The Case for Adaptive Neuromodulation to Treat Severe Intractable Mental Disorders

Effect of Anisotropic Brain Conductivity on Patient-Specific Volume of Tissue Activation in Deep Brain Stimulation for Parkinson Disease

OBJECTIVE

Deep brain stimulation (DBS) modeling can improve surgical targeting by quantifying the spatial extent of stimulation relative to subcortical structures of interest. A certain degree of model complexity is required to obtain accurate predictions, particularly complexity regarding electrical properties of the tissue around DBS electrodes. In this study, the effect of anisotropy on the volume of tissue activation (VTA) was evaluated in an individualized manner.

METHODS

Tissue activation models incorporating patient-specific tissue conductivity were built for 40 Parkinson disease patients who had received bilateral subthalamic nucleus (STN) DBS. To assess the impact of local changes in tissue anisotropy, one VTA was computed at each electrode contact using identical stimulation parameters. For comparison, VTAs were also computed assuming isotropic tissue conductivity. Stimulation location was considered by classifying the anisotropic VTAs relative to the STN. VTAs were characterized based on volume, spread in three directions, sphericity, and Dice coefficient.

RESULTS

Incorporating anisotropy generated significantly larger and less spherical VTAs overall. However, its effect on VTA size and shape was variable and more nuanced at the individual patient and implantation levels. Dorsal VTAs had significantly higher sphericity than ventral VTAs, suggesting more isotropic behavior. Contrastingly, lateral and posterior VTAs had significantly larger and smaller lateral-medial spreads, respectively. Volume and spread correlated negatively with sphericity.

CONCLUSION

The influence of anisotropy on VTA predictions is important to consider, and varies across patients and stimulation location.

SIGNIFICANCE

This study highlights the importance of considering individualized factors in DBS modeling to accurately characterize the VTA.

Chronic, Reusable, Multiday Neuropixels Recordings during Free-Moving Operant Behavior

Electrophysiological recording is a powerful technique to examine neuronal substrates underlying cognition and behavior. Neuropixels probes provide a unique capacity to capture neuronal activity across many brain areas with high spatiotemporal resolution. Neuropixels are also expensive and optimized for acute, head-fixed use, both of which limit the types of behaviors and manipulations that can be studied. Recent advances have addressed the cost issue by showing chronic implant, explant, and reuse of Neuropixels probes, but the methods were not optimized for use in free-moving behavior. There were specific needs for improvement in cabling/connection stability. Here, we extend that work to demonstrate chronic Neuropixels recording, explant, and reuse in a rat model during fully free-moving operant behavior. Similar to prior approaches, we house the probe and headstage within a 3D-printed housing that avoids direct fixation of the probe to the skull, enabling eventual explant. We demonstrate innovations to allow chronic headstage connection with protection against environmental factors and a more stable cabling setup to reduce the tension that can interrupt recording. We demonstrate this approach with rats performing two different behavioral tasks, in each case showing: (1) chronic single- or dual-probe recordings in free-moving rats in operant chambers and (2) reusability of Neuropixels 1.0 probes with continued good single-unit yield after retrieval and reimplant. We thus demonstrate the potential for Neuropixels recordings in a wider range of species and preparations.

Researcher Views on Changes in Personality, Mood, and Behavior in Next-Generation Deep Brain Stimulation

The literature on deep brain stimulation (DBS) and adaptive DBS (aDBS) raises concerns that these technologies may affect personality, mood, and behavior. We conducted semi-structured interviews with researchers (n = 23) involved in developing next-generation DBS systems, exploring their perspectives on ethics and policy topics including whether DBS/aDBS can cause such changes. The majority of researchers reported being aware of personality, mood, or behavioral (PMB) changes in recipients of DBS/aDBS. Researchers offered varying estimates of the frequency of PMB changes. A smaller majority reported changes in personality specifically. Some expressed reservations about the scientific status of the term 'personality,' while others used it freely. Most researchers discussed negative PMB changes, but a majority said that DBS/aDBS can also result in positive changes. Several researchers viewed positive PMB changes as part of the therapeutic goal in psychiatric applications of DBS/aDBS. Finally, several discussed potential causes of PMB changes other than the device itself.

Alternative patterns of deep brain stimulation in neurologic and neuropsychiatric disorders

Deep brain stimulation (DBS) is a widely used clinical therapy that modulates neuronal firing in subcortical structures, eliciting downstream network effects. Its effectiveness is determined by electrode geometry and location as well as adjustable stimulation parameters including pulse width, interstimulus interval, frequency, and amplitude. These parameters are often determined empirically during clinical or intraoperative programming and can be altered to an almost unlimited number of combinations. Conventional high-frequency stimulation uses a continuous high-frequency square-wave pulse (typically 130-160 Hz), but other stimulation patterns may prove efficacious, such as continuous or bursting theta-frequencies, variable frequencies, and coordinated reset stimulation. Here we summarize the current landscape and potential clinical applications for novel stimulation patterns.

Patient-specific connectomic models correlate with, but do not reliably predict, outcomes in deep brain stimulation for obsessive-compulsive disorder

Deep brain stimulation (DBS) of the ventral internal capsule/ventral striatum (VCVS) is an emerging treatment for obsessive-compulsive disorder (OCD). Recently, multiple studies using normative connectomes have correlated DBS outcomes to stimulation of specific white matter tracts. Those studies did not test whether these correlations are clinically predictive, and did not apply cross-validation approaches that are necessary for biomarker development. Further, they did not account for the possibility of systematic differences between DBS patients and the non-diagnosed controls used in normative connectomes. To address these gaps, we performed patient-specific diffusion imaging in 8 patients who underwent VCVS DBS for OCD. We delineated tracts connecting thalamus and subthalamic nucleus (STN) to prefrontal cortex via VCVS. We then calculated which tracts were likely activated by individual patients' DBS settings. We fit multiple statistical models to predict both OCD and depression outcomes from tract activation. We further attempted to predict hypomania, a VCVS DBS complication. We assessed all models' performance on held-out test sets. With this best-practices approach, no model predicted OCD response, depression response, or hypomania above chance. Coefficient inspection partly supported prior reports, in that capture of tracts projecting to cingulate cortex was associated with both YBOCS and MADRS response. In contrast to prior reports, however, tracts connected to STN were not reliably correlated with response. Thus, patient-specific imaging and a guideline-adherent analysis were unable to identify a tractographic target with sufficient effect size to drive clinical decision-making or predict individual outcomes. These findings suggest caution in interpreting the results of normative connectome studies.

The prefrontal cortex and neurosurgical treatment for intractable OCD

Over the past two decades, circuit-based neurosurgical procedures have gained increasing acceptance as a safe and efficacious approach to the treatment of the intractable obsessive-compulsive disorder (OCD). Lesions and deep brain stimulation (DBS) of the longitudinal corticofugal white matter tracts connecting the prefrontal cortex with the striatum, thalamus, subthalamic nucleus (STN), and brainstem implicate orbitofrontal, medial prefrontal, frontopolar, and ventrolateral cortical networks in the symptoms underlying OCD. The highly parallel distributed nature of these networks may explain the relative lack of adverse effects observed following surgery. Additional pre-post studies of cognitive tasks in more surgical patients are needed to confirm the role of these networks in OCD and to define therapeutic responses to surgical intervention.

Physiologically informed neuromodulation

The rapid evolution of neuromodulation techniques includes an increasing amount of research into stimulation paradigms that are guided by patients' neurophysiology, to increase efficacy and responder rates. Treatment personalisation and target engagement have shown to be effective in fields such as Parkinson's disease, and closed-loop paradigms have been successfully implemented in cardiac defibrillators. Promising avenues are being explored for physiologically informed neuromodulation in psychiatry. Matching the stimulation frequency to individual brain rhythms has shown some promise in transcranial magnetic stimulation (TMS). Matching the phase of those rhythms may further enhance neuroplasticity, for instance when combining TMS with electroencephalographic (EEG) recordings. Resting-state EEG and event-related potentials may be useful to demonstrate connectivity between stimulation sites and connected areas. These techniques are available today to the psychiatrist to diagnose underlying sleep disorders, epilepsy, or lesions as contributing factors to the cause of depression. These technologies may also be useful in assessing the patient's brain network status prior to deciding on treatment options. Ongoing research using invasive recordings may allow for future identification of mood biomarkers and network structure. A core limitation is that biomarker research may currently be limited by the internal heterogeneity of psychiatric disorders according to the current DSM-based classifications. New approaches are being developed and may soon be validated. Finally, care must be taken when incorporating closed-loop capabilities into neuromodulation systems, by ensuring the safe operation of the system and understanding the physiological dynamics. Neurophysiological tools are rapidly evolving and will likely define the next generation of neuromodulation therapies.

Long-term ecological assessment of intracranial electrophysiology synchronized to behavioral markers in obsessive-compulsive disorder

Detection of neural signatures related to pathological behavioral states could enable adaptive deep brain stimulation (DBS), a potential strategy for improving efficacy of DBS for neurological and psychiatric disorders. This approach requires identifying neural biomarkers of relevant behavioral states, a task best performed in ecologically valid environments. Here, in human participants with obsessive-compulsive disorder (OCD) implanted with recording-capable DBS devices, we synchronized chronic ventral striatum local field potentials with relevant, disease-specific behaviors. We captured over 1,000 h of local field potentials in the clinic and at home during unstructured activity, as well as during DBS and exposure therapy. The wide range of symptom severity over which the data were captured allowed us to identify candidate neural biomarkers of OCD symptom intensity. This work demonstrates the feasibility and utility of capturing chronic intracranial electrophysiology during daily symptom fluctuations to enable neural biomarker identification, a prerequisite for future development of adaptive DBS for OCD and other psychiatric disorders.

Spectral Features Based Decoding of Task Engagement: The Role of Theta and High Gamma Bands in Cognitive Control

This paper analyzes local field potentials (LFP) from 10 human subjects to discover frequency-dependent biomarkers of cognitive conflict. We utilize cortical and sub-cortical LFP recordings from the subjects during a cognitive task known as the Multi-Source Interference Task (MSIT). We decode the task engagement and discover biomarkers that may facilitate closed-loop neuromodulation to enhance cognitive control. First, we show that spectral power features in predefined frequency bands can be used to classify task and non-task segments with a median accuracy of 88.1%. Here the features are first ranked using the Bayes Factor and then used as inputs to subject-specific linear support vector machine classifiers. Second, we show that theta (4-8 Hz) band, and high gamma (65-200 Hz) band oscillations are modulated during the task performance. Third, by isolating time-series from specific brain regions of interest, we observe that a subset of the dorsolateral prefrontal cortex features is sufficient to decode the task states. The paper shows that cognitive control evokes robust neurological signatures, especially in the prefrontal cortex (PFC).

Decoding Human Cognitive Control Using Functional Connectivity of Local Field Potentials

Many patients with mental illnesses characterized by impaired cognitive control have no relief from gold-standard clinical treatments resulting in a pressing need for new alternatives. This paper develops a neural decoder to detect task engagement in ten human subjects during a conflict-based behavioral task known as the multi-source interference task (MSIT). Task engagement is of particular interest here because closed-loop brain stimulation during those states can augment decision-making. The functional connectivity patterns of the electrodes are extracted. A principal component analysis of these patterns is carried out and the ranked principal components are used as inputs to train subject-specific linear support vector machine classifiers. In this paper, we show that task engagement can be differentiated from background brain activity with a median accuracy of 89.7%. This was accomplished by constructing distributed functional networks from local field potentials recording during the task performance. A further challenge is that goal-directed efforts take place over higher temporal resolution. Task engagement must thus be detected at a similar rate for proactive intervention. We show that our algorithms can detect task engagement from neural recordings in less than 2 seconds; this can be further improved using an application-specific device.

Chronic electroencephalography in epilepsy with a responsive neurostimulation device: current status and future prospects

INTRODUCTION

Implanted neurostimulation devices are gaining traction as therapeutic options for people with certain forms of drug-resistant focal epilepsy. Some of these devices enable chronic electroencephalography (cEEG), which offers views of the dynamics of brain activity in epilepsy over unprecedented time horizons.

AREAS COVERED

This review focuses on clinical insights and basic neuroscience discoveries enabled by analyses of cEEG from an exemplar device, the NeuroPace RNS® System. Applications of RNS cEEG covered here include counting and lateralizing seizures, quantifying medication response, characterizing spells, forecasting seizures, and exploring mechanisms of cognition. Limitations of the RNS System are discussed in the context of next-generation devices in development.

EXPERT OPINION

The wide temporal lens of cEEG helps capture the dynamism of epilepsy, revealing phenomena that cannot be appreciated with short duration recordings. The RNS System is a vanguard device whose diagnostic utility rivals its therapeutic benefits, but emerging minimally invasive devices, including those with subscalp recording electrodes, promise to be more applicable within a broad population of people with epilepsy. Epileptology is on the precipice of a paradigm shift in which cEEG is a standard part of diagnostic evaluations and clinical management is predicated on quantitative observations integrated over long timescales.

Invasive Electrophysiology for Circuit Discovery and Study of Comorbid Psychiatric Disorders in Patients With Epilepsy: Challenges, Opportunities, and Novel Technologies

Intracranial electroencephalographic (iEEG) recordings from patients with epilepsy provide distinct opportunities and novel data for the study of co-occurring psychiatric disorders. Comorbid psychiatric disorders are very common in drug-resistant epilepsy and their added complexity warrants careful consideration. In this review, we first discuss psychiatric comorbidities and symptoms in patients with epilepsy. We describe how epilepsy can potentially impact patient presentation and how these factors can be addressed in the experimental designs of studies focused on the electrophysiologic correlates of mood. Second, we review emerging technologies to integrate long-term iEEG recording with dense behavioral tracking in naturalistic environments. Third, we explore questions on how best to address the intersection between epilepsy and psychiatric comorbidities. Advances in ambulatory iEEG and long-term behavioral monitoring technologies will be instrumental in studying the intersection of seizures, epilepsy, psychiatric comorbidities, and their underlying circuitry.

Honeycomb: a template for reproducible psychophysiological tasks for clinic, laboratory, and home use

Objective:

To improve the ability of psychiatry researchers to build, deploy, maintain, reproduce, and share their own psychophysiological tasks. Psychophysiological tasks are a useful tool for studying human behavior driven by mental processes such as cognitive control, reward evaluation, and learning. Neural mechanisms during behavioral tasks are often studied via simultaneous electrophysiological recordings. Popular online platforms such as Amazon Mechanical Turk (MTurk) and Prolific enable deployment of tasks to numerous participants simultaneously. However, there is currently no task-creation framework available for flexibly deploying tasks both online and during simultaneous electrophysiology.

Methods:

We developed a task creation template, termed Honeycomb, that standardizes best practices for building jsPsych-based tasks. Honeycomb offers continuous deployment configurations for seamless transition between use in research settings and at home. Further, we have curated a public library, termed BeeHive, of ready-to-use tasks.

Results:

We demonstrate the benefits of using Honeycomb tasks with a participant in an ongoing study of deep brain stimulation for obsessive compulsive disorder, who completed repeated tasks both in the clinic and at home.

Conclusion:

Honeycomb enables researchers to deploy tasks online, in clinic, and at home in more ecologically valid environments and during concurrent electrophysiology.

Analysis-rcs-data: Open-Source Toolbox for the Ingestion, Time-Alignment, and Visualization of Sense and Stimulation Data From the Medtronic Summit RC+S System

Closed-loop neurostimulation is a promising therapy being tested and clinically implemented in a growing number of neurological and psychiatric indications. This therapy is enabled by chronically implanted, bidirectional devices including the Medtronic Summit RC+S system. In order to successfully optimize therapy for patients implanted with these devices, analyses must be conducted offline on the recorded neural data, in order to inform optimal sense and stimulation parameters. The file format, volume, and complexity of raw data from these devices necessitate conversion, parsing, and time reconstruction ahead of time-frequency analyses and modeling common to standard neuroscientific analyses. Here, we provide an open-source toolbox written in Matlab which takes raw files from the Summit RC+S and transforms these data into a standardized format amenable to conventional analyses. Furthermore, we provide a plotting tool which can aid in the visualization of multiple data streams and sense, stimulation, and therapy settings. Finally, we describe an analysis module which replicates RC+S on-board power computations, a functionality which can accelerate biomarker discovery. This toolbox aims to accelerate the research and clinical advances made possible by longitudinal neural recordings and adaptive neurostimulation in people with neurological and psychiatric illnesses.

Uncovering biomarkers during therapeutic neuromodulation with PARRM: Period-based Artifact Reconstruction and Removal Method

Advances in therapeutic neuromodulation devices have enabled concurrent stimulation and electrophysiology in the central nervous system. However, stimulation artifacts often obscure the sensed underlying neural activity. Here, we develop a method, termed Period-based Artifact Reconstruction and Removal Method (PARRM), to remove stimulation artifacts from neural recordings by leveraging the exact period of stimulation to construct and subtract a high-fidelity template of the artifact. Benchtop saline experiments, computational simulations, five unique in vivo paradigms across animal and human studies, and an obscured movement biomarker are used for validation. Performance is found to exceed that of state-of-the-art filters in recovering complex signals without introducing contamination. PARRM has several advantages: (1) it is superior in signal recovery; (2) it is easily adaptable to several neurostimulation paradigms; and (3) it has low complexity for future on-device implementation. Real-time artifact removal via PARRM will enable unbiased exploration and detection of neural biomarkers to enhance efficacy of closed-loop therapies.

Predicting task performance from biomarkers of mental fatigue in global brain activity

Objective.

Detection and early prediction of mental fatigue (i.e. shifts in vigilance), could be used to adapt neuromodulation strategies to effectively treat patients suffering from brain injury and other indications with prominent chronic mental fatigue.

Approach.

In this study, we analyzed electrocorticography (ECoG) signals chronically recorded from two healthy non-human primates (NHP) as they performed a sustained attention task over extended periods of time. We employed a set of spectrotemporal and connectivity biomarkers of the ECoG signals to identify periods of mental fatigue and a gradient boosting classifier to predict performance, up to several seconds prior to the behavioral response.

Main results.

Wavelet entropy and the instantaneous amplitude and frequency were among the best single features across sessions in both NHPs. The classification performance using higher order spectral-temporal (HOST) features was significantly higher than that of conventional spectral power features in both NHPs. Across the 99 sessions analyzed, average F1 scores of 77.5%±8.2% and 91.2%±3.6%, and accuracy of 79.5%±8.9% and 87.6%±3.9 % for the classifier were obtained for each animal, respectively.

Significance.

Our results here demonstrate the feasibility of predicting performance and detecting periods of mental fatigue by analyzing ECoG signals, and that this general approach, in principle, could be used for closed-loop control of neuromodulation strategies.

Review of nanotheranostics for molecular mechanisms underlying psychiatric disorders and commensurate nanotherapeutics for neuropsychiatry: The mind knockout

Bio-neuronal led psychiatric abnormalities transpired by the loss of neuronal structure and function (neurodegeneration), pro-inflammatory cytokines, microglial dysfunction, altered neurotransmission, toxicants, serotonin deficiency, kynurenine pathway, and excessively produced neurotoxic substances. These uncontrolled happenings in the etiology of psychiatric disorders initiate further changes in neurotransmitter metabolism, pathologic microglial, cell activation, and impaired neuroplasticity. Inflammatory cytokines, the outcome of dysfunctional mitochondria, dysregulation of the immune system, and under stress functions of the brain are leading biochemical factors for depression and anxiety. Nanoscale drug delivery platforms, inexpensive diagnostics using nanomaterials, nano-scale imaging technologies, and ligand-conjugated nanocrystals used for elucidating the molecular mechanisms and foremost cellular communications liable for such disorders are highly capable features to study for efficient diagnosis and therapy of the mental illness. These theranostic tools made up of multifunctional nanomaterials have the potential for effective and accurate diagnosis, imaging of psychiatric disorders, and are at the forefront of leading technologies in nanotheranostics openings field as they can collectively and efficiently target the stimulated territories of the cerebellum (cells and tissues) through molecular-scale interactions with higher bioavailability, and bio-accessibility. Specifically, the nanoplatforms based neurological changes are playing a significant role in the diagnosis of psychiatric disorders and portraying the routes of functional restoration of mental disorders by newer imaging tools at nano-level in all directions. Because of these nanotherapeutic platforms, the molecules of nanomedicine can penetrate the Blood-Brain Barrier with an increased half-life of drug molecules. The discoveries in nanotheranostics and nanotherapeutics inbuilt unique multi-functionalities are providing the best multiplicities of novel nanotherapeutic potentialities with no toxicity concerns at the level of nano range.

NeuroDAC: an open-source arbitrary biosignal waveform generator

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

Researcher Perspectives on Data Sharing in Deep Brain Stimulation

The expansion of research on deep brain stimulation (DBS) and adaptive DBS (aDBS) raises important neuroethics and policy questions related to data sharing. However, there has been little empirical research on the perspectives of experts developing these technologies. We conducted semi-structured, open-ended interviews with aDBS researchers regarding their data sharing practices and their perspectives on ethical and policy issues related to sharing. Researchers expressed support for and a commitment to sharing, with most saying that they were either sharing their data or would share in the future and that doing so was important for advancing the field. However, those who are sharing reported a variety of sharing partners, suggesting heterogeneity in sharing practices and lack of the broad sharing that would reflect principles of open science. Researchers described several concerns and barriers related to sharing, including privacy and confidentiality, the usability of shared data by others, ownership and control of data (including potential commercialization), and limited resources for sharing. They also suggested potential solutions to these challenges, including additional safeguards to address privacy issues, standardization and transparency in analysis to address issues of data usability, professional norms and heightened cooperation to address issues of ownership and control, and streamlining of data transmission to address resource limitations. Researchers also offered a range of views on the sensitivity of neural activity data (NAD) and data related to mental health in the context of sharing. These findings are an important input to deliberations by researchers, policymakers, neuroethicists, and other stakeholders as they navigate ethics and policy questions related to aDBS research.

Harmonizing the Neurobiology and Treatment of Obsessive-Compulsive Disorder

Obsessive-compulsive disorder (OCD) is a common, chronic, and oftentimes disabling disorder. The only established first-line treatments for OCD are exposure and response prevention, and serotonin reuptake inhibitor medications (SRIs). However, a subset of patients fails to respond to either modality, and few experience complete remission. Beyond SRI monotherapy, antipsychotic augmentation is the only medication approach for OCD with substantial empirical support. Our incomplete understanding of the neurobiology of OCD has hampered efforts to develop new treatments or enhance extant interventions. This review focuses on several promising areas of research that may help elucidate the pathophysiology of OCD and advance treatment. Multiple studies support a significant genetic contribution to OCD, but pinpointing the specific genetic determinants requires additional investigation. The preferential efficacy of SRIs in OCD has neither led to discovery of serotonergic abnormalities in OCD nor to development of new serotonergic medications for OCD. Several lines of preclinical and clinical evidence suggest dysfunction of the glutamatergic system in OCD, prompting testing of several promising glutamate modulating agents. Functional imaging studies in OCD show consistent evidence for increased activity in brain regions that form a cortico-striato-thalamo-cortical (CSTC) loop. Neuromodulation treatments with either noninvasive devices (e.g., transcranial magnetic stimulation) or invasive procedures (e.g., deep brain stimulation) provide further support for the CSTC model of OCD. A common substrate for various interventions (whether drug, behavioral, or device) may be modulation (at different nodes or connections) of the CSTC circuit that mediates the symptoms of OCD.

Researcher Perspectives on Ethical Considerations in Adaptive Deep Brain Stimulation Trials

Interest and investment in closed-loop or adaptive deep brain stimulation (aDBS) systems have quickly expanded due to this neurotechnology's potential to more safely and effectively treat refractory movement and psychiatric disorders compared to conventional DBS. A large neuroethics literature outlines potential ethical concerns about conventional DBS and aDBS systems. Few studies, however, have examined stakeholder perspectives about ethical issues in aDBS research and other next-generation DBS devices. To help fill this gap, we conducted semi-structured interviews with researchers involved in aDBS trials (n = 23) to gain insight into the most pressing ethical questions in aDBS research and any concerns about specific features of aDBS devices, including devices' ability to measure brain activity, automatically adjust stimulation, and store neural data. Using thematic content analysis, we identified 8 central themes in researcher responses. The need to measure and store neural data for aDBS raised concerns among researchers about data privacy and security issues (noted by 91% of researchers), including the avoidance of unintended or unwanted third-party access to data. Researchers reflected on the risks and safety (83%) of aDBS due to the experimental nature of automatically modulating then observing stimulation effects outside a controlled clinical setting and in relation to need for surgical battery changes. Researchers also stressed the importance of ensuring informed consent and adequate patient understanding (74%). Concerns related to automaticity and device programming (65%) were discussed, including current uncertainties about biomarker validity. Additionally, researchers discussed the potential impacts of automatic stimulation on patients' autonomy and control over stimulation (57%). Lastly, researchers discussed concerns related to patient selection (defining criteria for candidacy) (39%), challenges of ensuring post-trial access to care and device maintenance (39%), and potential effects on personality and identity (30%). To help address researcher concerns, we discuss the need to minimize cybersecurity vulnerabilities, advance biomarker validity, promote the balance of device control between patients and clinicians, and enhance ongoing informed consent. The findings from this study will help inform policies that will maximize the benefits and minimize potential harms of aDBS and other next-generation DBS devices.

Developing Collaborative Platforms to Advance Neurotechnology and Its Translation

Neurotechnological devices are failing to deliver on their therapeutic promise because of the time it takes to translate them from bench to clinic. In this Perspective, we reflect on lessons learned from medical device successes and failures and consider how such lessons might shape a strategic vision for translating neurotechnologies in the future. We articulate how the intentional design and deployment of "scientific platforms," from the technology stack of hardware and software through the supporting ecosystem, could catalyze a new wave of innovation, discovery, and therapy. We also identify specific actions that could promote future neurotechnology roadmaps and industrial-academic-government collaborative activities. We believe that community-supported neurotechnology platforms will prove to be transformational in accelerating ideas from bench to bedside, maximizing scientific discovery and improving patient care.

The RNS System: brain-responsive neurostimulation for the treatment of epilepsy

Introduction: Epilepsy affects more than 1% of the US population, and over 30% of adults with epilepsy do not respond to antiseizure medications without life-impacting medication-related side effects. Resection of the seizure focus is not an option for many patients because it would cause unacceptable neurological or cognitive harm. For these patients, neuromodulation has emerged as a nondestructive, effective, and safe alternative. The NeuroPace® RNS® System, the only brain-responsive neurostimulation device, records neural activity from leads placed at one or two seizure foci. When the neurostimulator detects epileptiform activity, as defined for each patient by his or her physician, brief pulses of electrical stimulation are delivered to normalize the activity.Areas covered: This review describes the RNS System, the results of multi-year clinical trials, and the research discoveries enabled by the chronic ambulatory brain data collected by the RNS System.Expert commentary: Brain-responsive neurostimulation could potentially be used to treat any episodic neurological disorder that's accompanied by a neurophysiological biomarker of severity. Combining advanced machine learning approaches with the chronic ambulatory brain data collected by the RNS System could eventually enable automatic fine-tuning of detection and stimulation for each patient, creating a general-purpose neurotechnological platform for precision medicine.

Biomarker Optimization of Spinal Cord Stimulation Therapies

OBJECTIVES

We are in the process of designing and testing an intradural stimulation device that will shorten the distance between the location of the electrode array and the targeted neural tissue, thus improving the efficacy of electrical current delivery. Identifying a biomarker that accurately reflects the response to this intervention is highly valued because of the potential to optimize interventional parameters or predict a response before it is clinically measurable. In this report, we summarize the findings pertaining to the study of biomarkers so that we and others will have an up-to-date reference that critically evaluates the current approaches and select one or several for testing during the development of our device.

MATERIALS AND METHODS

We have conducted a broad survey of the existing literature to catalogue the biomarkers that could be coupled to intradural spinal cord stimulation. We describe in detail some of the most promising biomarkers, existing limitations, and suitability to managing chronic pain.

RESULTS

Chronic, intractable pain is an all-encompassing condition that is incurable. Many treatments for managing chronic pain are nonspecific in action and intermittently administered; therefore, patients are particularly susceptible to large fluctuations in pain control over the course of a day. The absence of a reliable biomarker challenges assessment of therapeutic efficacy and contributes to either incomplete and inconsistent pain relief or, alternatively, intolerable side effects. Fluctuations in metabolites or inflammatory markers, signals captured during dynamic imaging, and genomics will likely have a role in governing how a device is modulated.

CONCLUSIONS

Efforts to identify one or more biomarkers are well underway with some preliminary evidence supporting their efficacy. This has far-reaching implications, including improved outcomes, fewer adverse events, harmonization of treatment and individuals, performance gains, and cost savings. We anticipate that novel biomarkers will be used widely to manage chronic pain.

Oscillotherapeutics - Time-targeted interventions in epilepsy and beyond

Oscillatory brain activities support many physiological functions from motor control to cognition. Disruptions of the normal oscillatory brain activities are commonly observed in neurological and psychiatric disorders including epilepsy, Parkinson's disease, Alzheimer's disease, schizophrenia, anxiety/trauma-related disorders, major depressive disorders, and drug addiction. Therefore, these disorders can be considered as common oscillation defects despite having distinct behavioral manifestations and genetic causes. Recent technical advances of neuronal activity recording and analysis have allowed us to study the pathological oscillations of each disorder as a possible biomarker of symptoms. Furthermore, recent advances in brain stimulation technologies enable time- and space-targeted interventions of the pathological oscillations of both neurological disorders and psychiatric disorders as possible targets for regulating their symptoms.

Explainable Artificial Intelligence for Neuroscience: Behavioral Neurostimulation

The use of Artificial Intelligence and machine learning in basic research and clinical neuroscience is increasing. AI methods enable the interpretation of large multimodal datasets that can provide unbiased insights into the fundamental principles of brain function, potentially paving the way for earlier and more accurate detection of brain disorders and better informed intervention protocols. Despite AI's ability to create accurate predictions and classifications, in most cases it lacks the ability to provide a mechanistic understanding of how inputs and outputs relate to each other. Explainable Artificial Intelligence (XAI) is a new set of techniques that attempts to provide such an understanding, here we report on some of these practical approaches. We discuss the potential value of XAI to the field of neurostimulation for both basic scientific inquiry and therapeutic purposes, as well as, outstanding questions and obstacles to the success of the XAI approach.

Decoding task engagement from distributed network electrophysiology in humans

OBJECTIVE

Here, our objective was to develop a binary decoder to detect task engagement in humans during two distinct, conflict-based behavioral tasks. Effortful, goal-directed decision-making requires the coordinated action of multiple cognitive processes, including attention, working memory and action selection. That type of mental effort is often dysfunctional in mental disorders, e.g. when a patient attempts to overcome a depression or anxiety-driven habit but feels unable. If the onset of engagement in this type of focused mental activity could be reliably detected, decisional function might be augmented, e.g. through neurostimulation. However, there are no known algorithms for detecting task engagement with rapid time resolution.

APPROACH

We defined a new network measure, fixed canonical correlation (FCCA), specifically suited for neural decoding applications. We extracted FCCA features from local field potential recordings in human volunteers to give a temporally continuous estimate of mental effort, defined by engagement in experimental conflict tasks.

MAIN RESULTS

Using a small number of features per participant, we accurately decoded and distinguished task engagement from other mental activities. Further, the decoder distinguished between engagement in two different conflict-based tasks within seconds of their onset.

SIGNIFICANCE

These results demonstrate that network-level brain activity can detect specific types of mental efforts. This could form the basis of a responsive intervention strategy for decision-making deficits.