Age-Related Characteristics of Resting-State Electroencephalographic Signals and the Corresponding Analytic Approaches: A Review.

The study of the effects of aging on neural activity in the human brain has attracted considerable attention in neurophysiological, neuropsychiatric, and neurocognitive research, as it is directly linked to an understanding of the neural mechanisms underlying the disruption of the brain structures and functions that lead to age-related pathological disorders. Electroencephalographic (EEG) signals recorded during resting-state conditions have been widely used because of the significant advantage of non-invasive signal acquisition with higher temporal resolution. These advantages include the capability of a variety of linear and nonlinear signal analyses and state-of-the-art machine-learning and deep-learning techniques. Advances in artificial intelligence (AI) can not only reveal the neural mechanisms underlying aging but also enable the assessment of brain age reliably by means of the age-related characteristics of EEG signals. This paper reviews the literature on the age-related features, available analytic methods, large-scale resting-state EEG databases, interpretations of the resulting findings, and recent advances in age-related AI models.

Neural Biomarkers for Identifying Atopic Dermatitis and Assessing Acupuncture Treatment Response Using Resting-State fMRI.

Purpose: Only a few studies have focused on the brain mechanisms underlying the itch processing in AD patients, and a neural biomarker has never been studied in AD patients. We aimed to develop a deep learning model-based neural signature which can extract the relevant temporal dynamics, discriminate between AD and healthy control (HC), and between AD patients who responded well to acupuncture treatment and those who did not. Patients and Methods: We recruited 41 AD patients (22 male, age mean ± SD: 24.34 ± 5.29) and 40 HCs (20 male, age mean ± SD: 26.4 ± 5.32), and measured resting-state functional MRI signals. After preprocessing, 38 functional regions of interest were applied to the functional MRI signals. A long short-term memory (LSTM) was used to extract the relevant temporal dynamics for classification and train the prediction model. Bootstrapping and 4-fold cross-validation were used to examine the significance of the models. Results: For the identification of AD patients and HC, we found that the supplementary motor area (SMA), posterior cingulate cortex (PCC), temporal pole, precuneus, and dorsolateral prefrontal cortex showed significantly greater prediction accuracy than the chance level. For the identification of high and low responder to acupuncture treatment, we found that the lingual-parahippocampal-fusiform gyrus, SMA, frontal gyrus, PCC and precuneus, paracentral lobule, and primary motor and somatosensory cortex showed significantly greater prediction accuracy than the chance level. Conclusion: We developed and evaluated a deep learning model-based neural biomarker that can distinguish between AD and HC as well as between AD patients who respond well and those who respond less to acupuncture. Using the intrinsic neurological abnormalities, it is possible to diagnose AD patients and provide personalized treatment regimens.

Auditory influence on stickiness perception: an fMRI study of multisensory integration.

This study explored how the human brain perceives stickiness through tactile and auditory channels, especially when presented with congruent or incongruent intensity cues. In our behavioral and functional MRI (fMRI) experiments, we presented participants with adhesive tape stimuli at two different intensities. The congruent condition involved providing stickiness stimuli with matching intensity cues in both auditory and tactile channels, whereas the incongruent condition involved cues of different intensities. Behavioral results showed that participants were able to distinguish between the congruent and incongruent conditions with high accuracy. Through fMRI searchlight analysis, we tested which brain regions could distinguish between congruent and incongruent conditions, and as a result, we identified the superior temporal gyrus, known primarily for auditory processing. Interestingly, we did not observe any significant activation in regions associated with somatosensory or motor functions. This indicates that the brain dedicates more attention to auditory cues than to tactile cues, possibly due to the unfamiliarity of conveying the sensation of stickiness through sound. Our results could provide new perspectives on the complexities of multisensory integration, highlighting the subtle yet significant role of auditory processing in understanding tactile properties such as stickiness.

Impact of violence on the burnout status of paramedics in the emergency department: A multicenter survey study.

BACKGROUND: Violence in emergency departments poses a threaten to both medical personnel and patients. We investigated the risk factors for high burnout status and the association between the impact of violence and burnout status of paramedics in emergency departments. METHODS: We included paramedics of emergency departments in Korea. We conducted a survey on the emotional, physical, and social responses to violence using an assault response questionnaire. Additionally, we evaluated burnout status using the Maslach Burnout Inventory. RESULTS: This study included 141 participants (57 females), with a mean age of 25.9 years. The burnout status was positively correlated with physical, emotional, and social responses, and the overall impact of violence (r = 0.576, 0.559, 0.446, and 0.590, respectively). Female sex, specialized centers, and emergency departments with <20 beds were associated with a high burnout status (adjusted odds ratio [95% confidence interval] = 13.11 [3.33-51.60], 35.34 [2.19-572.45], and 9.27 [1.75-53.56], respectively). Increased burnout was associated with emotional and physical responses to violence (B = 0.200 and 0.353, respectively). CONCLUSIONS: Paramedics of emergency departments were directly affected by violence. Violence was also related to burnout status. Efforts to prevent violence and detect predictive responses signaling an increase in burnout are required, followed by necessary active interventions.

Effects of Frontal Theta Rhythms in a Prior Resting State on the Subsequent Motor Imagery Brain-Computer Interface Performance.

Dealing with subjects who are unable to attain a proper level of performance, that is, those with brain-computer interface (BCI) illiteracy or BCI inefficients, is still a major issue in human electroencephalography (EEG) BCI systems. The most suitable approach to address this issue is to analyze the EEG signals of individual subjects independently recorded before the main BCI tasks, to evaluate their performance on these tasks. This study mainly focused on non-linear analyses and deep learning techniques to investigate the significant relationship between the intrinsic characteristics of a prior idle resting state and the subsequent BCI performance. To achieve this main objective, a public EEG motor/movement imagery dataset that constituted two individual EEG signals recorded from an idle resting state and a motor imagery BCI task was used in this study. For the EEG processing in the prior resting state, spectral analysis but also non-linear analyses, such as sample entropy, permutation entropy, and recurrent quantification analyses (RQA), were performed to obtain individual groups of EEG features to represent intrinsic EEG characteristics in the subject. For the EEG signals in the BCI tasks, four individual decoding methods, as a filter-bank common spatial pattern-based classifier and three types of convolution neural network-based classifiers, quantified the subsequent BCI performance in the subject. Statistical linear regression and ANOVA with post hoc analyses verified the significant relationship between non-linear EEG features in the prior resting state and three types of BCI performance as low-, intermediate-, and high-performance groups that were statistically discriminated by the subsequent BCI performance. As a result, we found that the frontal theta rhythm ranging from 4 to 8 Hz during the eyes open condition was highly associated with the subsequent BCI performance. The RQA findings that higher determinism and lower mean recurrent time were mainly observed in higher-performance groups indicate that more regular and stable properties in the EEG signals over the frontal regions during the prior resting state would provide a critical clue to assess an individual BCI ability in the following motor imagery task.

Spatiotemporal Characteristics of Neural Dynamics in Theta Oscillations Related to the Inhibition of Habitual Behavior.

The human brain carries out cognitive control for the inhibition of habitual behaviors by suppressing some familiar but inappropriate behaviors instead of engaging specific goal-directed behavior flexibly in a given situation. To examine the characteristics of neural dynamics related to such inhibition of habitual behaviors, we used a modified rock-paper-scissors (RPS) task that consisted of a basic, a lose-, and a win-conditioned game. Spectral and phase synchrony analyses were conducted to examine the acquired electroencephalogram signals across the entire brain during all RPS tasks. Temporal variations in frontal theta power activities were directly in line with the stream of RPS procedures in accordance with the task conditions. The lose-conditioned RPS task gave rise to increases in the local frontal power and global phase-synchronized pairs of theta oscillations. The activation of the global phase-synchronized network preceded the activation of frontal theta power. These results demonstrate that the frontal regions play a pivotal role in the inhibition of habitual behaviors-stereotyped and ingrained stimulus-response mappings that have been established over time. This study suggests that frontal theta oscillations may be engaged during the cognitive inhibition of habitual behaviors and that these oscillations characterize the degree of cognitive load required to inhibit habitual behaviors.

A Wearable Wrist Band-Type System for Multimodal Biometrics Integrated with Multispectral Skin Photomatrix and Electrocardiogram Sensors.

Multimodal biometrics are promising for providing a strong security level for personal authentication, yet the implementation of a multimodal biometric system for practical usage need to meet such criteria that multimodal biometric signals should be easy to acquire but not easily compromised. We developed a wearable wrist band integrated with multispectral skin photomatrix (MSP) and electrocardiogram (ECG) sensors to improve the issues of collectability, performance and circumvention of multimodal biometric authentication. The band was designed to ensure collectability by sensing both MSP and ECG easily and to achieve high authentication performance with low computation, efficient memory usage, and relatively fast response. Acquisition of MSP and ECG using contact-based sensors could also prevent remote access to personal data. Personal authentication with multimodal biometrics using the integrated wearable wrist band was evaluated in 150 subjects and resulted in 0.2% equal error rate ( EER ) and 100% detection probability at 1% FAR (false acceptance rate) ( PD . 1 ), which is comparable to other state-of-the-art multimodal biometrics. An additional investigation with a separate MSP sensor, which enhanced contact with the skin, along with ECG reached 0.1% EER and 100% PD . 1 , showing a great potential of our in-house wearable band for practical applications. The results of this study demonstrate that our newly developed wearable wrist band may provide a reliable and easy-to-use multimodal biometric solution for personal authentication.

Changes in effective connectivity of sensorimotor rhythms in thalamocortical circuits during the induction and recovery of anesthesia in mice.

The thalamocortical network serves a role in both consciousness and sensorimotor processing. However, little is known regarding how changes in conscious states, via induction of and recovery from anesthesia, affect the processing of sensorimotor information in the thalamocortical network. To address this, we investigated the dynamics of causal interactions among sensorimotor rhythms (SMR; frequency range of 3-12Hz) across the thalamocortical network during transitions into and out of ketamine-induced unconsciousness. Two local field potentials from the ventral lateral and ventrobasal thalamic nuclei, as well as two intracranial electroencephalography signals from the primary sensory and primary motor regions, were recorded in 10 mice. Spectral Granger causality analysis revealed two distinct frequency-specific patterns in sensorimotor rhythms. For the low-frequency (3-6.5Hz) SMR, loss of consciousness evoked causal influences directed from the cortex to the thalamus. For the high-frequency (6.5-12Hz) SMR, causal influences from the primary sensory cortex to other regions during the conscious period were abruptly altered by loss of consciousness and gradually regenerated following recovery of consciousness. The results of the present study indicate that anesthesia alters the flow of sensorimotor information in the thalamocortical network and may provide evidence of the neural basis of loss and recovery of sensorimotor function associated with anesthesia.

Modulation of Alpha Oscillations in the Human EEG with Facial Preference.

Facial preference that results from the processing of facial information plays an important role in social interactions as well as the selection of a mate, friend, candidate, or favorite actor. However, it still remains elusive which brain regions are implicated in the neural mechanisms underlying facial preference, and how neural activities in these regions are modulated during the formation of facial preference. In the present study, we investigated the modulation of electroencephalography (EEG) oscillatory power with facial preference. For the reliable assessments of facial preference, we designed a series of passive viewing and active choice tasks. In the former task, twenty-four face stimuli were passively viewed by participants for multiple times in random order. In the latter task, the same stimuli were then evaluated by participants for their facial preference judgments. In both tasks, significant differences between the preferred and non-preferred faces groups were found in alpha band power (8-13 Hz) but not in other frequency bands. The preferred faces generated more decreases in alpha power. During the passive viewing task, significant differences in alpha power between the preferred and non-preferred face groups were observed at the left frontal regions in the early (0.15-0.4 s) period during the 1-s presentation. By contrast, during the active choice task when participants consecutively watched the first and second face for 1 s and then selected the preferred one, an alpha power difference was found for the late (0.65-0.8 s) period over the whole brain during the first face presentation and over the posterior regions during the second face presentation. These results demonstrate that the modulation of alpha activity by facial preference is a top-down process, which requires additional cognitive resources to facilitate information processing of the preferred faces that capture more visual attention than the non-preferred faces.

An efficient detection of epileptic seizure by differentiation and spectral analysis of electroencephalograms.

Epilepsy is a critical neurological disorder resulting from abnormal hyper-excitability of neurons in the brain. Studies have shown that epilepsy can be detected in electroencephalography (EEG) recordings of patients suffering from seizures. The performance of EEG-based epileptic seizure detection relies largely on how well one can extract features from an EEG that characterize seizure activity. Conventional feature extraction methods using time-series analysis, spectral analysis and nonlinear dynamic analysis have advanced in recent years to improve detection. The computational complexity has also increased to obtain a higher detection rate. This study aimed to develop an efficient feature extraction method based on Hjorth's mobility to reduce computational complexity while maintaining high detection accuracy. A new feature extraction method was proposed by computing the spectral power of Hjorth's mobility components, which were effectively estimated by differentiating EEG signals in real-time. Using EEG data in five epileptic patients, this method resulted in a detection rate of 99.46% between interictal and epileptic EEG signals and 99.78% between normal and epileptic EEG signals, which is comparable to most advanced nonlinear methods. These results suggest that the spectral features of Hjorth's mobility components in EEG signals can represent seizure activity and may pave the way for developing a fast and reliable epileptic seizure detection method.

Representation of cognitive reappraisal goals in frontal gamma oscillations.

Recently, numerous efforts have been made to understand the neural mechanisms underlying cognitive regulation of emotion, such as cognitive reappraisal. Many studies have reported that cognitive control of emotion induces increases in neural activity of the control system, including the prefrontal cortex and the dorsal anterior cingulate cortex, and increases or decreases (depending upon the regulation goal) in neural activity of the appraisal system, including the amygdala and the insula. It has been hypothesized that information about regulation goals needs to be processed through interactions between the control and appraisal systems in order to support cognitive reappraisal. However, how this information is represented in the dynamics of cortical activity remains largely unknown. To address this, we investigated temporal changes in gamma band activity (35-55 Hz) in human electroencephalograms during a cognitive reappraisal task that was comprised of three reappraisal goals: to decease, maintain, or increase emotional responses modulated by affect-laden pictures. We examined how the characteristics of gamma oscillations, such as spectral power and large-scale phase synchronization, represented cognitive reappraisal goals. We found that left frontal gamma power decreased, was sustained, or increased when the participants suppressed, maintained, or amplified their emotions, respectively. This change in left frontal gamma power appeared during an interval of 1926 to 2453 ms after stimulus onset. We also found that the number of phase-synchronized pairs of gamma oscillations over the entire brain increased when participants regulated their emotions compared to when they maintained their emotions. These results suggest that left frontal gamma power may reflect cortical representation of emotional states modulated by cognitive reappraisal goals and gamma phase synchronization across whole brain regions may reflect emotional regulatory efforts to achieve these goals. Our study may provide the basis for an electroencephalogram-based neurofeedback system for the cognitive regulation of emotion.

The modulation of parietal gamma oscillations in the human electroencephalogram with cognitive reappraisal.

The present study examines how gamma oscillations can be modulated by cognitive reappraisal. Thirty-two participants performed cognitive reappraisal tasks to increase or decrease their emotions while viewing neutral and emotional (positive or negative) pictures. As a control task, the participants also simply viewed the pictures without an attempt to manipulate emotions. Electroencephalograms of the participants were recorded during the cognitive reappraisal tasks to extract the gamma oscillations. Gamma activity was quantified by measuring event-related changes in the gamma band power (30-55 Hz). During the tasks, the greatest gamma activity was found at the parietal regions. Greater parietal gamma activity was induced by the emotional pictures compared with the neutral ones when the participants passively viewed the pictures. No such difference in the parietal gamma activity was found when the participants performed the cognitive reappraisal tasks. In addition, parietal gamma activity during the cognitive reappraisal tasks was greater than that during the control task in response to the neutral pictures, implying the role of parietal gamma activity in the top-down cognitive execution process. In contrast, parietal gamma activity during the cognitive reappraisal task to increase emotions was greater than the levels of activity during the task to decrease emotions and during the control task in response to the emotional pictures, implying the role of parietal gamma activity in the bottom-up sensory emotional process. These results suggest that parietal gamma activity may be implicated in the multiple cognitive reappraisal processes.

Correlation of fronto-central phase coupling with sensorimotor rhythm modulation.

We investigated neurophysiologic correlates of individual differences in the modulation of sensorimotor rhythms (SMRs) in the human electroencephalography (EEG) during motor imagery. The ability of modulating SMRs to different motor imageries was correlated with the strength of alpha phase synchronization across frontal and central sensorimotor areas. The results suggest that fronto-central coupling may elucidate individual variations in SMR modulation that is essential for using SMR-based brain-computer interfaces.

Modulation of theta phase synchronization in the human electroencephalogram during a recognition memory task.

To the extent that recognition memory relies on interactions among widely distributed neural assemblies across the brain, phase synchronization between brain rhythms may play an important role in meditating those interactions. As the theta rhythm is known to modulate in power during the recognition memory process, we aimed to determine how the phase synchronization of the theta rhythms across the brain changes with recognition memory. Fourteen human participants performed a visual object recognition task in a virtual reality environment. Electroencephalograms were recorded from the scalp of the participants while they either recognized objects that had been presented previously or identified new objects. From the electroencephalogram recordings, we analyzed the phase-locking value of the theta rhythms, which indicates the degree of phase synchronization. We found that the overall phase-locking value recorded during the recognition of previously viewed objects was greater than that recorded during the identification of new objects. Specifically, the theta rhythms became strongly synchronized between the frontal and the left parietal areas during the recognition of previously viewed objects. These results suggest that the recognition memory process may involve an interaction between the frontal and the left parietal cortical regions mediated by theta phase synchronization.

Changes in the thalamocortical connectivity during anesthesia-induced transitions in consciousness.

Thalamocortical networks play an important role in information integration during consciousness. However, little is known about how the information flows between the thalamus and the cortex are affected by a loss of consciousness. To investigate this issue, we analyzed effective connectivity between the cortex and the thalamus in animals during anesthesia-induced transitions. By recording the electroencephalogram from the primary motor and the primary somatosensory cortex and by recording local field potentials from the ventral lateral and the ventrobasal thalamic nuclei, we evaluated changes in the conditional Granger causality between cortical and thalamic electrical activity as mice gradually lost consciousness from the use of anesthesia (ketamine/xylazine). The point of loss of consciousness was indicated by a moment of loss of movement that was measured using a head-mounted motion sensor. The results showed that 65% of the thalamocortical information flows were changed by anesthesia-induced loss of consciousness. Specifically, the effective connectivity between the cortex and the ventral lateral thalamus was altered such that the primary motor and the primary somatosensory cortex Granger-caused the ventral lateral thalamus before loss of consciousness whereas the ventral lateral thalamus Granger-caused the primary motor cortex and the primary somatosensory cortex after loss of consciousness. In contrast, the primary somatosensory cortex consistently Granger-caused the ventrobasal thalamus, regardless of the loss of consciousness. These results suggest how information flows change across the thalamocortical network during transitions in consciousness.