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https://www.readbyqxmd.com/read/28300640/consistency-of-eeg-source-localization-and-connectivity-estimates
#1
Keyvan Mahjoory, Vadim V Nikulin, Loïc Botrel, Klaus Linkenkaer-Hansen, Marco M Fato, Stefan Haufe
As the EEG inverse problem does not have a unique solution, the sources reconstructed from EEG and their connectivity properties depend on forward and inverse modeling parameters such as the choice of an anatomical template and electrical model, prior assumptions on the sources, and further implementational details. In order to use source connectivity analysis as a reliable research tool, there is a need for stability across a wider range of standard estimation routines. Using resting state EEG recordings of N=65 participants acquired within two studies, we present the first comprehensive assessment of the consistency of EEG source localization and functional/effective connectivity metrics across two anatomical templates (ICBM152 and Colin27), three electrical models (BEM, FEM and spherical harmonics expansions), three inverse methods (WMNE, eLORETA and LCMV), and three software implementations (Brainstorm, Fieldtrip and our own toolbox)...
March 12, 2017: NeuroImage
https://www.readbyqxmd.com/read/28290245/differentiated-effective-connectivity-patterns-of-the-executive-control-network-in-progressive-mci-a-potential-biomarker-for-predicting-ad
#2
Suping Cai, Yanlin Peng, Tao Chong, Yun Zhang, Karen M von Deneen, Liyu Huang, Aibl Research Group
Mild cognitive impairment (MCI) is often a transitional state between normal aging and Alzheimer's disease (AD). When observed longitudinally, some MCI patients convert to AD, while a considerable portion either remain MCI or revert to a normal functioning state. This divergence has provided some enlightenment on a potential biomarker be represented in the resting state brain activities of MCI patients with different post-hoc labels. Recent studies have shown impaired executive functions, other than typically explicated memory impairment with AD/MCI patients...
March 9, 2017: Current Alzheimer Research
https://www.readbyqxmd.com/read/28290073/prefrontal-dysconnectivity-links-to-working-memory-deficit-in-first-episode-schizophrenia
#3
Xiaojing Fang, Yulin Wang, Luqi Cheng, Yuanchao Zhang, Yuan Zhou, Shihao Wu, Huan Huang, Jilin Zou, Cheng Chen, Jun Chen, Huiling Wang, Tianzi Jiang
Working memory (WM) deficit is a core feature of schizophrenia and is characterized by abnormal functional integration in the prefrontal cortex, including the dorsolateral prefrontal cortex (dLPFC), dorsal anterior cingulate cortex (dACC), and ventrolateral prefrontal cortex (vLPFC). However, the specific mechanism by which the abnormal neuronal circuits that involve these brain regions contribute to this deficit is still unclear. Therefore, this study focused on these regions and sought to answer which abnormal causal relationships in these regions can be linked to impaired WM in schizophrenia...
March 13, 2017: Brain Imaging and Behavior
https://www.readbyqxmd.com/read/28286918/altered-effective-connectivity-network-in-childhood-absence-epilepsy-a-multi-frequency-meg-study
#4
Caiyun Wu, Jing Xiang, Wenwen Jiang, Shuyang Huang, Yuan Gao, Lu Tang, Yuchen Zhou, Di Wu, Qiqi Chen, Zheng Hu, Xiaoshan Wang
Using multi-frequency magnetoencephalography (MEG) data, we investigated whether the effective connectivity (EC) network of patients with childhood absence epilepsy (CAE) is altered during the inter-ictal period in comparison with healthy controls. MEG data from 13 untreated CAE patients and 10 healthy controls were recorded. Correlation analysis and Granger causality analysis were used to construct an EC network at the source level in eight frequency bands. Alterations in the spatial pattern and topology of the network in CAE were investigated by comparing the patients with the controls...
March 12, 2017: Brain Topography
https://www.readbyqxmd.com/read/28274168/resting-state-effective-connectivity-allows-auditory-hallucination%C3%A2-discrimination
#5
Manuel Graña, Leire Ozaeta, Darya Chyzhyk
Hallucinations are elusive phenomena that have been associated with psychotic behavior, but that have a high prevalence in healthy population. Some generative mechanisms of Auditory Hallucinations (AH) have been proposed in the literature, but so far empirical evidence is scarce. The most widely accepted generative mechanism hypothesis nowadays consists in the faulty workings of a network of brain areas including the emotional control, the audio and language processing, and the inhibition and self-attribution of the signals in the auditive cortex...
February 1, 2017: International Journal of Neural Systems
https://www.readbyqxmd.com/read/28269499/a-time-domain-frequency-selective-multivariate-granger-causality-approach
#6
Lutz Leistritz, Herbert Witte
The investigation of effective connectivity is one of the major topics in computational neuroscience to understand the interaction between spatially distributed neuronal units of the brain. Thus, a wide variety of methods has been developed during the last decades to investigate functional and effective connectivity in multivariate systems. Their spectrum ranges from model-based to model-free approaches with a clear separation into time and frequency range methods. We present in this simulation study a novel time domain approach based on Granger's principle of predictability, which allows frequency-selective considerations of directed interactions...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28268904/quantifying-connectivity-in-a-physiology-based-model-using-adaptive-dynamic-causal-modelling
#7
W Xiang, C Yang, A Karfoul, R Le Bouquin Jeannes
This paper proposes an Adaptive Dynamic Causal Modelling based approach to detect and quantify effective connectivity in human brain structures injured by epileptic activities. The identification of the parameters in the physiology based model subtended the Electroencephalographic observations is performed by improving the optimization step in the Expectation Maximization algorithm. Considering unidirectional flow propagation, we show the efficiency of our proposed approach compared to the conventional technique...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28268637/effective-connectivity-matrix-for-neural-ensembles
#8
Qi She, Winnie K Y So, Rosa H M Chan
In this paper, we present an efficient framework to study the directional interactions within the multiple-input multiple-output (MIMO) biological neural network from spiketrain data. We used an efficient generalized linear model (GLM) with Laguerre basis functions to model a MIMO neural system, and developed an Effective Connectivity Matrix (ECM) to visualize excitatory and inhibitory connections within the neural network. A new causality representation was developed based on system dynamics. Statistical test was applied to identify the significance of the measured causality...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28268518/tle-lateralization-using-whole-brain-structural-connectivity
#9
Esmaeil Davoodi-Bojd, Kost V Elisevich, Jason Schwalb, Ellen Air, Hamid Soltanian-Zadeh
A prerequisite of temporal lobe epilepsy (TLE) surgery is to lateralize the disease. Recent studies have shown the capability of diffusion weighted MRI (DWMRI) in lateralizing TLE patients. This has been achieved by analyzing diffusion parameters of specific white matter tracts or regions known to be involved in the disease; however, other brain regions and connections have not been investigated for TLE lateralization. Whole brain structural connectivity using DWMRI provides a wealth of information regarding the structural connections in the brain...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28268430/analysis-of-magnetoencephalography-signals-from-alzheimer-s-disease-patients-using-granger-causality
#10
Celia Juan-Cruz, Carlos Gomez, Jesus Poza, Alberto Fernandez, Roberto Hornero
The aim of this study was to analyze resting-state magnetoencephalography (MEG) activity in Alzheimer's disease (AD) by means of Granger Causality (GC), an effective connectivity measure that provides an estimation of the information flow between brain regions. For this task, five minutes of MEG recordings were acquired with a 148-channel whole-head magnetometer from 36 AD patients and 26 healthy controls. Abnormalities in AD connectivity were found in the five typical frequency bands: delta (δ, 1-4 Hz), theta (θ, 4-8 Hz), alpha (α, 8-13 Hz), beta (β, 13-30 Hz), and gamma (γ, 30-65 Hz)...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28268428/estimation-of-resting-state-effective-connectivity-in-epilepsy-using-direct-directed-transfer-function
#11
Biswajit Maharathi, Jeffrey A Loeb, James Patton
There has been an increasing demand among neuroscientists to understand the complex network of functionally connected neural assemblies in the human brain. For this purpose, computational EEG research is widely used by researchers due to its remarkable advantage in providing high temporal resolution, and ease of analysis across different frequency bands. Here we analyzed Electrocorticographic (ECoG) signals of electrodes placed on frontal-parietal neocortex brain region of 8 pediatric epileptic patients. In order to evaluate the directed causal relationship among different brain regions, we employed a Granger causality based multivariate connectivity estimator named direct Directed Transfer Function (dDTF) to identify signal propagations among the selected set of electrode in the frequency range 1-50Hz...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28268283/measuring-the-agreement-between-brain-connectivity-networks
#12
J Toppi, N Sciaraffa, Y Antonacci, A Anzolin, S Caschera, M Petti, D Mattia, L Astolfi
Investigating the level of similarity between two brain networks, resulting from measures of effective connectivity in the brain, can be of interest from many respects. In this study, we propose and test the idea to borrow measures of association used in machine learning to provide a measure of similarity between the structure of (un-weighted) brain connectivity networks. The measures here explored are the accuracy, Cohen's Kappa (K) and Area Under Curve (AUC). We implemented two simulation studies, reproducing two contexts of application that can be particularly interesting for practical applications, namely: i) in methodological studies, performed on surrogate data, aiming at comparing the estimated network with the corresponding ground-truth network; ii) in applications to real data, when it is necessary to compare the structure of a network obtained in a specific subject with a reference (e...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28259857/auditory-training-changes-temporal-lobe-connectivity-in-wernicke-s-aphasia-a-randomised-trial
#13
Zoe Vj Woodhead, Jennifer Crinion, Sundeep Teki, Will Penny, Cathy J Price, Alexander P Leff
INTRODUCTION: Aphasia is one of the most disabling sequelae after stroke, occurring in 25%-40% of stroke survivors. However, there remains a lack of good evidence for the efficacy or mechanisms of speech comprehension rehabilitation. TRIAL DESIGN: This within-subjects trial tested two concurrent interventions in 20 patients with chronic aphasia with speech comprehension impairment following left hemisphere stroke: (1) phonological training using 'Earobics' software and (2) a pharmacological intervention using donepezil, an acetylcholinesterase inhibitor...
March 4, 2017: Journal of Neurology, Neurosurgery, and Psychiatry
https://www.readbyqxmd.com/read/28259780/regression-dcm-for-fmri
#14
Stefan Frässle, Ekaterina I Lomakina, Adeel Razi, Karl J Friston, Joachim M Buhmann, Klaas E Stephan
The development of large-scale network models that infer the effective (directed) connectivity among neuronal populations from neuroimaging data represents a key challenge for computational neuroscience. Dynamic causal models (DCMs) of neuroimaging and electrophysiological data are frequently used for inferring effective connectivity but are presently restricted to small graphs (typically up to 10 regions) in order to keep model inversion computationally feasible. Here, we present a novel variant of DCM for functional magnetic resonance imaging (fMRI) data that is suited to assess effective connectivity in large (whole-brain) networks...
March 1, 2017: NeuroImage
https://www.readbyqxmd.com/read/28256710/levodopa-response-differs-in-parkinson-s-motor-subtypes-a-task-based-effective-connectivity-study
#15
Brianne Mohl, Brian D Berman, Erika Shelton, Jody Tanabe
Parkinson's disease is a circuit-level disorder with clinically-determined motor subtypes. Despite evidence suggesting each subtype may have different pathophysiology, few neuroimaging studies have examined levodopa-induced differences in neural activation between tremor dominant and posterior instability/gait difficulty subtype patients during a motor task. The goal of this fMRI study was to examine task-induced activation and connectivity in the cortico-striatal-thalamo-cortical motor circuit in healthy controls, tremor dominant patients, and postural instability/gait difficulty patients before and after levodopa administration...
March 3, 2017: Journal of Comparative Neurology
https://www.readbyqxmd.com/read/28246760/-side-effects-of-pain-therapy-sufficient-analgesia-without-unnecessary-complications
#16
F Greul, A Zimmer, W Meißner
Interventions of acute and chronic pain treatment are associated with risks. Therefore, it is important to know about treatment side effects in order to avoid unnecessary complications and therapy interruption. This knowledge, however, is not to prevent/abandon this treatment altogether. Rather, it is intended to use pain treatment interventions rationally. The following article is to deepen the knowledge of unintended effects of analgetic treatments. Moreover, it will help find an optimal pain therapy in terms of efficacy and tolerable risks as well as limitations...
February 28, 2017: Der Urologe. Ausg. A
https://www.readbyqxmd.com/read/28246645/deep-reefs-are-not-universal-refuges-reseeding-potential-varies-among-coral-species
#17
Pim Bongaerts, Cynthia Riginos, Ramona Brunner, Norbert Englebert, Struan R Smith, Ove Hoegh-Guldberg
Deep coral reefs (that is, mesophotic coral ecosystems) can act as refuges against major disturbances affecting shallow reefs. It has been proposed that, through the provision of coral propagules, such deep refuges may aid in shallow reef recovery; however, this "reseeding" hypothesis remains largely untested. We conducted a genome-wide assessment of two scleractinian coral species with contrasting reproductive modes, to assess the potential for connectivity between mesophotic (40 m) and shallow (12 m) depths on an isolated reef system in the Western Atlantic (Bermuda)...
February 2017: Science Advances
https://www.readbyqxmd.com/read/28227751/a-time-domain-frequency-selective-multivariate-granger-causality-approach
#18
Lutz Leistritz, Herbert Witte, Lutz Leistritz, Herbert Witte, Lutz Leistritz, Herbert Witte
The investigation of effective connectivity is one of the major topics in computational neuroscience to understand the interaction between spatially distributed neuronal units of the brain. Thus, a wide variety of methods has been developed during the last decades to investigate functional and effective connectivity in multivariate systems. Their spectrum ranges from model-based to model-free approaches with a clear separation into time and frequency range methods. We present in this simulation study a novel time domain approach based on Granger's principle of predictability, which allows frequency-selective considerations of directed interactions...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227105/quantifying-connectivity-in-a-physiology-based-model-using-adaptive-dynamic-causal-modelling
#19
W Xiang, C Yang, A Karfoul, R Le Bouquin Jeannes, W Xiang, C Yang, A Karfoul, R Le Bouquin Jeannes, A Karfoul, W Xiang, C Yang
This paper proposes an Adaptive Dynamic Causal Modelling based approach to detect and quantify effective connectivity in human brain structures injured by epileptic activities. The identification of the parameters in the physiology based model subtended the Electroencephalographic observations is performed by improving the optimization step in the Expectation Maximization algorithm. Considering unidirectional flow propagation, we show the efficiency of our proposed approach compared to the conventional technique...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226817/effective-connectivity-matrix-for-neural-ensembles
#20
Qi She, Winnie K Y So, Rosa H M Chan, Qi She, Winnie K Y So, Rosa H M Chan, Rosa H M Chan, Qi She, Winnie K Y So
In this paper, we present an efficient framework to study the directional interactions within the multiple-input multiple-output (MIMO) biological neural network from spiketrain data. We used an efficient generalized linear model (GLM) with Laguerre basis functions to model a MIMO neural system, and developed an Effective Connectivity Matrix (ECM) to visualize excitatory and inhibitory connections within the neural network. A new causality representation was developed based on system dynamics. Statistical test was applied to identify the significance of the measured causality...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
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