Region Of Subthalamic Nucleus Research Articles

Characterization of subthalamic nucleus deep brain stimulation evoked resonant neural activity in a large animal model: A pilot study

Recent reports have described stimulation evoked resonant neural activity (ERNA) recorded in the subthalamic nucleus (STN) and globus pallidus internus (GPi) of patients during Deep Brain Stimulation (DBS) surgery. The constraints imposed during intraoperative recordings in patients limit the opportunity for in-depth study of new findings such as ERNA. In this pilot study, we leverage a large animal model to focus on detailed characterization of ERNA. Bilateral DBS leads were implanted in the STN in three ovine subjects and externalized for chronic use with custom stimulation and recording circuitry. ERNA was reliably recorded from the STN region in all three subjects with distinct specificity to recording and stimulation sites/contacts. Basic neural response characteristics such as input/output behavior, frequency response and strength/duration curves were evaluated. ERNA amplitude was highly dependent upon stimulation frequency, due to the interaction of the underlying resonant activity and the evoked response from each stimulus pulse. The results could be predicted by a mathematical model of constructive/destructive phase interference, and importantly, the evoked response latency. Significant time dependent dynamics in these evoked potentials were observed, which will be critically important to understand for future clinical applications. Based upon these recordings from leads in the STN region of healthy ovine subjects, these data confirm that DBS evokes high frequency resonant activity in the basal ganglia network. The clinical utility of ERNA remains to be demonstrated, but its direct association with DBS therapy makes it an interesting biomarker for potential use in contact selection and closed loop therapy.

Lightweight deep learning model for automated STN localization using MER in Parkinson’s disease

Deep brain stimulation (DBS) of Subthalamic nucleus (STN), using a permanent electrode has emerged as an effective treatment for Parkinson’s disease patients. However, accurately localizing STN region during DBS surgery remains a challenging task. Microelectrode recording (MER) is used to identify STN by neurosurgeons. Nevertheless, manual interpretation of STN functional activity might prompt false alarms. Although the existing technology based on deep learning (DL) can outline the STN anatomical borders, it suffers from computational complexity and skewed findings. An end-to-end attention-based lightweight model with residual connections and soft thresholding is designed specifically for STN region detection. Scalogram and spectrogram images are generated from MER to mimic the observation of neurosurgeons and gather contextual information in a more intuitive manner. Proposed model exhibits a simplified architecture with only 15 layers, distinguishing it from previous DL models that tend to be more complex. Spatial attention helped to focus on relevant patterns within the data, while residual connections enhanced the gradient flow throughout the developed network. Soft thresholding offered noise reduction and robustness to signal variability. This approach eliminated the requirement for excessively deep layers and the necessity for separate feature extraction and classification stages by integrating them into a single pipeline. Our model identified STN with an accuracy of 97.42%. Proposed model outperformed 15 existing cutting-edge techniques. Our findings highlight the effectiveness of lightweight DL framework to achieve precise STN localization. The proposed method has significant potential in improving the efficiency of DBS surgery by reducing reliance on the neurosurgeon experience.

Subthalamic nucleus–language network connectivity predicts dopaminergic modulation of speech function in Parkinson’s disease

Speech impediments are a prominent yet understudied symptom of Parkinson's disease (PD). While the subthalamic nucleus (STN) is an established clinical target for treating motor symptoms, these interventions can lead to further worsening of speech. The interplay between dopaminergic medication, STN circuitry, and their downstream effects on speech in PD is not yet fully understood. Here, we investigate the effect of dopaminergic medication on STN circuitry and probe its association with speech and cognitive functions in PD patients. We found that changes in intrinsic functional connectivity of the STN were associated with alterations in speech functions in PD. Interestingly, this relationship was characterized by altered functional connectivity of the dorsolateral and ventromedial subdivisions of the STN with the language network. Crucially, medication-induced changes in functional connectivity between the STN's dorsolateral subdivision and key regions in the language network, including the left inferior frontal cortex and the left superior temporal gyrus, correlated with alterations on a standardized neuropsychological test requiring oral responses. This relation was not observed in the written version of the same test. Furthermore, changes in functional connectivity between STN and language regions predicted the medication's downstream effects on speech-related cognitive performance. These findings reveal a previously unidentified brain mechanism through which dopaminergic medication influences speech function in PD. Our study sheds light into the subcortical-cortical circuit mechanisms underlying impaired speech control in PD. The insights gained here could inform treatment strategies aimed at mitigating speech deficits in PD and enhancing the quality of life for affected individuals.

Classification of DBS microelectrode recordings using a residual neural network with attention in the temporal domain

During the Deep Brain Stimulation (DBS) surgery for Parkinson’s disease (PD), the main goal is to place the permanent stimulating electrode into an area of the brain that becomes pathologically hyperactive. This area, called Subthalamic Nucleus (STN), is small and located deep within the brain. Therefore, the main challenge is the precise localization of the STN region, considering various measurement errors and artifacts. In this paper, we have designed and developed a computer-aided decision support system for neurosurgical DBS surgery. The implementation of this system provides a novel method for calculating the expected position of the stimulating electrode based on the recordings of the electrical activity of brain tissue. The artificial neural network with attention is used to classify the microelectrode recordings and determine the final position of the stimulating electrode within the STN area. Experiments have verified the utility and efficiency of our system. The tests were carried out on many recordings collected during DBS surgeries, giving encouraging results. The experimental results demonstrate that deep learning methods extended with self-attention blocks compete with the other solutions. They provide significant robustness to recording artifacts and improve the accuracy of the stimulating electrode placement.

Chaotic dynamics of the Hénon map and neuronal input-output: A comparison with neurophysiological data.

In this study, the Hénon map was analyzed using quantifiers from information theory in order to compare its dynamics to experimental data from brain regions known to exhibit chaotic behavior. The goal was to investigate the potential of the Hénon map as a model for replicating chaotic brain dynamics in the treatment of Parkinson's and epilepsy patients. The dynamic properties of the Hénon map were compared with data from the subthalamic nucleus, the medial frontal cortex, and a q-DG model of neuronal input-output with easy numerical implementation to simulate the local behavior of a population. Using information theory tools, Shannon entropy, statistical complexity, and Fisher's information were analyzed, taking into account the causality of the time series. For this purpose, different windows over the time series were considered. The findings revealed that neither the Hénon map nor the q-DG model could perfectly replicate the dynamics of the brain regions studied. However, with careful consideration of the parameters, scales, and sampling used, they were able to model some characteristics of neural activity. According to these results, normal neural dynamics in the subthalamic nucleus region may present a more complex spectrum within the complexity-entropy causality plane that cannot be represented by chaotic models alone. The dynamic behavior observed in these systems using these tools is highly dependent on the studied temporal scale. As the size of the sample studied increases, the dynamics of the Hénon map become increasingly different from those of biological and artificial neural systems.

Comparison of Two Electrode Placement Methods in Transcranial Direct Current Stimulation for Parkinson's Disease

Purpose: Therapeutic electrical stimulation of deep brain structures, such as the subthalamic nucleus and the Globus Pallidus (GP), is widely accepted as a treatment tool for patients with Parkinson's Disease (PD). Electrical stimulation of the cerebral cortex with electrodes or transcranial stimulation can increase motor function among PD patients. The present study aimed to evaluate the effects of non-invasive cortical stimulation with simulation of transcranial Direct Current Stimulation (tDCS) technique on parts of the basal ganglia among PD patients.
 Materials and Methods: tDCS was simulated using two different electrode placement methods (anodal stimulation of the primary motor cortex (M1) and anodal stimulation of the Dorsolateral Prefrontal Cortex (DLPFC)) and We evaluated the excitation procedure in the target area based on the excitation current distribution in GP and Subthalamic Nucleus according to the patient's condition in both electrode methods. All simulations were performed using head Magnetic Resonance Imaging (MRI) images of four people with PD. Also, according to the excitation current distribution obtained from the previous step, we studied how the excitation current distributed in the target areas is affected by using a model of the basal ganglia so that based on the membrane potential of each excitation in these areas, in all four patients, we compare both electrode-installation methods in a functional way. The effectiveness of brain stimulation was also studied using a basal ganglia model. Considering the membrane potential of GP and Subthalamic Nucleus regions, the effectiveness of each electrode placement method was evaluated in the Basal Ganglia (BG) model.
 Results: According to the results, direct current stimulation was propagated through electrodes placed on the scalp throughout the model. Also, anodal stimulation of the M1 had a better stimulation of GP and subthalamic nucleus than anodal stimulation of the DLPFC.
 Conclusion: Although, the procedures for performing tDCS and invasive brain stimulation in PD are different, the results show that this treatment can be appropriate and improve motor function in patients with PD.

Classification of electrically-evoked potentials in the parkinsonian subthalamic nucleus region

Electrically evoked compound action potentials (ECAPs) generated in the subthalamic nucleus (STN) contain features that may be useful for titrating deep brain stimulation (DBS) therapy for Parkinson’s disease. Delivering a strong therapeutic effect with DBS therapies, however, relies on selectively targeting neural pathways to avoid inducing side effects. In this study, we investigated the spatiotemporal features of ECAPs in and around the STN across parameter sweeps of stimulation current amplitude, pulse width, and electrode configuration, and used a linear classifier of ECAP responses to predict electrode location. Four non-human primates were implanted unilaterally with either a directional (n = 3) or non-directional (n = 1) DBS lead targeting the sensorimotor STN. ECAP responses were characterized by primary features (within 1.6 ms after a stimulus pulse) and secondary features (between 1.6 and 7.4 ms after a stimulus pulse). Using these features, a linear classifier was able to accurately differentiate electrodes within the STN versus dorsal to the STN in all four subjects. ECAP responses varied systematically with recording and stimulating electrode locations, which provides a subject-specific neuroanatomical basis for selecting electrode configurations in the treatment of Parkinson’s disease with DBS therapy.

Altered functional connectivity associated with striatal dopamine depletion in Parkinson's disease.

We aimed to clarify whether dopamine depletion in the posterior dorsal striatum in early-stage Parkinson's disease (PD) alters synchronized activity in the cortico-basal ganglia motor circuit. In sum, 14 PD patients and 16 matched healthy controls (HC) underwent [11C]-2-β-carbomethoxy-3-β-(4-fluorophenyl) tropane positron emission tomography to identify striatal dopamine-depleted areas. The identified map was applied to functional magnetic resonance imaging (fMRI) to discover abnormalities in functional connectivity (FC) during motor-task and rest-state in PD patients in the drug-off state relative to HC. Striatal dopamine-depleted areas formed synchronized fMRI activity that largely corresponded to the cortico-basal ganglia motor circuit. Group comparisons revealed that striatal dopamine-depleted areas exhibited decreased FC with the medial premotor cortex during motor-task and with the medial, lateral premotor and primary motor cortices during rest-state. Striatal dopamine-depleted areas also elucidated decreased FC in the subthalamic nucleus (STN) in PD both during motor-task and rest-state. The STN regions that exhibited reduced FC with striatal dopamine-depleted areas demonstrated excessive FC with the lateral premotor and primary motor cortices in PD only during rest-state. Our findings suggest that striatal dopamine-depleted area reduced synchronized activity with the motor cortices and STN, which, in turn, induces an abnormal increase in coupling between the areas in PD.

The impact of pulse timing on cortical and subthalamic nucleus deep brain stimulation evoked potentials.

The impact of pulse timing is an important factor in our understanding of how to effectively modulate the basal ganglia thalamocortical (BGTC) circuit. Single pulse low-frequency DBS-evoked potentials generated through electrical stimulation of the subthalamic nucleus (STN) provide insight into circuit activation, but how the long-latency components change as a function of pulse timing is not well-understood. We investigated how timing between stimulation pulses delivered in the STN region influence the neural activity in the STN and cortex. DBS leads implanted in the STN of five patients with Parkinson's disease were temporarily externalized, allowing for the delivery of paired pulses with inter-pulse intervals (IPIs) ranging from 0.2 to 10 ms. Neural activation was measured through local field potential (LFP) recordings from the DBS lead and scalp EEG. DBS-evoked potentials were computed using contacts positioned in dorsolateral STN as determined through co-registered post-operative imaging. We quantified the degree to which distinct IPIs influenced the amplitude of evoked responses across frequencies and time using the wavelet transform and power spectral density curves. The beta frequency content of the DBS evoked responses in the STN and scalp EEG increased as a function of pulse-interval timing. Pulse intervals <1.0 ms apart were associated with minimal to no change in the evoked response. IPIs from 1.5 to 3.0 ms yielded a significant increase in the evoked response, while those >4 ms produced modest, but non-significant growth. Beta frequency activity in the scalp EEG and STN LFP response was maximal when IPIs were between 1.5 and 4.0 ms. These results demonstrate that long-latency components of DBS-evoked responses are pre-dominantly in the beta frequency range and that pulse interval timing impacts the level of BGTC circuit activation.

Subthalamic high-beta oscillation informs the outcome of deep brain stimulation in patients with Parkinson's disease.

BackgroundThe therapeutic effect of deep brain stimulation (DBS) of the subthalamic nucleus (STN) for Parkinson's disease (PD) is related to the modulation of pathological neural activities, particularly the synchronization in the β band (13–35 Hz). However, whether the local β activity in the STN region can directly predict the stimulation outcome remains unclear.ObjectiveWe tested the hypothesis that low-β (13–20 Hz) and/or high-β (20–35 Hz) band activities recorded from the STN region can predict DBS efficacy.MethodsLocal field potentials (LFPs) were recorded in 26 patients undergoing deep brain stimulation surgery in the subthalamic nucleus area. Recordings were made after the implantation of the DBS electrode prior to its connection to a stimulator. The maximum normalized powers in the theta (4–7 Hz), alpha (7–13 Hz), low-β (13–20 Hz), high-β (20–35 Hz), and low-γ (40–55 Hz) subbands in the postoperatively recorded LFP were correlated with the stimulation-induced improvement in contralateral tremor or bradykinesia–rigidity. The distance between the contact selected for stimulation and the contact with the maximum subband power was correlated with the stimulation efficacy. Following the identification of the potential predictors by the significant correlations, a multiple regression analysis was performed to evaluate their effect on the outcome.ResultsThe maximum high-β power was positively correlated with bradykinesia–rigidity improvement (rs = 0.549, p < 0.0001). The distance to the contact with maximum high-β power was negatively correlated with bradykinesia–rigidity improvement (rs = −0.452, p < 0.001). No significant correlation was observed with low-β power. The maximum high-β power and the distance to the contact with maximum high-β power were both significant predictors for bradykinesia–rigidity improvement in the multiple regression analysis, explaining 37.4% of the variance altogether. Tremor improvement was not significantly correlated with any frequency.ConclusionHigh-β oscillations, but not low-β oscillations, recorded from the STN region with the DBS lead can inform stimulation-induced improvement in contralateral bradykinesia–rigidity in patients with PD. High-β oscillations can help refine electrode targeting and inform contact selection for DBS therapy.

Stability and Effect of Parkinsonian State on Deep Brain Stimulation Cortical Evoked Potentials

ObjectivesTo characterize and compare the stability of cortical potentials evoked by deep brain stimulation (DBS) of the subthalamic nucleus (STN) across the naïve, parkinsonian, and pharmacologically treated parkinsonian states. To advance cortical potentials as possible biomarkers for DBS programming. Materials and MethodsSerial electrocorticographic (ECoG) recordings were made more than nine months from a single non-human primate instrumented with bilateral ECoG grids spanning anterior parietal to prefrontal cortex. Cortical evoked potentials (CEPs) were generated through time-lock averaging of the ECoG recordings to DBS pulses delivered unilaterally in the STN region using a chronically implanted, six-contact, scaled DBS lead. Recordings were made across the naïve followed by mild and moderate parkinsonian conditions achieved by staged injections of the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) neurotoxin. In addition to characterizing the spatial distribution and stability of the response within each state, changes in the amplitude and latency of CEP components as well as in the frequency content were examined in relation to parkinsonian severity and dopamine replacement. ResultsIn the naïve state, the STN DBS CEP presented as a multiphase response maximal over M1 cortex, with components attributable to physiological activity distinguishable from stimulus artifact as early as 0.45–0.75 msec poststimulation. When delivered using therapeutically effective parameters in the parkinsonian state, the CEP was highly stable across multiple recording sessions within each behavioral state. Across states, significant differences were present with respect to both the latency and amplitude of individual response components, with greater differences present for longer-latency components (all p < 0.05). Power spectral density analysis revealed a high-beta peak within the evoked response, with significant changes in power between disease states across multiple frequency bands. ConclusionsOur findings underscore the spatiotemporal specificity and relative stability of the DBS-CEP associated with different disease states and with therapeutic benefit. DBS-CEP may be a viable biomarker for therapeutic programming.

Functional connectivity maps of theta/alpha and beta coherence within the subthalamic nucleus region

The subthalamic nucleus (STN) is a primary target for deep brain stimulation in Parkinson's disease (PD). Although small in size, the STN is commonly partitioned into sensorimotor, cognitive/associative, and limbic subregions based on its structural connectivity profile to cortical areas. We investigated whether such a regional specialization is also supported by functional connectivity between local field potential recordings and simultaneous magnetoencephalography. Using a novel data set of 21 PD patients, we replicated previously reported cortico-STN coherence networks in the theta/alpha and beta frequency ranges, and looked for the spatial distribution of these networks within the STN region. Although theta/alpha and beta coherence peaks were both observed in on-medication recordings from electrode contacts at several locations within and around the STN, sites with theta/alpha coherence peaks were situated at significantly more inferior MNI coordinates than beta coherence peaks. Sites with only theta/alpha coherence peaks, i.e. without distinct beta coherence, were mostly located near the border of sensorimotor and cognitive/associative subregions as defined by a tractography-based atlas of the STN. Peak coherence values were largely unaltered by the medication state of the subject, however, theta/alpha peaks were more often identified in recordings obtained after administration of dopaminergic medication. Our findings suggest the existence of a frequency-specific topography of cortico-STN coherence within the STN, albeit with considerable spatial overlap between functional networks. Consequently, optimization of deep brain stimulation targeting might remain a trade-off between alleviating motor symptoms and avoiding adverse neuropsychiatric side effects.

Impulsivity is associated with firing regularity in parkinsonian ventral subthalamic nucleus.

Impulsive–compulsive behaviors (ICB) are over‐represented in Parkinson's disease (PD) patients. Neurons in the ventral subthalamic nucleus (STN) might play a predominant role in the modulation of impulsivity. We characterized the firing regularity of 742 subthalamic neurons from 24 PD patients (12 ICB+ and 12 ICB‐) in an OFF medication state. We computed the firing regularity in the dorsal and ventral STN regions, and we compared their performance in discriminating ICB patients. Regularity of ventral neurons in ICB+ patients is higher and supports a significant discrimination between the two cohorts. These results substantiate a ventral location of neurons involved in impulsivity.

HP10: Alpha and gamma oscillations of the subthalamic nucleus during internally-guided and externally-triggered movements in parkinsonian patients

The aim of this study was to determine the differences in rhythmic activity in the subthalamic nucleus (STN) when performing external triggers (ET) and internally-guided (IG) voluntary movements in patients with Parkinson's disease (PD). It is known that the initiation of movements and the implementation of IG movements are difficult in PD patients. This is attributed to the fact that IG movements are performed with the involvement of the striato-thalamo-cortical connections, which are impaired in PD. While movements evoked by ET are carried out using the cerebello-thalamo-cortical loops. The study included 3 patients (OFF state) who underwent postoperative multichannel registration of STN local potentials (LFP) and EMG of the studied forearm muscles. During the study, patients were asked to perform motor tests triggered by external verbal commands, as well as self-initiated tests. We showed stabilization of high beta oscillations during both types of movements in comparison with spontaneous activity. We also found increased gamma rhythms caused IG movements. At the same time, an increase in alpha oscillations was observed in the ventral STN regions during ET. Our results showed the differences in the level of synchronization of alpha and gamma rhythms during transmission of motor signals in the basal ganglia during the performance of IG and ET movements in PD patients. We hypothesize that increased “prokinetic” gamma activity during IG movement may be a compensatory mechanism for suppressing the “antikinetic” beta rhythm in PD patients. The functional role of alpha synchronization in motor control remains unclear and needs further study.

Effects of diffusion signal modeling and segmentation approaches on subthalamic nucleus parcellation

The subthalamic nucleus (STN) is commonly used as a surgical target for deep brain stimulation in movement disorders such as Parkinson's Disease. Tractography-derived connectivity-based parcellation (CBP) has been recently proposed as a suitable tool for non-invasive in vivo identification and pre-operative targeting of specific functional territories within the human STN. However, a well-established, accurate and reproducible protocol for STN parcellation is still lacking. The present work aims at testing the effects of different tractography-based approaches for the reconstruction of STN functional territories.We reconstructed functional territories of the STN on the high-quality dataset of 100 unrelated healthy subjects and on the test-retest dataset of the Human Connectome Project (HCP) repository. Connectivity-based parcellation was performed with a hypothesis-driven approach according to cortico-subthalamic connectivity, after dividing cortical areas into three groups: associative, limbic and sensorimotor. Four parcellation pipelines were compared, combining different signal modeling techniques (single-fiber vs multi-fiber) and different parcellation approaches (winner takes all parcellation vs fiber density thresholding). We tested these procedures on STN regions of interest obtained from three different, commonly employed, subcortical atlases. We evaluated the pipelines both in terms of between-subject similarity, assessed on the cohort of 100 unrelated healthy subjects, and of within-subject similarity, using a second cohort of 44 subjects with available test-retest data. We found that each parcellation provides converging results in terms of location of the identified parcels, but with significative variations in size and shape. All pipelines obtained very high within-subject similarity, with tensor-based approaches outperforming multi-fiber pipelines. On the other hand, higher between-subject similarity was found with multi-fiber signal modeling techniques combined with fiber density thresholding. We suggest that a fine-tuning of tractography-based parcellation may lead to higher reproducibility and aid the development of an optimized surgical targeting protocol.

A novel deep recurrent convolutional neural network for subthalamic nucleus localization using local field potential signals

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is a well-established interventional treatment for improving motor symptoms of patients suffering from Parkinson’s disease (PD). While STN is originally localized using imaging modalities, additional intraoperative guidance such as microelectrode recording (MER) is crucial to refine the final electrode trajectory. Analysis of MER by an experienced neurophysiologist maintains good clinical outcomes, although the procedure requires long duration and jeopardizes the safety of the surgery. Lately, local field potentials (LFP) investigation has inspired the emergence of adaptive DBS and revealed beneficial perception of PD mechanisms. Several studies confronting LFP analysis to detect the anatomical borders of STN, have focused on handcrafted feature engineering, which does not certainly capture delicate differences in LFP. This study gauges the ability of deep learning to exhibit valuable insight into the electrophysiological neural rhythms of STN using LFP. A recurrent convolutional neural network (CNN) strategy is presented, where local features are extracted from LFP signals via CNN, followed by recurrent layers to aggregate the best features for classification. The proposed model outperformed the state-of-the-art techniques, yielding highest average accuracy of 96.79%. This is the first study on the analysis of LFP signals to localize STN using deep recurrent CNN. The developed model has the potential to extract high level biomarkers regarding STN region, which would boost the neurosurgeon’s confidence in adjusting the trajectory intraoperatively for optimal lead implantation. LFP is a robust guidance tool and could be an alternative solution to the current scenario using MER.

Detection of subthalamic nucleus using novel higher-order spectra features in microelectrode recordings signals

Successful deep brain stimulation surgery for Parkinson’s disease (PD) patients hinges on accurate clustering of the functional regions along the electrode insertion trajectory. Microelectrode recording (MER) is employed as a substantial tool for neuro-navigation and localizing the optimal target, such as the subthalamic nucleus (STN), intraoperatively. MER signals deliver a framework to reveal the underlying characteristics of STN. The motivation behind this work is to explore the application of Higher-order statistics and spectra (HOS) for an automated delineation of the neurophysiological borders of STN using MER signals. Database collected from 21 PD patients were used. Two HOS methods (Bispectrum and cumulant) were exploited to probe non-Gaussian properties of STN region. This is followed by utilizing classifiers, namely K-nearest neighbor, decision tree, Boosting and support vector machine (SVM), to choose the superior classifier. Comparison of the performance achieved via HOS alongside the state-of-the-art techniques shows that the proposed features are better suited for identifying STN borders and achieve higher results. Average classification accuracy, sensitivity, specificity, area under the curve and Youden’s J statistics of 94.81%, 96.73%, 92.15%, 0.9444% and 0.8888, respectively, were yielded using SVM with 8 bispectrum and 241 cumulants features. The proposed model can aid the neurosurgeon in STN detection.

Deep convolutional neural network for the automated detection of Subthalamic nucleus using MER signals

BackgroundDeep brain stimulation (DBS) surgery has been extensively conducted for treating advanced Parkinson's disease (PD) patient's symptoms. DBS hinges on the localization of the subthalamic nucleus (STN) in which a permanent electrode should be accurately placed to produce electrical current. Microelectrode recording (MER) signals are routinely recorded in the procedure of DBS surgery to validate the planned trajectories. However, manual MER signals interpretation with the goal of detecting STN borders requires expertise and prone to inter-observer variability. Therefore, a computerized aided system would be beneficial to automatic detection of the dorsal and ventral borders of the STN in MER. New methodIn this study, a new deep learning model based on convolutional neural system for automatic delineation of the neurophysiological borders of the STN along the electrode trajectory was developed. Comparison with existing methodsThe proposed model does not involve any conventional standardization, feature extraction or selection steps. ResultsPromising results of 98.67% accuracy, 99.03% sensitivity, 98.11% specificity, 98.79% precision and 98.91% F1-score for subject based testing were achieved using the proposed convolutional neural network (CNN) model. ConclusionsThis is the first study on the analysis of MER signals to detect STN using deep CNN. Traditional machine learning (ML) algorithms are often cumbersome and suffer from subjective evaluation. Though, the developed 10-layered CNN model has the capability of extracting substantial features at the convolution stage. Hence, the proposed model has the potential to deliver high performance on STN region detection which shows perspective in aiding the neurosurgeon intraoperatively.

Distinct Roles of the Human Subthalamic Nucleus and Dorsal Pallidum in Parkinson’s Disease Impulsivity

BackgroundImpulsivity and impulse control disorders are common in Parkinson’s disease and lead to increased morbidity and reduced quality of life. Impulsivity is thought to arise from aberrant reward processing and inhibitory control, but it is unclear why deep brain stimulation of either the subthalamic nucleus (STN) or globus pallidus internus (GPi) affects levels of impulsivity. Our aim was to assess the role of the STN and GPi in impulsivity using invasive local field potential (LFP) recordings from deep brain stimulation electrodes. MethodsWe measured LFPs during a simple rewarding Go/NoGo paradigm in 39 female and male human patients with Parkinson’s disease manifesting variable amounts of impulsivity who were undergoing unilateral deep brain stimulation of either the STN (18 nuclei) or GPi (28 nuclei). We identified reward-specific LFP event-related potentials and correlated them to impulsivity severity. ResultsLFPs in both structures modulated during reward-specific Go and NoGo stimulus evaluation, reward feedback, and loss feedback. Motor and limbic functions were anatomically separable in the GPi but not in the STN. Across participants, LFP reward processing responses in the STN and GPi uniquely depended on the severity of impulsivity. ConclusionsThis study establishes LFP correlates of impulsivity within the STN and GPi regions. We propose a model for basal ganglia reward processing that includes the bottom-up role of the GPi in reward salience and the top-down role of the STN in cognitive control.

Surprise: Unexpected Action Execution and Unexpected Inhibition Recruit the Same Fronto-Basal-Ganglia Network.

Unexpected and thus surprising events are omnipresent and oftentimes require adaptive behavior such as unexpected inhibition or unexpected action. The current theory of unexpected events suggests that such unexpected events just like global stopping recruit a fronto-basal-ganglia network. A global suppressive effect impacting ongoing motor responses and cognition is specifically attributed to the subthalamic nucleus (STN). Previous studies either used separate tasks or presented unexpected, task-unrelated stimuli during response inhibition tasks to relate the neural signature of unexpected events to that of stopping. Here, we aimed to test these predictions using a within task design with identical stimulus material for both unexpected action and unexpected inhibition using functional magnetic resonance imaging (fMRI) for the first time. To this end, 32 healthy human participants of both sexes performed a cue-informed go/nogo task comprising expected and unexpected action and inhibition trials during fMRI. Using conjunction, contrast, and Bayesian analyses, we demonstrate that unexpected action elicited by an unexpected go signal and unexpected inhibition elicited by an unexpected nogo signal recruited the same fronto-basal-ganglia network which is usually assigned to stopping. Furthermore, the stronger the unexpected action-related activity in the STN region was the more detrimental was the effect on response times. The present results thus complement earlier findings and provide direct evidence for the unified theory of unexpected events while ruling out alternative task and novelty effects.SIGNIFICANCE STATEMENT This is the first study using functional magnetic resonance imaging (fMRI) to test whether unexpected events regardless of whether they require unexpected action or inhibition recruit a fronto-basal-ganglia network just like stopping. In contrast to previous studies, we used identical stimulus material for both conditions within one task. This enabled us to directly test predictions of the current theory of unexpected events and, moreover, to test for condition-specific neural signatures. The present results underpin that both processes recruit the same neural network while excluding alternative task and novelty effects. The simple task design thus provides an avenue to studying surprise as a pure form of reactive inhibition in neuropsychiatric patients displaying inhibitory deficits who often have a limited testing capacity.