Adaptive deep brain stimulation (aDBS) controlled by local field potential oscillations

A Method for Electrical Stimulus Artifact Removal Exploiting Neural Refractoriness: Validation by Contrasting Cathodic and Anodic Stimulation

Objective

To present a novel method for removing stimulus transient that exploits the absolute refractory period of electrically excitable neural tissues.

Background

Electrical stimulation often generates significant signal artifacts that can obscure important physiological signals. Removal of the artifact and understanding latent information from these signals could provide objective measures of circuit engagement, potentially driving advancements in neuromodulation research and therapies.

Methods

We conducted intracranial physiology studies on five consecutive patients with Parkinson's disease who underwent deep brain stimulation (DBS) surgery as part of their routine care. Monopolar stimuli (either cathodic or anodic) were delivered in pairs through the DBS electrode across a range of inter-stimulus intervals. Recordings from adjacent unused electrode contacts used broadband sampling and precise synchronization to generate a robust template for the stimulus transient during the absolute refractory period. These templates of stimulus transient were then subtracted from recordings at different intervals to extract and analyze the residual neural potentials.

Results

After artifact removal, the residual signals exhibited absolute and relative refractory periods with timing indicative of neural activity. Cathodic and anodic DBS pulses generated distinct patterns of local tissue activation, showing phase independence from the prior stimulus. The earliest detectable neural responses occurred at short peak latencies (ranging from 0.19 to 0.38 ms post-stimulus) and were completely or partially obscured by the stimulus artifact prior to removal. Cathodic stimuli produced stronger local tissue responses than anodic stimuli, aligning with clinical observations of lower activation thresholds for cathodic stimulation. However, cathodic and anodic pulses induced artifact patterns that were equivalent but opposite.

Interpretation

The proposed artifact removal technique enhances prior approaches by allowing direct measurement of local tissue responses without requirements for stimulus polarity reversal, template scaling, or specialized filters. This approach could be integrated into future neuromodulation systems to visualize stimulus-evoked neural potentials that would otherwise be obscured by stimulus artifacts.

Development and Evaluation of a Real-Time Phase-Triggered Stimulation Algorithm for the CorTec Brain Interchange

With the development and characterization of biomarkers that may reflect neural network state as well as a patient's clinical deficits, there is growing interest in more complex stimulation designs. While current implantable neuromodulation systems offer pathways to expand the design and application of adaptive stimulation paradigms, technological drawbacks of these systems limit adaptive neuromodulation exploration. In this paper, we discuss the implementation of a phase-triggered stimulation paradigm using a research platform composed of an investigational system known as the CorTec Brain Interchange (CorTec GmbH, Freiburg, Germany), and an open-source software tool known as OMNI-BIC. We then evaluate the stimulation paradigm's performance in both benchtop and in vivo human demonstrations. Our findings indicate that the Brain Interchange and OMNI-BIC platform is capable of reliable administration of phase-triggered stimulation and has the potential to help expand investigation within the adaptive neuromodulation design space.

Adaptive Deep Brain Stimulation in Parkinson's Disease: A Delphi Consensus Study

Importance

If history teaches, as cardiac pacing moved from fixed-rate to on-demand delivery in in 80s of the last century, there are high probabilities that closed-loop and adaptive approaches will become, in the next decade, the natural evolution of conventional Deep Brain Stimulation (cDBS). However, while devices for aDBS are already available for clinical use, few data on their clinical application and technological limitations are available so far. In such scenario, gathering the opinion and expertise of leading investigators worldwide would boost and guide practice and research, thus grounding the clinical development of aDBS.

Observations

We identified clinical and academically experienced DBS clinicians (n=21) to discuss the challenges related to aDBS. A 5-point Likert scale questionnaire along with a Delphi method was employed. 42 questions were submitted to the panel, half of them being related to technical aspects while the other half to clinical aspects of aDBS. Experts agreed that aDBS will become clinical practice in 10 years. In the present scenario, although the panel agreed that aDBS applications require skilled clinicians and that algorithms need to be further optimized to manage complex PD symptoms, consensus was reached on aDBS safety and its ability to provide a faster and more stable treatment response than cDBS, also for tremor-dominant Parkinson's disease patients and for those with motor fluctuations and dyskinesias.

Conclusions and Relevance

Despite the need of further research, the panel concluded that aDBS is safe, promises to be maximally effective in PD patients with motor fluctuation and dyskinesias and therefore will enter into the clinical practice in the next years, with further research focused on algorithms and markers for complex symptoms.

Robust adaptive deep brain stimulation control of in-silico non-stationary Parkinsonian neural oscillatory dynamics

Objective. Closed-loop deep brain stimulation (DBS) is a promising therapy for Parkinson's disease (PD) that works by adjusting DBS patterns in real time from the guidance of feedback neural activity. Current closed-loop DBS mainly uses threshold-crossing on-off controllers or linear time-invariant (LTI) controllers to regulate the basal ganglia (BG) Parkinsonian beta band oscillation power. However, the critical cortex-BG-thalamus network dynamics underlying PD are nonlinear, non-stationary, and noisy, hindering accurate and robust control of Parkinsonian neural oscillatory dynamics.Approach. Here, we develop a new robust adaptive closed-loop DBS method for regulating the Parkinsonian beta oscillatory dynamics of the cortex-BG-thalamus network. We first build an adaptive state-space model to quantify the dynamic, nonlinear, and non-stationary neural activity. We then construct an adaptive estimator to track the nonlinearity and non-stationarity in real time. We next design a robust controller to automatically determine the DBS frequency based on the estimated Parkinsonian neural state while reducing the system's sensitivity to high-frequency noise. We adopt and tune a biophysical cortex-BG-thalamus network model as an in-silico simulation testbed to generate nonlinear and non-stationary Parkinsonian neural dynamics for evaluating DBS methods.Main results. We find that under different nonlinear and non-stationary neural dynamics, our robust adaptive DBS method achieved accurate regulation of the BG Parkinsonian beta band oscillation power with small control error, bias, and deviation. Moreover, the accurate regulation generalizes across different therapeutic targets and consistently outperforms current on-off and LTI DBS methods.Significance. These results have implications for future designs of closed-loop DBS systems to treat PD.

Lead-free dual-frequency ultrasound implants for wireless, biphasic deep brain stimulation

Ultrasound-driven bioelectronics could offer a wireless scheme with sustainable power supply; however, current ultrasound implantable systems present critical challenges in biocompatibility and harvesting performance related to lead/lead-free piezoelectric materials and devices. Here, we report a lead-free dual-frequency ultrasound implants for wireless, biphasic deep brain stimulation, which integrates two developed lead-free sandwich porous 1-3-type piezoelectric composite elements with enhanced harvesting performance in a flexible printed circuit board. The implant is ultrasonically powered through a portable external dual-frequency transducer and generates programmable biphasic stimulus pulses in clinically relevant frequencies. Furthermore, we demonstrate ultrasound-driven implants for long-term biosafety therapy in deep brain stimulation through an epileptic rodent model. With biocompatibility and improved electrical performance, the lead-free materials and devices presented here could provide a promising platform for developing implantable ultrasonic electronics in the future.

Model-based closed-loop control of thalamic deep brain stimulation

Introduction: Closed-loop control of deep brain stimulation (DBS) is beneficial for effective and automatic treatment of various neurological disorders like Parkinson's disease (PD) and essential tremor (ET). Manual (open-loop) DBS programming solely based on clinical observations relies on neurologists' expertise and patients' experience. Continuous stimulation in open-loop DBS may decrease battery life and cause side effects. On the contrary, a closed-loop DBS system uses a feedback biomarker/signal to track worsening (or improving) of patients' symptoms and offers several advantages compared to the open-loop DBS system. Existing closed-loop DBS control systems do not incorporate physiological mechanisms underlying DBS or symptoms, e.g., how DBS modulates dynamics of synaptic plasticity. Methods: In this work, we propose a computational framework for development of a model-based DBS controller where a neural model can describe the relationship between DBS and neural activity and a polynomial-based approximation can estimate the relationship between neural and behavioral activities. A controller is used in our model in a quasi-real-time manner to find DBS patterns that significantly reduce the worsening of symptoms. By using the proposed computational framework, these DBS patterns can be tested clinically by predicting the effect of DBS before delivering it to the patient. We applied this framework to the problem of finding optimal DBS frequencies for essential tremor given electromyography (EMG) recordings solely. Building on our recent network model of ventral intermediate nuclei (Vim), the main surgical target of the tremor, in response to DBS, we developed neural model simulation in which physiological mechanisms underlying Vim-DBS are linked to symptomatic changes in EMG signals. By using a proportional-integral-derivative (PID) controller, we showed that a closed-loop system can track EMG signals and adjust the stimulation frequency of Vim-DBS so that the power of EMG reaches a desired control target. Results and discussion: We demonstrated that the model-based DBS frequency aligns well with that used in clinical studies. Our model-based closed-loop system is adaptable to different control targets and can potentially be used for different diseases and personalized systems.

A Systematic Review of Neurophysiology-Based Localization Techniques Used in Deep Brain Stimulation Surgery of the Subthalamic Nucleus

OBJECTIVE

This systematic review is conducted to identify, compare, and analyze neurophysiological feature selection, extraction, and classification to provide a comprehensive reference on neurophysiology-based subthalamic nucleus (STN) localization.

MATERIALS AND METHODS

The review was carried out using the methods and guidelines of the Kitchenham systematic review and provides an in-depth analysis on methods proposed on STN localization discussed in the literature between 2000 and 2021. Three research questions were formulated, and 115 publications were identified to answer the questions.

RESULTS

The three research questions formulated are answered using the literature found on the respective topics. This review discussed the technologies used in past research, and the performance of the state-of-the-art techniques is also reviewed.

CONCLUSION

This systematic review provides a comprehensive reference on neurophysiology-based STN localization by reviewing the research questions other new researchers may also have.

Dynamical flexible inference of nonlinear latent factors and structures in neural population activity

Modelling the spatiotemporal dynamics in the activity of neural populations while also enabling their flexible inference is hindered by the complexity and noisiness of neural observations. Here we show that the lower-dimensional nonlinear latent factors and latent structures can be computationally modelled in a manner that allows for flexible inference causally, non-causally and in the presence of missing neural observations. To enable flexible inference, we developed a neural network that separates the model into jointly trained manifold and dynamic latent factors such that nonlinearity is captured through the manifold factors and the dynamics can be modelled in tractable linear form on this nonlinear manifold. We show that the model, which we named 'DFINE' (for 'dynamical flexible inference for nonlinear embeddings') achieves flexible inference in simulations of nonlinear dynamics and across neural datasets representing a diversity of brain regions and behaviours. Compared with earlier neural-network models, DFINE enables flexible inference, better predicts neural activity and behaviour, and better captures the latent neural manifold structure. DFINE may advance the development of neurotechnology and investigations in neuroscience.

Oscillatory vs. non-oscillatory subthalamic beta activity in Parkinson's disease

Parkinson's disease is characterized by exaggerated beta activity (13-35 Hz) in cortico-basal ganglia motor loops. Beta activity includes both periodic fluctuations (i.e. oscillatory activity) and aperiodic fluctuations reflecting spiking activity and excitation/inhibition balance (i.e. non-oscillatory activity). However, the relative contribution, dopamine dependency and clinical correlations of oscillatory vs. non-oscillatory beta activity remain unclear. We recorded, modelled and analysed subthalamic local field potentials in parkinsonian patients at rest while off or on medication. Autoregressive modelling with additive 1/f noise clarified the relationships between measures of beta activity in the time domain (i.e. amplitude and duration of beta bursts) or in the frequency domain (i.e. power and sharpness of the spectral peak) and oscillatory vs. non-oscillatory activity: burst duration and spectral sharpness are specifically sensitive to oscillatory activity, whereas burst amplitude and spectral power are ambiguously sensitive to both oscillatory and non-oscillatory activity. Our experimental data confirmed the model predictions and assumptions. We subsequently analysed the effect of levodopa, obtaining strong-to-extreme Bayesian evidence that oscillatory beta activity is reduced in patients on vs. off medication, with moderate evidence for absence of modulation of the non-oscillatory component. Finally, specifically the oscillatory component of beta activity correlated with the rate of motor progression of the disease. Methodologically, these results provide an integrative understanding of beta-based biomarkers relevant for adaptive deep brain stimulation. Biologically, they suggest that primarily the oscillatory component of subthalamic beta activity is dopamine dependent and may play a role not only in the pathophysiology but also in the progression of Parkinson's disease. KEY POINTS: Beta activity in Parkinson's disease includes both true periodic fluctuations (i.e. oscillatory activity) and aperiodic fluctuations reflecting spiking activity and synaptic balance (i.e. non-oscillatory activity). The relative contribution, dopamine dependency and clinical correlations of oscillatory vs. non-oscillatory beta activity remain unclear. Burst duration and spectral sharpness are specifically sensitive to oscillatory activity, while burst amplitude and spectral power are ambiguously sensitive to both oscillatory and non-oscillatory activity. Only the oscillatory component of subthalamic beta activity is dopamine-dependent. Stronger beta oscillatory activity correlates with faster motor progression of the disease.

Paradoxical Modulation of STN β-Band Activity with Medication Compared to Deep Brain Stimulation

BACKGROUND

Excessive subthalamic nucleus (STN) β-band (13-35 Hz) synchronized oscillations has garnered interest as a biomarker for characterizing disease state and developing adaptive stimulation systems for Parkinson's disease (PD).

OBJECTIVES

To report on a patient with abnormal treatment-responsive modulation in the β-band.

METHODS

We examined STN local field potentials from an externalized deep brain stimulation (DBS) lead while assessing PD motor signs in four conditions (OFF, MEDS, DBS, and MEDS+DBS).

RESULTS

The patient presented here exhibited a paradoxical increase in β power following administration of levodopa and pramipexole (MEDS), but an attenuation in β power during DBS and MEDS+DBS despite clinical improvement of 50% or greater under all three therapeutic conditions.

CONCLUSIONS

This case highlights the need for further study on the role of β oscillations in the pathophysiology of PD and the importance of personalized approaches to the development of β or other biomarker-based DBS closed loop algorithms. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Monitoring time domain characteristics of Parkinson's disease using 3D memristive neuromorphic system

Introduction

Parkinson's disease (PD) is a neurodegenerative disorder affecting millions of patients. Closed-Loop Deep Brain Stimulation (CL-DBS) is a therapy that can alleviate the symptoms of PD. The CL-DBS system consists of an electrode sending electrical stimulation signals to a specific region of the brain and a battery-powered stimulator implanted in the chest. The electrical stimuli in CL-DBS systems need to be adjusted in real-time in accordance with the state of PD symptoms. Therefore, fast and precise monitoring of PD symptoms is a critical function for CL-DBS systems. However, the current CL-DBS techniques suffer from high computational demands for real-time PD symptom monitoring, which are not feasible for implanted and wearable medical devices.

Methods

In this paper, we present an energy-efficient neuromorphic PD symptom detector using memristive three-dimensional integrated circuits (3D-ICs). The excessive oscillation at beta frequencies (13-35 Hz) at the subthalamic nucleus (STN) is used as a biomarker of PD symptoms.

Results

Simulation results demonstrate that our neuromorphic PD detector, implemented with an 8-layer spiking Long Short-Term Memory (S-LSTM), excels in recognizing PD symptoms, achieving a training accuracy of 99.74% and a validation accuracy of 99.52% for a 75%-25% data split. Furthermore, we evaluated the improvement of our neuromorphic CL-DBS detector using NeuroSIM. The chip area, latency, energy, and power consumption of our CL-DBS detector were reduced by 47.4%, 66.63%, 65.6%, and 67.5%, respectively, for monolithic 3D-ICs. Similarly, for heterogeneous 3D-ICs, employing memristive synapses to replace traditional Static Random Access Memory (SRAM) resulted in reductions of 44.8%, 64.75%, 65.28%, and 67.7% in chip area, latency, and power usage.

Discussion

This study introduces a novel approach for PD symptom evaluation by directly utilizing spiking signals from neural activities in the time domain. This method significantly reduces the time and energy required for signal conversion compared to traditional frequency domain approaches. The study pioneers the use of neuromorphic computing and memristors in designing CL-DBS systems, surpassing SRAM-based designs in chip design area, latency, and energy efficiency. Lastly, the proposed neuromorphic PD detector demonstrates high resilience to timing variations in brain neural signals, as confirmed by robustness analysis.

From dawn till dusk: Time-adaptive bayesian optimization for neurostimulation

Stimulation optimization has garnered considerable interest in recent years in order to efficiently parametrize neuromodulation-based therapies. To date, efforts focused on automatically identifying settings from parameter spaces that do not change over time. A limitation of these approaches, however, is that they lack consideration for time dependent factors that may influence therapy outcomes. Disease progression and biological rhythmicity are two sources of variation that may influence optimal stimulation settings over time. To account for this, we present a novel time-varying Bayesian optimization (TV-BayesOpt) for tracking the optimum parameter set for neuromodulation therapy. We evaluate the performance of TV-BayesOpt for tracking gradual and periodic slow variations over time. The algorithm was investigated within the context of a computational model of phase-locked deep brain stimulation for treating oscillopathies representative of common movement disorders such as Parkinson's disease and Essential Tremor. When the optimal stimulation settings changed due to gradual and periodic sources, TV-BayesOpt outperformed standard time-invariant techniques and was able to identify the appropriate stimulation setting. Through incorporation of both a gradual "forgetting" and periodic covariance functions, the algorithm maintained robust performance when a priori knowledge differed from observed variations. This algorithm presents a broad framework that can be leveraged for the treatment of a range of neurological and psychiatric conditions and can be used to track variations in optimal stimulation settings such as amplitude, pulse-width, frequency and phase for invasive and non-invasive neuromodulation strategies.

Pallidal and Cortical Oscillations in Freely Moving Patients With Dystonia

OBJECTIVES

To evaluate the correlation between the pallidal local field potentials (LFPs) activity and the cortical oscillations (at rest and during several motor tasks) in two freely moving patients with generalized dystonia and pallidal deep brain stimulation (DBS).

MATERIALS AND METHODS

Two women with isolated generalized dystonia were selected for bilateral globus pallidus internus (GPi) DBS. After the electrodes' implantation, cortical activity was recorded by a portable electroencephalography (EEG) system simultaneously with GPi LFPs activity, during several motor tasks, gait, and rest condition. Recordings were not performed during stimulation. EEG and LFPs signals relative to each specific movement were coupled together and grouped in neck/upper limbs movements and gait. Power spectral density (PSD), EEG-LFP coherence (through envelope of imaginary coherence operator), and 1/f exponent of LFP-PSD background were calculated.

RESULTS

In both patients, the pallidal LFPs PSD at rest was characterized by prominent 4-12 Hz activity. Voluntary movements increased activity in the theta (θ) band (4-7 Hz) compared to rest, in both LFPs and EEG signals. Gait induced a drastic raise of θ activity in both patients' pallidal activity, less marked for the EEG signal. A coherence peak within the 8-13 Hz range was found between pallidal LFPs and EEG recorded at rest.

CONCLUSIONS

Neck/upper limbs voluntary movements and gait suppressed the GPi-LFPs-cortical-EEG coherence and differently impacted both EEG and LFPs low frequency activity. These findings suggest a selective modulation of the cortico-basal ganglia network activity in dystonia.

Machine learning for adaptive deep brain stimulation in Parkinson's disease: closing the loop

Parkinson's disease (PD) is the second most common neurodegenerative disease bearing a severe social and economic impact. So far, there is no known disease modifying therapy and the current available treatments are symptom oriented. Deep Brain Stimulation (DBS) is established as an effective treatment for PD, however current systems lag behind today's technological potential. Adaptive DBS, where stimulation parameters depend on the patient's physiological state, emerges as an important step towards "smart" DBS, a strategy that enables adaptive stimulation and personalized therapy. This new strategy is facilitated by currently available neurotechnologies allowing the simultaneous monitoring of multiple signals, providing relevant physiological information. Advanced computational models and analytical methods are an important tool to explore the richness of the available data and identify signal properties to close the loop in DBS. To tackle this challenge, machine learning (ML) methods applied to DBS have gained popularity due to their ability to make good predictions in the presence of multiple variables and subtle patterns. ML based approaches are being explored at different fronts such as the identification of electrophysiological biomarkers and the development of personalized control systems, leading to effective symptom relief. In this review, we explore how ML can help overcome the challenges in the development of closed-loop DBS, particularly its role in the search for effective electrophysiology biomarkers. Promising results demonstrate ML potential for supporting a new generation of adaptive DBS, with better management of stimulation delivery, resulting in more efficient and patient-tailored treatments.

Closed-loop modulation of model parkinsonian beta oscillations based on CAR-fuzzy control algorithm

Closed-loop deep brain stimulation (DBS) can apply on-demand stimulation based on the feedback signal (e.g. beta band oscillation), which is deemed to lower side effects of clinically used open-loop DBS. To facilitate the application of model-based closed-loop DBS in clinical, studies must consider state variations, e.g., variation of desired signal with different movement conditions and variation of model parameters with time. This paper proposes to use the controlled autoregressive (CAR)-fuzzy control algorithm to modulate the pathological beta band (13-35 Hz) oscillation of a basal ganglia-cortex-thalamus model. The CAR model is used to identify the relationship between DBS frequency parameter and beta oscillation power. Then the error between the one-step-ahead predicted beta power of CAR model and the desired value is innovatively used as the input of fuzzy controller to calculate the next step stimulation frequency. Compared with 130 Hz open-loop DBS, the proposed closed-loop DBS method could lower the mean stimulation frequency to 74.04 Hz with similar beta oscillation suppression performance. The Mamdani fuzzy controller is selected because which could establish fuzzy controller rules according to human operation experience. Adding prediction module to closed-loop control improves the accuracy of fuzzy control, compared with proportional-integral control and fuzzy control, the proposed CAR-fuzzy control algorithm has higher tracking reliability, response speed and robustness.

Wavelet-Based Bracketing, Time-Frequency Beta Burst Detection: New Insights in Parkinson's Disease

Studies have shown that beta band activity is not tonically elevated but comprises exaggerated phasic bursts of varying durations and magnitudes, for Parkinson's disease (PD) patients. Current methods for detecting beta bursts target a single frequency peak in beta band, potentially ignoring bursts in the wider beta band. In this study, we propose a new robust framework for beta burst identification across wide frequency ranges. Chronic local field potential at-rest recordings were obtained from seven PD patients implanted with Medtronic SenSight™ deep brain stimulation (DBS) electrodes. The proposed method uses wavelet decomposition to compute the time-frequency spectrum and identifies bursts spanning multiple frequency bins by thresholding, offering an additional burst measure, ∆f, that captures the width of a burst in the frequency domain. Analysis included calculating burst duration, magnitude, and ∆f and evaluating the distribution and likelihood of bursts between the low beta (13-20 Hz) and high beta (21-35 Hz). Finally, the results of the analysis were correlated to motor impairment (MDS-UPDRS III) med off scores. We found that low beta bursts with longer durations and larger width in the frequency domain (∆f) were positively correlated, while high beta bursts with longer durations and larger ∆f were negatively correlated with motor impairment. The proposed method, finding clear differences between bursting behavior in high and low beta bands, has clearly demonstrated the importance of considering wide frequency bands for beta burst behavior with implications for closed-loop DBS paradigms.

A review of neurophysiological effects and efficiency of waveform parameters in deep brain stimulation

Neurostimulation has diverse clinical applications and potential as a treatment for medically refractory movement disorders, epilepsy, and other neurological disorders. However, the parameters used to program electrodes-polarity, pulse width, amplitude, and frequency-and how they are adjusted have remained largely untouched since the 1970 s. This review summarizes the state-of-the-art in Deep Brain Stimulation (DBS) and highlights the need for further research to uncover the physiological mechanisms of neurostimulation. We focus on studies that reveal the potential for clinicians to use waveform parameters to selectively stimulate neural tissue for therapeutic benefit, while avoiding activating tissue associated with adverse effects. DBS uses cathodic monophasic rectangular pulses with passive recharging in clinical practice to treat neurological conditions such as Parkinson's Disease. However, research has shown that stimulation efficiency can be improved, and side effects reduced, through modulating parameters and adding novel waveform properties. These developments can prolong implantable pulse generator lifespan, reducing costs and surgery-associated risks. Waveform parameters can stimulate neurons based on axon orientation and intrinsic structural properties, providing clinicians with more precise targeting of neural pathways. These findings could expand the spectrum of diseases treatable with neuromodulation and improve patient outcomes.

Soft monolithic infrared neural interface for simultaneous neurostimulation and electrophysiology

Controlling neuronal activity using implantable neural interfaces constitutes an important tool to understand and develop novel strategies against brain diseases. Infrared neurostimulation is a promising alternative to optogenetics for controlling the neuronal circuitry with high spatial resolution. However, bi-directional interfaces capable of simultaneously delivering infrared light and recording electrical signals from the brain with minimal inflammation have not yet been reported. Here, we have developed a soft fibre-based device using high-performance polymers which are >100-fold softer than conventional silica glass used in standard optical fibres. The developed implant is capable of stimulating the brain activity in localized cortical domains by delivering laser pulses in the 2 μm spectral region while recording electrophysiological signals. Action and local field potentials were recorded in vivo from the motor cortex and hippocampus in acute and chronic settings, respectively. Immunohistochemical analysis of the brain tissue indicated insignificant inflammatory response to the infrared pulses while the signal-to-noise ratio of recordings still remained high. Our neural interface constitutes a step forward in expanding infrared neurostimulation as a versatile approach for fundamental research and clinically translatable therapies.

Closed-Loop Adaptive Deep Brain Stimulation in Parkinson's Disease: Procedures to Achieve It and Future Perspectives

Parkinson's disease (PD) is a neurodegenerative disease with a heavy burden on patients, families, and society. Deep brain stimulation (DBS) can improve the symptoms of PD patients for whom medication is insufficient. However, current open-loop uninterrupted conventional DBS (cDBS) has inherent limitations, such as adverse effects, rapid battery consumption, and a need for frequent parameter adjustment. To overcome these shortcomings, adaptive DBS (aDBS) was proposed to provide responsive optimized stimulation for PD. This topic has attracted scientific interest, and a growing body of preclinical and clinical evidence has shown its benefits. However, both achievements and challenges have emerged in this novel field. To date, only limited reviews comprehensively analyzed the full framework and procedures for aDBS implementation. Herein, we review current preclinical and clinical data on aDBS for PD to discuss the full procedures for its achievement and to provide future perspectives on this treatment.

Open Mind Neuromodulation Interface for the CorTec Brain Interchange (OMNI-BIC): an investigational distributed research platform for next-generation clinical neuromodulation research

The rise of adaptive stimulation approaches has shown great therapeutic promise in the growing field of neuromodulation. The discovery and growth of these novel adaptive stimulation paradigms has been largely concentrated around several implantable devices with research application programming interfaces (APIs) that allow for custom applications to be created for clinical neuromodulation studies. However, the sunsetting of devices and ongoing development of new platforms is leading to an increased fragmentation in the research environment- resulting in the reinvention of system features and the inability to leverage previous development efforts for future studies. The Open Mind Neuromodulation Interface (OMNI) is a previously proposed solution to address the weaknesses of the DLL-driven API approach of past neuromodulation research by utilizing an alternative gRPC-enabled microservice framework. Here, we introduce OMNI-BIC, an implementation of the OMNI framework to the CorTec Brain Interchange system. This paper describes the design and implementation of the OMNI-BIC software tools and demonstrates the framework's capabilities for implementing customized neuromodulation therapies for clinical investigations. Through the development and deployment of the OMNI-BIC system, we hope to improve future clinical studies with the Brain Interchange system and aid in continuing the growth and momentum of the exciting field of adaptive neuromodulation.

Effects of Contralateral Deep Brain Stimulation and Levodopa on Subthalamic Nucleus Oscillatory Activity and Phase-Amplitude Coupling

BACKGROUND

The modulatory effects of medication and deep brain stimulation (DBS) on subthalamic nucleus (STN) neural activity in Parkinson's disease have been widely studied. However, effects on the contralateral side to the stimulated STN, in particular, changes in local field potential (LFP) oscillatory activity and phase-amplitude coupling (PAC), have not yet been reported.

OBJECTIVE

The aim of this study was to examine changes in STN LFP activity across a range of frequency bands and STN PAC for different combinations of DBS and medication on/off on the side contralateral to the applied stimulation.

MATERIALS AND METHODS

We examined STN LFPs that were recorded using externalized leads from eight parkinsonian patients during unilateral DBS from the side contralateral to the stimulation. LFP spectral power in alpha (5 to ∼13 Hz), low beta (13 to ∼20 Hz), high beta (20-30 Hz), and high gamma plus high-frequency oscillation (high gamma+HFO) (100-400 Hz) bands were estimated for different combinations of medication and unilateral stimulation (off/on). PAC between beta and high gamma+HFO in the STN LFPs was also investigated. The effect of the condition was examined using linear mixed models.

RESULTS

PAC in the STN LFP was reduced by DBS when compared to the baseline condition (no medication and stimulation). Medication had no significant effect on PAC. Alpha power decreased with DBS, both alone and when combined with medication. Beta power decreased with DBS, medication, and DBS and medication combined. High gamma+HFO power increased during the application of contralateral DBS and was unaltered by medication.

CONCLUSIONS

The results provide new insights into the effects of DBS and levodopa on STN LFP PAC and oscillatory activity on the side contralateral to stimulation. These may have important implications in understanding mechanisms underlying motor improvements with DBS, including changes on both contralateral and ipsilateral sides, while suggesting a possible role for contralateral sensing during unilateral DBS.

Effect and Relationship of Gait on Subcortical Local Field Potentials in Parkinson's Disease: A Systematic Review

OBJECTIVES

Developments in deep brain stimulation (DBS) technology have enabled the ability to detect local field potentials (LFPs) in Parkinson disease (PD). Gait dysfunction is one of the most prevalent deficits seen in PD. However, no consensus has been reached on the effect of gait on LFPs and the relationship between LFPs and clinical measures of gait. The objective of this systematic review was to synthesize existing research regarding the relationship between gait dysfunction and LFPs in PD.

METHODS

A systematic search of the literature yielded a total of ten articles, including 132 patients with PD, which met the criteria for inclusion.

RESULTS

Beta frequency band measures showed low-to-strong correlation to clinical gait measures (r = -0.50 to 0.82). Two studies found decreased beta power during gait; one found increased beta frequency peaks during gait; and one found higher beta power during dual-task gait than during single-task gait. One of the three studies comparing patients with and without freezing found significantly increased beta burst duration and power during gait in freezers compared with nonfreezers. All studies showed moderate-to-high methodologic quality.

CONCLUSIONS

This review highlights the need to consider the effect of gait on LFP recordings, particularly when used to guide DBS programming. Although sample sizes were small, it appears LFPs are associated to and modulated by gait in patients with PD. This evidence suggests that LFPs have the potential to be used as a biomarker of gait dysfunction in PD.

Efficient suppression of parkinsonian beta oscillations in a closed-loop model of deep brain stimulation with amplitude modulation

Introduction

Parkinson's disease (PD) is a movement disorder characterized by the pathological beta band (15-30 Hz) neural oscillations within the basal ganglia (BG). It is shown that the suppression of abnormal beta oscillations is correlated with the improvement of PD motor symptoms, which is a goal of standard therapies including deep brain stimulation (DBS). To overcome the stimulation-induced side effects and inefficiencies of conventional DBS (cDBS) and to reduce the administered stimulation current, closed-loop adaptive DBS (aDBS) techniques were developed. In this method, the frequency and/or amplitude of stimulation are modulated based on various disease biomarkers.

Methods

Here, by computational modeling of a cortico-BG-thalamic network in normal and PD conditions, we show that closed-loop aDBS of the subthalamic nucleus (STN) with amplitude modulation leads to a more effective suppression of pathological beta oscillations within the parkinsonian BG.

Results

Our results show that beta band neural oscillations are restored to their normal range and the reliability of the response of the thalamic neurons to motor cortex commands is retained due to aDBS with amplitude modulation. Furthermore, notably less stimulation current is administered during aDBS compared with cDBS due to a closed-loop control of stimulation amplitude based on the STN local field potential (LFP) beta activity.

Discussion

Efficient models of closed-loop stimulation may contribute to the clinical development of optimized aDBS techniques designed to reduce potential stimulation-induced side effects of cDBS in PD patients while leading to a better therapeutic outcome.

Closed-Loop Neuromodulation and Self-Perception in Clinical Treatment of Refractory Epilepsy

Background: Newer "closed-loop" neurostimulation devices in development could, in theory, induce changes to patients' personalities and self-perceptions. Empirically, however, only limited data of patient and family experiences exist. Responsive neurostimulation (RNS) as a treatment for refractory epilepsy is the first approved and commercially available closed-loop brain stimulation system in clinical practice, presenting an opportunity to observe how conceptual neuroethical concerns manifest in clinical treatment.Methods: We conducted ethnographic research at a single academic medical center with an active RNS treatment program and collected data via direct observation of clinic visits and in-depth interviews with 12 patients and their caregivers. We used deductive and inductive analyses to identify the relationship between these devices and patient changes in personality and self-perception.Results: Participants generally did not attribute changes in patients' personalities or self-perception to implantation of or stimulation using RNS. They did report that RNS affected patients' experiences and conceptions of illness. In particular, the capacity to store and display electrophysiological data produced a common frame of reference and a shared vocabulary among patients and clinicians.Discussion: Empirical experiences of a clinical population being treated with closed-loop neuromodulation do not corroborate theoretical concerns about RNS devices described by neuroethicists and technology developers. However, closed-loop devices demonstrated an ability to change illness experiences. Even without altering identify and self-perception, they provided new cultural tools and metaphors for conceiving of epilepsy as an illness and of the process of diagnosis and treatment. These findings call attention to the need to situate neuroethical concerns in the broader contexts of patients' illness experiences and social circumstances.

Movement decoding using spatio-spectral features of cortical and subcortical local field potentials

The first commercially sensing enabled deep brain stimulation (DBS) devices for the treatment of movement disorders have recently become available. In the future, such devices could leverage machine learning based brain signal decoding strategies to individualize and adapt therapy in real-time. As multi-channel recordings become available, spatial information may provide an additional advantage for informing machine learning models. To investigate this concept, we compared decoding performances from single channels vs. spatial filtering techniques using intracerebral multitarget electrophysiology in Parkinson's disease patients undergoing DBS implantation. We investigated the feasibility of spatial filtering in invasive neurophysiology and the putative utility of combined cortical ECoG and subthalamic local field potential signals for decoding grip-force, a well-defined and continuous motor readout. We found that adding spatial information to the model can improve decoding (6% gain in decoding), but the spatial patterns and additional benefit was highly individual. Beyond decoding performance results, spatial filters and patterns can be used to obtain meaningful neurophysiological information about the brain networks involved in target behavior. Our results highlight the importance of individualized approaches for brain signal decoding, for which multielectrode recordings and spatial filtering can improve precision medicine approaches for clinical brain computer interfaces.

Predictive neuromodulation of cingulo-frontal neural dynamics in major depressive disorder using a brain-computer interface system: A simulation study

Introduction

Deep brain stimulation (DBS) is a promising therapy for treatment-resistant major depressive disorder (MDD). MDD involves the dysfunction of a brain network that can exhibit complex nonlinear neural dynamics in multiple frequency bands. However, current open-loop and responsive DBS methods cannot track the complex multiband neural dynamics in MDD, leading to imprecise regulation of symptoms, variable treatment effects among patients, and high battery power consumption.

Methods

Here, we develop a closed-loop brain-computer interface (BCI) system of predictive neuromodulation for treating MDD. We first use a biophysically plausible ventral anterior cingulate cortex (vACC)-dorsolateral prefrontal cortex (dlPFC) neural mass model of MDD to simulate nonlinear and multiband neural dynamics in response to DBS. We then use offline system identification to build a dynamic model that predicts the DBS effect on neural activity. We next use the offline identified model to design an online BCI system of predictive neuromodulation. The online BCI system consists of a dynamic brain state estimator and a model predictive controller. The brain state estimator estimates the MDD brain state from the history of neural activity and previously delivered DBS patterns. The predictive controller takes the estimated MDD brain state as the feedback signal and optimally adjusts DBS to regulate the MDD neural dynamics to therapeutic targets. We use the vACC-dlPFC neural mass model as a simulation testbed to test the BCI system and compare it with state-of-the-art open-loop and responsive DBS treatments of MDD.

Results

We demonstrate that our dynamic model accurately predicts nonlinear and multiband neural activity. Consequently, the predictive neuromodulation system accurately regulates the neural dynamics in MDD, resulting in significantly smaller control errors and lower DBS battery power consumption than open-loop and responsive DBS.

Discussion

Our results have implications for developing future precisely-tailored clinical closed-loop DBS treatments for MDD.

Neuromorphic-Based Neuroprostheses for Brain Rewiring: State-of-the-Art and Perspectives in Neuroengineering

Neuroprostheses are neuroengineering devices that have an interface with the nervous system and supplement or substitute functionality in people with disabilities. In the collective imagination, neuroprostheses are mostly used to restore sensory or motor capabilities, but in recent years, new devices directly acting at the brain level have been proposed. In order to design the next-generation of neuroprosthetic devices for brain repair, we foresee the increasing exploitation of closed-loop systems enabled with neuromorphic elements due to their intrinsic energy efficiency, their capability to perform real-time data processing, and of mimicking neurobiological computation for an improved synergy between the technological and biological counterparts. In this manuscript, after providing definitions of key concepts, we reviewed the first exploitation of a real-time hardware neuromorphic prosthesis to restore the bidirectional communication between two neuronal populations in vitro. Starting from that 'case-study', we provide perspectives on the technological improvements for real-time interfacing and processing of neural signals and their potential usage for novel in vitro and in vivo experimental designs. The development of innovative neuroprosthetics for translational purposes is also presented and discussed. In our understanding, the pursuit of neuromorphic-based closed-loop neuroprostheses may spur the development of novel powerful technologies, such as 'brain-prostheses', capable of rewiring and/or substituting the injured nervous system.

Neuron matters: neuromodulation with electromagnetic stimulation must consider neurons as dynamic identities

Neuromodulation with electromagnetic stimulation is widely used for the control of abnormal neural activity, and has been proven to be a valuable alternative to pharmacological tools for the treatment of many neurological diseases. Tremendous efforts have been focused on the design of the stimulation apparatus (i.e., electrodes and magnetic coils) that delivers the electric current to the neural tissue, and the optimization of the stimulation parameters. Less attention has been given to the complicated, dynamic properties of the neurons, and their context-dependent impact on the stimulation effects. This review focuses on the neuronal factors that influence the outcomes of electromagnetic stimulation in neuromodulation. Evidence from multiple levels (tissue, cellular, and single ion channel) are reviewed. Properties of the neural elements and their dynamic changes play a significant role in the outcome of electromagnetic stimulation. This angle of understanding yields a comprehensive perspective of neural activity during electrical neuromodulation, and provides insights in the design and development of novel stimulation technology.

Clinical neuroscience and neurotechnology: An amazing symbiosis

In the last decades, clinical neuroscience found a novel ally in neurotechnologies, devices able to record and stimulate electrical activity in the nervous system. These technologies improved the ability to diagnose and treat neural disorders. Neurotechnologies are concurrently enabling a deeper understanding of healthy and pathological dynamics of the nervous system through stimulation and recordings during brain implants. On the other hand, clinical neurosciences are not only driving neuroengineering toward the most relevant clinical issues, but are also shaping the neurotechnologies thanks to clinical advancements. For instance, understanding the etiology of a disease informs the location of a therapeutic stimulation, but also the way stimulation patterns should be designed to be more effective/naturalistic. Here, we describe cases of fruitful integration such as Deep Brain Stimulation and cortical interfaces to highlight how this symbiosis between clinical neuroscience and neurotechnology is closer to a novel integrated framework than to a simple interdisciplinary interaction.

Principles of gait encoding in the subthalamic nucleus of people with Parkinson's disease

Disruption of subthalamic nucleus dynamics in Parkinson's disease leads to impairments during walking. Here, we aimed to uncover the principles through which the subthalamic nucleus encodes functional and dysfunctional walking in people with Parkinson's disease. We conceived a neurorobotic platform embedding an isokinetic dynamometric chair that allowed us to deconstruct key components of walking under well-controlled conditions. We exploited this platform in 18 patients with Parkinson's disease to demonstrate that the subthalamic nucleus encodes the initiation, termination, and amplitude of leg muscle activation. We found that the same fundamental principles determine the encoding of leg muscle synergies during standing and walking. We translated this understanding into a machine learning framework that decoded muscle activation, walking states, locomotor vigor, and freezing of gait. These results expose key principles through which subthalamic nucleus dynamics encode walking, opening the possibility to operate neuroprosthetic systems with these signals to improve walking in people with Parkinson's disease.

A Systematic Review of Treatment Outcome Predictors in Deep Brain Stimulation for Refractory Obsessive-Compulsive Disorder

Obsessive-compulsive disorder (OCD) is a chronic and debilitating mental disorder. Deep brain stimulation (DBS) is a promising approach for refractory OCD patients. Research aiming at treatment outcome prediction is vital to provide optimized treatments for different patients. The primary purpose of this systematic review was to collect and synthesize studies on outcome prediction of OCD patients with DBS implantations in recent years. This systematic review (PROSPERO registration number: CRD42022335585) followed the PRISMA (Preferred Reporting Items for Systematic Review and Meta-analysis) guidelines. The search was conducted using three different databases with the following search terms related to OCD and DBS. We identified a total of 3814 articles, and 17 studies were included in our review. A specific tract confirmed by magnetic resonance imaging (MRI) was predictable for DBS outcome regardless of implant targets, but inconsistencies still exist. Current studies showed various ways of successful treatment prediction. However, considering the heterogeneous results, we hope that future studies will use larger cohorts and more precise approaches for predictors and establish more personalized ways of DBS surgeries.

Long Term Performance of a Bi-Directional Neural Interface for Deep Brain Stimulation and Recording

Background: In prior reports, we described the design and initial performance of a fully implantable, bi-directional neural interface system for use in deep brain and other neurostimulation applications. Here we provide an update on the chronic, long-term neural sensing performance of the system using traditional 4-contact leads and extend those results to include directional 8-contact leads.

Methods: Seven ovine subjects were implanted with deep brain stimulation (DBS) leads at different nodes within the Circuit of Papez: four with unilateral leads in the anterior nucleus of the thalamus and hippocampus; two with bilateral fornix leads, and one with bilateral hippocampal leads. The leads were connected to either an Activa PC+S^® (Medtronic) or Percept PC^°ledR (Medtronic) deep brain stimulation and recording device. Spontaneous local field potentials (LFPs), evoked potentials (EPs), LFP response to stimulation, and electrode impedances were monitored chronically for periods of up to five years in these subjects.

Results: The morphology, amplitude, and latencies of chronic hippocampal EPs evoked by thalamic stimulation remained stable over the duration of the study. Similarly, LFPs showed consistent spectral peaks with expected variation in absolute magnitude dependent upon behavioral state and other factors, but no systematic degradation of signal quality over time. Electrode impedances remained within expected ranges with little variation following an initial stabilization period. Coupled neural activity between the two nodes within the Papez circuit could be observed in synchronized recordings up to 5 years post-implant. The magnitude of passive LFP power recorded from directional electrode segments was indicative of the contacts that produced the greatest stimulation-induced changes in LFP power within the Papez network.

Conclusion: The implanted device performed as designed, providing the ability to chronically stimulate and record neural activity within this network for up to 5 years of follow-up.

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.

DyNeuMo Mk-1: Design and pilot validation of an investigational motion-adaptive neurostimulator with integrated chronotherapy

There is growing interest in using adaptive neuromodulation to provide a more personalized therapy experience that might improve patient outcomes. Current implant technology, however, can be limited in its adaptive algorithm capability. To enable exploration of adaptive algorithms with chronic implants, we designed and validated the 'Picostim DyNeuMo Mk-1' (DyNeuMo Mk-1 for short), a fully-implantable, adaptive research stimulator that titrates stimulation based on circadian rhythms (e.g. sleep, wake) and the patient's movement state (e.g. posture, activity, shock, free-fall). The design leverages off-the-shelf consumer technology that provides inertial sensing with low-power, high reliability, and relatively modest cost. The DyNeuMo Mk-1 system was designed, manufactured and verified using ISO 13485 design controls, including ISO 14971 risk management techniques to ensure patient safety, while enabling novel algorithms. The system was validated for an intended use case in movement disorders under an emergency-device authorization from the Medicines and Healthcare Products Regulatory Agency (MHRA). The algorithm configurability and expanded stimulation parameter space allows for a number of applications to be explored in both central and peripheral applications. Intended applications include adaptive stimulation for movement disorders, synchronizing stimulation with circadian patterns, and reacting to transient inertial events such as posture changes, general activity, and walking. With appropriate design controls in place, first-in-human research trials are now being prepared to explore the utility of automated motion-adaptive algorithms.

Developments in Deep Brain Stimulators for Successful Aging Towards Smart Devices—An Overview

This work provides an overview of the present state-of-the-art in the development of deep brain Deep Brain Stimulation (DBS) and how such devices alleviate motor and cognitive disorders for a successful aging. This work reviews chronic diseases that are addressable via DBS, reporting also the treatment efficacies. The underlying mechanism for DBS is also reported. A discussion on hardware developments focusing on DBS control paradigms is included specifically the open- and closed-loop “smart” control implementations. Furthermore, developments towards a “smart” DBS, while considering the design challenges, current state of the art, and constraints, are also presented. This work also showcased different methods, using ambient energy scavenging, that offer alternative solutions to prolong the battery life of the DBS device. These are geared towards a low maintenance, semi-autonomous, and less disruptive device to be used by the elderly patient suffering from motor and cognitive disorders.

Deep brain stimulation for Parkinson’s Disease: A Review and Future Outlook

Parkinson's Disease (PD) is a neurodegenerative disorder that manifests as an impairment of motor and non-motor abilities due to a loss of dopamine input to deep brain structures. While there is presently no cure for PD, a variety of pharmacological and surgical therapeutic interventions have been developed to manage PD symptoms. This review explores the past, present and future outlooks of PD treatment, with particular attention paid to deep brain stimulation (DBS), the surgical procedure to deliver DBS, and its limitations. Finally, our group's efforts with respect to brain mapping for DBS targeting will be discussed.

Classification of Whisker Deflections From Evoked Responses in the Somatosensory Barrel Cortex With Spiking Neural Networks

Spike-based neuromorphic hardware has great potential for low-energy brain-machine interfaces, leading to a novel paradigm for neuroprosthetics where spiking neurons in silicon read out and control activity of brain circuits. Neuromorphic processors can receive rich information about brain activity from both spikes and local field potentials (LFPs) recorded by implanted neural probes. However, it was unclear whether spiking neural networks (SNNs) implemented on such devices can effectively process that information. Here, we demonstrate that SNNs can be trained to classify whisker deflections of different amplitudes from evoked responses in a single barrel of the rat somatosensory cortex. We show that the classification performance is comparable or even superior to state-of-the-art machine learning approaches. We find that SNNs are rather insensitive to recorded signal type: both multi-unit spiking activity and LFPs yield similar results, where LFPs from cortical layers III and IV seem better suited than those of deep layers. In addition, no hand-crafted features need to be extracted from the data—multi-unit activity can directly be fed into these networks and a simple event-encoding of LFPs is sufficient for good performance. Furthermore, we find that the performance of SNNs is insensitive to the network state—their performance is similar during UP and DOWN states.

Control of coupled neural oscillations using near-periodic inputs

Deep brain stimulation (DBS) is a commonly used treatment for medication resistant Parkinson's disease and is an emerging treatment for other neurological disorders. More recently, phase-specific adaptive DBS (aDBS), whereby the application of stimulation is locked to a particular phase of tremor, has been proposed as a strategy to improve therapeutic efficacy and decrease side effects. In this work, in the context of these phase-specific aDBS strategies, we investigate the dynamical behavior of large populations of coupled neurons in response to near-periodic stimulation, namely, stimulation that is periodic except for a slowly changing amplitude and phase offset that can be used to coordinate the timing of applied input with a specified phase of model oscillations. Using an adaptive phase-amplitude reduction strategy, we illustrate that for a large population of oscillatory neurons, the temporal evolution of the associated phase distribution in response to near-periodic forcing can be captured using a reduced order model with four state variables. Subsequently, we devise and validate a closed-loop control strategy to disrupt synchronization caused by coupling. Additionally, we identify strategies for implementing the proposed control strategy in situations where underlying model equations are unavailable by estimating the necessary terms of the reduced order equations in real-time from observables.

Brain oscillations and Parkinson disease

Brain oscillations have been associated with Parkinson's disease (PD) for a long time mainly due to the fundamental oscillatory nature of parkinsonian rest tremor. Over the years, this association has been extended to frequencies well above that of tremor, largely owing to the opportunities offered by deep brain stimulation (DBS) to record electrical activity directly from the patients' basal ganglia. This chapter reviews the results of research on brain oscillations in PD focusing on theta (4-7Hz), beta (13-35Hz), gamma (70-80Hz) and high-frequency oscillations (200-400Hz). For each of these oscillations, we describe localization and interaction with brain structures and between frequencies, changes due to dopamine intake, task-related modulation, and clinical relevance. The study of brain oscillations will also help to dissect the mechanisms of action of DBS. Overall, the chapter tentatively depicts PD in terms of "oscillopathy."

Double-blind cross-over pilot trial protocol to evaluate the safety and preliminary efficacy of long-term adaptive deep brain stimulation in patients with Parkinson's disease

INTRODUCTION

After several years of brain-sensing technology development and proof-of-concept studies, adaptive deep brain stimulation (aDBS) is ready to better treat Parkinson's disease (PD) using aDBS-capable implantable pulse generators (IPGs). New aDBS devices are capable of continuous sensing of neuronal activity from the subthalamic nucleus (STN) and contemporaneous stimulation automatically adapted to match the patient's clinical state estimated from the analysis of STN activity using proprietary algorithms. Specific studies are necessary to assess superiority of aDBS vs conventional DBS (cDBS) therapy. This protocol describes an original innovative multicentre international study aimed to assess safety and efficacy of aDBS vs cDBS using a new generation of DBS IPG in PD (AlphaDBS system by Newronika SpA, Milan, Italy).

METHODS

The study involves six investigational sites (in Italy, Poland and The Netherlands). The primary objective will be to evaluate the safety and tolerability of the AlphaDBS System, when used in cDBS and aDBS mode. Secondary objective will be to evaluate the potential efficacy of aDBS. After eligibility screening, 15 patients with PD already implanted with DBS systems and in need of battery replacement will be randomised to enter a two-phase protocol, including a 'short-term follow-up' (2 days experimental sessions during hospitalisation, 1 day per each mode) and a 'long-term follow-up' (1 month at home, 15 days per each mode).

ETHICS AND DISSEMINATION

The trial was approved as premarket study by the Italian, Polish, and Dutch Competent Authorities: Bioethics Committee at National Oncology Institute of Maria Skłodowska-Curie-National Research Institute in Warsaw; Comitato Etico Milano Area 2; Comitato Etico IRCCS Istituto Neurologico C. Besta; Comitato Etico interaziendale AOUC Città della Salute e della Scienza-AO Ordine Mauriziano di Torino-ASL Città di Torino; De Medisch Ethisch Toetsingscommissie van Maastricht UMC. The study started enrolling patients in January 2021.

TRIAL REGISTRATION NUMBER

NCT04681534.

Improving suction technology for nerve activity recording

BACKGROUND

Extracellular recording of nerve activities using suction electrodes is an easy yet powerful tool in characterizing neural activities in physiology and pathological conditions. The key factors that determine the quality of suction electrode recordings have not been fully investigated. New Methods: Here, we proposed a biophysical model to study the mechanisms underlying suction technology for axon recording. The model focuses on the interpretation of the recorded single neuron activity based on the location of the electrode, the integrity of the recorded tissue, and the tightness of the suction. To directly test these model predictions, we applied two channel recordings from the nerves in Aplysia californica, and analyzed the shape of the extracellularly recorded single neuron activity under various conditions.

RESULTS

We found that both the recording site and the integrity of the neural tissue impact the shape of the action potentials traveling along the axon. In practice, the tightness of the suction is the key parameter for high-quality recordings using a suction electrode. Comparison with Existing Methods: Experimental protocols that can improve precise positioning of the electrode tip to the target nerve, avoid tissue damage, enhance suction force, and maintain tightness are essential for high-quality suction recording from axons. Current methods have not emphasized on achieving and maintaining of the suction pressure during experimentation, and have sometimes ignored the impact of suction electrode position or tissue damage to the quality of the recorded neural signal.

CONCLUSIONS

A combined theoretical analysis and experimental approach is essential in improving neural recording technology. The work provides theoretical and practical guidelines to improve suction technology. This work also provides valuable insights to the improvement of several other extracellular recording technology in laboratory research or clinical settings.

Physiologically informed neuromodulation

The rapid evolution of neuromodulation techniques includes an increasing amount of research into stimulation paradigms that are guided by patients' neurophysiology, to increase efficacy and responder rates. Treatment personalisation and target engagement have shown to be effective in fields such as Parkinson's disease, and closed-loop paradigms have been successfully implemented in cardiac defibrillators. Promising avenues are being explored for physiologically informed neuromodulation in psychiatry. Matching the stimulation frequency to individual brain rhythms has shown some promise in transcranial magnetic stimulation (TMS). Matching the phase of those rhythms may further enhance neuroplasticity, for instance when combining TMS with electroencephalographic (EEG) recordings. Resting-state EEG and event-related potentials may be useful to demonstrate connectivity between stimulation sites and connected areas. These techniques are available today to the psychiatrist to diagnose underlying sleep disorders, epilepsy, or lesions as contributing factors to the cause of depression. These technologies may also be useful in assessing the patient's brain network status prior to deciding on treatment options. Ongoing research using invasive recordings may allow for future identification of mood biomarkers and network structure. A core limitation is that biomarker research may currently be limited by the internal heterogeneity of psychiatric disorders according to the current DSM-based classifications. New approaches are being developed and may soon be validated. Finally, care must be taken when incorporating closed-loop capabilities into neuromodulation systems, by ensuring the safe operation of the system and understanding the physiological dynamics. Neurophysiological tools are rapidly evolving and will likely define the next generation of neuromodulation therapies.

A New Implantable Closed-Loop Clinical Neural Interface: First Application in Parkinson's Disease

Deep brain stimulation (DBS) is used for the treatment of movement disorders, including Parkinson’s disease, dystonia, and essential tremor, and has shown clinical benefits in other brain disorders. A natural path for the improvement of this technique is to continuously observe the stimulation effects on patient symptoms and neurophysiological markers. This requires the evolution of conventional deep brain stimulators to bidirectional interfaces, able to record, process, store, and wirelessly communicate neural signals in a robust and reliable fashion. Here, we present the architecture, design, and first use of an implantable stimulation and sensing interface (AlphaDBS^R System) characterized by artifact-free recording and distributed data management protocols. Its application in three patients with Parkinson’s disease (clinical trial n. NCT04681534) is shown as a proof of functioning of a clinically viable implanted brain-computer interface (BCI) for adaptive DBS. Reliable artifact free-recordings, and chronic long-term data and neural signal management are in place.

Real-time removal of stimulation artifacts in closed-loop deep brain stimulation

Objective.Closed-loop deep brain stimulation (DBS) with neural feedback has shown great potential in improving the therapeutic effect and reducing side effects. However, the amplitude of stimulation artifacts is much larger than the local field potentials, which remains a bottleneck in developing a closed-loop stimulation strategy with varied parameters.Approach.We proposed an irregular sampling method for the real-time removal of stimulation artifacts. The artifact peaks were detected by applying a threshold to the raw recordings, and the samples within the contaminated period of the stimulation pulses were excluded and replaced with the interpolation of the samples prior to and after the stimulation artifact duration. This method was evaluated with both simulation signals andin vivoclosed-loop DBS applications in Parkinsonian animal models.Main results. The irregular sampling method was able to remove the stimulation artifacts effectively with the simulation signals. The relative errors between the power spectral density of the recovered and true signals within a wide frequency band (2-150 Hz) were 2.14%, 3.93%, 7.22%, 7.97% and 6.25% for stimulation at 20 Hz, 60 Hz, 130 Hz, 180 Hz, and stimulation with variable low and high frequencies, respectively. This stimulation artifact removal method was verified in real-time closed-loop DBS applicationsin vivo, and the artifacts were effectively removed during stimulation with frequency continuously changing from 130 Hz to 1 Hz and stimulation adaptive to beta oscillations.Significance.The proposed method provides an approach for real-time removal in closed-loop DBS applications, which is effective in stimulation with low frequency, high frequency, and variable frequency. This method can facilitate the development of more advanced closed-loop DBS strategies.

A nationwide survey on clinical neurophysiology education in Italian schools of specialization in neurology

Introduction

Clinical neurophysiology deals with nervous system functions assessed with electrophysiological and ultrasound-based imaging techniques. Even though the need for highly specialized neurophysiologists has increased, residency training rarely takes today’s requirements into account. This study aimed to snapshot the neurophysiological training provided by Italian specialization schools in neurology.

Methods

A single-page web-based survey comprising 13 multiple-choice categorical and interval scale questions was sent via e-mail to neurology specialization school directors. The survey addressed the programs’ structural neurophysiology organization, time dedicated to each clinical neurophysiology subspecialty, and descriptors assessing the discipline’s importance (e.g., residents who attempted residential courses, gained certifications, or awards gained).

Results

The most studied neurophysiological techniques were electroencephalography (EEG) and electromyography (EMG). Most specialization schools devoted less than 3 months each to multimodal evoked potentials (EPs), ultrasound sonography (US), and intra-operative monitoring. Of the 35 specialization schools surveyed, 77.1% reported that four students, or fewer, participated in the Italian Society of Clinical Neurophysiology Examination in Neurophysiology. Of the 35 specialization centers surveyed, 11.4% declared that the final evaluation required students to discuss a neurophysiological test.

Discussion

Our survey underlined the poorly standardized technical requirements in postgraduate neurology specialization schools, wide variability among training programs, and limited training on multi-modal evoked potentials, intraoperative monitoring, and sonography. These findings underline the need to reappraise and improve educational and training standards for clinical neurophysiology during postgraduate specialization schools in neurology with an international perspective.

Dopaminergic Modulation of Spectral and Spatial Characteristics of Parkinsonian Subthalamic Nucleus Beta Bursts

In Parkinson’s disease (PD), subthalamic nucleus (STN) beta burst activity is pathologically elevated. These bursts are reduced by dopamine and deep brain stimulation (DBS). Therefore, these bursts have been tested as a trigger for closed-loop DBS. To provide better targeted parameters for closed-loop stimulation, we investigate the spatial distribution of beta bursts within the STN and if they are specific to a beta sub-band. Local field potentials (LFP) were acquired in the STN of 27 PD patients while resting. Based on the orientation of segmented DBS electrodes, the LFPs were classified as anterior, postero-medial, and postero-lateral. Each recording lasted 30 min with (ON) and without (OFF) dopamine. Bursts were detected in three frequency bands: ±3 Hz around the individual beta peak frequency, low beta band (lBB), and high beta band (hBB). Medication reduced the duration and the number of bursts per minute but not the amplitude of the beta bursts. The burst amplitude was spatially modulated, while the burst duration and rate were frequency dependent. Furthermore, the hBB burst duration was positively correlated with the akinetic-rigid UPDRS III subscore. Overall, these findings on differential dopaminergic modulation of beta burst parameters suggest that hBB burst duration is a promising target for closed-loop stimulation and that burst parameters could guide DBS programming.

Modulation of dopamine tone induces frequency shifts in cortico-basal ganglia beta oscillations

Βeta oscillatory activity (human: 13-35 Hz; primate: 8-24 Hz) is pervasive within the cortex and basal ganglia. Studies in Parkinson's disease patients and animal models suggest that beta-power increases with dopamine depletion. However, the exact relationship between oscillatory power, frequency and dopamine tone remains unclear. We recorded neural activity in the cortex and basal ganglia of healthy non-human primates while acutely and chronically up- and down-modulating dopamine levels. We assessed changes in beta oscillations in patients with Parkinson's following acute and chronic changes in dopamine tone. Here we show beta oscillation frequency is strongly coupled with dopamine tone in both monkeys and humans. Power, coherence between single-units and local field potentials (LFP), spike-LFP phase-locking, and phase-amplitude coupling are not systematically regulated by dopamine levels. These results demonstrate that beta frequency is a key property of pathological oscillations in cortical and basal ganglia networks.