Emerging technologies for improved deep brain stimulation

Pathogenesis, diagnosis, and treatment of epilepsy: electromagnetic stimulation-mediated neuromodulation therapy and new technologies

Epilepsy is a severe, relapsing, and multifactorial neurological disorder. Studies regarding the accurate diagnosis, prognosis, and in-depth pathogenesis are crucial for the precise and effective treatment of epilepsy. The pathogenesis of epilepsy is complex and involves alterations in variables such as gene expression, protein expression, ion channel activity, energy metabolites, and gut microbiota composition. Satisfactory results are lacking for conventional treatments for epilepsy. Surgical resection of lesions, drug therapy, and non-drug interventions are mainly used in clinical practice to treat pain associated with epilepsy. Non-pharmacological treatments, such as a ketogenic diet, gene therapy for nerve regeneration, and neural regulation, are currently areas of research focus. This review provides a comprehensive overview of the pathogenesis, diagnostic methods, and treatments of epilepsy. It also elaborates on the theoretical basis, treatment modes, and effects of invasive nerve stimulation in neurotherapy, including percutaneous vagus nerve stimulation, deep brain electrical stimulation, repetitive nerve electrical stimulation, in addition to non-invasive transcranial magnetic stimulation and transcranial direct current stimulation. Numerous studies have shown that electromagnetic stimulation-mediated neuromodulation therapy can markedly improve neurological function and reduce the frequency of epileptic seizures. Additionally, many new technologies for the diagnosis and treatment of epilepsy are being explored. However, current research is mainly focused on analyzing patients' clinical manifestations and exploring relevant diagnostic and treatment methods to study the pathogenesis at a molecular level, which has led to a lack of consensus regarding the mechanisms related to the disease.

Clinical neurophysiology in the treatment of movement disorders: IFCN handbook chapter

In this review, different aspects of the use of clinical neurophysiology techniques for the treatment of movement disorders are addressed. First of all, these techniques can be used to guide neuromodulation techniques or to perform therapeutic neuromodulation as such. Neuromodulation includes invasive techniques based on the surgical implantation of electrodes and a pulse generator, such as deep brain stimulation (DBS) or spinal cord stimulation (SCS) on the one hand, and non-invasive techniques aimed at modulating or even lesioning neural structures by transcranial application. Movement disorders are one of the main areas of indication for the various neuromodulation techniques. This review focuses on the following techniques: DBS, repetitive transcranial magnetic stimulation (rTMS), low-intensity transcranial electrical stimulation, including transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS), and focused ultrasound (FUS), including high-intensity magnetic resonance-guided FUS (MRgFUS), and pulsed mode low-intensity transcranial FUS stimulation (TUS). The main clinical conditions in which neuromodulation has proven its efficacy are Parkinson's disease, dystonia, and essential tremor, mainly using DBS or MRgFUS. There is also some evidence for Tourette syndrome (DBS), Huntington's disease (DBS), cerebellar ataxia (tDCS), and axial signs (SCS) and depression (rTMS) in PD. The development of non-invasive transcranial neuromodulation techniques is limited by the short-term clinical impact of these techniques, especially rTMS, in the context of very chronic diseases. However, at-home use (tDCS) or current advances in the design of closed-loop stimulation (tACS) may open new perspectives for the application of these techniques in patients, favored by their easier use and lower rate of adverse effects compared to invasive or lesioning methods. Finally, this review summarizes the evidence for keeping the use of electromyography to optimize the identification of muscles to be treated with botulinum toxin injection, which is indicated and widely performed for the treatment of various movement disorders.

Synaptic Plasticity in the Injured Brain Depends on the Temporal Pattern of Stimulation

Neurostimulation protocols are increasingly used as therapeutic interventions, including for brain injury. In addition to the direct activation of neurons, these stimulation protocols are also likely to have downstream effects on those neurons' synaptic outputs. It is well known that alterations in the strength of synaptic connections (long-term potentiation, LTP; long-term depression, LTD) are sensitive to the frequency of stimulation used for induction; however, little is known about the contribution of the temporal pattern of stimulation to the downstream synaptic plasticity that may be induced by neurostimulation in the injured brain. We explored interactions of the temporal pattern and frequency of neurostimulation in the normal cerebral cortex and after mild traumatic brain injury (mTBI), to inform therapies to strengthen or weaken neural circuits in injured brains, as well as to better understand the role of these factors in normal brain plasticity. Whole-cell (WC) patch-clamp recordings of evoked postsynaptic potentials in individual neurons, as well as field potential (FP) recordings, were made from layer 2/3 of visual cortex in response to stimulation of layer 4, in acute slices from control (naive), sham operated, and mTBI rats. We compared synaptic plasticity induced by different stimulation protocols, each consisting of a specific frequency (1 Hz, 10 Hz, or 100 Hz), continuity (continuous or discontinuous), and temporal pattern (perfectly regular, slightly irregular, or highly irregular). At the individual neuron level, dramatic differences in plasticity outcome occurred when the highly irregular stimulation protocol was used at 1 Hz or 10 Hz, producing an overall LTD in controls and shams, but a robust overall LTP after mTBI. Consistent with the individual neuron results, the plasticity outcomes for simultaneous FP recordings were similar, indicative of our results generalizing to a larger scale synaptic network than can be sampled by individual WC recordings alone. In addition to the differences in plasticity outcome between control (naive or sham) and injured brains, the dynamics of the changes in synaptic responses that developed during stimulation were predictive of the final plasticity outcome. Our results demonstrate that the temporal pattern of stimulation plays a role in the polarity and magnitude of synaptic plasticity induced in the cerebral cortex while highlighting differences between normal and injured brain responses. Moreover, these results may be useful for optimization of neurostimulation therapies to treat mTBI and other brain disorders, in addition to providing new insights into downstream plasticity signaling mechanisms in the normal brain.

Beta activity in human anterior cingulate cortex mediates reward biases

The rewards that we get from our choices and actions can have a major influence on our future behavior. Understanding how reward biasing of behavior is implemented in the brain is important for many reasons, including the fact that diminution in reward biasing is a hallmark of clinical depression. We hypothesized that reward biasing is mediated by the anterior cingulate cortex (ACC), a cortical hub region associated with the integration of reward and executive control and with the etiology of depression. To test this hypothesis, we recorded neural activity during a biased judgment task in patients undergoing intracranial monitoring for either epilepsy or major depressive disorder. We found that beta (12-30 Hz) oscillations in the ACC predicted both associated reward and the size of the choice bias, and also tracked reward receipt, thereby predicting bias on future trials. We found reduced magnitude of bias in depressed patients, in whom the beta-specific effects were correspondingly reduced. Our findings suggest that ACC beta oscillations may orchestrate the learning of reward information to guide adaptive choice, and, more broadly, suggest a potential biomarker for anhedonia and point to future development of interventions to enhance reward impact for therapeutic benefit.

Practical Bayesian Inference in Neuroscience: Or How I Learned to Stop Worrying and Embrace the Distribution

Typical statistical practices in the biological sciences have been increasingly called into question due to difficulties in the replication of an increasing number of studies, many of which are confounded by the relative difficulty of null significance hypothesis testing designs and interpretation of p-values. Bayesian inference, representing a fundamentally different approach to hypothesis testing, is receiving renewed interest as a potential alternative or complement to traditional null significance hypothesis testing due to its ease of interpretation and explicit declarations of prior assumptions. Bayesian models are more mathematically complex than equivalent frequentist approaches, which have historically limited applications to simplified analysis cases. However, the advent of probability distribution sampling tools with exponential increases in computational power now allows for quick and robust inference under any distribution of data. Here we present a practical tutorial on the use of Bayesian inference in the context of neuroscientific studies in both rat electrophysiological and computational modeling data. We first start with an intuitive discussion of Bayes' rule and inference followed by the formulation of Bayesian-based regression and ANOVA models using data from a variety of neuroscientific studies. We show how Bayesian inference leads to easily interpretable analysis of data while providing an open-source toolbox to facilitate the use of Bayesian tools.

A biodegradable and flexible neural interface for transdermal optoelectronic modulation and regeneration of peripheral nerves

Optoelectronic neural interfaces can leverage the photovoltaic effect to convert light into electrical current, inducing charge redistribution and enabling nerve stimulation. This method offers a non-genetic and remote approach for neuromodulation. Developing biodegradable and efficient optoelectronic neural interfaces is important for achieving transdermal stimulation while minimizing infection risks associated with device retrieval, thereby maximizing therapeutic outcomes. We propose a biodegradable, flexible, and miniaturized silicon-based neural interface capable of transdermal optoelectronic stimulation for neural modulation and nerve regeneration. Enhancing the device interface with thin-film molybdenum significantly improves the efficacy of neural stimulation. Our study demonstrates successful activation of the sciatic nerve in rodents and the facial nerve in rabbits. Moreover, transdermal optoelectronic stimulation accelerates the functional recovery of injured facial nerves.

Advances in conductive hydrogels for neural recording and stimulation

The brain-computer interface (BCI) allows the human or animal brain to directly interact with the external environment through the neural interfaces, thus playing the role of monitoring, protecting, improving/restoring, enhancing, and replacing. Recording electrophysiological information such as brain neural signals is of great importance in health monitoring and disease diagnosis. According to the electrode position, it can be divided into non-implantable, semi-implantable, and implantable. Among them, implantable neural electrodes can obtain the highest-quality electrophysiological information, so they have the most promising application. However, due to the chemo-mechanical mismatch between devices and tissues, the adverse foreign body response and performance loss over time seriously restrict the development and application of implantable neural electrodes. Given the challenges, conductive hydrogel-based neural electrodes have recently attracted much attention, owing to many advantages such as good mechanical match with the native tissues, negligible foreign body response, and minimal signal attenuation. This review mainly focuses on the current development of conductive hydrogels as a biocompatible framework for neural tissue and conductivity-supporting substrates for the transmission of electrical signals of neural tissue to speed up electrical regeneration and their applications in neural sensing and recording as well as stimulation.

Lead-free dual-frequency ultrasound implants for wireless, biphasic deep brain stimulation

Ultrasound-driven bioelectronics could offer a wireless scheme with sustainable power supply; however, current ultrasound implantable systems present critical challenges in biocompatibility and harvesting performance related to lead/lead-free piezoelectric materials and devices. Here, we report a lead-free dual-frequency ultrasound implants for wireless, biphasic deep brain stimulation, which integrates two developed lead-free sandwich porous 1-3-type piezoelectric composite elements with enhanced harvesting performance in a flexible printed circuit board. The implant is ultrasonically powered through a portable external dual-frequency transducer and generates programmable biphasic stimulus pulses in clinically relevant frequencies. Furthermore, we demonstrate ultrasound-driven implants for long-term biosafety therapy in deep brain stimulation through an epileptic rodent model. With biocompatibility and improved electrical performance, the lead-free materials and devices presented here could provide a promising platform for developing implantable ultrasonic electronics in the future.

Multi-timescale neuromodulation strategy for closed-loop deep brain stimulation in Parkinson's disease

Objective.Beta triggered closed-loop deep brain stimulation (DBS) shows great potential for improving the efficacy while reducing side effect for Parkinson's disease. However, there remain great challenges due to the dynamics and stochasticity of neural activities. In this study, we aimed to tune the amplitude of beta oscillations with different time scales taking into account influence of inherent variations in the basal ganglia-thalamus-cortical circuit.Approach. A dynamic basal ganglia-thalamus-cortical mean-field model was established to emulate the medication rhythm. Then, a dynamic target model was designed to embody the multi-timescale dynamic of beta power with milliseconds, seconds and minutes. Moreover, we proposed a closed-loop DBS strategy based on a proportional-integral-differential (PID) controller with the dynamic control target. In addition, the bounds of stimulation amplitude increments and different parameters of the dynamic target were considered to meet the clinical constraints. The performance of the proposed closed-loop strategy, including beta power modulation accuracy, mean stimulation amplitude, and stimulation variation were calculated to determine the PID parameters and evaluate neuromodulation performance in the computational dynamic mean-field model.Main results. The Results show that the dynamic basal ganglia-thalamus-cortical mean-field model simulated the medication rhythm with the fasted and the slowest rate. The dynamic control target reflected the temporal variation in beta power from milliseconds to minutes. With the proposed closed-loop strategy, the beta power tracked the dynamic target with a smoother stimulation sequence compared with closed-loop DBS with the constant target. Furthermore, the beta power could be modulated to track the control target under different long-term targets, modulation strengths, and bounds of the stimulation increment.Significance. This work provides a new method of closed-loop DBS for multi-timescale beta power modulation with clinical constraints.

High frequency deep brain stimulation of the dorsal raphe nucleus prevents methamphetamine priming-induced reinstatement of drug seeking in rats

Drug addiction represents a multifaceted and recurrent brain disorder that possesses the capability to create persistent and ineradicable pathological memory. Deep brain stimulation (DBS) has shown a therapeutic potential for neuropsychological disorders, while the precise stimulation targets and therapeutic parameters for addiction remain deficient. Among the crucial brain regions implicated in drug addiction, the dorsal raphe nucleus (DRN) has been found to exert an essential role in the manifestation of addiction memory. Thus, we investigated the effects of DRN DBS in the treatment of addiction and whether it might produce side effects by a series of behavioral assessments, including methamphetamine priming-induced reinstatement of drug seeking behaviors, food-induced conditioned place preference (CPP), open field test and elevated plus-maze test, and examined brain activity and connectivity after DBS of DRN. We found that high-frequency DBS of the DRN significantly lowered the CPP scores and the number of active-nosepokes in the methamphetamine-primed CPP test and the self-administration model. Moreover, both high-frequency and sham DBS group rats were able to establish significant food-induced place preference, and no significant difference was observed in the open field test and in the elevated plus-maze test between the two groups. Immunofluorescence staining and functional magnetic resonance imaging revealed that high-frequency DBS of the DRN could alter the activity and functional connectivity of brain regions related to addiction. These results indicate that high-frequency DBS of the DRN effectively inhibits methamphetamine priming-induced relapse and seeking behaviors in rats and provides a new target for the treatment of drug addiction.

Biophysical Principles and Computational Modeling of Deep Brain Stimulation

BACKGROUND

Deep brain stimulation (DBS) has revolutionized the treatment of neurological disorders, yet the mechanisms of DBS are still under investigation. Computational models are important in silico tools for elucidating these underlying principles and potentially for personalizing DBS therapy to individual patients. The basic principles underlying neurostimulation computational models, however, are not well known in the clinical neuromodulation community.

OBJECTIVE

In this study, we present a tutorial on the derivation of computational models of DBS and outline the biophysical contributions of electrodes, stimulation parameters, and tissue substrates to the effects of DBS.

RESULTS

Given that many aspects of DBS are difficult to characterize experimentally, computational models have played an important role in understanding how material, size, shape, and contact segmentation influence device biocompatibility, energy efficiency, the spatial spread of the electric field, and the specificity of neural activation. Neural activation is dictated by stimulation parameters including frequency, current vs voltage control, amplitude, pulse width, polarity configurations, and waveform. These parameters also affect the potential for tissue damage, energy efficiency, the spatial spread of the electric field, and the specificity of neural activation. Activation of the neural substrate also is influenced by the encapsulation layer surrounding the electrode, the conductivity of the surrounding tissue, and the size and orientation of white matter fibers. These properties modulate the effects of the electric field and determine the ultimate therapeutic response.

CONCLUSION

This article describes biophysical principles that are useful for understanding the mechanisms of neurostimulation.

Pilot Study to Investigate the Use of In-Clinic Sensing to Identify Optimal Stimulation Parameters for Deep Brain Stimulation Therapy in Parkinson's Disease

BACKGROUND

Deep brain stimulation (DBS) programming is time intensive. Recent advances in sensing technology of local field potentials (LFPs) may enable improvements. Few studies have compared the use of this technology with standard of care.

OBJECTIVE/HYPOTHESIS

Sensing technology of subthalamic nucleus (STN) DBS leads in Parkinson's disease (PD) is reliable and predicts the optimal contacts and settings as predicted by clinical assessment.

MATERIALS AND METHODS

Five subjects with PD (n = 9 hemispheres) with bilateral STN DBS and sensing capable battery replacement were recruited. An LFP sensing review of all bipolar contact pairs was performed three times. Contact with the maximal beta peak power (MBP) was then clinically assessed in a double-blinded fashion, and five conditions were tested: 1) entry settings, 2) off stimulation, 3) MBP at 30 μs, 4) MBP at 60 μs, and 5) MBP at 90 μs.

RESULTS

Contact and frequency of the MBP power in all hemispheres did not differ across sessions. The entry settings matched with the contact with the MBP power in 5 of 9 hemispheres. No clinical difference was evident in the stimulation conditions. The clinician and subject preferred settings determined by MBP power in 7 of 9 and 5 of 7 hemispheres, respectively.

CONCLUSIONS

This study indicates that STN LFPs in PD recorded directly from contacts of the DBS lead provide consistent recordings across the frequency range and a reliably detected beta peak. Furthermore, programming based on the MBP power provides at least clinical equivalence to standard of care programming with STN DBS.

Novel Cyclic Homogeneous Oscillation Detection Method for High Accuracy and Specific Characterization of Neural Dynamics

Detecting temporal and spectral features of neural oscillations is essential to understanding dynamic brain function. Traditionally, the presence and frequency of neural oscillations are determined by identifying peaks over 1/f noise within the power spectrum. However, this approach solely operates within the frequency domain and thus cannot adequately distinguish between the fundamental frequency of a non-sinusoidal oscillation and its harmonics. Non-sinusoidal signals generate harmonics, significantly increasing the false-positive detection rate - a confounding factor in the analysis of neural oscillations. To overcome these limitations, we define the fundamental criteria that characterize a neural oscillation and introduce the Cyclic Homogeneous Oscillation (CHO) detection method that implements these criteria based on an auto-correlation approach that determines the oscillation's periodicity and fundamental frequency. We evaluated CHO by verifying its performance on simulated sinusoidal and non-sinusoidal oscillatory bursts convolved with 1/f noise. Our results demonstrate that CHO outperforms conventional techniques in accurately detecting oscillations. Specifically, we determined the sensitivity and specificity of CHO as a function of signal-to-noise ratio (SNR). We further assessed CHO by testing it on electrocorticographic (ECoG, 8 subjects) and electroencephalographic (EEG, 7 subjects) signals recorded during the pre-stimulus period of an auditory reaction time task and on electrocorticographic signals (6 SEEG subjects and 6 ECoG subjects) collected during resting state. In the reaction time task, the CHO method detected auditory alpha and pre-motor beta oscillations in ECoG signals and occipital alpha and pre-motor beta oscillations in EEG signals. Moreover, CHO determined the fundamental frequency of hippocampal oscillations in the human hippocampus during the resting state (6 SEEG subjects). In summary, CHO demonstrates high precision and specificity in detecting neural oscillations in time and frequency domains. The method's specificity enables the detailed study of non-sinusoidal characteristics of oscillations, such as the degree of asymmetry and waveform of an oscillation. Furthermore, CHO can be applied to identify how neural oscillations govern interactions throughout the brain and to determine oscillatory biomarkers that index abnormal brain function.

Real-time field-programmable gate array-based closed-loop deep brain stimulation platform targeting cerebellar circuitry rescues motor deficits in a mouse model of cerebellar ataxia

AIMS

The open-loop nature of conventional deep brain stimulation (DBS) produces continuous and excessive stimulation to patients which contributes largely to increased prevalence of adverse side effects. Cerebellar ataxia is characterized by abnormal Purkinje cells (PCs) dendritic arborization, loss of PCs and motor coordination, and muscle weakness with no effective treatment. We aim to develop a real-time field-programmable gate array (FPGA) prototype targeting the deep cerebellar nuclei (DCN) to close the loop for ataxia using conditional double knockout mice with deletion of PC-specific LIM homeobox (Lhx)1 and Lhx5, resulting in abnormal dendritic arborization and motor deficits.

METHODS

We implanted multielectrode array in the DCN and muscles of ataxia mice. The beneficial effect of open-loop DCN-DBS or closed-loop DCN-DBS was compared by motor behavioral assessments, electromyography (EMG), and neural activities (neurospike and electroencephalogram) in freely moving mice. FPGA board, which performed complex real-time computation, was used for closed-loop DCN-DBS system.

RESULTS

Closed-loop DCN-DBS was triggered only when symptomatic muscle EMG was detected in a real-time manner, which restored motor activities, electroencephalogram activities and neurospike properties completely in ataxia mice. Closed-loop DCN-DBS was more effective than an open-loop paradigm as it reduced the frequency of DBS.

CONCLUSION

Our real-time FPGA-based DCN-DBS system could be a potential clinical strategy for alleviating cerebellar ataxia and other movement disorders.

Detection of evoked resonant neural activity in Parkinson's disease

Objective. This study investigated a machine-learning approach to detect the presence of evoked resonant neural activity (ERNA) recorded during deep brain stimulation (DBS) of the subthalamic nucleus (STN) in people with Parkinson's disease.Approach. Seven binary classifiers were trained to distinguish ERNA from the background neural activity using eight different time-domain signal features.Main results. Nested cross-validation revealed a strong classification performance of 99.1% accuracy, with 99.6% specificity and 98.7% sensitivity to detect ERNA. Using a semi-simulated ERNA dataset, the results show that a signal-to-noise ratio of 15 dB is required to maintain a 90% classifier sensitivity. ERNA detection is feasible with an appropriate combination of signal processing, feature extraction and classifier. Future work should consider reducing the computational complexity for use in real-time applications.Significance. The presence of ERNA can be used to indicate the location of a DBS electrode array during implantation surgery. The confidence score of the detector could be useful for assisting clinicians to adjust the position of the DBS electrode array inside/outside the STN.

Flexible metallic core-shell nanostructured electrodes for neural interfacing

Electrodes with nanostructured surface have emerged as promising low-impedance neural interfaces that can avoid the charge-injection restrictions typically associated to microelectrodes. In this work, we propose a novel approximation, based on a two-step template assisted electrodeposition technique, to obtain flexible nanostructured electrodes coated with core-shell Ni-Au vertical nanowires. These nanowires benefit from biocompatibility of the Au shell exposed to the environment and the mechanical properties of Ni that allow for nanowires longer and more homogeneous in length than their only-Au counterparts. The nanostructured electrodes show impedance values, measured by electrochemical impedance spectroscopy (EIS), at least 9 times lower than those of flat reference electrodes. This ratio is in good accordance with the increased effective surface area determined both from SEM images and cyclic voltammetry measurements, evidencing that only Au is exposed to the medium. The observed EIS profile evolution of Ni-Au electrodes over 7 days were very close to those of Au electrodes and differently from Ni ones. Finally, the morphology, viability and neuronal differentiation of rat embryonic cortical cells cultured on Ni-Au NW electrodes were found to be similar to those on control (glass) substrates and Au NW electrodes, accompanied by a lower glial cell differentiation. This positive in-vitro neural cell behavior encourages further investigation to explore the tissue responses that the implantation of these nanostructured electrodes might elicit in healthy (damaged) neural tissues in vivo, with special emphasis on eventual tissue encapsulation.

Wireless closed-loop deep brain stimulation using microelectrode array probes

Deep brain stimulation (DBS), including optical stimulation and electrical stimulation, has been demonstrated considerable value in exploring pathological brain activity and developing treatments for neural disorders. Advances in DBS microsystems based on implantable microelectrode array (MEA) probes have opened up new opportunities for closed-loop DBS (CL-DBS) in situ. This technology can be used to detect damaged brain circuits and test the therapeutic potential for modulating the output of these circuits in a variety of diseases simultaneously. Despite the success and rapid utilization of MEA probe-based CL-DBS microsystems, key challenges, including excessive wired communication, need to be urgently resolved. In this review, we considered recent advances in MEA probe-based wireless CL-DBS microsystems and outlined the major issues and promising prospects in this field. This technology has the potential to offer novel therapeutic options for psychiatric disorders in the future.

Deep brain stimulation of the subthalamic nucleus in Parkinson disease 2013-2023: where are we a further 10 years on?

Deep brain stimulation has been in clinical use for 30 years and during that time it has changed markedly from a small-scale treatment employed by only a few highly specialized centers into a widespread keystone approach to the management of disorders such as Parkinson's disease. In the intervening decades, many of the broad principles of deep brain stimulation have remained unchanged, that of electrode insertion into stereotactically targeted brain nuclei, however the underlying technology and understanding around the approach have progressed markedly. Some of the most significant advances have taken place over the last decade with the advent of artificial intelligence, directional electrodes, stimulation/recording implantable pulse generators and the potential for remote programming among many other innovations. New therapeutic targets are being assessed for their potential benefits and a surge in the number of deep brain stimulation implantations has given birth to a flourishing scientific literature surrounding the pathophysiology of brain disorders such as Parkinson's disease. Here we outline the developments of the last decade and look to the future of deep brain stimulation to attempt to discern some of the most promising lines of inquiry in this fast-paced and rapidly evolving field.

Characterization and closed-loop control of infrared thalamocortical stimulation produces spatially constrained single-unit responses

Deep brain stimulation (DBS) is a powerful tool for the treatment of circuitopathy-related neurological and psychiatric diseases and disorders such as Parkinson's disease and obsessive-compulsive disorder, as well as a critical research tool for perturbing neural circuits and exploring neuroprostheses. Electrically mediated DBS, however, is limited by the spread of stimulus currents into tissue unrelated to disease course and treatment, potentially causing undesirable patient side effects. In this work, we utilize infrared neural stimulation (INS), an optical neuromodulation technique that uses near to midinfrared light to drive graded excitatory and inhibitory responses in nerves and neurons, to facilitate an optical and spatially constrained DBS paradigm. INS has been shown to provide spatially constrained responses in cortical neurons and, unlike other optical techniques, does not require genetic modification of the neural target. We show that INS produces graded, biophysically relevant single-unit responses with robust information transfer in rat thalamocortical circuits. Importantly, we show that cortical spread of activation from thalamic INS produces more spatially constrained response profiles than conventional electrical stimulation. Owing to observed spatial precision of INS, we used deep reinforcement learning (RL) for closed-loop control of thalamocortical circuits, creating real-time representations of stimulus-response dynamics while driving cortical neurons to precise firing patterns. Our data suggest that INS can serve as a targeted and dynamic stimulation paradigm for both open and closed-loop DBS.

Functional and anatomical connectivity predict brain stimulation's mnemonic effects

Closed-loop direct brain stimulation is a promising tool for modulating neural activity and behavior. However, it remains unclear how to optimally target stimulation to modulate brain activity in particular brain networks that underlie particular cognitive functions. Here, we test the hypothesis that stimulation's behavioral and physiological effects depend on the stimulation target's anatomical and functional network properties. We delivered closed-loop stimulation as 47 neurosurgical patients studied and recalled word lists. Multivariate classifiers, trained to predict momentary lapses in memory function, triggered the stimulation of the lateral temporal cortex (LTC) during the study phase of the task. We found that LTC stimulation specifically improved memory when delivered to targets near white matter pathways. Memory improvement was largest for targets near white matter that also showed high functional connectivity to the brain's memory network. These targets also reduced low-frequency activity in this network, an established marker of successful memory encoding. These data reveal how anatomical and functional networks mediate stimulation's behavioral and physiological effects, provide further evidence that closed-loop LTC stimulation can improve episodic memory, and suggest a method for optimizing neuromodulation through improved stimulation targeting.

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.

Ultrasound-Induced Cascade Amplification in a Mechanoluminescent Nanotransducer for Enhanced Sono-Optogenetic Deep Brain Stimulation

Remote and genetically targeted neuromodulation in the deep brain is important for understanding and treatment of neurological diseases. Ultrasound-triggered mechanoluminescent technology offers a promising approach for achieving remote and genetically targeted brain modulation. However, its application has thus far been limited to shallow brain depths due to challenges related to low sonochemical reaction efficiency and restricted photon yields. Here we report a cascaded mechanoluminescent nanotransducer to achieve efficient light emission upon ultrasound stimulation. As a result, blue light was generated under ultrasound stimulation with a subsecond response latency. Leveraging the high energy transfer efficiency of focused ultrasound in brain tissue and the high sensitivity to ultrasound of these mechanoluminescent nanotransducers, we are able to show efficient photon delivery and activation of ChR2-expressing neurons in both the superficial motor cortex and deep ventral tegmental area after intracranial injection. Our liposome nanotransducers enable minimally invasive deep brain stimulation for behavioral control in animals via a flexible, mechanoluminescent sono-optogenetic system.

Role of the volume conductor on simulations of local field potential recordings from deep brain stimulation electrodes

OBJECTIVE

Local field potential (LFP) recordings from deep brain stimulation (DBS) electrodes are commonly used in research analyses, and are beginning to be used in clinical practice. Computational models of DBS LFPs provide tools for investigating the biophysics and neural synchronization that underlie LFP signals. However, technical standards for DBS LFP model parameterization remain to be established. Therefore, the goal of this study was to evaluate the role of the volume conductor (VC) model complexity on simulated LFP signals in the subthalamic nucleus (STN).

APPROACH

We created a detailed human head VC model that explicitly represented the inhomogeneity and anisotropy associated with 12 different tissue structures. This VC model represented our "gold standard" for technical detail and electrical realism. We then incrementally decreased the complexity of the VC model and quantified the impact on the simulated LFP recordings. Identical STN neural source activity was used when comparing the different VC model variants. Results Ignoring tissue anisotropy reduced the simulated LFP amplitude by ~12%, while eliminating soft tissue heterogeneity had a negligible effect on the recordings. Simplification of the VC model to consist of a single homogenous isotropic tissue medium with a conductivity of 0.215 S/m contributed an additional ~3% to the error.

SIGNIFICANCE

Highly detailed VC models do generate different results than simplified VC models. However, with errors in the range of ~15%, the use of a well-parameterized simple VC model is likely to be acceptable in most contexts for DBS LFP modeling.

Spatially specific, closed-loop infrared thalamocortical deep brain stimulation

Deep brain stimulation (DBS) is a powerful tool for the treatment of circuitopathy-related neurological and psychiatric diseases and disorders such as Parkinson's disease and obsessive-compulsive disorder, as well as a critical research tool for perturbing neural circuits and exploring neuroprostheses. Electrically-mediated DBS, however, is limited by the spread of stimulus currents into tissue unrelated to disease course and treatment, potentially causing undesirable patient side effects. In this work, we utilize infrared neural stimulation (INS), an optical neuromodulation technique that uses near to mid-infrared light to drive graded excitatory and inhibitory responses in nerves and neurons, to facilitate an optical and spatially constrained DBS paradigm. INS has been shown to provide spatially constrained responses in cortical neurons and, unlike other optical techniques, does not require genetic modification of the neural target. We show that INS produces graded, biophysically relevant single-unit responses with robust information transfer in thalamocortical circuits. Importantly, we show that cortical spread of activation from thalamic INS produces more spatially constrained response profiles than conventional electrical stimulation. Owing to observed spatial precision of INS, we used deep reinforcement learning for closed-loop control of thalamocortical circuits, creating real-time representations of stimulus-response dynamics while driving cortical neurons to precise firing patterns. Our data suggest that INS can serve as a targeted and dynamic stimulation paradigm for both open and closed-loop DBS.

The Fifth Bioelectronic Medicine Summit: today's tools, tomorrow's therapies

The emerging field of bioelectronic medicine (BEM) is poised to make a significant impact on the treatment of several neurological and inflammatory disorders. With several BEM therapies being recently approved for clinical use and others in late-phase clinical trials, the 2022 BEM summit was a timely scientific meeting convening a wide range of experts to discuss the latest developments in the field. The BEM Summit was held over two days in New York with more than thirty-five invited speakers and panelists comprised of researchers and experts from both academia and industry. The goal of the meeting was to bring international leaders together to discuss advances and cultivate collaborations in this emerging field that incorporates aspects of neuroscience, physiology, molecular medicine, engineering, and technology. This Meeting Report recaps the latest findings discussed at the Meeting and summarizes the main developments in this rapidly advancing interdisciplinary field. Our hope is that this Meeting Report will encourage researchers from academia and industry to push the field forward and generate new multidisciplinary collaborations that will form the basis of new discoveries that we can discuss at the next BEM Summit.

A Nano-Biohybrid-Based Bio-Solar Cell to Regulate the Electrical Signal Transmission to Living Cells for Biomedical Application

Bio-solar cells are studied as sustainable and biocompatible energy sources with significant potential for biomedical applications. However, they are composed of light-harvesting biomolecules with narrow absorption wavelengths and weak transient photocurrent generation. In this study, a nano-biohybrid-based bio-solar cell composed of bacteriorhodopsin, chlorophyllin, and Ni/TiO2 nanoparticles is developed to overcome the current limitations and verify the possibility of biomedical applications. Bacteriorhodopsin and chlorophyllin are introduced as light-harvesting biomolecules to broaden the absorption wavelength. As a photocatalyst, Ni/TiO2 nanoparticles are introduced to generate a photocurrent and amplify the photocurrent generated by the biomolecules. The developed bio-solar cell absorbs a broad range of visible wavelengths and generates an amplified stationary photocurrent density (152.6 nA cm-2 ) with a long lifetime (up to 1 month). Besides, the electrophysiological signals of muscle cells at neuromuscular junctions are precisely regulated by motor neurons excited by the photocurrent of the bio-solar cell, indicating that the bio-solar cell can control living cells by signal transmission through other types of living cells. The proposed nano-biohybrid-based bio-solar cell can be used as a sustainable and biocompatible energy source for the development of wearable and implantable biodevices and bioelectronic medicines for humans.

Trends and disparities in deep brain stimulation utilization in the United States: a Nationwide Inpatient Sample analysis from 1993 to 2017

Background

Deep brain stimulation (DBS) is an approved treatment option for Parkinson's Disease (PD), essential tremor (ET), dystonia, obsessive-compulsive disorder and epilepsy in the United States. There are disparities in access to DBS, and clear understanding of the contextual factors driving them is important. Previous studies aimed at understanding these factors have been limited by single indications or small cohort sizes. The aim of this study is to provide an updated and comprehensive analysis of DBS utilization for multiple indications to better understand the factors driving disparities in access.

Methods

The United States based National Inpatient Sample (NIS) database was utilized to analyze the surgical volume and trends of procedures based on indication, using relevant ICD codes. Predictors of DBS use were analyzed using a logistic regression model. DBS-implanted patients in each indication were compared based on the patient-, hospital-, and outcome-related variables.

Findings

Our analysis of 104,356 DBS discharges from 1993 to 2017 revealed that the most frequent indications for DBS were PD (67%), ET (24%), and dystonia (4%). Although the number of DBS procedures has consistently increased over the years, radiofrequency ablation utilization has significantly decreased to only a few patients per year since 2003. Negative predictors for DBS utilization in PD and ET cohorts included age increase and female sex, while African American status was a negative predictor across all cohorts. Significant differences in patient-, hospital-, and outcome-related variables between DBS indications were also determined.

Interpretation

Demographic and socioeconomic-based disparities in DBS use are evident. Although racial disparities are present across all indications, other disparities such as age, sex, wealth, and insurance status are only relevant in certain indications.

Funding

This work was supported by Alan & Susan Hudson Cornerstone Chair in Neurosurgery at University Health Network.

Functional and anatomical connectivity predict brain stimulation's mnemonic effects

Closed-loop direct brain stimulation is a promising tool for modulating neural activity and behavior. However, it remains unclear how to optimally target stimulation to modulate brain activity in particular brain networks that underlie particular cognitive functions. Here, we test the hypothesis that stimulation's behavioral and physiological effects depend on the stimulation target's anatomical and functional network properties. We delivered closed-loop stimulation as 47 neurosurgical patients studied and recalled word lists. Multivariate classifiers, trained to predict momentary lapses in memory function, triggered stimulation of the lateral temporal cortex (LTC) during the study phase of the task. We found that LTC stimulation specifically improved memory when delivered to targets near white matter pathways. Memory improvement was largest for targets near white matter that also showed high functional connectivity to the brain's memory network. These targets also reduced low-frequency activity in this network, an established marker of successful memory encoding. These data reveal how anatomical and functional networks mediate stimulation's behavioral and physiological effects, provide further evidence that closed-loop LTC stimulation can improve episodic memory, and suggest a method for optimizing neuromodulation through improved stimulation targeting.

Implantable Zinc-Oxygen Battery for In Situ Electrical Stimulation-Promoted Neural Regeneration

Electrical stimulation is a promising strategy for treating neural diseases. However, current energy suppliers cannot provide effective power for in situ electrical stimulation. Here, an implantable tubular zinc-oxygen battery is reported as the power source for in situ electrical stimulation during the neural repair. The battery exhibited a high volumetric energy density of 231.4 mWh cm-3 based on the entire anode and cathode in vivo. Due to its superior electrochemical properties and biosafety, the battery can be directly wrapped around the nerve to provide in situ electrical stimulation with a minimal size of 0.86 mm3 . The cell and animal experiments demonstrated that the zinc-oxygen battery-based nerve tissue engineering conduit effectively promoted regeneration of the injured long-segment sciatic nerve, proving its promising applications for powering implantable neural electronics in the future.

Preliminary Experience with a Four-Lead Implantable Pulse Generator for Deep Brain Stimulation

BACKGROUND

Implantable pulse generators (IPGs) store energy and deliver electrical impulses for deep brain stimulation (DBS) to treat neurological and psychiatric disorders. IPGs have evolved over time to meet the demands of expanding clinical indications and more nuanced therapeutic approaches.

OBJECTIVES

The aim of this study was to examine the workflow of the first 4-lead IPG for DBS in patients with complex disease.

METHOD

The engineering capabilities, clinical use cases, and surgical technique are described in a cohort of 12 patients with epilepsy, essential tremor, Parkinson's disease, mixed tremor, and Tourette's syndrome with comorbid obsessive-compulsive disorder between July 2021 and July 2022.

RESULTS

This system is a rechargeable 32-channel, 4-port system with independent current control that can be connected to 8 contact linear or directionally segmented leads. The system is ideal for patients with mixed disease or those with multiple severe symptoms amenable to >2 lead implantations. A multidisciplinary team including neurologists, radiologists, and neurosurgeons is necessary to safely plan the procedure. There were no serious intraoperative or postoperative adverse events. One patient required revision surgery for bowstringing.

CONCLUSIONS

This new 4-lead IPG represents an important new tool for DBS surgery with the ability to expand lead implantation paradigms for patients with complex disease.

Oscillatory beta dynamics inform biomarker-driven treatment optimization for Parkinson's disease

Parkinson's disease (PD) is a neurodegenerative disorder characterized by loss of dopaminergic neurons and dysregulation of the basal ganglia. Cardinal motor symptoms include bradykinesia, rigidity, and tremor. Deep brain stimulation (DBS) of select subcortical nuclei is standard of care for medication-refractory PD. Conventional open-loop DBS delivers continuous stimulation with fixed parameters that do not account for a patient's dynamic activity state or medication cycle. In comparison, closed-loop DBS, or adaptive DBS (aDBS), adjusts stimulation based on biomarker feedback that correlates with clinical state. Recent work has identified several neurophysiological biomarkers in local field potential recordings from PD patients, the most promising of which are 1) elevated beta (∼13-30 Hz) power in the subthalamic nucleus (STN), 2) increased beta synchrony throughout basal ganglia-thalamocortical circuits, notably observed as coupling between the STN beta phase and cortical broadband gamma (∼50-200 Hz) amplitude, and 3) prolonged beta bursts in the STN and cortex. In this review, we highlight relevant frequency and time domain features of STN beta measured in PD patients and summarize how spectral beta power, oscillatory beta synchrony, phase-amplitude coupling, and temporal beta bursting inform PD pathology, neurosurgical targeting, and DBS therapy. We then review how STN beta dynamics inform predictive, biomarker-driven aDBS approaches for optimizing PD treatment. We therefore provide clinically useful and actionable insight that can be applied toward aDBS implementation for PD.

Deep brain stimulation (DBS) as a therapeutic approach in gait disorders: What does it bring to the table?

Gait deficits are found in various degenerative central nervous system conditions, and are particularly a hallmark of Parkinson's disease (PD). While there is no cure for such neurodegenerative disorders, Levodopa is considered as the standard medication in PD patients. Often times, the therapy of severe PD consists of deep brain stimulation (DBS) of the subthalamic nucleus. Earlier research exploring the effect of gait have reported contradictory results or insufficient efficacy. A change in gait includes various parameters, such as step length, cadence, Double-stance phase duration which may be positively affected by DBS. DBS could also be effective in correcting the levodopa-induced postural sway abnormalities. Moreover, during normal walking, interaction among the subthalamic nucleus and cortex -essential regions which exert a role in locomotion- are coupled. However, during the freezing of gait, the activity is desynchronized. The mechanisms underlying DBS-induced neurobehavioral improvements in such scenarios requires further study. The present review discusses DBS in the context of gait, the benefits associated with DBS compared to standard pharmacotherapy options, and provides insights into future research.

Postsynaptic neuronal activity promotes regeneration of retinal axons

The wiring of visual circuits requires that retinal neurons functionally connect to specific brain targets, a process that involves activity-dependent signaling between retinal axons and their postsynaptic targets. Vision loss in various ophthalmological and neurological diseases is caused by damage to the connections from the eye to the brain. How postsynaptic brain targets influence retinal ganglion cell (RGC) axon regeneration and functional reconnection with the brain targets remains poorly understood. Here, we established a paradigm in which the enhancement of neural activity in the distal optic pathway, where the postsynaptic visual target neurons reside, promotes RGC axon regeneration and target reinnervation and leads to the rescue of optomotor function. Furthermore, selective activation of retinorecipient neuron subsets is sufficient to promote RGC axon regeneration. Our findings reveal a key role for postsynaptic neuronal activity in the repair of neural circuits and highlight the potential to restore damaged sensory inputs via proper brain stimulation.

The blood-brain barrier: structure, regulation, and drug delivery

Blood-brain barrier (BBB) is a natural protective membrane that prevents central nervous system (CNS) from toxins and pathogens in blood. However, the presence of BBB complicates the pharmacotherapy for CNS disorders as the most chemical drugs and biopharmaceuticals have been impeded to enter the brain. Insufficient drug delivery into the brain leads to low therapeutic efficacy as well as aggravated side effects due to the accumulation in other organs and tissues. Recent breakthrough in materials science and nanotechnology provides a library of advanced materials with customized structure and property serving as a powerful toolkit for targeted drug delivery. In-depth research in the field of anatomical and pathological study on brain and BBB further facilitates the development of brain-targeted strategies for enhanced BBB crossing. In this review, the physiological structure and different cells contributing to this barrier are summarized. Various emerging strategies for permeability regulation and BBB crossing including passive transcytosis, intranasal administration, ligands conjugation, membrane coating, stimuli-triggered BBB disruption, and other strategies to overcome BBB obstacle are highlighted. Versatile drug delivery systems ranging from organic, inorganic, and biologics-derived materials with their synthesis procedures and unique physio-chemical properties are summarized and analyzed. This review aims to provide an up-to-date and comprehensive guideline for researchers in diverse fields, offering perspectives on further development of brain-targeted drug delivery system.

Noninvasive Electrical Stimulation Neuromodulation and Digital Brain Technology: A Review

We review the research progress on noninvasive neural regulatory systems through system design and theoretical guidance. We provide an overview of the development history of noninvasive neuromodulation technology, focusing on system design. We also discuss typical cases of neuromodulation that use modern noninvasive electrical stimulation and the main limitations associated with this technology. In addition, we propose a closed-loop system design solution of the "time domain", "space domain", and "multi-electrode combination". For theoretical guidance, this paper provides an overview of the "digital brain" development process used for noninvasive electrical-stimulation-targeted modeling and the development of "digital human" programs in various countries. We also summarize the core problems of the existing "digital brain" used for noninvasive electrical-stimulation-targeted modeling according to the existing achievements and propose segmenting the tissue. For this, the tissue parameters of a multimodal image obtained from a fresh cadaver were considered as an index. The digital projection of the multimodal image of the brain of a living individual was implemented, following which the segmented tissues could be reconstructed to obtain a "digital twin brain" model with personalized tissue structure differences. The "closed-loop system" and "personalized digital twin brain" not only enable the noninvasive electrical stimulation of neuromodulation to achieve the visualization of the results and adaptive regulation of the stimulation parameters but also enable the system to have individual differences and more accurate stimulation.

The effect of deep brain stimulation in Parkinson's disease reflected in EEG microstates

Mechanisms of deep brain stimulation (DBS) on cortical networks were explored mainly by fMRI. Advanced analysis of high-density EEG is a source of additional information and may provide clinically useful biomarkers. The presented study evaluates EEG microstates in Parkinson's disease and the effect of DBS of the subthalamic nucleus (STN). The association between revealed spatiotemporal dynamics of brain networks and changes in oscillatory activity and clinical examination were assessed. Thirty-seven patients with Parkinson's disease treated by STN-DBS underwent two sessions (OFF and ON stimulation conditions) of resting-state EEG. EEG microstates were analyzed in patient recordings and in a matched healthy control dataset. Microstate parameters were then compared across groups and were correlated with clinical and neuropsychological scores. Of the five revealed microstates, two differed between Parkinson's disease patients and healthy controls. Another microstate differed between ON and OFF stimulation conditions in the patient group and restored parameters in the ON stimulation state toward to healthy values. The mean beta power of that microstate was the highest in patients during the OFF stimulation condition and the lowest in healthy controls; sources were localized mainly in the supplementary motor area. Changes in microstate parameters correlated with UPDRS and neuropsychological scores. Disease specific alterations in the spatiotemporal dynamics of large-scale brain networks can be described by EEG microstates. The approach can reveal changes reflecting the effect of DBS on PD motor symptoms as well as changes probably related to non-motor symptoms not influenced by DBS.

Closed-loop optogenetic control of the dynamics of neural activity in non-human primates

Electrical neurostimulation is effective in the treatment of neurological disorders, but associated recording artefacts generally limit its applications to open-loop stimuli. Real-time and continuous closed-loop control of brain activity can, however, be achieved by pairing concurrent electrical recordings and optogenetics. Here we show that closed-loop optogenetic stimulation with excitatory opsins enables the precise manipulation of neural dynamics in brain slices from transgenic mice and in anaesthetized non-human primates. The approach generates oscillations in quiescent tissue, enhances or suppresses endogenous patterns in active tissue and modulates seizure-like bursts elicited by the convulsant 4-aminopyridine. A nonlinear model of the phase-dependent effects of optical stimulation reproduced the modulation of cycles of local-field potentials associated with seizure oscillations, as evidenced by the systematic changes in the variability and entropy of the phase-space trajectories of seizures, which correlated with changes in their duration and intensity. We also show that closed-loop optogenetic neurostimulation could be delivered using intracortical optrodes incorporating light-emitting diodes. Closed-loop optogenetic approaches may be translatable to therapeutic applications in humans.

Restoration of sensory information via bionic hands

Individuals who have lost the use of their hands because of amputation or spinal cord injury can use prosthetic hands to restore their independence. A dexterous prosthesis requires the acquisition of control signals that drive the movements of the robotic hand, and the transmission of sensory signals to convey information to the user about the consequences of these movements. In this Review, we describe non-invasive and invasive technologies for conveying artificial sensory feedback through bionic hands, and evaluate the technologies' long-term prospects.

Investigation of temperature variations on a Class-E inverter and proposing a compensation circuit to prevent harmful effects on biomedical implants

In this paper, a Class-E inverter and a thermal compensation circuit for wireless power transmission in biomedical implants are designed, simulated, and fabricated. In the analysis of the Class-E inverter, the voltage-dependent non-linearities of C_ds, C_gd, and R_ON as well as temperature-dependent non-linearity of R_ON of the transistor are considered simultaneously. Close agreement of theoretical, simulated and experimental results confirmed the validity of the proposed approach in taking into account these nonlinear effects. The paper investigated the effect of temperature variations on the characteristics of the inverter. Since both the output power and efficiency decrease with increasing temperature, a compensation circuit is proposed to keep them constant within a wide temperature range to enable its application as a reliable power source for medical implants in harsh environments. Simulations were performed and the results confirmed that the compensator enables significant improvements by maintaining the power and efficiency almost constant (8.46 ± 0.14 W and 90.4 ± 0.2%) within the temperature range of - 60 to 100 °C. Measurements performed at 25 °C and 80 °C with and without the compensation circuit were in good agreement with the theoretical and simulation results. The obtained measured output power and efficiency at 25 °C are equal to 7.42 W and 89.9%.

Discovering Themes in Deep Brain Stimulation Research Using Explainable Artificial Intelligence

Deep brain stimulation is a treatment that controls symptoms by changing brain activity. The complexity of how to best treat brain dysfunction with deep brain stimulation has spawned research into artificial intelligence approaches. Machine learning is a subset of artificial intelligence that uses computers to learn patterns in data and has many healthcare applications, such as an aid in diagnosis, personalized medicine, and clinical decision support. Yet, how machine learning models make decisions is often opaque. The spirit of explainable artificial intelligence is to use machine learning models that produce interpretable solutions. Here, we use topic modeling to synthesize recent literature on explainable artificial intelligence approaches to extracting domain knowledge from machine learning models relevant to deep brain stimulation. The results show that patient classification (i.e., diagnostic models, precision medicine) is the most common problem in deep brain stimulation studies that employ explainable artificial intelligence. Other topics concern attempts to optimize stimulation strategies and the importance of explainable methods. Overall, this review supports the potential for artificial intelligence to revolutionize deep brain stimulation by personalizing stimulation protocols and adapting stimulation in real time.

Principles and applications of sono-optogenetics

Optogenetics has revolutionized neuroscience research through its spatiotemporally precise activation of specific neurons by illuminating light on opsin-expressing neurons. A long-standing challenge of in vivo optogenetics arises from the limited penetration depth of visible light in the neural tissue due to scattering and absorption of photons. To address this challenge, sono-optogenetics has been developed to enable spatiotemporally precise light production in a three-dimensional volume of neural tissue by leveraging the deep tissue penetration and focusing ability of ultrasound as well as circulation-delivered mechanoluminescent nanotransducers. Here, we present a comprehensive review of the sono-optogenetics method from the physical principles of ultrasound and mechanoluminescence to its emerging applications for unique neuroscience studies. We also discuss a few promising directions in which sono-optogenetics can make a lasting transformative impact on neuroscience research from the perspectives of mechanoluminescent materials, ultrasound-tissue interaction, to the unique neuroscience opportunities of "scanning optogenetics".

Nanomedicine and nanobiotechnology applications of magnetoelectric nanoparticles

Unlike any other nanoparticles known to date, magnetoelectric nanoparticles (MENPs) can generate relatively strong electric fields locally via the application of magnetic fields and, vice versa, have their magnetization change in response to an electric field from the microenvironment. Hence, MENPs can serve as a wireless two-way interface between man-made devices and physiological systems at the molecular level. With the recent development of room-temperature biocompatible MENPs, a number of novel potential medical applications have emerged. These applications include wireless brain stimulation and mapping/recording of neural activity in real-time, targeted delivery across the blood-brain barrier (BBB), tissue regeneration, high-specificity cancer cures, molecular-level rapid diagnostics, and others. Several independent in vivo studies, using mice and nonhuman primates models, demonstrated the capability to deliver MENPs in the brain across the BBB via intravenous injection or, alternatively, bypassing the BBB via intranasal inhalation of the nanoparticles. Wireless deep brain stimulation with MENPs was demonstrated both in vitro and in vivo in different rodents models by several independent groups. High-specificity cancer treatment methods as well as tissue regeneration approaches with MENPs were proposed and demonstrated in in vitro models. A number of in vitro and in vivo studies were dedicated to understand the underlying mechanisms of MENPs-based high-specificity targeted drug delivery via application of d.c. and a.c. magnetic fields. This article is categorized under: Nanotechnology Approaches to Biology > Nanoscale Systems in Biology Therapeutic Approaches and Drug Discovery > Nanomedicine for Neurological Disease Therapeutic Approaches and Drug Discovery > Nanomedicine for Oncologic Disease Therapeutic Approaches and Drug Discovery > Emerging Technologies.

Commonly Overlooked Factors in Biocompatibility Studies of Neural Implants

Biocompatibility of cutting-edge neural implants, surgical tools and techniques, and therapeutic technologies is a challenging concept that can be easily misjudged. For example, neural interfaces are routinely gauged on how effectively they determine active neurons near their recording sites. Tissue integration and toxicity of neural interfaces are frequently assessed histologically in animal models to determine tissue morphological and cellular changes in response to surgical implantation and chronic presence. A disconnect between histological and efficacious biocompatibility exists, however, as neuronal numbers frequently observed near electrodes do not match recorded neuronal spiking activity. The downstream effects of the myriad surgical and experimental factors involved in such studies are rarely examined when deciding whether a technology or surgical process is biocompatible. Such surgical factors as anesthesia, temperature excursions, bleed incidence, mechanical forces generated, and metabolic conditions are known to have strong systemic and thus local cellular and extracellular consequences. Many tissue markers are extremely sensitive to the physiological state of cells and tissues, thus significantly impacting histological accuracy. This review aims to shed light on commonly overlooked factors that can have a strong impact on the assessment of neural biocompatibility and to address the mismatch between results stemming from functional and histological methods.

Adding a single pulse into high-frequency pulse stimulations can substantially alter the following episode of neuronal firing in rat hippocampus

Background. High-frequency stimulation (HFS) sequences of electrical pulses are commonly utilized in many types of neuromodulation therapies. The temporal pattern of pulse sequences characterized by varying inter-pulse intervals (IPI) has emerged as an adjustable dimension to generate diverse effects of stimulations to meet the needs for developing the therapies.Objective:To explore the hypothesis that a simple manipulation of IPI by inserting a pulse in HFS with a constant IPI can substantially change the neuronal responses.Approach. Antidromic HFS (A-HFS) and orthodromic HFS (O-HFS) sequences were respectively applied at the alveus (the efferent axons) and the Schaffer collaterals (the afferent axons) of hippocampal CA1 region in anesthetized ratsin-vivo. The HFS sequences lasted 120 s with a pulse frequency of 100 Hz and an IPI of 10 ms. In the late steady period (60-120 s) of the HFS, additional pulses were inserted into the original pulse sequences to investigate the alterations of neuronal responses to the changes in IPI. The amplitudes and latencies of antidromic/orthodromic population spikes (APS/OPS) evoked by pulses were measured to evaluate the alterations of the evoked firing of CA1 pyramidal neurons caused by the pulse insertions.Main Results. During the steady period of A-HFS at efferent axons, the evoked APSs were suppressed due to intermittent axonal block. Under this situation, inserting a pulse to shorten an IPI was able to redistribute the following neuronal firing thereby generating an episode of oscillation in the evoked APS sequence including APSs with significantly increased and decreased amplitudes. Also, during the steady period of O-HFS without obvious OPS, a pulse insertion was able to generate a large OPS, indicating a synchronized firing of a large population of post-synaptic neurons induced by a putative redistribution of activations at the afferent axons under O-HFS.Significance. This study firstly showed that under the situation of HFS-induced axonal block, changing an IPI by a single-pulse insertion can substantially redistribute the evoked neuronal responses to increase synchronized firing of neuronal populations during both antidromic and O-HFS with a constant IPI originally. The finding provides a potential way to enhance the HFS action on neuronal networks without losing some other functions of HFS such as generating axonal block.

Deep Brain Stimulation of the Interposed Cerebellar Nuclei in a Conditional Genetic Mouse Model with Dystonia

Dystonia is a neurological disease that is currently ranked as the third most common motor disorder. Patients exhibit repetitive and sometimes sustained muscle contractions that cause limb and body twisting and abnormal postures that impair movement. Deep brain stimulation (DBS) of the basal ganglia and thalamus can be used to improve motor function when other treatment options fail. Recently, the cerebellum has garnered interest as a DBS target for treating dystonia and other motor disorders. Here, we describe a procedure for targeting DBS electrodes to the interposed cerebellar nuclei to correct motor dysfunction in a mouse model with dystonia. Targeting cerebellar outflow pathways with neuromodulation opens new possibilities for using the expansive connectivity of the cerebellum to treat motor and non-motor diseases.

Movement decoding using spatio-spectral features of cortical and subcortical local field potentials

The first commercially sensing enabled deep brain stimulation (DBS) devices for the treatment of movement disorders have recently become available. In the future, such devices could leverage machine learning based brain signal decoding strategies to individualize and adapt therapy in real-time. As multi-channel recordings become available, spatial information may provide an additional advantage for informing machine learning models. To investigate this concept, we compared decoding performances from single channels vs. spatial filtering techniques using intracerebral multitarget electrophysiology in Parkinson's disease patients undergoing DBS implantation. We investigated the feasibility of spatial filtering in invasive neurophysiology and the putative utility of combined cortical ECoG and subthalamic local field potential signals for decoding grip-force, a well-defined and continuous motor readout. We found that adding spatial information to the model can improve decoding (6% gain in decoding), but the spatial patterns and additional benefit was highly individual. Beyond decoding performance results, spatial filters and patterns can be used to obtain meaningful neurophysiological information about the brain networks involved in target behavior. Our results highlight the importance of individualized approaches for brain signal decoding, for which multielectrode recordings and spatial filtering can improve precision medicine approaches for clinical brain computer interfaces.

Stretchable Surface Electrode Arrays Using an Alginate/PEDOT:PSS-Based Conductive Hydrogel for Conformal Brain Interfacing

An electrocorticogram (ECoG) is the electrical activity obtainable from the cerebral cortex and an informative source with considerable potential for future advanced applications in various brain-interfacing technologies. Considerable effort has been devoted to developing biocompatible, conformal, soft, and conductive interfacial materials for bridging devices and brain tissue; however, the implementation of brain-adaptive materials with optimized electrical and mechanical characteristics remains challenging. Herein, we present surface electrode arrays using the soft tough ionic conductive hydrogel (STICH). The newly proposed STICH features brain-adaptive softness with Young's modulus of ~9.46 kPa, which is sufficient to form a conformal interface with the cortex. Additionally, the STICH has high toughness of ~36.85 kJ/mm³, highlighting its robustness for maintaining the solid structure during interfacing with wet brain tissue. The stretchable metal electrodes with a wavy pattern printed on the elastomer were coated with the STICH as an interfacial layer, resulting in an improvement of the impedance from 60 kΩ to 10 kΩ at 1 kHz after coating. Acute in vivo experiments for ECoG monitoring were performed in anesthetized rodents, thereby successfully realizing conformal interfacing to the animal's cortex and the sensitive recording of electrical activity using the STICH-coated electrodes, which exhibited a higher visual-evoked potential (VEP) amplitude than that of the control device.

Closed-loop direct control of seizure focus in a rodent model of temporal lobe epilepsy via localized electric fields applied sequentially

Direct electrical stimulation of the seizure focus can achieve the early termination of epileptic oscillations. However, direct intervention of the hippocampus, the most prevalent seizure focus in temporal lobe epilepsy is thought to be not practicable due to its large size and elongated shape. Here, in a rat model, we report a sequential narrow-field stimulation method for terminating seizures, while focusing stimulus energy at the spatially extensive hippocampal structure. The effects and regional specificity of this method were demonstrated via electrophysiological and biological responses. Our proposed modality demonstrates spatiotemporal preciseness and selectiveness for modulating the pathological target region which may have potential for further investigation as a therapeutic approach.

Stable, interactive modulation of neuronal oscillations produced through brain-machine equilibrium

Closed-loop interaction has the potential to regulate ongoing brain activity by continuously binding an external stimulation to specific dynamics of a neural circuit. Achieving interactive modulation requires a stable brain-machine feedback loop. Here, we demonstrate that it is possible to maintain oscillatory brain activity in a desired state by delivering stimulation accurately aligned with the timing of each cycle. We develop a fast algorithm that responds on a cycle-by-cycle basis to stimulate basal ganglia nuclei at predetermined phases of successive cortical beta cycles in parkinsonian rats. Using this approach, an equilibrium emerges between the modified brain signal and feedback-dependent stimulation pattern, leading to sustained amplification or suppression of the oscillation depending on the phase targeted. Beta amplification slows movement speed by biasing the animal's mode of locomotion. Together, these findings show that highly responsive, phase-dependent stimulation can achieve a stable brain-machine interaction that leads to robust modulation of ongoing behavior.