Closed-loop optical neural stimulation based on a 32-channel low-noise recording system with online spike sorting

Development of a Smart Wireless Multisensor Platform for an Optogenetic Brain Implant

Implantable cell replacement therapies promise to completely restore the function of neural structures, possibly changing how we currently perceive the onset of neurodegenerative diseases. One of the major clinical hurdles for the routine implementation of stem cell therapies is poor cell retention and survival, demanding the need to better understand these mechanisms while providing precise and scalable approaches to monitor these cell-based therapies in both pre-clinical and clinical scenarios. This poses significant multidisciplinary challenges regarding planning, defining the methodology and requirements, prototyping and different stages of testing. Aiming toward an optogenetic neural stem cell implant controlled by a smart wireless electronic frontend, we show how an iterative development methodology coupled with a modular design philosophy can mitigate some of these challenges. In this study, we present a miniaturized, wireless-controlled, modular multisensor platform with fully interfaced electronics featuring three different modules: an impedance analyzer, a potentiostat and an optical stimulator. We show the application of the platform for electrical impedance spectroscopy-based cell monitoring, optical stimulation to induce dopamine release from optogenetically modified neurons and a potentiostat for cyclic voltammetry and amperometric detection of dopamine release. The multisensor platform is designed to be used as an opto-electric headstage for future in vivo animal experiments.

An unsupervised real-time spike sorting system based on optimized OSort

Objective. The OSort algorithm, a pivotal unsupervised spike sorting method, has been implemented in dedicated hardware devices for real-time spike sorting. However, due to the inherent complexity of neural recording environments, OSort still grapples with numerous transient cluster occurrences during the practical sorting process. This leads to substantial memory usage, heavy computational load, and complex hardware architectures, especially in noisy recordings and multi-channel systems.Approach. This study introduces an optimized OSort algorithm (opt-OSort) which utilizes correlation coefficient (CC), instead of Euclidean distance as classification criterion. TheCCmethod not only bolsters the robustness of spike classification amidst the diverse and ever-changing conditions of physiological and recording noise environments, but also can finish the entire sorting procedure within a fixed number of cluster slots, thus preventing a large number of transient clusters. Moreover, the opt-OSort incorporates two configurable validation loops to efficiently reject cluster outliers and track recording variations caused by electrode drifting in real-time.Main results. The opt-OSort significantly reduces transient cluster occurrences by two orders of magnitude and decreases memory usage by 2.5-80 times in the number of pre-allocated transient clusters compared with other hardware implementations of OSort. The opt-OSort maintains an accuracy comparable to offline OSort and other commonly-used algorithms, with a sorting time of 0.68µs as measured by the hardware-implemented system in both simulated datasets and experimental data. The opt-OSort's ability to handle variations in neural activity caused by electrode drifting is also demonstrated.Significance. These results present a rapid, precise, and robust spike sorting solution suitable for integration into low-power, portable, closed-loop neural control systems and brain-computer interfaces.

A Fast and Effective Spike Sorting Method Based on Multi-Frequency Composite Waveform Shapes

Accurate spike sorting to the appropriate neuron is crucial for neural activity analysis. To improve spike sorting performance, it is essential to fully leverage each processing step, including filtering, spike detection, feature extraction, and clustering. However, compared to the latter two steps that were widely studied and optimized, the filtering process was largely neglected. In this study, we proposed a fast and effective spike sorting method (MultiFq) based on multi-frequency composite waveform shapes acquired through an optimized filtering process. When combined with the classical PCA-Km spiking sorting algorithm, our proposed MultiFq significantly improved its sorting performance and achieved as high performance as the complex Wave-clus did in both the simulated and in vivo datasets. But, the combined method was about 10 times faster than Wave-clus (0.16 s vs. 2.06 s in simulated datasets; 0.46 s vs. 2.03 s in in vivo datasets). Furthermore, we demonstrated the compatibility of our MultiFq by combining it with other sorting algorithms, which consistently resulted in significant improvement in sorting accuracy with the maximum improvement at 35.04%. The above results demonstrated that our proposed method could significantly improve the sorting performance with low computation cost and good compatibility by leveraging the multi-frequency composite waveform shapes.

A Miniaturized Closed-Loop Optogenetic Brain Stimulation Device

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

Closed-loop Modulation of the Self-regulating Brain: A Review on Approaches, Emerging Paradigms, and Experimental Designs

Closed-loop approaches, setups, and experimental designs have been applied within the field of neuroscience to enhance the understanding of basic neurophysiology principles (closed-loop neuroscience; CLNS) and to develop improved procedures for modulating brain circuits and networks for clinical purposes (closed-loop neuromodulation; CLNM). The contents of this review are thus arranged into the following sections. First, we describe basic research findings that have been made using CLNS. Next, we provide an overview of the application, rationale, and therapeutic aspects of CLNM for clinical purposes. Finally, we summarize methodological concerns and critics in clinical practice of neurofeedback and novel applications of closed-loop perspective and techniques to improve and optimize its experiments. Moreover, we outline the theoretical explanations and experimental ideas to test animal models of neurofeedback and discuss technical issues and challenges associated with implementing closed-loop systems. We hope this review is helpful for both basic neuroscientists and clinical/ translationally-oriented scientists interested in applying closed-loop methods to improve mental health and well-being.

A Closed-Loop Optogenetic Stimulation Device

Closed-loop optogenetic stimulation devices deliver optical stimulations based on real-time measurement and analysis of neural responses to stimulations. However, the use of large bench-top and tethered devices hinders the naturalistic test environment, which is crucial in pre-clinical neuroscience studies involving small rodent subjects. This paper presents a tetherless, lightweight and miniaturized head-mountable closed-loop optogenetic stimulation device. The device consists of three hardware modules: a hybrid electrode, an action potential detector, and an optogenetic stimulator. In addition, the device includes three software modules: a feature extractor, a control algorithm, and a pulse generator. The details of the design, implementation, and bench-testing of the device are presented. Furthermore, an in vitro test environment is formed using synthetic neural signals, wherein the device is validated for its closed-loop performance. During the in vitro validation, the device was able to identify abnormal neural signals, and trigger optical stimulation. On the other hand, it was able to also distinguish normal neural signals and inhibit optical stimulation. The overall power consumption of the device is 24 mW. The device measures 6 mm in radius and weighs 0.44 g excluding the power source.

A Wireless Electro-Optic Headstage With a 0.13- μm CMOS Custom Integrated DWT Neural Signal Decoder for Closed-Loop Optogenetics

We present a wireless electro-optic headstage that uses a 0.13- μm CMOS custom integrated circuit (IC) implementing a digital neural decoder (ND-IC) for enabling real-time closed-loop (CL) optogenetics. The ND-IC processes the neural activity data using three digital cores: 1) the detector core detects and extracts the action potential (AP) of individual neurons by using an adaptive threshold; 2) the data compression core compresses the detected AP by using an efficient Symmlet-2 discrete wavelet transform (DWT) processor for decreasing the amount of data to be transmitted by the low-power wireless link; and 3) the classification core sorts the compressed AP into separated clusters on the fly according to their wave shapes. The ND-IC encompasses several innovations: 1) the compression core decreases the complexity from O(n ²) to O(n · log(n)) compared to the previous solutions, while using two times less memory, thanks to the use of a new coefficient sorting tree; and 2) the AP classification core reuses both the compressed DWT coefficients to perform implicit dimensionality reduction, which allows for performing intensive signal processing on-chip, while increasing power and hardware efficiency. This core also reuses the signal standard deviation already computed by the AP detector core as threshold for performing automatic AP sorting. The headstage also introduces innovations by enabling a new wireless CL scheme between the neural data acquisition module and the optical stimulator. Our CL scheme uses the AP sorting and timing information produced by the ND-IC for detecting complex firing patterns within the brain. The headstage is also smaller (1.13 cm ³), lighter (3.0 g with a 40 mAh battery) and less invasive than the previous solutions, while providing a measured autonomy of 2h40, with the ND-IC. The whole system and the ND-IC are first validated in vivo in the LD thalamus of a Long-Evans rat, and then in freely-moving CL experiments involving a mouse virally expressing ChR2-mCherry in inhibitory neurons of the prelimbic cortex, and the results show that our system works well within an in vivo experimental setting with a freely moving mouse.

A novel neural prosthesis providing long-term electrocorticography recording and cortical stimulation for epilepsy and brain-computer interface

OBJECTIVE

Wireless technology is a novel tool for the transmission of cortical signals. Wireless electrocorticography (ECoG) aims to improve the safety and diagnostic gain of procedures requiring invasive localization of seizure foci and also to provide long-term recording of brain activity for brain-computer interfaces (BCIs). However, no wireless devices aimed at these clinical applications are currently available. The authors present the application of a fully implantable and externally rechargeable neural prosthesis providing wireless ECoG recording and direct cortical stimulation (DCS). Prolonged wireless ECoG monitoring was tested in nonhuman primates by using a custom-made device (the ECoG implantable wireless 16-electrode [ECOGIW-16E] device) containing a 16-contact subdural grid. This is a preliminary step toward large-scale, long-term wireless ECoG recording in humans.

METHODS

The authors implanted the ECOGIW-16E device over the left sensorimotor cortex of a nonhuman primate (Macaca fascicularis), recording ECoG signals over a time span of 6 months. Daily electrode impedances were measured, aiming to maintain the impedance values below a threshold of 100 KΩ. Brain mapping was obtained through wireless cortical stimulation at fixed intervals (1, 3, and 6 months). After 6 months, the device was removed. The authors analyzed cortical tissues by using conventional histological and immunohistological investigation to assess whether there was evidence of damage after the long-term implantation of the grid.

RESULTS

The implant was well tolerated; no neurological or behavioral consequences were reported in the monkey, which resumed his normal activities within a few hours of the procedure. The signal quality of wireless ECoG remained excellent over the 6-month observation period. Impedance values remained well below the threshold value; the average impedance per contact remains approximately 40 KΩ. Wireless cortical stimulation induced movements of the upper and lower limbs, and elicited fine movements of the digits as well. After the monkey was euthanized, the grid was found to be encapsulated by a newly formed dural sheet. The grid removal was performed easily, and no direct adhesions of the grid to the cortex were found. Conventional histological studies showed no cortical damage in the brain region covered by the grid, except for a single microscopic spot of cortical necrosis (not visible to the naked eye) in a region that had undergone repeated procedures of electrical stimulation. Immunohistological studies of the cortex underlying the grid showed a mild inflammatory process.

CONCLUSIONS

This preliminary experience in a nonhuman primate shows that a wireless neuroprosthesis, with related long-term ECoG recording (up to 6 months) and multiple DCSs, was tolerated without sequelae. The authors predict that epilepsy surgery could realize great benefit from this novel prosthesis, providing an extended time span for ECoG recording.

Real-time classification of experience-related ensemble spiking patterns for closed-loop applications

Communication in neural circuits across the cortex is thought to be mediated by spontaneous temporally organized patterns of population activity lasting ~50 –200 ms. Closed-loop manipulations have the unique power to reveal direct and causal links between such patterns and their contribution to cognition. Current brain–computer interfaces, however, are not designed to interpret multi-neuronal spiking patterns at the millisecond timescale. To bridge this gap, we developed a system for classifying ensemble patterns in a closed-loop setting and demonstrated its application in the online identification of hippocampal neuronal replay sequences in the rat. Our system decodes multi-neuronal patterns at 10 ms resolution, identifies within 50 ms experience-related patterns with over 70% sensitivity and specificity, and classifies their content with 95% accuracy. This technology scales to high-count electrode arrays and will help to shed new light on the contribution of internally generated neural activity to coordinated neural assembly interactions and cognition.

Identification of Retinal Ganglion Cell Firing Patterns Using Clustering Analysis Supplied with Failure Diagnosis

An important goal in visual neuroscience is to understand how neuronal population coding in vertebrate retina mediates the broad range of visual functions. Microelectrode arrays interface on isolated retina registers a collective measure of the spiking dynamics of retinal ganglion cells (RGCs) by probing them simultaneously and in large numbers. The recorded data stream is then processed to identify spike trains of individual RGCs by efficient and scalable spike detection and sorting routines. Most spike sorting software packages, available either commercially or as freeware, combine automated steps with judgment calls by the investigator to verify the quality of sorted spikes. This work focused on sorting spikes of RGCs into clusters using an integrated analytical platform for the data recorded during visual stimulation of wild-type mice retinas with whole field stimuli. After spike train detection, we projected each spike onto two feature spaces: a parametric space and a principal components space. We then applied clustering algorithms to sort spikes into separate clusters. To eliminate the need for human intervention, the initial clustering results were submitted to diagnostic tests that evaluated the results to detect the sources of failure in cluster assignment. This failure diagnosis formed a decision logic for diagnosable electrodes to enhance the clustering quality iteratively through rerunning the clustering algorithms. The new clustering results showed that the spike sorting accuracy was improved. Subsequently, the number of active RGCs during each whole field stimulation was found, and the light responsiveness of each RGC was identified. Our approach led to error-resilient spike sorting in both feature extraction methods; however, using parametric features led to less erroneous spike sorting compared to principal components, particularly for low signal-to-noise ratios. As our approach is reliable for retinal signal processing in response to simple visual stimuli, it could be applied to the evaluation of disrupted physiological signaling in retinal neurodegenerative diseases.

Multimed: An Integrated, Multi-Application Platform for the Real-Time Recording and Sub-Millisecond Processing of Biosignals

Enhanced understanding and control of electrophysiology mechanisms are increasingly being hailed as key knowledge in the fields of modern biology and medicine. As more and more excitable cell mechanics are being investigated and exploited, the need for flexible electrophysiology setups becomes apparent. With that aim, we designed Multimed, which is a versatile hardware platform for the real-time recording and processing of biosignals. Digital processing in Multimed is an arrangement of generic processing units from a custom library. These can freely be rearranged to match the needs of the application. Embedded onto a Field Programmable Gate Array (FPGA), these modules utilize full-hardware signal processing to lower processing latency. It achieves constant latency, and sub-millisecond processing and decision-making on 64 channels. The FPGA core processing unit makes Multimed suitable as either a reconfigurable electrophysiology system or a prototyping platform for VLSI implantable medical devices. It is specifically designed for open- and closed-loop experiments and provides consistent feedback rules, well within biological microseconds timeframes. This paper presents the specifications and architecture of the Multimed system, then details the biosignal processing algorithms and their digital implementation. Finally, three applications utilizing Multimed in neuroscience and diabetes research are described. They demonstrate the system’s configurability, its multi-channel, real-time processing, and its feedback control capabilities.

Bioelectronic organ-based sensor for microfluidic real-time analysis of the demand in insulin

On-line and real-time analysis of micro-organ activity permits to use the endogenous analytical power of cellular signal transduction algorithms as biosensors. We have developed here such a sensor using only a few pancreatic endocrine islets and the avoidance of transgenes or chemical probes reduces bias and procures general usage. Nutrient and hormone-induced changes in islet ion fluxes through channels provide the first integrative read-out of micro-organ activity. Using extracellular electrodes we captured this read-out non-invasively as slow potentials which reflect glucose concentration-dependent (3-15 mM) micro-organ activation and coupling. Custom-made PDMS-based microfluidics with platinum black micro-electrode arrays required only some tens of islets and functioned at flow rates of 1-10 µl/min which are compatible with microdialysis. We developed hardware solutions for on-line real-time analysis on a reconfigurable Field-Programmable Gate Array (FPGA) that offered resource-efficient architecture and storage of intermediary processing stages. Moreover, real-time adaptive and reconfigurable algorithms accounted for signal disparities and noise distribution. Based on islet slow potentials, this integrated set-up allowed within less than 40 μs the discrimination and precise automatic ranking of small increases (2 mM steps) of glucose concentrations in real time and within the physiological glucose range. This approach shall permit further development in continuous monitoring of the demand for insulin in type 1 diabetes as well as monitoring of organs-on-chip or maturation of stem-cell derived islets.

A Compact Closed-Loop Optogenetics System Based on Artifact-Free Transparent Graphene Electrodes

Electrophysiology is a decades-old technique widely used for monitoring activity of individual neurons and local field potentials. Optogenetics has revolutionized neuroscience studies by offering selective and fast control of targeted neurons and neuron populations. The combination of these two techniques is crucial for causal investigation of neural circuits and understanding their functional connectivity. However, electrical artifacts generated by light stimulation interfere with neural recordings and hinder the development of compact closed-loop systems for precise control of neural activity. Here, we demonstrate that transparent graphene micro-electrodes fabricated on a clear polyethylene terephthalate film eliminate the light-induced artifact problem and allow development of a compact battery-powered closed-loop optogenetics system. We extensively investigate light-induced artifacts for graphene electrodes in comparison to metal control electrodes. We then design optical stimulation module using micro-LED chips coupled to optical fibers to deliver light to intended depth for optogenetic stimulation. For artifact-free integration of graphene micro-electrode recordings with optogenetic stimulation, we design and develop a compact closed-loop system and validate it for different frequencies of interest for neural recordings. This compact closed-loop optogenetics system can be used for various applications involving optogenetic stimulation and electrophysiological recordings.

A real-time spike sorting method based on the embedded GPU

Microelectrode arrays with hundreds of channels have been widely used to acquire neuron population signals in neuroscience studies. Online spike sorting is becoming one of the most important challenges for high-throughput neural signal acquisition systems. Graphic processing unit (GPU) with high parallel computing capability might provide an alternative solution for increasing real-time computational demands on spike sorting. This study reported a method of real-time spike sorting through computing unified device architecture (CUDA) which was implemented on an embedded GPU (NVIDIA JETSON Tegra K1, TK1). The sorting approach is based on the principal component analysis (PCA) and K-means. By analyzing the parallelism of each process, the method was further optimized in the thread memory model of GPU. Our results showed that the GPU-based classifier on TK1 is 37.92 times faster than the MATLAB-based classifier on PC while their accuracies were the same with each other. The high-performance computing features of embedded GPU demonstrated in our studies suggested that the embedded GPU provide a promising platform for the real-time neural signal processing.

In-vitro validation of a closed-loop optogenetic stimulation device

Closed-loop optogenetic stimulation (CLOS) involves real-time neural data collection and analysis, feedback control, and optical neuromodulation. Despite emergence of the CLOS devices, methods for validating such devices in-vitro are limited. This paper presents a CLOS device and introduces an in-vitro setup for validating CLOS devices. The CLOS device consists of an electrode, a neural detector, a control algorithm, and an optogenetic stimulator. The in-vitro setup consists of saline solution, a neural signal emitter, a photodiode and its amplifier, and a closed-loop simulation program. Synthetic neural signals are delivered to the CLOS device, and based on the sensed optical stimulations from the light source, the properties of the delivered neural signals are changed. Experiments are conducted to evaluate the closed-loop operation of the CLOS device, and verify the capability of the in-vitro setup to for validating the CLOS devices. The in-vitro setup enables refinement of CLOS devices before arduous in-vivo trials.

Algorithmic design of a noise-resistant and efficient closed-loop deep brain stimulation system: A computational approach

Advances in the field of closed-loop neuromodulation call for analysis and modeling approaches capable of confronting challenges related to the complex neuronal response to stimulation and the presence of strong internal and measurement noise in neural recordings. Here we elaborate on the algorithmic aspects of a noise-resistant closed-loop subthalamic nucleus deep brain stimulation system for advanced Parkinson’s disease and treatment-refractory obsessive-compulsive disorder, ensuring remarkable performance in terms of both efficiency and selectivity of stimulation, as well as in terms of computational speed. First, we propose an efficient method drawn from dynamical systems theory, for the reliable assessment of significant nonlinear coupling between beta and high-frequency subthalamic neuronal activity, as a biomarker for feedback control. Further, we present a model-based strategy through which optimal parameters of stimulation for minimum energy desynchronizing control of neuronal activity are being identified. The strategy integrates stochastic modeling and derivative-free optimization of neural dynamics based on quadratic modeling. On the basis of numerical simulations, we demonstrate the potential of the presented modeling approach to identify, at a relatively low computational cost, stimulation settings potentially associated with a significantly higher degree of efficiency and selectivity compared with stimulation settings determined post-operatively. Our data reinforce the hypothesis that model-based control strategies are crucial for the design of novel stimulation protocols at the backstage of clinical applications.

A Wireless 32-Channel Implantable Bidirectional Brain Machine Interface

All neural information systems (NIS) rely on sensing neural activity to supply commands and control signals for computers, machines and a variety of prosthetic devices. Invasive systems achieve a high signal-to-noise ratio (SNR) by eliminating the volume conduction problems caused by tissue and bone. An implantable brain machine interface (BMI) using intracortical electrodes provides excellent detection of a broad range of frequency oscillatory activities through the placement of a sensor in direct contact with cortex. This paper introduces a compact-sized implantable wireless 32-channel bidirectional brain machine interface (BBMI) to be used with freely-moving primates. The system is designed to monitor brain sensorimotor rhythms and present current stimuli with a configurable duration, frequency and amplitude in real time to the brain based on the brain activity report. The battery is charged via a novel ultrasonic wireless power delivery module developed for efficient delivery of power into a deeply-implanted system. The system was successfully tested through bench tests and in vivo tests on a behaving primate to record the local field potential (LFP) oscillation and stimulate the target area at the same time.

Graphene neural interfaces for artifact free optogenetics

Light-induced artifacts in metal-based microelectrodes significantly limit simultaneous use of electrophysiology with optogenetics or optical imaging. In this work, we systematically investigate light-induced artifacts in Au and transparent graphene electrodes fabricated in the same batch. We demonstrate that the Au electrodes show light artifacts resembling genuine local field potentials, while graphene electrodes are free of light-induced artifacts. With its other desirable properties including high mechanical strength, good conductivity, transparency, and biocompatibility, graphene electrodes show great promise for combining electrical and optical modalities in the same experiment.

A Multichannel Recording System with Optical Stimulation for Closed-Loop Optogenetic Experiments

Selective perturbation of the activity of specific cell types in the brain tissue is essential in understanding the function of neuronal circuits involved in cognition and behavior and might also provide therapeutic neuromodulation strategies. Such selective neuronal addressing can be achieved through the optical activation of light-sensitive proteins called opsins that are expressed in specific cell populations through genetic methods-hence the name "optogenetics." In optogenetic experiments, the electrical activity of the targeted cell populations is optically triggered and monitored using arrays of microelectrodes. In closed-loop studies, the optical stimulation parameters are adjusted based on the recorded activity, ideally in real time. Here we describe the basic tools and the protocols allowing closed-loop optogenic experiments in vivo.

Wirelessly powered, fully internal optogenetics for brain, spinal and peripheral circuits in mice

To enable sophisticated optogenetic manipulation of neural circuits throughout the nervous system with limited disruption of animal behavior, light-delivery systems beyond fiber optic tethering and large, head-mounted wireless receivers are desirable. We report the development of an easy-to-construct, implantable wireless optogenetic device. Our smallest version (20 mg, 10 mm(3)) is two orders of magnitude smaller than previously reported wireless optogenetic systems, allowing the entire device to be implanted subcutaneously. With a radio-frequency (RF) power source and controller, this implant produces sufficient light power for optogenetic stimulation with minimal tissue heating (<1 °C). We show how three adaptations of the implant allow for untethered optogenetic control throughout the nervous system (brain, spinal cord and peripheral nerve endings) of behaving mice. This technology opens the door for optogenetic experiments in which animals are able to behave naturally with optogenetic manipulation of both central and peripheral targets.

Closed-loop and activity-guided optogenetic control

Advances in optical manipulation and observation of neural activity have set the stage for widespread implementation of closed-loop and activity-guided optical control of neural circuit dynamics. Closing the loop optogenetically (i.e., basing optogenetic stimulation on simultaneously observed dynamics in a principled way) is a powerful strategy for causal investigation of neural circuitry. In particular, observing and feeding back the effects of circuit interventions on physiologically relevant timescales is valuable for directly testing whether inferred models of dynamics, connectivity, and causation are accurate in vivo. Here we highlight technical and theoretical foundations as well as recent advances and opportunities in this area, and we review in detail the known caveats and limitations of optogenetic experimentation in the context of addressing these challenges with closed-loop optogenetic control in behaving animals.

The PennBMBI: Design of a General Purpose Wireless Brain-Machine-Brain Interface System

In this paper, a general purpose wireless Brain-Machine-Brain Interface (BMBI) system is presented. The system integrates four battery-powered wireless devices for the implementation of a closed-loop sensorimotor neural interface, including a neural signal analyzer, a neural stimulator, a body-area sensor node and a graphic user interface implemented on the PC end. The neural signal analyzer features a four channel analog front-end with configurable bandpass filter, gain stage, digitization resolution, and sampling rate. The target frequency band is configurable from EEG to single unit activity. A noise floor of 4.69 μVrms is achieved over a bandwidth from 0.05 Hz to 6 kHz. Digital filtering, neural feature extraction, spike detection, sensing-stimulating modulation, and compressed sensing measurement are realized in a central processing unit integrated in the analyzer. A flash memory card is also integrated in the analyzer. A 2-channel neural stimulator with a compliance voltage up to ± 12 V is included. The stimulator is capable of delivering unipolar or bipolar, charge-balanced current pulses with programmable pulse shape, amplitude, width, pulse train frequency and latency. A multi-functional sensor node, including an accelerometer, a temperature sensor, a flexiforce sensor and a general sensor extension port has been designed. A computer interface is designed to monitor, control and configure all aforementioned devices via a wireless link, according to a custom designed communication protocol. Wireless closed-loop operation between the sensory devices, neural stimulator, and neural signal analyzer can be configured. The proposed system was designed to link two sites in the brain, bridging the brain and external hardware, as well as creating new sensory and motor pathways for clinical practice. Bench test and in vivo experiments are performed to verify the functions and performances of the system.

Neural ensemble communities: open-source approaches to hardware for large-scale electrophysiology

One often-overlooked factor when selecting a platform for large-scale electrophysiology is whether or not a particular data acquisition system is 'open' or 'closed': that is, whether or not the system's schematics and source code are available to end users. Open systems have a reputation for being difficult to acquire, poorly documented, and hard to maintain. With the arrival of more powerful and compact integrated circuits, rapid prototyping services, and web-based tools for collaborative development, these stereotypes must be reconsidered. We discuss some of the reasons why multichannel extracellular electrophysiology could benefit from open-source approaches and describe examples of successful community-driven tool development within this field. In order to promote the adoption of open-source hardware and to reduce the need for redundant development efforts, we advocate a move toward standardized interfaces that connect each element of the data processing pipeline. This will give researchers the flexibility to modify their tools when necessary, while allowing them to continue to benefit from the high-quality products and expertise provided by commercial vendors.

Real-time in vivo optogenetic neuromodulation and multielectrode electrophysiologic recording with NeuroRighter

Optogenetic channels have greatly expanded neuroscience’s experimental capabilities, enabling precise genetic targeting and manipulation of neuron subpopulations in awake and behaving animals. However, many barriers to entry remain for this technology – including low-cost and effective hardware for combined optical stimulation and electrophysiologic recording. To address this, we adapted the open-source NeuroRighter multichannel electrophysiology platform for use in awake and behaving rodents in both open and closed-loop stimulation experiments. Here, we present these cost-effective adaptations, including commercially available LED light sources; custom-made optical ferrules; 3D printed ferrule hardware and software to calibrate and standardize output intensity; and modifications to commercially available electrode arrays enabling stimulation proximally and distally to the recording target. We then demonstrate the capabilities and versatility of these adaptations in several open and closed-loop experiments, demonstrate spectrographic methods of analyzing the results, as well as discuss artifacts of stimulation.