Discovery of recurrent multiple brain states in non-convulsive status epilepticus

Objective We study burst-like patterns of epileptiform discharges in non-convulsive status epilepticus (SE). Methods Epileptiform burst-like transients were identified by estimating the amplitude envelope of the EEG signal, and recurrence and similarities were identified by pairwise matching in the time-domain. Results We identified similarities in the onset of a significant fraction of the epileptiform bursts, and a bimodal distribution of the burst durations. Conclusions Bursts of epileptiform discharges during a non-convulsive SE are manifestations of multiple patterns of recurring brain states. Significance Quantitative description of ictal phenomena in epilepsy and status epilepticus adds to the knowledge of abnormal brain behavior and may assist in improved patient care

Generalized periodic discharges: Pathophysiology and clinical considerations

Generalized periodic discharges (GPDs) are commonly encountered in metabolic encephalopathy and cerebral hypoxia/ischemia. The clinical significance of this EEG pattern is indistinct, and it is unclear whether treatment with antiepileptic drugs is beneficial. In this study, we discuss potential pathophysiological mechanisms. Based on the literature, supplemented with simulations in a minimal computational model, we conclude that selective synaptic failure or neuronal damage of inhibitory interneurons, leading to disinhibition of excitatory pyramidal cells, presumably plays a critical role. Reversibility probably depends on the potential for functional recovery of these interneurons. Whether antiepileptic drugs are helpful for regaining function is unclea

Predicting sex from brain rhythms with deep learning

We have excellent skills to extract sex from visual assessment of human faces, but assessing sex from human brain rhythms seems impossible. Using deep convolutional neural networks, with unique potential to find subtle differences in apparent similar patterns, we explore if brain rhythms from either sex contain sex specific information. Here we show, in a ground truth scenario, that a deep neural net can predict sex from scalp electroencephalograms with an accuracy of >80% (p < 10-5), revealing that brain rhythms are sex specific. Further, we extracted sex-specific features from the deep net filter layers, showing that fast beta activity (20-25 Hz) and its spatial distribution is a main distinctive attribute. This demonstrates the ability of deep nets to detect features in spatiotemporal data unnoticed by visual assessment, and to assist in knowledge discovery. We anticipate that this approach may also be successfully applied to other specialties where spatiotemporal data is abundant, including neurology, cardiology and neuropsychology

STQS:Interpretable multi-modal Spatial-Temporal-seQuential model for automatic Sleep scoring

Sleep scoring is an important step for the detection of sleep disorders and usually performed by visual analysis. Since manual sleep scoring is time consuming, machine-learning based approaches have been proposed. Though efficient, these algorithms are black-box in nature and difficult to interpret by clinicians. In this paper, we propose a deep learning architecture for multi-modal sleep scoring, investigate the model's decision making process, and compare the model's reasoning with the annotation guidelines in the AASM manual. Our architecture, called STQS, uses convolutional neural networks (CNN) to automatically extract spatio-temporal features from 3 modalities (EEG, EOG and EMG), a bidirectional long short-term memory (Bi-LSTM) to extract sequential information, and residual connections to combine spatio-temporal and sequential features. We evaluated our model on two large datasets, obtaining an accuracy of 85% and 77% and a macro F1 score of 79% and 73% on SHHS and an in-house dataset, respectively. We further quantify the contribution of various architectural components and conclude that adding LSTM layers improves performance over a spatio-temporal CNN, while adding residual connections does not. Our interpretability results show that the output of the model is well aligned with AASM guidelines, and therefore, the model's decisions correspond to domain knowledge. We also compare multi-modal models and single-channel models and suggest that future research should focus on improving multi-modal models

Small-world characteristics of EEG patterns in post-anoxic encephalopathy

Post-anoxic encephalopathy (PAE) has a heterogenous outcome which is difficult to predict. At present, it is possible to predict poor outcome using somatosensory evoked potentials in only a minority of the patients at an early stage. In addition, it remains difficult to predict good outcome at an early stage. Network architecture, as can be quantified with continuous electroencephalography (cEEG), may serve as a candidate measure for predicting neurological outcome. Here, we explore whether cEEG monitoring can be used to detect the integrity of neural network architecture in patients with PAE after cardiac arrest. From 56 patients with PAE treated with mild therapeutic hypothermia, 19-channel cEEG data were recorded starting as soon as possible after cardiac arrest. Adjacency matrices of shared frequencies between 1 and 25Hz of the EEG channels were obtained using Fourier transformations. Number of network nodes and connections, clustering coefficient (C), average path length (L), and small-world index (SWI) were derived. Outcome was quantified by the best cerebral performance category (CPC)-score within 6months. Compared to non-survivors, survivors showed significantly more nodes and connections. L was significantly higher and C and SWI were significantly lower in the survivor group than in the non-survivor group. The number of nodes, connections, and the L were negatively correlated with the CPC-score. C and SWI correlated positively with the CPC-score. The combination of number of nodes, connections, C, and L showed the most significant difference and correlation between survivors and non-survivors and CPC-score. Our data might implicate that non-survivors have insufficient distribution and differentiation of neural activity for regaining normal brain function. These network differences, already present during hypothermia, might be further developed as early prognostic markers. The predictive values are however still inferior to current practice parameters. Keywords: small-world network, continuous EEG, post-anoxic encephalopathy, prognosis, resuscitatio

Reduced Synaptic Vesicle Recycling during Hypoxia in Cultured Cortical Neurons

Improvement of neuronal recovery in the ischemic penumbra, an area around the core of a brain infarct with some remaining perfusion, has a large potential for the development of therapy against acute ischemic stroke. However, mechanisms that lead to either recovery or secondary damage in the penumbra largely remain unclear. Recent studies in cultured networks of cortical neurons showed that failure of synaptic transmission (referred to as synaptic failure) is a critical factor in the penumbral area, but the mechanisms that lead to synaptic failure are still under investigation. Here we used a Styryl dye, FM1-43, to quantify endocytosis and exocytosis in cultures of rat cortical neurons under normoxic and hypoxic conditions. Hypoxia in cultured cortical networks rapidly depressed endocytosis and, to a lesser extent, exocytosis. These findings support electrophysiological findings that synaptic failure occurs quickly after the induction of hypoxia, and confirms that the failing processes are at least in part presynaptic