Exploring the neural mechanisms of awareness is a fundamental task of cognitive neuroscience. There is an ongoing dispute regarding the role of the prefrontal cortex (PFC) in the emergence of awareness, which is partially raised by the confound between report- and awareness-related activity. To address this problem, we designed a visual awareness task that can minimize report-related motor confounding. Our results show that saccadic latency is significantly shorter in the aware trials than in the unaware trials. Local field potential (LFP) data from six patients consistently show early (200-300ms) awareness-related activity in the PFC, including event-related potential and high-gamma activity. Moreover, the awareness state can be reliably decoded by the neural activity in the PFC since the early stage, and the neural pattern is dynamically changed rather than being stable during the representation of awareness. Furthermore, the enhancement of dynamic functional connectivity, through the phase modulation at low frequency, between the PFC and other brain regions in the early stage of the awareness trials may explain the mechanism of conscious access. These results indicate that the PFC is critically involved in the emergence of awareness.

Neuroimaging and intracranial electrophysiological studies have consistently shown the largest and most consistent face-selective neural activity in the middle portion of the human right lateral fusiform gyrus (\'fusiform face area(s)\', FFA). Yet, direct evidence for the critical role of this region in face identity recognition (FIR) is still lacking. Here we report the first evidence of transient behavioral impairment of FIR during focal electrical stimulation of the right FFA. Upon stimulation of an electrode contact within this region, subject CJ, who shows typical FIR ability outside of stimulation, was transiently unable to point to pictures of famous faces among strangers and to match pictures of famous or unfamiliar faces presented simultaneously for their identity. Her performance at comparable tasks with other visual materials (written names, pictures of buildings) remained unaffected by stimulation at the same location. During right FFA stimulation, CJ consistently reported that simultaneously presented faces appeared as being the same identity, with little or no distortion of the spatial face configuration. Independent electrophysiological recordings showed the largest neural face-selective and face identity activity at the critical electrode contacts. Altogether, this extensive multimodal case report supports the causal role of the right FFA in FIR.

Epilepsy is a chronic disorder caused by excessive electrical discharges. Currently, clinical experts identify the seizure onset zone (SOZ) channel through visual judgment based on long-time intracranial electroencephalogram (iEEG), which is a very time-consuming, difficult and experience-based task. Therefore, there is a need for high-accuracy diagnostic aids to reduce the workload of clinical experts. In this article, we propose a method in which, the iEEG is split into the 20-s segment and for each patient, we ask clinical experts to label a part of the data, which is used to train a model and classify the remaining iEEG data. In recent years, machine learning methods have been successfully applied to solve some medical problems. Filtering, entropy and short-time Fourier transform (STFT) are used for extracting features. We compare them to wavelet transform (WT), empirical mode decomposition (EMD) and other traditional methods with the aim of obtaining the best possible discriminating features. Finally, we look for their medical interpretation, which is important for clinical experts. We achieve high-performance results for SOZ and non-SOZ data classification by using the labeled iEEG data and support vector machine (SVM), fully connected neural network (FCNN) and convolutional neural network (CNN) as classification models. In addition, we introduce the positive unlabeled (PU) learning to further reduce the workload of clinical experts. By using PU learning, we can learn a binary classifier with a small amount of labeled data and a large amount of unlabeled data. This can greatly reduce the amount and difficulty of annotation work by clinical experts. All together, we show that using 105 minutes of labeled data we achieve a classification result of 91.46% on average for multiple patients.

Objective.Patient-dependent seizure detection based on intracranial electroencephalography (iEEG) has made significant progress. However, due to the difference in the locations and number of iEEG electrodes used for each patient, patient-independent seizure detection based on iEEG has not been carried out. Additionally, current seizure detection algorithms based on deep learning have outperformed traditional machine learning algorithms in many performance metrics. However, they still have shortcomings of large memory footprints and slow inference speed.Approach.To solve the above problems of the current study, we propose a novel lightweight convolutional neural network model combining the Convolutional Block Attention Module (CBAM). Its performance for patient-independent seizure detection is evaluated on two long-term continuous iEEG datasets: SWEC-ETHZ and TJU-HH. Finally, we reproduce four other patient-independent methods to compare with our method and calculate the memory footprints and inference speed for all methods.Main results.Our method achieves 83.81% sensitivity (SEN) and 85.4% specificity (SPE) on the SWEC-ETHZ dataset and 86.63% SEN and 92.21% SPE on the TJU-HH dataset. In particular, it takes only 11 ms to infer 10 min iEEG (128 channels), and its memory footprint is only 22 kB. Compared to baseline methods, our method not only achieves better patient-independent seizure detection performance but also has a smaller memory footprint and faster inference speed.Significance.To our knowledge, this is the first iEEG-based patient-independent seizure detection study. This facilitates the application of seizure detection algorithms to the future clinic.

OBJECTIVE: Epilepsy is the second most prevalent neurological disorder of brain activity, affecting about seventy million people, or nearly 1% of the world population. Epileptic seizures prediction is extremely important for improving the epileptic patients\' life. This paper proposed a novel method to predict seizures by detecting the critical transition of brain activities with intracranial EEG (iEEG) signals.
METHODS: This article used three key measures of fluctuation, correlation and percolation to quantify pre-ictal states of epilepsy. Based on these measures, a ritical nucleus of iEEG signals was constructed and a composite index was introduced to detect the likelihood of impending seizures. In addition, we analyzed the dynamical mechanism of seizures at the tipping point from the perspective of spatial diffusion and temporal fluctuation.
RESULTS: The empirical results supported that the seizures are self-initiated via a critical transition in pre-ictal state and showed that the proposed model can achieve a good prediction performance. The average accuracy, sensitivity, specificity and false-positive rate (FPR) attain 87.96%, 82.93%, 89.33% and 0.11/h respectively. The results also suggest that the temporal and spatial factors have strong synergistic effect on triggering seizures. For those seizures consistent with critical transition, the predictive performance was greatly improved with sensitivity up to 96.88%.
CONCLUSIONS: This article proposed a critical nucleus model combined with spatial and temporal features of iEEG signals capable of seizure prediction. The proposed model brings insight from phase transition into epileptic iEEG signals analysis and quantifies the transition of the state to predict epileptic seizures with high accuracy.

The emotional status of a speaker is an important non-linguistic cue carried by human voice and can be perceived by a listener in vocal communication. Understanding the neural circuits involved in processing emotions carried by human voice is crucial for understanding the neural basis of social interaction. Previous studies have shown that human insula and amygdala responded more selectively to emotional sounds than non-emotional sounds. However, it is not clear whether the neural selectivity to emotional sounds in these brain structures is determined by the emotion presented by a speaker which is associated with the acoustic properties of the sounds or by the emotion perceived by a listener. In this study, we recorded intracranial electroencephalography (iEEG) responses to emotional human voices while subjects performed emotion recognition tasks. We found that the iEEG responses of Heschl\'s gyrus (HG) and posterior insula were determined by the presented emotion, whereas the iEEG responses of anterior insula and amygdala were driven by the perceived emotion. These results suggest that the anterior insula and amygdala play a crucial role in conscious perception of emotions carried by human voice.

Consciousness lies at the heart of our existence and experience. To probe how perceptual consciousness emerges in the brain, we recorded brain-wide intracranial electroencephalography signals from human patients while their perceptual consciousness was effectively manipulated using the continuous flash suppression paradigm. We observed substantial differences in brain activities when visual information gradually enters consciousness. Specifically, the functional connectivity first increases and then decreases, oscillations in the low-frequency band reduce in power, and those in the high-frequency band remain unchanged. We employed random forest-based classification to characterize the transitions from no perception to subconsciousness and then to consciousness, which showed an increase in signal variance at the second transition rather than the first. Further, the frontal-parietal junction dominates the first transition, whereas the temporal-frontal lobes dominate the second transition. Finally, we identified the most relevant neuronal features associated with consciousness. Altogether, these findings shed fresh light on the emergence of visual consciousness.

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.

Neurophysiological work in primates and rodents have shown the amygdala plays a central role in reward processing through connectivity with the orbitofrontal cortex (OFC) and hippocampus. However, understanding the role of oscillations in each region and their connectivity in different stages of reward processing in humans has been hampered by limitations with noninvasive methods such as poor spatial and temporal resolution. To overcome these limitations, we recorded local field potentials (LFPs) directly from the amygdala, OFC and hippocampus simultaneously in human male and female epilepsy patients performing a monetary incentive delay (MID) task. This allowed us to dissociate electrophysiological activity and connectivity patterns related to the anticipation and receipt of rewards and losses in real time. Anticipation of reward increased high-frequency gamma (HFG; 60-250 Hz) activity in the hippocampus and theta band (4-8 Hz) synchronization between amygdala and OFC, suggesting roles in memory and motivation. During receipt, HFG in the amygdala was involved in outcome value coding, the OFC cue context-specific outcome value comparison and the hippocampus reward coding. Receipt of loss decreased amygdala-hippocampus theta and increased amygdala-OFC HFG amplitude coupling which coincided with subsequent adjustments in behavior. Increased HFG synchronization between the amygdala and hippocampus during reward receipt suggested encoding of reward information into memory for reinstatement during anticipation. These findings extend what is known about the primate brain to humans, showing key spectrotemporal coding and communication dynamics for reward and punishment related processes which could serve as more precise targets for neuromodulation to establish causality and potential therapeutic applications.SIGNIFICANCE STATEMENT Dysfunctional reward processing contributes to many psychiatric disorders. Neurophysiological work in primates has shown the amygdala, orbitofrontal cortex (OFC), and hippocampus play a synergistic role in reward processing. However, because of limitations with noninvasive imaging, it is unclear whether the same interactions occur in humans and what oscillatory mechanisms underpin them. We addressed this issue by recording local field potentials (LFPs) from all three regions in human epilepsy patients during monetary reward processing. There was increased amygdala-OFC high-frequency coupling when losing money which coincided with subsequent adjustments in behavior. In contrast, increased amygdala-hippocampus high-frequency phase-locking suggested a role in reward memory. The findings highlight amygdala networks for reward and punishment processes that could act as more precise neuromodulation targets to treat psychiatric disorders.

During mammalian evolution, primate neocortex expanded, shifting hippocampal functional networks away from primary sensory cortices, towards association cortices. Reflecting this rerouting, human resting hippocampal functional networks preferentially include higher association cortices, while those in rodents retained primary sensory cortices. Research on human visual, auditory and somatosensory systems shows evidence of this rerouting. Olfaction, however, is unique among sensory systems in its relative structural conservation throughout mammalian evolution, and it is unknown whether human primary olfactory cortex was subject to the same rerouting. We combined functional neuroimaging and intracranial electrophysiology to directly compare hippocampal functional networks across human sensory systems. We show that human primary olfactory cortex-including the anterior olfactory nucleus, olfactory tubercle and piriform cortex-has stronger functional connectivity with hippocampal networks at rest, compared to other sensory systems. This suggests that unlike other sensory systems, olfactory-hippocampal connectivity may have been retained in mammalian evolution. We further show that olfactory-hippocampal connectivity oscillates with nasal breathing. Our findings suggest olfaction might provide insight into how memory and cognition depend on hippocampal interactions.