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After more than 30 years of extensive investigation, impressive progress has been made in identifying the neural correlates of consciousness (NCC). However, the functional role of spatiotemporally distinct consciousness-related neural activity in conscious perception is debated. An influential framework proposed that consciousness-related neural activities could be dissociated into two distinct processes: phenomenal and access consciousness. However, though hotly debated, its authenticity has not been examined in a single paradigm with more informative intracranial recordings. In the present study, we employed a visual awareness task and recorded the local field potential (LFP) of patients with electrodes implanted in cortical and subcortical regions. Overall, we found that the latency of visual awareness-related activity exhibited a bimodal distribution, and the recording sites with short and long latencies were largely separated in location, except in the lateral prefrontal cortex (lPFC). The mixture of short and long latencies in the lPFC indicates that it plays a critical role in linking phenomenal and access consciousness. However, the division between the two is not as simple as the central sulcus, as proposed previously. Moreover, in 4 patients with electrodes implanted in the bilateral prefrontal cortex, early awareness-related activity was confined to the contralateral side, while late awareness-related activity appeared on both sides. Finally, Granger causality analysis showed that awareness-related information flowed from the early sites to the late sites. These results provide the first LFP evidence of neural correlates of phenomenal and access consciousness, which sheds light on the spatiotemporal dynamics of NCC in the human brain.

In recent years, aberrant neural oscillations in various cortical areas have emerged as a common physiological hallmark across mouse models of amyloid pathology and patients with Alzheimer's disease. However, much less is known about the underlying effect of amyloid pathology on single cell activity. Here, we used high-density silicon probe recordings from frontal cortex area of 9-month-old APP/PS1 mice to show that local field potential power in the theta and beta band is increased in transgenic animals, whereas single-cell firing rates, specifically of putative pyramidal cells, are significantly reduced. At the same time, these sparsely firing pyramidal cells phase-lock their spiking activity more strongly to the ongoing theta and beta rhythms. Furthermore, we demonstrated that the antiepileptic drug, levetiracetam, counteracts these effects by increasing pyramidal cell firing rates in APP/PS1 mice and uncoupling pyramidal cells and interneurons. Overall, our results highlight reduced firing rates of cortical pyramidal cells as a pathophysiological phenotype in APP/PS1 mice and indicate a potentially beneficial effect of acute levetiracetam treatment., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Objective: We focused on a rare gene mutation causing dystonia in two siblings who received globus pallidus internus deep brain stimulation (GPi-DBS). The aim was to characterize the relationship between neuronal activity patterns and clinical syndromes., Methods: Whole exome sequencing was applied to identify the TWNK (previous symbol C10orf2) mutation; Two siblings with TWNK mutation presented as generalized dystonia with rigidity and bradykinesia; four other sporadic generalized dystonia patients underwent GPi-DBS and local field potentials (LFPs) were recorded. Oscillatory activities were illustrated with power spectra and temporal dynamics measured by the Lempel-Ziv complexity (LZC)., Results: Normalized power spectra of GPi LFPs differed between patients with TWNK mutation and dystonia over the low beta bands. Patients with TWNK mutation had higher low beta power (15-27 Hz, unpaired t-test, corrected P < 0.0022) and lower LZC (15-27 Hz, unpaired t-test, P < 0.01) than other patients with generalized dystonia. On the other hand, the TWNK mutation patients showed decreased low frequency and beta oscillation in the GPi after DBS, as well as improved movement performance., Conclusion: The LFPs were different in TWNK mutation dystonia siblings than other patients with generalized dystonia, which indicate the abnormal LFPs were related to symptoms rather than specific disease. In addition, the inhibited effect on oscillations also provided a potential evidence for DBS treatment on rare movement disorders., Significance: This study could potentially aid in the future development of adaptive DBS via rare disease LFPs comparison., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

Objective: Accurate and reliable detection of tremor onset in Parkinson's disease (PD) is critical to the success of adaptive deep brain stimulation (aDBS) therapy. Here, we investigated the potential use of feature engineering and machine learning methods for more accurate detection of rest tremor in PD., Methods: We analyzed the local field potential (LFP) recordings from the subthalamic nucleus region in 12 patients with PD (16 recordings). To explore the optimal biomarkers and the best performing classifier, the performance of state-of-the-art machine learning (ML) algorithms and various features of the subthalamic LFPs were compared. We further used a Kalman filtering technique in feature domain to reduce the false positive rate., Results: The Hjorth complexity showed a higher correlation with tremor, compared to other features in our study. In addition, by optimal selection of a maximum of five features with a sequential feature selection method and using the gradient boosted decision trees as the classifier, the system could achieve an average F1 score of up to 88.7% and a detection lead of 0.52 s. The use of Kalman filtering in feature space significantly improved the specificity by 17.0% (p = 0.002), thereby potentially reducing the unnecessary power dissipation of the conventional DBS system., Conclusion: The use of relevant features combined with Kalman filtering and machine learning improves the accuracy of tremor detection during rest., Significance: The proposed method offers a potential solution for efficient on-demand stimulation for PD tremor., (Copyright © 2019 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.)

Umami is one of the basic tastes along with sweet, bitter, sour and salty. It is often elicited by amino acids and can provide a palatable flavor for food. With taste epithelium as the sensing element, microelectrodes can be used to evaluate umami taste by biological responses of the tissue. The electrophysiological activities to umami stimuli are measured with a 60-channel microelectrode array (MEA). Local field potential (LFP) recorded by a MEA system showed different temporal characteristics respectively with l-glutamic acid (l-Glu), l-aspartic acid (l-Asp), l-monosodium glutamate (l-MSG) and l-monosodium aspartate (l-MSA), while remarkable differences were observed between amino acids and their sodium salts. We also found that a dose-dependent behavior in the increasing concentrations of umami stimulations and a synergistic enhancement between amino acids and purine nucleotides can be detected. The investigation of this evaluation for umami represents a promising approach for distinguishing and evaluating umami tastants., (Copyright © 2013 Elsevier Inc. All rights reserved.)