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PubMed Journals Articles About "Podcast China Growing Science Network Talking Brain Signals" RSS

18:43 EDT 17th September 2019 | BioPortfolio

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Showing "Podcast China growing science network talking brain signals" PubMed Articles 1–25 of 29,000+

Adaptive neural network classifier for decoding MEG signals.

We introduce two Convolutional Neural Network (CNN) classifiers optimized for inferring brain states from magnetoencephalographic (MEG) measurements. Network design follows a generative model of the electromagnetic (EEG and MEG) brain signals allowing explorative analysis of neural sources informing classification. The proposed networks outperform traditional classifiers as well as more complex neural networks when decoding evoked and induced responses to different stimuli across subjects. Importantly, thes...


Graph-to-signal transformation based classification of functional connectivity brain networks.

Complex network theory has been successful at unveiling the topology of the brain and showing alterations to the network structure due to brain disease, cognitive function and behavior. Functional connectivity networks (FCNs) represent different brain regions as the nodes and the connectivity between them as the edges of a graph. Graph theoretic measures provide a way to extract features from these networks enabling subsequent characterization and discrimination of networks across conditions. However, these...

Functional brain network topology in parents who lost their only child in China: Post-traumatic stress disorder and sex effects.

Post-traumatic stress disorder (PTSD) is associated with disruption of the brain network topology; however, little is known about the topological changes and sex effects in PTSD patients following a unique trauma, the loss of an only child, in China.


A graph representation of functional diversity of brain regions.

Modern network science techniques are popularly used to characterize the functional organization of the brain. A major challenge in network neuroscience is to understand how functional characteristics and topological architecture are related in the brain. Previous task-based functional neuroimaging studies have uncovered a core set of brain regions (e.g., frontal and parietal) supporting diverse cognitive tasks. However, the graph representation of functional diversity of brain regions remains to be underst...

Sense of Effort and Articulatory Contact Pressure Associated with Talking by Individuals Using Tracheoesophageal Speech.

The aims of this study were (1) to determine locations of increased effort in the body when talking using tracheoesophageal speech (TES); (2) to compare talking effort for participants using TES and those using laryngeal speech; (3) to compare tongue-palate contact pressure during talking for TES and laryngeal speech; and (4) to assess the relationship between talking effort and articulatory contact pressure (ACP).

Study on the spatial correlation structure and synergistic governance development of the haze emission in China.

To clarify the current situation of haze emission and governance in China, the study analyzed the characteristics of spatial correlation structure and synergistic governance development of the haze emission of 31 provinces in China, based on social network analysis and distance synergistic model. The results indicated that the spatial correlation of inter-provincial haze emission in China presented a typical "central-marginal" network structure. The provinces in the network center were mostly located in the...

Cysteine-rich peptides: signals for pollen tube guidance, species isolation and beyond.

Network analysis of whole-brain fMRI dynamics: A new framework based on dynamic communicability.

Neuroimaging techniques such as MRI have been widely used to explore the associations between brain areas. Structural connectivity (SC) captures the anatomical pathways across the brain and functional connectivity (FC) measures the correlation between the activity of brain regions. These connectivity measures have been much studied using network theory in order to uncover the distributed organization of brain structures, in particular FC for task-specific brain communication. However, the application of net...

The Orbitofrontal Cortex Processes Neurofeedback Failure Signals.

Receiving feedback from neural activity, dubbed neurofeedback, can reinforce brain self-regulation. In a real-time functional magnetic resonance imaging (fMRI) experiment, healthy participants received amygdala neurofeedback via a visual brain computer interface. The brain response to signals of reward and failure was modeled. In contrast to previous analyses, we take into account feedback that immediately preceded these signals. That means we tested whether responses were modulated while participants obser...

Normalization enhances brain network features that predict individual intelligence in children with epilepsy.

Architecture of the cerebral network has been shown to associate with IQ in children with epilepsy. However, subject-level prediction on this basis, a crucial step toward harnessing network analyses for the benefit of children with epilepsy, has yet to be achieved. We compared two network normalization strategies in terms of their ability to optimize subject-level inferences on the relationship between brain network architecture and brain function.

A hierarchical sequential neural network with feature fusion for sleep staging based on EOG and RR signals.

Currently, the automatic sleep staging methods mainly face two problems: the first problem is that although the algorithms which use electroencephalogram (EEG) signals have nice performance, acquiring EEG signals is complicated and uncomfortable; the second one is that if the methods utilize physiological signals collected by user-friendly devices, such as cardiorespiratory signals, whose accuracies are hard to be accepted by clinicians, although the employed signals are easy and comfortable to acquire.

The Hidden Control Architecture of Complex Brain Networks.

The brain controls various cognitive functions in a robust and efficient way. What is the control architecture of brain networks that enables such robust and optimal control? Is this brain control architecture distinct from that of other complex networks? Here, we developed a framework to delineate a control architecture of a complex network that is compatible with the behavior of the network and applied the framework to structural brain networks and other complex networks. As a result, we revealed that the...

Calculation of the contribution rate of China's hydraulic science and technology based on a feedforward neural network.

Quantitative analysis of the contribution rate of China's hydraulic science and technology and analysis of the underlying reasons behind changes provide an important foundation upon which the government can formulate water policies. This paper abandons the assumption of a scale economy and separates the changes of benefits brought about by the scale from scientific and technological progress, thus changing the C-D production function from linear to nonlinear. Based on a feedforward neural network, it calcul...

Inter-Network High-Order Functional Connectivity (IN-HOFC) and its Alteration in Patients with Mild Cognitive Impairment.

Little is known about the high-order interactions among brain regions measured by the similarity of higher-order features (other than the raw blood-oxygen-level-dependent signals) which can characterize higher-level brain functional connectivity (FC). Previously, we proposed FC topographical profile-based high-order FC (HOFC) and found that this metric could provide supplementary information to traditional FC for early Alzheimer's disease (AD) detection. However, whether such findings apply to network-level...

The Scientific Case for Brain Simulations.

A key element of the European Union's Human Brain Project (HBP) and other large-scale brain research projects is the simulation of large-scale model networks of neurons. Here, we argue why such simulations will likely be indispensable for bridging the scales between the neuron and system levels in the brain, and why a set of brain simulators based on neuron models at different levels of biological detail should therefore be developed. To allow for systematic refinement of candidate network models by compari...

Network stiffness: A new topological property in complex networks.

Aiming at serving the interdisciplinary demand in network science, this paper introduces a new concept for complex networks, named network stiffness, which is extracted from structural engineering by assuming that a complex network behaves similarly with a structured framework. This analogy allows interpreting that a complex network can resist against any cause attempting to induce deformation changes to the network's structure, regardless of whether the network is material or not. Within this framework, th...

Emotion Recognition Using Electrodermal Activity Signals and Multiscale Deep Convolution Neural Network.

Automated emotion recognition plays a vital role in problem solving, decision making and social activities of human life. An emotion is a set of reactions and experience to a given conditions, which are modeled as a linear combination of arousal and valence dimensions. Emotional pattern recognition based on physiological signals is a relatively new and fast growing area of research. Several physiological signals such as ECG, EEG, and EMG have been used for emotion recognition. Analysis of Electrodermal Acti...

Whole-brain functional network disruption in chronic pain with disc herniation.

Brain functional network properties are globally disrupted in multiple musculoskeletal chronic pain conditions. Back pain with lumbar disc herniation is highly prevalent and a major route for progression to chronic back pain. However, brain functional network properties remain unknown in such patients. Here, we examined resting-state fMRI-based functional connectivity networks in chronic back pain patients with clear evidence for lumbar disc herniation (LDH-CP, n = 146), in comparison to healthy controls (H...

Recovering Brain Dynamics During Concurrent tACS-M/EEG: An Overview of Analysis Approaches and Their Methodological and Interpretational Pitfalls.

Transcranial alternating current stimulation (tACS) is increasingly used as a tool to non-invasively modulate brain oscillations in a frequency specific manner. A growing body of neuroscience research utilizes tACS to probe causal relationships between neuronal oscillations and cognitive processes or explore its capability of restoring dysfunctional brain oscillations implicated in various neurological and psychiatric disease. However, the underlying mechanisms of action are yet poorly understood. Due to a ...

Increased segregation of functional networks in developing brains.

A growing literature conceptualises typical brain development from a network perspective. However, largely due to technical and methodological challenges inherent in paediatric functional neuroimaging, there remains an important gap in our knowledge regarding the typical development of functional brain networks in "preschool" childhood (i.e., children younger than 6 years of age). In this study, we recorded brain oscillatory activity using age-appropriate magnetoencephalography in 24 children, including 14 ...

Portable brain-computer interface based on novel convolutional neural network.

Electroencephalography (EEG) is a powerful, noninvasive tool that provides a high temporal resolution to directly reflect brain activities. Conventional electrodes require skin preparation and the use of conductive gels, while subjects must wear uncomfortable EEG hats. These procedures usually create a challenge for subjects. In the present study, we propose a portable EEG signal acquisition system. This study consists of two main parts: 1) A novel, portable dry-electrode and wireless brain-computer interfa...

Hemicraniectomy in traumatic brain injury: a noninvasive platform to investigate high gamma activity for brain machine interfaces.

Brain-machine interfaces (BMIs) translate brain signals into control signals for an external device, such as a computer cursor or robotic limb. These signals can be obtained either noninvasively or invasively. Invasive recordings, using electrocorticography (ECoG) or intracortical microelectrodes, provide higher bandwidth, more informative signals. Rehabilitative BMIs, which aim to drive plasticity in the brain to enhance recovery after brain injury, have almost exclusively used non-invasive recordings, suc...

Characterization of pallidocortical motor network in Parkinson disease through complex network analysis.

Deep brain stimulation (DBS) has demonstrated to be an advanced therapy for selected patients with Parkinson disease (PD) from numerous clinical trials, while its maximal therapeutic effect is capped by the inadequate understanding of precise neuronal mechanisms underlying PD. Recordings from multi-channel electrodes placed in subcortical and cortical regions of the basal ganglia-thalamocortical (BGTC) motor network during DBS surgical procedures can provide rich physiologic information from accessible netw...

Epileptic Focus Localization via Brain Network Analysis on Riemannian Manifolds.

Brain network connectivity analysis plays an important role in computer-aided automatic localization of seizure onset zone (SOZ) from Intracranial Electroencephalography (iEEG). However, how to accurately compute brain network dynamics is still not well addressed. This work aims to develop an effective measure to find out the dynamics for SOZ localization.

RE: Warnings and caveats in brain controllability.

The use of network control theory to analyze the organization of white matter fibers in the human brain has the potential to enable mechanistic theories of cognition, and to inform the development of novel diagnostics and treatments for neurological disease and psychiatric disorders (Gu et al., 2015). The recent article (Tu et al., 2018) aims to challenge several of the contributions of (Gu et al., 2015), and particularly the conclusions that brain networks are theoretically controllable from single regions...


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