Towards adaptive deep brain stimulation: clinical and technical notes on a novel commercial device for chronic brain sensing

Precisely-timed outpatient recordings of subcortical local field potentials from wireless streaming-capable deep-brain stimulators: a method and toolbox

BACKGROUND

Investigations of the electrophysiological mechanisms of the human subcortex have relied on recording local field potentials (LFPs) during deep-brain stimulation (DBS) neurosurgery. However, the neurosurgical setting severely restricts the research use of these recordings. Recently developed sensing-capable DBS devices wirelessly stream subcortical LFPs in outpatient settings. These recordings have tremendous potential for research. However, synchronizing them with other behavior or neural recordings is challenging, as the clinical devices do not accept digital timing information.

NEW METHOD

Switching the DBS device on introduces transient yet consistent artifacts in both the LFP and simultaneous scalp-EEG recordings. We use these artifacts as a reference to align these recordings (N = 20). We tested whether the alignment was precise enough to match a ground truth state (large artifacts produced by transcranial magnetic stimulation, TMS), yielded trial-averaged event-locked LFPs, and phase consistency across trials. We further evaluated the consistency of task-related LFPs across outpatient and perisurgical recordings.

RESULTS AND COMPARISON WITH EXISTING METHOD(S)

Previous alignment methods were limited because they relied on inconsistent on/offset features of DBS artifacts caused by ongoing stimulation. Moreover, they only provided limited validation. Our highly precise alignment method showed a maximum deviation of only 8 ms - clearly superior to prior techniques. Furthermore, event-related activity patterns were comparable across outpatient and perisurgical LFP recordings.

CONCLUSIONS

We present a method and a MATLAB toolbox that inserts the most precise digital timing information into wirelessly-streamed DBS-LFP recordings to date. By enabling event-related research with high-temporal precision, this method greatly enhances the utility of these recordings.

Management of freezing of gait - mechanism-based practical recommendations

Freezing of gait (FOG) is a debilitating motor symptom that commonly occurs in Parkinson disease, atypical parkinsonism and other neurodegenerative conditions. Management of FOG is complex and requires a multifaceted approach that includes pharmacological, surgical and non-pharmacological interventions. In this Expert Recommendation, we provide state-of-the-art practical recommendations for the management of FOG, based on the latest insights into the pathophysiology of the condition. We propose two complementary treatment flows, both of which are linked to the pathophysiology and tailored to specific FOG phenotypes. The first workflow focuses on the reduction of excessive inhibitory outflow from the basal ganglia through use of dopaminergic medication or advanced therapies such as deep brain stimulation and infusion therapy. The second workflow focuses on facilitation of processing across cerebral compensatory networks by use of non-pharmacological interventions. We also highlight interventions that have potential for FOG but are not supported by sufficient evidence to recommend for clinical application. Our updated recommendations are intended to enable effective symptomatic relief once FOG has developed, but we also consider potential targets for preventive approaches. The recommendations are based on scientific evidence where available, supplemented with practice-based evidence informed by our clinical experience.

Gamma entrainment induced by deep brain stimulation as a biomarker for motor improvement with neuromodulation

Finely tuned gamma (FTG) oscillations from the subthalamic nucleus (STN) and cortex in Parkinson's disease (PD) patients undergoing deep brain stimulation (DBS) are often associated with dyskinesia. Recently it was shown that DBS entrains gamma activity at 1:2 of the stimulation frequency; however, the functional role of this signal is not yet fully understood. We recorded local field potentials from the STN in 19 chronically implanted PD patients on dopaminergic medication during DBS, at rest, and during repetitive movements. Here we show that high-frequency DBS induced 1:2 gamma entrainment in 15/19 patients. Spontaneous FTG was present in 8 patients; in five of these patients dyskinesia occurred or were enhanced with entrained gamma activity during stimulation. Further, there was a significant increase in the power of 1:2 entrained gamma activity during movement in comparison to rest, while patients with entrainment had faster movements compared to those without. These findings argue for a functional relevance of the stimulation-induced 1:2 gamma entrainment in PD patients as a prokinetic activity that, however, is not necessarily promoting dyskinesia. DBS-induced entrainment can be a promising neurophysiological biomarker for identifying the optimal amplitude during closed-loop DBS.

Pattern Recognition of Neurotransmitters: Complexity Reduction for Serotonin and Dopamine

In this work, we simultaneously detected and predicted the concentration levels of serotonin (SE) and dopamine (DA) neurotransmitters (NTs) for in vitro mixtures, with measurements obtained using conventional glassy carbon electrodes (CGCEs) and differential pulse voltammetry (DPV). The NTs were estimated by deconvolving the multiplexed signals of both NTs using Principal Component Analysis with Gaussian Process Regression (PCA-GPR) and Partial Least Squares with Gaussian Process Regression (PLS-GPR), both with exponential-isotropic kernels. The average testing accuracies of estimation using PCA-GPR for DA alone, SE alone and their mixture (DA-SE) were 87.6%, 88.1%, and 96.7%, respectively. Using PLS-GPR, the testing accuracies of estimation for DA alone, SE alone, and their mixture (DA-SE) were 87.3%, 83.8%, and 95.1%, respectively. Furthermore, we explored methods of reducing the procedural complexity in estimating the NTs by finding reduced subsets of features for accurately detecting and predicting their concentrations. The reduced subsets of features found in the oxidation potential windows of the NTs improved the testing accuracy of the estimation of DA-SE to 97.4%. We thus believe that reducing complexity has the potential to increase the detection and prediction accuracies of NT measurements for practical clinical uses such as deep brain stimulation.

A technology framework for distributed preoperative planning and medical training in deep brain stimulation

Deep brain stimulation (DBS) is a groundbreaking therapy for movement disorders, necessitating precise planning and extensive training to ensure accurate electrode placement in critical brain regions, such as the thalamic nuclei. This paper introduces an innovative technology framework for DBS to support distributed, real-time preoperative planning and medical training. The system integrates advanced imaging techniques, interactive graphical representation, and real-time data synchronization to assist clinicians in accurately identifying essential anatomical structures and refining pre-surgical plans. At the platform's core are multi-volume rendering, segmentation, and virtual tool modeling algorithms that employ transparency and refinement controls to seamlessly merge and visualize different tissue types in 3D alongside their interactions with surgical tools. This method enhances visual clarity and provides a highly detailed depiction of crucial structures, ensuring the precision required for effective DBS planning. By delivering dynamic, real-time feedback, the framework supports improved decision-making and sets a new standard for collaborative DBS training and procedural preparation. The platform's web-based synchronization architecture enhances collaboration by allowing neurologists and surgeons to simultaneously interact with visualized data from any location. This functionality supports live feedback, promotes collaborative decision-making, and streamlines procedural planning, leading to improved surgical outcomes. Performance evaluations across various hardware configurations and web browsers demonstrate the platform's high rendering speed and low-latency data synchronization, ensuring responsive and reliable interactions essential for clinical use. Its adaptability makes it suitable for medical training, preoperative planning, and intraoperative support, accommodating a wide range of hardware setups and web environments to address the specific demands of DBS-related procedures. This research lays a robust foundation for advancing distributed clinical planning, comprehensive medical education, and improved patient care in neurostimulation therapies.

Individualized non-invasive deep brain stimulation of the basal ganglia using transcranial ultrasound stimulation

Transcranial ultrasound stimulation (TUS) offers precise, non-invasive neuromodulation, though its impact on human deep brain structures remains underexplored. Here we examined TUS-induced changes in the basal ganglia of 10 individuals with movement disorders (Parkinson's disease and dystonia) and 15 healthy participants. Local field potentials were recorded using deep brain stimulation (DBS) leads in the globus pallidus internus (GPi). Compared to sham, theta burst TUS (tbTUS) increased theta power during stimulation, while 10 Hz TUS enhanced beta power, with effects lasting up to 40 min. In healthy participants, a stop-signal task assessed tbTUS effects on the GPi, with pulvinar stimulation serving as an active sham. GPi TUS prolonged stop-signal reaction times, indicating impaired response inhibition, whereas pulvinar TUS had no effect. These findings provide direct electrophysiological evidence of TUS target engagement and specificity in deep brain structures, suggesting its potential as a noninvasive DBS strategy for neurological and psychiatric disorders.

Finely Tuned γ Tracks Medication Cycles in Parkinson's Disease: An Ambulatory Brain-Sense Study

BACKGROUND

Novel commercial brain-sense neurostimulators enable us to contextualize brain activity with symptom and medication states in real-life ambulatory settings in Parkinson's disease (PD). Although various candidate biomarkers have been proposed for adaptive deep brain stimulation (DBS), a comprehensive comparison of their ambulatory profiles is lacking.

OBJECTIVES

To systematically compare the ambulatory neurophysiological dynamics and clinical properties of three candidate biomarkers-low-frequency, beta (β), and finely tuned γ (FTG) activity.

METHODS

We investigated 14 PD patients implanted with the Medtronic Percept PC, who underwent up to two 4-week ambulatory multimodal recording periods on their regular medication and stimulation. Subthalamic nucleus local field potentials (LFPs) of low-frequency, β, and FTG activity were recorded. Additionally, objective motor symptom states, physical activity and heart rate using wearables, as well as medication-intake times, sleep-awake times, and subjective symptom states using diaries were co-registered. LFP dynamics were also compared to high-resolution in-hospital recordings under off/on dopaminergic medication and stimulation conditions.

RESULTS

FTG reliably indexed off to on medication states in the ambulatory setting at the group and individual levels, and these spectral dynamics could be anticipated by high-resolution in-hospital recordings. Both FTG and low-frequency correlated with wearable-based dyskinesia scores, whereas diary-based dyskinesia events were only linked to FTG. Importantly, FTG indicated on-medication states regardless of the presence of dyskinesia and despite potential motion and heart rate artifacts. The 24-hour profile revealed large circadian power shifts that may overdrive medication-intake dynamics.

CONCLUSION

Despite the limitations of low-temporal resolution recordings, this work provides valuable insights into the real-life dynamics of biomarkers. Specifically, it highlights the utility of FTG as a primary and reliable indicator of medication states for adaptive DBS. © 2025 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Comparison metrics and power trade-offs for BCI motor decoding circuit design

Brain signal decoders are increasingly being used in early clinical trials for rehabilitation and assistive applications such as motor control and speech decoding. As many Brain-Computer Interfaces (BCIs) need to be deployed in battery-powered or implantable devices, signal decoding must be performed using low-power circuits. This paper reviews existing hardware systems for BCIs, with a focus on motor decoding, to better understand the factors influencing the power and algorithmic performance of such systems. We propose metrics to compare the energy efficiency of a broad range of on-chip decoding systems covering Electroencephalography (EEG), Electrocorticography (ECoG), and Microelectrode Array (MEA) signals. Our analysis shows that achieving a given classification rate requires an Input Data Rate (IDR) that can be empirically estimated, a finding that is helpful for sizing new BCI systems. Counter-intuitively, our findings show a negative correlation between the power consumption per channel (PpC) and the Information Transfer Rate (ITR). This suggests that increasing the number of channels can simultaneously reduce the PpC through hardware sharing and increase the ITR by providing new input data. In fact, for EEG and ECoG decoding circuits, the power consumption is dominated by the complexity of signal processing. To better understand how to minimize this power consumption, we review the optimizations used in state-of-the-art decoding circuits.

Pitfalls and practical suggestions for using local field potential recordings in DBS clinical practice and research

Objective. Local field potential (LFP) recordings using chronically implanted sensing-enabled stimulators are a powerful tool for indexing symptom presence and severity in neurological and neuropsychiatric disorders, and for enhancing our neurophysiological understanding of brain processes. LFPs have gained interest as input signals for closed-loop deep brain stimulation (DBS) and can be used to inform DBS parameter selection. LFP recordings using chronically implanted sensing-enabled stimulators have various implementational challenges.Approach. Here we describe our collective experience using BrainSense (Medtronic®) for clinical and research work. We aim to provide insightful tips and practical advice to empower readers with the knowledge needed to navigate the intricacies of the device and make the most out of its features.Main results. The central issues that apply to several BrainSense features encompass restricted compatibility of stimulation configuration with sensing, differences in electrophysiological signal properties between 'stimulation OFF' and 'stimulation ON at 0.0 mA', and challenges associated with the internal clock of the neurostimulator. In addition, since recordings are obtained from bipolar and not monopolar channels, spatial certainty regarding the distribution of LFPs around the DBS electrode is limited. Several options exist to synchronize LFP time series with external data streams, but standardization and generalization are lacking. The use of at-home chronic LFP recording is limited by a low temporal and spectral resolution. Regarding at-home LFP snapshots, LFP time series are not stored, parts of the power spectrum are censored when stimulating at high or low frequencies, and the stimulation amplitude is not readily available.Significance. We discussed practical applications, implementation, system limitations, and pitfalls with the aim that sensing can be better applied for clinical practice and research.

N2GNet tracks gait performance from subthalamic neural signals in Parkinson's disease

Adaptive deep brain stimulation (DBS) provides individualized therapy for people with Parkinson's disease (PWP) by adjusting the stimulation in real-time using neural signals that reflect their motor state. Current algorithms, however, utilize condensed and manually selected neural features which may result in a less robust and biased therapy. In this study, we propose Neural-to-Gait Neural network (N2GNet), a novel deep learning-based regression model capable of tracking real-time gait performance from subthalamic nucleus local field potentials (STN LFPs). The LFP data were acquired when eighteen PWP performed stepping in place, and the ground reaction forces were measured to track their weight shifts representing gait performance. By exhibiting a stronger correlation with weight shifts compared to the higher-correlation beta power from the two leads and outperforming other evaluated model designs, N2GNet effectively leverages a comprehensive frequency band, not limited to the beta range, to track gait performance solely from STN LFPs.

Supraspinal contributions to defective antagonistic inhibition and freezing of gait in Parkinson's disease

The neuromuscular circuit mechanisms of freezing of gait in Parkinson's disease have received little study. Technological progress enables researchers chronically to sense local field potential activity of the basal ganglia in patients while walking. To study subthalamic activity and the circuit processes of supraspinal contributions to spinal motor integration, we recorded local field potentials, surface EMG of antagonistic leg muscles and gait kinematics in patients while walking and freezing. To evaluate the specificity of our findings, we controlled our findings to internally generated volitional stops. We found specific activation-deactivation abnormalities of oscillatory activity of the subthalamic nucleus both before and during a freeze. Furthermore, we were able to show with synchronization analyses that subthalamo-spinal circuits entrain the spinal motor neurons to a defective timing and activation pattern. The main neuromuscular correlates when turning into freezing were as follows: (i) disturbed reciprocity between antagonistic muscles; (ii) increased co-contraction of the antagonists; (iii) defective activation and time pattern of the gastrocnemius muscle; and (iv) increased subthalamo-muscular coherence with the gastrocnemius muscles before the freeze. Beyond the pathophysiological insights into the supraspinal mechanisms contributing to freezing of gait, our findings have potential to inform the conceptualization of future neurorestorative therapies.

Beta Oscillations in the Sensory Thalamus During Severe Facial Neuropathic Pain Using Novel Sensing Deep Brain Stimulation

OBJECTIVE

Advancements in deep brain stimulation (DBS) devices provide a unique opportunity to record local field potentials longitudinally to improve the efficacy of treatment for intractable facial pain. We aimed to identify potential electrophysiological biomarkers of pain in the ventral posteromedial nucleus (VPM) of the thalamus and periaqueductal gray (PAG) using a long-term sensing DBS system.

MATERIALS AND METHODS

We analyzed power spectra of ambulatory pain-related events from one patient implanted with a long-term sensing generator, representing different pain intensities (pain >7, pain >9) and pain qualities (no pain, burning, stabbing, and shocking pain). Power spectra were parametrized to separate oscillatory and aperiodic features and compared across the different pain states.

RESULTS

Overall, 96 events were marked during a 16-month follow-up. Parameterization of spectra revealed a total of 62 oscillatory peaks with most in the VPM (77.4%). The pain-free condition did not show any oscillations. In contrast, β peaks were observed in the VPM during all episodes (100%) associated with pain >9, 56% of episodes with pain >7, and 50% of burning pain events (center frequencies: 28.4 Hz, 17.8 Hz, and 20.7 Hz, respectively). Episodes of pain >9 indicated the highest relative β band power in the VPM and decreased aperiodic exponents (denoting the slope of the power spectra) in both the VPM and PAG.

CONCLUSIONS

For this patient, an increase in β band activity in the sensory thalamus was associated with severe facial pain, opening the possibility for closed-loop DBS in facial pain.

Chronic adaptive deep brain stimulation versus conventional stimulation in Parkinson's disease: a blinded randomized feasibility trial

Deep brain stimulation (DBS) is a widely used therapy for Parkinson's disease (PD) but lacks dynamic responsiveness to changing clinical and neural states. Feedback control might improve therapeutic effectiveness, but the optimal control strategy and additional benefits of 'adaptive' neurostimulation are unclear. Here we present the results of a blinded randomized cross-over pilot trial aimed at determining the neural correlates of specific motor signs in individuals with PD and the feasibility of using these signals to drive adaptive DBS. Four male patients with PD were recruited from a population undergoing DBS implantation for motor fluctuations, with each patient receiving adaptive DBS and continuous DBS. We identified stimulation-entrained gamma oscillations in the subthalamic nucleus or motor cortex as optimal markers of high versus low dopaminergic states and their associated residual motor signs in all four patients. We then demonstrated improved motor symptoms and quality of life with adaptive compared to clinically optimized standard stimulation. The results of this pilot trial highlight the promise of personalized adaptive neurostimulation in PD based on data-driven selection of neural signals. Furthermore, these findings provide the foundation for further larger clinical trials to evaluate the efficacy of personalized adaptive neurostimulation in PD and other neurological disorders. ClinicalTrials.gov registration: NCT03582891 .

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

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

Case report: Improvement of gait with adaptive deep brain stimulation in a patient with Parkinson's disease

Gait disturbance is a common and severe symptom of Parkinson's disease that severely impairs quality of life. Current treatments provide only partial benefits with wide variability in outcomes. Also, deep brain stimulation of the subthalamic nucleus (STN-DBS), a mainstay treatment for bradykinetic-rigid symptoms and parkinsonian tremor, is poorly effective on gait. We applied a novel DBS paradigm, adjusting the current amplitude linearly with respect to subthalamic beta power (adaptive DBS), in one parkinsonian patient with gait impairment and chronically stimulated with conventional DBS. We studied the kinematics of gait and gait initiation (anticipatory postural adjustments) as well as subthalamic beta oscillations with both conventional and adaptive DBS. With adaptive DBS, the patient showed a consistent and long-lasting improvement in walking while retaining benefits on other disease-related symptoms. We suggest that adaptive DBS can benefit gait in Parkinson's disease possibly by avoiding overstimulation and dysfunctional entrainment of the supraspinal locomotor network.

Comparison of beta peak detection algorithms for data-driven deep brain stimulation programming strategies in Parkinson's disease

Oscillatory activity within the beta frequency range (13-30 Hz) serves as a Parkinson's disease biomarker for tailoring deep brain stimulation (DBS) treatments. Currently, identifying clinically relevant beta signals, specifically frequencies of peak amplitudes within the beta spectral band, is a subjective process. To inform potential strategies for objective clinical decision making, we assessed algorithms for identifying beta peaks and devised a standardized approach for both research and clinical applications. Employing a novel monopolar referencing strategy, we utilized a brain sensing device to measure beta peak power across distinct contacts along each DBS electrode implanted in the subthalamic nucleus. We then evaluated the accuracy of ten beta peak detection algorithms against a benchmark established by expert consensus. The most accurate algorithms, all sharing similar underlying algebraic dynamic peak amplitude thresholding approaches, matched the expert consensus in performance and reliably predicted the clinical stimulation parameters during follow-up visits. These findings highlight the potential of algorithmic solutions to overcome the subjective bias in beta peak identification, presenting viable options for standardizing this process. Such advancements could lead to significant improvements in the efficiency and accuracy of patient-specific DBS therapy parameterization.

A method for the synchronization of inertial sensor signals and local field potentials from deep brain stimulation systems

Objective. Recent innovative neurostimulators allow recording local field potentials (LFPs) while performing motor tasks monitored by wearable sensors. Inertial sensors can provide quantitative measures of motor impairment in people with subthalamic nucleus deep brain stimulation. To the best of our knowledge, there is no validated method to synchronize inertial sensors and neurostimulators without an additional device. This study aims to define a new synchronization method to analyze disease-related brain activity patterns during specific motor tasks and evaluate how LFPs are affected by stimulation and medication.Approach. Fourteen male subjects treated with subthalamic nucleus deep brain stimulation were recruited to perform motor tasks in four different medication and stimulation conditions. In each condition, a synchronization protocol was performed consisting of taps on the implanted neurostimulator, which produces artifacts in the LFPs that a nearby inertial sensor can simultaneously record.Main results. In 64% of the recruited subjects, induced artifacts were detected at least in one condition. Among those subjects, 83% of the recordings could be synchronized offline analyzing LFPs and wearables data. The remaining recordings were synchronized by video analysis.Significance. The proposed synchronization method does not require an external system (e.g., TENS electrodes) and can be easily integrated into clinical practice. The procedure is simple and can be carried out in a short time. A proper and simple synchronization will also be useful to analyze subthalamic neural activity in the presence of specific events (e.g., freezing of gait events) to identify predictive biomarkers.

DBScope as a versatile computational toolbox for the visualization and analysis of sensing data from deep brain stimulation

Different neurostimulators for deep brain stimulation (DBS) come already with the ability to chronically sense local field potentials during stimulation. This invaluable new data has the potential to increase our understanding of disease-related brain activity patterns, their temporal evolution, and their modulation in response to therapies. It also gives the opportunity to unveil new electrophysiological biomarkers and ultimately bring adaptive stimulation therapies closer to clinical practice. Unfortunately, there are still very limited options on how to visualize, analyze, and exploit the full potential of the sensing data from these new DBS neurostimulators. To answer this need, we developed a free open-source toolbox, named DBScope, that imports data from neurostimulation devices and can be operated in two ways: via user interface and programmatically, as a library of functions. In this way, it can be used by both clinicians during clinical sessions (for instance, to visually inspect data from the current or previous in-clinic visits), and by researchers in their research pipelines (e.g., for pre-processing, feature extraction and biomarker search). All in all, the DBScope toolbox is set to facilitate the clinical decision-making process and the identification of clinically relevant biomarkers. The toolbox is already being used in clinical and research environments, and it is freely available to download at GitHub (where it is also fully documented).

Chronic intracranial recordings in the globus pallidus reveal circadian rhythms in Parkinson's disease

Circadian rhythms have been shown in the subthalamic nucleus (STN) in Parkinson's disease (PD), but only a few studies have focused on the globus pallidus internus (GPi). This retrospective study investigates GPi circadian rhythms in a large cohort of subjects with PD (130 recordings from 93 subjects) with GPi activity chronically recorded in their home environment. We found a significant change in GPi activity between daytime and nighttime in most subjects (82.4%), with a reduction in GPi activity at nighttime in 56.2% of recordings and an increase in activity in 26.2%. GPi activity in higher frequency bands ( > 20 Hz) was more likely to decrease at night and in patients taking extended-release levodopa medication. Our results suggest that circadian fluctuations in the GPi vary across individuals and that increased power at night might be due to the reemergence of pathological neural activity. These findings should be considered to ensure successful implementation of adaptive neurostimulation paradigms in the real-world.

Spontaneous blink-related beta power increase and theta phase reset in subthalamic nucleus of Parkinson patients during walking

OBJECTIVE

Both blinking and walking are altered in Parkinson's disease and both motor outputs have been shown to be linked in healthy subjects. Additionally, studies suggest an involvement of basal ganglia activity and striatal dopamine in blink generation. We investigated the role of the basal ganglia circuitry on spontaneous blinking and if this role is dependent on movement state and striatal dopamine.

METHODS

We analysed subthalamic nucleus (STN) activity in seven chronically implanted patients for deep brain stimulation (DBS) with respect to blinks and movement state (resting state and unperturbed walking). Neurophysiological recordings were combined with individual molecular brain imaging assessing the dopamine reuptake transporter (DAT) density for the left and right striatum separately.

RESULTS

We found a significantly higher blink rate during walking compared to resting. The blink rate during walking positively correlated with the DAT density of the left caudate nucleus. During walking only, spontaneous blinking was followed by an increase in the right STN beta power and a bilateral subthalamic phase reset in the low frequencies. The right STN blink-related beta power modulation correlated negatively with the DAT density of the contralateral putamen. The left STN blink-related beta power correlated with the DAT density of the putamen in the less dopamine-depleted hemisphere. Both correlations were specific to the walking condition and to beta power following a blink.

CONCLUSION

Our findings show that spontaneous blinking is related to striatal dopamine and has a frequency specific deployment in the STN. This correlation depends on the current movement state such as walking.

SIGNIFICANCE

This work indicates that subcortical activity following a motor event as well as the relationship between dopamine and motor events can be dependent on the motor state. Accordingly, disease related changes in brain activity should be assessed during natural movement.

Chronic Pallidal Local Field Potentials Are Associated With Dystonic Symptoms in Children

BACKGROUND

Novel deep brain stimulation devices can record local field potentials (LFPs), which represent the synchronous synaptic activity of neuronal populations. The clinical relevance of LFPs in patients with dystonia remains unclear.

OBJECTIVES

We sought to determine whether chronic LFPs recorded from the globus pallidus internus (GPi) were associated with symptoms of dystonia in children.

MATERIALS AND METHODS

Ten patients with heterogeneous forms of dystonia (genetic and acquired) were implanted with neurostimulators that recorded LFP spectral snapshots. Spectra were compared across parent-reported asymptomatic and symptomatic periods, with daily narrowband data superimposed in 24 one-hour bins.

RESULTS

Spectral power increased during periods of registered dystonic symptoms: mean increase = 102%, CI: (76.7, 132). Circadian rhythms within the LFP narrowband time series correlated with dystonic symptoms: for delta/theta-waves, correlation = 0.33, CI: (0.18, 0.47) and for alpha waves, correlation = 0.27, CI: (0.14, 0.40).

CONCLUSIONS

LFP spectra recorded in the GPi indicate a circadian pattern and are associated with the manifestation of dystonic symptoms.

Gait-related beta-gamma phase amplitude coupling in the subthalamic nucleus of parkinsonian patients

Analysis of coupling between the phases and amplitudes of neural oscillations has gained increasing attention as an important mechanism for large-scale brain network dynamics. In Parkinson's disease (PD), preliminary evidence indicates abnormal beta-phase coupling to gamma-amplitude in different brain areas, including the subthalamic nucleus (STN). We analyzed bilateral STN local field potentials (LFPs) in eight subjects with PD chronically implanted with deep brain stimulation electrodes during upright quiet standing and unperturbed walking. Phase-amplitude coupling (PAC) was computed using the Kullback-Liebler method, based on the modulation index. Neurophysiological recordings were correlated with clinical and kinematic measurements and individual molecular brain imaging studies ([123I]FP-CIT and single-photon emission computed tomography). We showed a dopamine-related increase in subthalamic beta-gamma PAC from standing to walking. Patients with poor PAC modulation and low PAC during walking spent significantly more time in the stance and double support phase of the gait cycle. Our results provide new insights into the subthalamic contribution to human gait and suggest cross-frequency coupling as a gateway mechanism to convey patient-specific information of motor control for human locomotion.

Neural signal data collection and analysis of Percept™ PC BrainSense recordings for thalamic stimulation in epilepsy

Deep brain stimulation (DBS) using Medtronic's Percept™ PC implantable pulse generator is FDA-approved for treating Parkinson's disease (PD), essential tremor, dystonia, obsessive compulsive disorder, and epilepsy. Percept™ PC enables simultaneous recording of neural signals from the same lead used for stimulation. Many Percept™ PC sensing features were built with PD patients in mind, but these features are potentially useful to refine therapies for many different disease processes. When starting our ongoing epilepsy research study, we found it difficult to find detailed descriptions about these features and have compiled information from multiple sources to understand it as a tool, particularly for use in patients other than those with PD. Here we provide a tutorial for scientists and physicians interested in using Percept™ PC's features and provide examples of how neural time series data is often represented and saved. We address characteristics of the recorded signals and discuss Percept™ PC hardware and software capabilities in data pre-processing, signal filtering, and DBS lead performance. We explain the power spectrum of the data and how it is shaped by the filter response of Percept™ PC as well as the aliasing of the stimulation due to digitally sampling the data. We present Percept™ PC's ability to extract biomarkers that may be used to optimize stimulation therapy. We show how differences in lead type affects noise characteristics of the implanted leads from seven epilepsy patients enrolled in our clinical trial. Percept™ PC has sufficient signal-to-noise ratio, sampling capabilities, and stimulus artifact rejection for neural activity recording. Limitations in sampling rate, potential artifacts during stimulation, and shortening of battery life when monitoring neural activity at home were observed. Despite these limitations, Percept™ PC demonstrates potential as a useful tool for recording neural activity in order to optimize stimulation therapies to personalize treatment.

Variation Minimization Based Electrocardiogram Artifacts Removal for Local Field Potentials From Neurostimulator

Local field potential (LFP) recorded by sensing-enabled neurostimulators provided chronic observation of deep brain activities for the research of brain disorders. However, the contamination from the electrocardiogram (ECG) deteriorated the extraction of effective information from LFP. This study proposed a novel algorithm based on minimizing the variance combining template subtraction to improve the performance of ECG artifact removal for LFP. Four patients with implanted electrodes were recruited, and eight real LFP records were collected from their left and right hemispheres, respectively. The results showed that the proposed method improved the accuracy of artifact peak detection in LFP, and the subsequent signal quality after template subtraction compared to the traditional Pan-Tompkins (PT) method. The outcome of this study benefited the LFP-based brain research, promoting the application of sensing-enabled neurostimulators in more areas.

Single threshold adaptive deep brain stimulation in Parkinson's disease depends on parameter selection, movement state and controllability of subthalamic beta activity

BACKGROUND

Deep brain stimulation (DBS) is an invasive treatment option for patients with Parkinson's disease. Recently, adaptive DBS (aDBS) systems have been developed, which adjust stimulation timing and amplitude in real-time. However, it is unknown how changes in parameters, movement states and the controllability of subthalamic beta activity affect aDBS performance.

OBJECTIVE

To characterize how parameter choice, movement state and controllability interactively affect the electrophysiological and behavioral response to single threshold aDBS.

METHODS

We recorded subthalamic local field potentials in 12 patients with Parkinson's disease receiving single threshold aDBS in the acute post-operative state. We investigated changes in two aDBS parameters: the onset time and the smoothing of real-time beta power. Electrophysiological patterns and motor performance were assessed while patients were at rest and during a simple motor task. We further studied the impact of controllability on aDBS performance by comparing patients with and without beta power modulation during continuous stimulation.

RESULTS

Our findings reveal that changes in the onset time control the extent of beta power suppression achievable with single threshold adaptive stimulation during rest. Behavioral data indicate that only specific parameter combinations yield a beneficial effect of single threshold aDBS. During movement, action induced beta power suppression reduces the responsivity of the closed loop algorithm. We further demonstrate that controllability of beta power is a prerequisite for effective parameter dependent modulation of subthalamic beta activity.

CONCLUSION

Our results highlight the interaction between single threshold aDBS parameter selection, movement state and controllability in driving subthalamic beta activity and motor performance. By this means, we identify directions for the further development of closed-loop DBS algorithms.

Unraveling the complexities of programming neural adaptive deep brain stimulation in Parkinson's disease

Over the past three decades, deep brain stimulation (DBS) for Parkinson's disease (PD) has been applied in a continuous open loop fashion, unresponsive to changes in a given patient's state or symptoms over the course of a day. Advances in recent neurostimulator technology enable the possibility for closed loop adaptive DBS (aDBS) for PD as a treatment option in the near future in which stimulation adjusts in a demand-based manner. Although aDBS offers great clinical potential for treatment of motor symptoms, it also brings with it the need for better understanding how to implement it in order to maximize its benefits. In this perspective, we outline considerations for programing several key parameters for aDBS based on our experience across several aDBS-capable research neurostimulators. At its core, aDBS hinges on successful identification of relevant biomarkers that can be measured reliably in real-time working in cohesion with a control policy that governs stimulation adaption. However, auxiliary parameters such as the window in which stimulation is allowed to adapt, as well as the rate it changes, can be just as impactful on performance and vary depending on the control policy and patient. A standardize protocol for programming aDBS will be crucial to ensuring its effective application in clinical practice.

Sensing with deep brain stimulation device in epilepsy: Aperiodic changes in thalamic local field potential during seizures

OBJECTIVE

Thalamic deep brain stimulation (DBS) is an effective therapeutic option in patients with drug-resistant epilepsy. Recent DBS devices with sensing capabilities enable chronic, outpatient local field potential (LFP) recordings. Whereas beta oscillations have been demonstrated to be a useful biomarker in movement disorders, the clinical utility of DBS sensing in epilepsy remains unclear. Our aim was to determine LFP features that distinguish ictal from inter-ictal states, which may aid in tracking seizure outcomes with DBS.

METHODS

Electrophysiology data were obtained from DBS devices implanted in the anterior nucleus (N = 12) or centromedian nucleus (N = 2) of the thalamus. Power spectra recorded during patient/caregiver-marked seizure events were analyzed with a method that quantitatively separates the oscillatory and non-oscillatory/aperiodic components of the LFP using non-parametric statistics, without the need for pre-specification of the frequency bands of interest. Features of the LFP parameterized using this algorithm were compared with those from inter-ictal power spectra recorded in clinic.

RESULTS

Oscillatory activity in multiple canonical frequency bands was identified from the power spectra in 86.48% of patient-marked seizure events. Delta oscillations were present in all patients, followed by theta (N = 10) and beta (N = 9). Although there were no differences in oscillatory LFP features between the ictal and inter-ictal states, there was a steeper decline in the 1/f slope of the aperiodic component of the LFP during seizures.

SIGNIFICANCE

Our work highlights the potential and shortcomings of chronic LFP recordings in thalamic DBS for epilepsy. Findings suggest that no single frequency band in isolation clearly differentiates seizures, and that features of aperiodic LFP activity may be clinically-relevant biomarkers of seizures.

Initial case series of a novel sensing deep brain stimulation device in drug-resistant epilepsy and consistent identification of alpha/beta oscillatory activity: A feasibility study

OBJECTIVE

Here, we report a retrospective, single-center experience with a novel deep brain stimulation (DBS) device capable of chronic local field potential (LFP) recording in drug-resistant epilepsy (DRE) and explore potential electrophysiological biomarkers that may aid DBS programming and outcome tracking.

METHODS

Five patients with DRE underwent thalamic DBS, targeting either the bilateral anterior (n = 3) or centromedian (n = 2) nuclei. Postoperative electrode lead localizations were visualized in Lead-DBS software. Local field potentials recorded over 12-18 months were tracked, and changes in power were associated with patient events, medication changes, and stimulation. We utilized a combination of lead localization, in-clinic broadband LFP recordings, real-time LFP response to stimulation, and chronic recordings to guide DBS programming.

RESULTS

Four patients (80%) experienced a >50% reduction in seizure frequency, whereas one patient had no significant reduction. Peaks in the alpha and/or beta frequency range were observed in the thalamic LFPs of each patient. Stimulation suppressed these LFP peaks in a dose-dependent manner. Chronic timeline data identified changes in LFP amplitude associated with stimulation, seizure occurrences, and medication changes. We also noticed a circadian pattern of LFP amplitudes in all patients. Button-presses during seizure events via a mobile application served as a digital seizure diary and were associated with elevations in LFP power.

SIGNIFICANCE

We describe an initial cohort of patients with DRE utilizing a novel sensing DBS device to characterize potential LFP biomarkers of epilepsy that may be associated with seizure control after DBS in DRE. We also present a new workflow utilizing the Percept device that may optimize DBS programming using real-time and chronic LFP recording.

Intelligibility of speech in Parkinson's disease relies on anatomically segregated subthalamic beta oscillations

BACKGROUND

Speech impairment is commonly reported in Parkinson's disease and is not consistently improved by available therapies - including deep brain stimulation of the subthalamic nucleus (STN-DBS), which can worsen communication performance in some patients. Improving the outcome of STN-DBS on speech is difficult due to our incomplete understanding of the contribution of the STN to fluent speaking.

OBJECTIVE

To assess the relationship between subthalamic neural activity and speech production and intelligibility.

METHODS

We investigated bilateral STN local field potentials (LFPs) in nine parkinsonian patients chronically implanted with DBS during overt reading. LFP spectral features were correlated with clinical scores and measures of speech intelligibility.

RESULTS

Overt reading was associated with increased beta-low ([1220) Hz) power in the left STN, whereas speech intelligibility correlated positively with beta-high ([2030) Hz) power in the right STN.

CONCLUSION

We identified separate contributions from frequency and brain lateralization of the STN in the execution of an overt reading motor task and its intelligibility. This subcortical organization could be exploited for new adaptive stimulation strategies capable of identifying the occurrence of speaking behavior and facilitating its functional execution.

Physiology of dystonia: Human studies

In this chapter, we discuss neurophysiological techniques that have been used in the study of dystonia. We examine traditional disease models such as inhibition and excessive plasticity and review the evidence that these play a causal role in pathophysiology. We then review the evidence for sensory and peripheral influences within pathophysiology and look at an emergent literature that tries to probe how oscillatory brain activity may be linked to dystonia pathophysiology.

Ambulatory Local Field Potential Recordings from the Thalamus in Epilepsy: A Feasibility Study

INTRODUCTION

Stimulation of the thalamus is gaining favor in the treatment of medically refractory multifocal and generalized epilepsy. Implanted brain stimulators capable of recording ambulatory local field potentials (LFPs) have recently been introduced, but there is little information to guide their use in thalamic stimulation for epilepsy. This study sought to assess the feasibility of chronically recording ambulatory interictal LFP from the thalamus in patients with epilepsy.

METHODS

In this pilot study, ambulatory LFP was recorded from patients who underwent sensing-enabled deep brain stimulation (DBS, 2 participants) or responsive neurostimulation (RNS, 3 participants) targeting the anterior nucleus of the thalamus (ANT, 2 electrodes), centromedian nucleus (CM, 7 electrodes), or medial pulvinar (PuM, 1 electrode) for multifocal or generalized epilepsy. Time-domain and frequency-domain LFP was investigated for epileptiform discharges, spectral peaks, circadian variation, and peri-ictal patterns.

RESULTS

Thalamic interictal discharges were visible on ambulatory recordings from both DBS and RNS. At-home interictal frequency-domain data could be extracted from both devices. Spectral peaks were noted at 10-15 Hz in CM, 6-11 Hz in ANT, and 19-24 Hz in PuM but varied in prominence and were not visible in all electrodes. In CM, 10-15 Hz power exhibited circadian variation and was attenuated by eye opening.

CONCLUSION

Chronic ambulatory recording of thalamic LFP is feasible. Common spectral peaks can be observed but vary between electrodes and across neural states. DBS and RNS devices provide a wealth of complementary data that have the potential to better inform thalamic stimulation for epilepsy.

Case report: Bilateral double beta peak activity is influenced by stimulation, levodopa concentrations, and motor tasks, in a Parkinson's disease patient on chronic deep brain stimulation

Introduction

Subthalamic (STN) local field potentials (LFPs) in the beta band are considered potential biomarkers for closed-loop deep brain stimulation (DBS) in Parkinson's disease (PD). The beta band is further dissected into low-and high-frequency components with somewhat different functions, although their concomitance and association in the single patient is far to be defined. We present a 56-year-old male PD patient undergoing DBS showing a double-beta peak activity on both sides. The aim of the study was to investigate how low-and high-beta peaks were influenced by plasma levodopa (L-dopa) levels, stimulation, and motor performances.

Methods

A systematic evaluation of raw LFPs, plasma L-dopa levels, and motor tasks was performed in the following four conditions: OFF medications/ON stimulation, OFF medications/OFF stimulation, ON medications/OFF stimulation, and ON medications/ON stimulation.

Results

The analysis of the LFP spectra suggests the following results: (1) the high-beta peak was suppressed by stimulation, while the low-beta peak showed a partial and not consistent response to stimulation; (2) the high-beta peak is also influenced by plasma L-dopa concentration, showing a progressive amplitude increment concordant with plasma L-dopa levels, while the low-beta peak shows a different behavir; and (3) motor performances seem to impact beta peaks behavior.

Conclusion

This single exploratory case study illustrates a complex behavior of low-and high-beta peaks in a PD patient, in response to stimulation, L-dopa plasma levels, and motor performances. Our results suggest the importance to investigate patient-specific individual LFP patterns in view of upcoming closed-loop stimulation.

Adaptive Deep Brain Stimulation: From Experimental Evidence Toward Practical Implementation

Closed-loop adaptive deep brain stimulation (aDBS) can deliver individualized therapy at an unprecedented temporal precision for neurological disorders. This has the potential to lead to a breakthrough in neurotechnology, but the translation to clinical practice remains a significant challenge. Via bidirectional implantable brain-computer-interfaces that have become commercially available, aDBS can now sense and selectively modulate pathophysiological brain circuit activity. Pilot studies investigating different aDBS control strategies showed promising results, but the short experimental study designs have not yet supported individualized analyses of patient-specific factors in biomarker and therapeutic response dynamics. Notwithstanding the clear theoretical advantages of a patient-tailored approach, these new stimulation possibilities open a vast and mostly unexplored parameter space, leading to practical hurdles in the implementation and development of clinical trials. Therefore, a thorough understanding of the neurophysiological and neurotechnological aspects related to aDBS is crucial to develop evidence-based treatment regimens for clinical practice. Therapeutic success of aDBS will depend on the integrated development of strategies for feedback signal identification, artifact mitigation, signal processing, and control policy adjustment, for precise stimulation delivery tailored to individual patients. The present review introduces the reader to the neurophysiological foundation of aDBS for Parkinson's disease (PD) and other network disorders, explains currently available aDBS control policies, and highlights practical pitfalls and difficulties to be addressed in the upcoming years. Finally, it highlights the importance of interdisciplinary clinical neurotechnological research within and across DBS centers, toward an individualized patient-centered approach to invasive brain stimulation. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Deep brain stimulation - New programming algorithms and teleprogramming

INTRODUCTION

Thanks to a variety of factors, the field of neuromodulation has evolved significantly over the past decade. Developments include new indications and innovations of hardware, software, and stimulation techniques leading to an expansion in scope and role of these techniques as powerful therapies. They also imply the realization that practical application involves new nuances that make patient selection, surgical technique and the programming process even more complex, requiring continuous education and an organized structured approach.

AREAS COVERED

In this review, the authors explore the developments in deep brain stimulation technology, including electrodes, implantable pulse generators, contact configurations (i.e, directional leads and independent current control), remote programming and sensing using local field potentials.

EXPERT OPINION

The innovations in the field of deep brain stimulation discussed in this review potentially provide increased effectiveness and flexibility not only to improve therapeutic response but also to address troubleshooting challenges seen in clinical practice. Directional leads and shorter pulse widths may broaden the therapeutic window of stimulation, avoiding current spread to structures that might trigger stimulation-related side effects. Similarly, independent control of current to individual contacts allows for the shaping of the electric field. Finally, sensing and remote programming represent important developments for more effective and individualized patient care.

Pallidal Recordings in Chronically Implanted Dystonic Patients: Mitigation of Tremor-Related Artifacts

Low-frequency oscillatory patterns of pallidal local field potentials (LFPs) have been proposed as a physiomarker for dystonia and hold the promise for personalized adaptive deep brain stimulation. Head tremor, a low-frequency involuntary rhythmic movement typical of cervical dystonia, may cause movement artifacts in LFP signals, compromising the reliability of low-frequency oscillations as biomarkers for adaptive neurostimulation. We investigated chronic pallidal LFPs with the PerceptTM PC (Medtronic PLC) device in eight subjects with dystonia (five with head tremors). We applied a multiple regression approach to pallidal LFPs in patients with head tremors using kinematic information measured with an inertial measurement unit (IMU) and an electromyographic signal (EMG). With IMU regression, we found tremor contamination in all subjects, whereas EMG regression identified it in only three out of five. IMU regression was also superior to EMG regression in removing tremor-related artifacts and resulted in a significant power reduction, especially in the theta-alpha band. Pallido-muscular coherence was affected by a head tremor and disappeared after IMU regression. Our results show that the Percept PC can record low-frequency oscillations but also reveal spectral contamination due to movement artifacts. IMU regression can identify such artifact contamination and be a suitable tool for its removal.

Adaptive deep brain stimulation for Parkinson's disease: looking back at the past decade on motor outcomes

BACKGROUND

Adaptive deep brain stimulation (aDBS) has been reported to be an effective treatment for motor symptoms in patients with Parkinson's disease (PD). However, it remains unclear whether and in which motor domain aDBS provides greater/less benefits than conventional DBS (cDBS).

OBJECTIVE

To conduct a meta-analysis and systematic review to explore the improvement of the motor symptoms of PD patients undergoing aDBS and the comparison between aDBS and cDBS.

METHODS

Nineteen studies from PubMed, Embase, and the Cochrane Library database were eligible for the main analysis. Twelve studies used quantitative plus qualitative analysis; seven studies were only qualitatively analyzed. The efficacy of aDBS was evaluated and compared to cDBS through overall motor function improvements, changes in symptoms of rigidity-bradykinesia, dyskinesia, tremor, and speech function, and total electrical energy delivered (TEED). The overall motor improvement and TEED were investigated through meta-analyses, while other variables were investigated by systematic review.

RESULTS

Quantitative analysis showed that aDBS, with a reduction of TEED (55% of that of cDBS), significantly improved motor functions (33.9%, p < 0.01) and may be superior to cDBS in overall motor improvement (p = 0.002). However, significant publication bias was detected regarding the superiority (p = 0.006, Egger's test). In the qualitative analysis, rigidity-bradykinesia, dyskinesia, and speech function outcomes after aDBS and cDBS were comparable. Beta-based aDBS may not be as efficient as cDBS for tremor control.

CONCLUSIONS

aDBS can effectively relieve the clinical symptoms of advanced PD as did cDBS, at least in acute trials, delivering less stimulation than cDBS. Specific symptoms including tremor and axial disability remain to be compared between aDBS and cDBS in long-term studies.

Association of subthalamic beta frequency sub-bands to symptom severity in patients with Parkinson's disease: A systematic review

OBJECTIVE

Local field potentials (LFP), specifically beta (13-30Hz) frequency measures, have been found to be associated with motor dysfunction in people with Parkinson's disease (PwPD). A consensus on beta subband (low- and high-beta) relationships to clinical state or therapy response has yet to be determined. The objective of this review is to synthesize literature reporting the association of low- and high-beta characteristics to clinical ratings of motor symptoms in PwPD.

METHODS

A systematic search of existing literature was completed using EMBASE. Articles which collected subthalamic nucleus (STN) LFPs using macroelectrodes in PwPD, analyzed low- (13-20 Hz) and high-beta (21-35 Hz) bands, collected UPDRS-III, and reported correlational strength or predictive capacity of LFPs to UPDRS-III scores.

RESULTS

The initial search yielded 234 articles, with 11 articles achieving inclusion. Beta measures included power spectral density, peak characteristics, and burst characteristics. High-beta was a significant predictor of UPDRS-III responses to therapy in 5 (100%) articles. Low-beta was significantly associated with UPDRS-III total score in 3 (60%) articles. Low- and high-beta associations to UPDRS-III subscores were mixed.

CONCLUSION

This systematic review reinforces previous reports that beta band oscillatory measures demonstrate a consistent relationship to Parkinsonian motor symptoms and ability to predict motor response to therapy. Specifically, high-beta, demonstrated a consistent ability to predict UPDRS-III responses to common PD therapies, while low-beta measures were associated with general Parkinsonian symptom severity. Continued research is needed to determine which beta subband demonstrates the greatest association to motor symptom subtypes and potentially offers clinical utility toward LFP-guided DBS programming and adaptive DBS.

Use of Invasive Brain-Computer Interfaces in Pediatric Neurosurgery: Technical and Ethical Considerations

Invasive brain-computer interfaces hold promise to alleviate disabilities in individuals with neurologic injury, with fully implantable brain-computer interface systems expected to reach the clinic in the upcoming decade. Children with severe neurologic disabilities, like quadriplegic cerebral palsy or cervical spine trauma, could benefit from this technology. However, they have been excluded from clinical trials of intracortical brain-computer interface to date. In this manuscript, we discuss the ethical considerations related to the use of invasive brain-computer interface in children with severe neurologic disabilities. We first review the technical hardware and software considerations for the application of intracortical brain-computer interface in children. We then discuss ethical issues related to motor brain-computer interface use in pediatric neurosurgery. Finally, based on the input of a multidisciplinary panel of experts in fields related to brain-computer interface (functional and restorative neurosurgery, pediatric neurosurgery, mathematics and artificial intelligence research, neuroengineering, pediatric ethics, and pragmatic ethics), we then formulate initial recommendations regarding the clinical use of invasive brain-computer interfaces in children.

Experimental Verification for Numerical Simulation of Thalamic Stimulation-Evoked Calcium-Sensitive Fluorescence and Electrophysiology with Self-Assembled Multifunctional Optrode

Owing to its capacity to eliminate a long-standing methodological limitation, fiber photometry can assist research gaining novel insight into neural systems. Fiber photometry can reveal artifact-free neural activity under deep brain stimulation (DBS). Although evoking neural potential with DBS is an effective method for mediating neural activity and neural function, the relationship between DBS-evoked neural Ca²⁺ change and DBS-evoked neural electrophysiology remains unknown. Therefore, in this study, a self-assembled optrode was demonstrated as a DBS stimulator and an optical biosensor capable of concurrently recording Ca²⁺ fluorescence and electrophysiological signals. Before the in vivo experiment, the volume of tissue activated (VTA) was estimated, and the simulated Ca²⁺ signals were presented using Monte Carlo (MC) simulation to approach the realistic in vivo environment. When VTA and the simulated Ca²⁺ signals were combined, the distribution of simulated Ca²⁺ fluorescence signals matched the VTA region. In addition, the in vivo experiment revealed a correlation between the local field potential (LFP) and the Ca²⁺ fluorescence signal in the evoked region, revealing the relationship between electrophysiology and the performance of neural Ca²⁺ concentration behavior. Concurrent with the VTA volume, simulated Ca²⁺ intensity, and the in vivo experiment, these data suggested that the behavior of neural electrophysiology was consistent with the phenomenon of Ca²⁺ influx to neurons.

A comparison of methods to suppress electrocardiographic artifacts in local field potential recordings

OBJECTIVE

Local field potential (LFP) recordings from deep brain stimulation (DBS) electrodes are often contaminated with electrocardiographic (ECG) artifacts that hinder the detection of disease-specific electrical brain activity.

METHODS

Three ECG suppression methods were evaluated: (1) QRS interpolation of the Perceive toolbox, (2) template subtraction, and (3) singular value decomposition (SVD). LFPs were recorded with the Medtronic Percept^TM PC system in nine Parkinson's disease patients with stimulation OFF ("OFF-DBS"; anode disconnected) and ON at 0 mA ("ON-DBS 0 mA"; anode connected). Findings were verified with simulated ECG-contaminated time series.

RESULTS

ECG artifacts were present in 10 out of 18 ON-DBS 0 mA recordings. All ECG suppression methods drastically reduced artifact-induced beta band (13-35 Hz) power and at least partly recovered the beta peak and beta burst dynamics. Using external ECG recordings and lengthening artifact epoch length improved the performance of the suppression methods. Increasing epoch length, however, elevated the risk of flattening the beta peak and losing beta burst dynamics.

CONCLUSIONS

The SVD method formed the preferred trade-off between artifact cleaning and signal loss, as long as its parameter settings are adequately chosen.

SIGNIFICANCE

ECG suppression methods enable analysis of disease-specific neural activity from signals affected by ECG artifacts.

Proceedings of the 10th annual deep brain stimulation think tank: Advances in cutting edge technologies, artificial intelligence, neuromodulation, neuroethics, interventional psychiatry, and women in neuromodulation

The deep brain stimulation (DBS) Think Tank X was held on August 17-19, 2022 in Orlando FL. The session organizers and moderators were all women with the theme women in neuromodulation. Dr. Helen Mayberg from Mt. Sinai, NY was the keynote speaker. She discussed milestones and her experiences in developing depression DBS. The DBS Think Tank was founded in 2012 and provides an open platform where clinicians, engineers and researchers (from industry and academia) can freely discuss current and emerging DBS technologies as well as the logistical and ethical issues facing the field. The consensus among the DBS Think Tank X speakers was that DBS has continued to expand in scope however several indications have reached the "trough of disillusionment." DBS for depression was considered as "re-emerging" and approaching a slope of enlightenment. DBS for depression will soon re-enter clinical trials. The group estimated that globally more than 244,000 DBS devices have been implanted for neurological and neuropsychiatric disorders. This year's meeting was focused on advances in the following areas: neuromodulation in Europe, Asia, and Australia; cutting-edge technologies, closed loop DBS, DBS tele-health, neuroethics, lesion therapy, interventional psychiatry, and adaptive DBS.

Parkinsonian daytime sleep-wake classification using deep brain stimulation lead recordings

Excessive daytime sleepiness is a recognized non-motor symptom that adversely impacts the quality of life of people with Parkinson's disease (PD), yet effective treatment options remain limited. Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for PD motor signs. Reliable daytime sleep-wake classification using local field potentials (LFPs) recorded from DBS leads implanted in STN can inform the development of closed-loop DBS approaches for prompt detection and disruption of sleep-related neural oscillations. We performed STN DBS lead recordings in three nonhuman primates rendered parkinsonian by administrating neurotoxin 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP). Reference sleep-wake states were determined on a second-by-second basis by video monitoring of eyes (eyes-open, wake and eyes-closed, sleep). The spectral power in delta (1-4 Hz), theta (4-8 Hz), low-beta (8-20 Hz), high-beta (20-35 Hz), gamma (35-90 Hz), and high-frequency (200-400 Hz) bands were extracted from each wake and sleep epochs for training (70% data) and testing (30% data) a support vector machines classifier for each subject independently. The spectral features yielded reasonable daytime sleep-wake classification (sensitivity: 90.68 ± 1.28; specificity: 88.16 ± 1.08; accuracy: 89.42 ± 0.68; positive predictive value; 88.70 ± 0.89, n = 3). Our findings support the plausibility of monitoring daytime sleep-wake states using DBS lead recordings. These results could have future clinical implications in informing the development of closed-loop DBS approaches for automatic detection and disruption of sleep-related neural oscillations in people with PD to promote wakefulness.

Local Field Potential-Guided Contact Selection Using Chronically Implanted Sensing Devices for Deep Brain Stimulation in Parkinson's Disease

Intra- and perioperatively recorded local field potential (LFP) activity of the nucleus subthalamicus (STN) has been suggested to guide contact selection in patients undergoing deep brain stimulation (DBS) for Parkinson's disease (PD). Despite the invention of sensing capacities in chronically implanted devices, a comprehensible algorithm that enables contact selection using such recordings is still lacking. We evaluated a fully automated algorithm that uses the weighted average of bipolar recordings to determine effective monopolar contacts based on elevated activity in the beta band. LFPs from 14 hemispheres in seven PD patients with newly implanted directional DBS leads of the STN were recorded. First, the algorithm determined the stimulation level with the highest beta activity. Based on the prior determined level, the directional contact with the highest beta activity was chosen in the second step. The mean clinical efficacy of the contacts chosen using the algorithm did not statistically differ from the mean clinical efficacy of standard contact selection as performed in clinical routine. All recording sites were projected into MNI standard space to investigate the feasibility of the algorithm with respect to the anatomical boundaries of the STN. We conclude that the proposed algorithm is a first step towards LFP-based contact selection in STN-DBS for PD using chronically implanted devices.

Artifact characterization and mitigation techniques during concurrent sensing and stimulation using bidirectional deep brain stimulation platforms

Bidirectional deep brain stimulation (DBS) platforms have enabled a surge in hours of recordings in naturalistic environments, allowing further insight into neurological and psychiatric disease states. However, high amplitude, high frequency stimulation generates artifacts that contaminate neural signals and hinder our ability to interpret the data. This is especially true in psychiatric disorders, for which high amplitude stimulation is commonly applied to deep brain structures where the native neural activity is miniscule in comparison. Here, we characterized artifact sources in recordings from a bidirectional DBS platform, the Medtronic Summit RC + S, with the goal of optimizing recording configurations to improve signal to noise ratio (SNR). Data were collected from three subjects in a clinical trial of DBS for obsessive-compulsive disorder. Stimulation was provided bilaterally to the ventral capsule/ventral striatum (VC/VS) using two independent implantable neurostimulators. We first manipulated DBS amplitude within safe limits (2–5.3 mA) to characterize the impact of stimulation artifacts on neural recordings. We found that high amplitude stimulation produces slew overflow, defined as exceeding the rate of change that the analog to digital converter can accurately measure. Overflow led to expanded spectral distortion of the stimulation artifact, with a six fold increase in the bandwidth of the 150.6 Hz stimulation artifact from 147–153 to 140–180 Hz. By increasing sense blank values during high amplitude stimulation, we reduced overflow by as much as 30% and improved artifact distortion, reducing the bandwidth from 140–180 Hz artifact to 147–153 Hz. We also identified artifacts that shifted in frequency through modulation of telemetry parameters. We found that telemetry ratio changes led to predictable shifts in the center-frequencies of the associated artifacts, allowing us to proactively shift the artifacts outside of our frequency range of interest. Overall, the artifact characterization methods and results described here enable increased data interpretability and unconstrained biomarker exploration using data collected from bidirectional DBS devices.

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.

Spectral and spatial distribution of subthalamic beta peak activity in Parkinson's disease patients

Current efforts to optimize subthalamic deep brain stimulation in Parkinson's disease patients aim to harness local oscillatory activity in the beta frequency range (13-35 Hz) as a feedback-signal for demand-based adaptive stimulation paradigms. A high prevalence of beta peak activity is prerequisite for this approach to become routine clinical practice. In a large dataset of postoperative rest recordings from 106 patients we quantified occurrence and identified determinants of spectral peaks in the alpha, low and high beta bands. At least one peak in beta band occurred in 92% of patients and 84% of hemispheres off medication, irrespective of demographic parameters, clinical subtype or motor symptom severity. Distance to previously described clinical sweet spot was significantly related both to beta peak occurrence and to spectral power (rho -0.21, p 0.006), particularly in the high beta band. Electrophysiological landscapes of our cohort's dataset in normalised space showed divergent heatmaps for alpha and beta but found similar regions for low and high beta frequency bands. We discuss potential ramifications for clinicians' programming decisions. In summary, this report provides robust evidence that spectral peaks in beta frequency range can be detected in the vast majority of Parkinsonian subthalamic nuclei, increasing confidence in the broad applicability of beta-guided deep brain stimulation.

Troubleshooting Gait Disturbances in Parkinson's Disease With Deep Brain Stimulation

Deep brain stimulation (DBS) of the subthalamic nucleus or the globus pallidus is an established treatment for Parkinson's disease (PD) that yields a marked and lasting improvement of motor symptoms. Yet, DBS benefit on gait disturbances in PD is still debated and can be a source of dissatisfaction and poor quality of life. Gait disturbances in PD encompass a variety of clinical manifestations and rely on different pathophysiological bases. While gait disturbances arising years after DBS surgery can be related to disease progression, early impairment of gait may be secondary to treatable causes and benefits from DBS reprogramming. In this review, we tackle the issue of gait disturbances in PD patients with DBS by discussing their neurophysiological basis, providing a detailed clinical characterization, and proposing a pragmatic programming approach to support their management.

Single-interface bioelectronic medicines - concept, clinical applications and preclinical data

Presently, large groups of patients with various diseases are either intolerant, or irresponsive to drug therapies and also intractable by surgery. For several diseases, one option which is available for such patients is the implantable neurostimulation therapy. However, lacking closed-loop control and selective stimulation capabilities, the present neurostimulation therapies are not optimal and are therefore used as only "third" therapeutic options when a disease cannot be treated by drugs or surgery. Addressing those limitations, a next generation class of closed-loop controlled and selective neurostimulators generically named bioelectronic medicines seems within reach. A sub-class of such devices is meant to monitor and treat impaired functions by intercepting, analyzing and modulating neural signals involved in the regulation of such functions using just one neural interface for those purposes. The primary objective of this review is to provide a first broad perspective on this type of single-interface devices for bioelectronic therapies. For this purpose, the concept, clinical applications and preclinical studies for further developments with such devices are here analyzed in a narrative manner.

Deep Brain Stimulation: Emerging Tools for Simulation, Data Analysis, and Visualization

Deep brain stimulation (DBS) is a well-established neurosurgical procedure for movement disorders that is also being explored for treatment-resistant psychiatric conditions. This review highlights important consideration for DBS simulation and data analysis. The literature on DBS has expanded considerably in recent years, and this article aims to identify important trends in the field. During DBS planning, surgery, and follow up sessions, several large data sets are created for each patient, and it becomes clear that any group analysis of such data is a big data analysis problem and has to be handled with care. The aim of this review is to provide an update and overview from a neuroengineering perspective of the current DBS techniques, technical aids, and emerging tools with the focus on patient-specific electric field (EF) simulations, group analysis, and visualization in the DBS domain. Examples are given from the state-of-the-art literature including our own research. This work reviews different analysis methods for EF simulations, tractography, deep brain anatomical templates, and group analysis. Our analysis highlights that group analysis in DBS is a complex multi-level problem and selected parameters will highly influence the result. DBS analysis can only provide clinically relevant information if the EF simulations, tractography results, and derived brain atlases are based on as much patient-specific data as possible. A trend in DBS research is creation of more advanced and intuitive visualization of the complex analysis results suitable for the clinical environment.