Subthalamic nucleus deep brain stimulation with a multiple independent constant current-controlled device in Parkinson's disease (INTREPID): a multicentre, double-blind, randomised, sham-controlled study.

BACKGROUND: Deep brain stimulation (DBS) of the subthalamic nucleus is an established therapeutic option for managing motor symptoms of Parkinson's disease. We conducted a double-blind, sham-controlled, randomised controlled trial to assess subthalamic nucleus DBS, with a novel multiple independent contact current-controlled (MICC) device, in patients with Parkinson's disease. METHODS: This trial took place at 23 implanting centres in the USA. Key inclusion criteria were age between 22 and 75 years, a diagnosis of idiopathic Parkinson's disease with over 5 years of motor symptoms, and stable use of anti-parkinsonian medications for 28 days before consent. Patients who passed screening criteria were implanted with the DBS device bilaterally in the subthalamic nucleus. Patients were randomly assigned in a 3:1 ratio to receive either active therapeutic stimulation settings (active group) or subtherapeutic stimulation settings (control group) for the 3-month blinded period. Randomisation took place with a computer-generated data capture system using a pre-generated randomisation table, stratified by site with random permuted blocks. During the 3-month blinded period, both patients and the assessors were masked to the treatment group while the unmasked programmer was responsible for programming and optimisation of device settings. The primary outcome was the difference in mean change from baseline visit to 3 months post-randomisation between the active and control groups in the mean number of waking hours per day with good symptom control and no troublesome dyskinesias, with no increase in anti-parkinsonian medications. Upon completion of the blinded phase, all patients received active treatment in the open-label period for up to 5 years. Primary and secondary outcomes were analysed by intention to treat. All patients who provided informed consent were included in the safety analysis. The open-label phase is ongoing with no new enrolment, and current findings are based on the prespecified interim analysis of the first 160 randomly assigned patients. The study is registered with ClinicalTrials.gov, NCT01839396. FINDINGS: Between May 17, 2013, and Nov 30, 2017, 313 patients were enrolled across 23 sites. Of these 313 patients, 196 (63%) received the DBS implant and 191 (61%) were randomly assigned. Of the 160 patients included in the interim analysis, 121 (76%) were randomly assigned to the active group and 39 (24%) to the control group. The difference in mean change from the baseline visit (post-implant) to 3 months post-randomisation in increased ON time without troublesome dyskinesias between the active and control groups was 3·03 h (SD 4·52, 95% CI 1·3-4·7; p<0·0001). 26 serious adverse events in 20 (13%) patients occurred during the 3-month blinded period. Of these, 18 events were reported in the active group and 8 in the control group. One death was reported among the 196 patients before randomisation, which was unrelated to the procedure, device, or stimulation. INTERPRETATION: This double-blind, sham-controlled, randomised controlled trial provides class I evidence of the safety and clinical efficacy of subthalamic nucleus DBS with a novel MICC device for the treatment of motor symptoms of Parkinson's disease. Future trials are needed to investigate potential benefits of producing a more defined current field using MICC technology, and its effect on clinical outcomes. FUNDING: Boston Scientific.

LFP-Net: A deep learning framework to recognize human behavioral activities using brain STN-LFP signals.

BACKGROUND: Recognition of human behavioral activities using local field potential (LFP) signals recorded from the Subthalamic Nuclei (STN) has applications in developing the next generation of deep brain stimulation (DBS) systems. DBS therapy is often used for patients with Parkinson's disease (PD) when medication cannot effectively tackle patients' motor symptoms. A DBS system capable of adaptively adjusting its parameters based on patients' activities may optimize therapy while reducing the stimulation side effects and improving the battery life. METHOD: STN-LFP reveals motor and language behavior, making it a reliable source for behavior classification. This paper presents LFP-Net, an automated machine learning framework based on deep convolutional neural networks (CNN) for classification of human behavior using the time-frequency representation of STN-LFPs within the beta frequency range. CNNs learn different features based on the beta power patterns associated with different behaviors. The features extracted by the CNNs are passed through fully connected layers and then to the softmax layer for classification. RESULTS: Our experiments on ten PD patients performing three behavioral tasks including "button press", "target reaching", and "speech" show that the proposed approach obtains an average classification accuracy of ∼88 %. Comparison with existing methods: The proposed method outperforms other state-of-the-art classification methods based on STN-LFP signals. Compared to well-known deep neural networks such as AlexNet, our approach gives a higher accuracy using significantly fewer parameters. CONCLUSIONS: CNNs show a high performance in decoding the brain neural response, which is crucial in designing the automatic brain-computer interfaces and closed-loop systems.

Studying the Effects of Deep Brain Stimulation and Medication on the Dynamics of STN-LFP Signals for Human Behavior Analysis.

This paper presents the results of our recent work on studying the effects of deep brain stimulation (DBS) and medication on the dynamics of brain local field potential (LFP) signals used for behavior analysis of patients with Parkinson's disease (PD). DBS is a technique used to alleviate the severe symptoms of PD when pharmacotherapy is not very effective. Behavior recognition from the LFP signals recorded from the subthalamic nucleus (STN) has application in developing closed-loop DBS systems, where the stimulation pulse is adaptively generated according to subjects' performing behavior. Most of the existing studies on behavior recognition that use STN-LFPs are based on the DBS being "off". This paper discovers how the performance and accuracy of automated behavior recognition from the LFP signals are affected under different paradigms of stimulation on/off. We first study the notion of beta power suppression in LFP signals under different scenarios (stimulation on/off and medication on/off). Afterward, we explore the accuracy of support vector machines in predicting human actions ("button press" and "reach") using the spectrogram of STN-LFP signals. Our experiments on the recorded LFP signals of three subjects confirm that the beta power is suppressed significantly when the patients take medication (p-value < 0.002) or stimulation (p-value < 0.0003). The results also show that we can classify different behaviors with a reasonable accuracy of 85% even when the high-amplitude stimulation is applied.

Semi-automated application for estimating subthalamic nucleus boundaries and optimal target selection for deep brain stimulation implantation surgery.

OBJECTIVE: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has become standard care for the surgical treatment of Parkinson's disease (PD). Reliable interpretation of microelectrode recording (MER) data, used to guide DBS implantation surgery, requires expert electrophysiological evaluation. Recent efforts have endeavored to use electrophysiological signals for automatic detection of relevant brain structures and optimal implant target location.The authors conducted an observational case-control study to evaluate a software package implemented on an electrophysiological recording system to provide online objective estimates for entry into and exit from the STN. In addition, they evaluated the accuracy of the software in selecting electrode track and depth for DBS implantation into STN, which relied on detecting changes in spectrum activity. METHODS: Data were retrospectively collected from 105 MER-guided STN-DBS surgeries (4 experienced neurosurgeons; 3 sites), in which estimates for entry into and exit from the STN, DBS track selection, and implant depth were compared post hoc between those determined by the software and those determined by the implanting neurosurgeon/neurophysiologist during surgery. RESULTS: This multicenter study revealed submillimetric agreement between surgeon/neurophysiologist and software for entry into and exit out of the STN as well as optimal DBS implant depth. CONCLUSIONS: The results of this study demonstrate that the software can reliably and accurately estimate entry into and exit from the STN and select the track corresponding to ultimate DBS implantation.

Motor Task Detection From Human STN Using Interhemispheric Connectivity.

Deep brain stimulation (DBS) provides significant therapeutic benefit for movement disorders, such as Parkinson's disease (PD). Current DBS devices lack real-time feedback (thus are open loop) and stimulation parameters are adjusted during scheduled visits with a clinician. A closed-loop DBS system may reduce power consumption and side effects by adjusting stimulation parameters based on patient's behavior. Subthalamic nucleus (STN) local field potential (LFP) is a great candidate signal for the neural feedback, because it can be recorded from the stimulation lead and does not require additional sensors. In this paper, we introduce a behavior detection method capable of asynchronously detecting the finger movements of PD patients. Our study indicates that there is a motor-modulated inter-hemispheric connectivity between LFP signals recorded bilaterally from the STN. We utilize a non-linear regression method to measure this inter-hemispheric connectivity for detecting finger movement. Our experimental results, using the recordings from 11 patients with PD, demonstrate that this approach is applicable for behavior detection in the majority of subjects (average area under curve of 70±12%).

A hierarchical structure for human behavior classification using STN local field potentials.

BACKGROUND: Classification of human behavior from brain signals has potential application in developing closed-loop deep brain stimulation (DBS) systems. This paper presents a human behavior classification using local field potential (LFP) signals recorded from subthalamic nuclei (STN). METHOD: A hierarchical classification structure is developed to perform the behavior classification from LFP signals through a multi-level framework (coarse to fine). At each level, the time-frequency representations of all six signals from the DBS leads are combined through an MKL-based SVM classifier to classify five tasks (speech, finger movement, mouth movement, arm movement, and random segments). To lower the computational cost, we alternatively use the inter-hemispheric synchronization of the LFPs to make three pairs out of six bipolar signals. Three classifiers are separately trained at each level of the hierarchical approach, which lead to three labels. A fusion function is then developed to combine these three labels and determine the label of the corresponding trial. RESULTS: Using all six LFPs with the proposed hierarchical approach improves the classification performance. Moreover, the synchronization-based method reduces the computational burden considerably while the classification performance remains relatively unchanged. COMPARISON WITH EXISTING METHODS: Our experiments on two different datasets recorded from nine subjects undergoing DBS surgery show that the proposed approaches remarkably outperform other methods for behavior classification based on LFP signals. CONCLUSION: The LFP signals acquired from STNs contain useful information for recognizing human behavior. This can be a precursor for designing the next generation of closed-loop DBS systems.

A Multiple Kernel Learning approach for human behavioral task classification using STN-LFP signal.

Deep Brain Stimulation (DBS) has gained increasing attention as an effective method to mitigate Parkinson's disease (PD) disorders. Existing DBS systems are open-loop such that the system parameters are not adjusted automatically based on patient's behavior. Classification of human behavior is an important step in the design of the next generation of DBS systems that are closed-loop. This paper presents a classification approach to recognize such behavioral tasks using the subthalamic nucleus (STN) Local Field Potential (LFP) signals. In our approach, we use the time-frequency representation (spectrogram) of the raw LFP signals recorded from left and right STNs as the feature vectors. Then these features are combined together via Support Vector Machines (SVM) with Multiple Kernel Learning (MKL) formulation. The MKL-based classification method is utilized to classify different tasks: button press, mouth movement, speech, and arm movement. Our experiments show that the lp-norm MKL significantly outperforms single kernel SVM-based classifiers in classifying behavioral tasks of five subjects even using signals acquired with a low sampling rate of 10 Hz. This leads to a lower computational cost.

Motor task event detection using Subthalamic Nucleus Local Field Potentials.

Deep Brain Stimulation (DBS) provides significant therapeutic benefit for movement disorders such as Parkinson's disease. Current DBS devices lack real-time feedback (thus are open loop) and stimulation parameters are adjusted during scheduled visits with a clinician. A closed-loop DBS system may reduce power consumption and DBS side effects. In such systems, DBS parameters are adjusted based on patient's behavior, which means that behavior detection is a major step in designing such systems. Various physiological signals can be used to recognize the behaviors. Subthalamic Nucleus (STN) Local Field Potential (LFP) is a great candidate signal for the neural feedback, because it can be recorded from the stimulation lead and does not require additional sensors. A practical behavior detection method should be able to detect behaviors asynchronously meaning that it should not use any prior knowledge of behavior onsets. In this paper, we introduce a behavior detection method that is able to asynchronously detect the finger movements of Parkinson patients. As a result of this study, we learned that there is a motor-modulated inter-hemispheric connectivity between LFP signals recorded bilaterally from STN. We used non-linear regression method to measure this connectivity and use it to detect the finger movements. Performance of this method is evaluated using Receiver Operating Characteristic (ROC).

Task specific inter-hemispheric coupling in human subthalamic nuclei.

Cortical networks and quantitative measures of connectivity are integral to the study of brain function. Despite lack of direct connections between left and right subthalamic nuclei (STN), there are apparent physiological connections. During clinical examination of patients with Parkinson's disease (PD), this connectivity is exploited to enhance signs of PD, yet our understanding of this connectivity is limited. We hypothesized that movement leads to synchronization of neural oscillations in bilateral STN, and we implemented phase coherence, a measure of phase-locking between cortical sites in a narrow frequency band, to demonstrate this synchronization. We analyzed task specific phase synchronization and causality between left and right STN local field potentials (LFPs) recorded from both hemispheres simultaneously during a cued movement task in four subjects with PD who underwent Deep Brain Stimulation (DBS) surgery. We used a data driven approach to determine inter-hemispheric channel pairs and frequencies with a task specific increase in phase locking.We found significant phase locking between hemispheres in alpha frequency (8-12 Hz) in all subjects concurrent with movement of either hand. In all subjects, phase synchronization increased over baseline upon or prior to hand movement onset and lasted until the motion ceased. Left and right hand movement showed similar patterns. Granger causality (GC) at the phase-locking frequencies between synchronized electrodes revealed a unidirectional causality from right to left STN regardless of which side was moved.Phase synchronization across hemispheres between basal ganglia supports existence of a bilateral network having lateralized regions of specialization for motor processing. Our results suggest this bilateral network is activated by a unilateral motor program. Understanding phase synchronization in natural brain functions is critical to development of future DBS systems that augment goal directed behavioral function.

Creating the feedback loop: closed-loop neurostimulation.

Current DBS therapy delivers a train of electrical pulses at set stimulation parameters. This open-loop design is effective for movement disorders, but therapy may be further optimized by a closed loop design. The technology to record biosignals has outpaced our understanding of their relationship to the clinical state of the whole person. Neuronal oscillations may represent or facilitate the cooperative functioning of brain ensembles, and may provide critical information to customize neuromodulation therapy. This review addresses advances to date, not of the technology per se, but of the strategies to apply neuronal signals to trigger or modulate stimulation systems.

Single trial behavioral task classification using subthalamic nucleus local field potential signals.

Deep Brain Stimulation (DBS) has been a successful technique for alleviating Parkinson's disease (PD) symptoms especially for whom drug therapy is no longer efficient. Existing DBS therapy is open-loop, providing a time invariant stimulation pulse train that is not customized to the patient's current behavioral task. By customizing this pulse train to the patient's current task the side effects may be suppressed. This paper introduces a method for single trial recognition of the patient's current task using the local field potential (LFP) signals. This method utilizes wavelet coefficients as features and support vector machine (SVM) as the classifier for recognition of a selection of behaviors: speech, motor, and random. The proposed method is 82.4% accurate for the binary classification and 73.2% for classifying three tasks. These algorithms will be applied in a closed loop feedback control system to optimize DBS parameters to the patient's real time behavioral goals.

Psychosis from subthalamic nucleus deep brain stimulator lesion effect.

BACKGROUND: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) in particular is highly effective in relieving symptoms of Parkinson's disease (PD). However, it can also have marked psychiatric side effects, including delirium, mania, and psychosis. The etiologies of those effects are not well-understood, and both surgeons and consulting psychiatrists are in need of treatment strategies. CASE DESCRIPTION: Two patients with young onset of PD and without significant prior psychiatric problems presented for bilateral STN DBS when medications became ineffective. Both had uneventful operative courses but developed florid psychosis 1-2 weeks later, before stimulator activation. Neither showed signs of delirium, but both required hospitalization, and one required treatment with a first-generation antipsychotic drug. Use of that drug did not worsen PD symptoms, contrary to usual expectations. CONCLUSION: These cases describe a previously unreported post-DBS syndrome in which local tissue reaction to lead implantation produces psychosis even without electrical stimulation of subcortical circuits. The lesion effect also appears to have anti-Parkinsonian effects that may allow the safe use of otherwise contraindicated medications. These cases have implications for management of PD DBS patients postoperatively, and may also be relevant as DBS is further used in other brain regions to treat behavioral disorders.

Famous face identification in temporal lobe epilepsy: support for a multimodal integration model of semantic memory.

This study aims to demonstrate that the left and right anterior temporal lobes (ATLs) perform critical but unique roles in famous face identification, with damage to either leading to differing deficit patterns reflecting decreased access to lexical or semantic concepts but not their degradation. Famous face identification was studied in 22 presurgical and 14 postsurgical temporal lobe epilepsy (TLE) patients and 20 healthy comparison subjects using free recall and multiple choice (MC) paradigms. Right TLE patients exhibited presurgical deficits in famous face recognition, and postsurgical deficits in both famous face recognition and familiarity judgments. However, they did not exhibit any problems with naming before or after surgery. In contrast, left TLE patients demonstrated both pre- and postsurgical deficits in famous face naming but no significant deficits in recognition or familiarity. Double dissociations in performance between groups were alleviated by altering task demands. Postsurgical right TLE patients provided with MC options correctly identified greater than 70% of famous faces they initially rated as unfamiliar. Left TLE patients accurately chose the name for nearly all famous faces they recognized (based on their verbal description) but initially failed to name, although they tended to rapidly lose access to this name. We believe alterations in task demands activate alternative routes to semantic and lexical networks, demonstrating that unique pathways to such stored information exist, and suggesting a different role for each ATL in identifying visually presented famous faces. The right ATL appears to play a fundamental role in accessing semantic information from a visual route, with the left ATL serving to link semantic information to the language system to produce a specific name. These findings challenge several assumptions underlying amodal models of semantic memory, and provide support for the integrated multimodal theories of semantic memory and a distributed representation of concepts.

The thalamus and language revisited.

Regionalization of language function within the left thalamus has been established with language and verbal memory effects of thalamic stimulation during surgery for movement disorders. Three distinct language effects of thalamic stimulation were established: anomia from posterior ventrolateral (VL) and pulvinar regions; perseveration from mid-VL regions; and, a memory and acceleratory effect from anterior VL, described as a "specific alerting response" (SAR). These studies are reviewed in context of pertinent contemporary and recent literature on the thalamic role in memory and language. An explicit mechanistic model for the anomia and SAR effect is proposed. The suggested model for the SAR effect involves secondary switching in the striatum by the activation of thalamostriatal projections, whereas the anomia effect implicates the disruption of the cortical synchronization action of pulvinar via the cortico-pulvinar-cortical projection system. Further experimental data is required to firmly establish these mechanisms.

Human motor cortical activity is selectively phase-entrained on underlying rhythms.

The functional significance of electrical rhythms in the mammalian brain remains uncertain. In the motor cortex, the 12-20 Hz beta rhythm is known to transiently decrease in amplitude during movement, and to be altered in many motor diseases. Here we show that the activity of neuronal populations is phase-coupled with the beta rhythm on rapid timescales, and describe how the strength of this relation changes with movement. To investigate the relationship of the beta rhythm to neuronal dynamics, we measured local cortical activity using arrays of subdural electrocorticographic (ECoG) electrodes in human patients performing simple movement tasks. In addition to rhythmic brain processes, ECoG potentials also reveal a spectrally broadband motif that reflects the aggregate neural population activity beneath each electrode. During movement, the amplitude of this broadband motif follows the dynamics of individual fingers, with somatotopically specific responses for different fingers at different sites on the pre-central gyrus. The 12-20 Hz beta rhythm, in contrast, is widespread as well as spatially coherent within sulcal boundaries and decreases in amplitude across the pre- and post-central gyri in a diffuse manner that is not finger-specific. We find that the amplitude of this broadband motif is entrained on the phase of the beta rhythm, as well as rhythms at other frequencies, in peri-central cortex during fixation. During finger movement, the beta phase-entrainment is diminished or eliminated. We suggest that the beta rhythm may be more than a resting rhythm, and that this entrainment may reflect a suppressive mechanism for actively gating motor function.

Cortical stimulation mapping and Wada results demonstrate a normal variant of right hemisphere language organization.

PURPOSE: Exclusive right hemisphere language lateralization is rarely observed in the Wada angiography results of epilepsy surgery patients. Cortical stimulation mapping (CSM) is infrequently performed in such patients, as most undergo nondominant left hemisphere resections, which are presumed not to pose any risk to language. Early language reorganization is typically assumed in such individuals, taking left hemisphere epileptiform activity as confirmation of change resulting from a pathologic process. We present data from CSM and Wada studies demonstrating that right hemisphere language occurs in the absence of left hemisphere pathology, suggesting it can exist as a normal, but rare variant, in some individuals. Furthermore, these data confirm the Wada test findings of atypical dominance. METHODS: Cortical stimulation mapping data were examined for all right hemisphere surgical patients with right hemisphere speech at our center between 1974 and 2006. Of 1,209 interpretable Wada procedures, 89 patients (7.4%) had exclusive right hemisphere speech, and 21 (1.7%) of these patients underwent surgery involving the right hemisphere. Language site location was determined by examining intraoperative photographs, and site distribution was statistically compared to published findings from left hemisphere language dominant patients. KEY FINDINGS: Language cortex was identified in the right hemisphere during CSM for all patients with available data. All sites could be classified in superior or middle temporal gyri, inferior parietal lobe, or inferior frontal gyrus, all of which were common zones where language was identified in the left hemisphere dominant comparison sample. SIGNIFICANCE: Results suggest that the Wada procedure is a valid measure for identifying right hemisphere language processing without any false lateralization found in the patients mapped with CSM (i.e., a positive Wada is 100% sensitive for finding right hemisphere language sites), and that the distribution of language sites is consistent across right hemisphere and left hemisphere language dominant patients, supporting the theory that right hemisphere language can occur as a normal variant of language lateralization.

Long-term outcome of extratemporal resection in posttraumatic epilepsy.

OBJECT: Posttraumatic epilepsy (PTE) is a common cause of medically intractable epilepsy. While much of PTE is extratemporal, little is known about factors associated with good outcomes in extratemporal resections in medically intractable PTE. The authors investigated and characterized the long-term outcome and patient factors associated with outcome in this population. METHODS: A single-institution retrospective query of all epilepsy surgeries at Regional Epilepsy Center at the University of Washington was performed for a 17-year time span with search terms indicative of trauma or brain injury. The query was limited to adult patients who underwent an extratemporal resection (with or without temporal lobectomy), in whom no other cause of epilepsy could be identified, and for whom minimum 1-year follow-up data were available. Surgical outcomes (in terms of seizure reduction) and clinical data were analyzed and compared. RESULTS: Twenty-one patients met inclusion and exclusion criteria. In long-term follow-up 6 patients (28%) were seizure-free and an additional 6 (28%) had a good outcome of 2 or fewer seizures per year. Another 5 patients (24%) experienced a reduction in seizures, while only 4 (19%) did not attain significant benefit. The presence of focal encephalomalacia on imaging was associated with good or excellent outcomes in 83%. In 8 patients with the combination of encephalomalacia and invasive intracranial EEG, 5 (62.5%) were found to be seizure free. Normal MRI examinations preoperatively were associated with worse outcomes, particularly when combined with multifocal or poorly localized EEG findings. Two patients suffered complications but none were life threatening or disabling. CONCLUSIONS: Many patients with extratemporal PTE can achieve good to excellent seizure control with epilepsy surgery. The risks of complications are acceptably low. Patients with focal encephalomalacia on MRI generally do well. Excellent outcomes can be achieved when extratemporal resection is guided by intracranial EEG electrodes defining the extent of resection.