Comparative analysis of resting-state EEG-based multiscale entropy between schizophrenia and bipolar disorder.

BACKGROUND: Studies that use nonlinear methods to identify abnormal brain dynamics in patients with psychiatric disorders are limited. This study investigated brain dynamics based on EEG using multiscale entropy (MSE) analysis in patients with schizophrenia (SZ) and bipolar disorder (BD). METHODS: The eyes-closed resting-state EEG data were collected from 51 patients with SZ, 51 patients with BD, and 51 healthy controls (HCs). Patients with BD were further categorized into type I (n = 23) and type II (n = 16), and then compared with patients with SZ. A sample entropy-based MSE was evaluated from the bilateral frontal, central, and parieto-occipital regions using 30-s artifact-free EEG data for each individual. Correlation analyses of MSE values and psychiatric symptoms were performed. RESULTS: For patients with SZ, higher MSE values were observed at higher-scale factors (i.e., 41-70) across all regions compared with both HCs and patients with BD. Furthermore, there were positive correlations between the MSE values in the left frontal and parieto-occipital regions and PANSS scores. For patients with BD, higher MSE values were observed at middle-scale factors (i.e., 13-40) in the bilateral frontal and central regions compared with HCs. Patients with BD type I exhibited higher MSE values at higher-scale factors across all regions compared with those with BD type II. In BD type I, positive correlations were found between MSE values in all left regions and YMRS scores. CONCLUSIONS: Patients with psychiatric disorders exhibited group-dependent MSE characteristics. These results suggest that MSE features may be useful biomarkers that reflect pathophysiological characteristics.

Influence of inter-stimulus interval on 40-Hz auditory steady-state response in patients with schizophrenia.

Decreased 40-Hz auditory steady-state response (ASSR) is believed to reflect abnormal gamma oscillation in patients with schizophrenia (SZ). However, previous studies have reported conflicting results due to variations in inter-stimulus interval (ISI) used. In this study, we aimed to investigate the influence of varying ISI on the 40-Hz ASSR, particularly for patients with SZ and healthy controls (HCs). Twenty-four SZ patients (aged 40.8 ± 13.9 years, male: n = 11) and 21 HCs (aged 33.3 ± 11.3 years, male: n = 8) were recruited. For every participant, 40-Hz ASSRs were acquired for three different stimulus types: 500, 2000, and 3500 ms of ISIs. Two conventional ASSR measures (total power and inter-trial coherence, ITC) were calculated. Several additional ASSR measures were also analyzed: (i) ISI-dependent power; (ii) power onset slope; (iii) power centroid latency; (iv) ISI-dependent ITC; (v) ITC onset slope (500, 2000, 3500 ms); (vi) ITC centroid latency (500, 2000, 3500 ms). As ISI increased, total power and ITC increased in patients with SZ but decreased in HCs. In addition, patients with SZ showed higher ISI-dependent ITC, which was positively correlated with the psychotic symptom severity. The abnormal ITC onset slope and centroid latency for the ISI-500 ms condition were associated with cognitive speed decline in patients with SZ. Our study confirmed that the 40-Hz ASSR could be severely influenced by ISI. Furthermore, our results showed that the additional ASSR measures (ISI-dependent ITC, ITC onset slope, ITC centroid latency) could represent psychotic symptom severity or impairment in cognitive function in patients with SZ.

Inflammatory Markers and Brain Volume in Patients with Post-traumatic Stress Disorder.

Objective: Posttraumatic stress disorder (PTSD) is characterized by increased inflammatory processing and altered brain volume. In this study, we investigated the relationship between inflammatory markers and brain volume in patients with PTSD. Methods: Forty-five patients with PTSD, and 70 healthy controls (HC) completed clinical assessments and self-reported psychopathology scales. Factors associated with inflammatory responses including brain-derived neurotrophic factor and four inflammatory biomarkers (C-reactive protein, cortisol, Interleukin-6, and homocysteine) and T1-magnetic resonance imaging of the brain were measured. Results: In the PTSD group, cortisol level was significantly lower (t = 2.438, p = 0.046) than that of the HC. Cortisol level was significantly negatively correlated with the left thalamus proper (r = -0.369, p = 0.035), right thalamus proper (r = -0.394, p = 0.014), right frontal pole (r = -0.348, p = 0.039), left occipital pole (r = -0.338, p = 0.044), and right superior occipital gyrus (r = -0.397, p = 0.008) in patients with PTSD. However, these significant correlations were not observed in HC. Conclusion: Our results indicate that increased cortisol level, even though its average level was lower than that of HC, is associated with smaller volumes of the thalamus, right frontal pole, left occipital pole, and right superior occipital gyrus in patients with PTSD. Cortisol, a major stress hormone, might be a reliable biomarker to brain volumes and pathophysiological pathways in patients with PTSD.

Selection of the optimal channel configuration for implementing wearable EEG devices for the diagnosis of mild cognitive impairment.

BACKGROUND: Early diagnosis of mild cognitive impairment (MCI) is essential for timely treatment planning. With recent advances in the wearable technology, interest has increasingly shifted toward computer-aided self-diagnosis of MCI using wearable electroencephalography (EEG) devices in daily life. However, no study so far has investigated the optimal electrode configurations for the efficient diagnosis of MCI while considering the design factors of wearable EEG devices. In this study, we aimed to determine the optimal channel configurations of wearable EEG devices for the computer-aided diagnosis of MCI. METHOD: We employed an EEG dataset collected from 21 patients with MCI and 21 healthy control subjects. After evaluating the classification accuracies for all possible electrode configurations for the two-, four-, six-, and eight-electrode conditions using a support vector machine, the optimal electrode configurations that provide the highest diagnostic accuracy were suggested for each electrode condition. RESULTS: The highest classification accuracies of 74.04% ± 4.82, 82.43% ± 6.14, 86.28% ± 2.81, and 86.85% ± 4.97 were achieved for the optimal two-, four-, six-, and eight-electrode configurations, respectively, which demonstrated the possibility of precise machine-learning-based diagnosis of MCI with a limited number of EEG electrodes. Additionally, further simulations with the EEG dataset revealed that the optimal electrode configurations had significantly higher classification accuracies than commercial EEG devices with the same number of electrodes, which suggested the importance of electrode configuration optimization for wearable EEG devices based on clinical EEG datasets. CONCLUSIONS: This study highlighted that the optimization of the electrode configuration, assuming the wearable EEG devices can potentially be utilized for daily life monitoring of MCI, is necessary to enhance the performance and portability.

Treatment efficacy of tDCS and predictors of treatment response in patients with post-traumatic stress disorder.

BACKGROUND: Although transcranial direct stimulation (tDCS) has been proposed as an alternative treatment option for various psychiatric disorders, there is inconsistent information regarding the treatment effects of tDCS for patients with post-traumatic stress disorder (PTSD). This study aimed to investigate the tDCS efficacy and identify predictors of treatment response to tDCS in patients with PTSD. METHOD: Fifty-one patients received 10 sessions of tDCS involving the position of the anode over the F3 area and cathode over the F4 as a condition of 2.0 mA and 20 min duration. Digit span test and 10 questionnaires (Clinician-Administered PTSD Scale (CAPS), Cognitive Emotion Regulation Questionnaire (CERQ), Multidimensional Experiential Avoidance Questionnaire (MEAQ), etc.) were used to measure tDCS effects on PTSD symptoms and identify predictors of response to tDCS. RESULTS: 1) 50.9 % of patients had a significant reduction in the frequency and severity of PTSD symptoms, 2) PTSD-related symptoms such as depression, anxiety, rumination, and quality of life were significantly improved, 3) baseline scores on rumination and digit span test significantly predicted treatment response to tDCS. LIMITATIONS: This study was open design without a sham control group. Also, the patients' medications were not controlled. CONCLUSION: This study highlighted the efficacy of frontal tDCS for the treatment of patients with PTSD and identified rumination and digit span as favorable predictive factors for the outcomes of tDCS.

Comparative analysis of default mode networks in major psychiatric disorders using resting-state EEG.

Default mode network (DMN) is a set of functional brain structures coherently activated when individuals are in resting-state. In this study, we constructed multi-frequency band resting-state EEG-based DMN functional network models for major psychiatric disorders to easily compare their pathophysiological characteristics. Phase-locking values (PLVs) were evaluated to quantify functional connectivity; global and nodal clustering coefficients (CCs) were evaluated to quantify global and local connectivity patterns of DMN nodes, respectively. DMNs of patients with post-traumatic stress disorder (PTSD), obsessive compulsive disorder (OCD), panic disorder, major depressive disorder (MDD), bipolar disorder, schizophrenia (SZ), mild cognitive impairment (MCI), and Alzheimer's disease (AD) were constructed relative to their demographically-matched healthy control groups. Overall DMN patterns were then visualized and compared with each other. In global CCs, SZ and AD showed hyper-clustering in the theta band; OCD, MCI, and AD showed hypo-clustering in the low-alpha band; OCD and MDD showed hypo-clustering and hyper-clustering in low-beta, and high-beta bands, respectively. In local CCs, disease-specific patterns were observed. In the PLVs, lowered theta-band functional connectivity between the left lingual gyrus and the left hippocampus was frequently observed. Our comprehensive comparisons suggest EEG-based DMN as a useful vehicle for understanding altered brain networks of major psychiatric disorders.

Comparison of Visual Stimuli for Steady-State Visual Evoked Potential-Based Brain-Computer Interfaces in Virtual Reality Environment in terms of Classification Accuracy and Visual Comfort.

Recent studies on brain-computer interfaces (BCIs) based on the steady-state visual evoked potential (SSVEP) have demonstrated their use to control objects or generate commands in virtual reality (VR) environments. However, most SSVEP-based BCI studies performed in VR environments have adopted visual stimuli that are typically used in conventional LCD environments without considering the differences in the rendering devices (head-mounted displays (HMDs) used in the VR environments). The proximity between the visual stimuli and the eyes in HMDs can readily cause eyestrain, degrading the overall performance of SSVEP-based BCIs. Therefore, in the present study, we have tested two different types of visual stimuli-pattern-reversal checkerboard stimulus (PRCS) and grow/shrink stimulus (GSS)-on young healthy participants wearing HMDs. Preliminary experiments were conducted to investigate the visual comfort of each participant during the presentation of the visual stimuli. In subsequent online avatar control experiments, we observed considerable differences in the classification accuracy of individual participants based on the type of visual stimuli used to elicit SSVEP. Interestingly, there was a close relationship between the subjective visual comfort score and the online performance of the SSVEP-based BCI: most participants showed better classification accuracy under visual stimulus they were more comfortable with. Our experimental results suggest the importance of an appropriate visual stimulus to enhance the overall performance of the SSVEP-based BCIs in VR environments. In addition, it is expected that the appropriate visual stimulus for a certain user might be readily selected by surveying the user's visual comfort for different visual stimuli, without the need for the actual BCI experiments.