Anti-PD-1 treatment protects against seizure by suppressing sodium channel function.

AIMS: Although programmed cell death protein 1 (PD-1) typically serves as a target for immunotherapies, a few recent studies have found that PD-1 is expressed in the nervous system and that neuronal PD-1 might play a crucial role in regulating neuronal excitability. However, whether brain-localized PD-1 is involved in seizures and epileptogenesis is still unknown and worthy of in-depth exploration. METHODS: The existence of PD-1 in human neurons was confirmed by immunohistochemistry, and PD-1 expression levels were measured by real-time quantitative PCR (RT-qPCR) and western blotting. Chemoconvulsants, pentylenetetrazol (PTZ) and cyclothiazide (CTZ), were applied for the establishment of in vivo (rodents) and in vitro (primary hippocampal neurons) models of seizure, respectively. SHR-1210 (a PD-1 monoclonal antibody) and sodium stibogluconate (SSG, a validated inhibitor of SH2-containing protein tyrosine phosphatase-1 [SHP-1]) were administrated to investigate the impact of PD-1 pathway blockade on epileptic behaviors of rodents and epileptiform discharges of neurons. A miRNA strategy was applied to determine the impact of PD-1 knockdown on neuronal excitability. The electrical activities and sodium channel function of neurons were determined by whole-cell patch-clamp recordings. The interaction between PD-1 and α-6 subunit of human voltage-gated sodium channel (Nav1.6) was validated by performing co-immunostaining and co-immunoprecipitation (co-IP) experiments. RESULTS: Our results reveal that PD-1 protein and mRNA levels were upregulated in lesion cores compared with perifocal tissues of surgically resected specimens from patients with intractable epilepsy. Furthermore, we show that anti-PD-1 treatment has anti-seizure effects both in vivo and in vitro. Then, we reveal that PD-1 blockade can alter the electrophysiological properties of sodium channels. Moreover, we reveal that PD-1 acts together with downstream SHP-1 to regulate sodium channel function and hence neuronal excitability. Further investigation suggests that there is a direct interaction between neuronal PD-1 and Nav1.6. CONCLUSION: Our study reveals that neuronal PD-1 plays an important role in epilepsy and that anti-PD-1 treatment protects against seizures by suppressing sodium channel function, identifying anti-PD-1 treatment as a novel therapeutic strategy for epilepsy.

Spatio-temporal evolution of human neural activity during visually cued hand movements.

Making hand movements in response to visual cues is common in daily life. It has been well known that this process activates multiple areas in the brain, but how these neural activations progress across space and time remains largely unknown. Taking advantage of intracranial electroencephalographic (iEEG) recordings using depth and subdural electrodes from 36 human subjects using the same task, we applied single-trial and cross-trial analyses to high-frequency iEEG activity. The results show that the neural activation was widely distributed across the human brain both within and on the surface of the brain, and focused specifically on certain areas in the parietal, frontal, and occipital lobes, where parietal lobes present significant left lateralization on the activation. We also demonstrate temporal differences across these brain regions. Finally, we evaluated the degree to which the timing of activity within these regions was related to sensory or motor function. The findings of this study promote the understanding of task-related neural processing of the human brain, and may provide important insights for translational applications.

Deep Learning With Convolutional Neural Networks for Motor Brain-Computer Interfaces Based on Stereo-Electroencephalography (SEEG).

OBJECTIVE: Deep learning based on convolutional neural networks (CNN) has achieved success in brain-computer interfaces (BCIs) using scalp electroencephalography (EEG). However, the interpretation of the so-called 'black box' method and its application in stereo-electroencephalography (SEEG)-based BCIs remain largely unknown. Therefore, in this paper, an evaluation is performed on the decoding performance of deep learning methods on SEEG signals. METHODS: Thirty epilepsy patients were recruited, and a paradigm including five hand and forearm motion types was designed. Six methods, including filter bank common spatial pattern (FBCSP) and five deep learning methods (EEGNet, shallow and deep CNN, ResNet, and a deep CNN variant named STSCNN), were used to classify the SEEG data. Various experiments were conducted to investigate the effect of windowing, model structure, and the decoding process of ResNet and STSCNN. RESULTS: The average classification accuracy for EEGNet, FBCSP, shallow CNN, deep CNN, STSCNN, and ResNet were 35 ± 6.1%, 38 ± 4.9%, 60 ± 3.9%, 60 ± 3.3%, 61 ± 3.2%, and 63 ± 3.1% respectively. Further analysis of the proposed method demonstrated clear separability between different classes in the spectral domain. CONCLUSION: ResNet and STSCNN achieved the first- and second-highest decoding accuracy, respectively. The STSCNN demonstrated that an extra spatial convolution layer was beneficial, and the decoding process can be partially interpreted from spatial and spectral perspectives. SIGNIFICANCE: This study is the first to investigate the performance of deep learning on SEEG signals. In addition, this paper demonstrated that the so-called 'black-box' method can be partially interpreted.

Visual cortex encodes timing information in humans and mice.

Despite the importance of timing in our daily lives, our understanding of how the human brain mediates second-scale time perception is limited. Here, we combined intracranial stereoelectroencephalography (SEEG) recordings in epileptic patients and circuit dissection in mice to show that visual cortex (VC) encodes timing information. We first asked human participants to perform an interval-timing task and found VC to be a key timing brain area. We then conducted optogenetic experiments in mice and showed that VC plays an important role in the interval-timing behavior. We further found that VC neurons fired in a time-keeping sequential manner and exhibited increased excitability in a timed manner. Finally, we used a computational model to illustrate a self-correcting learning process that generates interval-timed activities with scalar-timing property. Our work reveals how localized oscillations in VC occurring in the seconds to deca-seconds range relate timing information from the external world to guide behavior.

E2SGAN: EEG-to-SEEG translation with generative adversarial networks.

High-quality brain signal data recorded by Stereoelectroencephalography (SEEG) electrodes provide clinicians with clear guidance for presurgical assessments for epilepsy surgeries. SEEG, however, is limited to selected patients with epilepsy due to its invasive procedure. In this work, a brain signal synthesis framework is presented to synthesize SEEG signals from non-invasive EEG signals. First, a strategy to determine the matching relation between EEG and SEEG channels is presented by considering both signal correlation and spatial distance. Second, the EEG-to-SEEG generative adversarial network (E2SGAN) is proposed to precisely synthesize SEEG data from the simultaneous EEG data. Although the widely adopted magnitude spectra has proved to be informative in EEG tasks, it leaves much to be desired in the setting of signal synthesis. To this end, instantaneous frequency spectra is introduced to further represent the alignment of the signal. Correlative spectral attention (CSA) is proposed to enhance the discriminator of E2SGAN by capturing the correlation between each pair of EEG and SEEG frequencies. The weighted patch prediction (WPP) technique is devised to ensure robust temporal results. Comparison experiments on real-patient data demonstrate that E2SGAN outperforms baseline methods in both temporal and frequency domains. The perturbation experiment reveals that the synthesized results have the potential to capture abnormal discharges in epileptic patients before seizures.

Surgery for Epilepsy Involving Rolandic and Perirolandic Cortex: A Case Series Assessing Complications and Efficacy.

BACKGROUND: Surgical removal of lesions around the rolandic cortex remains a challenge for neurosurgeons owing to the high risk of neurological deficits. Evaluating the risk factors associated with motor deficits after surgery in this region may help reduce the occurrence of motor deficits. OBJECTIVE: To report our surgical experience in treating epileptic lesions involving the rolandic and perirolandic cortices. METHODS: We performed a single-center retrospective review of patients undergoing epilepsy surgeries with lesions located in the rolandic and perirolandic cortices. Patients with detailed follow-up information were included. The lesion locations, resected regions, and invasive exploration techniques were studied to assess their relationship with postoperative motor deficits. RESULTS: Forty-one patients were included. Twenty-three patients suffered from a transient motor deficit, and 2 had permanent disabilities after surgery. Six patients with lesions at the posterior bank of the precentral sulcus underwent resection, and 5 experienced short-term motor deficits. Two patients with lesions adjacent to the anterior part of the precentral gyrus, in whom the adjacent precentral gyrus was removed, experienced permanent motor deficits. Lesions located at the bottom of the central sulcus and invading the anterior bank of the central sulcus were observed in 3 patients. The patients did not experience permanent motor deficits after surgery. CONCLUSION: The anterior bank of the central sulcus is indispensable for motor function, and destruction of this region would inevitably cause motor deficits. The anterior bank of the precentral gyrus can also be removed without motor impairment if there is a preexisting epileptogenic lesion.

Neural excitatory rebound induced by valproic acid may predict its inadequate control of seizures.

BACKGROUND: Valproic acid (VPA) represents one of the most efficient antiseizure medications (ASMs) for both general and focal seizures, but some patients may have inadequate control by VPA monotherapy. In this study, we aimed to verify the hypothesis that excitatory dynamic rebound induced by inhibitory power may contribute to the ineffectiveness of VPA therapy and become a predictor of post-operative inadequate control of seizures. METHODS: Awake craniotomy surgeries were performed in 16 patients with intro-operative high-density electrocorticogram (ECoG) recording. The relationship between seizure control and the excitatory rebound was further determined by diagnostic test and univariate analysis. Thereafter, kanic acid (KA)-induced epileptic mouse model was used to confirm that its behavior and neural activity would be controlled by VPA. Finally, a computational simulation model was established to verify the hypothesis. FINDINGS: Inadequate control of seizures by VPA monotherapy and post-operative status epilepticus are closely related to a significant excitatory rebound after VPA injection (rebound electrodes≧5/64, p = 0.008), together with increased synchronization of the local field potential (LFP). In addition, the neural activity in the model mice showed a significant rebound on spike firing (53/77 units, 68.83%). The LFP increased the power spectral density in multiple wavebands after VPA injection in animal experiments (p < 0.001). Computational simulation experiments revealed that inhibitory power-induced excitatory rebound is an intrinsic feature in the neural network. INTERPRETATION: Despite the limitations, we provide evidence that inadequate control of seizures by VPA monotherapy could be associated with neural excitatory rebounds, which were predicted by intraoperative ECoG analysis. Combined with the evidence from computational models and animal experiments, our findings suggested that ineffective ASMs may be because of the excitatory rebound, which is mediated by increased inhibitory power. FUNDING: This work was supported by National Natural Science Foundation of China (62127810, 81970418), Shanghai Municipal Science and Technology Major Project (2018SHZDZX03) and ZJLab; Science and Technology Commission of Shanghai Municipality (18JC1410403, 19411969000, 19ZR1477700, 20Z11900100); MOE Frontiers Center for Brain Science; Shanghai Key Laboratory of Health Identification and Assessment (21DZ2271000); Shanghai Shenkang (SHDC2020CR3073B).

Reoperation for residual or recurrent hemifacial spasm after microvascular decompression.

PURPOSE: Microvascular decompression (MVD) surgery is the only potential curative method for hemifacial spasm (HFS). Little attention is paid to those recurrent/residual HFS cases. We want to study the potential etiology of those recurrent/residual HFS cases and evaluate the value of reoperation. METHODS: We retrospectively reviewed reoperation hemifacial spasm patients in our hospital. Intraoperative videos or images were carefully reviewed, and the etiology of recurrent/residual HFS is roughly divided into three categories. Intraoperative findings, surgical outcomes, and complications were carefully studied to assess the value of reoperation for recurrent/residual HFS patients. RESULTS: A total of 28 cases were included in our case series. Twenty-three of them are recurrent HFS cases, and 5 of them are residual HFS cases. The mean follow-up duration is 24.96 months. There are seventeen patients with missed culprit vessels or insufficient decompression of root exit zone (REZ), eight patients with Teflon adhesion, and three patients with improper application of decompression materials in our case series. The final reoperation outcome with 17 excellent, seven good, and four fair, respectively. Eight (28.57%) of them experienced long-term complications after reoperation. CONCLUSION: Re-operation for recurrent/residual HFS is an effective therapy and can achieve a higher cure rate. However, the complication rate is higher compared to the first MVD surgery. Accurately identifying REZ and proper decompression strategies to deal with the culprit vessels are very important for surgical success. TRIAL REGISTRATION NUMBER: UIN: researchregistry7603. Date of registration: Jan. 31st, 2022 "retrospectively registered".

Decoding continuous kinetic information of grasp from stereo-electroencephalographic (SEEG) recordings.

Objective.Brain-computer interfaces (BCIs) have the potential to bypass damaged neural pathways and restore functionality lost due to injury or disease. Approaches to decoding kinematic information are well documented; however, the decoding of kinetic information has received less attention. Additionally, the possibility of using stereo-electroencephalography (SEEG) for kinetic decoding during hand grasping tasks is still largely unknown. Thus, the objective of this paper is to demonstrate kinetic parameter decoding using SEEG in patients performing a grasping task with two different force levels under two different ascending rates.Approach.Temporal-spectral representations were studied to investigate frequency modulation under different force tasks. Then, force amplitude was decoded from SEEG recordings using multiple decoders, including a linear model, a partial least squares model, an unscented Kalman filter, and three deep learning models (shallow convolutional neural network, deep convolutional neural network and the proposed CNN+RNN neural network).Main results.The current study showed that: (a) for some channel, both low-frequency modulation (event-related desynchronization (ERD)) and high-frequency modulation (event-related synchronization) were sustained during prolonged force holding periods; (b) continuously changing grasp force can be decoded from the SEEG signals; (c) the novel CNN+RNN deep learning model achieved the best decoding performance, with the predicted force magnitude closely aligned to the ground truth under different force amplitudes and changing rates.Significance.This work verified the possibility of decoding continuously changing grasp force using SEEG recordings. The result presented in this study demonstrated the potential of SEEG recordings for future BCI application.

Assessing differential representation of hand movements in multiple domains using stereo-electroencephalographic recordings.

Invasive brain-computer interfaces (BCI) have made great progress in the reconstruction of fine hand movement parameters for paralyzed patients, where superficial measurement modalities including electrocorticography (ECoG) and micro-array recordings are mostly used. However, these recording techniques typically focus on the signals from the sensorimotor cortex, leaving subcortical regions and other cortical regions related to the movements largely unexplored. As an intracranial recording technique for the presurgical assessments of brain surgery, stereo-encephalography (SEEG) inserts depth electrodes containing multiple contacts into the brain and thus provides the unique opportunity for investigating movement-related neural representation throughout the brain. Although SEEG samples neural signals with high spatial-temporal resolutions, its potential of being used to build BCIs has just been realized recently, and the decoding of SEEG activity related to hand movements has not been comprehensively investigated yet. Here, we systematically evaluated the factors influencing the performance of movement decoding using SEEG signals recorded from 32 human subjects performing a visually-cued hand movement task. Our results suggest that multiple regions in both lateral and depth directions present significant neural selectivity to the task, whereas the sensorimotor area, including both precentral and postcentral cortex, carries the richest discriminative neural information for the decoding. The posterior parietal and prefrontal cortex contribute gradually less, but still rich sources for extracting movement parameters. The insula, temporal and occipital cortex also contains useful task-related information for decoding. Under the cortex layer, white matter presents decodable neural patterns but yields a lower accuracy (42.0 ± 0.8%) than the cortex on average (44.2 ± 0.8%, p<0.01). Notably, collectively using neural signals from multiple task-related areas can significantly enhance the movement decoding performance by 6.9% (p<0.01) on average compared to using a single region. Among the different spectral components of SEEG activity, the high gamma and delta bands offer the most informative features for hand movements reconstruction. Additionally, the phase-amplitude coupling strength between these two frequency ranges correlates positively with the performance of movement decoding. In the temporal domain, maximum decoding accuracy is first reached around 2 s after the onset of movement commands. In sum, this study provides valuable insights for the future motor BCIs design employing both SEEG recordings and other recording modalities.

Investigating Data Cleaning Methods to Improve Performance of Brain-Computer Interfaces Based on Stereo-Electroencephalography.

Stereo-electroencephalography (SEEG) utilizes localized and penetrating depth electrodes to directly measure electrophysiological brain activity. The implanted electrodes generally provide a sparse sampling of multiple brain regions, including both cortical and subcortical structures, making the SEEG neural recordings a potential source for the brain-computer interface (BCI) purpose in recent years. For SEEG signals, data cleaning is an essential preprocessing step in removing excessive noises for further analysis. However, little is known about what kinds of effect that different data cleaning methods may exert on BCI decoding performance and, moreover, what are the reasons causing the differentiated effects. To address these questions, we adopted five different data cleaning methods, including common average reference, gray-white matter reference, electrode shaft reference, bipolar reference, and Laplacian reference, to process the SEEG data and evaluated the effect of these methods on improving BCI decoding performance. Additionally, we also comparatively investigated the changes of SEEG signals induced by these different methods from multiple-domain (e.g., spatial, spectral, and temporal domain). The results showed that data cleaning methods could improve the accuracy of gesture decoding, where the Laplacian reference produced the best performance. Further analysis revealed that the superiority of the data cleaning method with excellent performance might be attributed to the increased distinguishability in the low-frequency band. The findings of this work highlighted the importance of applying proper data clean methods for SEEG signals and proposed the application of Laplacian reference for SEEG-based BCI.

Detection of human white matter activation and evaluation of its function in movement decoding using stereo-electroencephalography (SEEG).

Objective. White matter tissue takes up approximately 50% of the human brain volume and it is widely known as a messenger conducting information between areas of the central nervous system. However, the characteristics of white matter neural activity and whether white matter neural recordings can contribute to movement decoding are often ignored and still remain largely unknown. In this work, we make quantitative analyses to investigate these two important questions using invasive neural recordings.Approach. We recorded stereo-electroencephalography (SEEG) data from 32 human subjects during a visually-cued motor task, where SEEG recordings can tap into gray and white matter electrical activity simultaneously. Using the proximal tissue density method, we identified the location (i.e. gray or white matter) of each SEEG contact. Focusing on alpha oscillatory and high gamma activities, we compared the activation patterns between gray matter and white matter. Then, we evaluated the performance of such white matter activation in movement decoding.Main results. The results show that white matter also presents activation under the task, in a similar way with the gray matter but at a significantly lower amplitude. Additionally, this work also demonstrates that combing white matter neural activities together with that of gray matter significantly promotes the movement decoding accuracy than using gray matter signals only.Significance. Taking advantage of SEEG recordings from a large number of subjects, we reveal the response characteristics of white matter neural signals under the task and demonstrate its enhancing function in movement decoding. This study highlights the importance of taking white matter activities into consideration in further scientific research and translational applications.

Prestimulus dynamics blend with the stimulus in neural variability quenching.

Neural responses to the same stimulus show significant variability over trials, with this variability typically reduced (quenched) after a stimulus is presented. This trial-to-trial variability (TTV) has been much studied, however how this neural variability quenching is influenced by the ongoing dynamics of the prestimulus period is unknown. Utilizing a human intracranial stereo-electroencephalography (sEEG) data set, we investigate how prestimulus dynamics, as operationalized by standard deviation (SD), shapes poststimulus activity through trial-to-trial variability (TTV). We first observed greater poststimulus variability quenching in those real trials exhibiting high prestimulus variability as observed in all frequency bands. Next, we found that the relative effect of the stimulus was higher in the later (300-600ms) than the earlier (0-300ms) poststimulus period. Lastly, we replicate our findings in a separate EEG dataset and extend them by finding that trials with high prestimulus variability in the theta and alpha bands had faster reaction times. Together, our results demonstrate that stimulus-related activity, including its variability, is a blend of two factors: 1) the effects of the external stimulus itself, and 2) the effects of the ongoing dynamics spilling over from the prestimulus period - the state at stimulus onset - with the second dwarfing the influence of the first.

Characterizing Brain Signals for Epileptic Pre-ictal Signal Classification.

Epilepsy is a kind of neurological disorder characterized by recurrent epileptic seizures. While it is crucial to characterize pre-ictal brain electrical activities, the problem to this day still remains computationally challenging. Using brain signal acquisition and advances in deep learning technology, we aim to classify pre-ictal signals and characterize the brain waveforms of patients with epilepsy during the pre-ictal period. We develop a novel machine learning model called Pre-ictal Signal Classification (PiSC) for pre-ictal signal classification and for identifying brain waveform patterns critical for seizure onset early detection. In PiSC, a unique preprocessing procedure is developed to convert the stereo-electroencephalography (sEEG) signals to data blocks ready for pre-ictal signal classification. Also, a novel deep learning framework is developed to integrate deep neural networks and meta-learning to effectively mitigate patient-to-patient variances as well as fine-tuning a trained classification model for new patients. The unique network architecture ensures model stability and generalization in sEEG data modeling. The experimental results on a real-world patient dataset show that PiSC improved the accuracy and F1 score by 10% compared with the existing models. Two types of sEEG patterns were discovered to be associated with seizure development in nocturnal epileptic patients.

Quantitative analysis of cerebellar lobule morphology and clinical cognitive correlates in refractory temporal lobe epilepsy patients.

PURPOSE: This study was conducted to explore the cerebellar substructure volumetric alterations in refractory unilateral temporal lobe epilepsy (TLE) patients and the relationship with clinical factors and cognitive scores. METHODS: A total of 48 unilateral refractory TLE patients and 48 age- and gender-matched normal controls (NCs) were retrospectively studied. All subjects underwent high-resolution magnetic resonance imaging (MRI) and automatically segmented volumetric brain information was obtained using volBrain and Data Processing Assistant for Resting-State fMRI (DPARSF) separately. Clinical seizure features and cognitive scores were acquired by a structured review of medical records. RESULTS: The total volumes (TVs) of bilateral crus I, crus II, and IX were significantly smaller in the refractory unilateral TLE epilepsy patients. The gray matter volumes (GMVs) of cerebellar lobules showed lateralized reduction in ipsilateral III, IX, and contralateral crus II. Contralateral crus II GMV showed significant negative correlation with the duration of epilepsy (r = -0.31, p = 0.035) and positive association with the cognitive scores including long-term memory (LTM) (r = 0.39, p = 0.017), short-term memory (STM) (r = 0.51, p = 0.001) verbal comprehension index (VCI) (r = 0.37, p = 0.024), and perceptual organization index (POI) (r = 0.36, p = 0.030). The voxel-based morphometry (VBM) analysis proved similar results. The contralateral crus I GMV was significantly smaller in the generalized onset group (t = 2.536, p = 0.015). CONCLUSIONS: The lobules of the cerebellar in refractory TLE patients manifest different volumetric change characteristics. Crus II contralateral GMV is negatively correlated with the duration of epilepsy and positively associated with the cognitive scores.

Ictal stereo-electroencephalography onset patterns of mesial temporal lobe epilepsy and their clinical implications.

OBJECTIVE: The differences in mesial temporal epilepsy (MTE) stereo-electroencephalography (SEEG) seizure-onset patterns and their clinical implications remains unclear. METHODS: We analyzed consecutive patients with MTE undergoing non-invasive workup, SEEG evaluation and resective surgery. Cases were classified into either mesial temporal sclerosis (MTS) group or non-MTS group based on magnetic resonance imaging (MRI). Seizure-onset patterns of SEEG were classified to analyze their correlation with surgical outcome and clinical subtypes. RESULTS: Twenty-eight patients were studied. Twenty (71.4%) patients had Engel I outcome. Thirteen patients had one seizure-onset pattern, 15 had two or more patterns. Five patterns of seizure-onset were identified and seizure-onset zones differed significantly across the 5 patterns. No difference was observed in surgical outcome between patients with single or multiple seizure-onset patterns. Periodic spike-onset pattern was associated with MTS (P = 0.003) while burst-onset was associated with non-MTS lesions (P = 0.003). Patients with seizure-onsets outside the resected temporal lobe (multiple onsets) had poorer prognosis (P = 0.0046). CONCLUSION: We identified 5 distinct onset patterns of MTE and correlated two of them with MRI findings. Multiple seizure-onset patterns in MTE may not necessarily suggest poor outcome. Patients with multi-focal seizure-onsets including seizures originating outside the resected temporal lobe have poorer outcome. SIGNIFICANCE: This study identifies distinct onset patterns of MTE and their clinical implications.

Enhancing gesture decoding performance using signals from posterior parietal cortex: a stereo-electroencephalograhy (SEEG) study.

OBJECTIVE: Hand movement is a crucial function for humans' daily life. Developing brain-machine interface (BMI) to control a robotic hand by brain signals would help the severely paralyzed people partially regain the functional independence. Previous intracranial electroencephalography (iEEG)-based BMIs towards gesture decoding mostly used neural signals from the primary sensorimotor cortex while ignoring the hand movement related signals from posterior parietal cortex (PPC). Here, we propose combining iEEG recordings from PPC with that from primary sensorimotor cortex to enhance the gesture decoding performance of iEEG-based BMI. APPROACH: Stereoelectroencephalography (SEEG) signals from 25 epilepsy subjects were recorded when they performed a three-class hand gesture task. Across all 25 subjects, we identified 524, 114 and 221 electrodes from three regions of interest (ROIs), including PPC, postcentral cortex (POC) and precentral cortex (PRC), respectively. Based on the time-varying high gamma power (55-150 Hz) of SEEG signal, both the general activation in the task and the fine selectivity to gestures of each electrode in these ROIs along time was evaluated by the coefficient of determination r 2. According to the activation along time, we further assessed the first activation time of each ROI. Finally, the decoding accuracy for gestures was obtained by linear support vector machine classifier to comparatively explore if the PPC will assist PRC and POC for gesture decoding. MAIN RESULTS: We find that a majority(L: [Formula: see text] 60%, R: [Formula: see text] 40%) of electrodes in all the three ROIs present significant activation during the task. A large scale temporal activation sequence exists among the ROIs, where PPC activates first, PRC second and POC last. Among the activated electrodes, 15% (PRC), 26% (POC) and 4% (left PPC) of electrodes are significantly selective to gestures. Moreover, decoding accuracy obtained by combining the selective electrodes from three ROIs together is 5%, 3.6%, and 8% higher than that from only PRC and POC when decoding features across, before, and after the movement onset, were used. SIGNIFICANCE: This is the first human iEEG study demonstrating that PPC contains neural information about fine hand movement, supporting the role of PPC in hand shape encoding. Combining PPC with primary sensorimotor cortex can provide more information to improve the gesture decoding performance. Our results suggest that PPC could be a rich neural source for iEEG-based BMI. Our findings also demonstrate the early involvement of human PPC in visuomotor task and thus may provide additional implications for further scientific research and BMI applications.

iEEGview: an open-source multifunction GUI-based Matlab toolbox for localization and visualization of human intracranial electrodes.

OBJECTIVE: The precise localization of intracranial electrodes is a fundamental step relevant to the analysis of intracranial electroencephalography (iEEG) recordings in various fields. With the increasing development of iEEG studies in human neuroscience, higher requirements have been posed on the localization process, resulting in urgent demand for more integrated, easy-operation and versatile tools for electrode localization and visualization. With the aim of addressing this need, we develop an easy-to-use and multifunction toolbox called iEEGview, which can be used for the localization and visualization of human intracranial electrodes. APPROACH: iEEGview is written in Matlab scripts and implemented with a GUI. From the GUI, by taking only pre-implant MRI and post-implant CT images as input, users can directly run the full localization pipeline including brain segmentation, image co-registration, electrode reconstruction, anatomical information identification, activation map generation and electrode projection from native brain space into common brain space for group analysis. Additionally, iEEGview implements methods for brain shift correction, visual location inspection on MRI slices and computation of certainty index in anatomical label assignment. MAIN RESULTS: All the introduced functions of iEEGview work reliably and successfully, and are tested by images from 28 human subjects implanted with depth and/or subdural electrodes. SIGNIFICANCE: iEEGview is the first public Matlab GUI-based software for intracranial electrode localization and visualization that holds integrated capabilities together within one pipeline. iEEGview promotes convenience and efficiency for the localization process, provides rich localization information for further analysis and offers solutions for addressing raised technical challenges. Therefore, it can serve as a useful tool in facilitating iEEG studies.

Optimal referencing for stereo-electroencephalographic (SEEG) recordings.

Stereo-electroencephalography (SEEG) is an intracranial recording technique in which depth electrodes are inserted in the brain as part of presurgical assessments for invasive brain surgery. SEEG recordings can tap into neural signals across the entire brain and thereby sample both cortical and subcortical sites. However, even though signal referencing is important for proper assessment of SEEG signals, no previous study has comprehensively evaluated the optimal referencing method for SEEG. In our study, we recorded SEEG data from 15 human subjects during a motor task, referencing them against the average of two white matter contacts (monopolar reference). We then subjected these signals to 5 different re-referencing approaches: common average reference (CAR), gray-white matter reference (GWR), electrode shaft reference (ESR), bipolar reference, and Laplacian reference. The results from three different signal quality metrics suggest the use of the Laplacian re-reference for study of local population-level activity and low-frequency oscillatory activity.

GIRK1-mediated inwardly rectifying potassium current suppresses the epileptiform burst activities and the potential antiepileptic effect of ML297.

G protein-gated inwardly rectifying potassium (GIRK) channels are important inhibitory regulators of neuronal excitability in central nervous system, and the impairment of GIRK channel function has been reported to be associated with the susceptibility of epilepsy. However, the dynamics of GIRK channels in the pathogenesis of epilepsy are still unclear. In this study, our results showed that cyclothiazide, a potent convulsant, dose dependently increased the epileptiform bursting activities and suppressed the baclofen induced GIRK currents. In addition, TPQ, a selective GIRK antagonist, significantly decreased the total inwardly rectifying potassium (Kir) current, and increased the neuronal epileptiform activities. In contrast, ML297, a potent and selective GIRK channel agonist, reversed the cyclothiazide induced decrease of GIRK currents and the increase of neuronal excitability in cultured hippocampal neurons. Further investigation revealed that GIRK1, but not GIRK2, played a key role in suppressing epileptic activities. Finally, in pilocarpine mice seizure model, we demonstrated that ML297 significantly suppressed the seizure behavior. In summary, our current results indicate that GIRK channels, especially GIRK1-containing channels, are involved in epileptic activities and ML297 has a potential antiepileptic effect.