REM sleep behavior disorder (RBD) is characterized by loss of normal REM sleep skeletal muscle atonia, resulting in complex motor behaviors associated with dream mentation. The polysonographical hallmarks of RBD include tonic/phasic loss of the skeletal muscle atonia of REM sleep (REM sleep without atonia; RWA), and the International Classification of Sleep Disorders 2nd edition (ICSDF-2) requires this finding of polysomnography (PSG) in the diagnosis of RBD. However, a scoring rule for RWA is not well established. We describe our methods of a scoring rule for RWA and some unsolved issues for RWA scoring were discussed.
Methods: The subjects were 10,745 consecutive patients who presented with sleep and/or wake problems at our sleep center between April 1998 and March 2006. Diagnosis of RBD was made based on ICSD-2 criteria. Our original analysis for RWA was that each page of REM sleep is marked either tonic or atonic (tonic=50% of the page or more is occupied by EMG activity). In our new RWA scoring, increased EMG activity was counted separately according to EMG activity patterns; tonic EMG, phasic pattern, and combined EMG activities. Date of 13 patients who underwent PSG with the same digital polygraph among patients with RBD were used for analyze according to new scoring methods, and we also calculated %tonic REM and REM density in 13 patients.
Results: Sixty-seven patients (0.6%) were diagnosed as having RBD. There was a strong male predominancy (85.1%). In 13 of 67 patients, there were no differences between the original and the new methods in %REM and %RWA. Patients with Parkinson disease had increased tonic chin EMG activity during REM sleep (39.5±17.1% vs. 22.3±14.6%) and REM density (60.9±11.0% vs. 44.6±11.9%) compared to idiopathic RBD patients, however it was not statically significant.
Conclusions: The pathogenesis of RBD is still unclear; therefore, detailed analysis of RWA give hints for finding predictors for the development of neurodegerative disease and for establishing its diagnostic classification.

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The objective of this study was to evaluate the functional relationships among brain sites using electroencephalography (EEG) with emotional stimuli, and to further evaluate discrimination using Artificial Neural Network (ANN). Twenty-two healthy subjects were assessed using the Cornell Medical Index (CMI) and divided into two groups: normal and pre-neurotic groups. EEG was measured with emotional tasks using audio-visual stimuli and analyzed using coherence analysis. The coherence values in each group, session, frequency band, and region were analyzed using analysis of variance (ANOVA), and discrimination was evaluated in the two groups using ANN. The coherence values of the pre-neurotic group in the pleasant and unpleasant sessions toward lateral sagittal and medial coronal were significantly larger than those of the normal group. Discrimination between the normal and pre-neurotic groups showed over 80% accuracy, sensitivity, and specificity. In addition, the coherence values using ANN differed in subjects with different neurotic states. These results suggest that cross-correlation of the information processes concerning emotional stimuli in the brain differs depending on the psychosomatic state.

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[Objective] To evaluate our proposed method, which incorporate results of sensor signal analyses as weighting of recorded data prior to execution of source estimation, with simulated data by comparing with conventional method. The simulated data were generated by assuming hippocampal-neocortical circuits, which had a functional coupling between hippocampal theta activity and neocortical gamma activity.
[Method] First, we formulated analysis procedure to apply our proposed method to cross-frequency correlation analysis to detect brain portions connected each other with different frequency range. In this procedure, Spearman's rank correlation was used to evaluate cross-frequency correlation so as to execute statistical evaluation of recorded data which can not be limited to normal distribution. Sensor signal analyses with cross-frequency correlation maps and correlation topography were utilized to identify significant characteristics (e.g. frequency pair at which significant cross-frequency correlation were observed.). And the results of the sensor signal analysis were used to weight recorded signals. Then source estimation was executed by standardized low-resolution brain electromagnetic tomography (sLORETA) in frequency domain and sLORETA modified for quantifiable method (sLORETA-qm) was employed to obtain averaged source image. Second, we simulated connection between hippocampal theta activity and correlated neo-cortical gamma activity. Three sources (s1: hippocampal theta activity, s2: neocortical gamma activity correlated to phase of s1, s3: non-correlated occipital theta activity) were placed by referring MR image of normal subject and sensor signals were generated by four layers spherical model and additional white noise of 16 or 4 of signal to noise ratio. Then, the formulated procedure were applied to the simulated data to detect and estimate the correlated sources.
[Results] Our proposed method could estimate only correlated sources corresponded to the correlated sources (s1 and s2), while conventional method estimated non-correlated source corresponded non-correlated source (s3) as if correlated source.
[Conclusion] We demonstrated that our proposed method could extract the sources of specific neural activities in the brain such as cross-frequency correlation. Our proposed method will become a useful and helpful tool for obtaining detailed information on neural activities.

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Measuring local cerebral blood flow and metabolism by various mapping methods such as positron emission tomography or perfusion computed tomography helps us to evaluate detailed functions of brain areas containing a focal ischemic lesion, but does not necessarily represent neural activities of the areas. Scalp electroencephalography (EEG), reflecting volume-conducted neural activities, demonstrates that slow wave activity is dominant in an acute ischemic cerebral region; but, this technique presents major problems with the lack of objective indices for brain functions and low spatial resolution. Magnetoencephalography (MEG), an important new method in neuroscience to directly detect neural activities with high spatial resolution, has been applied in stroke patients. However, the usefulness of MEG for assessing neural activities in an ischemic brain area has not been fully established as yet. The present study reviewed MEG studies of cerebral stroke using internet searches of the bibliography to identify scientific evidence for the clinical effectiveness of MEG. We searched for stroke-related manuscripts published before July 2010 on MEDLINE using the keywords (stroke OR cerebral ischemia) AND (MEG OR magnetoencephalography), and retrieved 58 papers. We narrowed the search to 25 papers based on the levels of evidence and abstract contents. Then, we selected 12 papers with evidence level higher than 2 to assess the clinical utility of MEG. Most papers stressed the clinical usefulness of MEG, but a few claimed the superiority of MEG compared to EEG for the diagnosis or treatment indication for ischemic conditions. Therefore, more objective analysis of MEG findings in ischemic conditions is needed for future development.

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Magnetoencephalography (MEG) has been applied for various neurological diseases including epilepsy, cerebrovascular disease, and cognitive dysfunction. However, apart from epilepsy, the scientific evidence validating the use of MEG for neurological diseases in children remains unclear. In this study, we reviewed clinical MEG studies of neurological diseases in children based on a website bibliographic survey. We searched MEG research articles on pediatric neurological diseases published before June 2010 on MEDLINE using the following key words: child AND (MEG OR magnetoencephalography), and retrieved 93 papers. We further narrowed the search to 14 papers by excluding review articles, single case reports, and papers on epilepsy. The diagnoses of patients described in the 14 papers were dioxin exposure, periventricle leukomaracia, polymicrogyria, albinism, moyo-moya disease, Angelman syndrome, dyslexia, ADHD, and pervasive developmental disorders. The levels of evidences for the conclusions were classified as 1: no paper, 2a: 10 papers and 2b: 4 papers, respectively. The recommendation grade was B in all articles. MEG is not popular at present for assessing childhood neurological diseases other than epilepsy. However, it has the potential to become a valuable tool for studying the functions of underdeveloped brain because it is total noninvasive and therefore permits repeat examinations in children.

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