Electrical brain stimulation and continuous behavioral state tracking in ambulatory humans

Objective. Electrical deep brain stimulation (DBS) is an established treatment for patients with drug-resistant epilepsy. Sleep disorders are common in people with epilepsy, and DBS may actually further disturb normal sleep patterns and sleep quality. Novel implantable devices capable of DBS and streaming of continuous intracranial electroencephalography (iEEG) signals enable detailed assessments of therapy efficacy and tracking of sleep related comorbidities. Here, we investigate the feasibility of automated sleep classification using continuous iEEG data recorded from Papez's circuit in four patients with drug resistant mesial temporal lobe epilepsy using an investigational implantable sensing and stimulation device with electrodes implanted in bilateral hippocampus (HPC) and anterior nucleus of thalamus (ANT). Approach. The iEEG recorded from HPC is used to classify sleep during concurrent DBS targeting ANT. Simultaneous polysomnography (PSG) and sensing from HPC were used to train, validate and test an automated classifier for a range of ANT DBS frequencies: no stimulation, 2 Hz, 7 Hz, and high frequency (>100 Hz). Main results. We show that it is possible to build a patient specific automated sleep staging classifier using power in band features extracted from one HPC iEEG sensing channel. The patient specific classifiers performed well under all thalamic DBS frequencies with an average F1-score 0.894, and provided viable classification into awake and major sleep categories, rapid eye movement (REM) and non-REM. We retrospectively analyzed classification performance with gold-standard PSG annotations, and then prospectively deployed the classifier on chronic continuous iEEG data spanning multiple months to characterize sleep patterns in ambulatory patients living in their home environment. Significance. The ability to continuously track behavioral state and fully characterize sleep should prove useful for optimizing DBS for epilepsy and associated sleep, cognitive and mood comorbidities

Visual vs. Narrative Truth in The Winter’s Tale

This paper explores the relationship between sight and proof in The Winter’s Tale and analyses the imagery of eyesight specifically in relation to the representation of visual truth in the play. The main concern is a close analysis of Leontes’ways of seeing and his ironical perception of ocular proof. The theme of parental resemblance and visual veracity is elaborated on through a general consideration of truth and falsity, original and copy. The statue of Hermione revealed in the last act of The Winter’s Tale stands for a secondary reality and nature’s imitation, contributing to Leontes’s earlier understanding of vision. Leontes falls into belief that what he encounters is a work of art transformed into life. As a result, the ways of seeing are constantly questioned in the play and the illusionistic qualities of the visual, verbal and theatrical arts thus become central.Cette étude se penche sur les rapports entre le regard et la notion de preuve dansLe Conte d’hiver, en s’intéressant aux images associées à la vue et aux liens avec la représentation de la vérité visible dans la pièce. Il s’agit notamment d’examiner les manières de voir de Leontes, et sa perception ironique des preuves oculaires. La thématique de la resemblance familiale et de la réalité du visible est développée en comparant le vrai et le faux, l’original et sa copie. La statue d’Hermione, révélée dans le dernier acte de la pièce, symbolise alors une réalité secondaire et une imitation de la nature, contribuant à informer l’interprétation par Leontes du monde visible. Le roi croit que ce qu’il voit est une ouvre d’art transformée en un être vivant. Ainsi, les manières de voir sont sans cesse remises en question dans la pièce, suggérant le caractère central des qualités illusioninstes des arts visuels, poétiques ou dramatiques

The validity of parental-reported body height and weight: a comparison with objective measurements of 7-8-year-old Czech children

The values of body weight and height can be recorded in various ways. Self-reports and parental-report methods are amongst the most typical ways to collect data. These methods have advantages, but also limits. Anthropometric measures are recommended to improve measurement precision. The aim of this study was to investigate whether the parental-reported body weight and height of 7-8-year-old Czech children corresponded with the measured body weight and height. Data concerning children’s body weight and body height were collected via parental informed consent and anthropometric measurements. The research sample consisted of 388 children from 7 to 8 years-old (boys, n = 176; girls, n = 162). Only children with parental informed consent were included. Correlations between parental-reported and measured data were analysed with the Pearson correlation coefficient to examine the strength of linear dependence between the two methods. The differences between parental-reported and measured data were tested using the Wilcoxon signed-rank test. P-values below α = 0.05 were considered statistically significant. Parents manifested a tendency to underestimate body weight and especially the body height of their children. This trend was seen in boys and girls in both age groups. Out of the 338 children with parent-reported height, parents under-reported their child’s height by 1 cm or more in 37.1% of the children, 39.6% of the parents reported a height within 0.99 cm of the measured height, and 23.3% of parents over-reported their child’s height by 1 cm or more. The same number of children had parent-reported weights, parents under-reported their child’s weight by 1 kg and more in 25.2% of the children, 57.7% of the parents reported a weight within 0.99 kg of the measured weight, and 17.1% of the parents over-reported their child’s weight by 1 kg or more. The Pearson correlation coefficient between the measured and parental-reported height and weight revealed a statistically significant strong positive linear relationship in both genders (rheight = 0.912, rweight = 0.943; all p< 0.001). The differences between the measured and parental-reported height and weight were not significantly different (all p< 0.05). The high agreement and correlation between measured and parental-reported body height and weight suggest that parental-report methods can be an appropriate alternative to objective measurement and can be used as a valid tool to classify body height and weight for large population studies of Czech children in school-based research when anthropometric measures are not available

Deep generative networks for algorithm development in implantable neural technology

Electrical stimulation of deep brain structures is an established therapy for drug-resistant focal epilepsy. The emerging implantable neural sensing and stimulating (INSS) technology enables simultaneous delivery of chronic deep brain stimulation (DBS) and recording of electrical brain activity from deep brain structures while patients live in their home environment. Long-term intracranial electroencephalography (iEEG) iEEG signals recorded by INSS devices represent an opportunity to investigate brain neurophysiology and how DBS affects neural circuits. However, novel algorithms and data processing pipelines need to be developed to facilitate research of these long-term iEEG signals. Early-stage analytical infrastructure development for INSS applications can be limited by lacking iEEG data that might not always be available. Here, we investigate the feasibility of utilizing the Deep Generative Adversarial Network (DCGAN) for synthetic iEEG data generation. We trained DCGAN using 3-second iEEG segments and validated synthetic iEEG usability by training a classification model, using synthetic iEEG only and providing a good classification performance on unseen real iEEG with an F1 score 0.849. Subsequently, we demonstrated the feasibility of utilizing the synthetic iEEG in the INSS application development by training a deep learning network for DBS artifact removal using synthetic data only and demonstrated the performance on real iEEG signals. The presented strategy of on-demand generating synthetic iEEG will benefit early-stage algorithm development for INSS applications