Closed-loop Brain Stimulation Research Articles

Ambulatory Brain Biomarkers of Chronic Pain: Towards Closed Loop Brain Stimulation

Ambulatory Brain Biomarkers of Chronic Pain: Towards Closed Loop Brain Stimulation

Disrupting abnormal neuronal oscillations with adaptive delayed feedback control.

Closed-loop neuronal stimulation has a strong therapeutic potential for neurological disorders such as Parkinson's disease. However, at the moment, standard stimulation protocols rely on continuous open-loop stimulation and the design of adaptive controllers is an active field of research. Delayed feedback control (DFC), a popular method used to control chaotic systems, has been proposed as a closed-loop technique for desynchronisation of neuronal populations but, so far, was only tested in computational studies. We implement DFC for the first time in neuronal populations and access its efficacy in disrupting unwanted neuronal oscillations. To analyse in detail the performance of this activity control algorithm, we used specialised in vitro platforms with high spatiotemporal monitoring/stimulating capabilities. We show that the conventional DFC in fact worsens the neuronal population oscillatory behaviour, which was never reported before. Conversely, we present an improved control algorithm, adaptive DFC (aDFC), which monitors the ongoing oscillation periodicity and self-tunes accordingly. aDFC effectively disrupts collective neuronal oscillations restoring a more physiological state. Overall, these results support aDFC as a better candidate for therapeutic closed-loop brain stimulation.

Repetitive Transcranial Alternating Current Stimulation to Improve Working Memory: An EEG-fNIRS Study.

Transcranial electrical stimulation has demonstrated the potential to enhance cognitive functions such as working memory, learning capacity, and attentional allocation. Recently, it was shown that periodic stimulation within a specific duration could augment the human brain's neuroplasticity. This study investigates the effects of repetitive transcranial alternating current stimulation (tACS; 1 mA, 5 Hz, 2 min duration) on cognitive function, functional connectivity, and topographic changes using both electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). Fifteen healthy subjects were recruited to measure brain activity in the pre-, during-, and post-stimulation sessions under tACS and sham stimulation conditions. Fourteen trials of working memory tasks and eight repetitions of tACS/sham stimulation with a 1-minute intersession interval were applied to the frontal cortex of the participants. The working memory score, EEG band-wise powers, EEG topography, concentration changes of oxygenated hemoglobin, and functional connectivity (FC) were individually analyzed to quantify the behavioral and neurophysiological effects of tACS. Our results indicate that tACS increases: i) behavioral scores (i.e., 15.08, ) and EEG band-wise powers (i.e., theta and beta bands) compared to the sham stimulation condition, ii) FC of both EEG-fNIRS signals, especially in the large-scale brain network communication and interhemispheric connections, and iii) the hemodynamic response in comparison to the pre-stimulation session and the sham condition. Conclusively, the repetitive theta-band tACS stimulation improves the working memory capacity regarding behavioral and neuroplasticity perspectives. Additionally, the proposed fNIRS biomarkers (mean, slope), EEG band-wise powers, and FC can be used as neuro-feedback indices for closed-loop brain stimulation.

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.

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...

DELMEP: a deep learning algorithm for automated annotation of motor evoked potential latencies

The analysis of motor evoked potentials (MEPs) generated by transcranial magnetic stimulation (TMS) is crucial in research and clinical medical practice. MEPs are characterized by their latency and the treatment of a single patient may require the characterization of thousands of MEPs. Given the difficulty of developing reliable and accurate algorithms, currently the assessment of MEPs is performed with visual inspection and manual annotation by a medical expert; making it a time-consuming, inaccurate, and error-prone process. In this study, we developed DELMEP, a deep learning-based algorithm to automate the estimation of MEP latency. Our algorithm resulted in a mean absolute error of about 0.5 ms and an accuracy that was practically independent of the MEP amplitude. The low computational cost of the DELMEP algorithm allows employing it in on-the-fly characterization of MEPs for brain-state-dependent and closed-loop brain stimulation protocols. Moreover, its learning ability makes it a particularly promising option for artificial-intelligence-based personalized clinical applications.

Regulation of arousal and performance of a healthy non-human primate using closed-loop central thalamic deep brain stimulation.

Application of closed-loop approaches in systems neuroscience and brain-computer interfaces holds great promise for revolutionizing our understanding of the brain and for developing novel neuromodulation strategies to restore lost function. The anterior forebrain mesocircuit (AFM) of the mammalian brain is hypothesized to underlie arousal regulation of the cortex and striatum, and support cognitive functions during wakefulness. Dysfunction of arousal regulation is hypothesized to contribute to cognitive dysfunctions in various neurological disorders, and most prominently in patients following traumatic brain injury (TBI). Several clinical studies have explored the use of daily central thalamic deep brain stimulation (CT-DBS) within the AFM to restore consciousness and executive attention in TBI patients. In this study, we explored the use of closed-loop CT-DBS in order to episodically regulate arousal of the AFM of a healthy non-human primate (NHP) with the goal of restoring behavioral performance. We used pupillometry and near real-time analysis of ECoG signals to episodically initiate closed-loop CT-DBS and here we report on our ability to enhance arousal and restore the animal's performance. The initial computer based approach was then experimentally validated using a customized clinical-grade DBS device, the DyNeuMo-X, a bi-directional research platform used for rapidly testing closed-loop DBS. The successful implementation of the DyNeuMo-X in a healthy NHP supports ongoing clinical trials employing the internal DyNeuMo system (NCT05437393, NCT05197816) and our goal of developing and accelerating the deployment of novel neuromodulation approaches to treat cognitive dysfunction in patients with structural brain injuries and other etiologies.

On closed-loop brain stimulation systems for improving the quality of life of patients with neurological disorders.

Emerging brain technologies have significantly transformed human life in recent decades. For instance, the closed-loop brain-computer interface (BCI) is an advanced software-hardware system that interprets electrical signals from neurons, allowing communication with and control of the environment. The system then transmits these signals as controlled commands and provides feedback to the brain to execute specific tasks. This paper analyzes and presents the latest research on closed-loop BCI that utilizes electric/magnetic stimulation, optogenetic, and sonogenetic techniques. These techniques have demonstrated great potential in improving the quality of life for patients suffering from neurodegenerative or psychiatric diseases. We provide a comprehensive and systematic review of research on the modalities of closed-loop BCI in recent decades. To achieve this, the authors used a set of defined criteria to shortlist studies from well-known research databases into categories of brain stimulation techniques. These categories include deep brain stimulation, transcranial magnetic stimulation, transcranial direct-current stimulation, transcranial alternating-current stimulation, and optogenetics. These techniques have been useful in treating a wide range of disorders, such as Alzheimer's and Parkinson's disease, dementia, and depression. In total, 76 studies were shortlisted and analyzed to illustrate how closed-loop BCI can considerably improve, enhance, and restore specific brain functions. The analysis revealed that literature in the area has not adequately covered closed-loop BCI in the context of cognitive neural prosthetics and implanted neural devices. However, the authors demonstrate that the applications of closed-loop BCI are highly beneficial, and the technology is continually evolving to improve the lives of individuals with various ailments, including those with sensory-motor issues or cognitive deficiencies. By utilizing emerging techniques of stimulation, closed-loop BCI can safely improve patients' cognitive and affective skills, resulting in better healthcare outcomes.

Resonance Scanning as an Efficiency Enhancer for EEG-Guided Adaptive Neurostimulation.

Electroencephalogram (EEG)-guided adaptive neurostimulation is an innovative kind of non-invasive closed-loop brain stimulation technique that uses audio-visual stimulation on-line modulated by rhythmical EEG components of the individual. However, the opportunity to enhance its effectiveness is a challenging task and needs further investigation. The present study aims to experimentally test whether it is possible to increase the efficiency of EEG-guided adaptive neurostimulation by pre- strengthening the modulating factor (subject's EEG) through the procedure of resonance scanning, i.e., LED photostimulation with the frequency gradually increasing in the range of main EEG rhythms (4-20 Hz). Thirty-six university students in a state of exam stress were randomly assigned to two matched groups. One group was presented with the EEG-guided adaptive neurostimulation alone, whereas another matched group was presented with the combination of resonance scanning and EEG-guided adaptive neurostimulation. The changes in psychophysiological indicators after stimulation relative to the initial level were used. Although both types of stimulation led to an increase in the power of EEG rhythms, accompanied by a decrease in the number of errors in the word recognition test and a decrease in the degree of emotional maladjustment, these changes reached the level of significance only in experiments with preliminary resonance scanning. Resonance scanning increases the brain's responsiveness to subsequent EEG-guided adaptive neurostimulation, acting as a tool to enhance its efficiency. The results obtained clearly indicate that the combination of resonance scanning and EEG-guided adaptive neurostimulation is an effective way to reach the signs of cognitive improvement in stressed individuals.

Cross-Frequency Coupling and Intelligent Neuromodulation.

Cross-frequency coupling (CFC) reflects (nonlinear) interactions between signals of different frequencies. Evidence from both patient and healthy participant studies suggests that CFC plays an essential role in neuronal computation, interregional interaction, and disease pathophysiology. The present review discusses methodological advances and challenges in the computation of CFC with particular emphasis on potential solutions to spurious coupling, inferring intrinsic rhythms in a targeted frequency band, and causal interferences. We specifically focus on the literature exploring CFC in the context of cognition/memory tasks, sleep, and neurological disorders, such as Alzheimer's disease, epilepsy, and Parkinson's disease. Furthermore, we highlight the implication of CFC in the context and for the optimization of invasive and noninvasive neuromodulation and rehabilitation. Mainly, CFC could support advancing the understanding of the neurophysiology of cognition and motor control, serve as a biomarker for disease symptoms, and leverage the optimization of therapeutic interventions, e.g., closed-loop brain stimulation. Despite the evident advantages of CFC as an investigative and translational tool in neuroscience, further methodological improvements are required to facilitate practical and correct use in cyborg and bionic systems in the field.

Accuracy of eeg-phase estimation for closed-loop brain stimulation inside the mri device

Accuracy of eeg-phase estimation for closed-loop brain stimulation inside the mri device

Removal of Transcranial Alternating Current Stimulation EEG Artifacts Using Blind Source Separation and Wavelets.

Transcranial alternating current stimulation (tACS) is a non-invasive technology for modulating brain activity, with significant potential for improving motor and cognitive functions. To investigate the effects of tACS, many studies have used electroencephalographic (EEG) data recorded during brain stimulation. However, the large artifacts induced by tACS make the analysis of tACS-EEG recordings challenging, which in turn has prevented the implementation of closed-loop brain stimulation schemes. Here, we propose a novel combination of blind source separation (BSS) and wavelets to achieve removal of tACS-EEG artifacts with improved performance. We examined the performance of several BSS methods both applied individually, as well as combined with the empirical wavelet transform (EWT) in terms of denoising realistic simulated and experimental tACS-EEG data. EWT combined with BSS yielded considerably improved performance compared to BSS alone for both simulated and experimental data. Overall, independent vector analysis (IVA) combined with EWT yielded the best performance. The proposed method yields promise for quantifying the effects of tACS on simultaneously recorded EEG data, which can in turn contribute towards understanding the effects of tACS on brain activity, as well as extracting reliable biomarkers that may be used to develop closed-loop tACS strategies for modulating the underlying brain activity in real time.

The Portiloop: A deep learning-based open science tool for closed-loop brain stimulation.

Closed-loop brain stimulation refers to capturing neurophysiological measures such as electroencephalography (EEG), quickly identifying neural events of interest, and producing auditory, magnetic or electrical stimulation so as to interact with brain processes precisely. It is a promising new method for fundamental neuroscience and perhaps for clinical applications such as restoring degraded memory function; however, existing tools are expensive, cumbersome, and offer limited experimental flexibility. In this article, we propose the Portiloop, a deep learning-based, portable and low-cost closed-loop stimulation system able to target specific brain oscillations. We first document open-hardware implementations that can be constructed from commercially available components. We also provide a fast, lightweight neural network model and an exploration algorithm that automatically optimizes the model hyperparameters to the desired brain oscillation. Finally, we validate the technology on a challenging test case of real-time sleep spindle detection, with results comparable to off-line expert performance on the Massive Online Data Annotation spindle dataset (MODA; group consensus). Software and plans are available to the community as an open science initiative to encourage further development and advance closed-loop neuroscience research [https://github.com/Portiloop].

Designing and validating a robust adaptive neuromodulation algorithm for closed-loop control of brain states

Objective. Neuromodulation systems that use closed-loop brain stimulation to control brain states can provide new therapies for brain disorders. To date, closed-loop brain stimulation has largely used linear time-invariant controllers. However, nonlinear time-varying brain network dynamics and external disturbances can appear during real-time stimulation, collectively leading to real-time model uncertainty. Real-time model uncertainty can degrade the performance or even cause instability of time-invariant controllers. Three problems need to be resolved to enable accurate and stable control under model uncertainty. First, an adaptive controller is needed to track the model uncertainty. Second, the adaptive controller additionally needs to be robust to noise and disturbances. Third, theoretical analyses of stability and robustness are needed as prerequisites for stable operation of the controller in practical applications. Approach. We develop a robust adaptive neuromodulation algorithm that solves the above three problems. First, we develop a state-space brain network model that explicitly includes nonlinear terms of real-time model uncertainty and design an adaptive controller to track and cancel the model uncertainty. Second, to improve the robustness of the adaptive controller, we design two linear filters to increase steady-state control accuracy and reduce sensitivity to high-frequency noise and disturbances. Third, we conduct theoretical analyses to prove the stability of the neuromodulation algorithm and establish a trade-off between stability and robustness, which we further use to optimize the algorithm design. Finally, we validate the algorithm using comprehensive Monte Carlo simulations that span a broad range of model nonlinearity, uncertainty, and complexity. Main results. The robust adaptive neuromodulation algorithm accurately tracks various types of target brain state trajectories, enables stable and robust control, and significantly outperforms state-of-the-art neuromodulation algorithms. Significance. Our algorithm has implications for future designs of precise, stable, and robust closed-loop brain stimulation systems to treat brain disorders and facilitate brain functions.

A Miniaturized Closed-Loop Optogenetic Brain Stimulation Device

This paper presents a tetherless and miniaturized closed-loop optogenetic brain stimulation device, designed as a back mountable device for laboratory mice. The device has the ability to sense the biomarkers corresponding to major depressive disorder (MDD) from local field potential (LFP), and produces a feedback signal to control the closed-loop operation after on-device processing of the sensed signals. MDD is a chronic neurological disorder and there are still many unanswered questions about the underlying neurological mechanisms behind its occurrence. Along with other brain stimulation paradigms, optogenetics has recently proved effective in the study of MDD. Most of these experiments have used tethered and connected devices. However, the use of tethered devices in optogenetic brain stimulation experiments has the drawback of hindering the free movement of the laboratory animal subjects undergoing stimulation. To address this issue, the proposed device is small, light-weight, untethered, and back-mountable. The device consists of: (i) an optrode which houses an electrode for collecting neural signals, an optical source for delivering light stimulations, and a temperature sensor for monitoring the temperature increase at the stimulation site, (ii) a neural sensor for acquisition and pre-processing of the neural signals to obtain LFP signals in the frequency range of 4 to 200 Hz, as electrophysiological biomarkers of MDD (iii) a classifier for classification of the signal into four classes: normal, abnormal alpha, abnormal theta, and abnormal gamma oscillations, (iv) a control algorithm to select stimulation parameters based on the input class, and (v) a stimulator for generating light stimulations. The design, implementation, and evaluation of the device are presented, and the results are discussed. The neural sensor and the stimulator are circular in shape with a radius of 8 mm. Pre-recorded neural signals from the mouse hippocampus are used for the evaluation of the device.

Adaptive closed-loop control strategy inhibiting pathological basal ganglia oscillations

The presence of pathological basal ganglia oscillations in the beta (12–35 Hz) frequency band is associated with Parkinson’s disease (PD). Suppressing the abnormal beta rhythm can effectively alleviate prominent PD movement disorders such as bradykinesia and rigidity. Brain stimulations, such as deep brain stimulation or transcranial stimulation, are effective therapeutic methods in managing the beta rhythm. However, electrostimulation using the current open-loop paradigm for stimulation is not optimal, especially when the controlled system experiences a substantial unknown disturbance. In this work we propose an adaptive radial basis function (ARBF) neural network strategy to achieve closed-loop brain stimulation based on real-time observed neural oscillation feedback. The underlying system is assumed to be an unknown nonlinear system, and the closed-loop strategy adaptively modulates the stimulation signal to cope with the abnormal neuronal discharge fluctuations, so as to eliminate the beta rhythm of the STN-GPe network. The proposed ARBF neural network closed-loop scheme is tested in a neural mass model composed of the subthalamic nucleus and external globus pallidus. It is shown that the performance of the ARBF controller is robust, including when internal synaptic connections within the basal ganglia network are enhanced to endogenously impact pathological conditions, and also when pathological oscillations are induced by exogenous cortical inputs. Simulation results demonstrate the effectiveness of the proposed closed-loop neuromodulation pattern based on an ARBF neural network. This work may help to develop DBS control systems with adaptive optimization and less network complexity.

Researchers' Ethical Concerns About Using Adaptive Deep Brain Stimulation for Enhancement.

The capacity of next-generation closed-loop or adaptive deep brain stimulation devices (aDBS) to read (measure neural activity) and write (stimulate brain regions or circuits) shows great potential to effectively manage movement, seizure, and psychiatric disorders, and also raises the possibility of using aDBS to electively (non-therapeutically) modulate mood, cognition, and prosociality. What separates aDBS from most neurotechnologies (e.g. transcranial stimulation) currently used for enhancement is that aDBS remains an invasive, surgically-implanted technology with a risk-benefit ratio significantly different when applied to diseased versus non-diseased individuals. Despite a large discourse about the ethics of enhancement, no empirical studies yet examine perspectives on enhancement from within the aDBS research community. We interviewed 23 aDBS researchers about their attitudes toward expanding aDBS use for enhancement. A thematic content analysis revealed that researchers share ethical concerns related to (1) safety and security; (2) enhancement as unnecessary, unnatural or aberrant; and (3) fairness, equality, and distributive justice. Most (70%) researchers felt that enhancement applications for DBS will eventually be technically feasible and that attempts to develop such applications for DBS are already happening (particularly for military purposes). However, researchers unanimously (100%) felt that DBS ideally should not be considered for enhancement until researchers better understand brain target localization and functioning. While many researchers acknowledged controversies highlighted by scholars and ethicists, such as potential impacts on personhood, authenticity, autonomy and privacy, their ethical concerns reflect considerations of both gravity and perceived near-term likelihood.

TinyLFP: A Tiny Local-Field-Potential Sensor

Efficient acquisition and analysis of neural signals play a significant role in enhancing our understanding of the brain function and its abnormalities. Neural interfaces involve electrodes inserted into the brain tissues, and also hardware circuits and software to record neural activities and analyze them. Local field potentials (LFPs) are the low frequency neural signals recorded invasively capturing the dynamic extracellular neuronal activities. The information on different bands of neural oscillations (alpha, beta, theta, and gamma) in LFP signals has been used to determine brain abnormalities. LFP signals contain components with amplitudes in the range of 10- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$100 ~\mu \text{V}$ </tex-math></inline-formula> , which are small and can be easily distorted by external noise sources. With the advancements in closed-loop brain stimulation paradigms, there is a need for miniaturized sensors to capture and monitor LFP signals. The sensed LFP signals can then be used to close the feedback loop in a control system to adjust stimulation parameters in a closed-loop brain stimulation device. A LFP sensor requires a low power circuit to ensure prolonged operation. In addition, an area-efficient and low-noise front end stage needs to be formed. Considering these requirements, a single-channel, power-efficient, low-noise, compact neural sensor for acquisition of LFP signals is presented. The miniaturized LFP sensor, named TinyLFP, has a low-noise first stage instrumentation amplifier for providing a pre-amplification of 100 V/V, a band pass filter with a pass band of 4–200 Hz, and a post amplifier stage with an amplification factor of 200 V/V. Overall, the device provides a gain of around 85 dB for the signals in the range of 4 to 200 Hz to be used as the first stage of a closed loop optogenetic brain stimulation device.

Treating Refractory Mental Illness With Closed-Loop Brain Stimulation: Progress Towards a Patient-Specific Transdiagnostic Approach.

Treating Refractory Mental Illness With Closed-Loop Brain Stimulation: Progress Towards a Patient-Specific Transdiagnostic Approach.

A Robust and Adaptive Control Algorithm for Closed-Loop Brain Stimulation.

Developing closed-loop brain stimulation systems can benefit the treatment of neurological and neuropsychiatric disorders and facilitate brain functions. Current designs of closed-loop controllers have used time-invariant linear models of brain activity to devise non-adaptive controllers. However, unmodeled nonlinear dynamics can happen during real-time closed-loop control, leading to nonlinear uncertainty in the brain activity model. Current non-adaptive controllers cannot track the nonlinear model uncertainty and are not robust to noise, both of which can compromise their control performance. Here, within an ℒ1 adaptive control framework, we develop a new discrete-time robust and adaptive closed-loop control algorithm that addresses a general form of nonlinear model uncertainty. We conduct Monte Carlo simulations to validate the robust and adaptive control algorithm and show that it significantly outperforms existing closed-loop control algorithms. Our results can facilitate future designs of precise and safe closed-loop brain stimulation systems to treat neurological and neuropsychiatric disorders and modulate brain functions.