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1.
J Neurosci ; 42(25): 5007-5020, 2022 06 22.
Artigo em Inglês | MEDLINE | ID: mdl-35589391

RESUMO

Consolidation of memory is believed to involve offline replay of neural activity. While amply demonstrated in rodents, evidence for replay in humans, particularly regarding motor memory, is less compelling. To determine whether replay occurs after motor learning, we sought to record from motor cortex during a novel motor task and subsequent overnight sleep. A 36-year-old man with tetraplegia secondary to cervical spinal cord injury enrolled in the ongoing BrainGate brain-computer interface pilot clinical trial had two 96-channel intracortical microelectrode arrays placed chronically into left precentral gyrus. Single- and multi-unit activity was recorded while he played a color/sound sequence matching memory game. Intended movements were decoded from motor cortical neuronal activity by a real-time steady-state Kalman filter that allowed the participant to control a neurally driven cursor on the screen. Intracortical neural activity from precentral gyrus and 2-lead scalp EEG were recorded overnight as he slept. When decoded using the same steady-state Kalman filter parameters, intracortical neural signals recorded overnight replayed the target sequence from the memory game at intervals throughout at a frequency significantly greater than expected by chance. Replay events occurred at speeds ranging from 1 to 4 times as fast as initial task execution and were most frequently observed during slow-wave sleep. These results demonstrate that recent visuomotor skill acquisition in humans may be accompanied by replay of the corresponding motor cortex neural activity during sleep.SIGNIFICANCE STATEMENT Within cortex, the acquisition of information is often followed by the offline recapitulation of specific sequences of neural firing. Replay of recent activity is enriched during sleep and may support the consolidation of learning and memory. Using an intracortical brain-computer interface, we recorded and decoded activity from motor cortex as a human research participant performed a novel motor task. By decoding neural activity throughout subsequent sleep, we find that neural sequences underlying the recently practiced motor task are repeated throughout the night, providing direct evidence of replay in human motor cortex during sleep. This approach, using an optimized brain-computer interface decoder to characterize neural activity during sleep, provides a framework for future studies exploring replay, learning, and memory.


Assuntos
Aprendizagem/fisiologia , Córtex Motor/fisiologia , Sono/fisiologia , Adulto , Interfaces Cérebro-Computador , Vértebras Cervicais , Eletroencefalografia/métodos , Humanos , Masculino , Projetos Piloto , Quadriplegia/etiologia , Quadriplegia/fisiopatologia , Traumatismos da Medula Espinal/complicações , Traumatismos da Medula Espinal/fisiopatologia
2.
eNeuro ; 7(5)2020.
Artigo em Inglês | MEDLINE | ID: mdl-33060183

RESUMO

In vivo electrophysiology experiments require the collection of data from multiple subjects, often for extended periods. Studying multiple subjects for extended periods can be made more efficient through simultaneous recordings, but scaling up recordings to accommodate larger numbers of subjects simultaneously requires coordination and consideration of costs and flexibility. To facilitate this process, we have developed OpBox, an open source set of tools to acquire electroencephalography (EEG) and electromyography (EMG) flexibly from multiple rodent subjects simultaneously. OpBox combines open source hardware and software with off-the-shelf components to create a system that costs less than commercial solutions ($500 per subject), and can be easily deployed for multiple subjects. Coded in MATLAB, OpBox scripts can simultaneously and flexibly collect and display multiple analog and digital data streams, for instance real-time EEG and EMG, event triggers from a behavioral system, and rotary encoder data. OpBox also calculates and displays real-time spectral representations and event-related potentials (ERPs). To verify the performance of our system, we compare our amplifiers with two other commercial amplifiers, a Grass P55 AC preamplifier and an Intan RHD2000-series amplifier. The OpBox amplifier performs comparably to commercial amplifiers for signal-to-noise ratios (SNRs), noise floors, and common mode rejection. We also demonstrate that our acquisition system can reliably record multichannel data from multiple subjects, and has been successfully tested with 12 subjects running simultaneously on a single standard desktop computer. Together, OpBox increases the flexibility and lowers the cost for simultaneous acquisition of electrophysiology data from multiple subjects.


Assuntos
Eletroencefalografia , Software , Eletromiografia , Potenciais Evocados , Processamento de Sinais Assistido por Computador , Razão Sinal-Ruído
3.
Epilepsia ; 61(9): 1906-1918, 2020 09.
Artigo em Inglês | MEDLINE | ID: mdl-32761902

RESUMO

OBJECTIVE: Seizure detection is a major facet of electroencephalography (EEG) analysis in neurocritical care, epilepsy diagnosis and management, and the instantiation of novel therapies such as closed-loop stimulation or optogenetic control of seizures. It is also of increased importance in high-throughput, robust, and reproducible pre-clinical research. However, seizure detectors are not widely relied upon in either clinical or research settings due to limited validation. In this study, we create a high-performance seizure-detection approach, validated in multiple data sets, with the intention that such a system could be available to users for multiple purposes. METHODS: We introduce a generalized linear model trained on 141 EEG signal features for classification of seizures in continuous EEG for two data sets. In the first (Focal Epilepsy) data set consisting of 16 rats with focal epilepsy, we collected 1012 spontaneous seizures over 3 months of 24/7 recording. We trained a generalized linear model on the 141 features representing 20 feature classes, including univariate and multivariate, linear and nonlinear, time, and frequency domains. We tested performance on multiple hold-out test data sets. We then used the trained model in a second (Multifocal Epilepsy) data set consisting of 96 rats with 2883 spontaneous multifocal seizures. RESULTS: From the Focal Epilepsy data set, we built a pooled classifier with an Area Under the Receiver Operating Characteristic (AUROC) of 0.995 and leave-one-out classifiers with an AUROC of 0.962. We validated our method within the independently constructed Multifocal Epilepsy data set, resulting in a pooled AUROC of 0.963. We separately validated a model trained exclusively on the Focal Epilepsy data set and tested on the held-out Multifocal Epilepsy data set with an AUROC of 0.890. Latency to detection was under 5 seconds for over 80% of seizures and under 12 seconds for over 99% of seizures. SIGNIFICANCE: This method achieves the highest performance published for seizure detection on multiple independent data sets. This method of seizure detection can be applied to automated EEG analysis pipelines as well as closed loop interventional approaches, and can be especially useful in the setting of research using animals in which there is an increased need for standardization and high-throughput analysis of large number of seizures.


Assuntos
Eletrocorticografia/métodos , Epilepsias Parciais/diagnóstico , Aprendizado de Máquina , Convulsões/diagnóstico , Processamento de Sinais Assistido por Computador , Animais , Área Sob a Curva , Modelos Animais de Doenças , Eletroencefalografia , Epilepsias Parciais/fisiopatologia , Agonistas de Aminoácidos Excitatórios/toxicidade , Ácido Caínico/toxicidade , Modelos Lineares , Curva ROC , Ratos , Reprodutibilidade dos Testes , Convulsões/induzido quimicamente , Convulsões/fisiopatologia
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