Mapping of subthalamic nucleus using microelectrode recordings during deep brain stimulation

Nanomedicine in Neuroprotection, Neuroregeneration, and Blood-Brain Barrier Modulation: A Narrative Review

Nanomedicine is a newer, promising approach to promote neuroprotection, neuroregeneration, and modulation of the blood-brain barrier. This review includes the integration of various nanomaterials in neurological disorders. In addition, gelatin-based hydrogels, which have huge potential due to biocompatibility, maintenance of porosity, and enhanced neural process outgrowth, are reviewed. Chemical modification of these hydrogels, especially with guanidine moieties, has shown improved neuron viability and underscores tailored biomaterial design in neural applications. This review further discusses strategies to modulate the blood-brain barrier-a factor critically associated with the effective delivery of drugs to the central nervous system. These advances bring supportive solutions to the solving of neurological conditions and innovative therapies for their treatment. Nanomedicine, as applied to neuroscience, presents a significant leap forward in new therapeutic strategies that might help raise the treatment and management of neurological disorders to much better levels. Our aim was to summarize the current state-of-knowledge in this field.

Neurophysiologic Characteristics of the Anterior Nucleus of the Thalamus during Deep Brain Stimulation Surgery for Epilepsy

INTRODUCTION

Anterior nucleus of the thalamus (ANT) deep brain stimulation (DBS) is an increasingly promising treatment option for refractory epilepsy. Optimal therapeutic benefit has been associated with stimulation at the junction of ANT and the mammillothalamic tract (mtt), but electrophysiologic markers of this target are lacking. The present study examined microelectrode recordings (MER) during DBS to identify unique electrophysiologic characteristics of ANT and the ANT-mtt junction.

METHODS

Ten patients with medically refractory epilepsy underwent MER during ANT-DBS implantation under general anesthesia. MER locations were determined based on coregistration of preoperative MRI, postoperative CT, and a stereotactic atlas of the thalamus (Morel atlas). Several neurophysiological parameters including single unit spiking rate, bursting properties, theta and alpha power and cerebrospinal fluid (CSF)-normalized root mean square (NRMS) of multiunit activity were characterized at recording depths and compared to anatomic boundaries.

RESULTS

From sixteen hemispheres, 485 recordings locations were collected from a mean of 30.3 (15.64 ± 5.0 mm) recording spans. Three-hundred and ninety-four of these recording locations were utilized further for analysis of spiking and bursting rates, after excluding recordings that were more than 8 mm above the putative ventral ANT border. The ANT region exhibited discernible features including: (1) mean spiking rate (7.52 Hz ± 6.9 Hz; one-way analysis of variance test, p = 0.014 when compared to mediodorsal nucleus of the thalamus [MD], mtt, and CSF), (2) the presence of bursting activity with 40% of ANT locations (N = 59) exhibited bursting versus 24% the mtt (χ2; p < 0.001), and 32% in the MD (p = 0.38), (3) CSF-NRMS, a proxy for neuronal density, exhibited well demarcated changes near the entry and exit of ANT (linear regression, R = -0.33, p < 0.001). Finally, in the ANT, both theta (4-8 Hz) and alpha band power (9-12 Hz) were negatively correlated with distance to the ventral ANT border (linear regression, p < 0.001 for both). The proportion of recordings with spiking and bursting activity was consistently highest 0-2 mm above the ventral ANT border with the mtt.

CONCLUSION

We observed several electrophysiological markers demarcating the ANT superior and inferior borders including multiple single cell and local field potential features. A local maximum in neural activity just above the ANT-mtt junction was consistent with the previously described optimal target for seizure reduction. These features may be useful for successful targeting of ANT-DBS for epilepsy.

Modeling Instantaneous Firing Rate of Deep Brain Stimulation Target Neuronal Ensembles in the Basal Ganglia and Thalamus

OBJECTIVE

Deep brain stimulation (DBS) is an effective treatment for movement disorders, including Parkinson disease and essential tremor. However, the underlying mechanisms of DBS remain elusive. Despite the capability of existing models in interpreting experimental data qualitatively, there are very few unified computational models that quantitatively capture the dynamics of the neuronal activity of varying stimulated nuclei-including subthalamic nucleus (STN), substantia nigra pars reticulata (SNr), and ventral intermediate nucleus (Vim)-across different DBS frequencies.

MATERIALS AND METHODS

Both synthetic and experimental data were used in the model fitting; the synthetic data were generated by an established spiking neuron model that was reported in our previous work, and the experimental data were provided using single-unit microelectrode recordings (MERs) during DBS (microelectrode stimulation). Based on these data, we developed a novel mathematical model to represent the firing rate of neurons receiving DBS, including neurons in STN, SNr, and Vim-across different DBS frequencies. In our model, the DBS pulses were filtered through a synapse model and a nonlinear transfer function to formulate the firing rate variability. For each DBS-targeted nucleus, we fitted a single set of optimal model parameters consistent across varying DBS frequencies.

RESULTS

Our model accurately reproduced the firing rates observed and calculated from both synthetic and experimental data. The optimal model parameters were consistent across different DBS frequencies.

CONCLUSIONS

The result of our model fitting was in agreement with experimental single-unit MER data during DBS. Reproducing neuronal firing rates of different nuclei of the basal ganglia and thalamus during DBS can be helpful to further understand the mechanisms of DBS and to potentially optimize stimulation parameters based on their actual effects on neuronal activity.

Advances in Deep Brain Stimulation: From Mechanisms to Applications

Deep brain stimulation (DBS) is an effective therapy for various neurologic and neuropsychiatric disorders, involving chronic implantation of electrodes into target brain regions for electrical stimulation delivery. Despite its safety and efficacy, DBS remains an underutilized therapy. Advances in the field of DBS, including in technology, mechanistic understanding, and applications have the potential to expand access and use of DBS, while also improving clinical outcomes. Developments in DBS technology, such as MRI compatibility and bidirectional DBS systems capable of sensing neural activity while providing therapeutic stimulation, have enabled advances in our understanding of DBS mechanisms and its application. In this review, we summarize recent work exploring DBS modulation of target networks. We also cover current work focusing on improved programming and the development of novel stimulation paradigms that go beyond current standards of DBS, many of which are enabled by sensing-enabled DBS systems and have the potential to expand access to DBS.

Multifunctional fibers enable modulation of cortical and deep brain activity during cognitive behavior in macaques

Recording and modulating neural activity in vivo enables investigations of the neurophysiology underlying behavior and disease. However, there is a dearth of translational tools for simultaneous recording and localized receptor-specific modulation. We address this limitation by translating multifunctional fiber neurotechnology previously only available for rodent studies to enable cortical and subcortical neural recording and modulation in macaques. We record single-neuron and broader oscillatory activity during intracranial GABA infusions in the premotor cortex and putamen. By applying state-space models to characterize changes in electrophysiology, we uncover that neural activity evoked by a working memory task is reshaped by even a modest local inhibition. The recordings provide detailed insight into the electrophysiological effect of neurotransmitter receptor modulation in both cortical and subcortical structures in an awake macaque. Our results demonstrate a first-time application of multifunctional fibers for causal studies of neuronal activity in behaving nonhuman primates and pave the way for clinical translation of fiber-based neurotechnology.

Wavelet-Based Bracketing, Time-Frequency Beta Burst Detection: New Insights in Parkinson's Disease

Studies have shown that beta band activity is not tonically elevated but comprises exaggerated phasic bursts of varying durations and magnitudes, for Parkinson's disease (PD) patients. Current methods for detecting beta bursts target a single frequency peak in beta band, potentially ignoring bursts in the wider beta band. In this study, we propose a new robust framework for beta burst identification across wide frequency ranges. Chronic local field potential at-rest recordings were obtained from seven PD patients implanted with Medtronic SenSight™ deep brain stimulation (DBS) electrodes. The proposed method uses wavelet decomposition to compute the time-frequency spectrum and identifies bursts spanning multiple frequency bins by thresholding, offering an additional burst measure, ∆f, that captures the width of a burst in the frequency domain. Analysis included calculating burst duration, magnitude, and ∆f and evaluating the distribution and likelihood of bursts between the low beta (13-20 Hz) and high beta (21-35 Hz). Finally, the results of the analysis were correlated to motor impairment (MDS-UPDRS III) med off scores. We found that low beta bursts with longer durations and larger width in the frequency domain (∆f) were positively correlated, while high beta bursts with longer durations and larger ∆f were negatively correlated with motor impairment. The proposed method, finding clear differences between bursting behavior in high and low beta bands, has clearly demonstrated the importance of considering wide frequency bands for beta burst behavior with implications for closed-loop DBS paradigms.

Subthalamic Deep Brain Stimulation in Parkinson's Disease: A Boon or Bane - A Single Centre Retrospective Observational Study from India

Background  Subthalamic deep brain stimulation (STN-DBS) for refractory Parkinson's disease (PD) is more of a modality of treatment that is empirical, for which a physiological explanation is being sought. This study was done to determine the outcome and complications of patients undergoing STN-DBS for PD. Methods  This retrospective observational cohort study was conducted in an advanced neuromedicine facility in eastern India for 9 years (August 2013-August 2022), which included all patients undergoing STN-DBS. Results  A total of 53 patients were operated on during the study period. The mean age group of the study population was 60.5 (standard deviation [SD]: 8.2) years with a male (33 [62.3%]) predominance. The most common presenting complaints included rigidity and hypokinesia (27), severe dyskinesia (21), and tremors (17). During the postoperative period, rigidity and hypokinesia (21), severe dyskinesia (16), and tremors (12) improved significantly in a subset of the patients. The majority (45 [84.9%]) of these cases received bilateral monopolar simulation, whereas three patients (5.7%) had bilateral bipolar stimulation. Unilateral bipolar stimulation was used in five (9.4%) patients. In the immediate postoperative period, they were initiated on limb, speech, and swallowing therapy as indicated. Surgery-related complications were seen in five (9.4%) cases. At 6 months of follow-up, a significant improvement in the Unified PD rating scale component (mainly motor examination and complication of PD therapy) was noted in the majority (36 [67.9%]) of patients. One patient developed neuroleptic malignant syndrome and succumbed to his illness on the fourth postoperative day. Conclusion  Given these findings, STN-DBS appears to be a good, safe, and effective treatment for a subset of medically refractory PD with an overall improvement in two-thirds of the study cohort and less than 10% risk of complications.

Complications of Deep Brain Stimulation for Parkinson Disease and Relationship between Micro-electrode tracks and hemorrhage: Systematic Review and Meta-Analysis

BACKGROUND

Deep brain stimulation is a common treatment for Parkinson's disease (PD). Despite strong efficacy in well-selected patients, complications can occur. Intraoperative micro-electrode recording (MER) can enhance efficacy by improving lead accuracy. However, there is controversy as to whether MER increases risk of hemorrhage.

OBJECTIVES

To provide a comprehensive systematic review and meta-analysis reporting complication rates from deep brain stimulation in PD. We also interrogate the association between hemorrhage and MER.

METHODS

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were implemented while querying the Pubmed, Embase, and Cochrane databases. All included studies were randomized controlled trials and prospective case series with 5 or more patients. Primary outcomes included rates of overall revision, infection, lead malposition, surgical site and wound complications, hardware-related complications, and seizure. The secondary outcome was the relationship between number of MER tracks and hemorrhage rate.

RESULTS

262 articles with 21,261 patients were included in the analysis. Mean follow-up was 25.8 months (range 0-133). Complication rates were: revision 4.9%, infection 4.2%, lead malposition 3.3%, surgical site complications 2.8%, hemorrhage 2.4%, hardware-related complications 2.4%, and seizure 1.9%. While hemorrhage rate did not increase with single-track MER (odds ratio, 3.49; P = 0.29), there was a significant non-linear increase with each additional track.

CONCLUSION

Infection and lead malposition were the most common complications. Hemorrhage risk increases with more than one MER track. These results highlight the challenge of balancing surgical accuracy and perioperative risk.

Localization of deep brain stimulation trajectories via automatic mapping of microelectrode recordings to MRI

Objective. Suboptimal electrode placement during subthalamic nucleus deep brain stimulation (STN DBS) surgery may arise from several sources, including frame-based targeting errors and intraoperative brain shift. We present a computer algorithm that can accurately localize intraoperative microelectrode recording (MER) tracks on preoperative magnetic resonance imaging (MRI) in real-time, thereby predicting deviation between the surgical plan and the MER trajectories.Approach. Random forest (RF) modeling was used to derive a statistical relationship between electrophysiological features on intraoperative MER and voxel intensity on preoperative T2-weighted MR imaging. This model was integrated into a larger algorithm that can automatically localize intraoperative MER recording tracks on preoperative MRI in real-time. To verify accuracy, targeting error of both the planned intraoperative trajectory ('planned') and the algorithm-derived trajectory ('calculated') was estimated by measuring deviation from the final DBS lead location on postoperative high-resolution computed tomography ('actual').Main results. MR imaging and MERs were obtained from 24 STN DBS implant trajectories. The cross-validated RF model could accurately distinguish between gray and white matter regions along MER trajectories (AUC 0.84). When applying this model within the localization algorithm, thecalculatedMER trajectory estimate was found to be significantly closer to theactualDBS lead when compared to theplannedtrajectory recorded during surgery (1.04 mm vs 1.52 mm deviation,p< 0.002), with improvement shown in 19/24 cases (79%). When applying the algorithm to simulated DBS trajectory plans with randomized targeting error, up to 4 mm of error could be resolved to <2 mm on average (p< 0.0001).Significance. This work presents an automated system for intraoperative localization of electrodes during STN DBS surgery. This neuroengineering solution may enhance the accuracy of electrode position estimation, particularly in cases where high-resolution intraoperative imaging is not available.

Automatic extraction of upper-limb kinematic activity using deep learning-based markerless tracking during deep brain stimulation implantation for Parkinson's disease: A proof of concept study

Optimal placement of deep brain stimulation (DBS) therapy for treating movement disorders routinely relies on intraoperative motor testing for target determination. However, in current practice, motor testing relies on subjective interpretation and correlation of motor and neural information. Recent advances in computer vision could improve assessment accuracy. We describe our application of deep learning-based computer vision to conduct markerless tracking for measuring motor behaviors of patients undergoing DBS surgery for the treatment of Parkinson's disease. Video recordings were acquired during intraoperative kinematic testing (N = 5 patients), as part of standard of care for accurate implantation of the DBS electrode. Kinematic data were extracted from videos post-hoc using the Python-based computer vision suite DeepLabCut. Both manual and automated (80.00% accuracy) approaches were used to extract kinematic episodes from threshold derived kinematic fluctuations. Active motor epochs were compressed by modeling upper limb deflections with a parabolic fit. A semi-supervised classification model, support vector machine (SVM), trained on the parameters defined by the parabolic fit reliably predicted movement type. Across all cases, tracking was well calibrated (i.e., reprojection pixel errors 0.016-0.041; accuracies >95%). SVM predicted classification demonstrated high accuracy (85.70%) including for two common upper limb movements, arm chain pulls (92.30%) and hand clenches (76.20%), with accuracy validated using a leave-one-out process for each patient. These results demonstrate successful capture and categorization of motor behaviors critical for assessing the optimal brain target for DBS surgery. Conventional motor testing procedures have proven informative and contributory to targeting but have largely remained subjective and inaccessible to non-Western and rural DBS centers with limited resources. This approach could automate the process and improve accuracy for neuro-motor mapping, to improve surgical targeting, optimize DBS therapy, provide accessible avenues for neuro-motor mapping and DBS implantation, and advance our understanding of the function of different brain areas.

Radiomic features of amygdala nuclei and hippocampus subfields help to predict subthalamic deep brain stimulation motor outcomes for Parkinson‘s disease patients

Background and purpose

The aim of the study is to predict the subthalamic nucleus (STN) deep brain stimulation (DBS) outcomes for Parkinson’s disease (PD) patients using the radiomic features extracted from pre-operative magnetic resonance images (MRI).

Methods

The study included 34 PD patients who underwent DBS implantation in the STN. Five patients (15%) showed poor DBS motor outcome. All together 9 amygdalar nuclei and 12 hippocampus subfields were segmented using Freesurfer 7.0 pipeline from pre-operative MRI images. Furthermore, PyRadiomics platform was used to extract 120 radiomic features for each nuclei and subfield resulting in 5,040 features. Minimum Redundancy Maximum Relevance (mRMR) feature selection method was employed to reduce the number of features to 20, and 8 machine learning methods (regularized binary logistic regression (LR), decision tree classifier (DT), linear discriminant analysis (LDA), naive Bayes classifier (NB), kernel support vector machine (SVM), deep feed-forward neural network (DNN), one-class support vector machine (OC-SVM), feed-forward neural network-based autoencoder for anomaly detection (DNN-A)) were applied to build the models for poor vs. good and very good STN-DBS motor outcome prediction.

Results

The highest mean prediction accuracy was obtained using regularized LR (96.65 ± 7.24%, AUC 0.98 ± 0.06) and DNN (87.25 ± 14.80%, AUC 0.87 ± 0.18).

Conclusion

The results show the potential power of the radiomic features extracted from hippocampus and amygdala MRI in the prediction of STN-DBS motor outcomes for PD patients.

Interleukin-4 receptor signaling modulates neuronal network activity

Evidence is emerging that immune responses not only play a part in the central nervous system (CNS) in diseases but may also be relevant for healthy conditions. We discovered a major role for the interleukin-4 (IL-4)/IL-4 receptor alpha (IL-4Rα) signaling pathway in synaptic processes, as indicated by transcriptome analysis in IL-4Rα–deficient mice and human neurons with/without IL-4 treatment. Moreover, IL-4Rα is expressed presynaptically, and locally available IL-4 regulates synaptic transmission. We found reduced synaptic vesicle pools, altered postsynaptic currents, and a higher excitatory drive in cortical networks of IL-4Rα–deficient neurons. Acute effects of IL-4 treatment on postsynaptic currents in wild-type neurons were mediated via PKCγ signaling release and led to increased inhibitory activity supporting the findings in IL-4Rα–deficient neurons. In fact, the deficiency of IL-4Rα resulted in increased network activity in vivo, accompanied by altered exploration and anxiety-related learning behavior; general learning and memory was unchanged. In conclusion, neuronal IL-4Rα and its presynaptic prevalence appear relevant for maintaining homeostasis of CNS synaptic function.

Lead-OR: A multimodal platform for deep brain stimulation surgery

Background

Deep brain stimulation (DBS) electrode implant trajectories are stereotactically defined using preoperative neuroimaging. To validate the correct trajectory, microelectrode recordings (MERs) or local field potential recordings can be used to extend neuroanatomical information (defined by MRI) with neurophysiological activity patterns recorded from micro- and macroelectrodes probing the surgical target site. Currently, these two sources of information (imaging vs. electrophysiology) are analyzed separately, while means to fuse both data streams have not been introduced.

Methods

Here, we present a tool that integrates resources from stereotactic planning, neuroimaging, MER, and high-resolution atlas data to create a real-time visualization of the implant trajectory. We validate the tool based on a retrospective cohort of DBS patients (N = 52) offline and present single-use cases of the real-time platform.

Results

We establish an open-source software tool for multimodal data visualization and analysis during DBS surgery. We show a general correspondence between features derived from neuroimaging and electrophysiological recordings and present examples that demonstrate the functionality of the tool.

Conclusions

This novel software platform for multimodal data visualization and analysis bears translational potential to improve accuracy of DBS surgery. The toolbox is made openly available and is extendable to integrate with additional software packages.

Funding

Deutsche Forschungsgesellschaft (410169619, 424778381), Deutsches Zentrum für Luft- und Raumfahrt (DynaSti), National Institutes of Health (2R01 MH113929), and Foundation for OCD Research (FFOR).

Inhibition of the enzyme autotaxin reduces cortical excitability and ameliorates the outcome in stroke

Stroke penumbra injury caused by excess glutamate is an important factor in determining stroke outcome; however, several therapeutic approaches aiming to rescue the penumbra have failed, likely due to unspecific targeting and persistent excitotoxicity, which continued far beyond the primary stroke event. Synaptic lipid signaling can modulate glutamatergic transmission via presynaptic lysophosphatidic acid (LPA) 2 receptors modulated by the LPA-synthesizing molecule autotaxin (ATX) present in astrocytic perisynaptic processes. Here, we detected long-lasting increases in brain ATX concentrations after experimental stroke. In humans, cerebrospinal fluid ATX concentration was increased up to 14 days after stroke. Using astrocyte-specific deletion and pharmacological inhibition of ATX at different time points after experimental stroke, we showed that inhibition of LPA-related cortical excitability improved stroke outcome. In transgenic mice and in individuals expressing a single-nucleotide polymorphism that increased LPA-related glutamatergic transmission, we found dysregulated synaptic LPA signaling and subsequent negative stroke outcome. Moreover, ATX inhibition in the animal model ameliorated stroke outcome, suggesting that this approach might have translational potential for improving the outcome after stroke.

The Accuracy of Imaging Guided Targeting with Microelectrode Recoding in Subthalamic Nucleus for Parkinson's Disease: A Single-Center Experience

Background:

Accurate electrode targeting was essential for the efficacy of deep brain stimulation (DBS). There is ongoing debate about the necessary of microelectrode recording (MER) in subthalamic nucleus (STN)-DBS surgery for accurate targeting.

Objective:

This study aimed to analyze the accuracy of imaging-guided awake DBS with MER in STN for Parkinson’s disease in a single center.

Methods:

The authors performed a retrospective analysis of 161 Parkinson’s disease patients undergoing STN-DBS at our center from March 2013 to June 2021. The implantation was performed by preoperative magnetic resonance imaging (MRI)-based direct targeting with intraoperative MER and macrostimulation testing. 285 electrode tracks with preoperative and postoperative coordinates were included to calculate the placement error in STN targeting.

Results:

85.9% of electrodes guided by preoperative MRI were implanted without intraoperative adjustment. 31 (10.2%) and 12 (3.9%) electrodes underwent intraoperative adjustment due to MER and intraoperative testing, respectively. We found 86.2% (245/285) of electrodes with trajectory error ≤2 mm. The MER physiological signals length < 4 mm and ≥4 mm group showed trajectory error > 2 mm in 38.0% and 8.8% of electrodes, respectively. Compared to non-adjustment electrodes, the final positioning of MER-adjusted electrodes deviated from the center of STN.

Conclusion:

The preoperative MRI guided STN targeting results in approximately 14% cases that require electrode repositioning. MER physiological signals length < 4 mm at first penetration implied deviation off planned target. MER combined with intraoperative awake testing served to rescue such deviation based on MRI alone.

Clinically-derived oscillatory biomarker predicts optimal subthalamic stimulation for Parkinson's disease

OBJECTIVE

Choosing the optimal electrode trajectory, stimulation location, and stimulation amplitude in subthalamic nucleus deep brain stimulation (STN DBS) for Parkinson's disease (PD) remains a time-consuming empirical effort. In this retrospective study, we derive a data-driven electrophysiological biomarker that predicts clinical DBS location and parameters, and we consolidate this information into a quantitative score that may facilitate an objective approach to STN DBS surgery and programming.

APPROACH

Random-forest feature selection was applied to a dataset of 1046 microelectrode recordings sites across 20 DBS implant trajectories to identify features of oscillatory activity that predict clinically programmed volumes of tissue activation (VTA). A cross-validated classifier was used to retrospectively predict VTA regions from these features. Spatial convolution of probabilistic classifier outputs along MER trajectories produced a biomarker score that reflects the probability of localization within a clinically optimized VTA.

MAIN RESULTS

Biomarker scores peaked within the VTA region and were significantly correlated with percent improvement in postoperative motor symptoms (MDS-UPRDS Part III, R = 0.61, p = 0.004). Notably, the length of STN, a common criterion for trajectory selection, did not show similar correlation (R = -0.31, p = 0.18). These findings suggest that biomarker-based trajectory selection and programming may improve motor outcomes by 9 ± 3 percentage points (p = 0.047) in this dataset.

SIGNIFICANCE

A clinically defined electrophysiological biomarker not only predicts VTA size and location but also correlates well with motor outcomes. Use of this biomarker for trajectory selection and initial stimulation may potentially simplify STN DBS surgery and programming.

Past, Present, and Future of Deep Brain Stimulation: Hardware, Software, Imaging, Physiology and Novel Approaches

Deep brain stimulation (DBS) has advanced treatment options for a variety of neurologic and neuropsychiatric conditions. As the technology for DBS continues to progress, treatment efficacy will continue to improve and disease indications will expand. Hardware advances such as longer-lasting batteries will reduce the frequency of battery replacement and segmented leads will facilitate improvements in the effectiveness of stimulation and have the potential to minimize stimulation side effects. Targeting advances such as specialized imaging sequences and "connectomics" will facilitate improved accuracy for lead positioning and trajectory planning. Software advances such as closed-loop stimulation and remote programming will enable DBS to be a more personalized and accessible technology. The future of DBS continues to be promising and holds the potential to further improve quality of life. In this review we will address the past, present and future of DBS.

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.