Differential relationship of observer-rated and self-rated depression and anxiety scales with heart rate variability features.

Heart rate variability (HRV) is a known psychophysiological marker for diverse psychiatric symptoms. In this study, we aimed to explore the potential for clinical use of HRV by investigating the interrelationship between HRV indices and clinical measures mainly used to assess depressive and anxious symptoms. Participants who reported depressive and anxious symptoms were designated into the following groups: group 1, clinician-rated and self-rated depression; group 2, only self-rated depression; group 3, clinician-rated and self-rated anxiety; group 4, only self-rated anxiety. Statistical comparisons were performed between these groups to investigate the association between HRV and clinical measures. As a result, HRV variables showed significant correlations only with the clinician-rated assessments. Moreover, both time and frequency domain HRV indices were significantly different between groups 1 and 2, but groups 3 and 4 showed significant differences only in frequency domain HRV indices. Our study showed that HRV is an objective indicator for depressive or anxious symptoms. Additionally, it is considered a potential indicator for predicting the severity or state of depressive symptoms rather than of anxious symptoms. This study will contribute to increasing the diagnostic utility of discriminating those symptoms based on HRV in the future.

Vowel speech recognition from rat electroencephalography using long short-term memory neural network.

Over the years, considerable research has been conducted to investigate the mechanisms of speech perception and recognition. Electroencephalography (EEG) is a powerful tool for identifying brain activity; therefore, it has been widely used to determine the neural basis of speech recognition. In particular, for the classification of speech recognition, deep learning-based approaches are in the spotlight because they can automatically learn and extract representative features through end-to-end learning. This study aimed to identify particular components that are potentially related to phoneme representation in the rat brain and to discriminate brain activity for each vowel stimulus on a single-trial basis using a bidirectional long short-term memory (BiLSTM) network and classical machine learning methods. Nineteen male Sprague-Dawley rats subjected to microelectrode implantation surgery to record EEG signals from the bilateral anterior auditory fields were used. Five different vowel speech stimuli were chosen, /a/, /e/, /i/, /o/, and /u/, which have highly different formant frequencies. EEG recorded under randomly given vowel stimuli was minimally preprocessed and normalized by a z-score transformation to be used as input for the classification of speech recognition. The BiLSTM network showed the best performance among the classifiers by achieving an overall accuracy, f1-score, and Cohen's κ values of 75.18%, 0.75, and 0.68, respectively, using a 10-fold cross-validation approach. These results indicate that LSTM layers can effectively model sequential data, such as EEG; hence, informative features can be derived through BiLSTM trained with end-to-end learning without any additional hand-crafted feature extraction methods.

Quantification of stroke lesion volume using epidural EEG in a cerebral ischaemic rat model.

Precise monitoring of the brain after a stroke is essential for clinical decision making. Due to the non-invasive nature and high temporal resolution of electroencephalography (EEG), it is widely used to evaluate real-time cortical activity. In this study, we investigated the stroke-related EEG biomarkers and developed a predictive model for quantifying the structural brain damage in a focal cerebral ischaemic rat model. We enrolled 31 male Sprague-Dawley rats and randomly assigned them to mild stroke, moderate stroke, severe stroke, and control groups. We induced photothrombotic stroke targeting the right auditory cortex. We then acquired EEG signal responses to sound stimuli (frequency linearly increasing from 8 to 12 kHz with 750 ms duration). Power spectral analysis revealed a significant correlation of the relative powers of alpha, theta, delta, delta/alpha ratio, and (delta + theta)/(alpha + beta) ratio with the stroke lesion volume. The auditory evoked potential analysis revealed a significant association of amplitude and latency with stroke lesion volume. Finally, we developed a multiple regression model combining EEG predictors for quantifying the ischaemic lesion (R2 = 0.938, p value < 0.001). These findings demonstrate the potential application of EEG as a valid modality for monitoring the brain after a stroke.

Susceptibility of pentylenetetrazole-induced seizures in mice with Cereblon gene knockout.

Epilepsy is a neurological disorder characterized by unpredictable seizures, which are bursts of electrical activity that temporarily affect the brain. Cereblon (CRBN), a DCAFs (DDB1 and CUL4-associated factors), is a well-established protein associated with human mental retardation. Being a substrate receptor of the cullin-RING E3 ubiquitin ligase (CRL) 4 complex, CRBN mediates ubiquitination of several substrates and conducts multiple biological processes. In the central nervous system, the largeconductance Ca2+-activated K+ (BKCa) channel, which is the substrate of CRBN, is an important regulator of epilepsy. Despite the functional role and importance of CRBN in the brain, direct injection of pentylenetetrazole (PTZ) to induce seizures in CRBN knock-out mice has not been challenged. In this study, we investigated the effect of PTZ in CRBN knock-out mice. Here, we demonstrate that, compared with WT mice, CRBN knock-out mice do not show the intensification of seizures by PTZ induction. Moreover, electroencephalography recordings were also performed in the brains of both WT and CRBN knockout mice to identify the absence of significant differences in the pattern of seizure activities. Consistently, immunoblot analysis for validating the protein level of the CRL4 complex containing CRBN (CRL4Crbn) in the mouse brain was carried out. Taken together, we found that the deficiency of CRBN does not affect PTZ-induced seizure. [BMB Reports 2020; 53(9): 484-489].

The Effects of Transcranial Direct Current Stimulation on the Cognitive and Behavioral Changes After Electrode Implantation Surgery in Rats.

Postoperative delirium can lead to increased morbidity and mortality, and may even be a potentially life-threatening clinical syndrome. However, the neural mechanism underlying this condition has not been fully understood and there is little knowledge regarding potential preventive strategies. To date, investigation of transcranial direct current stimulation (tDCS) for the relief of symptoms caused by neuropsychiatric disorders and the enhancement of cognitive performance has led to promising results. In this study, we demonstrated that tDCS has a possible effect on the fast recovery from delirium in rats after microelectrode implant surgery, as demonstrated by postoperative behavior and neurophysiology compared with sham stimulation. This is the first study to describe the possible effects of tDCS for the fast recovery from delirium based on the study of both electroencephalography and behavioral changes. Postoperative rats showed decreased attention, which is the core symptom of delirium. However, anodal tDCS over the right frontal area immediately after surgery exhibited positive effects on acute attentional deficit. It was found that relative power of theta was lower in the tDCS group than in the sham group after surgery, suggesting that the decrease might be the underlying reason for the positive effects of tDCS. Connectivity analysis revealed that tDCS could modulate effective connectivity and synchronization of brain activity among different brain areas, including the frontal cortex, parietal cortex, and thalamus. It was concluded that anodal tDCS on the right frontal regions may have the potential to help patients recover quickly from delirium.

The changes of cerebral hemodynamics during ketamine induced anesthesia in a rat model.

Current electroencephalogram (EEG) based-consciousness monitoring technique is vulnerable to specific clinical conditions (eg, epilepsy and dementia). However, hemodynamics is the most fundamental and well-preserved parameter to evaluate, even under severe clinical situations. In this study, we applied near-infrared spectroscopy (NIRS) system to monitor hemodynamic change during ketamine-induced anesthesia to find its correlation with the level of consciousness. Oxy-hemoglobin (OHb) and deoxy-hemoglobin concentration levels were continuously acquired throughout the experiment, and the reflectance ratio between 730 and 850 nm was calculated to quantify the hemodynamic changes. The results showed double peaks of OHb concentration change during ketamine anesthesia, which seems to be closely related to the consciousness state of the rat. This finding suggests the possibility of NIRS based-hemodynamic monitoring as a supplementary parameter for consciousness monitoring, compensating drawbacks of EEG signal based monitoring.

Detection of Stress Levels from Biosignals Measured in Virtual Reality Environments Using a Kernel-Based Extreme Learning Machine.

Virtual reality (VR) is a computer technique that creates an artificial environment composed of realistic images, sounds, and other sensations. Many researchers have used VR devices to generate various stimuli, and have utilized them to perform experiments or to provide treatment. In this study, the participants performed mental tasks using a VR device while physiological signals were measured: a photoplethysmogram (PPG), electrodermal activity (EDA), and skin temperature (SKT). In general, stress is an important factor that can influence the autonomic nervous system (ANS). Heart-rate variability (HRV) is known to be related to ANS activity, so we used an HRV derived from the PPG peak interval. In addition, the peak characteristics of the skin conductance (SC) from EDA and SKT variation can also reflect ANS activity; we utilized them as well. Then, we applied a kernel-based extreme-learning machine (K-ELM) to correctly classify the stress levels induced by the VR task to reflect five different levels of stress situations: baseline, mild stress, moderate stress, severe stress, and recovery. Twelve healthy subjects voluntarily participated in the study. Three physiological signals were measured in stress environment generated by VR device. As a result, the average classification accuracy was over 95% using K-ELM and the integrated feature (IT = HRV + SC + SKT). In addition, the proposed algorithm can embed a microcontroller chip since K-ELM algorithm have very short computation time. Therefore, a compact wearable device classifying stress levels using physiological signals can be developed.

Differential modulation of thalamo-parietal interactions by varying depths of isoflurane anesthesia.

The thalamus is thought to relay peripheral sensory information to the somatosensory cortex in the parietal lobe. Long-range thalamo-parietal interactions play an important role in inducing the effect of anesthetic. However, whether these interaction changes vary with different levels of anesthesia is not known. In the present study, we investigated the influence of different levels of isoflurane-induced anesthesia on the functional connectivity between the thalamus and the parietal region. Microelectrodes were implanted in rats to record local field potentials (LFPs). The rats underwent different levels of isoflurane anesthesia [deep anesthesia: isoflurane (ISO) 2.5 vol%, light anesthesia (ISO 1 vol%), awake, and recovery state] and LFPs were recorded from four different brain areas (left parietal, right parietal, left thalamus, and right thalamus). Partial directed coherence (PDC) was calculated for these areas. With increasing depth of anesthesia, the PDC in the thalamus-to-parietal direction was significantly increased mainly in the high frequency ranges; however, in the parietal-to-thalamus direction, the increase was mainly in the low frequency band. For both directions, the PDC changes were prominent in the alpha frequency band. Functional interactions between the thalamus and parietal area are augmented proportionally to the anesthesia level. This relationship may pave the way for better understanding of the neural processing of sensory inputs from the periphery under different levels of anesthesia.

Monitoring cerebral oxygenation and local field potential with a variation of isoflurane concentration in a rat model.

We aimed to investigate experimentally how anesthetic levels affect cerebral metabolism measured by near-infrared spectroscopy (NIRS) and to identify a robust marker among NIRS parameters to discriminate various stages of anesthetic depth in rats under isoflurane anesthesia. In order to record the hemodynamic changes and local field potential (LFP) in the brain, fiber-optic cannulae and custom-made microelectrodes were implanted in the frontal cortex of the skull. The NIRS and LFP signals were continuously monitored before, during and after isoflurane anesthesia. As isoflurane concentration is reduced, the level of oxyhemoglobin and total hemoglobin concentrations of the frontal cortex decreased gradually, while deoxyhemoglobin increased. The reflectance ratio between 730nm and 850nm and burst suppression ratio (BSR) correspond similarly with the change of oxyhemoglobin during the variation of isoflurane concentration. These results suggest that NIRS signals in addition to EEG may provide a possibility of developing a new anesthetic depth index.

Changes in thalamo-frontal interaction under different levels of anesthesia in rats.

Anesthesia is thought to be mediated by inhibiting the integration of information between different areas of the brain. Long-range thalamo-cortical interaction plays a critical role in inducing anesthesia-related unconsciousness. However, it remains unclear how this interaction change according to anesthetic depth. In this study, we aimed to investigate how different levels of anesthesia affect thalamo-frontal interactions. Prior to the experiment, electrodes were implanted to record local field potentials (LFPs). Isoflurane (ISO) was administered and LFPs were measured in rats from four different brain areas (left frontal, right frontal, left thalamus and right thalamus) at four different anesthesia levels: awake, deep (ISO 2.5vol%), light (ISO 1vol%) and recovery. Spectral granger causality (Spectral-GC) were calculated at the measured areas in accordance with anesthetic levels. Anesthesia led to a decrease in connectivity in the thalamo-frontal direction and an increase in connectivity in the frontal-thalamic direction. The changes in thalamo-frontal functional connectivity were prominent during deep anesthesia at high frequency bands. The connection strengths between the thalamus and the frontal area changed depending on the depth of anesthesia. The relationships between anesthetic levels and thalamo-frontal activity may shed light on the neural mechanism by which different levels of anesthesia act.