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.

Challenges and future trends in wearable closed-loop neuromodulation to efficiently treat methamphetamine addiction

Achieving abstinence from drugs is a long journey and can be particularly challenging in the case of methamphetamine, which has a higher relapse rate than other drugs. Therefore, real-time monitoring of patients' physiological conditions before and when cravings arise to reduce the chance of relapse might help to improve clinical outcomes. Conventional treatments, such as behavior therapy and peer support, often cannot provide timely intervention, reducing the efficiency of these therapies. To more effectively treat methamphetamine addiction in real-time, we propose an intelligent closed-loop transcranial magnetic stimulation (TMS) neuromodulation system based on multimodal electroencephalogram-functional near-infrared spectroscopy (EEG-fNIRS) measurements. This review summarizes the essential modules required for a wearable system to treat addiction efficiently. First, the advantages of neuroimaging over conventional techniques such as analysis of sweat, saliva, or urine for addiction detection are discussed. The knowledge to implement wearable, compact, and user-friendly closed-loop systems with EEG and fNIRS are reviewed. The features of EEG and fNIRS signals in patients with methamphetamine use disorder are summarized. EEG biomarkers are categorized into frequency and time domain and topography-related parameters, whereas for fNIRS, hemoglobin concentration variation and functional connectivity of cortices are described. Following this, the applications of two commonly used neuromodulation technologies, transcranial direct current stimulation and TMS, in patients with methamphetamine use disorder are introduced. The challenges of implementing intelligent closed-loop TMS modulation based on multimodal EEG-fNIRS are summarized, followed by a discussion of potential research directions and the promising future of this approach, including potential applications to other substance use disorders.

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

Introduction

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

Methods

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

Results

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

Discussion

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

NeuralTree: A 256-Channel 0.227-μJ/Class Versatile Neural Activity Classification and Closed-Loop Neuromodulation SoC

Closed-loop neural interfaces with on-chip machine learning can detect and suppress disease symptoms in neurological disorders or restore lost functions in paralyzed patients. While high-density neural recording can provide rich neural activity information for accurate disease-state detection, existing systems have low channel counts and poor scalability, which could limit their therapeutic efficacy. This work presents a highly scalable and versatile closed-loop neural interface SoC that can overcome these limitations. A 256-channel time-division multiplexed (TDM) front-end with a two-step fast-settling mixed-signal DC servo loop (DSL) is proposed to record high-spatial-resolution neural activity and perform channel-selective brain-state inference. A tree-structured neural network (NeuralTree) classification processor extracts a rich set of neural biomarkers in a patient- and disease-specific manner. Trained with an energy-aware learning algorithm, the NeuralTree classifier detects the symptoms of underlying disorders (e.g., epilepsy and movement disorders) at an optimal energy-accuracy trade-off. A 16-channel high-voltage (HV) compliant neurostimulator closes the therapeutic loop by delivering charge-balanced biphasic current pulses to the brain. The proposed SoC was fabricated in 65nm CMOS and achieved a 0.227μJ/class energy efficiency in a compact area of 0.014mm2/channel. The SoC was extensively verified on human electroencephalography (EEG) and intracranial EEG (iEEG) epilepsy datasets, obtaining 95.6%/94% sensitivity and 96.8%/96.9% specificity, respectively. In-vivo neural recordings using soft μECoG arrays and multi-domain biomarker extraction were further performed on a rat model of epilepsy. In addition, for the first time in literature, on-chip classification of rest-state tremor in Parkinson's disease (PD) from human local field potentials (LFPs) was demonstrated.

Closed-Loop Neural Prostheses With On-Chip Intelligence: A Review and a Low-Latency Machine Learning Model for Brain State Detection

The application of closed-loop approaches in systems neuroscience and therapeutic stimulation holds great promise for revolutionizing our understanding of the brain and for developing novel neuromodulation therapies to restore lost functions. Neural prostheses capable of multi-channel neural recording, on-site signal processing, rapid symptom detection, and closed-loop stimulation are critical to enabling such novel treatments. However, the existing closed-loop neuromodulation devices are too simplistic and lack sufficient on-chip processing and intelligence. In this paper, we first discuss both commercial and investigational closed-loop neuromodulation devices for brain disorders. Next, we review state-of-the-art neural prostheses with on-chip machine learning, focusing on application-specific integrated circuits (ASIC). System requirements, performance and hardware comparisons, design trade-offs, and hardware optimization techniques are discussed. To facilitate a fair comparison and guide design choices among various on-chip classifiers, we propose a new energy-area (E-A) efficiency figure of merit that evaluates hardware efficiency and multi-channel scalability. Finally, we present several techniques to improve the key design metrics of tree-based on-chip classifiers, both in the context of ensemble methods and oblique structures. A novel Depth-Variant Tree Ensemble (DVTE) is proposed to reduce processing latency (e.g., by 2.5× on seizure detection task). We further develop a cost-aware learning approach to jointly optimize the power and latency metrics. We show that algorithm-hardware co-design enables the energy- and memory-optimized design of tree-based models, while preserving a high accuracy and low latency. Furthermore, we show that our proposed tree-based models feature a highly interpretable decision process that is essential for safety-critical applications such as closed-loop stimulation.

Bioelectronic medicine for the autonomic nervous system: clinical applications and perspectives

Bioelectronic medicine (BM) is an emerging new approach for developing novel neuromodulation therapies for pathologies that have been previously treated with pharmacological approaches. In this review, we will focus on the neuromodulation of autonomic nervous system (ANS) activity with implantable devices, a field of BM that has already demonstrated the ability to treat a variety of conditions, from inflammation to metabolic and cognitive disorders. Recent discoveries about immune responses to ANS stimulation are the laying foundation for a new field holding great potential for medical advancement and therapies and involving an increasing number of research groups around the world, with funding from international public agencies and private investors. Here, we summarize the current achievements and future perspectives for clinical applications of neural decoding and stimulation of the ANS. First, we present the main clinical results achieved so far by different BM approaches and discuss the challenges encountered in fully exploiting the potential of neuromodulatory strategies. Then, we present current preclinical studies aimed at overcoming the present limitations by looking for optimal anatomical targets, developing novel neural interface technology, and conceiving more efficient signal processing strategies. Finally, we explore the prospects for translating these advancements into clinical practice.

NeuroDAC: an open-source arbitrary biosignal waveform generator

Objective.Researchers are developing biomedical devices with embedded closed-loop algorithms for providing advanced adaptive therapies. As these devices become more capable and algorithms become more complex, tasked with integrating and interpreting multi-channel, multi-modal electrophysiological signals, there is a need for flexible bench-top testing and prototyping. We present a methodology for leveraging off-the-shelf audio equipment to construct a biosignal waveform generator capable of streaming pre-recorded biosignals from a host computer. By re-playing known, well-characterized, but physiologically relevant real-world biosignals into a device under test, researchers can evaluate their systems without the need for expensivein vivoexperiments.Approach.An open-source design based on the proposed methodology is described and validated, the NeuroDAC. NeuroDAC allows for 8 independent channels of biosignal playback using a simple, custom designed attenuation and buffering circuit. Applications can communicate with the device over a USB interface using standard audio drivers. On-board analog amplitude adjustment is used to maximize the dynamic range for a given signal and can be independently tuned for each channel.Main results.Low noise component selection yields a no-signal noise floor of just 5.35 ± 0.063. NeuroDAC's frequency response is characterized with a high pass -3 dB rolloff at 0.57 Hz, and is capable of accurately reproducing a wide assortment of biosignals ranging from EMG, EEG, and ECG to extracellularly recorded neural activity. We also present an application example using the device to test embedded algorithms on a closed-loop neural modulation device, the Medtronic RC+S.Significance.By making the design of NeuroDAC open-source we aim to present an accessible tool for rapidly prototyping new biomedical devices and algorithms than can be easily modified based on individual testing needs.ClinicalTrials.gov Identifiers: NCT04281134, NCT03437928, NCT03582891.

A Brain-Spinal Interface (BSI) System-on-Chip (SoC) for Closed-Loop Cortically-Controlled Intraspinal Microstimulation

This paper reports on a fully miniaturized brain-spinal interface (BSI) system for closed-loop cortically-controlled intraspinal microstimulation (ISMS). Fabricated in AMS 0.35μm two-poly four-metal complementary metal-oxide-semiconductor (CMOS) technology, this system-on-chip (SoC) measures ~ 3.46mm × 3.46mm and incorporates two identical 4-channel modules, each comprising a spike-recording front-end, embedded digital signal processing (DSP) unit, and programmable stimulating back-end. The DSP unit is capable of generating multichannel trigger signals for a wide array of ISMS triggering patterns based on real-time discrimination of a programmable number of intracortical neural spikes within a pre-specified time-bin duration via thresholding and user-adjustable time-amplitude windowing. The system is validated experimentally using an anesthetized rat model of a spinal cord contusion injury at the T8 level. Multichannel neural spikes are recorded from the cerebral cortex and converted in real time into electrical stimuli delivered to the lumbar spinal cord below the level of the injury, resulting in distinct patterns of hindlimb muscle activation.

A Bidirectional Neural Interface IC With Chopper Stabilized BioADC Array and Charge Balanced Stimulator

We present a bidirectional neural interface with a 4-channel biopotential analog-to-digital converter (bioADC) and a 4-channel current-mode stimulator in 180 nm CMOS. The bioADC directly transduces microvolt biopotentials into a digital representation without a voltage-amplification stage. Each bioADC channel comprises a continuous-time first-order ∆Σ modulator with a chopper-stabilized OTA input and current feedback, followed by a second-order comb-filter decimator with programmable oversampling ratio. Each stimulator channel contains two independent digital-to-analog converters for anodic and cathodic current generation. A shared calibration circuit matches the amplitude of the anodic and cathodic currents for charge balancing. Powered from a 1.5 V supply, the analog and digital circuits in each recording channel draw on average [Formula: see text] and [Formula: see text] of supply current, respectively. The bioADCs achieve an SNR of [Formula: see text] and a SFDR of [Formula: see text] , for better than 9-b ENOB. Intracranial EEG recordings from an anesthetized rat are shown and compared to simultaneous recordings from a commercial reference system to validate performance in-vivo . Additionally, we demonstrate bidirectional operation by recording cardiac modulation induced through vagus nerve stimulation, and closed-loop control of cardiac rhythm. The micropower operation, direct digital readout, and integration of electrical stimulation circuits make this interface ideally suited for closed-loop neuromodulation applications.

Neurochemostat: A Neural Interface SoC With Integrated Chemometrics for Closed-Loop Regulation of Brain Dopamine

This paper presents a 3.3×3.2 mm(2) system-on-chip (SoC) fabricated in AMS 0.35 μm 2P/4M CMOS for closed-loop regulation of brain dopamine. The SoC uniquely integrates neurochemical sensing, on-the-fly chemometrics, and feedback-controlled electrical stimulation to realize a "neurochemostat" by maintaining brain levels of electrically evoked dopamine between two user-set thresholds. The SoC incorporates a 90 μW, custom-designed, digital signal processing (DSP) unit for real-time processing of neurochemical data obtained by 400 V/s fast-scan cyclic voltammetry (FSCV) with a carbon-fiber microelectrode (CFM). Specifically, the DSP unit executes a chemometrics algorithm based upon principal component regression (PCR) to resolve in real time electrically evoked brain dopamine levels from pH change and CFM background-current drift, two common interferents encountered using FSCV with a CFM in vivo. Further, the DSP unit directly links the chemically resolved dopamine levels to the activation of the electrical microstimulator in on-off-keying (OOK) fashion. Measured results from benchtop testing, flow injection analysis (FIA), and biological experiments with an anesthetized rat are presented.

Technological advances in the surgical treatment of movement disorders

Technological innovations have driven the advancement of the surgical treatment of movement disorders, from the invention of the stereotactic frame to the adaptation of deep brain stimulation (DBS). Along these lines, this review will describe recent advances in inserting neuromodulation modalities, including DBS, to the target, and in the delivery of therapy at the target. Recent radiological advances are altering the way that DBS leads are targeted and inserted, by refining the ability to visualize the subcortical targets using high-field strength magnetic resonance imaging and other innovations, such as diffusion tensor imaging, and the development of novel targeting devices enabling purely anatomical implantations without the need for neurophysiological monitoring. New portable computed tomography scanners also are facilitating lead implantation without monitoring, as well as improving radiological verification of DBS lead location. Advances in neurophysiological mapping include efforts to develop automatic target verification algorithms, and probabilistic maps to guide target selection. The delivery of therapy at the target is being improved by the development of the next generation of internal pulse generators (IPGs). These include constant current devices that mitigate the variability introduced by impedance changes of the stimulated tissue and, in the near future, devices that deliver novel stimulation patterns with improved efficiency. Closed-loop adaptive IPGs are being tested, which may tailor stimulation to ongoing changes in the nervous system, reflected in biomarkers continuously recorded by the devices. Finer-grained DBS leads, in conjunction with new IPGs and advanced programming tools, may offer improved outcomes via current steering algorithms. Finally, even thermocoagulation-essentially replaced by DBS-is being advanced by new minimally-invasive approaches that may improve this therapy for selected patients in whom it may be preferred. Functional neurosurgery has a history of being driven by technological innovation, a tradition that continues into its future.