Measurement of evoked potentials during thalamic deep brain stimulation

Thalamo-cortical evoked potentials during stimulation of the dentato-rubro-thalamic tract demonstrate synaptic filtering

Essential tremor DBS targeting the ventral intermediate nucleus (Vim) of the thalamus and its input, the dentato-rubro-thalamic tract (DRTt), has proven to be an effective treatment strategy. We examined thalamo-cortical evoked potentials (TCEPs) and cortical dynamics during stimulation of the DRTt. We recorded TCEPs in primary motor cortex during clinical and supra-clinical stimulation of the DRTt in ten essential tremor patients. Stimulation was varied over pulse amplitude (2-10 ​mA) and pulse width (30-250 ​μs) to allow for strength-duration testing. Testing at clinical levels (3 ​mA, 60 ​μs) for stimulation frequencies of 1-160 ​Hz was performed and phase amplitude coupling (PAC) of beta phase and gamma power was calculated. Primary motor cortex TCEPs displayed two responses: early and all-or-none (<20 ​ms) or delayed and charge-dependent (>50 ​ms). Strength-duration curve approximation indicates that the chronaxie of the neural elements related to the TCEPs is <200 ​μs. At the range of clinical stimulation (amplitude 2-5 ​mA, pulse width 30-60 ​μs), TCEPs were not noted over primary motor cortex. Decreased pathophysiological phase-amplitude coupling was seen above 70 ​Hz stimulation without changes in power spectra and below the threshold of TCEPs. Our findings demonstrate that DRTt stimulation within normal clinical bounds does not excite fibers directly connected with primary motor cortex but that supra-clinical stimulation can excite a direct axonal tract. Both clinical efficacy and phase-amplitude coupling were frequency-dependent, favoring a synaptic filtering model as a possible mechanism of action.

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.

Cross-Frequency Coupling and Intelligent Neuromodulation

Cross-frequency coupling (CFC) reflects (nonlinear) interactions between signals of different frequencies. Evidence from both patient and healthy participant studies suggests that CFC plays an essential role in neuronal computation, interregional interaction, and disease pathophysiology. The present review discusses methodological advances and challenges in the computation of CFC with particular emphasis on potential solutions to spurious coupling, inferring intrinsic rhythms in a targeted frequency band, and causal interferences. We specifically focus on the literature exploring CFC in the context of cognition/memory tasks, sleep, and neurological disorders, such as Alzheimer's disease, epilepsy, and Parkinson's disease. Furthermore, we highlight the implication of CFC in the context and for the optimization of invasive and noninvasive neuromodulation and rehabilitation. Mainly, CFC could support advancing the understanding of the neurophysiology of cognition and motor control, serve as a biomarker for disease symptoms, and leverage the optimization of therapeutic interventions, e.g., closed-loop brain stimulation. Despite the evident advantages of CFC as an investigative and translational tool in neuroscience, further methodological improvements are required to facilitate practical and correct use in cyborg and bionic systems in the field.

Classification of electrically-evoked potentials in the parkinsonian subthalamic nucleus region

Electrically evoked compound action potentials (ECAPs) generated in the subthalamic nucleus (STN) contain features that may be useful for titrating deep brain stimulation (DBS) therapy for Parkinson's disease. Delivering a strong therapeutic effect with DBS therapies, however, relies on selectively targeting neural pathways to avoid inducing side effects. In this study, we investigated the spatiotemporal features of ECAPs in and around the STN across parameter sweeps of stimulation current amplitude, pulse width, and electrode configuration, and used a linear classifier of ECAP responses to predict electrode location. Four non-human primates were implanted unilaterally with either a directional (n = 3) or non-directional (n = 1) DBS lead targeting the sensorimotor STN. ECAP responses were characterized by primary features (within 1.6 ms after a stimulus pulse) and secondary features (between 1.6 and 7.4 ms after a stimulus pulse). Using these features, a linear classifier was able to accurately differentiate electrodes within the STN versus dorsal to the STN in all four subjects. ECAP responses varied systematically with recording and stimulating electrode locations, which provides a subject-specific neuroanatomical basis for selecting electrode configurations in the treatment of Parkinson's disease with DBS therapy.

Mitigating Mismatch Compression in Differential Local Field Potentials

Deep brain stimulation (DBS) devices capable of measuring differential local field potentials ( ∂ LFP) enable neural recordings alongside clinical therapy. Efforts to identify oscillatory correlates of various brain disorders, or disease readouts, are growing but must proceed carefully to ensure readouts are not distorted by brain environment. In this report we identified, characterized, and mitigated a major source of distortion in ∂ LFP that we introduce as mismatch compression (MC). Using in vivo, in silico, and in vitro models of MC, we showed that impedance mismatches in the two recording electrodes can yield incomplete rejection of stimulation artifact and subsequent gain compression that distorts oscillatory power. We then developed and validated an opensource mitigation pipeline that mitigates the distortions arising from MC. This work enables more reliable oscillatory readouts for adaptive DBS applications.

Concurrent stimulation and sensing in bi-directional brain interfaces: a multi-site translational experience

Objective. To provide a design analysis and guidance framework for the implementation of concurrent stimulation and sensing during adaptive deep brain stimulation (aDBS) with particular emphasis on artifact mitigations.Approach. We defined a general architecture of feedback-enabled devices, identified key components in the signal chain which might result in unwanted artifacts and proposed methods that might ultimately enable improved aDBS therapies. We gathered data from research subjects chronically-implanted with an investigational aDBS system, Summit RC + S, to characterize and explore artifact mitigations arising from concurrent stimulation and sensing. We then used a prototype investigational implantable device, DyNeuMo, and a bench-setup that accounts for tissue-electrode properties, to confirm our observations and verify mitigations. The strategies to reduce transient stimulation artifacts and improve performance during aDBS were confirmed in a chronic implant using updated configuration settings.Main results.We derived and validated a 'checklist' of configuration settings to improve system performance and areas for future device improvement. Key considerations for the configuration include (a) active instead of passive recharge, (b) sense-channel blanking in the amplifier, (c) high-pass filter settings, (d) tissue-electrode impedance mismatch management, (e) time-frequency trade-offs in the classifier, (f) algorithm blanking and transition rate limits. Without proper channel configuration, the aDBS algorithm was susceptible to limit-cycles of oscillating stimulation independent of physiological state. By applying the checklist, we could optimize each block's performance characteristics within the overall system. With system-level optimization, a 'fast' aDBS prototype algorithm was demonstrated to be feasible without reentrant loops, and with noise performance suitable for subcortical brain circuits.Significance. We present a framework to study sources and propose mitigations of artifacts in devices that provide chronic aDBS. This work highlights the trade-offs in performance as novel sensing devices translate to the clinic. Finding the appropriate balance of constraints is imperative for successful translation of aDBS therapies.Clinical trial:Institutional Review Board and Investigational Device Exemption numbers: NCT02649166/IRB201501021 (University of Florida), NCT04043403/IRB52548 (Stanford University), NCT03582891/IRB1824454 (University of California San Francisco). IDE #180 097.

Local and distant cortical responses to single pulse intracranial stimulation in the human brain are differentially modulated by specific stimulation parameters

BACKGROUND

Electrical neuromodulation via direct electrical stimulation (DES) is an increasingly common therapy for a wide variety of neuropsychiatric diseases. Unfortunately, therapeutic efficacy is inconsistent, likely due to our limited understanding of the relationship between the massive stimulation parameter space and brain tissue responses.

OBJECTIVE

To better understand how different parameters induce varied neural responses, we systematically examined single pulse-induced cortico-cortico evoked potentials (CCEP) as a function of stimulation amplitude, duration, brain region, and whether grey or white matter was stimulated.

METHODS

We measured voltage peak amplitudes and area under the curve (AUC) of intracranially recorded stimulation responses as a function of distance from the stimulation site, pulse width, current injected, location relative to grey and white matter, and brain region stimulated (N = 52, n = 719 stimulation sites).

RESULTS

Increasing stimulation pulse width increased responses near the stimulation location. Increasing stimulation amplitude (current) increased both evoked amplitudes and AUC nonlinearly. Locally (<15 mm), stimulation at the boundary between grey and white matter induced larger responses. In contrast, for distant sites (>15 mm), white matter stimulation consistently produced larger responses than stimulation in or near grey matter. The stimulation location-response curves followed different trends for cingulate, lateral frontal, and lateral temporal cortical stimulation.

CONCLUSION

These results demonstrate that a stronger local response may require stimulation in the grey-white boundary while stimulation in the white matter could be needed for network activation. Thus, stimulation parameters tailored for a specific anatomical-functional outcome may be key to advancing neuromodulatory therapy.

Deep Brain Stimulation for Tremor: Update on Long-Term Outcomes, Target Considerations and Future Directions

Deep brain stimulation (DBS) of the thalamic ventral intermediate nucleus is one of the main advanced neurosurgical treatments for drug-resistant tremor. However, not every patient may be eligible for this procedure. Nowadays, various other functional neurosurgical procedures are available. In particular cases, radiofrequency thalamotomy, focused ultrasound and radiosurgery are proven alternatives to DBS. Besides, other DBS targets, such as the posterior subthalamic area (PSA) or the dentato-rubro-thalamic tract (DRT), may be appraised as well. In this review, the clinical characteristics and pathophysiology of tremor syndromes, as well as long-term outcomes of DBS in different targets, will be summarized. The effectiveness and safety of lesioning procedures will be discussed, and an evidence-based clinical treatment approach for patients with drug-resistant tremor will be presented. Lastly, the future directions in the treatment of severe tremor syndromes will be elaborated.

Subcortical short-term plasticity elicited by deep brain stimulation

Objective

To investigate local short‐term neuroplasticity elicited by subthalamic, thalamic, and pallidal deep brain stimulation (DBS) for movement disorders.

Methods

During DBS surgery, we delivered pairs of stimulus pulses with both circular and directional leads across 90 interstimulus intervals in 17 participants and recorded local field potentials from unused contacts on the implanted electrode array. We removed the stimulus artifact, validated the neural origin of the underlying signals, and examined short‐term plasticity as a function of interstimulus interval and DBS target, using linear mixed effects models.

Results

DBS evokes short latency local field potentials that are readily detected with both circular and directional leads at all stimulation targets (0.31 ± 0.10 msec peak latency, mean ± SD). Peak amplitude, area, and latency are modified strongly by interstimulus interval (P < 0.001) and display absolute and relative refractory periods (0.56 ± 0.08 and 2.94 ± 1.05 msec, respectively). We also identified later oscillatory activity in the subthalamic‐pallidal circuit (4.50 ± 1.11 msec peak latency) that displays paired pulse facilitation (present in 5/8 subthalamic, 4/5 pallidal, and 0/6 thalamic trajectories, P = 0.018, Fisher’s exact test), and correlates with resting beta frequency power (P < 0.001), therapeutic DBS frequencies, and stimulation sites chosen later for therapy in the ambulatory setting (P = 0.031).

Interpretation

Paired DBS pulses synchronize local circuit electrophysiology and elicit short‐term neuroplasticity in the subthalamic‐pallidal circuit. Collectively, these responses likely represent the earliest detectable interaction between the DBS pulse and local neuronal tissue in humans. Evoked subcortical field potentials could serve as a predictive biomarker to guide the implementation of next‐generation directional and adaptive stimulation devices.

Electroceutically induced subthalamic high-frequency oscillations and evoked compound activity may explain the mechanism of therapeutic stimulation in Parkinson's disease

Despite having remarkable utility in treating movement disorders, the lack of understanding of the underlying mechanisms of high-frequency deep brain stimulation (DBS) is a main challenge in choosing personalized stimulation parameters. Here we investigate the modulations in local field potentials induced by electrical stimulation of the subthalamic nucleus (STN) at therapeutic and non-therapeutic frequencies in Parkinson's disease patients undergoing DBS surgery. We find that therapeutic high-frequency stimulation (130-180 Hz) induces high-frequency oscillations (~300 Hz, HFO) similar to those observed with pharmacological treatment. Along with HFOs, we also observed evoked compound activity (ECA) after each stimulation pulse. While ECA was observed in both therapeutic and non-therapeutic (20 Hz) stimulation, the HFOs were induced only with therapeutic frequencies, and the associated ECA were significantly more resonant. The relative degree of enhancement in the HFO power was related to the interaction of stimulation pulse with the phase of ECA. We propose that high-frequency STN-DBS tunes the neural oscillations to their healthy/treated state, similar to pharmacological treatment, and the stimulation frequency to maximize these oscillations can be inferred from the phase of ECA waveforms of individual subjects. The induced HFOs can, therefore, be utilized as a marker of successful re-calibration of the dysfunctional circuit generating PD symptoms.

Image-based biophysical modeling predicts cortical potentials evoked with subthalamic deep brain stimulation

BACKGROUND

Subthalamic deep brain stimulation (DBS) is an effective surgical treatment for Parkinson's disease and continues to advance technologically with an enormous parameter space. As such, in-silico DBS modeling systems have become common tools for research and development, but their underlying methods have yet to be standardized and validated.

OBJECTIVE

Evaluate the accuracy of patient-specific estimates of neural pathway activations in the subthalamic region against intracranial, cortical evoked potential (EP) recordings.

METHODS

Pathway activations were modeled in eleven patients using the latest advances in connectomic modeling of subthalamic DBS, focusing on the hyperdirect pathway (HDP) and corticospinal/bulbar tract (CSBT) for their relevance in human research studies. Correlations between pathway activations and respective EP amplitudes were quantified.

RESULTS

Good model performance required accurate lead localization and image fusions, as well as appropriate selection of fiber diameter in the biophysical model. While optimal model parameters varied across patients, good performance could be achieved using a global set of parameters that explained 60% and 73% of electrophysiologic activations of CSBT and HDP, respectively. Moreover, restricted models fit to only EP amplitudes of eight standard (monopolar and bipolar) electrode configurations were able to extrapolate variation in EP amplitudes across other directional electrode configurations and stimulation parameters, with no significant reduction in model performance across the cohort.

CONCLUSIONS

Our findings demonstrate that connectomic models of DBS with sufficient anatomical and electrical details can predict recruitment dynamics of white matter. These results will help to define connectomic modeling standards for preoperative surgical targeting and postoperative patient programming applications.

EEG-based diagnostics of the auditory system using cochlear implant electrodes as sensors

The cochlear implant is one of the most successful medical prostheses, allowing deaf and severely hearing-impaired persons to hear again by electrically stimulating the auditory nerve. A trained audiologist adjusts the stimulation settings for good speech understanding, known as "fitting" the implant. This process is based on subjective feedback from the user, making it time-consuming and challenging, especially in paediatric or communication-impaired populations. Furthermore, fittings only happen during infrequent sessions at a clinic, and therefore cannot take into account variable factors that affect the user's hearing, such as physiological changes and different listening environments. Objective audiometry, in which brain responses evoked by auditory stimulation are collected and analysed, removes the need for active patient participation. However, recording of brain responses still requires expensive equipment that is cumbersome to use. An elegant solution is to record the neural signals using the implant itself. We demonstrate for the first time the recording of continuous electroencephalographic (EEG) signals from the implanted intracochlear electrode array in human subjects, using auditory evoked potentials originating from different brain regions. This was done using a temporary recording set-up with a percutaneous connector used for research purposes. Furthermore, we show that the response morphologies and amplitudes depend crucially on the recording electrode configuration. The integration of an EEG system into cochlear implants paves the way towards chronic neuro-monitoring of hearing-impaired patients in their everyday environment, and neuro-steered hearing prostheses, which can autonomously adjust their output based on neural feedback.

Deep brain stimulation: a review of the open neural engineering challenges

OBJECTIVE

Deep brain stimulation (DBS) is an established and valid therapy for a variety of pathological conditions ranging from motor to cognitive disorders. Still, much of the DBS-related mechanism of action is far from being understood, and there are several side effects of DBS whose origin is unclear. In the last years DBS limitations have been tackled by a variety of approaches, including adaptive deep brain stimulation (aDBS), a technique that relies on using chronically implanted electrodes on 'sensing mode' to detect the neural markers of specific motor symptoms and to deliver on-demand or modulate the stimulation parameters accordingly. Here we will review the state of the art of the several approaches to improve DBS and summarize the main challenges toward the development of an effective aDBS therapy.

APPROACH

We discuss models of basal ganglia disorders pathogenesis, hardware and software improvements for conventional DBS, and candidate neural and non-neural features and related control strategies for aDBS.

MAIN RESULTS

We identify then the main operative challenges toward optimal DBS such as (i) accurate target localization, (ii) increased spatial resolution of stimulation, (iii) development of in silico tests for DBS, (iv) identification of specific motor symptoms biomarkers, in particular (v) assessing how LFP oscillations relate to behavioral disfunctions, and (vi) clarify how stimulation affects the cortico-basal-ganglia-thalamic network to (vii) design optimal stimulation patterns.

SIGNIFICANCE

This roadmap will lead neural engineers novel to the field toward the most relevant open issues of DBS, while the in-depth readers might find a careful comparison of advantages and drawbacks of the most recent attempts to improve DBS-related neuromodulatory strategies.

Physiological Artifacts and the Implications for Brain-Machine-Interface Design

The accurate measurement of brain activity by Brain-Machine-Interfaces (BMI) and closed-loop Deep Brain Stimulators (DBS) is one of the most important steps in communicating between the brain and subsequent processing blocks. In conventional chest-mounted systems, frequently used in DBS, a significant amount of artifact can be induced in the sensing interface, often as a common-mode signal applied between the case and the sensing electrodes. Attenuating this common-mode signal can be a serious challenge in these systems due to finite common-mode-rejection-ratio (CMRR) capability in the interface. Emerging BMI and DBS devices are being developed which can mount on the skull. Mounting the system on the cranial region can potentially suppress these induced physiological signals by limiting the artifact amplitude. In this study, we model the effect of artifacts by focusing on cardiac activity, using a current- source dipole model in a torso-shaped volume conductor. Performing finite element simulation with the different DBS architectures, we estimate the ECG common mode artifacts for several device architectures. Using this model helps define the overall requirements for the total system CMRR to maintain resolution of brain activity. The results of the simulations estimate that the cardiac artifacts for skull-mounted systems will have a significantly lower effect than non-cranial systems that include the pectoral region. It is expected that with a pectoral mounted device, a minimum of 60-80 dB CMRR is required to suppress the ECG artifact, depending on device placement relative to the cardiac dipole, while in cranially mounted devices, a 0 dB CMRR is sufficient, in the worst-case scenario. In addition, the model suggests existing commercial devices could optimize performance with a right-hand side placement. The methods used for estimating cardiac artifacts can be extended to other sources such as motion/muscle sources. The susceptibility of the device to artifacts has significant implications for the practical translation of closed-loop DBS and BMI, including the choice of biomarkers, the system design requirements, and the surgical placement of the device relative to artifact sources.

Evoked potentials reveal neural circuits engaged by human deep brain stimulation

BACKGROUND

Deep brain stimulation (DBS) is an effective therapy for reducing the motor symptoms of Parkinson's disease, but the mechanisms of action of DBS and neural correlates of symptoms remain unknown.

OBJECTIVE

To use the neural response to DBS to reveal connectivity of neural circuits and interactions between groups of neurons as potential mechanisms for DBS.

METHODS

We recorded activity evoked by DBS of the subthalamic nucleus (STN) in humans with Parkinson's disease. In follow up experiments we also simultaneously recorded activity in the contralateral STN or the ipsilateral globus pallidus from both internal (GPi) and external (GPe) segments.

RESULTS

DBS local evoked potentials (DLEPs) were stereotyped across subjects, and a biophysical model of reciprocal connections between the STN and the GPe recreated DLEPs. Simultaneous STN and GP recordings during STN DBS demonstrate that DBS evoked potentials were present throughout the basal ganglia and confirmed that DLEPs arose from the reciprocal connections between the STN and GPe. The shape and amplitude of the DLEPs were dependent on the frequency and duration of DBS and were correlated with resting beta band oscillations. In the frequency domain, DLEPs appeared as a 350 Hz high frequency oscillation (HFO) independent of the frequency of DBS.

CONCLUSIONS

DBS evoked potentials suggest that the intrinsic dynamics of the STN and GP are highly interlinked and may provide a promising new biomarker for adaptive DBS.

The Emerging Role of Biomarkers in Adaptive Modulation of Clinical Brain Stimulation

Therapeutic brain stimulation has proven efficacious for treatment of nervous system diseases, exerting widespread influence via disease-specific neural networks. Activation or suppression of neural networks could theoretically be assessed by either clinical symptom modification (ie, tremor, rigidity, seizures) or development of specific biomarkers linked to treatment of symptomatic disease states. For example, biomarkers indicative of disease state could aid improved intraoperative localization of electrode position, optimize device efficacy or efficiency through dynamic control, and eventually serve to guide automatic adjustment of stimulation settings. Biomarkers to control either extracranial or intracranial stimulation span from continuous physiological brain activity, intermittent pathological activity, and triggered local phenomena or potentials, to wearable devices, blood flow, biochemical or cardiac signals, temperature perturbations, optical or magnetic resonance imaging changes, or optogenetic signals. The goal of this review is to update new approaches to implement control of stimulation through relevant biomarkers. Critical questions include whether adaptive systems adjusted through biomarkers can optimize efficiency and eventually efficacy, serve as inputs for stimulation adjustment, and consequently broaden our fundamental understanding of abnormal neural networks in pathologic states. Neurosurgeons are at the forefront of translating and developing biomarkers embedded within improved brain stimulation systems. Thus, criteria for developing and validating biomarkers for clinical use are important for the adaptation of device approaches into clinical practice.

Proceedings of the Seventh Annual Deep Brain Stimulation Think Tank: Advances in Neurophysiology, Adaptive DBS, Virtual Reality, Neuroethics and Technology

The Seventh Annual Deep Brain Stimulation (DBS) Think Tank held on September 8th of 2019 addressed the most current: (1) use and utility of complex neurophysiological signals for development of adaptive neurostimulation to improve clinical outcomes; (2) Advancements in recent neuromodulation techniques to treat neuropsychiatric disorders; (3) New developments in optogenetics and DBS; (4) The use of augmented Virtual reality (VR) and neuromodulation; (5) commercially available technologies; and (6) ethical issues arising in and from research and use of DBS. These advances serve as both "markers of progress" and challenges and opportunities for ongoing address, engagement, and deliberation as we move to improve the functional capabilities and translational value of DBS. It is in this light that these proceedings are presented to inform the field and initiate ongoing discourse. As consistent with the intent, and spirit of this, and prior DBS Think Tanks, the overarching goal is to continue to develop multidisciplinary collaborations to rapidly advance the field and ultimately improve patient outcomes.

Latency of subthalamic nucleus deep brain stimulation-evoked cortical activity as a potential biomarker for postoperative motor side effects

OBJECTIVE

Here, we investigate whether cortical activation predicts motor side effects of deep brain stimulation (DBS) and whether these potential biomarkers have utility under general anesthesia.

METHODS

We recorded scalp potentials elicited by DBS during surgery (n = 11), both awake and under general anesthesia, and in an independent ambulatory cohort (n = 8). Across a range of stimulus configurations, we measured the amplitude and timing of short- and long-latency response components and linked them to motor side effects.

RESULTS

Regardless of anesthesia state, in both cohorts, DBS settings with capsular side effects elicited early responses with peak latencies clustering at <1 ms. This early response was preserved under anesthesia in all participants (11/11). In contrast, the long-latency components were suppressed completely in 6/11 participants. Finally, the latency of the earliest response could predict the presence of postoperative motor side effects both awake and under general anesthesia (84.8% and 75.8% accuracy, awake and under anesthesia, respectively).

CONCLUSION

DBS elicits short-latency cortical activation, both awake and under general anesthesia, which appears to reveal interactions between the stimulus and the corticospinal tract.

SIGNIFICANCE

Short-latency evoked cortical activity can potentially be used to aid both DBS lead placement and post-operative programming.

MR imaging central thalamic deep brain stimulation restored autistic-like social deficits in the rat

BACKGROUND

Social deficit is a core symptom in autism spectrum disorder (ASD). Although deep brain stimulation (DBS) has been proposed as a potential treatment for ASD, an ideal target nucleus is yet to be identified. DBS at the central thalamic nucleus (CTN) is known to alter corticostriatal and limbic circuits, and subsequently increase the exploratory motor behaviors, cognitive performance, and skill learning in neuropsychiatric and neurodegenerative disorders.

OBJECTIVE

We first investigated the ability of CTN-DBS to selectively engage distinct brain circuits and compared the spatial distribution of evoked network activity and modulation. Second, we investigated whether CTN-DBS intervention improves social interaction in a valproic acid-exposed ASD rat offspring model.

METHODS

Brain regions activated through CTN-DBS by using a magnetic resonance (MR)-compatible neural probe, which is capable of inducing site-selective microstimulations during functional MRI (fMRI), were investigated. We then performed functional connectivity MRI, the three-chamber social interaction test, and Western blotting analyses to evaluate the therapeutic efficacy of CTN-DBS in an ASD rat offspring model.

RESULTS

The DBS-evoked fMRI results indicated that the activated brain regions were mainly located in cortical areas, limbic-related areas, and the dorsal striatum. We observed restoration of brain functional connectivity (FC) in corticostriatal and corticolimbic circuits after CTN-DBS, accompanied with increased social interaction and decreased social avoidance in the three-chamber social interaction test. The dopamine D2 receptor decreased significantly after CTN-DBS treatment, suggesting changes in synaptic plasticity and alterations in the brain circuits.

CONCLUSIONS

Applying CTN-DBS to ASD rat offspring increased FC and altered the synaptic plasticity in the corticolimbic and the corticostriatal circuits. This suggests that CTN-DBS could be an effective treatment for improving the social behaviors of individuals with ASD.

Consistent linear and non-linear responses to invasive electrical brain stimulation across individuals and primate species with implanted electrodes

BACKGROUND

Electrical neuromodulation via implanted electrodes is used in treating numerous neurological disorders, yet our knowledge of how different brain regions respond to varying stimulation parameters is sparse.

OBJECTIVE/HYPOTHESIS

We hypothesized that the neural response to electrical stimulation is both region-specific and non-linearly related to amplitude and frequency.

METHODS

We examined evoked neural responses following 400 ms trains of 10-400 Hz electrical stimulation ranging from 0.1 to 10 mA. We stimulated electrodes implanted in cingulate cortex (dorsal anterior cingulate and rostral anterior cingulate) and subcortical regions (nucleus accumbens, amygdala) of non-human primates (NHP, N = 4) and patients with intractable epilepsy (N = 15) being monitored via intracranial electrodes. Recordings were performed in prefrontal, subcortical, and temporal lobe locations.

RESULTS

In subcortical regions as well as dorsal and rostral anterior cingulate cortex, response waveforms depended non-linearly on frequency (Pearson's linear correlation r < 0.39), but linearly on current (r > 0.58). These relationships between location, and input-output characteristics were similar in homologous brain regions with average Pearson's linear correlation values r > 0.75 between species and linear correlation values between participants r > 0.75 across frequency and current values per brain region. Evoked waveforms could be described by three main principal components (PCs) which allowed us to successfully predict response waveforms across individuals and across frequencies using PC strengths as functions of current and frequency using brain region specific regression models.

CONCLUSIONS

These results provide a framework for creation of an atlas of input-output relationships which could be used in the principled selection of stimulation parameters per brain region.

A neural mass model to predict electrical stimulation evoked responses in human and non-human primate brain

OBJECTIVE

Deep brain stimulation (DBS) is a valuable tool for ameliorating drug resistant pathologies such as movement disorders and epilepsy. DBS is also being considered for complex neuro-psychiatric disorders, which are characterized by high variability in symptoms and slow responses that hinder DBS setting optimization. The objective of this work was to develop an in silico platform to examine the effects of electrical stimulation in regions neighboring a stimulated brain region.

APPROACH

We used the Jansen-Rit neural mass model of single and coupled nodes to simulate the response to a train of electrical current pulses at different frequencies (10-160 Hz) of the local field potential recorded in the amygdala and cortical structures in human subjects and a non-human primate.

RESULTS

We found that using a single node model, the evoked responses could be accurately modeled following a narrow range of stimulation frequencies. Including a second coupled node increased the range of stimulation frequencies whose evoked responses could be efficiently modeled. Furthermore, in a chronic recording from a non-human primate, features of the in vivo evoked response remained consistent for several weeks, suggesting that model re-parameterization for chronic stimulation protocols would be infrequent.

SIGNIFICANCE

Using a model of neural population activity, we reproduced the evoked response to cortical and subcortical stimulation in human and non-human primate. This modeling framework provides an environment to explore, safely and rapidly, a wide range of stimulation settings not possible in human brain stimulation studies. The model can be trained on a limited dataset of stimulation responses to develop an optimal stimulation strategy for an individual patient.

Systems approaches to optimizing deep brain stimulation therapies in Parkinson's disease

Over the last 30 years, deep brain stimulation (DBS) has been used to treat chronic neurological diseases like dystonia, obsessive-compulsive disorders, essential tremor, Parkinson's disease, and more recently, dementias, depression, cognitive disorders, and epilepsy. Despite its wide use, DBS presents numerous challenges for both clinicians and engineers. One challenge is the design of novel, more efficient DBS therapies, which are hampered by the lack of complete understanding about the cellular mechanisms of therapeutic DBS. Another challenge is the existence of redundancy in clinical outcomes, that is, different DBS programs can result in similar clinical benefits but very little information (e.g., predictive models, longitudinal data, metrics, etc.) is available to select one program over another. Finally, there is high variability in patients' responses to DBS, which forces clinicians to carefully adjust the stimulation settings to each patient via lengthy programming sessions. Researchers in neural engineering and systems biology have been tackling these challenges over the past few years with the specific goal of developing novel DBS therapies, design methodologies, and computational tools that optimize the therapeutic effects of DBS in each patient. Furthermore, efforts are being made to automatically adapt the DBS treatment to the fluctuations of disease symptoms. A review of the quantitative approaches currently available for the treatment of Parkinson's disease is presented here with an emphasis on the contributions that systems theoretical approaches have provided to understand the global dynamics of complex neuronal circuits in the brain under DBS. This article is categorized under: Translational, Genomic, and Systems Medicine > Therapeutic Methods Analytical and Computational Methods > Computational Methods Analytical and Computational Methods > Dynamical Methods Physiology > Mammalian Physiology in Health and Disease.

Closed-loop low-frequency DBS restores thalamocortical relay fidelity in a computational model of the motor loop

Closed-loop modulation of deep brain stimulation (DBS) of the subthalamic nucleus (STN) in Parkinson's disease (PD) is investigated to automatically adjust the stimulation to the patients' conditions, optimize the clinical outcomes, and reduce the energy requirements. This study proposes a closed-loop control system for real-time adaptation of the STN-DBS amplitude based on the neural activity in the motor thalamus. Population-averaged post-stimulus time histograms are used to measure the average effects of STN-DBS on the thalamocortical neurons and a L₂-norm minimization problem is solved to design the control algorithm, while the frequency of stimulation is kept constant. Applied on a large-scale, biophysically-based, anatomically-compliant model of the cortico-basal ganglia-thalamo-cortical motor loop under PD conditions, our adaptive DBS significantly (P-value P<;0.05) improved the relay fidelity of the thalamocortical neurons and restored the average power of the thalamocortical spike trains in the band [3, 100] Hz, two indicators of restored thalamocortical activity. Furthermore, adaptive-DBS significantly decreased the energy requirements when compared with non-adaptive-DBS at the same frequency. Finally, 30- and 60-Hz-adaptive-DBS determined the maximal restoration of thalamocortical activity and outperformed high-frequency, non-adaptive-DBS. Overall, results suggest that a feedback-controlled, low-frequency DBS pattern may result in significant restoration of the thalamocortical encoding while lowering the energy requirements.

Subthalamic nucleus deep brain stimulation evokes resonant neural activity

Deep brain stimulation (DBS) is a rapidly expanding treatment for neurological and psychiatric conditions; however, a target-specific biomarker is required to optimize therapy. Here, we show that DBS evokes a large-amplitude resonant neural response focally in the subthalamic nucleus. This response is greatest in the dorsal region (the clinically optimal stimulation target for Parkinson disease), coincides with improved clinical performance, is chronically recordable, and is present under general anesthesia. These features make it a readily utilizable electrophysiological signal that could potentially be used for guiding electrode implantation surgery and tailoring DBS therapy to improve patient outcomes.

Model-based deconstruction of cortical evoked potentials generated by subthalamic nucleus deep brain stimulation

Parkinson's disease is associated with altered neural activity in the motor cortex. Chronic high-frequency deep brain stimulation (DBS) of the subthalamic nucleus (STN) is effective in suppressing parkinsonian motor symptoms and modulates cortical activity. However, the anatomical pathways responsible for STN DBS-mediated cortical modulation remain unclear. Cortical evoked potentials (cEP) generated by STN DBS reflect the response of cortex to subcortical stimulation, and the goal of this study was to determine the neural origin of STN DBS-generated cEP using a two-step approach. First, we recorded cEP over ipsilateral primary motor cortex during different frequencies of STN DBS in awake healthy and unilateral 6-OHDA-lesioned parkinsonian rats. Second, we used a detailed, biophysically based model of the thalamocortical network to deconstruct the neural origin of the recorded cEP. The in vivo cEP included short (R1)-, intermediate (R2)-, and long-latency (R3) responses. Model-based cortical responses to simulated STN DBS matched remarkably well the in vivo responses. The short-latency response was generated by antidromic activation of layer 5 pyramidal neurons, whereas recurrent activation of layer 5 pyramidal neurons via excitatory axon collaterals reproduced the intermediate-latency response. The long-latency response was generated by polysynaptic activation of layer 2/3 pyramidal neurons via the cortico-thalamic-cortical pathway. Antidromic activation of the hyperdirect pathway and subsequent intracortical and cortico-thalamo-cortical synaptic interactions were sufficient to generate cortical potential evoked by STN DBS, and orthodromic activation through basal ganglia-thalamus-cortex pathways was not required. These results demonstrate the utility of cEP to determine the neural elements activated by STN DBS that might modulate cortical activity and contribute to the suppression of parkinsonian symptoms. NEW & NOTEWORTHY Subthalamic nucleus (STN) deep brain stimulation (DBS) is increasingly used to treat Parkinson's disease (PD). Cortical potentials evoked by STN DBS in patients with PD exhibit consistent short-latency (1-3 ms), intermediate-latency (5-15 ms), and long-latency (18-25 ms) responses. The short-latency response occurs as a result of antidromic activation of the hyperdirect pathway comprising corticosubthalamic axons. However, the neural origins of intermediate- and long-latency responses remain elusive, and the dominant view is that these are produced through the orthodromic pathway (basal ganglia-thalamus-cortex). By combining in vivo electrophysiology with computational modeling, we demonstrate that antidromic activation of the cortico-thalamic-cortical pathway is sufficient to generate the intermediate- and long-latency cortical responses to STN DBS.

Biomarkers and Stimulation Algorithms for Adaptive Brain Stimulation

The goal of this review is to describe in what ways feedback or adaptive stimulation may be delivered and adjusted based on relevant biomarkers. Specific treatment mechanisms underlying therapeutic brain stimulation remain unclear, in spite of the demonstrated efficacy in a number of nervous system diseases. Brain stimulation appears to exert widespread influence over specific neural networks that are relevant to specific disease entities. In awake patients, activation or suppression of these neural networks can be assessed by either symptom alleviation (i.e., tremor, rigidity, seizures) or physiological criteria, which may be predictive of expected symptomatic treatment. Secondary verification of network activation through specific biomarkers that are linked to symptomatic disease improvement may be useful for several reasons. For example, these biomarkers could aid optimal intraoperative localization, possibly improve efficacy or efficiency (i.e., reduced power needs), and provide long-term adaptive automatic adjustment of stimulation parameters. Possible biomarkers for use in portable or implanted devices span from ongoing physiological brain activity, evoked local field potentials (LFPs), and intermittent pathological activity, to wearable devices, biochemical, blood flow, optical, or magnetic resonance imaging (MRI) changes, temperature changes, or optogenetic signals. First, however, potential biomarkers must be correlated directly with symptom or disease treatment and network activation. Although numerous biomarkers are under consideration for a variety of stimulation indications the feasibility of these approaches has yet to be fully determined. Particularly, there are critical questions whether the use of adaptive systems can improve efficacy over continuous stimulation, facilitate adjustment of stimulation interventions and improve our understanding of the role of abnormal network function in disease mechanisms.

Optimized temporal pattern of brain stimulation designed by computational evolution

Brain stimulation is a promising therapy for several neurological disorders, including Parkinson's disease. Stimulation parameters are selected empirically and are limited to the frequency and intensity of stimulation. We varied the temporal pattern of deep brain stimulation to ameliorate symptoms in a parkinsonian animal model and in humans with Parkinson's disease. We used model-based computational evolution to optimize the stimulation pattern. The optimized pattern produced symptom relief comparable to that from standard high-frequency stimulation (a constant rate of 130 or 185 Hz) and outperformed frequency-matched standard stimulation in a parkinsonian rat model and in patients. Both optimized and standard high-frequency stimulation suppressed abnormal oscillatory activity in the basal ganglia of rats and humans. The results illustrate the utility of model-based computational evolution of temporal patterns to increase the efficiency of brain stimulation in treating Parkinson's disease and thereby reduce the energy required for successful treatment below that of current brain stimulation paradigms.

Behavioral assessment of sensitivity to intracortical microstimulation of primate somatosensory cortex

Intracortical microstimulation (ICMS) is a powerful tool to investigate the functional role of neural circuits and may provide a means to restore sensation for patients for whom peripheral stimulation is not an option. In a series of psychophysical experiments with nonhuman primates, we investigate how stimulation parameters affect behavioral sensitivity to ICMS. Specifically, we deliver ICMS to primary somatosensory cortex through chronically implanted electrode arrays across a wide range of stimulation regimes. First, we investigate how the detectability of ICMS depends on stimulation parameters, including pulse width, frequency, amplitude, and pulse train duration. Then, we characterize the degree to which ICMS pulse trains that differ in amplitude lead to discriminable percepts across the range of perceptible and safe amplitudes. We also investigate how discriminability of pulse amplitude is modulated by other stimulation parameters-namely, frequency and duration. Perceptual judgments obtained across these various conditions will inform the design of stimulation regimes for neuroscience and neuroengineering applications.

Modeling sequence and quasi-uniform assumption in computational neurostimulation

Computational neurostimulation aims to develop mathematical constructs that link the application of neuromodulation with changes in behavior and cognition. This process is critical but daunting for technical challenges and scientific unknowns. The overarching goal of this review is to address how this complex task can be made tractable. We describe a framework of sequential modeling steps to achieve this: (1) current flow models, (2) cell polarization models, (3) network and information processing models, and (4) models of the neuroscientific correlates of behavior. Each step is explained with a specific emphasis on the assumptions underpinning underlying sequential implementation. We explain the further implementation of the quasi-uniform assumption to overcome technical limitations and unknowns. We specifically focus on examples in electrical stimulation, such as transcranial direct current stimulation. Our approach and conclusions are broadly applied to immediate and ongoing efforts to deploy computational neurostimulation.