register, ID: NL7889; NL9392).

ID: 210553 Clinical Feasibility of ECAP-Controlled, Closed-Loop Spinal Cord Stimulation With Cervically Placed Leads

Recent advances in spinal cord stimulation (SCS) have focused on two highly novel areas; Differential Target MultiplexedTM SCS (DTM) [1], which has demonstrated superior pain relief, and the spinal Evoked Compound Action Potential (ECAP), a bioelectrical signal used as a measure of neural activation with closed-loop (CL) SCS systems to compensate for motion at the electrode-tissue interface [2]. Due to thinner cerebral spinal fluid (CSF) and the large mobility in the cervical space compared to the thoracic space [3], the use of ECAP-controlled SCS systems for upper limb pain may reduce uncomfortable stimulation delivered to patients and decrease the variation in stimulation delivered to the cord. In this study, ECAP-controlled closed-loop DTMTM SCS was explored to assess feasibility with cervically placed SCS leads in an in-clinic environment.

Tracking the Navigation Behavior of Drosophila Larvae in Real and Virtual Odor Gradients by Using the Raspberry Pi Virtual Reality (PiVR) System.

In a closed-loop experimental paradigm, an animal experiences a modulation of its sensory input as a function of its own behavior. Tools enabling closed-loop experiments are crucial for delineating causal relationships between the activity of genetically labeled neurons and specific behavioral responses. We have recently developed an experimental platform known as "Raspberry Pi Virtual Reality" (PiVR) that is used to perform closed-loop optogenetic stimulation of neurons in unrestrained animals. PiVR is a system that operates at high temporal resolution (>30-Hz) and with low latencies. Larvae of the fruit fly Drosophila melanogaster are ideal to study the role of individual neurons in modulating behavior to aid the understanding of the neural pathways underlying various guided behaviors. Here, we introduce larval chemotaxis as an example of a navigational behavior in which an animal seeks to locate a target-in this case, the attractive source of an odor-by tracking a concentration gradient. The methodologies that we describe here combine the use of PiVR with the study of larval chemotaxis in real and virtual odor gradients, but these can also be readily adapted to other sensory modalities.

Cerebellar associative learning underlies skilled reach adaptation.

The cerebellum is hypothesized to refine movement through online adjustments. We examined how such predictive control may be generated using a mouse reach paradigm, testing whether the cerebellum uses within-reach information as a predictor to adjust reach kinematics. We first identified a population-level response in Purkinje cells that scales inversely with reach velocity, pointing to the cerebellar cortex as a potential site linking kinematic predictors and anticipatory control. Next, we showed that mice can learn to compensate for a predictable reach perturbation caused by repeated, closed-loop optogenetic stimulation of pontocerebellar mossy fiber inputs. Both neural and behavioral readouts showed adaptation to position-locked mossy fiber perturbations and exhibited aftereffects when stimulation was removed. Surprisingly, position-randomized stimulation schedules drove partial adaptation but no opposing aftereffects. A model that recapitulated these findings suggests that the cerebellum may decipher cause-and-effect relationships through time-dependent generalization mechanisms.