Pre-frontal control of closed-loop limbic neurostimulation by rodents using a brain-computer interface

Modulation Steering Motion by Quantitative Electrical Stimulation in Pigeon Robots

The pigeon robot has attracted significant attention in the field of animal robotics thanks to its outstanding mobility and adaptive capability in complex environments. However, research on pigeon robots is currently facing bottlenecks, and achieving fine control over the motion behavior of pigeon robots through brain-machine interfaces remains challenging. Here, we systematically quantify the relationship between electrical stimulation and stimulus-induced motion behaviors, and provide an analytical method to demonstrate the effectiveness of pigeon robots based on electrical stimulation. In this study, we investigated the influence of gradient voltage intensity (1.2-3.0 V) on the indoor steering motion control of pigeon robots. Additionally, we discussed the response time of electrical stimulation and the effective period of the brain-machine interface. The results indicate that pigeon robots typically exhibit noticeable behavioral responses at a 2.0 V voltage stimulus. Increasing the stimulation intensity significantly controls the steering angle and turning radius (p < 0.05), enabling precise control of pigeon robot steering motion through stimulation intensity regulation. When the threshold voltage is reached, the average response time of a pigeon robot to the electrical stimulation is 220 ms. This study quantifies the role of each stimulation parameter in controlling pigeon robot steering behavior, providing valuable reference information for the precise steering control of pigeon robots. Based on these findings, we offer a solution for achieving precise control of pigeon robot steering motion and contribute to solving the problem of encoding complex trajectory motion in pigeon robots.

Closing the loop in psychiatric deep brain stimulation: physiology, psychometrics, and plasticity

Deep brain stimulation (DBS) is an invasive approach to precise modulation of psychiatrically relevant circuits. Although it has impressive results in open-label psychiatric trials, DBS has also struggled to scale to and pass through multi-center randomized trials. This contrasts with Parkinson disease, where DBS is an established therapy treating thousands of patients annually. The core difference between these clinical applications is the difficulty of proving target engagement, and of leveraging the wide range of possible settings (parameters) that can be programmed in a given patient's DBS. In Parkinson's, patients' symptoms change rapidly and visibly when the stimulator is tuned to the correct parameters. In psychiatry, those same changes take days to weeks, limiting a clinician's ability to explore parameter space and identify patient-specific optimal settings. I review new approaches to psychiatric target engagement, with an emphasis on major depressive disorder (MDD). Specifically, I argue that better engagement may come by focusing on the root causes of psychiatric illness: dysfunction in specific, measurable cognitive functions and in the connectivity and synchrony of distributed brain circuits. I overview recent progress in both those domains, and how it may relate to other technologies discussed in companion articles in this issue.

Somatosensory ECoG-based brain-machine interface with electrical stimulation on medial forebrain bundle

Brain-machine interface (BMI) provides an alternative route for controlling an external device with one's intention. For individuals with motor-related disability, the BMI technologies can be used to replace or restore motor functions. Therefore, BMIs for movement restoration generally decode the neural activity from the motor-related brain regions. In this study, however, we designed a BMI system that uses sensory-related neural signals for BMI combined with electrical stimulation for reward. Four-channel electrocorticographic (ECoG) signals were recorded from the whisker-related somatosensory cortex of rats and converted to extract the BMI signals to control the one-dimensional movement of a dot on the screen. At the same time, we used operant conditioning with electrical stimulation on medial forebrain bundle (MFB), which provides a virtual reward to motivate the rat to move the dot towards the desired center region. The BMI task training was performed for 7 days with ECoG recording and MFB stimulation. Animals successfully learned to move the dot location to the desired position using S1BF neural activity. This study successfully demonstrated that it is feasible to utilize the neural signals from the whisker somatosensory cortex for BMI system. In addition, the MFB electrical stimulation is effective for rats to learn the behavioral task for BMI.

Mixed vine copula flows for flexible modeling of neural dependencies

Recordings of complex neural population responses provide a unique opportunity for advancing our understanding of neural information processing at multiple scales and improving performance of brain computer interfaces. However, most existing analytical techniques fall short of capturing the complexity of interactions within the concerted population activity. Vine copula-based approaches have shown to be successful at addressing complex high-order dependencies within the population, disentangled from the single-neuron statistics. However, most applications have focused on parametric copulas which bear the risk of misspecifying dependence structures. In order to avoid this risk, we adopted a fully non-parametric approach for the single-neuron margins and copulas by using Neural Spline Flows (NSF). We validated the NSF framework on simulated data of continuous and discrete types with various forms of dependency structures and with different dimensionality. Overall, NSFs performed similarly to existing non-parametric estimators, while allowing for considerably faster and more flexible sampling which also enables faster Monte Carlo estimation of copula entropy. Moreover, our framework was able to capture low and higher order heavy tail dependencies in neuronal responses recorded in the mouse primary visual cortex during a visual learning task while the animal was navigating a virtual reality environment. These findings highlight an often ignored aspect of complexity in coordinated neuronal activity which can be important for understanding and deciphering collective neural dynamics for neurotechnological applications.

The Influence of Frequency Bands and Brain Region on ECoG-Based BMI Learning Performance

Numerous brain–machine interface (BMI) studies have shown that various frequency bands (alpha, beta, and gamma bands) can be utilized in BMI experiments and modulated as neural information for machine control after several BMI learning trial sessions. In addition to frequency range as a neural feature, various areas of the brain, such as the motor cortex or parietal cortex, have been selected as BMI target brain regions. However, although the selection of target frequency and brain region appears to be crucial in obtaining optimal BMI performance, the direct comparison of BMI learning performance as it relates to various brain regions and frequency bands has not been examined in detail. In this study, ECoG-based BMI learning performances were compared using alpha, beta, and gamma bands, respectively, in a single rodent model. Brain area dependence of learning performance was also evaluated in the frontal cortex, the motor cortex, and the parietal cortex. The findings indicated that BMI learning performance was best in the case of the gamma frequency band and worst in the alpha band (one-way ANOVA, F = 4.41, p < 0.05). In brain area dependence experiments, better BMI learning performance appears to be shown in the primary motor cortex (one-way ANOVA, F = 4.36, p < 0.05). In the frontal cortex, two out of four animals failed to learn the feeding tube control even after a maximum of 10 sessions. In conclusion, the findings reported in this study suggest that the selection of target frequency and brain region should be carefully considered when planning BMI protocols and for performing optimized BMI.

Circumventing neural damage in a C. elegans chemosensory circuit using genetically engineered synapses

Neuronal loss can considerably diminish neural circuit function, impairing normal behavior by disrupting information flow in the circuit. Here, we use genetically engineered electrical synapses to reroute the flow of information in a C. elegans damaged chemosensory circuit in order to restore organism behavior. We impaired chemotaxis by removing one pair of interneurons from the circuit then artificially coupled two other adjacent neuron pairs by ectopically expressing the gap junction protein, connexin, in them. This restored chemotaxis in the animals. We expected to observe linear and direct information flow between the connexin-coupled neurons in the recovered circuit but also revealed the formation of new potent left-right lateral electrical connections within the connexin-expressing neuron pairs. Our analysis suggests that these additional electrical synapses help restore circuit function by amplifying weakened neuronal signals in the damaged circuit in addition to emulating the wild-type circuit. A record of this paper's transparent peer review process is included in the Supplemental Information.

Behavioral validation of a wireless low-power neurostimulation technology in a conditioned place preference task

OBJECTIVE

Neurostimulation technologies are important for studying neural circuits and the connections that underlie neurological and psychiatric disorders. However, current methods come with limitations such as the restraint on movement imposed by the wires delivering stimulation. The objective of this study was to assess whether the e-Particle (EP), a novel wireless neurostimulator, could sufficiently stimulate the brain to modify behavior without these limitations.

APPROACH

Rats were implanted with the EP and a commercially available stimulating electrode. Animals received rewarding brain stimulation, and performance in a conditioned place preference (CPP) task was measured. To ensure stimulation-induced neuronal activation, immediate early gene c-fos expression was also measured.

MAIN RESULTS

The EP was validated in a commonly used CPP task by demonstrating that (1) wireless stimulation via the EP induced preference behavior that was comparable to that induced by standard wired electrodes and (2) neuronal activation was observed in projection targets of the stimulation site.

SIGNIFICANCE

The EP may help achieve a better understanding of existing brain stimulation methods while overcoming their limitations. Validation of the EP in a behavioral model suggests that the benefits of this technology may extend to other areas of animal research and potentially to human clinical applications.

Invasive Brain Machine Interface System

Because of high spatial-temporal resolution of neural signals obtained by invasive recording, the invasive brain-machine interfaces (BMI) have achieved great progress in the past two decades. With success in animal research, BMI technology is transferring to clinical trials for helping paralyzed people to restore their lost motor functions. This chapter gives a brief review of BMI development from animal experiments to human clinical studies in the following aspects: (1) BMIs based on rodent animals; (2) BMI based on non-human primates; and (3) pilot BMIs studies in clinical trials. In the end, the chapter concludes with a summary of potential opportunities and future challenges in BMI technology.

Closing the loop on impulsivity via nucleus accumbens delta-band activity in mice and man

We reveal prominent delta oscillations in the nucleus accumbens preceding food reward in mice and use them to guide responsive neurostimulation to suppress binge-like behavior. Similar electrographic signatures are observed in human nucleus accumbens during reward anticipation as well, suggesting their translational potential in the development of a treatment for loss of impulse control in obesity and perhaps additional brain disorders.

Reward hypersensitization is a common feature of neuropsychiatric disorders, manifesting as impulsivity for anticipated incentives. Temporally specific changes in activity within the nucleus accumbens (NAc), which occur during anticipatory periods preceding consummatory behavior, represent a critical opportunity for intervention. However, no available therapy is capable of automatically sensing and therapeutically responding to this vulnerable moment in time when anticipation-related neural signals may be present. To identify translatable biomarkers for an off-the-shelf responsive neurostimulation system, we record local field potentials from the NAc of mice and a human anticipating conventional rewards. We find increased power in 1- to 4-Hz oscillations predominate during reward anticipation, which can effectively trigger neurostimulation that reduces consummatory behavior in mice sensitized to highly palatable food. Similar oscillations are present in human NAc during reward anticipation, highlighting the translational potential of our findings in the development of a treatment for a major unmet need.

High-frequency epidural stimulation across the respiratory cycle evokes phrenic short-term potentiation after incomplete cervical spinal cord injury

C2 spinal hemilesion (C2Hx) paralyzes the ipsilateral diaphragm, but recovery is possible through activation of "crossed spinal" synaptic inputs to ipsilateral phrenic motoneurons. We tested the hypothesis that high-frequency epidural stimulation (HF-ES) would potentiate ipsilateral phrenic output after subacute and chronic C2Hx. HF-ES (300 Hz) was applied to the ventrolateral C4 or T2 spinal cord ipsilateral to C2Hx in anesthetized and mechanically ventilated adult rats. Stimulus duration was 60 s, and currents ranged from 100 to 1,000 µA. Bilateral phrenic nerve activity and ipsilateral hypoglossal (XII) nerve activity were recorded before and after HF-ES. Higher T2 stimulus currents potentiated ipsilateral phasic inspiratory activity at both 2 and 12 wk post-C2Hx, whereas higher stimulus currents delivered at C4 potentiated ipsilateral phasic phrenic activity only at 12 wk (P = 0.028). Meanwhile, tonic output in the ipsilateral phrenic nerve reached 500% of baseline values at the high currents with no difference between 2 and 12 wk. HF-ES did not trigger inspiratory burst-frequency changes. Similar responses occurred following T2 HF-ES. Increases in contralateral phrenic and XII nerve output were induced by C4 and T2 HF-ES at higher currents, but the relative magnitude of these changes was small compared with the ipsilateral phrenic response. We conclude that following incomplete cervical spinal cord injury, HF-ES of the ventrolateral midcervical or thoracic spinal cord can potentiate efferent phrenic motor output with little impact on inspiratory burst frequency. However, the substantial increases in tonic output indicate that the uninterrupted 60-s stimulation paradigm used is unlikely to be useful for respiratory muscle activation after spinal injury.NEW & NOTEWORTHY Previous studies reported that high-frequency epidural stimulation (HF-ES) activates the diaphragm following acute spinal transection. This study examined HF-ES and phrenic motor output following subacute and chronic incomplete cervical spinal cord injury. Short-term potentiation of phrenic bursting following HF-ES illustrates the potential for spinal stimulation to induce respiratory neuroplasticity. Increased tonic phrenic output indicates that alternatives to the continuous stimulation paradigm used in this study will be required for respiratory muscle activation after spinal cord injury.

Closed-loop neuromodulation systems: next-generation treatments for psychiatric illness

Despite deep brain stimulation's positive early results in psychiatric disorders, well-designed clinical trials have yielded inconsistent clinical outcomes. One path to more reliable benefit is closed-loop therapy: stimulation that is automatically adjusted by a device or algorithm in response to changes in the patient's electrical brain activity. These interventions may provide more precise and patient-specific treatments. This article first introduces the available closed-loop neuromodulation platforms, which have shown clinical efficacy in epilepsy and strong early results in movement disorders. It discusses the strengths and limitations of these devices in the context of psychiatric illness. It then describes emerging technologies to address these limitations, including pre-clinical developments such as wireless deep neurostimulation and genetically targeted neuromodulation. Finally, ongoing challenges and limitations for closed-loop psychiatric brain stimulation development, most notably the difficulty of identifying meaningful biomarkers for titration, are discussed. This is considered in the recently-released Research Domain Criteria (RDoC) framework, and how neuromodulation and RDoC are jointly very well suited to address the problem of treatment-resistant illness is described.

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.

Operant conditioning of neural activity in freely behaving monkeys with intracranial reinforcement

Operant conditioning of neural activity has typically been performed under controlled behavioral conditions using food reinforcement. This has limited the duration and behavioral context for neural conditioning. To reward cell activity in unconstrained primates, we sought sites in nucleus accumbens (NAc) whose stimulation reinforced operant responding. In three monkeys, NAc stimulation sustained performance of a manual target-tracking task, with response rates that increased monotonically with increasing NAc stimulation. We recorded activity of single motor cortex neurons and documented their modulation with wrist force. We conditioned increased firing rates with the monkey seated in the training booth and during free behavior in the cage using an autonomous head-fixed recording and stimulating system. Spikes occurring above baseline rates triggered single or multiple electrical pulses to the reinforcement site. Such rate-contingent, unit-triggered stimulation was made available for periods of 1-3 min separated by 3-10 min time-out periods. Feedback was presented as event-triggered clicks both in-cage and in-booth, and visual cues were provided in many in-booth sessions. In-booth conditioning produced increases in single neuron firing probability with intracranial reinforcement in 48 of 58 cells. Reinforced cell activity could rise more than five times that of non-reinforced activity. In-cage conditioning produced significant increases in 21 of 33 sessions. In-cage rate changes peaked later and lasted longer than in-booth changes, but were often comparatively smaller, between 13 and 18% above non-reinforced activity. Thus intracranial stimulation reinforced volitional increases in cortical firing rates during both free behavior and a controlled environment, although changes in the latter were more robust.NEW & NOTEWORTHY Closed-loop brain-computer interfaces (BCI) were used to operantly condition increases in muscle and neural activity in monkeys by delivering activity-dependent stimuli to an intracranial reinforcement site (nucleus accumbens). We conditioned increased firing rates with the monkeys seated in a training booth and also, for the first time, during free behavior in a cage using an autonomous head-fixed BCI.

Restoring motor function with bidirectional neural interfaces

Closed-loop brain-computer interfaces have bidirectional connections that allow activity-dependent stimulation of the brain, spinal cord, or muscles. Such bidirectional brain-computer interfaces (BBCI) have three major applications that can be used to restore lost motor function. First, the brain could learn to incorporate a long-term artificial recurrent connection into normal behavior, exploiting the brain's ability to adapt to consistent sensorimotor conditions. The obvious clinical application for restoring motor function is to use an artificial recurrent connection to bridge a lost biological connection. Second, activity-dependent stimulation can generate synaptic plasticity on the cellular level. The corresponding clinical application is to strengthen weakened neural connections, such as occur in stroke. A third application involves delivery of activity-dependent deep brain stimulation at subcortical reward sites, which can operantly reinforce the activity that generates the stimulation. The BBCI paradigm has numerous specific applications, depending on the source of the signals and the stimulated targets.

Affective Brain-Computer Interfaces As Enabling Technology for Responsive Psychiatric Stimulation

There is a pressing clinical need for responsive neurostimulators, which sense a patient's brain activity and deliver targeted electrical stimulation to suppress unwanted symptoms. This is particularly true in psychiatric illness, where symptoms can fluctuate throughout the day. Affective BCIs, which decode emotional experience from neural activity, are a candidate control signal for responsive stimulators targeting the limbic circuit. Present affective decoders, however, cannot yet distinguish pathologic from healthy emotional extremes. Indiscriminate stimulus delivery would reduce quality of life and may be actively harmful. We argue that the key to overcoming this limitation is to specifically decode volition, in particular the patient's intention to experience emotional regulation. Those emotion-regulation signals already exist in prefrontal cortex (PFC), and could be extracted with relatively simple BCI algorithms. We describe preliminary data from an animal model of PFC-controlled limbic brain stimulation and discuss next steps for pre-clinical testing and possible translation.