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Eeg Brain Connectivity

Rebecca S Wilson, Stephen D Mayhew, David T Rollings, Aimee Goldstone, Joanne R Hale, Andrew P Bagshaw
INTRODUCTION: Prior sleep behavior has been shown to correlate with waking resting-state functional connectivity (FC) in the default mode network (DMN). However, the impact of sleep history on FC during sleep has not been investigated. The aim of this study was to establish whether there is an association between intersubject variability in habitual sleep behaviors and the strength of FC within the regions of the DMN during non-rapid eye movement (NREM) sleep. METHODS: Wrist actigraphy and sleep questionnaires were used as objective and subjective measures of habitual sleep behavior, and EEG-functional MRI during NREM sleep was used to quantify sleep...
December 4, 2018: Brain and Behavior
M Rubega, M Carboni, M Seeber, D Pascucci, S Tourbier, G Toscano, P Van Mierlo, P Hagmann, G Plomp, S Vulliemoz, C M Michel
In the last decade, the use of high-density electrode arrays for EEG recordings combined with the improvements of source reconstruction algorithms has allowed the investigation of brain networks dynamics at a sub-second scale. One powerful tool for investigating large-scale functional brain networks with EEG is time-varying effective connectivity applied to source signals obtained from electric source imaging. Due to computational and interpretation limitations, the brain is usually parcelled into a limited number of regions of interests (ROIs) before computing EEG connectivity...
December 3, 2018: Brain Topography
Norman Scheel, Eric Franke, Thomas F Münte, Amir Madany Mamlouk
Spontaneous fluctuations of resting-state functional connectivity have been studied in many ways, but grasping the complexity of brain activity has been difficult. Dimensional complexity measures, which are based on Eigenvalue (EV) spectrum analyses (e.g., Ω entropy) have been successfully applied to EEG data, but have not been fully evaluated on functional MRI recordings, because only through the recent introduction of fast multiband fMRI sequences, feasable temporal resolutions are reached. Combining the Eigenspectrum normalization of Ω entropy and the scalable architecture of the so called Multivariate Principal Subspace Entropy (MPSE) leads to a new complexity measure, namely normalized MPSE (nMPSE)...
2018: Frontiers in Human Neuroscience
Alessandro Principe, Miguel Ley, Gerardo Conesa, Rodrigo Rocamora
Several models have been proposed to explain brain regional and interregional communication, the majority of them using methods that tap the frequency domain, like spectral coherence. Considering brain interareal communication as binary interactions, we describe a novel method devised to predict dynamics and thus highlight abrupt changes marked by unpredictability. Based on a variable-order Markov model algorithm developed in-house for data compression, the prediction error connectivity (PEC) estimates network transitions by calculating error matrices (EMs)...
November 30, 2018: NeuroImage
Loukianos Spyrou, Mario Parra, Javier Escudero
OBJECTIVE: The coupling between neuronal populations and its magnitude have been shown to be informative for various clinical applications. One method to estimate functional brain connectivity is with electroencephalography (EEG) from which the cross-spectrum between different sensor locations is derived. We wish to test the efficacy of tensor factorisation in the estimation of brain connectivity. METHODS: An EEG model in the complex domain is derived that shows the suitability of the PARAFAC2 model...
November 28, 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Iulia M Comsa, Tristan A Bekinschtein, Srivas Chennu
As we fall sleep, our brain traverses a series of gradual changes at physiological, behavioural and cognitive levels, which are not yet fully understood. The loss of responsiveness is a critical event in the transition from wakefulness to sleep. Here we seek to understand the electrophysiological signatures that reflect the loss of capacity to respond to external stimuli during drowsiness using two complementary methods: spectral connectivity and EEG microstates. Furthermore, we integrate these two methods for the first time by investigating the connectivity patterns captured during individual microstate lifetimes...
November 29, 2018: Brain Topography
Upasana Talukdar, Shyamanta M Hazarika, John Q Gan
Even though it has long been felt that psychological state influences the performance of brain-computer interfaces (BCI), formal analysis to support this hypothesis has been scant. This study investigates the inter-relationship between motor imagery (MI) and mental fatigue using EEG: a. whether prolonged sequences of MI produce mental fatigue and b. whether mental fatigue affects MI EEG class separability. Eleven participants participated in the MI experiment, 5 of which quit in the middle because of experiencing high fatigue...
November 29, 2018: Journal of Computational Neuroscience
Jichi Chen, Hong Wang, Chengcheng Hua, Qiaoxiu Wang, Chong Liu
A large number of traffic accidents due to driver drowsiness have been under more attention of many countries. The organization of the functional brain network is associated with drowsiness, but little is known about the brain network topology that is modulated by drowsiness. To clarify this problem, in this study, we introduce a novel approach to detect driver drowsiness. Electroencephalogram (EEG) signals have been measured during a simulated driving task, in which participants are recruited to undergo both alert and drowsy states...
December 2018: Cognitive Neurodynamics
André Fonseca, Scott Kerick, Jung-Tai King, Chin-Teng Lin, Tzyy-Ping Jung
The analysis of neurophysiological changes during driving can clarify the mechanisms of fatigue, considered an important cause of vehicle accidents. The fluctuations in alertness can be investigated as changes in the brain network connections, reflected in the direction and magnitude of the information transferred. Those changes are induced not only by the time on task but also by the quality of sleep. In an unprecedented 5-month longitudinal study, daily sampling actigraphy and EEG data were collected during a sustained-attention driving task within a near-real-world environment...
2018: Frontiers in Human Neuroscience
Nadia Mammone, Simona De Salvo, Cosimo Ieracitano, Silvia Marino, Emanuele Cartella, Alessia Bramanti, Roberto Giorgianni, Francesco C Morabito
Stroke is a critical event that causes the disruption of neural connections. There is increasing evidence that the brain tries to reorganize itself and to replace the damaged circuits, by establishing compensatory pathways. Intra- and extra-cellular currents are involved in the communication between neurons and the macroscopic effects of such currents can be detected at the scalp through electroencephalographic (EEG) sensors. EEG can be used to study the lesions in the brain indirectly, by studying their effects on the brain electrical activity...
November 23, 2018: Sensors
Pierpaolo Busan, Giovanni Del Ben, Lucia Roberta Russo, Simona Bernardini, Giulia Natarelli, Giorgio Arcara, Paolo Manganotti, Piero Paolo Battaglini
OBJECTIVE: Brain dynamics in developmental stuttering (DS) are not well understood. The supplementary motor area (SMA) plays a crucial role, since it communicates with regions related to planning/execution of movements, and with sub-cortical regions involved in paced/voluntary acts (such as speech). We used TMS combined with EEG to shed light on connections in DS, stimulating the SMA. METHODS: TMS/EEG was recorded in adult DS and fluent speakers (FS), stimulating the SMA during rest...
November 10, 2018: Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology
Evan Houldin, Zhuo Fang, Laura B Ray, Adrian M Owen, Stuart M Fogel
Resting state network (RSN) functional connectivity (FC) has been investigated under a wealth of different healthy and compromised conditions. However such investigations are often dependent on the defined spatial boundaries and nodes of so-called canonical RSNs, themselves the product of extensive deliberations over distinctions between functional magnetic resonance imaging (fMRI) noise and neural signal, specifically in the context of the healthy waking state. However, a similar unbiased cataloguing of noise and networks remains to be done in other states, particularly sleep, a healthy alternate mode of the brain that supports distinct operations from wakefulness, such as dreaming and memory consolidation...
November 26, 2018: Sleep
Ramy Hussein, Hamid Palangi, Rabab K Ward, Z Jane Wang
OBJECTIVE: Automatic detection of epileptic seizures based on deep learning methods received much attention last year. However, the potential of deep neural networks in seizure detection has not been fully exploited in terms of the optimal design of the model architecture and the detection power of the time-series brain data. In this work, a deep neural network architecture is introduced to learn the temporal dependencies in Electroencephalogram (EEG) data for robust detection of epileptic seizures...
November 15, 2018: Clinical Neurophysiology: Official Journal of the International Federation of Clinical Neurophysiology
Christina Pressl, Philip Brandner, Stefan Schaffelhofer, Karen Blackmon, Patricia Dugan, Manisha Holmes, Thomas Thesen, Ruben Kuzniecky, Orrin Devinsky, Winrich A Freiwald
There are no functional imaging based biomarkers for pharmacological treatment response in temporal lobe epilepsy (TLE). In this study, we investigated whether there is an association between resting state functional brain connectivity (RsFC) and seizure control in TLE. We screened a large database containing resting state functional magnetic resonance imaging (Rs-fMRI) data from 286 epilepsy patients. Patient medical records were screened for seizure characterization, EEG reports for lateralization and location of seizure foci to establish uniformity of seizure localization within patient groups...
November 17, 2018: Epilepsy Research
Saúl J Ruiz-Gómez, Carlos Gómez, Jesús Poza, Mario Martínez-Zarzuela, Miguel A Tola-Arribas, Mónica Cano, Roberto Hornero
Alzheimer's Disease (AD) represents the most prevalent form of dementia and is considered a major health problem due to its high prevalence and its economic costs. An accurate characterization of the underlying neural dynamics in AD is crucial in order to adopt effective treatments. In this regard, mild cognitive impairment (MCI) is an important clinical entity, since it is a risk-state for developing dementia. In the present study, coupling patterns of 111 resting-state electroencephalography (EEG) recordings were analyzed...
2018: Frontiers in Neuroinformatics
Martin Spüler, Eduardo López-Larraz, Ander Ramos-Murguialday
BACKGROUND: Brain machine interface (BMI) technology has demonstrated its efficacy for rehabilitation of paralyzed chronic stroke patients. The critical component in BMI-training consists of the associative connection (contingency) between the intention and the feedback provided. However, the relationship between the BMI design and its performance in stroke patients is still an open question. METHODS: In this study we compare different methodologies to design a BMI for rehabilitation and evaluate their effects on movement intention decoding performance...
November 20, 2018: Journal of Neuroengineering and Rehabilitation
Ting Li, Tao Xue, Baozeng Wang, Jinhua Zhang
Research about decoding neurophysiological signals mainly aims to elucidate the details of human motion control from the perspective of neural activity. We performed brain connectivity analysis with EEG to propose a brain functional network (BFN) and used a feature extraction algorithm for decoding the voluntary hand movement of a subject. By analyzing the characteristic parameters obtained from the BFN, we extracted the most important electrode nodes and frequencies for identifying the direction of movement of a hand...
2018: Frontiers in Human Neuroscience
Wiebke Theilmann, Okko Alitalo, Iris Yorke, Tomi Rantamäki
OBJECTIVES: Deep burst-suppressing isoflurane anesthesia regulates signaling pathways connected with antidepressant responses in the rodent brain: activation of TrkB neurotrophin receptor and inhibition of GSK3β kinase (glycogen synthase kinase 3β). The main objective of this study was to investigate whether EEG (electroencephalogram) burst suppression correlates with these intriguing molecular alterations induced by isoflurane. METHODS: Adult male mice pre-implanted with EEG recording electrodes were subjected to varying concentrations of isoflurane (1...
November 15, 2018: Neuroscience Letters
Anusha Mohan, Christian Davidson, Dirk De Ridder, Sven Vanneste
Tinnitus, the perception of a phantom sound, is accompanied by loudness and distress components. Distress however accompanies not just tinnitus, but several disorders. Several functional connectivity studies show that distress is characterized by disconnectivity of fronto-limbic circuits or hyperconnectivity of default mode/salience networks. The drawback, however, is that it considers only the magnitude of connectivity, not the direction. Thus, the current study aims to identify the core network of the domain-general distress component in tinnitus by comparing whole brain directed functional networks calculated from 5 min of resting state EEG data collected from 310 tinnitus patients and 256 non-tinnitus controls...
November 15, 2018: Brain Imaging and Behavior
Dheeraj Rathee, Hubert Cecotti, Girijesh Prasad
Recent progress in the number of studies involving brain connectivity analysis of motor imagery (MI) tasks for brain-computer interface (BCI) systems has warranted the need for pre-processing methods. The objective of this study is to evaluate the impact of current source density (CSD) estimation from raw electroencephalogram (EEG) signals on the classification performance of scalp level brain connectivity feature based MI-BCI. In particular, time-domain partial Granger causality (PGC) method was implemented on the raw EEG signals and CSD signals of a publicly available dataset for the estimation of brain connectivity features...
July 2018: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
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