Posterior cingulate and medial prefrontal excitation-inhibition balance in euthymic bipolar disorder.

BACKGROUND: Persistent cognitive deficits and functional impairments are associated with bipolar disorder (BD), even during the euthymic phase. The dysfunction of default mode network (DMN) is critical for self-referential and emotional mental processes and is implicated in BD. The current study aims to explore the balance of excitatory and inhibitory neurotransmitters, i.e. glutamate and γ-aminobutyric acid (GABA), in hubs of the DMN during the euthymic patients with BD (euBD). METHOD: Thirty-four euBD and 55 healthy controls (HC) were recruited to the study. Using proton magnetic resonance spectroscopy (1H-MRS), glutamate (with PRESS sequence) and GABA levels (with MEGAPRESS sequence) were measured in the medial prefrontal cortex/anterior cingulate cortex (mPFC/ACC) and the posterior cingulate gyrus (PCC). Measured concentrations of excitatory glutamate/glutamine (Glx) and inhibitory GABA were used to calculate the excitatory/inhibitory (E/I) ratio. Executive and attentional functions were respectively assessed using the Wisconsin card-sorting test and continuous performance test. RESULTS: euBD performed worse on attentional function than controls (p = 0.001). Compared to controls, euBD had higher E/I ratios in the PCC (p = 0.023), mainly driven by a higher Glx level in the PCC of euBD (p = 0.002). Only in the BD group, a marginally significant negative association between the mPFC E/I ratio (Glx/GABA) and executive function was observed (p = 0.068). CONCLUSIONS: Disturbed E/I balance, particularly elevated Glx/GABA ratio in PCC is observed in euBD. The E/I balance in hubs of DMN may serve as potential biomarkers for euBD, which may also contribute to their poorer executive function.

Seed correlation analysis based on brain region activation for ADHD diagnosis in a large-scale resting state data set.

Background: Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder of multifactorial pathogenesis, which is often accompanied by dysfunction in several brain functional connectivity. Resting-state functional MRI have been used in ADHD, and they have been proposed as a possible biomarker of diagnosis information. This study's primary aim was to offer an effective seed-correlation analysis procedure to investigate the possible biomarker within resting state brain networks as diagnosis information. Method: Resting-state functional magnetic resonance imaging (rs-fMRI) data of 149 childhood ADHD were analyzed. In this study, we proposed a two-step hierarchical analysis method to extract functional connectivity features and evaluation by linear classifiers and random sampling validation. Result: The data-driven method-ReHo provides four brain regions (mPFC, temporal pole, motor area, and putamen) with regional homogeneity differences as second-level seeds for analyzing functional connectivity differences between distant brain regions. The procedure reduces the difficulty of seed selection (location, shape, and size) in estimations of brain interconnections, improving the search for an effective seed; The features proposed in our study achieved a success rate of 83.24% in identifying ADHD patients through random sampling (saving 25% as the test set, while the remaining data was the training set) validation (using a simple linear classifier), surpassing the use of traditional seeds. Conclusion: This preliminary study examines the feasibility of diagnosing ADHD by analyzing the resting-state fMRI data from the ADHD-200 NYU dataset. The data-driven model provides a precise way to find reliable seeds. Data-driven models offer precise methods for finding reliable seeds and are feasible across different datasets. Moreover, this phenomenon may reveal that using a data-driven approach to build a model specific to a single data set may be better than combining several data and creating a general model.

Time-course analysis of frontal gene expression profiles in the rat model of posttraumatic stress disorder and a comparison with the conditioned fear model.

Posttraumatic stress disorder (PTSD) is a complex disorder that involves physiological, emotional, and cognitive dysregulation that may occur after exposure to a life-threatening event. In contrast with the condition of learned fear with resilience to extinction, abnormal fear with impaired fear extinction and exaggeration are considered crucial factors for the pathological development of PTSD. The prefrontal cortex (mPFC) is considered a critical region of top-down control in fear regulation, which involves the modulation of fear expression and extinction. The pathological course of PTSD is usually chronic and persistent; a number of studies have indicated temporal progression in gene expression and phenotypes may be involved in PTSD pathology. In the current study, we use a well-established modified single-prolonged stress (SPS&FS) rat model to feature PTSD-like phenotypes and compared it with a footshock fear conditioning model (FS model); we collected the frontal tissue after extreme stress exposure or fear conditioning and extracted RNA for transcriptome-level gene sequencing. We compared the genetic profiling of the mPFC at early (<2 h after solely FS or SPS&FS exposure) and late (7 days after solely FS or SPS&FS exposure) stages in these two models. First, we identified temporal differences in the expressional patterns between these two models and found pathways such as protein synthesis factor eukaryotic initiation factor 2 (EIF2), transcription factor NF-E2-related factor 2 (NRF2)-mediated oxidative stress response, and acute phase responding signaling enriched in the early stage in both models with significant p-values. Furthermore, in the late stage, the sirtuin signaling pathway was enriched in both models; other pathways such as STAT3, cAMP, lipid metabolism, Gα signaling, and increased fear were especially enriched in the late stage of the SPS&FS model. However, pathways such as VDR/RXR, GP6, and PPAR signaling were activated significantly in the FS model's late stage. Last, the network analysis revealed the temporal dynamics of psychological disorder, the endocrine system, and also genes related to increased fear in the two models. This study could help elucidate the genetic temporal alteration and stage-specific pathways in these two models, as well as a better understanding of the transcriptome-level differences between them.

Early- and late-phase changes of brain activity and early-phase neuromodulation in the posttraumatic stress disorder rat model.

Posttraumatic stress disorder (PTSD) is a complex syndrome that may occur after life-threatening events. Fear memory abnormalities may play vital roles in the pathogenesis of PTSD. Previous work has found that fear memories are not rigid; the retrieval of fear memories may change over time. Furthermore, prior studies suggest that theta wave (4 Hz) activity is highly correlated with fear expression in an animal model. However, the relationship between pathological fear memory and potential brain wave features in PTSD remains largely uncharacterized. Here, we hypothesized that after traumatic stress exposure, the longitudinal dynamics of abnormal fears in PTSD animal models could be reflected by the measurement of local field potentials (LFPs). Using a well-established modified single-prolonged stress and footshock (SPS & FS) PTSD rat model, animals were restrained for 2 h and subsequently subjected to 20 min of forced swimming, then exposed to diethyl ether until they lost consciousness and placed in a conditioning chamber for fear conditioning. To characterize the temporal changes, we characterized freezing behavior brain wave features during the conditioning chamber re-exposure in the early (10 and 30 min; 2, 4, and 6 h) and late (day 1, 3, 7, and 14) phases after traumatic stress exposure. Our results indicate that SPS & FS rats showed co-morbid PTSD phenotypes including significantly higher levels of anxiety-, depression-, and anhedonia-like behaviors, and impaired fear extinction. Delta wave (0.5-4 Hz) suppression in the medial prefrontal cortex, amygdala, and ventral hippocampus occurred 10 and 30 min after traumatic stress, followed by continuous delta wave activity from 2 h to day 14, correlating with fear levels. tDCS reduced delta activity and alleviated PTSD-like phenotypes in the SPS & FS group. In this study, profiling abnormal fears with brain wave correlates may improve our understanding of time-dependent pathological fear memory retrieval in PTSD and facilitate the development of effective intervention strategies.

Equivalent current dipole sources of neurofeedback training-induced alpha activity through temporal/spectral analytic techniques.

Much of the work in alpha NFT has focused on evaluating changes in alpha amplitude. However, the generation mechanism of training-induced alpha activity has not yet been clarified. The present study aimed to identify sources of training-induced alpha activity through four temporal/spectral analytic techniques, i.e., the max peak average (MPA), positive average (PA), negative average (NA) and event-related spectral perturbation average (ERSPA) methods. Thirty-five healthy participants were recruited into an alpha group receiving feedback of 8-12-Hz amplitudes, and twenty-eight healthy participants were recruited into a control group receiving feedback of random 4-Hz amplitudes from the range of 7 to 20 Hz. Twelve sessions were performed within 4 weeks (3 sessions per week). The control group had no change in the amplitude spectrum. In contrast, twenty-nine participants in the alpha group showed significant alpha amplitude increases exclusively and were identified as "responders". A whole-head EEG was recorded for the "responders" after NFT. The epochs of training-induced alpha activity from whole-head EEG were averaged by four different methods for equivalent current dipole source analysis. High agreement and Cohen's kappa coefficients on dipole source localization between each method were observed, showing that the dipole clusters of training-induced alpha activity were consistently located in the precuneus, posterior cingulate cortex (PCC) and middle temporal gyrus. The residual variance (goodness of fit) for dipole estimation of the MPA was significantly smaller than that of the others. Our findings indicate that the precuneus, PCC and middle temporal gyrus play important roles in enhancing training-induced alpha activity. The four averaging methods (especially the MPA method) were suitable for investigating sources of brainwaves. Additionally, three dipoles can be used for dipole source analysis of training-induced alpha activity in future research, especially the training sites are around the central regions.

Validation of eyes-closed resting alpha amplitude predicting neurofeedback learning of upregulation alpha activity.

Neurofeedback training (NFT) enables users to learn self-control of EEG activity of interest and then to create many benefits on cognitive function. A considerable number of nonresponders who fail to achieve successful NFT have often been reported in the within-session prediction. This study aimed to investigate successful EEG NFT of upregulation alpha activity in terms of trainability, independence, and between-session predictability validation. Forty-six participants completed 12 training sessions. Spectrotemporal analysis revealed the upregulation success on brain activity of 8-12 Hz exclusively to demonstrate trainability and independence of alpha NFT. Three learning indices of between-session changes exhibited significant correlations with eyes-closed resting state (ECRS) alpha amplitude before the training exclusively. Through a stepwise linear discriminant analysis, the prediction model of ECRS's alpha frequency band amplitude exhibited the best accuracy (89.1%) validation regarding the learning index of increased alpha amplitude on average. This study performed a systematic analysis on NFT success, the performance of the 3 between-session learning indices, and the validation of ECRS alpha activity for responder prediction. The findings would assist researchers in obtaining insight into the training efficacy of individuals and then attempting to adapt an efficient strategy in NFT success.

Directions of Deep Brain Stimulation for Epilepsy and Parkinson's Disease.

BACKGROUND: Deep brain stimulation (DBS) is an effective treatment for movement disorders and neurological/psychiatric disorders. DBS has been approved for the control of Parkinson disease (PD) and epilepsy. OBJECTIVES: A systematic review and possible future direction of DBS system studies is performed in the open loop and closed-loop configuration on PD and epilepsy. METHODS: We searched Google Scholar database for DBS system and development. DBS search results were categorized into clinical device and research system from the open-loop and closed-loop perspectives. RESULTS: We performed literature review for DBS on PD and epilepsy in terms of system development by the open loop and closed-loop configuration. This study described development and trends for DBS in terms of electrode, recording, stimulation, and signal processing. The closed-loop DBS system raised a more attention in recent researches. CONCLUSION: We overviewed development and progress of DBS. Our results suggest that the closed-loop DBS is important for PD and epilepsy.

Portable rotating grating stimulation for anisometropic amblyopia with 6 months training.

Treatment of grating stimulation has been used in amblyopia for decades, but high dropout rate and inconvenience for daily practice occur in previous studies. We developed a home-based portable system with rotating grating stimulation on a tablet. Thirty anisometropic amblyopic children were randomly allocated into the control or Grating group. They drew contour of the picture under patch of a better eye for 6 months. Best-corrected visual acuity (BCVA), grating acuity (GA), and contrast sensitivity (CS) were assessed at the baseline, 1st, 2nd, 3rd, and 6th months of training. All participants completed the 6-month training. Patched eyes of both groups exhibited no difference. Trained eyes of the control group had significantly slight improvement in BCVA and GA. In particular, the Grating group exhibited significantly higher BCVA, GA, and CS compared with those of the control group at the 3rd and 6th months of training. Moreover, percentage of the Grating group with great improvement (BCVA ≥ 0.3 or CS ≥ 0.3) was significantly larger than those of the control group at the 3rd or 6th months of training. The portable grating stimulation system demonstrates its trainability by no dropout and effectiveness by significant improvements in all assessments through a well experimental design.Trial Registration: ClinicalTrials.gov NCT04213066, registered 30/12/2019, https://clinicaltrials.gov/ct2/show/NCT04213066 .

Neurofeedback of Alpha Activity on Memory in Healthy Participants: A Systematic Review and Meta-Analysis.

Background: Neurofeedback training (NFT) has recently been proposed as a valuable technique for cognitive enhancement and psychiatric amelioration. However, effect of NFT of alpha activity on memory is controversial. The current study analyzed previous works in terms of randomized and blinded analyses, training paradigms, and participant characteristics to validate the efficacy of alpha NFT on memory in a healthy population. Objectives: A systematic meta-analysis of studies with randomized controlled trials was performed to explore the effect of alpha NFT on working memory (WM) and episodic memory (EM) in a healthy population. Methods: We searched PubMed, Embase, and Cochrane Library from January 1, 1999, to November 30, 2019. Previous studies were evaluated with the Cochrane risk of bias (RoB). A meta-analysis calculating absolute weighted standardized mean difference (SMD) using random-effects models was employed. Heterogeneity was estimated using I 2 statistics. Funnel plots and Egger's test were performed to evaluate the quality of evidence. Results: Sixteen studies with 217 healthy participants in the control group and 210 participants in the alpha group met the eligibility criteria. Alpha NFT studies with WM measures presented little publication bias (P = 0.116), and 5 of 7 domains in the Cochrane RoB exhibited a low risk of bias. The overall effect size from 14 WM studies was 0.56 (95% CI 0.31-0.81, P < 0.0001; I 2 = 28%). Six EM studies exhibited an effect size of 0.77 (95% CI 0.06-1.49, P = 0.03; I 2 = 77%). Conclusion: Meta-analysis results suggest that alpha NFT seems to have a positive effect on the WM and EM of healthy participants. Future efforts should focus on the neurophysiological mechanisms of alpha NFT in memory.

Development of a rule-based automatic five-sleep-stage scoring method for rats.

BACKGROUND: Sleep problem or disturbance often exists in pain or neurological/psychiatric diseases. However, sleep scoring is a time-consuming tedious labor. Very few studies discuss the 5-stage (wake/NREM1/NREM2/transition sleep/REM) automatic fine analysis of wake-sleep stages in rodent models. The present study aimed to develop and validate an automatic rule-based classification of 5-stage wake-sleep pattern in acid-induced widespread hyperalgesia model of the rat. RESULTS: The overall agreement between two experts' consensus and automatic scoring in the 5-stage and 3-stage analyses were 92.32% (κ = 0.88) and 94.97% (κ = 0.91), respectively. Standard deviation of the accuracy among all rats was only 2.93%. Both frontal-occipital EEG and parietal EEG data showed comparable accuracies. The results demonstrated the performance of the proposed method with high accuracy and reliability. Subtle changes exhibited in the 5-stage wake-sleep analysis but not in the 3-stage analysis during hyperalgesia development of the acid-induced pain model. Compared with existing methods, our method can automatically classify vigilance states into 5-stage or 3-stage wake-sleep pattern with a promising high agreement with sleep experts. CONCLUSIONS: In this study, we have performed and validated a reliable automated sleep scoring system in rats. The classification algorithm is less computation power, a high robustness, and consistency of results. The algorithm can be implanted into a versatile wireless portable monitoring system for real-time analysis in the future.

Cortical and Subcortical Neural Correlates for Respiratory Sensation in Response to Transient Inspiratory Occlusions in Humans.

Cortical and subcortical mechanosensation of breathing can be measured by short respiratory occlusions. However, the corresponding neural substrates involved in the respiratory sensation elicited by a respiratory mechanical stimulus remained unclear. Therefore, we applied the functional magnetic resonance imaging (fMRI) technique to study cortical activations of respiratory mechanosensation. We hypothesized that thalamus, frontal cortex, somatosensory cortex, and inferior parietal cortex would be significantly activated in response to respiratory mechanical stimuli. We recruited 23 healthy adults to participate in our event-designed fMRI experiment. During the 12-min scan, participants breathed with a specialized face-mask. Single respiratory occlusions of 150 ms were delivered every 2-4 breaths. At least 32 successful occlusions were collected for data analysis. The results showed significant neural activations in the thalamus, supramarginal gyrus, middle frontal gyrus, inferior frontal triangularis, and caudate (AlphaSim corrected p < 0.05). In addition, subjective ratings of breathlessness were significantly correlated with the levels of neural activations in bilateral thalamus, right caudate, right supramarginal gyrus, left middle frontal gyrus, left inferior triangularis. Our results demonstrated cortical sources of respiratory sensations elicited by the inspiratory occlusion paradigm in healthy adults were located in the thalamus, supramarginal gyrus, and the middle frontal cortex, inferior frontal triangularis, suggesting subcortical, and cortical neural sources of the respiratory mechanosensation are thalamo-cortical based, especially the connections to the premotor area, middle and ventro-lateral prefrontal cortex, as well as the somatosensory association cortex. Finally, level of neural activation in thalamus is associated with the subjective rating of breathlessness, suggesting respiratory sensory information is gated at the thalamic level.

Spike densities of the amygdala and neocortex reflect progression of kindled motor seizures.

Amygdala kindling is a common temporal lobe-like seizure model. In the present study, temporal and spectral analyses of the ictal period were investigated throughout amygdala kindling in response to different behavioral seizures. Right-side amygdala was kindled to induce epileptiform afterdischarges (ADs). ADs of both the frontal cortex and amygdala were analyzed. Powers of the low (0-9 Hz)- and high (12-30 Hz)-frequency bands in response to different behavioral seizures were calculated. Densities of upward and downward peaks of spikes, which reflected information of spike count and spike pattern, throughout kindle-induced ADs were calculated. Progression was seen in the temporal and spectral characteristics of amygdala-kindled ADs in response to behaviors. Numbers of significant differences of all 1-s AD segments between two Racine's seizure stages were significantly higher in upward and downward indexes of the temporal spike than those using the spectral method in both the amygdala and neocortex. Ability for distinguishing seizure stages was significantly higher in temporal spike density of amygdala ADs compared to those of frontal ADs. Our results showed that amygdala kindling caused spectrotemporal changes of activities in the amygdala and frontal cortex. The density of spike-related peaks had better distinguishability in response to behavioral seizures, particularly in a seizure zone of amygdala. The present study provides a new temporal index of spike's peak density to understand progression of motor seizures in the kindling process.

Portable wireless neurofeedback system of EEG alpha rhythm enhances memory.

BACKGROUND: Effect of neurofeedback training (NFT) on enhancement of cognitive function or amelioration of clinical symptoms is inconclusive. The trainability of brain rhythm using a neurofeedback system is uncertainty because various experimental designs are used in previous studies. The current study aimed to develop a portable wireless NFT system for alpha rhythm and to validate effect of the NFT system on memory with a sham-controlled group. METHODS: The proposed system contained an EEG signal analysis device and a smartphone with wireless Bluetooth low-energy technology. Instantaneous 1-s EEG power and contiguous 5-min EEG power throughout the training were developed as feedback information. The training performance and its progression were kept to boost usability of our device. Participants were blinded and randomly assigned into either the control group receiving random 4-Hz power or Alpha group receiving 8-12-Hz power. Working memory and episodic memory were assessed by the backward digital span task and word-pair task, respectively. RESULTS: The portable neurofeedback system had advantages of a tiny size and long-term recording and demonstrated trainability of alpha rhythm in terms of significant increase of power and duration of 8-12 Hz. Moreover, accuracies of the backward digital span task and word-pair task showed significant enhancement in the Alpha group after training compared to the control group. CONCLUSIONS: Our tiny portable device demonstrated success trainability of alpha rhythm and enhanced two kinds of memories. The present study suggest that the portable neurofeedback system provides an alternative intervention for memory enhancement.

Development and Evaluation of a Wearable Device for Sleep Quality Assessment.

OBJECTIVE: In this study, a wearable actigraphy recording device with low sampling rate (1 Hz) for power saving and data reduction and a high accuracy wake-sleep scoring method for the assessment of sleep were developed. METHODS: The developed actigraphy recorder was successfully applied to overnight recordings of 81 subjects with simultaneous polysomnography (PSG) measurements. The total length of recording reached 639.8 h. A wake-sleep scoring method based on the concept of movement density evaluation and adaptive windowing was proposed. Data from subjects with good (N = 43) and poor (N = 16) sleep efficiency (SE) in the range of 52.7-97.42% were used for testing. The Bland-Altman technique was used to evaluate the concordance of various sleep measurements between the manual PSG scoring and the proposed actigraphy method. RESULTS: For wake-sleep staging, the average accuracy, sensitivity, specificity, and kappa coefficient of the proposed system were 92.16%, 95.02%, 71.30%, and 0.64, respectively. For the assessment of SE, the accuracy of classifying the subject with good or poor SE reached 91.53%. The mean biases of SE, sleep onset time, wake after sleep onset, and total sleep time were -0.95%, 0.74 min, 2.84 min, and -4.3 min, respectively. CONCLUSION: These experimental results demonstrate the robustness and reliability of our method using limited activity information to estimate wake-sleep stages during overnight recordings. SIGNIFICANCE: The results suggest that the proposed wearable actigraphy system is practical for the in-home screening of objective sleep measurements and objective evaluation of sleep improvement after treatment.

Distinct Somatic Discrimination Reflected by Laser-Evoked Potentials Using Scalp EEG Leads.

Discrimination is an important function in pain processing of the somatic cortex. The involvement of the somatic cortex has been studied using equivalent dipole analysis and neuroimaging, but the results are inconsistent. Scalp electroencephalography (EEG) can reflect functional changes of particular brain regions underneath a lead. However, the responses of EEG leads close to the somatic cortex in response to pain have not been systematically evaluated. The present study applied CO2 laser stimulation to the dorsum of the left hand. Laser-evoked potentials (LEPs) of C4, T3, and T4 leads and pain ratings in response to four stimulus intensities were analyzed. LEPs started earlier at the C4 and T4 leads. The onset latency and peak latency of LEPs for C4 and T4 leads were the same. Only 10 of 22 subjects (45 %) presented equivalent current dipoles within the primary somatosensory or motor cortices. LEP amplitudes of these leads increased as stimulation intensity increased. The stimulus-response pattern of the C4 lead was highly correlated with pain rating. In contrast, an S-shaped stimulus-response curve was obtained for the T3 and T4 leads. The present study provides supporting evidence that particular scalp channels are able to reflect the functional characteristics of their underlying cortical areas. Our data strengthen the clinical application of somatic-cortex-related leads for pain discrimination.

Rapid Amygdala Kindling Causes Motor Seizure and Comorbidity of Anxiety- and Depression-Like Behaviors in Rats.

Amygdala kindling is a model of temporal lobe epilepsy (TLE) with convulsion. The rapid amygdala kindling has an advantage on quick development of motor seizures and for antiepileptic drugs screening. The rapid amygdala kindling causes epileptogenesis accompanied by an anxiolytic response in early isolation of rat pups or depressive behavior in immature rats. However, the effect of rapid amygdala kindling on comorbidity of anxiety- and depression-like behaviors is unexplored in adult rats with normal breeding. In the present study, 40 amygdala stimulations given within 2 days were applied in adult Wistar rats. Afterdischarge (AD) and seizure stage were recorded throughout the amygdala kindling. Anxiety-like behaviors were evaluated by the elevated plus maze (EPM) test and open field (OF) test, whereas depression-like behaviors were assessed by the forced swim (FS) and sucrose consumption (SC) tests. A tonic-clonic convulsion was provoked in the kindle group. Rapid amygdala kindling resulted in a significantly lower frequency entering an open area of either open arms of the EPM or the central zone of an OF, lower sucrose intake, and longer immobility of the FS test in the kindle group. Our results suggest that rapid amygdala kindling elicited severe motor seizures comorbid with anxiety- and depression-like behaviors.

Neurofeedback training of EEG alpha rhythm enhances episodic and working memory.

Neurofeedback training (NFT) of the alpha rhythm has been used for several decades but is still controversial in regards to its trainability and effects on working memory. Alpha rhythm of the frontoparietal region are associated with either the intelligence or memory of healthy subjects and are also related to pathological states. In this study, alpha NFT effects on memory performances were explored. Fifty healthy participants were recruited and randomly assigned into a group receiving a 8-12-Hz amplitude (Alpha) or a group receiving a random 4-Hz amplitude from the range of 7 to 20 Hz (Ctrl). Three NFT sessions per week were conducted for 4 weeks. Working memory was assessed by both a backward digit span task and an operation span task, and episodic memory was assessed using a word pair task. Four questionnaires were used to assess anxiety, depression, insomnia, and cognitive function. The Ctrl group had no change in alpha amplitude and duration. In contrast, the Alpha group showed a progressive significant increase in the alpha amplitude and total alpha duration of the frontoparietal region. Accuracies of both working and episodic memories were significantly improved in a large proportion of participants of the Alpha group, particularly for those with remarkable alpha-amplitude increases. Scores of four questionnaires fell in a normal range before and after NFT. The current study provided supporting evidence for alpha trainability within a small session number compared with that of therapy. The findings suggested the enhancement of working and episodic memory through alpha NFT. Hum Brain Mapp 37:2662-2675, 2016. © 2016 Wiley Periodicals, Inc.

Epileptic Pattern Recognition and Discovery of the Local Field Potential in Amygdala Kindling Process.

Epileptogenesis, which occurs in an epileptic brain, is an important focus for epilepsy. The spectral analysis has been popularly applied to study the electrophysiological activities. However, the resolution is dominated by the window function of the algorithm used and the sample size. In this report, a temporal waveform analysis method is proposed to investigate the relationship of electrophysiological discharges and motor outcomes with a kindling process. Wistar rats were subjected to electrical amygdala kindling to induce temporal lobe epilepsy. During the kindling process, different morphologies of afterdischarges (ADs) were found and a recognition method, using template matching techniques combined with morphological comparators, was developed to automatically detect the epileptic patterns. The recognition results were compared to manually labeled results, and 79%-91% sensitivity was found. In addition, the initial ADs (the first 10 s) of different seizure stages were specifically utilized for recognition, and an average of 85% sensitivity was achieved. Our study provides an alternative viewpoint away from frequency analysis and time-frequency analysis to investigate epileptogenesis in an epileptic brain. The recognition method can be utilized as a preliminary inspection tool to identify remarkable changes in a patient's electrophysiological activities for clinical use. Moreover, we demonstrate the feasibility of predicting behavioral seizure stages from the early epileptiform discharges.

Combination of Expert Knowledge and a Genetic Fuzzy Inference System for Automatic Sleep Staging.

OBJECTIVE: In this paper, the genetic fuzzy inference system based on expert knowledge for automatic sleep staging was developed. METHODS: Eight features, including temporal and spectrum analyses of the EEG and EMG signals, were utilized as the input variables. The fuzzy rules and the fuzzy sets were constructed based on expert knowledge and the distributions of feature values at different sleep stages. Three experiments were designed to develop and evaluate the proposed system. PSGs of 32 healthy subjects and 16 subjects with insomnia were included in the experiment to develop and evaluate the proposed method. Finally, a complete sleep scoring system integrating two fuzzy inference models with robust performance on various subject groups is developed. RESULTS: The overall agreement and kappa coefficient of this integrated system applied to PSG data from 8 subjects with good sleep efficiency, 8 subjects with poor sleep efficiency and 8 subjects with insomnia were 86.44 % and 0.81, respectively. CONCLUSION: Due to the high performance of the proposed system, it is expected to integrate the proposed method with various PSG systems for sleep monitoring in clinical or homecare applications in the future. SIGNIFICANCE: An automatic sleep staging system integrating knowledge of the experts in scoring of PSG data and the elasticity of fuzzy systems in reasoning and decision making is proposed and the robustness and clinical applicability of the proposed method is demonstrated on data from healthy subjects and subjects with insomnia.

Using off-the-shelf lossy compression for wireless home sleep staging.

BACKGROUND: Recently, there has been increasing interest in the development of wireless home sleep staging systems that allow the patient to be monitored remotely while remaining in the comfort of their home. However, transmitting large amount of Polysomnography (PSG) data over the Internet is an important issue needed to be considered. In this work, we aim to reduce the amount of PSG data which has to be transmitted or stored, while having as little impact as possible on the information in the signal relevant to classify sleep stages. NEW METHOD: We examine the effects of off-the-shelf lossy compression on an all-night PSG dataset from 20 healthy subjects, in the context of automated sleep staging. The popular compression method Set Partitioning in Hierarchical Trees (SPIHT) was used, and a range of compression levels was selected in order to compress the signals with various degrees of loss. In addition, a rule-based automatic sleep staging method was used to automatically classify the sleep stages. RESULTS: Considering the criteria of clinical usefulness, the experimental results show that the system can achieve more than 60% energy saving with a high accuracy (>84%) in classifying sleep stages by using a lossy compression algorithm like SPIHT. COMPARISON WITH EXISTING METHOD(S): As far as we know, our study is the first that focuses how much loss can be tolerated in compressing complex multi-channel PSG data for sleep analysis. CONCLUSIONS: We demonstrate the feasibility of using lossy SPIHT compression for wireless home sleep staging.