A flexible intracortical brain-computer interface for typing using finger movements.

Keyboard typing with finger movements is a versatile digital interface for users with diverse skills, needs, and preferences. Currently, such an interface does not exist for people with paralysis. We developed an intracortical brain-computer interface (BCI) for typing with attempted flexion/extension movements of three finger groups on the right hand, or both hands, and demonstrated its flexibility in two dominant typing paradigms. The first paradigm is "point-and-click" typing, where a BCI user selects one key at a time using continuous real-time control, allowing selection of arbitrary sequences of symbols. During cued character selection with this paradigm, a human research participant with paralysis achieved 30-40 selections per minute with nearly 90% accuracy. The second paradigm is "keystroke" typing, where the BCI user selects each character by a discrete movement without real-time feedback, often giving a faster speed for natural language sentences. With 90 cued characters per minute, decoding attempted finger movements and correcting errors using a language model resulted in more than 90% accuracy. Notably, both paradigms matched the state-of-the-art for BCI performance and enabled further flexibility by the simultaneous selection of multiple characters as well as efficient decoder estimation across paradigms. Overall, the high-performance interface is a step towards the wider accessibility of BCI technology by addressing unmet user needs for flexibility.

An accurate and rapidly calibrating speech neuroprosthesis.

Brain-computer interfaces can enable rapid, intuitive communication for people with paralysis by transforming the cortical activity associated with attempted speech into text on a computer screen. Despite recent advances, communication with brain-computer interfaces has been restricted by extensive training data requirements and inaccurate word output. A man in his 40's with ALS with tetraparesis and severe dysarthria (ALSFRS-R = 23) was enrolled into the BrainGate2 clinical trial. He underwent surgical implantation of four microelectrode arrays into his left precentral gyrus, which recorded neural activity from 256 intracortical electrodes. We report a speech neuroprosthesis that decoded his neural activity as he attempted to speak in both prompted and unstructured conversational settings. Decoded words were displayed on a screen, then vocalized using text-to-speech software designed to sound like his pre-ALS voice. On the first day of system use, following 30 minutes of attempted speech training data, the neuroprosthesis achieved 99.6% accuracy with a 50-word vocabulary. On the second day, the size of the possible output vocabulary increased to 125,000 words, and, after 1.4 additional hours of training data, the neuroprosthesis achieved 90.2% accuracy. With further training data, the neuroprosthesis sustained 97.5% accuracy beyond eight months after surgical implantation. The participant has used the neuroprosthesis to communicate in self-paced conversations for over 248 hours. In an individual with ALS and severe dysarthria, an intracortical speech neuroprosthesis reached a level of performance suitable to restore naturalistic communication after a brief training period.

Measuring instability in chronic human intracortical neural recordings towards stable, long-term brain-computer interfaces.

Intracortical brain-computer interfaces (iBCIs) enable people with tetraplegia to gain intuitive cursor control from movement intentions. To translate to practical use, iBCIs should provide reliable performance for extended periods of time. However, performance begins to degrade as the relationship between kinematic intention and recorded neural activity shifts compared to when the decoder was initially trained. In addition to developing decoders to better handle long-term instability, identifying when to recalibrate will also optimize performance. We propose a method to measure instability in neural data without needing to label user intentions. Longitudinal data were analyzed from two BrainGate2 participants with tetraplegia as they used fixed decoders to control a computer cursor spanning 142 days and 28 days, respectively. We demonstrate a measure of instability that correlates with changes in closed-loop cursor performance solely based on the recorded neural activity (Pearson r = 0.93 and 0.72, respectively). This result suggests a strategy to infer online iBCI performance from neural data alone and to determine when recalibration should take place for practical long-term use.

A real-time, high-performance brain-computer interface for finger decoding and quadcopter control.

People with paralysis express unmet needs for peer support, leisure activities, and sporting activities. Many within the general population rely on social media and massively multiplayer video games to address these needs. We developed a high-performance finger brain-computer-interface system allowing continuous control of 3 independent finger groups with 2D thumb movements. The system was tested in a human research participant over sequential trials requiring fingers to reach and hold on targets, with an average acquisition rate of 76 targets/minute and completion time of 1.58 ± 0.06 seconds. Performance compared favorably to previous animal studies, despite a 2-fold increase in the decoded degrees-of-freedom (DOF). Finger positions were then used for 4-DOF velocity control of a virtual quadcopter, demonstrating functionality over both fixed and random obstacle courses. This approach shows promise for controlling multiple-DOF end-effectors, such as robotic fingers or digital interfaces for work, entertainment, and socialization.

Brain control of bimanual movement enabled by recurrent neural networks.

Brain-computer interfaces have so far focused largely on enabling the control of a single effector, for example a single computer cursor or robotic arm. Restoring multi-effector motion could unlock greater functionality for people with paralysis (e.g., bimanual movement). However, it may prove challenging to decode the simultaneous motion of multiple effectors, as we recently found that a compositional neural code links movements across all limbs and that neural tuning changes nonlinearly during dual-effector motion. Here, we demonstrate the feasibility of high-quality bimanual control of two cursors via neural network (NN) decoders. Through simulations, we show that NNs leverage a neural 'laterality' dimension to distinguish between left and right-hand movements as neural tuning to both hands become increasingly correlated. In training recurrent neural networks (RNNs) for two-cursor control, we developed a method that alters the temporal structure of the training data by dilating/compressing it in time and re-ordering it, which we show helps RNNs successfully generalize to the online setting. With this method, we demonstrate that a person with paralysis can control two computer cursors simultaneously. Our results suggest that neural network decoders may be advantageous for multi-effector decoding, provided they are designed to transfer to the online setting.

Thalamic deep brain stimulation in traumatic brain injury: a phase 1, randomized feasibility study.

Converging evidence indicates that impairments in executive function and information-processing speed limit quality of life and social reentry after moderate-to-severe traumatic brain injury (msTBI). These deficits reflect dysfunction of frontostriatal networks for which the central lateral (CL) nucleus of the thalamus is a critical node. The primary objective of this feasibility study was to test the safety and efficacy of deep brain stimulation within the CL and the associated medial dorsal tegmental (CL/DTTm) tract.Six participants with msTBI, who were between 3 and 18 years post-injury, underwent surgery with electrode placement guided by imaging and subject-specific biophysical modeling to predict activation of the CL/DTTm tract. The primary efficacy measure was improvement in executive control indexed by processing speed on part B of the trail-making test.All six participants were safely implanted. Five participants completed the study and one was withdrawn for protocol non-compliance. Processing speed on part B of the trail-making test improved 15% to 52% from baseline, exceeding the 10% benchmark for improvement in all five cases.CL/DTTm deep brain stimulation can be safely applied and may improve executive control in patients with msTBI who are in the chronic phase of recovery.ClinicalTrials.gov identifier: NCT02881151 .

Plug-and-Play Stability for Intracortical Brain-Computer Interfaces: A One-Year Demonstration of Seamless Brain-to-Text Communication.

Intracortical brain-computer interfaces (iBCIs) have shown promise for restoring rapid communication to people with neurological disorders such as amyotrophic lateral sclerosis (ALS). However, to maintain high performance over time, iBCIs typically need frequent recalibration to combat changes in the neural recordings that accrue over days. This requires iBCI users to stop using the iBCI and engage in supervised data collection, making the iBCI system hard to use. In this paper, we propose a method that enables self-recalibration of communication iBCIs without interrupting the user. Our method leverages large language models (LMs) to automatically correct errors in iBCI outputs. The self-recalibration process uses these corrected outputs ("pseudo-labels") to continually update the iBCI decoder online. Over a period of more than one year (403 days), we evaluated our Continual Online Recalibration with Pseudo-labels (CORP) framework with one clinical trial participant. CORP achieved a stable decoding accuracy of 93.84% in an online handwriting iBCI task, significantly outperforming other baseline methods. Notably, this is the longest-running iBCI stability demonstration involving a human participant. Our results provide the first evidence for long-term stabilization of a plug-and-play, high-performance communication iBCI, addressing a major barrier for the clinical translation of iBCIs.

Subject and Family Perspectives from the Central Thalamic Deep Brain Stimulation Trial for Traumatic Brain Injury: Part II.

This is the second paper in a two-part series describing subject and family perspectives from the CENTURY-S (CENtral Thalamic Deep Brain Stimulation for the Treatment of Traumatic Brain InjURY-Safety) first-in-human invasive neurological device trial to achieve cognitive restoration in moderate to severe traumatic brain injury (msTBI). To participate, subjects were independently assessed to formally establish decision-making capacity to provide voluntary informed consent. Here, we report on post-operative interviews conducted after a successful trial of thalamic stimulation. All five msTBI subjects met a pre-selected primary endpoint of at least a 10% improvement in completion time on Trail-Making-Test Part B, a marker of executive function. We describe narrative responses of subjects and family members, refracted against that success. Interviews following surgery and the stimulation trial revealed the challenge of adaptation to improvements in cognitive function and emotional regulation as well as altered (and restored) relationships and family dynamics. These improvements exposed barriers to social reintegration made relevant by recoveries once thought inconceivable. The study's success sparked concerns about post-trial access to implanted devices, financing of device maintenance, battery replacement, and on-going care. Most subjects and families identified the need for supportive counseling to adapt to the new trajectory of their lives.

An Individualized Tractography Pipeline for the Nucleus Basalis of Meynert Lateral Tract.

Background: At the center of the cortical cholinergic network, the nucleus basalis of Meynert (NBM) is crucial for the cognitive domains most vulnerable in PD. Preclinical evidence has demonstrated the positive impact of NBM deep brain stimulation (DBS) on cognition but early human trials have had mixed results. It is possible that DBS of the lateral NBM efferent white matter fiber bundle may be more effective at improving cognitive-motor function. However, precise tractography modelling is required to identify the optimal target for neurosurgical planning. Individualized tractography approaches have been shown to be highly effective for accurately identifying DBS targets but have yet to be developed for the NBM. Methods: Using structural and diffusion weighted imaging, we developed a tractography pipeline for precise individualized identification of the lateral NBM target tract. Using dice similarity coefficients, the reliability of the tractography outputs was assessed across three cohorts to investigate: 1) whether this manual pipeline is more reliable than an existing automated pipeline currently used in the literature; 2) the inter- and intra-rater reliability of our pipeline in research scans of patients with PD; and 3) the reliability and practicality of this pipeline in clinical scans of DBS patients. Results: The individualized manual pipeline was found to be significantly more reliable than the existing automated pipeline for both the segmentation of the NBM region itself (p<0.001) and the reconstruction of the target lateral tract (p=0.002). There was also no significant difference between the reliability of two different raters in the PD cohort (p=0.25), which showed high inter- (mean Dice coefficient >0.6) and intra-rater (mean Dice coefficient >0.7) reliability across runs. Finally, the pipeline was shown to be highly reliable within the clinical scans (mean Dice coefficient = 0.77). However, accurate reconstruction was only evident in 7/10 tracts. Conclusion: We have developed a reliable tractography pipeline for the identification and analysis of the NBM lateral tract in research and clinical grade imaging of healthy young adult and PD patient scans.

A high-performance speech neuroprosthesis.

Speech brain-computer interfaces (BCIs) have the potential to restore rapid communication to people with paralysis by decoding neural activity evoked by attempted speech into text1,2 or sound3,4. Early demonstrations, although promising, have not yet achieved accuracies sufficiently high for communication of unconstrained sentences from a large vocabulary1-7. Here we demonstrate a speech-to-text BCI that records spiking activity from intracortical microelectrode arrays. Enabled by these high-resolution recordings, our study participant-who can no longer speak intelligibly owing to amyotrophic lateral sclerosis-achieved a 9.1% word error rate on a 50-word vocabulary (2.7 times fewer errors than the previous state-of-the-art speech BCI2) and a 23.8% word error rate on a 125,000-word vocabulary (the first successful demonstration, to our knowledge, of large-vocabulary decoding). Our participant's attempted speech was decoded  at 62 words per minute, which is 3.4 times as fast as the previous record8 and begins to approach the speed of natural conversation (160 words per minute9). Finally, we highlight two aspects of the neural code for speech that are encouraging for speech BCIs: spatially intermixed tuning to speech articulators that makes accurate decoding possible from only a small region of cortex, and a detailed articulatory representation of phonemes that persists years after paralysis. These results show a feasible path forward for restoring rapid communication to people with paralysis who can no longer speak.

A brain-computer typing interface using finger movements.

Intracortical brain computer interfaces (iBCIs) decode neural activity from the cortex and enable motor and communication prostheses, such as cursor control, handwriting and speech, for people with paralysis. This paper introduces a new iBCI communication prosthesis using a 3D keyboard interface for typing using continuous, closed loop movement of multiple fingers. A participant-specific BCI keyboard prototype was developed for a BrainGate2 clinical trial participant (T5) using neural recordings from the hand-knob area of the left premotor cortex. We assessed the relative decoding accuracy of flexion/extension movements of individual single fingers (5 degrees of freedom (DOF)) vs. three groups of fingers (thumb, index-middle, and ring-small fingers, 3 DOF). Neural decoding using 3 independent DOF was more accurate (95%) than that using 5 DOF (76%). A virtual keyboard was then developed where each finger group moved along a flexion-extension arc to acquire targets that corresponded to English letters and symbols. The locations of these letter/symbols were optimized using natural language statistics, resulting in an approximately a 2× reduction in distance traveled by fingers on average compared to a random keyboard layout. This keyboard was tested using a simple real-time closed loop decoder enabling T5 to type with 31 symbols at 90% accuracy and approximately 2.3 sec/symbol (excluding a 2 second hold time) on average.

Translating deep learning to neuroprosthetic control.

Advances in deep learning have given rise to neural network models of the relationship between movement and brain activity that appear to far outperform prior approaches. Brain-computer interfaces (BCIs) that enable people with paralysis to control external devices, such as robotic arms or computer cursors, might stand to benefit greatly from these advances. We tested recurrent neural networks (RNNs) on a challenging nonlinear BCI problem: decoding continuous bimanual movement of two computer cursors. Surprisingly, we found that although RNNs appeared to perform well in offline settings, they did so by overfitting to the temporal structure of the training data and failed to generalize to real-time neuroprosthetic control. In response, we developed a method that alters the temporal structure of the training data by dilating/compressing it in time and re-ordering it, which we show helps RNNs successfully generalize to the online setting. With this method, we demonstrate that a person with paralysis can control two computer cursors simultaneously, far outperforming standard linear methods. Our results provide evidence that preventing models from overfitting to temporal structure in training data may, in principle, aid in translating deep learning advances to the BCI setting, unlocking improved performance for challenging applications.

Long-term unsupervised recalibration of cursor BCIs.

Intracortical brain-computer interfaces (iBCIs) require frequent recalibration to maintain robust performance due to changes in neural activity that accumulate over time. Compensating for this nonstationarity would enable consistently high performance without the need for supervised recalibration periods, where users cannot engage in free use of their device. Here we introduce a hidden Markov model (HMM) to infer what targets users are moving toward during iBCI use. We then retrain the system using these inferred targets, enabling unsupervised adaptation to changing neural activity. Our approach outperforms the state of the art in large-scale, closed-loop simulations over two months and in closed-loop with a human iBCI user over one month. Leveraging an offline dataset spanning five years of iBCI recordings, we further show how recently proposed data distribution-matching approaches to recalibration fail over long time scales; only target-inference methods appear capable of enabling long-term unsupervised recalibration. Our results demonstrate how task structure can be used to bootstrap a noisy decoder into a highly-performant one, thereby overcoming one of the major barriers to clinically translating BCIs.

Interim Safety Profile From the Feasibility Study of the BrainGate Neural Interface System.

BACKGROUND AND OBJECTIVES: Brain-computer interfaces (BCIs) are being developed to restore mobility, communication, and functional independence to people with paralysis. Though supported by decades of preclinical data, the safety of chronically implanted microelectrode array BCIs in humans is unknown. We report safety results from the prospective, open-label, nonrandomized BrainGate feasibility study (NCT00912041), the largest and longest-running clinical trial of an implanted BCI. METHODS: Adults aged 18-75 years with quadriparesis from spinal cord injury, brainstem stroke, or motor neuron disease were enrolled through 7 clinical sites in the United States. Participants underwent surgical implantation of 1 or 2 microelectrode arrays in the motor cortex of the dominant cerebral hemisphere. The primary safety outcome was device-related serious adverse events (SAEs) requiring device explantation or resulting in death or permanently increased disability during the 1-year postimplant evaluation period. The secondary outcomes included the type and frequency of other adverse events and the feasibility of the BrainGate system for controlling a computer or other assistive technologies. RESULTS: From 2004 to 2021, 14 adults enrolled in the BrainGate trial had devices surgically implanted. The average duration of device implantation was 872 days, yielding 12,203 days of safety experience. There were 68 device-related adverse events, including 6 device-related SAEs. The most common device-related adverse event was skin irritation around the percutaneous pedestal. There were no safety events that required device explantation, no unanticipated adverse device events, no intracranial infections, and no participant deaths or adverse events resulting in permanently increased disability related to the investigational device. DISCUSSION: The BrainGate Neural Interface system has a safety record comparable with other chronically implanted medical devices. Given rapid recent advances in this technology and continued performance gains, these data suggest a favorable risk/benefit ratio in appropriately selected individuals to support ongoing research and development. TRIAL REGISTRATION INFORMATION: ClinicalTrials.gov Identifier: NCT00912041. CLASSIFICATION OF EVIDENCE: This study provides Class IV evidence that the neurosurgically placed BrainGate Neural Interface system is associated with a low rate of SAEs defined as those requiring device explantation, resulting in death, or resulting in permanently increased disability during the 1-year postimplant period.

Changes in the Cerebello-Thalamo-Cortical Network After Magnetic Resonance-Guided Focused Ultrasound Thalamotomy.

Objective: In recent years, transcranial magnetic resonance-guided focused ultrasound (tcMRgFUS) has been established as a potential treatment option for movement disorders, including essential tremor (ET). So far, however, little is known about the impact of tcMRgFUS on structural connectivity. The objective of this study was to detect microstructural changes in tremor- and motor-related white matter tracts in ET patients treated with tcMRgFUS thalamotomy. Methods: Eleven patients diagnosed with ET were enrolled in this tcMRgFUS thalamotomy study. For each patient, 3 Tesla magnetic resonance imaging (3T MRI) including structural and diffusion MRI were acquired and the Clinical Rating Scale for Tremor was assessed before the procedure as well as 1 year after the treatment. Diffusion MRI tractography was performed to identify the cerebello-thalamo-cortical tract (CTCT), the medial lemniscus, and the corticospinal tract in both hemispheres on pre-treatment data. Pre-treatment tractography results were co-registered to post-treatment diffusion data. Diffusion tensor imaging (DTI) metrics, including fractional anisotropy (FA), mean diffusivity (MD) and radial diffusivity (RD), were averaged across the tracts in the pre- and post-treatment data. Results: The mean value of tract-specific DTI metrics changed significantly within the thalamic lesion and in the CTCT on the treated side (p < 0.05). Changes of DTI-derived indices within the CTCT correlated well with lesion overlap (FA: r = -0.54, p = 0.04; MD: r = 0.57, p = 0.04); RD: r = 0.67, p = 0.036). Further, a trend was seen for the correlation between changes of DTI-derived indices within the CTCT and clinical improvement (FA: r = 0.58; p = 0.062; MD: r = -0.52, p = 0.64; RD: r = -0.61 p = 0.090). Conclusions: Microstructural changes were detected within the CTCT after tcMRgFUS, and these changes correlated well with lesion-tract overlap. Our results show that diffusion MRI is able to detect the microstructural effects of tcMRgFUS, thereby further elucidating the treatment mechanism, and ultimately to improve targeting prospectively. Impact statement The results of this study demonstrate microstructural changes within the cerebello-thalamo-cortical pathways 1 year after MR-guided focused ultrasound thalamotomy. Even more, microstructural changes within the cerebello-thalamo-cortical pathways correlated significantly with clinical outcome. These findings do not only highly emphasize the need of new targeting strategies for MR-guided focused ultrasound thalamotomy but also help to elucidate the treatment mechanism of it.

A Retrospective Cohort Study of Implantable Pulse Generator Surgical Site Infections After Deep Brain Stimulation Surgery With an Antibacterial Envelope.

INTRODUCTION: Deep brain stimulation (DBS) surgery is an established treatment for many patients with neurologic disease, and a common complication of DBS is surgical site infection (SSI). In 2016, neurosurgeons at our institution began enclosing implantable pulse generators (IPGs) within fully absorbable, antibacterial envelopes in patients who underwent initial DBS implantation. We sought to determine whether the use of antibacterial envelopes reduced IPG-related SSIs. MATERIALS AND METHODS: We performed a retrospective chart review of all adult patients who underwent initial DBS implantation at Stanford Hospital between November 14, 2012, and November 9, 2020. Operative details, perioperative antibiotics, comorbidities, and postoperative complications were extracted for all patients. Univariate and multivariate logistic regression were used to identify factors associated with SSIs within three months of surgery, and interrupted time-series analysis was performed to assess whether the departmental adoption of the antibacterial envelope led to a reduction in IPG SSIs. RESULTS: Of 344 patients who underwent initial IPG implantation with the antibacterial envelope, one developed an SSI within three months of surgery (0.3%), compared with six of 204 patients (2.9%) who underwent the same procedure without the antibacterial envelope (odds ratio: 0.10, 95% CI: 0.01-0.80, p = 0.031). Univariate logistic regression revealed that the antibacterial envelope and 2000-mg intravenous cefazolin perioperatively were associated with reduced SSI risk, whereas no other factors reached statistical significance. After adjusting for comorbidities, no association remained statistically significant. Interrupted time-series analysis showed a reduction in SSIs after 2016, but the effect was not significant. CONCLUSIONS: The adoption of antibacterial envelopes was found to reduce IPG SSIs at the univariate level, but this association did not remain significant after controlling for confounding variables including perioperative antibiotic administration. Although encouraging, this study does not conclusively establish that the use of antibacterial pouches in patients who underwent initial DBS implantation reduces the incidence of IPG SSIs. Future prospective studies that control for confounding variables are necessary to determine the efficacy of antibacterial envelopes in reducing post-DBS infections at the IPG site before clear recommendations can be made.

Plug-and-Play Stability for Intracortical Brain-Computer Interfaces: A One-Year Demonstration of Seamless Brain-to-Text Communication.

Intracortical brain-computer interfaces (iBCIs) have shown promise for restoring rapid communication to people with neurological disorders such as amyotrophic lateral sclerosis (ALS). However, to maintain high performance over time, iBCIs typically need frequent recalibration to combat changes in the neural recordings that accrue over days. This requires iBCI users to stop using the iBCI and engage in supervised data collection, making the iBCI system hard to use. In this paper, we propose a method that enables self-recalibration of communication iBCIs without interrupting the user. Our method leverages large language models (LMs) to automatically correct errors in iBCI outputs. The self-recalibration process uses these corrected outputs ("pseudo-labels") to continually update the iBCI decoder online. Over a period of more than one year (403 days), we evaluated our Continual Online Recalibration with Pseudo-labels (CORP) framework with one clinical trial participant. CORP achieved a stable decoding accuracy of 93.84% in an online handwriting iBCI task, significantly outperforming other baseline methods. Notably, this is the longest-running iBCI stability demonstration involving a human participant. Our results provide the first evidence for long-term stabilization of a plug-and-play, high-performance communication iBCI, addressing a major barrier for the clinical translation of iBCIs.

Subject and Family Perspectives from the Central Thalamic Deep Brain Stimulation for Traumatic Brain Injury Study: Part I.

This is the first article in a two-part series describing subject and family perspectives from the central thalamic deep brain stimulation for the treatment of traumatic brain injury using the Medtronic PC + S first-in-human invasive neurological device trial to achieve cognitive restoration in moderate to severe traumatic brain injury, with subjects who were deemed capable of providing voluntary informed consent. In this article, we report on interviews conducted prior to surgery wherein we asked participants about their experiences recovering from brain injury and their perspectives on study enrollment and participation. We asked how risks and benefits were weighed, what their expectations and fears were, and how decisions were reached about trial participation. We found that informed consent and enrollment decisions are fraught. Subjects and families were often split, with subjects more focused on putative benefits and families concerned about incremental risk. Both subjects and families viewed brain injury as disruptive to personal identity and relationships. As decisions were made about study enrollment, families struggled with recognizing the re-emergent agency of subjects and ceding decision-making authority to subjects who had previously been dependent upon them for protection and guidance. Subjects and family members reported a hope for the relief of cognitive disabilities, improved quality of life, normalization of interpersonal interactions, and a return to work or school as reasons for study participation, along with altruism and a desire to advance science. Despite these aspirations, both subjects and families appreciated the risks of the intervention and did not suffer from a therapeutic misconception. A second essay to be published in the next issue of Cambridge Quarterly of Healthcare Ethics-Clinical Neuroethics will describe interviews conducted after surgery, the effects of cognitive restoration for subjects, families, and challenges presented to the social structures they will call upon to support them through recovery. This subsequent article will be available online prior to its formal publication in October 2023.

Proceedings of the Ninth Annual Deep Brain Stimulation Think Tank: Advances in Cutting Edge Technologies, Artificial Intelligence, Neuromodulation, Neuroethics, Pain, Interventional Psychiatry, Epilepsy, and Traumatic Brain Injury.

DBS Think Tank IX was held on August 25-27, 2021 in Orlando FL with US based participants largely in person and overseas participants joining by video conferencing technology. The DBS Think Tank was founded in 2012 and provides an open platform where clinicians, engineers and researchers (from industry and academia) can freely discuss current and emerging deep brain stimulation (DBS) technologies as well as the logistical and ethical issues facing the field. The consensus among the DBS Think Tank IX speakers was that DBS expanded in its scope and has been applied to multiple brain disorders in an effort to modulate neural circuitry. After collectively sharing our experiences, it was estimated that globally more than 230,000 DBS devices have been implanted for neurological and neuropsychiatric disorders. As such, this year's meeting was focused on advances in the following areas: neuromodulation in Europe, Asia and Australia; cutting-edge technologies, neuroethics, interventional psychiatry, adaptive DBS, neuromodulation for pain, network neuromodulation for epilepsy and neuromodulation for traumatic brain injury.

Proceedings of the 10th annual deep brain stimulation think tank: Advances in cutting edge technologies, artificial intelligence, neuromodulation, neuroethics, interventional psychiatry, and women in neuromodulation.

The deep brain stimulation (DBS) Think Tank X was held on August 17-19, 2022 in Orlando FL. The session organizers and moderators were all women with the theme women in neuromodulation. Dr. Helen Mayberg from Mt. Sinai, NY was the keynote speaker. She discussed milestones and her experiences in developing depression DBS. The DBS Think Tank was founded in 2012 and provides an open platform where clinicians, engineers and researchers (from industry and academia) can freely discuss current and emerging DBS technologies as well as the logistical and ethical issues facing the field. The consensus among the DBS Think Tank X speakers was that DBS has continued to expand in scope however several indications have reached the "trough of disillusionment." DBS for depression was considered as "re-emerging" and approaching a slope of enlightenment. DBS for depression will soon re-enter clinical trials. The group estimated that globally more than 244,000 DBS devices have been implanted for neurological and neuropsychiatric disorders. This year's meeting was focused on advances in the following areas: neuromodulation in Europe, Asia, and Australia; cutting-edge technologies, closed loop DBS, DBS tele-health, neuroethics, lesion therapy, interventional psychiatry, and adaptive DBS.