RESUMO
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.
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Sinais (Psicologia) , Mãos , Humanos , Encéfalo/fisiologia , Movimento/fisiologia , Mapeamento Encefálico/métodos , Eletroencefalografia/métodosRESUMO
The persistence of cells latently infected with HIV-1, named the latent reservoir, is the major barrier to HIV-1 eradication, and the formation and maintenance of the latent reservoir might be exacerbated by activation of the immunoinhibitory pathway and dysfunction of CD8+ T cells during HIV-1 infection. Our previous findings demonstrated that prophylactic vaccination combined with PD-1 blockade generated distinct immune response profiles and conferred effective control of highly pathogenic SIVmac239 infection in rhesus macaques. However, to our surprise, herein we found that a therapeutic vaccination in combination with PD-1 blockade resulted in activation of the viral reservoir, faster viral rebound after treatment interruption, accelerated AIDS progression, and, ultimately, death in chronically SIV-infected macaques after antiretroviral therapy (ART) interruption. Our study further demonstrated that the SIV provirus was preferentially enriched in PD-1+CD4+ T cells due to their susceptibility to viral entry, potent proliferative ability, and inability to perform viral transcription. In addition, the viral latency was effectively reactivated upon PD-1 blockade. Together, these results suggest that PD-1 blockade may be a double-edged sword for HIV-1 immunotherapy and provide important insight toward the rational design of immunotherapy strategies for an HIV-1 cure. IMPORTANCE As it is one of the most challenging public health problems, there are no clinically effective cure strategies against HIV-1 infection. We demonstrated that prophylactic vaccination combined with PD-1 blockade generated distinct immune response profiles and conferred better control of highly pathogenic SIVmac239 infection in rhesus macaques. In the present study, to our surprise, PD-1 blockade during therapeutic vaccination accelerated the reactivation of latent reservoir and AIDS progression in chronically SIV-infected macaques after ART interruption. Our study further demonstrated that the latent SIV provirus was preferentially enriched in PD-1+CD4+ T cells because of its susceptibility to viral entry, inhibition of SIV transcription, and potent ability of proliferation, and the viral latency was effectively reactivated by PD-1 blockade. Therefore, PD-1 blockade might be a double-edged sword for AIDS therapy. These findings provoke interest in further exploring novel treatments against HIV-1 infection and other emerging infectious diseases.
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Receptor de Morte Celular Programada 1/antagonistas & inibidores , Síndrome de Imunodeficiência Adquirida dos Símios/imunologia , Síndrome de Imunodeficiência Adquirida dos Símios/virologia , Vírus da Imunodeficiência Símia/efeitos dos fármacos , Vírus da Imunodeficiência Símia/imunologia , Animais , Antirretrovirais/farmacologia , Antirretrovirais/uso terapêutico , Terapia Antirretroviral de Alta Atividade , Biópsia , Biologia Computacional , Progressão da Doença , Imuno-Histoquímica , Imunomodulação/efeitos dos fármacos , Macaca mulatta , Vacinas contra a SAIDS/administração & dosagem , Vacinas contra a SAIDS/imunologia , Síndrome de Imunodeficiência Adquirida dos Símios/tratamento farmacológico , Subpopulações de Linfócitos T/efeitos dos fármacos , Subpopulações de Linfócitos T/imunologia , Subpopulações de Linfócitos T/metabolismo , Transcriptoma , Carga Viral , Ativação Viral/efeitos dos fármacos , Latência Viral/efeitos dos fármacos , Replicação Viral/efeitos dos fármacosRESUMO
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.
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Mapeamento Encefálico/métodos , Interfaces Cérebro-Computador , Eletroencefalografia/métodos , Mãos/fisiologia , Movimento/fisiologia , Adulto , Sinais (Psicologia) , Epilepsia Resistente a Medicamentos/fisiopatologia , Feminino , Humanos , Masculino , Técnicas EstereotáxicasRESUMO
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.
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Ondas Encefálicas/fisiologia , Encéfalo/fisiologia , Eletrocorticografia/normas , Processamento de Sinais Assistido por Computador , Técnicas Estereotáxicas , Adulto , Encéfalo/anatomia & histologia , Eletrocorticografia/métodos , Eletromiografia , Epilepsia/fisiopatologia , Epilepsia/cirurgia , Substância Cinzenta/anatomia & histologia , Substância Cinzenta/fisiologia , Humanos , Atividade Motora/fisiologia , Substância Branca/anatomia & histologia , Substância Branca/fisiologiaRESUMO
Brain-computer interfaces (BCIs) can enable direct communication with assistive devices by recording and decoding signals from the brain. To achieve high performance, many electrodes will be used, such as the recently developed invasive BCIs with channel numbers up to hundreds or even thousands. For those high-throughput BCIs, channel selection is important to reduce signal redundancy and invasiveness while maintaining decoding performance. However, such endeavour is rarely reported for invasive BCIs, especially those using deep learning methods. Two deep learning-based methods, referred to as Gumbel and STG, were proposed in this paper. They were evaluated using the Stereo-electroencephalography (SEEG) signals, and compared with three other methods, including manual selection, mutual information-based method (MI), and all channels (all channels without selection). The task is to classify the SEEG signals into five movements using channels selected by each method. When 10 channels were selected, the mean classification accuracies using Gumbel, STG (referred to as STG-10), manual selection, and MI selection were 65%, 60%, 60%, and 47%, respectively, whilst the accuracy was 59% using all channels (no selection). In addition, an investigation of the selected channels showed that Gumbel and STG have successfully identified the pre-central and post-central areas, which are closely related to motor control. Both Gumbel and STG successfully selected the informative channels in SEEG recordings while maintaining decoding accuracy. This study enables future high-throughput BCIs using deep learning methods, to identify useful channels and reduce computing and wireless transmission pressure.
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Interfaces Cérebro-Computador , Aprendizado Profundo , Humanos , Eletroencefalografia/métodos , Encéfalo , Movimento , Algoritmos , ImaginaçãoRESUMO
Objective.Deep learning is increasingly used for brain-computer interfaces (BCIs). However, the quantity of available data is sparse, especially for invasive BCIs. Data augmentation (DA) methods, such as generative models, can help to address this sparseness. However, all the existing studies on brain signals were based on convolutional neural networks and ignored the temporal dependence. This paper attempted to enhance generative models by capturing the temporal relationship from a time-series perspective.Approach. A conditional generative network (conditional transformer-based generative adversarial network (cTGAN)) based on the transformer model was proposed. The proposed method was tested using a stereo-electroencephalography (SEEG) dataset which was recorded from eight epileptic patients performing five different movements. Three other commonly used DA methods were also implemented: noise injection (NI), variational autoencoder (VAE), and conditional Wasserstein generative adversarial network with gradient penalty (cWGANGP). Using the proposed method, the artificial SEEG data was generated, and several metrics were used to compare the data quality, including visual inspection, cosine similarity (CS), Jensen-Shannon distance (JSD), and the effect on the performance of a deep learning-based classifier.Main results. Both the proposed cTGAN and the cWGANGP methods were able to generate realistic data, while NI and VAE outputted inferior samples when visualized as raw sequences and in a lower dimensional space. The cTGAN generated the best samples in terms of CS and JSD and outperformed cWGANGP significantly in enhancing the performance of a deep learning-based classifier (each of them yielding a significant improvement of 6% and 3.4%, respectively).Significance. This is the first time that DA methods have been applied to invasive BCIs based on SEEG. In addition, this study demonstrated the advantages of the model that preserves the temporal dependence from a time-series perspective.
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Interfaces Cérebro-Computador , Humanos , Benchmarking , Encéfalo , Fontes de Energia Elétrica , EletroencefalografiaRESUMO
Objective.Brain switches provide a tangible solution to asynchronized brain-computer interface, which decodes user intention without a pre-programmed structure. However, most brain switches based on electroencephalography signals have high false positive rates (FPRs), resulting in less practicality. This research aims to improve the operating mode and usability of the brain switch.Approach.Here, we propose a novel virtual physical model-based brain switch that leverages periodic active modulation. An optimization problem of minimizing the triggering time subject to a required FPR is formulated, numerical and analytical approximate solutions are obtained based on the model.Main results.Our motor imagery (MI)-based brain switch can reach 0.8FP/h FPR with a median triggering time of 58 s. We evaluated the proposed brain switch during online device control, and their average FPRs substantially outperformed the conventional brain switches in the literature. We further improved the proposed brain switch with the Common Spatial Pattern (CSP) and optimization method. An average FPR of 0.3 FPs/h was obtained for the MI-CSP-based brain switch, and the average triggering time improved to 21.6 s.Significance.This study provides a new approach that could significantly reduce the brain switch's FPR to less than 1 Fps/h, which was less than 10% of the FPR (decreasing by more than a magnitude of order) by other endogenous methods, and the reaction time was comparable to the state-of-the-art approaches. This represents a significant advancement over the current non-invasive asynchronous BCI and will open widespread avenues for translating BCI towards clinical applications.
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Interfaces Cérebro-Computador , Eletroencefalografia , Imaginação , Humanos , Imaginação/fisiologia , Eletroencefalografia/métodos , Encéfalo/fisiologia , Modelos Neurológicos , Movimento/fisiologiaRESUMO
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.
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Interfaces Cérebro-Computador , Aprendizado Profundo , Epilepsia , Humanos , Redes Neurais de Computação , Epilepsia/diagnóstico , Eletroencefalografia/métodos , AlgoritmosRESUMO
Objective. Revealing the relationship between simultaneous scalp electroencephalography (EEG) and intracranial electroencephalography (iEEG) is of great importance for both neuroscientific research and translational applications. However, whether prominent iEEG features in the high-gamma band can be reflected by scalp EEG is largely unknown. To address this, we investigated the phase-amplitude coupling (PAC) phenomenon between the low-frequency band of scalp EEG and the high-gamma band of iEEG.Approach. We analyzed a simultaneous iEEG and scalp EEG dataset acquired under a verbal working memory paradigm from nine epilepsy subjects. The PAC values between pairs of scalp EEG channel and identified iEEG channel were explored. After identifying the frequency combinations and electrode locations that generated the most significant PAC values, we compared the PAC values of different task periods (encoding, maintenance, and retrieval) and memory loads.Main results. We demonstrated that the amplitude of high-gamma activities in the entorhinal cortex, hippocampus, and amygdala was correlated to the delta or theta phase at scalp locations such as Cz and Pz. In particular, the frequency bin that generated the maximum PAC value centered at 3.16-3.84 Hz for the phase and 50-85 Hz for the amplitude. Moreover, our results showed that PAC values for the retrieval period were significantly higher than those of the encoding and maintenance periods, and the PAC was also influenced by the memory load.Significance. This is the first human simultaneous iEEG and scalp EEG study demonstrating that the amplitude of iEEG high-gamma components is associated with the phase of low-frequency components in scalp EEG. These findings enhance our understanding of multiscale neural interactions during working memory, and meanwhile, provide a new perspective to estimate intracranial high-frequency features with non-invasive neural recordings.
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Eletrocorticografia , Epilepsia , Eletrocorticografia/métodos , Eletroencefalografia/métodos , Epilepsia/diagnóstico , Humanos , Memória de Curto Prazo , Couro CabeludoRESUMO
Current myoelectric hands are limited in their ability to provide effective sensory feedback to the users, which highly affects their functionality and utility. Although the sensory information of a myoelectric hand can be acquired with equipped sensors, transforming the sensory signals into effective tactile sensations on users for functional tasks is a largely unsolved challenge. The purpose of this study aims to demonstrate that electrotactile feedback of the grip force improves the sensorimotor control of a myoelectric hand and enables object stiffness recognition. The grip force of a sensorized myoelectric hand was delivered to its users via electrotactile stimulation based on four kinds of typical encoding strategies, including graded (G), linear amplitude (LA), linear frequency (LF), and biomimetic (B) modulation. Object stiffness was encoded with the change of electrotactile sensations triggered by final grip force, as the prosthesis grasped the objects. Ten able-bodied subjects and two transradial amputees were recruited to participate in a dual-task virtual eggs test (VET) and an object stiffness discrimination test (OSDT) to quantify the prosthesis users' ability to handle fragile objects and recognize object stiffnesses, respectively. The quantified results showed that with electrotactile feedback enabled, the four kinds of encoding strategies allowed subjects to better able to handle fragile objects with similar performance, and the subjects were able to differentiate four levels of object stiffness at favorable accuracies (>86%) and high manual efficiency. Strategy LA presented the best stiffness discrimination performance, while strategy B was able to reduce the discrimination time but the discrimination accuracy was not better than the other three strategies. Electrotactile feedback also enhanced prosthesis embodiment and improved the users' confidence in prosthetic control. Outcomes indicate electrotactile feedback can be effectively exploited by the prosthesis users for grip force control and object stiffness recognition, proving the feasibility of functional sensory restoration of myoelectric prostheses equipped with electrotactile feedback.
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Membros Artificiais , Força da Mão , Eletromiografia/métodos , Retroalimentação , Retroalimentação Sensorial/fisiologia , Mãos/fisiologia , Força da Mão/fisiologia , Humanos , Desenho de Prótese , Tato/fisiologiaRESUMO
As a minimally invasive recording technique, stereo-electroencephalography (SEEG) measures intracranial signals directly by inserting depth electrodes shafts into the human brain, and thus can capture neural activities in both cortical layers and subcortical structures. Despite gradually increasing SEEG-based brain-computer interface (BCI) studies, the features utilized were usually confined to the amplitude of the event-related potential (ERP) or band power, and the decoding capabilities of other time-frequency and time-domain features have not been demonstrated for SEEG recordings yet. In this study, we aimed to verify the validity of time-domain and time-frequency features of SEEG, where classification performances served as evaluating indicators. To do this, using SEEG signals under intermittent auditory stimuli, we extracted features including the average amplitude, root mean square, slope of linear regression, and line-length from the ERP trace and three traces of band power activities (high-gamma, beta, and alpha). These features were used to detect the active state (including activations to two types of names) against the idle state. Results suggested that valid time-domain and time-frequency features distributed across multiple regions, including the temporal lobe, parietal lobe, and deeper structures such as the insula. Among all feature types, the average amplitude, root mean square, and line-length extracted from high-gamma (60-140 Hz) power and the line-length extracted from ERP were the most informative. Using a hidden Markov model (HMM), we could precisely detect the onset and the end of the active state with a sensitivity of 95.7 ± 1.3% and a precision of 91.7 ± 1.6%. The valid features derived from high-gamma power and ERP in this work provided new insights into the feature selection procedure for further SEEG-based BCI applications.
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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.
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Interfaces Cérebro-Computador , Eletroencefalografia , Eletroencefalografia/métodos , Força da Mão , Humanos , Modelos Lineares , Redes Neurais de ComputaçãoRESUMO
Prime editors (PEs) were developed to induce versatile edits at a guide-specified genomic locus. With all RNA-guided genome editors, guide-dependent off-target (OT) mutations can occur at other sites bearing similarity to the intended target. However, whether PEs carry the additional risk of guide-independent mutations elicited by their unique enzymatic moiety (i.e., reverse transcriptase) has not been examined systematically in mammalian cells. Here, we developed a cost-effective sensitive platform to profile guide-independent OT effects in human cells. We did not observe guide-independent OT mutations in the DNA or RNA of prime editor 3 (PE3)-edited cells, or alterations to their telomeres, endogenous retroelements, alternative splicing events, or gene expression. Together, our results showed undetectable prime editing guide RNA-independent OT effects of PE3 in human cells, suggesting the high editing specificity of its reverse-transcriptase moiety.
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Edição de Genes , RNA Guia de Cinetoplastídeos , Animais , Sistemas CRISPR-Cas/genética , Edição de Genes/métodos , Humanos , Mamíferos/genética , RNA/genética , RNA Guia de Cinetoplastídeos/genética , TranscriptomaRESUMO
Prime editor (PE), which is developed by combining Cas9 nickase and an engineered reverse transcriptase, can mediate all twelve types of base substitutions and small insertions or deletions in living cells but its efficiency remains low. Here, we develop spegRNA by introducing same-sense mutations at proper positions in the reverse-transcription template of pegRNA to increase PE's base-editing efficiency up-to 4,976-fold (on-average 353-fold). We also develop apegRNA by altering the pegRNA secondary structure to increase PE's indel-editing efficiency up-to 10.6-fold (on-average 2.77-fold). The spegRNA and apegRNA can be combined to further enhance editing efficiency. When spegRNA and apegRNA are used in PE3 and PE5 systems, the efficiencies of sPE3, aPE3, sPE5 and aPE5 systems are all enhanced significantly. The strategies developed in this study realize highly efficient prime editing at certain previously uneditable sites.
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Sistemas CRISPR-Cas , Edição de Genes , Sistemas CRISPR-Cas/genética , Mutação INDEL , Mutação , DNA Polimerase Dirigida por RNA/genéticaRESUMO
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.
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Objective.The electroencephalography (EEG)-based brain-computer interfaces (BCIs) have been used in the control of robotic arms. The performance of non-invasive BCIs may not be satisfactory due to the poor quality of EEG signals, so the shared control strategies were tried as an alternative solution. However, most of the existing shared control methods set the arbitration rules manually, which highly depended on the specific tasks and developer's experience. In this study, we proposed a novel shared control model that automatically optimized the control commands in a dynamical way based on the context in real-time control. Besides, we employed the hybrid BCI to better allocate commands with multiple functions. The system allowed non-invasive BCI users to manipulate a robotic arm moving in a three-dimensional (3D) space and complete a pick-place task of multiple objects.Approach.Taking the scene information obtained by computer vision as a knowledge base, a machine agent was designed to infer the user's intention and generate automatic commands. Based on the inference confidence and user's characteristic, the proposed shared control model fused the machine autonomy and human intention dynamically for robotic arm motion optimization during the online control. In addition, we introduced a hybrid BCI scheme that applied steady-state visual evoked potentials and motor imagery to the divided primary and secondary BCI interfaces to better allocate the BCI resources (e.g. decoding computing power, screen occupation) and realize the multi-dimensional control of the robotic arm.Main results.Eleven subjects participated in the online experiments of picking and placing five objects that scattered at different positions in a 3D workspace. The results showed that most of the subjects could control the robotic arm to complete accurate and robust picking task with an average success rate of approximately 85% under the shared control strategy, while the average success rate of placing task controlled by pure BCI was 50% approximately.Significance.In this paper, we proposed a novel shared controller for motion automatic optimization, together with a hybrid BCI control scheme that allocated paradigms according to the importance of commands to realize multi-dimensional and effective control of a robotic arm. Our study indicated that the shared control strategy with hybrid BCI could greatly improve the performance of the brain-actuated robotic arm system.
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Interfaces Cérebro-Computador , Procedimentos Cirúrgicos Robóticos , Encéfalo , Eletroencefalografia , Potenciais Evocados Visuais , HumanosRESUMO
Name recognition plays important role in self-related cognitive processes and also contributes to a variety of clinical applications, such as autism spectrum disorder diagnosis and consciousness disorder analysis. However, most previous name-related studies usually adopted noninvasive EEG or fMRI recordings, which were limited by low spatial resolution and temporal resolution, respectively, and thus millisecond-level response latencies in precise brain regions could not be measured using these noninvasive recordings. By invasive stereo-electroencephalography (SEEG) recordings that have high resolution in both the spatial and temporal domain, the current study distinguished the neural response to one's own name or a stranger's name, and explored common active brain regions in both auditory and visual modalities. The neural activities were classified using spatiotemporal features of high-gamma, beta, and alpha band. Results showed that different names could be decoded using multi-region SEEG signals, and the best classification performance was achieved at high gamma (60-145 Hz) band. In this case, auditory and visual modality-based name classification accuracies were 84.5 ± 8.3 and 79.9 ± 4.6%, respectively. Additionally, some single regions such as the supramarginal gyrus, middle temporal gyrus, and insula could also achieve remarkable accuracies for both modalities, supporting their roles in the processing of self-related information. The average latency of the difference between the two responses in these precise regions was 354 ± 63 and 285 ± 59 ms in the auditory and visual modality, respectively. This study suggested that name recognition was attributed to a distributed brain network, and the subsets with decoding capabilities might be potential implanted regions for awareness detection and cognition evaluation.
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Cholesterol-25-hydroxylase (CH25H) and its enzymatic product 25-hydroxycholesterol (25HC) exert broadly antiviral activity including inhibiting HIV-1 infection. However, their antiviral immunity and therapeutic efficacy in a nonhuman primate model are unknown. Here, we report that the regimen of 25HC combined with antiretroviral therapy (ART), provides profound immunological modulation towards inhibiting viral replication in chronically SIVmac239-infected rhesus macaques (RMs). Compared to the ART alone, this regimen more effectively controlled SIV replication, enhanced SIV-specific cellular immune responses, restored the ratio of CD4/CD8 cells, reversed the hyperactivation state of CD4+ T cells, and inhibited the secretion of proinflammatory cytokines by CD4+ and CD8+ T lymphocytes in chronically SIV-infected RMs. Furthermore, the in vivo safety and the preliminary pharmacokinetics of the 25HC compound were assessed in this RM model. Taken together, these assessments help explain the profound relationship between cholesterol metabolism, immune modulation, and antiviral activities by 25HC. These results provide insight for developing novel therapeutic drug candidates against HIV-1 infection and other related diseases.
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Infecções por HIV , Síndrome de Imunodeficiência Adquirida dos Símios , Vírus da Imunodeficiência Símia , Animais , Antivirais/farmacologia , Antivirais/uso terapêutico , Linfócitos T CD4-Positivos , Infecções por HIV/tratamento farmacológico , Hidroxicolesteróis , Macaca mulatta , Síndrome de Imunodeficiência Adquirida dos Símios/tratamento farmacológicoRESUMO
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.
Assuntos
Substância Branca , Córtex Cerebral , Eletroencefalografia , Substância Cinzenta/diagnóstico por imagem , Humanos , Movimento , Substância Branca/diagnóstico por imagemRESUMO
The deployment of electroencephalographic techniques for commercial applications has undergone a rapid growth in recent decades. As they continue to expand in the consumer markets as suitable techniques for monitoring the brain activity, their transformative potential necessitates equally significant ethical inquiries. One of the main questions, which arises then when evaluating these kinds of applications, is whether they should be aligned or not with the main ethical concerns reported by scholars and experts. Thus, the present work attempts to unify these disciplines of knowledge by performing a comprehensive scan of the major electroencephalographic market applications as well as their most relevant ethical concerns arising from the existing literature. In this literature review, different databases were consulted, which presented conceptual and empirical discussions and findings about commercial and ethical aspects of electroencephalography. Subsequently, the content was extracted from the articles and the main conclusions were presented. Finally, an external assessment of the outcomes was conducted in consultation with an expert panel in some of the topic areas such as biomedical engineering, biomechatronics, and neuroscience. The ultimate purpose of this review is to provide a genuine insight into the cutting-edge practical attempts at electroencephalography. By the same token, it seeks to highlight the overlap between the market needs and the ethical standards that should govern the deployment of electroencephalographic consumer-grade solutions, providing a practical approach that overcomes the engineering myopia of certain ethical discussions.