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Background: Impaired movement after stroke is closely associated with altered brain functions, and thus the investigation on neural substrates of patients with stroke can pave a way for not only un...

In recent years, machine learning techniques have been frequently applied to uncovering neuropsychiatric biomarkers with the aim of accurately diagnosing neuropsychiatric diseases and predicting treatment prognosis. However, many studies did not perform cross validation (CV) when using machine learning techniques, or others performed CV in an incorrect manner, leading to significantly biased results due to overfitting problem. The aim of this study is to investigate the impact of CV on the prediction performance of neuropsychiatric biomarkers, in particular, for feature selection performed with high-dimensional features. To this end, we evaluated prediction performances using both simulation data and actual electroencephalography (EEG) data. The overall prediction accuracies of the feature selection method performed outside of CV were considerably higher than those of the feature selection method performed within CV for both the simulation and actual EEG data. The differences between the prediction accuracies of the two feature selection approaches can be thought of as the amount of overfitting due to selection bias. Our results indicate the importance of correctly using CV to avoid biased results of prediction performance of neuropsychiatric biomarkers.

A resting-state EEG-based computer-aided diagnosis (CAD) system could complement the traditional diagnostic error for post-traumatic stress disorder (PTSD) patients. The aim of this study is to develop an EEG-based CAD system for diagnosis of PTSD patients. To this end, eyes-closed resting-state EEG data were recorded from 77 PTSD patients and 58 healthy controls. Two different types of functional connectivity-based features were extracted, i.e., phase locking values (PLVs) and network indices (strength, clustering coefficient, and path length), for both sensor- and source-level. The classification performances of each feature set were evaluated using a support vector machine with leave-one out cross-validation. The best classification performance was achieved when using source-level PLVs (accuracy — 70.37% and area under curve (AUC) — 0.85). In our future studies, we will attempt to enhance the performance of our proposed CAD system by using deep-learning algorithms.

The altered cognitive function related to intrusive memory is a distinguished symptom of post-traumatic stress disorder (PTSD). In the present study, we investigated the altered cortical activation and functional network in PTSD patients using electroencephalogram (EEG) measured during an auditory cognitive task. A time-series of neuronal activities was reconstructed using EEG from 25 PTSD patients and 25 healthy controls (HC). PTSD patients showed an unclear P300 event-related potential (ERP) waveform and significantly different power spectrum compared to HC. In addition, the altered network indices (low strength and clustering coefficient, but enhanced path length) appeared in PTSD patients, and the reduced nodal clustering coefficients were significantly related to symptom scores. Finally, we achieved the best classification of 80.00 % when differentiating PTSD patients and HC based on network features.

Transcranial direct current stimulation (tDCS) is an emerging technique that applies DC current to the scalp to improve the motor function of patients with neurological disorders. The target location of tDCS is generally defined by the motor hotspot identified by using motor-evoked potential (MEP). However, the traditional MEP-based method needs use of transcranial magnetic stimulation (TMS), and it requires the empirical judgement. In this study, we propose a new motor hotspot detection method based on electroencephalography (EEG) acquired during a motor task. EEG data were measured from twelve subjects while they repeatedly performed a simple finger tapping task. Power spectral densities (PSDs) were extracted frin the EEG data as features, and a linear regression and artificial neural network (ANN) were used to estimate an individual motor hotspot location. The 3D coordinate information of individual motor hotspots detected by TMS were compared with those estimated by our proposed EEG-based approach. ANN outperformed a linear regression method in terms of the error distance, and a minimum distance between the motor hotspots identified by TMS and ANN was only 0.18 cm, demonstrating the feasibility of our proposed novel EEG-based motor hotspot detection method.

Individuals who have prior history of childhood traumatic experiences are at a high risk for a variety of psychological and behavioral problems throughout their lifetime. This study aimed to investigate whether such individuals exhibit altered cortical functional networks during a behavioral inhibition task. One hundred fifty-three non-clinical individuals were recruited and instructed to perform a Go/NoGo task during an electroencephalograph. Source-level weighted functional networks based on the graph theory were analyzed for NoGo-P3 processing. Based on their total scores on the childhood trauma questionnaire (CTQ) participants were divided into three groups: low CTQ, middle CTQ, and high CTQ. Results at the global level indicated decreased strength, clustering coefficient, and efficiency for the low and gamma bands in the high CTQ group. In addition, the path length of the low beta band was observed to be longer in the high CTQ group than the low CTQ group. At the nodal level, the nodal clustering coefficient of high CTQ group was decreased in left primary somatosensory cortex and middle occipital gyrus for the low beta band, and in left superior temporal gyrus for the gamma band. The nodal clustering coefficient of the left primary somatosensory cortex showed a significant negative correlation with the total CTQ score for the low beta band. In addition, the nodal clustering coefficient of the left middle occipital gyrus for the low beta band and superior temporal gyrus for the gamma band showed significant negative correlations with the emotional neglect score. Our results demonstrate an altered cortical functional network in individuals who experienced childhood trauma. In particular, the left primary somatosensory cortex, middle occipital gyrus, and superior temporal gyrus were found to be vulnerable in individuals who experienced childhood trauma, especially emotional neglect.

Background Electroencephalogram (EEG)-based brain network analysis is a useful biological correlate reflecting brain function. Sensor-level network analysis might be contaminated by volume conduction and does not explain regional brain characteristics. Source-level network analysis could be a useful alternative. We analyzed EEG-based source-level network in major depressive disorder (MDD). Method Resting-state EEG was recorded in 87 MDD and 58 healthy controls, and cortical source signals were estimated. Network measures were calculated: global indices (strength, clustering coefficient (CC), path length (PL), and efficiency) and nodal indices (eigenvector centrality and nodal CC) in six frequency. Correlation analyses were performed between network indices and symptom scales. Results At the global level, MDD showed decreased strength, CC in theta and alpha bands, and efficiency in alpha band, while enhanced PL in alpha band. At nodal level, eigenvector centrality of alpha band showed region dependent changes in MDD. Nodal CCs of alpha band were reduced in MDD and were negatively correlated with depression and anxiety scales. Conclusion Disturbances in EEG-based brain network indices might reflect altered emotional processing in MDD. These source-level network indices might provide useful biomarkers to understand regional brain pathology in MDD., Highlights • We investigated the altered function of electroencephalogram-based cortical network for major depressive disorder (MDD). • MDD showed altered cortical brain network at both global and nodal level network in alpha frequency band. • Abnormal network indices in MDD were significantly correlated with depression and anxiety symptom scale scores. • Disrupted cortical networks band might reflect altered neural pathway during emotional processing in patients with MDD.

Aim The Sewol ferry capsizing accident on South Korea's southern coast resulted in the death of 304 people, and serious bereavement problems for their families. Electroencephalography (EEG) beta frequency is associated with psychiatric symptoms, such as insomnia. The aim of this study was to investigate the relation between frontal beta power, psychological symptoms, and insomnia in the bereaved families. Methods Eighty-four family members of the Sewol ferry victims (32 men and 52 women) were recruited and their EEG was compared with that of 25 (13 men and 12 women) healthy controls. A two-channel EEG device was used to measure cortical activity in the frontal lobe. Symptom severity of insomnia, post-traumatic stress disorder, complicated grief, and anxiety were evaluated. Results The bereaved families showed a higher frontal beta power than healthy controls. Subgroup analysis showed that frontal beta power was lower in the individuals with severe insomnia than in those with normal sleep. There was a significant inverse correlation between frontal beta power and insomnia symptom in the bereaved families. Conclusion This study suggests that increased beta power, reflecting the psychopathology in the bereaved families of the Sewol ferry disaster, may be a compensatory mechanism that follows complex trauma. Frontal beta power could be a potential marker indicating the severity of sleep disturbances. Our results suggest that sleep disturbance is an important symptom in family members of the Sewol ferry disaster's victims, which may be screened by EEG beta power.

Background: Proinflammatory cytokines, such as tumor necrosis factor-alpha (TNF-&alpha, ), are associated with the pathophysiology of major depressive disorder (MDD). Several studies have reported that increased TNF-&alpha, might be associated with tryptophan depletion, which eventually could result in MDD. However, other studies revealed that TNF-&alpha, increased serotonin firing in raphe. Therefore, whether TNF-&alpha, increases or decreases serotonin activity remains unclear. Here, we aimed to determine the relationship between serum TNF-&alpha, level and central serotonergic activity using the loudness dependence of auditory evoked potentials (LDAEP) and standardized low-resolution brain electromagnetic tomography (sLORETA), as well as to evaluate the effects of antidepressants on TNF-&alpha, levels. Methods: LDAEP, serum TNF-&alpha, level, and depression severity were measured in 64 MDD outpatients pre and post 3 months of treatment. Results: Pretreatment TNF-&alpha, levels were negatively correlated with the pretreatment N1 sLORETA-LDAEP, P2 sLORETA-LDAEP, and N1/P2 sLORETA-LDAEP (p &lt, 0.05). In multiple regression analysis for N1/P2 sLORETA-LDAEP, lower N1/P2 sLORETA-LDAEP was significantly related to higher TNF-&alpha, (CE = &minus, 0.047, p = 0.017) when all subjects were dichotomized based on the median TNF-&alpha, level (7.16 pg/mL) into pretreatment low- and high-TNF-&alpha, groups. In addition, the pretreatment Beck Depression Inventory, P2 LDAEP, and N1/P2 sLORETA-LDAEP were greater in the high-TNF-&alpha, groups than in the low-TNF-&alpha, groups (p &lt, 0.05). Moreover, the posttreatment TNF-&alpha, level was significantly decreased compared to the pretreatment TNF-&alpha, level (z = &minus, 2.581, p = 0.01). However, the posttreatment TNF-&alpha, levels were not associated with posttreatment LDAEP. Conclusions: Higher TNF-&alpha, level is associated with decreased LDAEP, which could indicate compensatory elevation of central serotonin activity in outpatients with MDD, although this effect disappeared and TNF-&alpha, level was reduced after three months of antidepressant treatment.

Background Disturbances in thought, speech, and linguistic processing are frequently observed in bipolar manic patients, but the underlying neurophysiological mechanisms are not well understood. P600 is a distinct, positive event-related potential component elicited by syntactic violations. Using the P600 ERP, we examined neural processing of syntactic language comprehension in patients with bipolar mania compared to patients with schizophrenia and healthy people. Method P600s were recorded from 21 manic patients with bipolar disorder, 26 patients with schizophrenia, and 29 healthy subjects during the presentation of 120 auditory sentences with syntactic violations or non-violations. Subjects were asked to judge whether each sentence was correct or incorrect. Results Patients with mania and schizophrenia had significantly smaller P600 amplitudes associated with syntactic violations compared with healthy subjects. There was no difference in P600 amplitude between patient groups. For behavioral performance, patients with schizophrenia had significantly less accurate rates and longer reaction times compared with healthy subjects, whereas manic patients exhibited no significant differences in accuracy and only showed increased reaction times in comparison with healthy subjects. Limitations Psychotropic drug usage and small sample size. Conclusion Patients with bipolar mania have reduced P600 amplitude, comparable to patients with schizophrenia. Our findings may represent the first neurophysiological evidence of abnormal syntactic linguistic processing in bipolar mania.

Background Deficits in facial emotion processing is a major characteristic of patients with panic disorder. It is known that visual stimuli with different spatial frequencies take distinct neural pathways. This study investigated facial emotion processing involving stimuli presented at broad, high, and low spatial frequencies in patients with panic disorder. Methods Eighteen patients with panic disorder and 19 healthy controls were recruited. Seven event-related potential (ERP) components: (P100, N170, early posterior negativity (EPN); vertex positive potential (VPP), N250, P300; and late positive potential (LPP)) were evaluated while the participants looked at fearful and neutral facial stimuli presented at three spatial frequencies. Results When a fearful face was presented, panic disorder patients showed a significantly increased P100 amplitude in response to low spatial frequency compared to high spatial frequency; whereas healthy controls demonstrated significant broad spatial frequency dependent processing in P100 amplitude. Vertex positive potential amplitude was significantly increased in high and broad spatial frequency, compared to low spatial frequency in panic disorder. Early posterior negativity amplitude was significantly different between HSF and BSF, and between LSF and BSF processing in both groups, regardless of facial expression. Limitation The possibly confounding effects of medication could not be controlled. Conclusions During early visual processing, patients with panic disorder prefer global to detailed information. However, in later processing, panic disorder patients overuse detailed information for the perception of facial expressions. These findings suggest that unique spatial frequency-dependent facial processing could shed light on the neural pathology associated with panic disorder.

Recently, objective and automated methods for the diagnosis of post-traumatic stress disorder (PTSD) have attracted increasing attention. However, previous studies on machine-learning-based diagnosis of PTSD with resting-state electroencephalogram (EEG) have reported poor accuracies of as low as 60%. Here, a Riemannian geometry-based classifier, the Fisher geodesic minimum distance to the mean (FgMDM), was employed for PTSD classification for the first time. Eyes-closed resting-state EEG data of 39 healthy individuals and 42 PTSD patients were used for the analysis. EEG source activities in 148 cortical regions were parcellated based on the Destrieux atlas, and their covariances were evaluated for each individual. Thirty epochs of preprocessed EEG were employed to calculate source activities. In addition, the FgMDM approach was applied to each EEG source covariance to construct the classifier. For a comparison, linear discriminant analysis (LDA), support vector machine (SVM), and random forest (RF) classifiers employing source band powers and network features as feature candidates were also tested. The FgMDM classifier showed an average classification accuracy of 75.240.80%. In contrast, the maximum accuracies of LDA, SVM, and RF classifiers were 66.54 ± 2.99%, 61.11 ± 2.98%, and 60.99 ± 2.19%, respectively. Our study demonstrated that the diagnostic accuracy of PTSD with resting-state EEG could be significantly improved by employing the FgMDM framework, which is a type of Riemannian geometry-based classifier.

Background Abnormalities in the 40-Hz auditory steady-state response (ASSR) of the gamma range have been reported in schizophrenia (SZ) and are regarded as important pathophysiological features. Many of the previous studies reported diminished gamma oscillations in SZ, although some studies reported increased spontaneous gamma oscillations. Furthermore, brain morphological correlates of the gamma band ASSR deficits have rarely examined. We investigated different measures of the 40-Hz ASSR and their association with brain volumes and psychological measures of SZ. Methods The 40-Hz ASSR was measured for 80 dB click sounds (1 ms, 500-ms trains at 40-Hz, with 3050 to 3500 inter-train interval) using electroencephalography with 64 electrodes in 33 patients with SZ (male: 16, female: 17 (age range: 21–60)) and 30 healthy controls (HCs) (male: 13, female: 17 (age range: 23–64)). Four gamma oscillation measures (evoked power, spontaneous oscillations (baseline and total power), and inter-trial phase coherence (ITC)) were assessed. The source activities of the ASSR were also analyzed. Brain volumes were assessed using high-resolution magnetic resonance imaging and voxel-based morphometry and superior temporal gyrus (STG) volume measures were obtained. Results Patients with SZ had larger total and evoked powers and higher ITC than HCs. Both groups showed significantly different association between mean evoked power and right STG volume. In HCs but not SZ, mean evoked power showed significant positive correlation with right STG volume. In addition, the two groups showed significantly different association between verbal fluency and mean evoked power. High evoked power was significantly correlated with poor verbal fluency in SZ. Conclusions The current study found increased gamma oscillation in SZ and suggests significant involvement of the STG in gamma oscillations., Highlights • SZ had larger total and evoked powers and higher ITC than HCs. • Evoked power positively correlated with right STG volume in HCs. • High evoked power correlated with poor verbal fluency in SZ.

BACKGROUND: People with schizophrenia (SZ) have deficits in context processing (CP), which refers to an executive process that guides adaptive behaviors according to goals and stored contextual information. Similar deficits were also found in their biological relatives, suggesting that CP-related dysfunctions might be endophenotypic markers of schizophrenia that reflect central pathology of the disorder. Abnormal cortical neural oscillations might be the brain mechanisms underlying CP deficits in schizophrenia. However, the precise spatial, temporal, and frequency abnormalities associated with the illness and genetic liability of schizophrenia are largely unknown. METHODS: We analyzed high-density electroencephalography (HD-EEG) and functional MRI data collected from 22 SZ, 20 biological relatives of SZ (RSZ), and 30 healthy controls [HC]) during an optimized CP task called stimulus response reversal task (SRRT). ExGaussian reaction time (RT) distribution analysis was used to determine abnormalities in intra-individual RT variability in CP. We identified fMRI cortical regions of interest (ROIs) activated for CP, then the ROI source signals of HD-EEG were computed using individual realistic head models and a source localization algorithm employing boundary element method (BEM). Cohen’s class time-frequency (TF) measures of the ROI signals were computed to define accurate time and frequency dynamics of CP-related cortical oscillations in high and low CP (HCP and LCP) load conditions. RESULTS: RSZ were normal in all behavioral performance indices, and SZ were also normal in response accuracy and motor speed (RT-mu). However, SZ had larger response instability (RT-sigma) than HC only in HCP trials, suggesting their specific deficits in contextual motor control. We identified task-related fMRI ROIs including dorsal middle frontal gyrus (dMFG), inferior middle frontal gyrus (iMFG), inferior frontal gyrus (IFG), inferior parietal lobules (IPL), anterior cingulate cortex (ACC), motor cortex (MC), and middle occipital gyrus (mOG) in the both hemispheres. TF analyses of the ROI signals in 3 time-windows of the task trials identified CP-related neural oscillatory activities (HCP-LCP differences) in low-frequency (1–5 Hz) event-related synchrony (LF-ERS) and alpha band (8–12 Hz) event-related desynchrony (A-ERD) that varied across the groups. Unlike HC who had simultaneous LF-ERS increases in dMFG and IPL during contextual cue encoding for HCP trials, SZ and RSZ had LF-ERS increases only in IFG and iMFG respectively. During delay period (maintenance of cue information) in HCP trials, HC had increased LF-ERS in dMFG and IPL, while RSZ had increased LF-ERS in iMFG. In contrast, SZ had A-ERD increases in mOG during delays in HCP trials. The HCP-related RT-sigma increases in SZ were predicted by their CP-related LF-ERS (1–4 Hz) during maintenance (1300–1500 ms after cue onsets) in IPL (r=-.71, p=.002). Before responses to probes in HCP trials where response conflicts should be resolved, HC had LF-ERS increases in dMFG, ACC, and IPL while RSZ had such increases in iMFG, ACC, and MC. In contrast, SZ had increases in pre-response A-ERD for HCP trials in mOG and MC. DISCUSSION: Results suggested that SZ and RSZ had common deficits in LF-ERS of the dorsal prefrontal and parietal network, but they utilized resources of other cortical regions to compensate their core deficits in CP. HCP-specific response instability in SZ might be caused by their parietal dysfunction to update contextual rules and additional ACC-motor dysfunctions. In summary, CP-related deficits in the dorsal prefrontal-parietal network might be the endophenotypic markers of schizophrenia, while ACC-motor cortical deficits might be disease-specific marker unique to SZ.

Background: Animal and clinical studies have demonstrated that the loudness dependence of auditory evoked potentials (LDAEP) is inversely related to central serotonergic activity, with a high LDAEP reflecting weak serotonergic neurotransmission and vice versa, though the findings in humans have been less consistent. In addition, a high pretreatment LDAEP appears to predict a favorable response to antidepressant treatments that augment the actions of serotonin. The aim of this study was to test whether the baseline LDAEP is correlated with response to long-term maintenance treatment in patients with major depressive disorder (MDD). Methods: Scalp N1, P2 and N1/P2 LDAEP and standardized low resolution brain electromagnetic tomography-localized N1, P2, and N1/P2 LDAEP were evaluated in 41 MDD patients before and after they received antidepressant treatment (escitalopram (n = 32, 10.0 ± 4.0 mg/day), sertraline (n = 7, 78.6 ± 26.7 mg/day), and paroxetine controlled-release formulation (n = 2, 18.8 ± 8.8 mg/day)) for more than 12 weeks. A treatment response was defined as a reduction in the Beck Depression Inventory (BDI) score of &gt, 50% between baseline and follow-up. Results: The responders had higher baseline scalp P2 and N1/P2 LDAEP than nonresponders (p = 0.017, p = 0.036). In addition, changes in total BDI score between baseline and follow-up were larger in subjects with a high baseline N1/P2 LDAEP than those with a low baseline N1/P2 LDAEP (p = 0.009). There were significantly more responders in the high-LDAEP group than in the low-LDAEP group (p = 0.041). Conclusions: The findings of this study reveal that a high baseline LDAEP is associated with a clinical response to long-term antidepressant treatment.

Abnormal facial emotion recognition is considered as one of the key symptoms of schizophrenia. Only few studies have considered deficits in the spatial frequency (SF)-dependent visual pathway leading to abnormal facial emotion recognition in schizophrenia. Twenty-one patients with schizophrenia and 19 matched healthy controls (HC) were recruited for this study. Event-related potentials (ERP) were measured during presentation of SF-modulated face stimuli and their source imaging was analyzed. The patients showed reduced P100 amplitude for low-spatial frequency (LSF) pictures of fearful faces compared with the HC group. The P100 amplitude for high-spatial frequency (HSF) pictures of neutral faces was increased in the schizophrenia group, but not in the HC group. The neural source activities of the LSF fearful faces and HSF neutral faces led to hypo- and hyperactivation of the frontal lobe of subjects from the schizophrenia group and HC group, respectively. In addition, patients with schizophrenia showed enhanced N170 activation in the right hemisphere in the LSF condition, while the HC group did not. Our results suggest that deficits in the LSF-dependent visual pathway, which involves magnocellular neurons, impair early visual processing leading to dysfunctional facial emotion recognition in schizophrenia. Moreover, it suggests impaired bottom-up processing rather than top-down dysfunction for facial emotion recognition in these patients.

Precise diagnosis of psychiatric diseases and a comprehensive assessment of a patient's symptom severity are important in order to establish a successful treatment strategy for each patient. Although great efforts have been devoted to searching for diagnostic biomarkers of schizophrenia over the past several decades, no study has yet investigated how accurately these biomarkers are able to estimate an individual patient's symptom severity. In this study, we applied electrophysiological biomarkers obtained from electroencephalography (EEG) analyses to an estimation of symptom severity scores of patients with schizophrenia. EEG signals were recorded from 23 patients while they performed a facial affect discrimination task. Based on the source current density analysis results, we extracted voxels that showed a strong correlation between source activity and symptom scores. We then built a prediction model to estimate the symptom severity scores of each patient using the source activations of the selected voxels. The symptom scores of the Positive and Negative Syndrome Scale (PANSS) were estimated using the linear prediction model. The results of leave-one-out cross validation (LOOCV) showed that the mean errors of the estimated symptom scores were 3.34 ± 2.40 and 3.90 ± 3.01 for the Positive and Negative PANSS scores, respectively. The current pilot study is the first attempt to estimate symptom severity scores in schizophrenia using quantitative EEG features. It is expected that the present method can be extended to other cognitive paradigms or other psychological illnesses.

Women tend to respond to emotional stimuli differently from men. This study aimed at investigating whether neural responses to perceptually “invisible” emotional stimuli differ between men and women by exploiting event-related potential (ERP). Forty healthy participants (21 women) were recruited for the main experiment. A control experiment was conducted by excluding nine (7 women) participants from the main experiment and replacing them with additional ten (6 women) participants (total 41 participants) where Beck's Anxiety Inventory (BAI) and Beck's Depression Inventory (BDI) scores were controlled. Using the visual backward masking paradigm, either a fearful or a neutral face stimulus was presented in varied durations (subthreshold, near-threshold, or suprathreshold) followed by a mask. Participants performed a two-alternative forced choice (2-AFC) emotion discrimination task on each face. Behavioral analysis showed that participants were unaware of masked stimuli of which duration was the shortest and, therefore, processed at subthreshold. Nevertheless, women showed significantly larger response in P100 amplitude to subthreshold fearful faces than men. This result remained consistent in the control experiment. Our findings indicate gender-differences in neural response to subthreshold emotional face, which is reflected in the early processing stage.

P300 deficits in patients with schizophrenia have previously been investigated using EEGs recorded during auditory oddball tasks. However, small-world cortical functional networks during auditory oddball tasks and their relationships with symptom severity scores in schizophrenia have not yet been investigated. In this study, the small-world characteristics of source-level functional connectivity networks of EEG responses elicited by an auditory oddball paradigm were evaluated using two representative graph-theoretical measures, clustering coefficient and path length. EEG signals from 34 patients with schizophrenia and 34 healthy controls were recorded while each subject was asked to attend to oddball tones. The results showed reduced clustering coefficients and increased path lengths in patients with schizophrenia, suggesting that the small-world functional network is disrupted in patients with schizophrenia. In addition, the negative and cognitive symptom components of positive and negative symptom scales were negatively correlated with the clustering coefficient and positively correlated with path length, demonstrating that both indices are indicators of symptom severity in patients with schizophrenia. Our study results suggest that disrupted small-world characteristics are potential biomarkers for patients with schizophrenia.

Addiction to Massive Multiple Online Role-Playing Games (MMORPGs) among juveniles has become a serious problem in Korea and has led to legislation prohibiting juveniles from playing games after midnight. One key factor in gaming addiction is the so-called narrative, or story, gamers create for themselves while playing. This study investigated how a course in writing and speaking using narrative characteristics and content borrowed from the MMORPG ''Dungeon & Fighter (DF)'' influenced language expression and gaming addiction. A total of 59 male high school students who were addicted to online gaming voluntarily participated in an experiment involving an educational course aimed at improving their writing and speaking. Participants were randomly divided into two groups, an experimental group of 27 students and a control group of 32 students (the control group participated in a general course addressing topics and various social issues unrelated to gaming). The experiment consisted of a total of 21 sessions lasting 2 h per day over a period of 2 months. The results of a follow-up examination revealed that participants in the experimental group improved their writing and speaking ability far more than those in the control group. In addition, a pilot ERP study suggested that the educational course in the experiment may reflect how gaming activity is processed in the brain, especially in the fronto-central areas, and thereby influence the course of addiction.

Faces are processed best when they are presented in the left visual field (LVF), a phenomenon known as LVF superiority. Although one eye contributes more when perceiving faces, it is unclear how the dominant eye (DE), the eye we unconsciously use when performing a monocular task, affects face processing. Here, we examined the influence of the DE on the LVF superiority for faces using event-related potentials. Twenty left-eye-dominant (LDE group) and 23 right-eye-dominant (RDE group) participants performed the experiments. Face stimuli were randomly presented in the LVF or right visual field (RVF). The RDE group exhibited significantly larger N170 amplitudes compared with the LDE group. Faces presented in the LVF elicited N170 amplitudes that were significantly more negative in the RDE group than they were in the LDE group, whereas the amplitudes elicited by stimuli presented in the RVF were equivalent between the groups. The LVF superiority was maintained in the RDE group but not in the LDE group. Our results provide the first neural evidence of the DE’s effects on the LVF superiority for faces. We propose that the RDE may be more biologically specialized for face processing.