A 57-year-old man presented with a progressive flaccid symmetrical motor and sensory neuropathy following a 1-week history of cough and malaise. He was diagnosed with Guillain-Barré syndrome secondary to COVID-19 and started on intravenous immunoglobulin. He proceeded to have worsening respiratory function and needed intubation and mechanical ventilation. This is the first reported case of this rare neurological complication of COVID-19 in the UK, but it adds to a small but growing body of international evidence to suggest a significant association between these two conditions. Increasing appreciation of this by clinicians will ensure earlier diagnosis, monitoring and treatment of patients presenting with this.

Interictal epileptiform discharges (IEDs) are transient neural electrical activities that occur in the brain of patients with epilepsy. A problem with the inspection of IEDs from the scalp electroencephalogram (sEEG) is that for a subset of epileptic patients, there are no visually discernible IEDs on the scalp, rendering the above procedures ineffective, both for detection purposes and algorithm evaluation. On the other hand, intracranially placed electrodes yield a much higher incidence of visible IEDs as compared to concurrent scalp electrodes. In this work, we utilize concurrent scalp and intracranial EEG (iEEG) from a group of temporal lobe epilepsy (TLE) patients with low number of scalp-visible IEDs. The aim is to determine whether by considering the timing information of the IEDs from iEEG, the resulting concurrent sEEG contains enough information for the IEDs to be reliably distinguished from non-IED segments. We develop an automatic detection algorithm which is tested in a leave-subject-out fashion, where each test subject's detection algorithm is based on the other patients' data. The algorithm obtained a [Formula: see text] accuracy in recognizing scalp IED from non-IED segments with [Formula: see text] accuracy when trained and tested on the same subject. Also, it was able to identify nonscalp-visible IED events for most patients with a low number of false positive detections. Our results represent a proof of concept that IED information for TLE patients is contained in scalp EEG even if they are not visually identifiable and also that between subject differences in the IED topology and shape are small enough such that a generic algorithm can be used.

Abstract-Detection algorithms for electroencephalography (EEG) data, especially in the field of interictal epileptiform discharge (IED) detection, have traditionally employed handcrafted features which utilised specific characteristics of neural responses. Although these algorithms achieve high accuracy, mere detection of an IED holds little clinical significance. In this work, we consider deep learning for epileptic subjects to accommodate automatic feature generation from intracranial EEG data, while also providing clinical insight. Convolutional neural networks are trained in a subject independent fashion to demonstrate how meaningful features are automatically learned in a hierarchical process. We illustrate how the convolved filters in the deepest layers provide insight towards the different types of IEDs within the group, as confirmed by our expert clinicians. The morphology of the IEDs found in filters can help evaluate the treatment of a patient. To improve the learning of the deep model, moderately different score classes are utilised as opposed to binary IED and non-IED labels. The resulting model achieves state of the art classification performance and is also invariant to time differences between the IEDs. This study suggests that deep learning is suitable for automatic feature generation from intracranial EEG data, while also providing insight into the data.

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.

Cortical and thalamic stimulation appear to be effective and well tolerated in children with refractory epilepsy. SCS can be used to identify the focus and predict the effects of resective surgery or chronic cortical stimulation. Further larger studies are necessary.

Cortical interactions shown by SPES can be described as control systems which can predict cortical oscillatory behavior. The method is unique as it describes connectivity as well as dynamic interactions.

The incidence of functional connections between human temporal lobes and their latencies were investigated using intracranial EEG responses to electrical stimulation with 1 msec single pulses in 91 patients assessed for surgery for treatment of epilepsy. The areas studied were amygdala, hippocampus, parahippocampal gyrus, fusiform gyrus, inferior and mid temporal gyrus. Furthermore, we assessed whether the presence of such connections are related to seizure onset extent and postsurgical seizure control. Responses were seen in any region of the contralateral temporal lobe when stimulating temporal regions in 30 patients out of the 91 (32.96%). Bi-hippocampal or bi-amygdalar projections were seen in only 5% of temporal lobes (N = 60) and between both fusiform gyri in 7.1% (N = 126). All other bilateral connections occurred in less than 5% of hemispheres. Depending on the structures, latencies ranged between 20 and 90 msec, with an average value of 60.2 msec. There were no statistical difference in the proportion of patients showing Engel Class I between patients with and without contralateral temporal connections. No difference was found in the proportion of patients showing bilateral or unilateral seizure onset among patients with and without contralateral temporal projections. The present findings corroborate that the functionality of bilateral temporal connections in humans is limited and does not affect the surgical outcome.

In human generalized seizures, the thalamus may become involved early or late in the seizure but, once it becomes involved, it leads the cortex. In contrast, in human frontal seizures the thalamus gets involved late in the seizure and, once it becomes involved, it lags behind the cortex. In addition, the centromedian nucleus of the thalamus is capable of autonomous epileptogenesis as suggested by the presence of independent focal unilateral epileptiform discharges restricted to thalamic structures. The thalamus may also be responsible for maintaining the rhythmicity of ictal discharges.