Uncovering biomarkers during therapeutic neuromodulation with PARRM: Period-based Artifact Reconstruction and Removal Method

Modulation of premotor cortex excitability mitigates the behavioral and electrophysiological abnormalities in a Parkinson's disease mouse model

The subthalamic nucleus (STN) plays a crucial role in suppressing prepotent response tendency. The prefrontal regions innervating the STN exhibit increased activity during the stop-signal responses, and the optogenetic activation of these neurons suppresses ongoing behavior. High-frequency electrical stimulation of the STN effectively treats the motor symptoms of Parkinson's disease (PD), yet its underlying circuit mechanisms remain unclear. Here, we investigated the involvement of STN-projecting premotor (M2) neurons in PD mouse models and the impact of deep brain stimulation targeting the STN (DBS-STN). We found that the M2 neurons exhibited enhanced burst firing and synchronous oscillations in the PD mouse model. Remarkably, high-frequency stimulation of STN-projecting M2 neurons, simulating antidromic activation during DBS-STN relieved motor symptoms and hyperexcitability. These changes were attributed to reduced firing frequency vs. current relationship through normalized hyperpolarization-activated inward current (Ih). The M2 neurons in the PD model mouse displayed increased Ih, which was reversed by high-frequency stimulation. Additionally, the infusion of ZD7288, an HCN channel blocker, into the M2 replicated the effects of high-frequency stimulation. In conclusion, our study reveals excessive excitability and suppressive motor control through M2-STN synapses in a PD mouse model. Antidromic excitation of M2 neurons during DBS-STN alleviates this suppression, thereby improving motor impairment. These findings provide insights into the circuit-level dynamics underlying deep brain stimulation's therapeutic effects in PD, suggesting that M2-STN synapses could serve as potential targets for future therapeutic strategies.

Challenges and opportunities of acquiring cortical recordings for chronic adaptive deep brain stimulation

Deep brain stimulation (DBS), a proven treatment for movement disorders, also holds promise for the treatment of psychiatric and cognitive conditions. However, for DBS to be clinically effective, it may require DBS technology that can alter or trigger stimulation in response to changes in biomarkers sensed from the patient's brain. A growing body of evidence suggests that such adaptive DBS is feasible, it might achieve clinical effects that are not possible with standard continuous DBS and that some of the best biomarkers are signals from the cerebral cortex. Yet capturing those markers requires the placement of cortex-optimized electrodes in addition to standard electrodes for DBS. In this Perspective we argue that the need for cortical biomarkers in adaptive DBS and the unfortunate convergence of regulatory and financial factors underpinning the unavailability of cortical electrodes for chronic uses threatens to slow down or stall research on adaptive DBS and propose public-private partnerships as a potential solution to such a critical technological gap.

Shared pathway-specific network mechanisms of dopamine and deep brain stimulation for the treatment of Parkinson's disease

Deep brain stimulation is a brain circuit intervention that can modulate distinct neural pathways for the alleviation of neurological symptoms in patients with brain disorders. In Parkinson's disease, subthalamic deep brain stimulation clinically mimics the effect of dopaminergic drug treatment, but the shared pathway mechanisms on cortex - basal ganglia networks are unknown. To address this critical knowledge gap, we combined fully invasive neural multisite recordings in patients undergoing deep brain stimulation surgery with normative MRI-based whole-brain connectomics. Our findings demonstrate that dopamine and stimulation exert distinct mesoscale effects through modulation of local neural population activity. In contrast, at the macroscale, stimulation mimics dopamine in its suppression of excessive interregional network synchrony associated with indirect and hyperdirect cortex - basal ganglia pathways. Our results provide a better understanding of the circuit mechanisms of dopamine and deep brain stimulation, laying the foundation for advanced closed-loop neurostimulation therapies.

Low-Frequency Dual Target Deep Brain Stimulation May Relieve Parkinsonian Symptoms

Background

Deep brain stimulation (DBS) reduces the motor symptoms of Parkinson's disease. The two most common targets are the subthalamic nucleus and the globus pallidus. Dual target deep brain stimulation may better reduce symptoms and minimize side effects, but the optimal parameters of dual target deep brain stimulation and their potential interactions are unknown.

Objective

Our purpose was to quantify the frequency response of dual target DBS on bradykinesia and beta oscillations in participants with Parkinson's disease, and to explore intrahemispheric pulse delays as a means to reduce total energy delivered.

Methods

We applied dual target DBS using the Summit RC+S in six participants, varying deep brain stimulation frequency.

Results

Dual target DBS at 50 Hz was effective at reducing bradykinesia, whereas increasing deep brain stimulation frequency up to 125 Hz also significantly reduced beta power. This frequency effect on beta power was replicated in a biophysical model. The model suggested that 22 Hz dual target deep brain stimulation, with an intrahemispheric delay of 40 ms, can reduce beta power by 87%.

Conclusion

We conclude that dual target DBS at 125 Hz best reduced bradykinesia. However, low frequency DBS with an appropriate intrahemispheric delay could improve symptom relief.

Stimulation artefact removal: review and evaluation of applications in evoked responses

Objective.This study investigated software methods for removing stimulation artefacts in recordings undertaken during deep brain stimulation (DBS). We aimed to evaluate artefact attenuation using sample recordings of evoked resonant neural activity (ERNA), as well as a synthetic ground-truth waveform that emulated observed ERNA characteristics.Approach.The synthetic waveform and eight raw DBS recordings were processed by fourteen algorithms spanning the following categories: signal modification, signal decomposition, and template subtraction. For the synthetic waveform, performance was quantified by comparing each reconstructed signal against the ground-truth waveform. For DBS recordings, performance was contrasted amongst each other. The stimulation artefact was quantified by its amplitude and subsequent decay to baseline by the time to first zero-crossing. Each reconstructed ERNA signal was characterised by peak-to-peak-amplitude, root-mean-square amplitude, latency, and number of zero-crossings.Main results.None of the methods performed overall as well as the Backward Filter. Signal decomposition techniques were able to attenuate stimulation artefact albeit with unacceptable ERNA distortion.Significance.Upon evaluation of common software methods for DBS artefact attenuation, we advocate the use of the Backward Filter for reducing such artefacts while reconstructing ERNA.

Irregular optogenetic stimulation waveforms can induce naturalistic patterns of hippocampal spectral activity

Objective. Therapeutic brain stimulation is conventionally delivered using constant-frequency stimulation pulses. Several recent clinical studies have explored how unconventional and irregular temporal stimulation patterns could enable better therapy. However, it is challenging to understand which irregular patterns are most effective for different therapeutic applications given the massively high-dimensional parameter space.Approach. Here we applied many irregular stimulation patterns in a single neural circuit to demonstrate how they can enable new dimensions of neural control compared to conventional stimulation, to guide future exploration of novel stimulation patterns in translational settings. We optogenetically excited the septohippocampal circuit with constant-frequency, nested pulse, sinusoidal, and randomized stimulation waveforms, systematically varying their amplitude and frequency parameters.Main results.We first found equal entrainment of hippocampal oscillations: all waveforms provided similar gamma-power increase, whereas no parameters increased theta-band power above baseline (despite the mechanistic role of the medial septum in driving hippocampal theta oscillations). We then compared each of the effects of each waveform on high-dimensional multi-band activity states using dimensionality reduction methods. Strikingly, we found that conventional stimulation drove predominantly 'artificial' (different from behavioral activity) effects, whereas all irregular waveforms induced activity patterns that more closely resembled behavioral activity.Significance. Our findings suggest that irregular stimulation patterns are not useful when the desired mechanism is to suppress or enhance a single frequency band. However, novel stimulation patterns may provide the greatest benefit for neural control applications where entraining a particular mixture of bands (e.g. if they are associated with different symptoms) or behaviorally-relevant activity is desired.

Multifunctional Fiber-Based Optoacoustic Emitter as a Bidirectional Brain Interface

A bidirectional brain interface with both "write" and "read" functions can be an important tool for fundamental studies and potential clinical treatments for neurological diseases. Herein, a miniaturized multifunctional fiber-based optoacoustic emitter (mFOE) is reported thatintegrates simultaneous optoacoustic stimulation for "write" and electrophysiology recording of neural circuits for "read". Because of the intrinsic ability of neurons to respond to acoustic wave, there is no requirement of the viral transfection. The orthogonality between optoacoustic waves and electrical field provides a solution to avoid the interference between electrical stimulation and recording. The stimulation function of the mFOE is first validated in cultured ratcortical neurons using calcium imaging. In vivo application of mFOE for successful simultaneous optoacoustic stimulation and electrical recording of brain activities is confirmed in mouse hippocampus in both acute and chronical applications up to 1 month. Minor brain tissue damage is confirmed after these applications. The capability of simultaneous neural stimulation and recording enabled by mFOE opens up new possibilities for the investigation of neural circuits and brings new insights into the study of ultrasound neurostimulation.

Time-locked acute alpha-frequency stimulation of subthalamic nuclei during the evaluation of emotional stimuli and its effect on power modulation

Introduction

Deep brain stimulation (DBS) studies in Parkinson's Disease (PD) targeting the subthalamic nucleus (STN) have characterized its spectral properties across cognitive processes. In emotional evaluation tasks, specific alpha frequency (8-12 Hz) event-related de-synchronization (ERD) (reduced power) has been demonstrated. The time-locked stimulation of STN relative to stimuli onset has shown subjective positive valence shifts with 10 Hz but not with 130 Hz. However, neurophysiological effects of stimulation on power modulation have not been investigated. We aim to investigate effects of acute stimulation of the right STN on concurrent power modulation in the contralateral STN and frontal scalp EEG. From our previous study, we had a strong a priori hypothesis that negative imagery without stimulation would be associated with alpha ERD; negative imagery with 130 Hz stimulation would be also associated with alpha ERD given the lack of its effect on subjective valence ratings; negative imagery with 10 Hz stimulation was to be associated with enhanced alpha power given the shift in behavioral valence ratings.

Methods

Twenty-four subjects with STN DBS underwent emotional picture-viewing tasks comprising neutral and negative pictures. In a subset of these subjects, the negative images were associated with time-locked acute stimulation at either 10 or 130 Hz. Power of signals was estimated relative to the baseline and subjected to non-parametric statistical testing.

Results

As hypothesized, in 130 Hz stimulation condition, we show a decrease in alpha power to negative vs. neutral images irrespective of stimulation. In contrast, this alpha power decrease was no longer evident in the negative 10 Hz stimulation condition consistent with a predicted increase in alpha power. Greater beta power in the 10 Hz stimulation condition along with correlations between beta power across the 10 Hz stimulation and unstimulated conditions suggest physiological and cognitive generalization effects.

Conclusion

Acute alpha-specific frequency stimulation presumably was associated with a loss of this expected decrease or desynchronization in alpha power to negative images suggesting the capacity to facilitate the synchronization of alpha and enhance power. Acute time-locked stimulation has the potential to provide causal insights into the spectral frequencies and temporal dynamics of emotional processing.

Robust Removal of Slow Artifactual Dynamics Induced by Deep Brain Stimulation in Local Field Potential Recordings Using SVD-Based Adaptive Filtering

Deep brain stimulation (DBS) is widely used as a treatment option for patients with movement disorders. In addition to its clinical impact, DBS has been utilized in the field of cognitive neuroscience, wherein the answers to several fundamental questions underpinning the mechanisms of neuromodulation in decision making rely on the ways in which a burst of DBS pulses, usually delivered at a clinical frequency, i.e., 130 Hz, perturb participants' choices. It was observed that neural activities recorded during DBS were contaminated with large artifacts, which lasts for a few milliseconds, as well as a low-frequency (slow) signal (~1-2 Hz) that can persist for hundreds of milliseconds. While the focus of most of methods for removing DBS artifacts was on the former, the artifact removal capabilities of the slow signal have not been addressed. In this work, we propose a new method based on combining singular value decomposition (SVD) and normalized adaptive filtering to remove both large (fast) and slow artifacts in local field potentials, recorded during a cognitive task in which bursts of DBS were utilized. Using synthetic data, we show that our proposed algorithm outperforms four commonly used techniques in the literature, namely, (1) normalized least mean square adaptive filtering, (2) optimal FIR Wiener filtering, (3) Gaussian model matching, and (4) moving average. The algorithm's capabilities are further demonstrated by its ability to effectively remove DBS artifacts in local field potentials recorded from the subthalamic nucleus during a verbal Stroop task, highlighting its utility in real-world applications.

Soft Fiber Electronics Based on Semiconducting Polymer

Fibers, originating from nature and mastered by human, have woven their way throughout the entire history of human civilization. Recent developments in semiconducting polymer materials have further endowed fibers and textiles with various electronic functions, which are attractive in applications such as information interfacing, personalized medicine, and clean energy. Owing to their ability to be easily integrated into daily life, soft fiber electronics based on semiconducting polymers have gained popularity recently for wearable and implantable applications. Herein, we present a review of the previous and current progress in semiconducting polymer-based fiber electronics, particularly focusing on smart-wearable and implantable areas. First, we provide a brief overview of semiconducting polymers from the viewpoint of materials based on the basic concepts and functionality requirements of different devices. Then we analyze the existing applications and associated devices such as information interfaces, healthcare and medicine, and energy conversion and storage. The working principle and performance of semiconducting polymer-based fiber devices are summarized. Furthermore, we focus on the fabrication techniques of fiber devices. Based on the continuous fabrication of one-dimensional fiber and yarn, we introduce two- and three-dimensional fabric fabricating methods. Finally, we review challenges and relevant perspectives and potential solutions to address the related problems.

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.

Periodic Artifact Removal With Applications to Deep Brain Stimulation

Deep brain stimulation (DBS) therapies have shown clinical success in the treatment of a number of neurological illnesses, including obsessive-compulsive disorder, epilepsy, and Parkinson's disease. An emerging strategy for increasing the efficacy of DBS therapies is to develop closed-loop, adaptive DBS systems that can sense biomarkers associated with particular symptoms and in response, adjust DBS parameters in real-time. The development of such systems requires extensive analysis of the underlying neural signals while DBS is on, so that candidate biomarkers can be identified and the effects of varying the DBS parameters can be better understood. However, DBS creates high amplitude, high frequency stimulation artifacts that prevent the underlying neural signals and thus the biological mechanisms underlying DBS from being analyzed. Additionally, DBS devices often require low sampling rates, which alias the artifact frequency, and rely on wireless data transmission methods that can create signal recordings with missing data of unknown length. Thus, traditional artifact removal methods cannot be applied to this setting. We present a novel periodic artifact removal algorithm for DBS applications that can accurately remove stimulation artifacts in the presence of missing data and in some cases where the stimulation frequency exceeds the Nyquist frequency. The numerical examples suggest that, if implemented on dedicated hardware, this algorithm has the potential to be used in embedded closed-loop DBS therapies to remove DBS stimulation artifacts and hence, to aid in the discovery of candidate biomarkers in real-time. Code for our proposed algorithm is publicly available on Github.

PELP: Accounting for Missing Data in Neural Time Series by Periodic Estimation of Lost Packets

Recent advances in wireless data transmission technology have the potential to revolutionize clinical neuroscience. Today sensing-capable electrical stimulators, known as "bidirectional devices", are used to acquire chronic brain activity from humans in natural environments. However, with wireless transmission come potential failures in data transmission, and not all available devices correctly account for missing data or provide precise timing for when data losses occur. Our inability to precisely reconstruct time-domain neural signals makes it difficult to apply subsequent neural signal processing techniques and analyses. Here, our goal was to accurately reconstruct time-domain neural signals impacted by data loss during wireless transmission. Towards this end, we developed a method termed Periodic Estimation of Lost Packets (PELP). PELP leverages the highly periodic nature of stimulation artifacts to precisely determine when data losses occur. Using simulated stimulation waveforms added to human EEG data, we show that PELP is robust to a range of stimulation waveforms and noise characteristics. Then, we applied PELP to local field potential (LFP) recordings collected using an implantable, bidirectional DBS platform operating at various telemetry bandwidths. By effectively accounting for the timing of missing data, PELP enables the analysis of neural time series data collected via wireless transmission-a prerequisite for better understanding the brain-behavior relationships underlying neurological and psychiatric disorders.

Molecular Mechanisms and Therapeutic Strategies for Levodopa-Induced Dyskinesia in Parkinson's Disease: A Perspective Through Preclinical and Clinical Evidence

Parkinson's disease (PD) is the second leading neurodegenerative disease that is characterized by severe locomotor abnormalities. Levodopa (L-DOPA) treatment has been considered a mainstay for the management of PD; however, its prolonged treatment is often associated with abnormal involuntary movements and results in L-DOPA-induced dyskinesia (LID). Although LID is encountered after chronic administration of L-DOPA, the appearance of dyskinesia after weeks or months of the L-DOPA treatment has complicated our understanding of its pathogenesis. Pathophysiology of LID is mainly associated with alteration of direct and indirect pathways of the cortico-basal ganglia-thalamic loop, which regulates normal fine motor movements. Hypersensitivity of dopamine receptors has been involved in the development of LID; moreover, these symptoms are worsened by concurrent non-dopaminergic innervations including glutamatergic, serotonergic, and peptidergic neurotransmission. The present study is focused on discussing the recent updates in molecular mechanisms and therapeutic approaches for the effective management of LID in PD patients.