Electroencephalography characteristics related to risk of sudden unexpected death in epilepsy in patients with Dravet syndrome.

Objective: To investigate the quantitative electroencephalography (EEG) features associated with a high risk of sudden unexpected death in epilepsy (SUDEP) in patients with Dravet syndrome (DS). Methods: Patients with DS and healthy controls (HCs) who underwent EEG were included in the study. EEG signals were recorded using a 21 channel digital EEG system, and pre-processed data were analyzed to identify quantitative EEG features associated with a high SUDEP risk. To assess the risk of SUDEP, SUDEP-7 scores were used. Results: A total of 64 patients with DS [38 males and 26 females, aged: 128.51 ± 75.50 months (range: 23-380 months)], and 13 HCs [7 males and 6 females, aged: 95.46 ± 86.48 months (range: 13-263 months)] were included. For the absolute band power, the theta power was significantly higher in the high-SUDEP group than in the low-SUDEP group in the central brain region. For the relative band power, the theta power was also significantly higher in the high-SUDEP group than in the low-SUDEP group in the central and occipital brain regions. The alpha power was significantly lower in the high-SUDEP group than in the low-SUDEP group in the central and parietal brain regions. Conclusion: Patients with high SUDEP-7 scores have different EEG features from those with low SUDEP-7 scores, suggesting that EEG may be used as a biomarker of SUDEP in DS. Significance: Early intervention in patients with DS at a high risk of SUDEP can reduce mortality and morbidity. Patients with high theta band powers warrant high-level supervision.

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.

EEG Source Network for the Diagnosis of Schizophrenia and the Identification of Subtypes Based on Symptom Severity-A Machine Learning Approach.

A precise diagnosis and a comprehensive assessment of symptom severity are important clinical issues in patients with schizophrenia (SZ). We investigated whether electroencephalography (EEG) features obtained from EEG source network analyses could be effectively applied to classify the SZ subtypes based on symptom severity. Sixty-four electrode EEG signals were recorded from 119 patients with SZ (53 males and 66 females) and 119 normal controls (NC, 51 males and 68 females) during resting-state with closed eyes. Brain network features (global and local clustering coefficient and global path length) were calculated from EEG source activities. According to positive, negative, and cognitive/disorganization symptoms, the SZ patients were divided into two groups (high and low) by positive and negative syndrome scale (PANSS). To select features for classification, we used the sequential forward selection (SFS) method. The classification accuracy was evaluated using 10 by 10-fold cross-validation with the linear discriminant analysis (LDA) classifier. The best classification accuracy was 80.66% for estimating SZ patients from the NC group. The best classification accuracy between low and high groups in positive, negative, and cognitive/disorganization symptoms were 88.10%, 75.25%, and 77.78%, respectively. The selected features well-represented the pathological brain regions of SZ. Our study suggested that resting-state EEG network features could successfully classify between SZ patients and the NC, and between low and high SZ groups in positive, negative, and cognitive/disorganization symptoms.

Empathy and Social Attribution Skills Moderate the Relationship between Temporal Lobe Volume and Facial Expression Recognition Ability in Schizophrenia.

OBJECTIVE: While impaired facial expression recognition has been closely associated with reduced temporal lobe volume in patients with schizophrenia, this study aimed at examining whether empathy and social attribution affect such a relationship. METHODS: A total of 43 patients with schizophrenia and 43 healthy controls underwent a facial expression recognition task (FERT) and magnetic resonance imaging. Basic empathy scale and the social attribution task-multiple choice were used to measure empathy and social attribution. RESULTS: Patients with schizophrenia showed significant positive correlations between the total temporal lobe volume and the FERT-accuracy (FERT-ACC). Diminished temporal lobe volume predicted the impaired facial emotion recognition ability. Both empathy and social attribution played roles as moderators of the path from the left amygdala volume, left fusiform gyrus volume, both sides of the superior temporal gyrus volume, and left middle temporal gyrus volume to the FERT-ACC. In contrast, empathy alone functioned as a moderator between the right fusiform gyrus volume, right middle temporal gyrus volume, and FERT-ACC. No significant interaction was found for healthy controls. CONCLUSION: Our results suggest that social cognition remediation training on empathy and social attribution, could buffer the negative effects of small temporal lobe volume on interpersonal emotional communication in patients with schizophrenia.

A Novel Singular Value Decomposition-Based Denoising Method in 4-Dimensional Computed Tomography of the Brain in Stroke Patients with Statistical Evaluation.

Computed tomography (CT) is a widely used medical imaging modality for diagnosing various diseases. Among CT techniques, 4-dimensional CT perfusion (4D-CTP) of the brain is established in most centers for diagnosing strokes and is considered the gold standard for hyperacute stroke diagnosis. However, because the detrimental effects of high radiation doses from 4D-CTP may cause serious health risks in stroke survivors, our research team aimed to introduce a novel image-processing technique. Our singular value decomposition (SVD)-based image-processing technique can improve image quality, first, by separating several image components using SVD and, second, by reconstructing signal component images to remove noise, thereby improving image quality. For the demonstration in this study, 20 4D-CTP dynamic images of suspected acute stroke patients were collected. Both the images that were and were not processed via the proposed method were compared. Each acquired image was objectively evaluated using contrast-to-noise and signal-to-noise ratios. The scores of the parameters assessed for the qualitative evaluation of image quality improved to an excellent rating (p < 0.05). Therefore, our SVD-based image-denoising technique improved the diagnostic value of images by improving their quality. The denoising technique and statistical evaluation can be utilized in various clinical applications to provide advanced medical services.

Prediction of Antidepressant Treatment Outcome Using Event-Related Potential in Patients with Major Depressive Disorder.

(1) Background: Prediction of treatment outcome has been one of the core objectives in clinical research of patients with major depressive disorder (MDD). This study explored the possibility of event-related potential (ERP) markers to predict antidepressant treatment outcomes among MDD patients; (2) Methods: Fifty-two patients with MDD were recruited and evaluated through Hamilton depression (HAM-D), Hamilton anxiety rating scale (HAM-A), and CORE. Patients underwent a battery of ERP measures including frontal alpha symmetry (FAA) in the low alpha band (8-10 Hz), mismatch negativity (MMN), and loudness-dependent auditory evoked potentials (LDAEP); (3) Results: During the eight weeks of study, 61% of patients achieved remission, and 77% showed successful treatment responsiveness. Patients with low FAA in F5/F6 demonstrated a significantly higher remission/response ratio and better treatment responsiveness (F (2.560, 117.755) = 3.84, p = 0.016) compared to patients with high FAA. In addition, greater FAA in F7/F8 EEG channels was significantly associated with greater melancholia scores (r = 0.34, p = 0.018). Other ERP markers lacked any significant effect; (4) Conclusions: Our results suggested low FAA (i.e., greater left frontal activity) could reflect a good treatment response in MDD patients. These findings support that FAA could be a promising index in understanding both MDD and melancholic subtype.

Self-Awareness of Psychopathology and Brain Volume in Patients With First Episode Psychosis.

Memory impairment, excessive rumination, and increased interpersonal sensitivity are major characteristics of high psychosis risk or first episode psychosis (FEP). Herein, we investigated the relationship between brain volume and self-awareness of psychopathology in patients with FEP. All participants (FEP: 34 and HCs: 34) completed clinical assessments and the following self-reported psychopathology evaluations: prospective and retrospective memory questionnaire (PRMQ), ruminative response scale (RRS), and interpersonal sensitivity measure (IPSM). Structural magnetic resonance imaging was then conducted. The PRMQ, RRS, and IPSM scores were significantly higher in the FEP group than in the healthy controls (HCs). The volumes of the amygdala, hippocampus, and superior temporal gyrus (STG) were significantly lower in the FEP group than in the HCs. There was a significant group-dependent moderation effect between self-awareness of psychopathology (PRMQ, RRS, and IPSM scores) and right STG (rSTG) volume. In the FEP group, self-awareness of psychopathology was positively associated with rSTG volume, while in the HCs, this correlation was negative. Our results indicate that self-awareness of psychopathology impacts rSTG volume in the opposite direction between patients with FEP and HCs. In patients with FEP, awareness of impairment may induce increases in rSTG brain volume. However, HCs showed decreased rSTG volume when they were aware of impairment.

Prefrontal Asymmetry during Cognitive Tasks and its Relationship with Suicide Ideation in Major Depressive Disorder: An fNIRS Study.

Reduced oxygenation changes in the prefrontal cortex during cognitive tasks have been reported in major depressive disorder (MDD). However, prefrontal asymmetry during cognitive tasks and its relation to suicide ideations have been less frequently examined in patients with MDD. This study investigated prefrontal asymmetry and its moderating effect on the relationship between depression severity and suicidal ideation in MDD patients during cognitive tasks. Forty-two patients with MDD and 64 healthy controls (HCs) were assessed for changes in oxygenated and deoxygenated hemoglobin (Hb) in the prefrontal cortex using functional near-infrared spectroscopy (fNIRS) during the verbal fluency task (VFT), Stroop task, and two-back task. Depression, anxiety, and suicide ideation were measured through self-report questionnaires. Relatively smaller left oxy-Hb changes during VFT, but not during the Stroop or two-back tasks, were found in MDD patients compared with HCs. Furthermore, prefrontal asymmetry during VFT moderated the effect of depression severity on suicide ideation, and was significantly and positively correlated with suicide ideation in patients with MDD. Specifically, relatively greater left oxy-Hb changes were associated with greater suicide ideation. These findings suggest fNIRS-measured prefrontal asymmetry as a potential biomarker for MDD and for the assessment of suicidal risk in patients with MDD.

Transient Global Amnesia Deteriorates the Network Efficiency of the Theta Band.

Acute perturbation of the hippocampus, one of the connector hubs in the brain, is a key step in the pathophysiological cascade of transient global amnesia (TGA). We tested the hypothesis that network efficiency, meaning the efficiency of information exchange over a network, is impaired during the acute stage of TGA. Graph theoretical analysis was applied to resting-state EEG data collected from 21 patients with TGA. The EEG data were obtained twice, once during the acute stage (< 24 hours after symptom onset) and once during the resolved stage (> 2 months after symptom onset) of TGA. Characteristic path lengths and clustering coefficients of functional networks constructed using phase-locking values were computed and normalized as a function of the degree in the delta, theta, alpha, beta 1, beta 2 and gamma frequency bands of the EEG. We investigated whether the normalized characteristic path length (nCPL) and normalized clustering coefficients (nCC) differed significantly between the acute and resolved stages of TGA at each frequency band using the Wilcoxon signed-rank test. For networks where the nCPL or nCC differed significantly between the two stages, we also evaluated changes in the connections of the brain networks. During the acute stage of TGA, the nCPL of the theta band networks with mean degrees of 8, 8.5, 9 and 9.5 significantly increased (P < 0.05). During the acute stage, the lost edges for these networks were mostly found between the anterior (frontal and anterior temporal) and posterior (parieto-occipital and posterior temporal) brain regions, whereas newly developed edges were primarily found between the left and right frontotemporal regions. The nCC of the theta band with a mean degree of 5.5 significantly decreased during the acute stage (P < 0.05). Our results indicate that TGA deteriorates the network efficiency of the theta frequency band. This effect might be related to the desynchronization between the anterior and posterior brain areas.

Fast and Robust Real-Time Estimation of Respiratory Rate from Photoplethysmography.

Respiratory rate (RR) is a useful vital sign that can not only provide auxiliary information on physiological changes within the human body, but also indicate early symptoms of various diseases. Recently, methods for the estimation of RR from photoplethysmography (PPG) have attracted increased interest, because PPG can be readily recorded using wearable sensors such as smart watches and smart bands. In the present study, we propose a new method for the fast and robust real-time estimation of RR using an adaptive infinite impulse response (IIR) notch filter, which has not yet been applied to the PPG-based estimation of RR. In our offline simulation study, the performance of the proposed method was compared to that of recently developed RR estimation methods called an adaptive lattice-type RR estimator and a Smart Fusion. The results of the simulation study show that the proposed method could not only estimate RR more quickly and more accurately than the conventional methods, but also is most suitable for online RR monitoring systems, as it does not use any overlapping moving windows that require increased computational costs. In order to demonstrate the practical applicability of the proposed method, an online RR estimation system was implemented.

Long working hours and overweight and obesity in working adults.

BACKGROUND: Previous studies have identified a link between gender and the various risk factors associated with obesity. We examined obesity risk factors in working adults to identify the effects of differences in body mass index (BMI) and percentage body fat (PBF) between women and men. METHODS: A total of 1,120 adults agreed to participate in the study. Data from 711 participants, including 411 women and 300 men, were analyzed. Multiple logistic regression analysis was used to estimate the effects of risk factors on obesity and being overweight. In addition, the least-squares (LS) means of both BMI and PBF were estimated by analysis of covariance (ANCOVA) in a generalized linear model. RESULTS: Increases in BMI and PBF were significantly related to an age > 50 years and long working hours in women after compensating for confounding factors. Using the PBF criterion, the odds ratio (OR) of being overweight or obese in women > 50 years of age who worked for > 9 h a day was 3.9 (95% confidence interval [CI], 1.05-11.00). For BMI, women who were > 50 years of age and worked for > 9 h a day were 3.82 times (95% CI, 1.31-11.14) more likely to be overweight or obese than those who were < 50 years of age and worked for < 9 h a day. CONCLUSION: Obesity in working adults was associated with > 50 years of age and long working hours in women. Further studies are needed to investigate the underlying mechanisms of this relationship and its potential implications for the prevention and management of excess weight and obesity.

Toward more intuitive brain-computer interfacing: classification of binary covert intentions using functional near-infrared spectroscopy.

In traditional brain-computer interface (BCI) studies, binary communication systems have generally been implemented using two mental tasks arbitrarily assigned to "yes" or "no" intentions (e.g., mental arithmetic calculation for "yes"). A recent pilot study performed with one paralyzed patient showed the possibility of a more intuitive paradigm for binary BCI communications, in which the patient's internal yes/no intentions were directly decoded from functional near-infrared spectroscopy (fNIRS). We investigated whether such an "fNIRS-based direct intention decoding" paradigm can be reliably used for practical BCI communications. Eight healthy subjects participated in this study, and each participant was administered 70 disjunctive questions. Brain hemodynamic responses were recorded using a multichannel fNIRS device, while the participants were internally expressing "yes" or "no" intentions to each question. Different feature types, feature numbers, and time window sizes were tested to investigate optimal conditions for classifying the internal binary intentions. About 75% of the answers were correctly classified when the individual best feature set was employed (75.89% ± 1.39 and 74.08% ± 2.87 for oxygenated and deoxygenated hemoglobin responses, respectively), which was significantly higher than a random chance level (68.57% for p < 0.001). The kurtosis feature showed the highest mean classification accuracy among all feature types. The grand-averaged hemodynamic responses showed that wide brain regions are associated with the processing of binary implicit intentions. Our experimental results demonstrated that direct decoding of internal binary intention has the potential to be used for implementing more intuitive and user-friendly communication systems for patients with motor disabilities.

Disruption of the Posterior Medial Network during the Acute Stage of Transient Global Amnesia: A Preliminary Study.

Acute perturbation of the corticohippocampal circuitry is a primary pathophysiological mechanism underlying transient global amnesia (TGA). With regard to memory, 2 distinct corticohippocampal circuitries potentially exist: the anterior temporal network and the posterior medial network. We used electroencephalography (EEG) spectral analysis to determine which network is disrupted during the acute stage of TGA. Patients with TGA who visited Seoul National University Bundang Hospital within 24 hours after symptom onset were retrospectively identified. Twenty patients underwent EEG twice, once in the acute stage (<24 hours after symptom onset) and once in the resolved stage (>2 months after symptom onset). A fast Fourier transform was applied to compute the spectral power of the 6 frequency bands: delta, theta, alpha, beta 1, beta 2, and gamma. We assumed that the frontocentral and temporal regions belonged to the anterior temporal network, whereas the parieto-occipital regions belonged to the posterior medial network. A paired Student's t test was used to evaluate the difference in the regional spectral powers in each frequency band between the acute and resolved TGA stages. Compared with the resolved stage, relative theta power in the left parieto-occipital region was increased and relative alpha power in the right parieto-occipital region was reduced during the acute stage of TGA, with a statistical significance of P<.05 (uncorrected). The cortical regions that belonged to the posterior medial network showed alterations of neuronal activity, which reflects disruption of the posterior medial network during the acute stage of TGA.

Localization of epileptogenic zones in Lennox-Gastaut syndrome (LGS) using graph theoretical analysis of ictal intracranial EEG: a preliminary investigation.

INTRODUCTION: Precise localization of epileptogenic zones is essential for the successful surgical treatment of refractory epilepsy including Lennox-Gastaut syndrome (LGS). The surgical resection areas are generally determined by epileptologists based on diverse neuroimaging modalities; however, exact epileptogenic zones cannot be accurately localized in many patients with LGS using the conventional methods. Therefore, new reliable algorithms are still required for enhancing the success rate of the resective epilepsy surgery. In the present study, we introduce an approach to localize epileptogenic zones in LGS based on the graph theoretical analysis of ical intracranial EEG (iEEG). METHODS: Four patients with LGS who became seizure-free after the resective epilepsy surgery were selected. Before the surgery, their epileptogenic zones were delineated using EEG, iEEG, and several conventional imaging modalities. Phase locking value (PLV) analysis was applied to construct functional connectivity networks during ictal events, and then several graph theoretical indices including betweenness centrality (BC) were evaluated for each iEEG sensor to find the primary hubs of the ictal epileptic network. The graph theoretical index values were then overlaid on 3D individual cortical surface. RESULTS: The iEEG channels with high BC values coincided well with the surgical resection areas. Among various graph theoretical measures such as local efficiency, participation coefficient, and eigenvector centrality, only BC showed fair correspondence with the surgical resection areas. CONCLUSIONS: The primary hubs in the ictal epileptic networks coincided well with areas of surgical resection in LGS patients with successful surgical outcomes. This observation warrants further studies to determine if the graph theoretical network analysis of ictal iEEG recordings can serve as a new auxiliary tool to localize epileptogenic zones in LGS.

Combined use of multiple computational intracranial EEG analysis techniques for the localization of epileptogenic zones in Lennox-Gastaut syndrome.

Traditionally, identification of epileptogenic zones primarily relied on visual inspection of intracranial electroencephalography (iEEG) recordings by experienced epileptologists; however, removal of epileptogenic zones identified by iEEG does not always guarantee favorable surgical outcomes. To confirm visual inspection results, and assist in making decisions about surgical resection areas, computational iEEG analysis methods have recently been used for the localization of epileptogenic zones. In this study, we have proposed a new approach for the localization of epileptogenic zones in Lennox-Gastaut syndrome (LGS), and have investigated whether the proposed approach could confirm surgical resection areas and predict seizure outcome before surgery. The proposed approach simultaneously used results of 2 iEEG analysis methods, directed transfer function (DTF) and time delay estimation, to enhance localization accuracy. This new combined method was applied to patients who became seizure-free after resective epilepsy surgery, as well as those who had unsuccessful surgery. A quantitative metric was also introduced that can measure how well the localized epileptogenic zones coincided with the surgical resection areas, with the aim of verifying whether the approach could confirm surgical resection areas determined by epileptologists. The estimated epileptogenic zones more strongly coincided with surgical resection areas in patients with successful, compared to those with unsuccessful surgical outcomes. Both qualitative and quantitative analyses showed that the combined use of 2 iEEG analyses resulted in a more accurate estimate of epileptogenic zones in LGS than the use of a single method. A combination of multiple iEEG analyses could not only enhance overall accuracy of localizing epileptogenic zones in LGS, but also has the potential to predict outcomes before resective surgery.

Localization of epileptogenic zones in Lennox-Gastaut syndrome using frequency domain source imaging of intracranial electroencephalography: a preliminary investigation.

Although intracranial electroencephalography (iEEG) has been widely used to localize epileptogenic zones in epilepsy, visual inspection of iEEG recordings does not always result in a favorable surgical outcome, especially in secondary generalized epilepsy such as Lennox-Gastaut syndrome (LGS). Various computational iEEG analysis methods have recently been introduced to confirm the visual inspection results. Of these methods, high gamma oscillation in iEEG has attracted interest because a series of studies have reported a close relationship between epileptogenic zones and cortical areas with high gamma oscillation. Meanwhile, frequency domain source imaging of EEG and MEG oscillations has proven to be a useful auxiliary tool for identifying rough locations of epileptogenic zones. To the best of our knowledge, however, frequency domain source imaging of high gamma iEEG oscillations has not been studied. In this study, we investigated whether the iEEG-based frequency domain source imaging of high gamma oscillation (60-100 Hz) would be a useful supplementary tool for identifying epileptogenic zones in patients with secondary generalized epilepsy. The method was applied to three successfully operated on LGS patients, whose iEEG contained some ictal events with distinct high gamma oscillations before seizure onset. The resultant cortical source distributions were compared with surgical resection areas and with high gamma spectral power distributions on the intracranial sensor plane. While the results of the sensor-level analyses contained many spurious activities, the results of frequency domain source imaging coincided better with the surgical resection areas, suggesting that the frequency domain source imaging of iEEG high gamma oscillations might help enhance the accuracy of pre-surgical evaluations of patients with secondary generalized epilepsy.

The risk of hospitalisation for infectious pneumonia in mineral dust exposed industries.

OBJECTIVES: The purpose of the study was to elucidate the relationship between industries characterised by mineral dust exposure and infectious pneumonia. METHODS: The authors applied a retrospective record linkage design and used the nationwide specific health examination database and identified industries where mineral dust exposure occurs and a control group composed of workers who had been exposed to noise-only during a 2000-2004 period. The database was matched with National Health Insurance claim records to identify pneumonia admissions from 2000 to 2005. The indirectly standardised admission ratios (SARs) for pneumonia admissions were estimated by comparing mineral dust exposed industry workers with noise-only exposed workers. RESULTS: The authors found significantly elevated SARs in both men (1.54, 95% CI 1.13 to 2.05) and women (3.23, 95% CI 1.40 to 6.37) working in the cement, lime, plaster and plaster products industries, and only in men working in the cast-metals industry (foundry) (1.64, 95% CI 1.25 to 2.11). CONCLUSIONS: These results support the association between mineral dust exposure, as well as metal fumes, and infectious pneumonia.