Surgical and Hardware-Related Adverse Events of Deep Brain Stimulation: A Ten-Year Single-Center Experience.

INTRODUCTION: Although deep brain stimulation (DBS) is effective for treating a number of neurological and psychiatric indications, surgical and hardware-related adverse events (AEs) can occur that affect quality of life. This study aimed to give an overview of the nature and frequency of those AEs in our center and to describe the way they were managed. Furthermore, an attempt was made at identifying possible risk factors for AEs to inform possible future preventive measures. MATERIALS AND METHODS: Patients undergoing DBS-related procedures between January 2011 and July 2020 were retrospectively analyzed to inventory AEs. The mean follow-up time was 43 ± 31 months. Univariate logistic regression analysis was used to assess the predictive value of selected demographic and clinical variables. RESULTS: From January 2011 to July 2020, 508 DBS-related procedures were performed including 201 implantations of brain electrodes in 200 patients and 307 implantable pulse generator (IPG) replacements in 142 patients. Surgical or hardware-related AEs following initial implantation affected 40 of 200 patients (20%) and resolved without permanent sequelae in all instances. The most frequent AEs were surgical site infections (SSIs) (9.95%, 20/201) and wire tethering (2.49%, 5/201), followed by hardware failure (1.99%, 4/201), skin erosion (1.0%, 2/201), pain (0.5%, 1/201), lead migration (0.52%, 2/386 electrode sites), and hematoma (0.52%, 2/386 electrode sites). The overall rate of AEs for IPG replacement was 5.6% (17/305). No surgical, ie, staged or nonstaged, electrode fixation, or patient-related risk factors were identified for SSI or wire tethering. CONCLUSIONS: Major AEs including intracranial surgery-related AEs or AEs requiring surgical removal or revision of hardware are rare. In particular, aggressive treatment is required in SSIs involving multiple sites or when Staphylococcus aureus is identified. For future benchmarking, the development of a uniform reporting system for surgical and hardware-related AEs in DBS surgery would be useful.

Tremor assessment using smartphone sensor data and fuzzy reasoning.

BACKGROUND: Tremor severity assessment is an important step for the diagnosis and treatment decision-making of essential tremor (ET) patients. Traditionally, tremor severity is assessed by using questionnaires (e.g., ETRS and QUEST surveys). In this work we assume the possibility of assessing tremor severity using sensor data and computerized analyses. The goal of this work is to assess severity of tremor objectively, to be better able to asses improvement in ET patients due to deep brain stimulation or other treatments. METHODS: We collect tremor data by strapping smartphones to the wrists of ET patients. The resulting raw sensor data is then pre-processed to remove any artifact due to patient's intentional movement. Finally, this data is exploited to automatically build a transparent, interpretable, and succinct fuzzy model for the severity assessment of ET. For this purpose, we exploit pyFUME, a tool for the data-driven estimation of fuzzy models. It leverages the FST-PSO swarm intelligence meta-heuristic to identify optimal clusters in data, reducing the possibility of a premature convergence in local minima which would result in a sub-optimal model. pyFUME was also combined with GRABS, a novel methodology for the automatic simplification of fuzzy rules. RESULTS: Our model is able to assess tremor severity of patients suffering from Essential Tremor, notably without the need for subjective questionnaires nor interviews. The fuzzy model improves the mean absolute error (MAE) metric by 78-81% compared to linear models and by 71-74% compared to a model based on decision trees. CONCLUSION: This study confirms that tremor data gathered using the smartphones is useful for the constructing of machine learning models that can be used to support the diagnosis and monitoring of patients who suffer from Essential Tremor. The model produced by our methodology is easy to inspect and, notably, characterized by a lower error with respect to approaches based on linear models or decision trees.

Rapid Dynamic Naturalistic Monitoring of Bradykinesia in Parkinson's Disease Using a Wrist-Worn Accelerometer.

Motor fluctuations in Parkinson's disease are characterized by unpredictability in the timing and duration of dopaminergic therapeutic benefits on symptoms, including bradykinesia and rigidity. These fluctuations significantly impair the quality of life of many Parkinson's patients. However, current clinical evaluation tools are not designed for the continuous, naturalistic (real-world) symptom monitoring needed to optimize clinical therapy to treat fluctuations. Although commercially available wearable motor monitoring, used over multiple days, can augment neurological decision making, the feasibility of rapid and dynamic detection of motor fluctuations is unclear. So far, applied wearable monitoring algorithms are trained on group data. In this study, we investigated the influence of individual model training on short timescale classification of naturalistic bradykinesia fluctuations in Parkinson's patients using a single-wrist accelerometer. As part of the Parkinson@Home study protocol, 20 Parkinson patients were recorded with bilateral wrist accelerometers for a one hour OFF medication session and a one hour ON medication session during unconstrained activities in their own homes. Kinematic metrics were extracted from the accelerometer data from the bodyside with the largest unilateral bradykinesia fluctuations across medication states. The kinematic accelerometer features were compared over the 1 h duration of recording, and medication-state classification analyses were performed on 1 min segments of data. Then, we analyzed the influence of individual versus group model training, data window length, and total number of training patients included in group model training, on classification. Statistically significant areas under the curves (AUCs) for medication induced bradykinesia fluctuation classification were seen in 85% of the Parkinson patients at the single minute timescale using the group models. Individually trained models performed at the same level as the group trained models (mean AUC both 0.70, standard deviation respectively 0.18 and 0.10) despite the small individual training dataset. AUCs of the group models improved as the length of the feature windows was increased to 300 s, and with additional training patient datasets. We were able to show that medication-induced fluctuations in bradykinesia can be classified using wrist-worn accelerometry at the time scale of a single minute. Rapid, naturalistic Parkinson motor monitoring has the clinical potential to evaluate dynamic symptomatic and therapeutic fluctuations and help tailor treatments on a fast timescale.

Deep brain stimulation of the anterior nucleus of the thalamus for drug-resistant epilepsy.

Despite the use of first-choice anti-epileptic drugs and satisfactory seizure outcome rates after resective epilepsy surgery, a considerable percentage of patients do not become seizure free. ANT-DBS may provide for an alternative treatment option in these patients. This literature review discusses the rationale, mechanism of action, clinical efficacy, safety, and tolerability of ANT-DBS in drug-resistant epilepsy patients. A review using systematic methods of the available literature was performed using relevant databases including Medline, Embase, and the Cochrane Library pertaining to the different aspects ANT-DBS. ANT-DBS for drug-resistant epilepsy is a safe, effective and well-tolerated therapy, where a special emphasis must be given to monitoring and neuropsychological assessment of both depression and memory function. Three patterns of seizure control by ANT-DBS are recognized, of which a delayed stimulation effect may account for an improved long-term response rate. ANT-DBS remotely modulates neuronal network excitability through overriding pathological electrical activity, decrease neuronal cell loss, through immune response inhibition or modulation of neuronal energy metabolism. ANT-DBS is an efficacious treatment modality, even when curative procedures or lesser invasive neuromodulative techniques failed. When compared to VNS, ANT-DBS shows slightly superior treatment response, which urges for direct comparative trials. Based on the available evidence ANT-DBS and VNS therapies are currently both superior compared to non-invasive neuromodulation techniques such as t-VNS and rTMS. Additional in-vivo research is necessary in order to gain more insight into the mechanism of action of ANT-DBS in localization-related epilepsy which will allow for treatment optimization. Randomized clinical studies in search of the optimal target in well-defined epilepsy patient populations, will ultimately allow for optimal patient stratification when applying DBS for drug-resistant patients with epilepsy.

An update on adaptive deep brain stimulation in Parkinson's disease.

Advancing conventional open-loop DBS as a therapy for PD is crucial for overcoming important issues such as the delicate balance between beneficial and adverse effects and limited battery longevity that are currently associated with treatment. Closed-loop or adaptive DBS aims to overcome these limitations by real-time adjustment of stimulation parameters based on continuous feedback input signals that are representative of the patient's clinical state. The focus of this update is to discuss the most recent developments regarding potential input signals and possible stimulation parameter modulation for adaptive DBS in PD. Potential input signals for adaptive DBS include basal ganglia local field potentials, cortical recordings (electrocorticography), wearable sensors, and eHealth and mHealth devices. Furthermore, adaptive DBS can be applied with different approaches of stimulation parameter modulation, the feasibility of which can be adapted depending on specific PD phenotypes. Implementation of technological developments like machine learning show potential in the design of such approaches; however, energy consumption deserves further attention. Furthermore, we discuss future considerations regarding the clinical implementation of adaptive DBS in PD. © 2018 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Methodology, outcome, safety and in vivo accuracy in traditional frame-based stereoelectroencephalography.

BACKGROUND: Stereoelectroencephalography (SEEG) is an established diagnostic technique for the localization of the epileptogenic zone in drug-resistant epilepsy. In vivo accuracy of SEEG electrode positioning is of paramount importance since higher accuracy may lead to more precise resective surgery, better seizure outcome and reduction of complications. OBJECTIVE: To describe experiences with the SEEG technique in our comprehensive epilepsy center, to illustrate surgical methodology, to evaluate in vivo application accuracy and to consider the diagnostic yield of SEEG implantations. METHODS: All patients who underwent SEEG implantations between September 2008 and April 2016 were analyzed. Planned electrode trajectories were compared with post-implantation trajectories after fusion of pre- and postoperative imaging. Quantitative analysis of deviation using Euclidean distance and directional errors was performed. Explanatory variables for electrode accuracy were analyzed using linear regression modeling. The surgical methodology, procedure-related complications and diagnostic yield were reported. RESULTS: Seventy-six implantations were performed in 71 patients, and a total of 902 electrodes were implanted. Median entry and target point deviations were 1.54 mm and 2.93 mm. Several factors that predicted entry and target point accuracy were identified. The rate of major complications was 2.6%. SEEG led to surgical therapy of various modalities in 53 patients (69.7%). CONCLUSIONS: This study demonstrated that entry and target point localization errors can be predicted by linear regression models, which can aid in identification of high-risk electrode trajectories and further enhancement of accuracy. SEEG is a reliable technique, as demonstrated by the high accuracy of conventional frame-based implantation methodology and the good diagnostic yield.

Technical feasibility of integrating 7 T anatomical MRI in image-guided radiotherapy of glioblastoma: a preparatory study.

OBJECTIVES: The use of 7 Tesla (T) magnetic resonance imaging (MRI) has recently shown great potential for high-resolution soft-tissue neuroimaging and visualization of microvascularization in glioblastoma (GBM). We have designed a clinical trial to explore the value of 7 T MRI in radiation treatment of GBM. For this aim we performed a preparatory study to investigate the technical feasibility of incorporating 7 T MR images into the neurosurgical navigation and radiotherapy treatment planning (RTP) systems via qualitative and quantitative assessment of the image quality. MATERIALS AND METHODS: The MR images were acquired with a Siemens Magnetom 7 T whole-body scanner and a Nova Medical 32-channel head coil. The 7 T MRI pulse sequences included magnetization-prepared two rapid acquisition gradient echoes (MP2RAGE), T2-SPACE, SPACE-FLAIR and gradient echo sequences (GRE). A pilot study with three healthy volunteers and an anthropomorphic 3D phantom was used to assess image quality and geometrical image accuracy. RESULTS: The MRI scans were well tolerated by the volunteers. Susceptibility artefacts were observed in both the cortex and subcortical white matter at close proximity to air-tissue interfaces. Regional loss of signal and contrast could be minimized by the use of dielectric pads. Image transfer and processing did not degrade image quality. The system-related spatial uncertainty of geometrical distortion-corrected MP2RAGE pulse sequences was ≤2 mm. CONCLUSION: Integration of high-quality and geometrically-reliable 7 T MR images into neurosurgical navigation and RTP software is technically feasible and safe.

TREMOR12: An Open-Source Mobile App for Tremor Quantification.

BACKGROUND: Evaluating the effect of treatment of tremor is mostly performed with clinical rating scales. Mobile applications facilitate a more rapid, objective, and quantitative evaluation of treatment effect. Existing mobile apps do not offer raw data access, which limits algorithm development. OBJECTIVE: To develop a novel open-source mobile app for tremor quantification. METHODS: TREMOR12 is an open-source mobile app that samples acceleration, rotation, rotation speed, and gravity, each in 3 axes and time-stamped in a frequency up to 100 Hz. The raw measurement data can be exported as a comma-separated value file for further analysis in the TREMOR12P data processing module. The app was evaluated with 3 patients suffering from essential tremor, who were between 55 and 71 years of age. RESULTS: This proof-of-concept study shows that the TREMOR12 app is able to detect and register tremor characteristics such as acceleration, rotation, rotation speed, and gravity in a simple and nonburdensome way. The app is compatible with current regulatory oversight by the European Union (MEDDEV regulations) and the Food and Drug Administration (FDA) guidance on mobile medical applications. CONCLUSION: TREMOR12 offers low-cost tremor quantification for research purposes and algorithm development, and may help to improve treatment evaluation.

The start and development of epilepsy surgery in Europe: a historical review.

Epilepsy has not always been considered a brain disease, but was believed to be a demonic possession in the past. Therefore, trepanation was done not only for medical but also for religious or spiritual reasons, originating in the Neolithic period (3000 BC). The earliest documentation of trepanation for epilepsy is found in the writings of the Hippocratic Corpus and consisted mainly of just skull surgery. The transition from skull surgery to brain surgery took place in the middle of the nineteenth century when the insight of epilepsy as a cortical disorder of the brain emerged. This led to the start of modern epilepsy surgery. The pioneer countries in which epilepsy surgery was performed in Europe were the UK, Germany, and The Netherlands. Neurosurgical forerunners like Sir Victor Horsley, William Macewen, Fedor Krause, and Otfrid Foerster started with "modern" epilepsy surgery. Initially, epilepsy surgery was mainly done with the purpose to resect traumatic lesions or large surface tumours. In the course of the twentieth century, this changed to highly specialized microscopic navigation-guided surgery to resect lesional and non-lesional epileptogenic cortex. The development of epilepsy surgery in Southern Europe, which has not been described until now, will be elaborated in this manuscript. To summarize, in this paper, we provide (1) a detailed description of the evolution of European epilepsy surgery with special emphasis on the pioneer countries; (2) novel, never published information about the development of epilepsy surgery in Southern Europe; and (3) we review the historical dichotomy of invasive electrode implantation strategy (Anglo-Saxon surface electrodes versus French-Italian stereoencephalography (SEEG) model).

Brain vascularization in deep brain stimulation surgeries: epilepsy, Parkinson's disease, and obsessive-compulsive disorder.

BACKGROUND: In our experience, we encountered more blood vessels during deep brain stimulation (DBS) surgeries in epilepsy. In this study, we have quantified and compared the cerebral vascularization in epilepsy, Parkinson's disease (PD) and obsessive-compulsive disorder (OCD). METHODS: A retrospective observational study in 15 epilepsy and 15 PD patients was performed. The amount, location, and size of blood vessels within 5 millimeters (mm) of all DBS electrode trajectories (N.=120) for both targets (anterior nucleus of the thalamus: ANT and subthalamic nucleus: STN) in both patient groups were quantified and compared on a Medtronic workstation (Dublin, Ireland). Additionally, blood vessels in the trajectories (N.=120) of another group of 15 PD (STN) and 15 OCD (ventral capsule-ventral striatum [VC-VS]) patients were quantified and compared (trajectories N.=120), also to the first group. Statistical analyses were performed with SPSS version 27.0 (descriptive statistics, independent samples t-tests, Mann Whitney U Test, ANOVA Test and post-hoc Tukey Test). A P value <0.05 was considered statistically significant. RESULTS: Our results showed a significant greater amount of cerebral blood vessels in epilepsy patients (10 SD±4) compared to PD (PD1 6 SD±1 and PD2 5 SD±3) and OCD (5 SD±1) with P<0.0001. Also, all other subanalyses showed more vascularization in the epilepsy group. CONCLUSIONS: Our results show that the brain of epilepsy patients seems to be more vascularized compared to PD and OCD patients. This can make the surgical planning for DBS more challenging, and the use of multiple trajectories limited.

Towards hippocampal navigation for brain-computer interfaces.

Automatic wheelchairs directly controlled by brain activity could provide autonomy to severely paralyzed individuals. Current approaches mostly rely on non-invasive measures of brain activity and translate individual commands into wheelchair movements. For example, an imagined movement of the right hand would steer the wheelchair to the right. No research has investigated decoding higher-order cognitive processes to accomplish wheelchair control. We envision an invasive neural prosthetic that could provide input for wheelchair control by decoding navigational intent from hippocampal signals. Navigation has been extensively investigated in hippocampal recordings, but not for the development of neural prostheses. Here we show that it is possible to train a decoder to classify virtual-movement speeds from hippocampal signals recorded during a virtual-navigation task. These results represent the first step toward exploring the feasibility of an invasive hippocampal BCI for wheelchair control.

Decoding executed and imagined grasping movements from distributed non-motor brain areas using a Riemannian decoder.

Using brain activity directly as input for assistive tool control can circumventmuscular dysfunction and increase functional independence for physically impaired people. The motor cortex is commonly targeted for recordings, while growing evidence shows that there exists decodable movement-related neural activity outside of the motor cortex. Several decoding studies demonstrated significant decoding from distributed areas separately. Here, we combine information from all recorded non-motor brain areas and decode executed and imagined movements using a Riemannian decoder. We recorded neural activity from 8 epilepsy patients implanted with stereotactic-electroencephalographic electrodes (sEEG), while they performed an executed and imagined grasping tasks. Before decoding, we excluded all contacts in or adjacent to the central sulcus. The decoder extracts a low-dimensional representation of varying number of components, and classified move/no-move using a minimum-distance-to-geometric-mean Riemannian classifier. We show that executed and imagined movements can be decoded from distributed non-motor brain areas using a Riemannian decoder, reaching an area under the receiver operator characteristic of 0.83 ± 0.11. Furthermore, we highlight the distributedness of the movement-related neural activity, as no single brain area is the main driver of performance. Our decoding results demonstrate a first application of a Riemannian decoder on sEEG data and show that it is able to decode from distributed brain-wide recordings outside of the motor cortex. This brief report highlights the perspective to explore motor-related neural activity beyond the motor cortex, as many areas contain decodable information.

Intraoperative MRI-guided resection of glioblastoma multiforme: a systematic review.

We did a systematic review to address the added value of intraoperative MRI (iMRI)-guided resection of glioblastoma multiforme compared with conventional neuronavigation-guided resection, with respect to extent of tumour resection (EOTR), quality of life, and survival. 12 non-randomised cohort studies matched all selection criteria and were used for qualitative synthesis. Most of the studies included descriptive statistics of patient populations of mixed pathology, and iMRI systems of varying field strengths between 0·15 and 1·5 Tesla. Most studies provided information on EOTR, but did not always mention how iMRI affected the surgical strategy. Only a few studies included information on quality of life or survival for subpopulations with glioblastoma multiforme or high-grade glioma. Several limitations and sources of bias were apparent, which affected the conclusions drawn and might have led to overestimation of the added value of iMRI-guided surgery for resection of glioblastoma multiforme. Based on the available literature, there is, at best, level 2 evidence that iMRI-guided surgery is more effective than conventional neuronavigation-guided surgery in increasing EOTR, enhancing quality of life, or prolonging survival after resection of glioblastoma multiforme.

Dataset of Speech Production in intracranial.Electroencephalography.

Speech production is an intricate process involving a large number of muscles and cognitive processes. The neural processes underlying speech production are not completely understood. As speech is a uniquely human ability, it can not be investigated in animal models. High-fidelity human data can only be obtained in clinical settings and is therefore not easily available to all researchers. Here, we provide a dataset of 10 participants reading out individual words while we measured intracranial EEG from a total of 1103 electrodes. The data, with its high temporal resolution and coverage of a large variety of cortical and sub-cortical brain regions, can help in understanding the speech production process better. Simultaneously, the data can be used to test speech decoding and synthesis approaches from neural data to develop speech Brain-Computer Interfaces and speech neuroprostheses.

The Role of Microelectrode Recording and Stereotactic Computed Tomography in Verifying Lead Placement During Awake MRI-Guided Subthalamic Nucleus Deep Brain Stimulation for Parkinson's Disease.

BACKGROUND: Bilateral deep brain stimulation of the subthalamic nucleus (STN-DBS) has become a cornerstone in the advanced treatment of Parkinson's disease (PD). Despite its well-established clinical benefit, there is a significant variation in the way surgery is performed. Most centers operate with the patient awake to allow for microelectrode recording (MER) and intraoperative clinical testing. However, technical advances in MR imaging and MRI-guided surgery raise the question whether MER and intraoperative clinical testing still have added value in DBS-surgery. OBJECTIVE: To evaluate the added value of MER and intraoperative clinical testing to determine final lead position in awake MRI-guided and stereotactic CT-verified STN-DBS surgery for PD. METHODS: 29 consecutive patients were analyzed retrospectively. Patients underwent awake bilateral STN-DBS with MER and intraoperative clinical testing. The role of MER and clinical testing in determining final lead position was evaluated. Furthermore, interobserver variability in determining the MRI-defined STN along the planned trajectory was investigated. Clinical improvement was evaluated at 12 months follow-up and adverse events were recorded. RESULTS: 98% of final leads were placed in the central MER-track with an accuracy of 0.88±0.45 mm. Interobserver variability of the MRI-defined STN was 0.84±0.09. Compared to baseline, mean improvement in MDS-UPDRS-III, PDQ-39 and LEDD were 26.7±16.0 points (54%) (p < 0.001), 9.0±20.0 points (19%) (p = 0.025), and 794±434 mg/day (59%) (p < 0.001) respectively. There were 19 adverse events in 11 patients, one of which (lead malposition requiring immediate postoperative revision) was a serious adverse event. CONCLUSION: MER and intraoperative clinical testing had no additional value in determining final lead position. These results changed our daily clinical practice to an asleep MRI-guided and stereotactic CT-verified approach.

Continuously Decoding Grasping Movements using Stereotactic Depth Electrodes.

Brain-Computer Interfaces (BCIs) that decode a patient's movement intention to control a prosthetic device could restore some independence to paralyzed patients. An important step on the road towards naturalistic prosthetic control is to decode movement continuously with low-latency. BCIs based on intracortical micro-arrays provide continuous control of robotic arms, but require a minor craniotomy. Surface recordings of neural activity using EEG have made great advances over the last years, but suffer from high noise levels and large intra-session variance. Here, we investigate the use of minimally invasive recordings using stereotactically implanted EEG (sEEG). These electrodes provide a sparse sampling across many brain regions. So far, promising decoding results have been presented using data measured from the subthalamic nucleus or trial-to-trial based methods using depth electrodes. In this work, we demonstrate that grasping movements can continuously be decoded using sEEG electrodes, as well. Beta and high-gamma activity was extracted from eight participants performing a grasping task. We demonstrate above chance level decoding of movement vs rest and left vs right, from both frequency bands with accuracies up to 0.94 AUC. The vastly different electrode locations between participants lead to large variability. In the future, we hope that sEEG recordings will provide additional information for the decoding process in neuroprostheses.

Speech Synthesis from Stereotactic EEG using an Electrode Shaft Dependent Multi-Input Convolutional Neural Network Approach.

Neurological disorders can lead to significant impairments in speech communication and, in severe cases, cause the complete loss of the ability to speak. Brain-Computer Interfaces have shown promise as an alternative communication modality by directly transforming neural activity of speech processes into a textual or audible representations. Previous studies investigating such speech neuroprostheses relied on electrocorticography (ECoG) or microelectrode arrays that acquire neural signals from superficial areas on the cortex. While both measurement methods have demonstrated successful speech decoding, they do not capture activity from deeper brain structures and this activity has therefore not been harnessed for speech-related BCIs. In this study, we bridge this gap by adapting a previously presented decoding pipeline for speech synthesis based on ECoG signals to implanted depth electrodes (sEEG). For this purpose, we propose a multi-input convolutional neural network that extracts speech-related activity separately for each electrode shaft and estimates spectral coefficients to reconstruct an audible waveform. We evaluate our approach on open-loop data from 5 patients who conducted a recitation task of Dutch utterances. We achieve correlations of up to 0.80 between original and reconstructed speech spectrograms, which are significantly above chance level for all patients (p < 0.001). Our results indicate that sEEG can yield similar speech decoding performance to prior ECoG studies and is a promising modality for speech BCIs.

Variability in subthalamic nucleus targeting for deep brain stimulation with 3 and 7 Tesla magnetic resonance imaging.

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective surgical treatment for Parkinson's disease (PD). Side-effects may, however, be induced when the DBS lead is placed suboptimally. Currently, lower field magnetic resonance imaging (MRI) at 1.5 or 3 Tesla (T) is used for targeting. Ultra-high-field MRI (7 T and above) can obtain superior anatomical information and might therefore be better suited for targeting. This study aims to test whether optimized 7 T imaging protocols result in less variable targeting of the STN for DBS compared to clinically utilized 3 T images. Three DBS-experienced neurosurgeons determined the optimal STN DBS target site on three repetitions of 3 T-T2, 7 T-T2*, 7 T-R2* and 7 T-QSM images for five PD patients. The distance in millimetres between the three repetitive coordinates was used as an index of targeting variability and was compared between field strength, MRI contrast and repetition with a Bayesian ANOVA. Further, the target coordinates were registered to MNI space, and anatomical coordinates were compared between field strength, MRI contrast and repetition using a Bayesian ANOVA. The results indicate that the neurosurgeons are stable in selecting the DBS target site across MRI field strength, MRI contrast and repetitions. The analysis of the coordinates in MNI space however revealed that the actual selected location of the electrode is seemingly more ventral when using the 3 T scan compared to the 7 T scans.

Digital Health Integration With Neuromodulation Therapies: The Future of Patient-Centric Innovation in Neuromodulation.

Digital health can drive patient-centric innovation in neuromodulation by leveraging current tools to identify response predictors and digital biomarkers. Iterative technological evolution has led us to an ideal point to integrate digital health with neuromodulation. Here, we provide an overview of the digital health building-blocks, the status of advanced neuromodulation technologies, and future applications for neuromodulation with digital health integration.

Real-time synthesis of imagined speech processes from minimally invasive recordings of neural activity.

Speech neuroprosthetics aim to provide a natural communication channel to individuals who are unable to speak due to physical or neurological impairments. Real-time synthesis of acoustic speech directly from measured neural activity could enable natural conversations and notably improve quality of life, particularly for individuals who have severely limited means of communication. Recent advances in decoding approaches have led to high quality reconstructions of acoustic speech from invasively measured neural activity. However, most prior research utilizes data collected during open-loop experiments of articulated speech, which might not directly translate to imagined speech processes. Here, we present an approach that synthesizes audible speech in real-time for both imagined and whispered speech conditions. Using a participant implanted with stereotactic depth electrodes, we were able to reliably generate audible speech in real-time. The decoding models rely predominately on frontal activity suggesting that speech processes have similar representations when vocalized, whispered, or imagined. While reconstructed audio is not yet intelligible, our real-time synthesis approach represents an essential step towards investigating how patients will learn to operate a closed-loop speech neuroprosthesis based on imagined speech.