Showing papers in "International Journal of Neural Systems in 2018"
TL;DR: These results demonstrated an SSVEP-based BCI could provide accurate and efficient high-level control of a robotic arm, showing the feasibility of a BCI-based robotic arm control system for hand-assistance.
Abstract: Although robot technology has been successfully used to empower people who suffer from motor disabilities to increase their interaction with their physical environment, it remains a challenge for individuals with severe motor impairment, who do not have the motor control ability to move robots or prosthetic devices by manual control. In this study, to mitigate this issue, a noninvasive brain-computer interface (BCI)-based robotic arm control system using gaze based steady-state visual evoked potential (SSVEP) was designed and implemented using a portable wireless electroencephalogram (EEG) system. A 15-target SSVEP-based BCI using a filter bank canonical correlation analysis (FBCCA) method allowed users to directly control the robotic arm without system calibration. The online results from 12 healthy subjects indicated that a command for the proposed brain-controlled robot system could be selected from 15 possible choices in 4[Formula: see text]s (i.e. 2[Formula: see text]s for visual stimulation and 2[Formula: see text]s for gaze shifting) with an average accuracy of 92.78%, resulting in a 15 commands/min transfer rate. Furthermore, all subjects (even naive users) were able to successfully complete the entire move-grasp-lift task without user training. These results demonstrated an SSVEP-based BCI could provide accurate and efficient high-level control of a robotic arm, showing the feasibility of a BCI-based robotic arm control system for hand-assistance.
117 citations
TL;DR: It is proved that one type of spike is enough to guarantee the Turing universality of SNQ P systems, which have previously been proved to be universal when two types of spikes are considered.
Abstract: Spiking neural P systems are a class of third generation neural networks belonging to the framework of membrane computing. Spiking neural P systems with communication on request (SNQ P systems) are...
101 citations
TL;DR: Experimental results from a publicly available benchmark database demonstrate that the proposed approach provides better classification accuracy than the recently proposed methods in the literature, indicating the effectiveness of the proposed method in the detection of epileptic seizures.
Abstract: The electroencephalogram (EEG) signal analysis is a valuable tool in the evaluation of neurological disorders, which is commonly used for the diagnosis of epileptic seizures. This paper presents a novel automatic EEG signal classification method for epileptic seizure detection. The proposed method first employs a continuous wavelet transform (CWT) method for obtaining the time-frequency images (TFI) of EEG signals. The processed EEG signals are then decomposed into five sub-band frequency components of clinical interest since these sub-band frequency components indicate much better discriminative characteristics. Both Gaussian Mixture Model (GMM) features and Gray Level Co-occurrence Matrix (GLCM) descriptors are then extracted from these sub-band TFI. Additionally, in order to improve classification accuracy, a compact feature selection method by combining the ReliefF and the support vector machine-based recursive feature elimination (RFE-SVM) algorithm is adopted to select the most discriminative feature subset, which is an input to the SVM with the radial basis function (RBF) for classifying epileptic seizure EEG signals. The experimental results from a publicly available benchmark database demonstrate that the proposed approach provides better classification accuracy than the recently proposed methods in the literature, indicating the effectiveness of the proposed method in the detection of epileptic seizures.
83 citations
TL;DR: The aim of this work was to circumvent the unavailability of iEEG and the limitations of sEEG, and has achieved a classification accuracy of 68% an increase of 6% over the previously proposed linear regression mapping.
Abstract: Data is often plagued by noise which encumbers machine learning of clinically useful biomarkers and electroencephalogram (EEG) data is no exemption. Intracranial EEG (iEEG) data enhances the training of deep learning models of the human brain, yet is often prohibitive due to the invasive recording process. A more convenient alternative is to record brain activity using scalp electrodes. However, the inherent noise associated with scalp EEG data often impedes the learning process of neural models, achieving substandard performance. Here, an ensemble deep learning architecture for nonlinearly mapping scalp to iEEG data is proposed. The proposed architecture exploits the information from a limited number of joint scalp-intracranial recording to establish a novel methodology for detecting the epileptic discharges from the sEEG of a general population of subjects. Statistical tests and qualitative analysis have revealed that the generated pseudo-intracranial data are highly correlated with the true intracranial data. This facilitated the detection of IEDs from the scalp recordings where such waveforms are not often visible. As a real-world clinical application, these pseudo-iEEGs are then used by a convolutional neural network for the automated classification of intracranial epileptic discharges (IEDs) and non-IED of trials in the context of epilepsy analysis. Although the aim of this work was to circumvent the unavailability of iEEG and the limitations of sEEG, we have achieved a classification accuracy of 68% an increase of 6% over the previously proposed linear regression mapping.
81 citations
TL;DR: The experimental results show that the active brain locations of the two tasks were quite distinctive and spatially specific if using the initial dip map at 4 s in comparison to the map of HRs at 14 s and the average classification accuracy was improved from 59% to 74.9% when using the phase diagram of dual threshold circles.
Abstract: In this paper, a new vector phase diagram differentiating the initial decreasing phase (i.e. initial dip) and the delayed hemodynamic response (HR) phase of oxy-hemoglobin changes (ΔHbO) of functio...
72 citations
TL;DR: This work has trained four different CNN models based on well-established architectures, using or not different spatial and intensity normalization preprocessings, and shows that a sufficiently complex model such as the three-dimensional version of the ALEXNET can effectively account for spatial differences.
Abstract: Spatial and intensity normalizations are nowadays a prerequisite for neuroimaging analysis. Influenced by voxel-wise and other univariate comparisons, where these corrections are key, they are commonly applied to any type of analysis and imaging modalities. Nuclear imaging modalities such as PET-FDG or FP-CIT SPECT, a common modality used in Parkinson's disease diagnosis, are especially dependent on intensity normalization. However, these steps are computationally expensive and furthermore, they may introduce deformations in the images, altering the information contained in them. Convolutional neural networks (CNNs), for their part, introduce position invariance to pattern recognition, and have been proven to classify objects regardless of their orientation, size, angle, etc. Therefore, a question arises: how well can CNNs account for spatial and intensity differences when analyzing nuclear brain imaging? Are spatial and intensity normalizations still needed? To answer this question, we have trained four different CNN models based on well-established architectures, using or not different spatial and intensity normalization preprocessings. The results show that a sufficiently complex model such as our three-dimensional version of the ALEXNET can effectively account for spatial differences, achieving a diagnosis accuracy of 94.1% with an area under the ROC curve of 0.984. The visualization of the differences via saliency maps shows that these models are correctly finding patterns that match those found in the literature, without the need of applying any complex spatial normalization procedure. However, the intensity normalization - and its type - is revealed as very influential in the results and accuracy of the trained model, and therefore must be well accounted.
66 citations
TL;DR: The proposed algorithm for seizure prediction using a novel feature - diffusion distance (DD) in intracranial Electroencephalograph (iEEG) recordings yields a high sensitivity as well as a low false prediction rate, which demonstrates its potential for real-time prediction of seizures.
Abstract: Epilepsy is a chronic neurological disorder characterized by sudden and apparently unpredictable seizures. A system capable of forecasting the occurrence of seizures is crucial and could open new therapeutic possibilities for human health. This paper addresses an algorithm for seizure prediction using a novel feature - diffusion distance (DD) in intracranial Electroencephalograph (iEEG) recordings. Wavelet decomposition is conducted on segmented electroencephalograph (EEG) epochs and subband signals at scales 3, 4 and 5 are utilized to extract the diffusion distance. The features of all channels composing a feature vector are then fed into a Bayesian Linear Discriminant Analysis (BLDA) classifier. Finally, postprocessing procedure is applied to reduce false prediction alarms. The prediction method is evaluated on the public intracranial EEG dataset, which consists of 577.67[Formula: see text]h of intracranial EEG recordings from 21 patients with 87 seizures. We achieved a sensitivity of 85.11% for a seizure occurrence period of 30[Formula: see text]min and a sensitivity of 93.62% for a seizure occurrence period of 50[Formula: see text]min, both with the seizure prediction horizon of 10[Formula: see text]s. Our false prediction rate was 0.08/h. The proposed method yields a high sensitivity as well as a low false prediction rate, which demonstrates its potential for real-time prediction of seizures.
60 citations
TL;DR: Emerging evidence of FC is provided to understand that specific thalamic subdivisions contribute to the abnormalities of thalamo-cortical networks in JME, and the posterior thalamus could play a crucial role in generalized epileptic activity inJME.
Abstract: The purpose of this study was to investigate the functional connectivity (FC) of thalamic subdivisions in patients with juvenile myoclonic epilepsy (JME). Resting state functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) data were acquired from 22 JME and 25 healthy controls. We first divided the thalamus into eight subdivisions by performing independent component analysis on tracking fibers and clustering thalamus-related FC maps. We then analyzed abnormal FC in each subdivision in JME compared with healthy controls, and we investigated their associations with clinical features. Eight thalamic sub-regions identified in the current study showed unbalanced thalamic FC in JME: decreased FC with the superior frontal gyrus and enhanced FC with the supplementary motor area in the posterior thalamus increased thalamic FC with the salience network (SN) and reduced FC with the default mode network (DMN). Abnormalities in thalamo-prefrontocortical networks might be related to the propagation of generalized spikes with frontocentral predominance in JME, and the network connectivity differences with the SN and DMN might be implicated in emotional and cognitive defects in JME. JME was also associated with enhanced FC among thalamic sub-regions and with the basal ganglia and cerebellum, suggesting the regulatory role of subcortical nuclei and the cerebellum on the thalamo-cortical circuit. Additionally, increased FC with the pallidum was positive related with the duration of disease. The present study provides emerging evidence of FC to understand that specific thalamic subdivisions contribute to the abnormalities of thalamic-cortical networks in JME. Moreover, the posterior thalamus could play a crucial role in generalized epileptic activity in JME.
54 citations
TL;DR: It is suggested that the STE-DW algorithm can be used as a reliable identification algorithm for training-free SSVEP-based BCIs, because of the good balance between ease of use, recognition accuracy, ITR and user applicability.
Abstract: The past decade has witnessed rapid development in the field of brain–computer interfaces (BCIs). While the performance is no longer the biggest bottleneck in the BCI application, the tedious train...
52 citations
TL;DR: A novel method using the local binary pattern (LBP) based on the wavelet transform (WT) is proposed to characterize the behavior of EEG activities and can obtain the much shorter histogram which greatly reduces the computational burden of classification and enables it to detect ictal EEG signals in real time.
Abstract: The automatic identification of epileptic electroencephalogram (EEG) signals can give assistance to doctors in diagnosis of epilepsy, and provide the higher security and quality of life for people ...
41 citations
TL;DR: An automated detector is able to accurately calculate a dynamic iEEG baseline in different HFO activity channels using the maximum distributed peak points method, resulting in higher sensitivity and specificity than other available HFO detectors.
Abstract: High frequency oscillations (HFOs) are considered as biomarker for epileptogenicity. Reliable automation of HFOs detection is necessary for rapid and objective analysis, and is determined by accurate computation of the baseline. Although most existing automated detectors measure baseline accurately in channels with rare HFOs, they lose accuracy in channels with frequent HFOs. Here, we proposed a novel algorithm using the maximum distributed peak points method to improve baseline determination accuracy in channels with wide HFOs activity ranges and calculate a dynamic baseline. Interictal ripples (80-200[Formula: see text]Hz), fast ripples (FRs, 200-500[Formula: see text]Hz) and baselines in intracerebral EEGs from seven patients with intractable epilepsy were identified by experienced reviewers and by our computer-automated program, and the results were compared. We also compared the performance of our detector to four well-known detectors integrated in RIPPLELAB. The sensitivity and specificity of our detector were, respectively, 71% and 75% for ripples and 66% and 84% for FRs. Spearman's rank correlation coefficient comparing automated and manual detection was [Formula: see text] for ripples and [Formula: see text] for FRs ([Formula: see text]). In comparison to other detectors, our detector had a relatively higher sensitivity and specificity. In conclusion, our automated detector is able to accurately calculate a dynamic iEEG baseline in different HFO activity channels using the maximum distributed peak points method, resulting in higher sensitivity and specificity than other available HFO detectors.
TL;DR: The findings revealed that SGC does not need a comparison with null-hypothesis networks constructed by a surrogate process, and results on the real dataset suggest that schizophrenia is associated with a deficit in the brain dynamic reorganization related to secondary pathways of the brain network.
Abstract: The aim of this study was to introduce a novel global measure of graph complexity: Shannon graph complexity (SGC) This measure was specifically developed for weighted graphs, but it can also be applied to binary graphs The proposed complexity measure was designed to capture the interplay between two properties of a system: the 'information' (calculated by means of Shannon entropy) and the 'order' of the system (estimated by means of a disequilibrium measure) SGC is based on the concept that complex graphs should maintain an equilibrium between the aforementioned two properties, which can be measured by means of the edge weight distribution In this study, SGC was assessed using four synthetic graph datasets and a real dataset, formed by electroencephalographic (EEG) recordings from controls and schizophrenia patients SGC was compared with graph density (GD), a classical measure used to evaluate graph complexity Our results showed that SGC is invariant with respect to GD and independent of node degree distribution Furthermore, its variation with graph size [Formula: see text] is close to zero for [Formula: see text] Results from the real dataset showed an increment in the weight distribution balance during the cognitive processing for both controls and schizophrenia patients, although these changes are more relevant for controls Our findings revealed that SGC does not need a comparison with null-hypothesis networks constructed by a surrogate process In addition, SGC results on the real dataset suggest that schizophrenia is associated with a deficit in the brain dynamic reorganization related to secondary pathways of the brain network
TL;DR: The optimization of volumes of interest (VOIs) to extract three-dimensional textures from Magnetic Resonance Image (MRI) in order to diagnose AD, Mild Cognitive Impairment converter (MCIc), Mild Cognitiveimpairment nonconverter (MCInc) and Normal subjects obtains excellent results in multi-class classification achieving accuracies of 94.4% and extracting significant information on the location of the most relevant points of the brain.
Abstract: Computer-Aided Diagnosis (CAD) represents a relevant instrument to automatically classify between patients with and without Alzheimer's Disease (AD) using several actual imaging techniques. This study analyzes the optimization of volumes of interest (VOIs) to extract three-dimensional (3D) textures from Magnetic Resonance Image (MRI) in order to diagnose AD, Mild Cognitive Impairment converter (MCIc), Mild Cognitive Impairment nonconverter (MCInc) and Normal subjects. A relevant feature of the proposed approach is the use of 3D features instead of traditional two-dimensional (2D) features, by using 3D discrete wavelet transform (3D-DWT) approach for performing feature extraction from T-1 weighted MRI. Due to the high number of coefficients when applying 3D-DWT to each of the VOIs, a feature selection algorithm based on mutual information is used, as is the minimum Redundancy Maximum Relevance (mRMR) algorithm. Region optimization has been performed in order to discover the most relevant regions (VOIs) in the brain with the use of Multi-Objective Genetic Algorithms, being one of the objectives to be optimize the accuracy of the system. The error index of the system is computed by the confusion matrix obtained by the multi-class support vector machine (SVM) classifier. Principal Component Analysis (PCA) is used with the purpose of reducing the number of features to the classifier. The cohort of subjects used in the study consisted of 296 different patients. A first group of 206 patients was used to optimize VOI selection and another group of 90 independent subjects (that did not belong to the first group) was used to test the solutions yielded by the genetic algorithm. The proposed methodology obtains excellent results in multi-class classification achieving accuracies of 94.4% and also extracting significant information on the location of the most relevant points of the brain. This suggests that the proposed method could aid in the research of other neurodegenerative diseases, improving the accuracy of the diagnosis and finding the most relevant regions of the brain associated with them.
TL;DR: Examining the dynamic FC in default-mode network (DMN) and motor-related network (MN) using Blood-Oxygenation-Level-Dependent-fMRI data from 26 healthy subjects demonstrated that dynamic FC analysis could offer unique insights in understanding how the brain reorganizes itself during rest and task states, and the ways in which the brain adaptively responds to the cognitive requirements of tasks.
Abstract: Task-related reorganization of functional connectivity (FC) has been widely investigated. Under classic static FC analysis, brain networks under task and rest have been demonstrated a general similarity. However, brain activity and cognitive process are believed to be dynamic and adaptive. Since static FC inherently ignores the distinct temporal patterns between rest and task, dynamic FC may be more a suitable technique to characterize the brain's dynamic and adaptive activities. In this study, we adopted [Formula: see text]-means clustering to investigate task-related spatiotemporal reorganization of dynamic brain networks and hypothesized that dynamic FC would be able to reveal the link between resting-state and task-state brain organization, including broadly similar spatial patterns but distinct temporal patterns. In order to test this hypothesis, this study examined the dynamic FC in default-mode network (DMN) and motor-related network (MN) using Blood-Oxygenation-Level-Dependent (BOLD)-fMRI data from 26 healthy subjects during rest (REST) and a hand closing-and-opening (HCO) task. Two principal FC states in REST and one principal FC state in HCO were identified. The first principal FC state in REST was found similar to that in HCO, which appeared to represent intrinsic network architecture and validated the broadly similar spatial patterns between REST and HCO. However, the second FC principal state in REST with much shorter "dwell time" implied the transient functional relationship between DMN and MN during REST. In addition, a more frequent shifting between two principal FC states indicated that brain network dynamically maintained a "default mode" in the motor system during REST, whereas the presence of a single principal FC state and reduced FC variability implied a more temporally stable connectivity during HCO, validating the distinct temporal patterns between REST and HCO. Our results further demonstrated that dynamic FC analysis could offer unique insights in understanding how the brain reorganizes itself during rest and task states, and the ways in which the brain adaptively responds to the cognitive requirements of tasks.
TL;DR: The outcomes of this study show that the APFP can reliably decompose at least a subset of MUs in the high density SEMG signals recorded from the human FDI muscle during low contraction levels using a protocol analog to clinical EMG examination.
Abstract: This study aims to assess the accuracy of a novel high density surface electromyogram (SEMG) decomposition method, namely automatic progressive FastICA peel-off (APFP), for automatic decomposition ...
TL;DR: A pipeline for the detection and analysis of pathologic High-Frequency Oscillations is developed based on spectral kurtosis-driven selection and wavelet-based detection of HFOs, which had average sensitivity and average specificity in identifying the HFO area which overlapped with the SOZ as defined by clinical presurgical workup.
Abstract: Pathological High-Frequency Oscillations (HFOs) have been recently proposed as potential biomarker of the seizure onset zone (SOZ) and have shown superior accuracy to interictal epileptiform discharges in delineating its anatomical boundaries. Characterization of HFOs is still in its infancy and this is reflected in the heterogeneity of analysis and reporting methods across studies and in clinical practice. The clinical approach to HFOs identification and quantification usually still relies on visual inspection of EEG data. In this study, we developed a pipeline for the detection and analysis of HFOs. This includes preliminary selection of the most informative channels exploiting statistical properties of the pre-ictal and ictal intracranial EEG (iEEG) time series based on spectral kurtosis, followed by wavelet-based characterization of the time-frequency properties of the signal. We performed a preliminary validation analyzing EEG data in the ripple frequency band (80-250 Hz) from six patients with drug-resistant epilepsy who underwent pre-surgical evaluation with stereo-EEG (SEEG) followed by surgical resection of pathologic brain areas, who had at least two-year positive post-surgical outcome. In this series, kurtosis-driven selection and wavelet-based detection of HFOs had average sensitivity of 81.94% and average specificity of 96.03% in identifying the HFO area which overlapped with the SOZ as defined by clinical presurgical workup. Furthermore, the kurtosis-based channel selection resulted in an average reduction in computational time of 66.60%.
TL;DR: The model was able to capture the alteration in EBCC consolidation caused by TMS and showed that TMS affected plasticity at cortical synapses thereby altering the fast learning phase, suggesting how TMS affects local circuit computation and memory processing in the cerebellum.
Abstract: During natural learning, synaptic plasticity is thought to evolve dynamically and redistribute within and among subcircuits. This process should emerge in plastic neural networks evolving under beh...
TL;DR: This research presents a novel approach that requires the effective integration of different dynamical time scales within a unified framework of neural responses, where the rod, cone, amacrine, bipolar, and ganglion cells correspond to the implemented pathways.
Abstract: Existing computational models of the retina often compromise between the biophysical accuracy and a hardware-adaptable methodology of implementation. When compared to the current modes of vision re...
TL;DR: This work shows for the first time that the neural signature extracted from the spectral shape of the EEG is to a large extent independent of the recorded cognitive task and experimental condition, and proposes to use this task-independent neural signature for more precise biometric identity verification.
Abstract: Genetic and neurophysiological studies of electroencephalogram (EEG) have shown that an individual’s brain activity during a given cognitive task is, to some extent, determined by their genes. In f...
TL;DR: A novel Gaussian discriminant analysis-based algorithm is introduced to achieve a more effective and accurate classification performance than existing state-of-the-art algorithms for discriminating the subtle differences between MCI participants and the CN group.
Abstract: Over the past few years, several approaches have been proposed to assist in the early diagnosis of Alzheimer’s disease (AD) and its prodromal stage of mild cognitive impairment (MCI). Using multimo...
TL;DR: The results suggest that combining multiple types of stimuli to evoke distinguishable ERPs might be a promising direction to improve the command generation rate in the brain-computer interface.
Abstract: Increasing command generation rate of an event-related potential-based brain-robot system is challenging, because of limited information transfer rate of a brain-computer interface system. To impro...
TL;DR: This study investigated whether the performance of an NIRS-BCI discriminating a mental arithmetic task from the baseline state could be predicted using resting-state functional connectivity (RSFC) of the prefrontal cortex and found that the RSFC between bilateral channels in the prefrontal area was negatively correlated with subsequent BCI performance.
Abstract: One of the most important issues in current brain-computer interface (BCI) research is the prediction of a user's BCI performance prior to the main BCI session because it would be useful to reduce the time required to determine the BCI paradigm best suited to that user. In electroencephalography (EEG)-BCI research, whether a user has low BCI performance toward a specific BCI paradigm has been estimated using a variety of resting-state EEG features. However, no previous study has attempted to predict the performance of near-infrared spectroscopy (NIRS)-BCI using resting-state NIRS data recorded before the main BCI experiment. In this study, we investigated whether the performance of an NIRS-BCI discriminating a mental arithmetic task from the baseline state could be predicted using resting-state functional connectivity (RSFC) of the prefrontal cortex. The investigation of NIRS signals recorded from 29 participants revealed that the RSFC between bilateral channels in the prefrontal area was negatively correlated with subsequent BCI performance (e.g. a fitted line for the RSFC between L2 and R2 channels explains 41% of BCI performance variation). We expect that our indicator can be used to predict BCI performance of an individual user prior to the main NIRS-BCI experiments, thereby facilitating implementation of more efficient NIRS-BCI systems.
TL;DR: A three layered 3D CNN was able to predict the ganglion cells firing rates with high correlations and low prediction error, as measured with Mean Squared Error and Dynamic Time Warping in test sets.
Abstract: Deep Learning offers flexible powerful tools that have advanced our understanding of the neural coding of neurosensory systems. In this work, a 3D Convolutional Neural Network (3D CNN) is used to mimic the behavior of a population of mice retinal ganglion cells in response to different light patterns. For this purpose, we projected homogeneous RGB flashes and checkerboards stimuli with variable luminances and wavelength spectrum to mimic a more naturalistic stimuli environment onto the mouse retina. We also used white moving bars in order to localize the spatial position of the recorded cells. Then recorded spikes were smoothed with a Gaussian kernel and used as the output target when training a 3D CNN in a supervised way. To find a suitable model, two hyperparameter search stages were performed. In the first stage, a trial and error process allowed us to obtain a system that is able to fit the neurons firing rates. In the second stage, a systematic procedure was used to compare several gradient-based optimizers, loss functions and the model's convolutional layers number. We found that a three layered 3D CNN was able to predict the ganglion cells firing rates with high correlations and low prediction error, as measured with Mean Squared Error and Dynamic Time Warping in test sets. These models were either competitive or outperformed other models used already in neuroscience, as Feed Forward Neural Networks and Linear-Nonlinear models. This methodology allowed us to capture the temporal dynamic response patterns in a robust way, even for neurons with high trial-to-trial variable spontaneous firing rates, when providing the peristimulus time histogram as an output to our model.
TL;DR: This approach of characterizing the vagus nerve can be used in closed loop systems to determine when to initiate VNS and also to tune the stimulation dose, which is patient-specific and changes over time.
Abstract: Objective: Vagus Nerve Stimulation (VNS) has shown great promise as a potential therapy for a number of conditions, such as epilepsy, depression and for Neurometabolic Therapies, especially for tre...
TL;DR: In this article, the authors tried to find the logical explanation of the phenomenon whereby some nuclear proliferators are absolved regardless of their active accumulation of nuclear arsenals while others are labeled as "rogue states" and ordered to disarm.
Abstract: There is no doubt that the NPT regime is far from being equal for all states involved. As the predominant hegemonic power since WWII, the United States plays a major role in deciding the fates of non-great power proliferators. This article tries to find the logical explanation of the phenomenon whereby some nuclear proliferators are absolved regardless of their active accumulation of nuclear arsenals while others are labeled as “rogue states” and ordered to disarm. The article suggests that a particular proliferator’s political regime could affect the way in which its state is approached by the U.S., known for its loyalty to democratic values. To check this argument’s feasibility, the author analyzes and compares types of political regimes of proliferators—which refer to non-great powers that commenced their nuclear programs since 1964. The study also shows that alignment with the U.S. and presence or absence of hostility toward the West are also influential factors. Along with the proliferator’s political regime, these factors determine whether the country is necessarily absolved, required to disarm, or heavily punished.
The author finds that the U.S. tends to free democratic proliferators from charges, especially those aligned with the U.S., officially or unofficially. In most cases, the autocratic governments are coerced to give up their proliferation ambitions, though an alignment with the U.S. may work as an extenuating circumstance to render the punishment less harmful. The article aims to demonstrate that the U.S.’s tendency to coerce undemocratic de facto nuclear powers, while avoiding coercion against democratic partners, is not a mere “double standard” or bias, but rather, a part of the U.S.’s strategic policy.
TL;DR: A possible physiological mechanism to explain the observed properties associated with an ideal filter is proposed, and the potential use of this approach for the evaluation of anticonvulsant strategies is discussed.
Abstract: During neocortical seizures in patients with epilepsy, microelectrode array recordings from the ictal core show a strong correlation between the fast, cellular spiking activities and the low-frequency component of the potential field, reflected in the electrocorticogram (ECoG). Here, we model the relationship between the cellular spike activity and this low-frequency component as the input and output signals of a linear time invariant system. Our approach is based on the observation that this relationship can be characterized by a so-called sinc function, the unit impulse response of an ideal (brick-wall) filter. Accordingly, using a brick-wall filter, we are able to convert ictal cellular spike inputs into an output that significantly correlates with the observed seizure activity in the ECoG (r=0.40-0.56,p<0.01) , while ECoG recordings of subsequent seizures within patients also show significant, but lower, correlations (r=0.10-0.30,p<0.01) . Furthermore, we can produce seizure-like output signals using synthetic spike trains with ictal properties. We propose a possible physiological mechanism to explain the observed properties associated with an ideal filter, and discuss the potential use of our approach for the evaluation of anticonvulsant strategies.
TL;DR: History of coronary artery diseases (CADs) and low neurologic grade at presentation (Hunt-Hess grade 4/5) are independent risk factors for increasing morbidity and mortality in patients with MIA.
Abstract: Objectives Multiple intracranial aneurysms (MIAs) are fairly common entities. Unless MIAs are incidentally diagnosed, they remain asymptomatic until they rupture. In this study, the authors investigated factors affecting the surgical outcomes in patients with MIA by evaluating the surgical outcomes of 90 consecutive cases. Material and Methods Medical records were retrospectively reviewed for 409 consecutive cerebral aneurysm cases that underwent surgery in the hospital from 2011 to 2013. The patients’ data were prospectively collected. All MIA patients ( n = 90) constituted the core sample for this study. Results The authors detected 221 aneurysms in 90 patients (49 females and 41 males; mean age: 50.8 ± 11.9 years; range: 25–82 years). Of the patients, 67 presented with subarachnoid hemorrhage, whereas 23 were incidentally diagnosed with unruptured aneurysms. The mortality rate was 13.3% ( n = 12). The morbidity rate was 18.8% ( n = 17). Of the patients, 67.8% ( n = 61) had returned to their jobs and normal daily activities by their last follow-up (average: 52.3 months). History of coronary artery diseases (CADs) and low neurologic grade at presentation (Hunt-Hess grade 4/5) are independent risk factors for increasing morbidity and mortality in patients with MIA (odds ratio [OR]: 18.46; p = 0.007); (OR: 30.0; p = 0.002) and (OR: 0.06; p = 0.0001); (OR: 0.07; p = 0.002), respectively. Conclusion History of CADs and high Hunt-Hess grade are independent risk factors for poor surgical outcomes of patients with MIA.
TL;DR: A Statistical Machine Translation (SMT) system which fully integrates NNLMs in the decoding stage is introduced, breaking the traditional approach based on [Formula: see text]-best list rescoring.
Abstract: Neural Network Language Models (NNLMs) are a successful approach to Natural Language Processing tasks, such as Machine Translation. We introduce in this work a Statistical Machine Translation (SMT)...
TL;DR: A multiple neural mass model (NMM) is proposed, the output of which is the linear combination of the outputs of all NMMs, and the results showed that the NMM number was significantly lower during the ictal period than during the interictalperiod, andThe strength of major NMMs increased, indicating that neural masses fuse into fewer larger neural masses with greater strength.
Abstract: Electroencephalography (EEG) is an important method to investigate the neurophysiological mechanism underlying epileptogenesis to identify new therapies for the treatment of epilepsy. The neurophysiologically based neural mass model (NMM) can build a bridge between signal processing and neurophysiology, which can be used as a platform to explore the neurophysiological mechanism of epileptogenesis. Most EEG signals cannot be regarded as the outputs of a single NMM with identical model parameters. The outputs of NMM are simple because the diversity of neural signals in the same NMM is ignored. To improve the simulation of EEG signals, a multiple NMM is proposed, the output of which is the linear combination of the outputs of all NMMs. The NMM number is not fixed and is minimized under the premise of guaranteeing the fitting effect. Orthogonal matching pursuit is used to solve a constrained [Formula: see text] norm minimization problem for NMM number and the strength of every NMM. The results showed that the NMM number was significantly lower during the ictal period than during the interictal period, and the strength of major NMMs increased. This indicates that neural masses fuse into fewer larger neural masses with greater strength. The distribution of excitatory and inhibitory strength during the ictal and interictal periods was similar, whereas the excitation/inhibition ratio was higher during the ictal period than during the interictal period.
TL;DR: By highlighting the relation between ancient Indian methodology and modern chronomedicine, the author describes the odyssey of antiquity to modern science.
Abstract: Pineal gland or “spiritual third eye” is regarded as the gateway of spiritual life as per ancient concepts about the soul. Recently, modern neuroscience has proven that pineal gland is not only the melatonin-secreting neuroendocrine organ which controls the circardian rhythm, but it also has mystical and energetic associations with spirituality. It acts as a tremendous coordinator between molecular, hormonal, physiological, and chemical rhythmic orchestra. Thus, in this article, by highlighting the relation between ancient Indian methodology and modern chronomedicine, the author describes the odyssey of antiquity to modern science.