Mutant Tau knock-in mice display frontotemporal dementia relevant behaviour and histopathology

Peer reviewedPostprin

The status of textile-based dry EEG electrodes

Electroencephalogram (EEG) is the biopotential recording of electrical signals generated by brain activity. It is useful for monitoring sleep quality and alertness, clinical applications, diagnosis, and treatment of patients with epilepsy, disease of Parkinson and other neurological disorders, as well as continuous monitoring of tiredness/ alertness in the field. We provide a review of textile-based EEG. Most of the developed textile-based EEGs remain on shelves only as published research results due to a limitation of flexibility, stickability, and washability, although the respective authors of the works reported that signals were obtained comparable to standard EEG. In addition, nearly all published works were not quantitatively compared and contrasted with conventional wet electrodes to prove feasibility for the actual application. This scenario would probably continue to give a publication credit, but does not add to the growth of the specific field, unless otherwise new integration approaches and new conductive polymer composites are evolved to make the application of textile-based EEG happen for bio-potential monitoring

Experimental Design Modulates Variance in BOLD Activation: The Variance Design General Linear Model

Typical fMRI studies have focused on either the mean trend in the blood-oxygen-level-dependent (BOLD) time course or functional connectivity (FC). However, other statistics of the neuroimaging data may contain important information. Despite studies showing links between the variance in the BOLD time series (BV) and age and cognitive performance, a formal framework for testing these effects has not yet been developed. We introduce the Variance Design General Linear Model (VDGLM), a novel framework that facilitates the detection of variance effects. We designed the framework for general use in any fMRI study by modeling both mean and variance in BOLD activation as a function of experimental design. The flexibility of this approach allows the VDGLM to i) simultaneously make inferences about a mean or variance effect while controlling for the other and ii) test for variance effects that could be associated with multiple conditions and/or noise regressors. We demonstrate the use of the VDGLM in a working memory application and show that engagement in a working memory task is associated with whole-brain decreases in BOLD variance.Comment: 18 pages, 7 figure

Neurophysiological Responses to Different Product Experiences

It is well known that the evaluation of a product from the shelf considers the simultaneous cerebral and emotional evaluation of the different qualities of the product such as its colour, the eventual images shown, and the envelope’s texture (hereafter all included in the term “product experience”). However, the measurement of cerebral and emotional reactions during the interaction with food products has not been investigated in depth in specialized literature. (e aim of this paper was to investigate such reactions by the EEG and the autonomic activities, as elicited by the cross-sensory interaction (sight and touch) across several different products. In addition, we investigated whether (i) the brand (Major Brand or Private Label), (ii) the familiarity (Foreign or Local Brand), and (iii) the hedonic value of products (Comfort Food or Daily Food) influenced the reaction of a group of volunteers during their interaction with the products. Results showed statistically significantly higher tendency of cerebral approach (as indexed by EEG frontal alpha asymmetry) in response to comfort food during the visual exploration and the visual and tactile exploration phases. Furthermore, for the same index, a higher tendency of approach has been found toward foreign food products in comparison with local food products during the visual and tactile exploration phase. Finally, the same comparison performed on a different index (EEG frontal theta) showed higher mental effort during the interaction with foreign products during the visual exploration and the visual and tactile exploration phases. Results from the present study could deepen the knowledge on the neurophysiological response to food products characterized by different nature in terms of hedonic value familiarity; moreover, they could have implications for food marketers and finally lead to further study on how people make food choices through the interactions with their commercial envelope

Comparison of smoothing filters in analysis of EEG data for the medical diagnostics purposes

This paper covers a brief review of both the advantages and disadvantages of the implementation of various smoothing filters in the analysis of electroencephalography (EEG) data for the purpose of potential medical diagnostics. The EEG data are very prone to the occurrence of various internal and external artifacts and signal distortions. In this paper, three types of smoothing filters were compared: smooth filter, median filter and Savitzky-Golay filter. The authors of this paper compared those filters and proved their usefulness, as they made the analyzed data more legible for diagnostic purposes. The obtained results were promising, however, the studies on finding perfect filtering methods are still in progress.Web of Science203art. no. 80

Abnormal hippocampal melatoninergic system: a potential link between absence epilepsy and depression-like behavior in WAG/Rij rats?

Absence epilepsy and depression are comorbid disorders, but the molecular link between the two disorders is unknown. Here, we examined the role of the melatoninergic system in the pathophysiology of spike and wave discharges (SWDs) and depression-like behaviour in the Wistar Albino Glaxo from Rijswijk (WAG/Rij) rat model of absence epilepsy. In WAG/Rij rats, SWD incidence was higher during the dark period of the light-dark cycle, in agreement with previous findings. However, neither pinealectomy nor melatonin administration had any effect on SWD incidence, suggesting that the melatoninergic system was not involved in the pathophysiology of absence-like seizures. Endogenous melatonin levels were lower in the hippocampus of WAG/Rij rats as compared to non-epileptic control rats, and this was associated with higher levels of melatonin receptors in the hippocampus, but not in the thalamus. In line with the reduced melatonin levels, cell density was lower in the hippocampus ofWAG/Rij rats and was further reduced by pinealectomy. As expected, WAG/Rij rats showed an increased depression-like behaviour in the sucrose preference and forced swim tests, as compared to non-epileptic controls. Pinealectomy abolished the difference between the two strains of rats by enhancing depression-like behaviour in non-epileptic controls. Melatonin replacement displayed a significant antidepressant-like effect in bothWAG/Rij and control rats. These findings suggest that a defect of hippocampal melatoninergic system may be one of the mechanisms underlying the depression-like phenotype inWAG/Rij rats and that activation of melatonin receptors might represent a valuable strategy in the treatment of depression associated with absence epilepsy

Convolutional Neural Networks for Epileptic Seizure Prediction

Epilepsy is the most common neurological disorder and an accurate forecast of seizures would help to overcome the patient's uncertainty and helplessness. In this contribution, we present and discuss a novel methodology for the classification of intracranial electroencephalography (iEEG) for seizure prediction. Contrary to previous approaches, we categorically refrain from an extraction of hand-crafted features and use a convolutional neural network (CNN) topology instead for both the determination of suitable signal characteristics and the binary classification of preictal and interictal segments. Three different models have been evaluated on public datasets with long-term recordings from four dogs and three patients. Overall, our findings demonstrate the general applicability. In this work we discuss the strengths and limitations of our methodology.Comment: accepted for MLESP 201

Distinct phase-amplitude couplings distinguish cognitive processes in human attention

Abstract Spatial attention is the cognitive function that coordinates the selection of visual stimuli with appropriate behavioral responses. Recent studies have reported that phase-amplitude coupling (PAC) of low and high frequencies covaries with spatial attention, but differ on the direction of covariation and the frequency ranges involved. We hypothesized that distinct phase-amplitude frequency pairs have differentiable contributions during tasks that manipulate spatial attention. We investigated this hypothesis with electrocorticography (ECoG) recordings from participants who engaged in a cued spatial attention task. To understand the contribution of PAC to spatial attention we classified cortical sites by their relationship to spatial variables or behavioral performance. Local neural activity in spatial sites was sensitive to spatial variables in the task, while local neural activity in behavioral sites correlated with reaction time. We found two PAC frequency clusters that covaried with different aspects of the task. During a period of cued attention, delta-phase/high-gamma (DH) PAC was sensitive to cue direction in spatial sites. In contrast, theta-alpha-phase/beta-low-gamma-amplitude (TABL) PAC robustly correlated with future reaction times in behavioral sites. Finally, we investigated the origins of TABL PAC and found it corresponded to behaviorally relevant, sharp waveforms, which were also coupled to a low frequency rhythm. We conclude that TABL and DH PAC correspond to distinct mechanisms during spatial attention tasks and that sharp waveforms are elements of a coupled dynamical process

Abnormal cognition, sleep, EEG and brain metabolism in a novel knock-in Alzheimer mouse, PLB1

Peer reviewedPublisher PD

To Design and Implement a Recommender System based on Brainwave: Applying Empirical Model Decomposition (EMD) and Neural Networks

Recommender systems collect and analyze users’ preferences to help users overcome information overload and make their decisions. In this research, we develop an online book recommender system based on users’ brainwave information. We collect users’ brainwave data by utilizing electroencephalography (EEG) device and apply empirical mode decomposition (EMD) to decompose the brainwave signals into intrinsic mode functions (IMFs). We propose a back-propagation neural networks (BPNN) model to portrait the user’s brainwave preference correlations based on IMFs of brainwave signals, thereby designing and developing the book recommender system. The experimental results show that the recommender system combined with the brainwave analysis can improve accuracy significantly. This research has highlighted a future direction for research and development on human-computer interaction (HCI) design and recommender system