scholarly journals Simultaneous EEG and fMRI: towards the characterization of structure and dynamics of brain networks

2013 ◽  
Vol 15 (3) ◽  
pp. 381-386 ◽  
Keyword(s):  
Spatial Resolution ◽  
Brain Function ◽  
Methodological Approach ◽  
Brain Networks ◽  
Indirect Measurement ◽  
Synaptic Activity ◽  
High Temporal Resolution ◽  
Neural Mass ◽  
The Brain

Progress in the understanding of normal and disturbed brain function is critically dependent on the methodological approach that is applied. Both electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) are extremely efficient methods for the assessment of human brain function. The specific appeal of the combination is related to the fact that both methods are complementary in terms of basic aspects: EEG is a direct measurement of neural mass activity and provides high temporal resolution. FMRI is an indirect measurement of neural activity and based on hemodynamic changes, and offers high spatial resolution. Both methods are very sensitive to changes of synaptic activity, suggesting that with simultaneous EEG and fMRI the same neural events can be characterized with both high temporal and spatial resolution. Since neural oscillations that can be assessed with EEG are a key mechanism for multi-site communication in the brain, EEG-fMRI can offer new insights into the connectivity mechanisms of brain networks.

Finding Community of Brain Networks Based on Neighbor Index and DPSO with Dynamic Crossover

2020 ◽  
Vol 15 (4) ◽  
pp. 287-299
Author(s):  
Jie Zhang ◽  
Junhong Feng ◽  
Fang-Xiang Wu
Keyword(s):  
Brain Function ◽  
Brain Networks ◽  
Discrete Particle Swarm Optimization ◽  
Mri Brain ◽  
Mutation Operators ◽  
Neural Unit ◽  
Crossover And Mutation ◽  
The Brain ◽  
Index Table ◽  
Dynamic Crossover

Background: : The brain networks can provide us an effective way to analyze brain function and brain disease detection. In brain networks, there exist some import neural unit modules, which contain meaningful biological insights. Objective:: Therefore, we need to find the optimal neural unit modules effectively and efficiently. Method:: In this study, we propose a novel algorithm to find community modules of brain networks by combining Neighbor Index and Discrete Particle Swarm Optimization (DPSO) with dynamic crossover, abbreviated as NIDPSO. The differences between this study and the existing ones lie in that NIDPSO is proposed first to find community modules of brain networks, and dose not need to predefine and preestimate the number of communities in advance. Results: : We generate a neighbor index table to alleviate and eliminate ineffective searches and design a novel coding by which we can determine the community without computing the distances amongst vertices in brain networks. Furthermore, dynamic crossover and mutation operators are designed to modify NIDPSO so as to alleviate the drawback of premature convergence in DPSO. Conclusion: The numerical results performing on several resting-state functional MRI brain networks demonstrate that NIDPSO outperforms or is comparable with other competing methods in terms of modularity, coverage and conductance metrics.

Implantable neural interfaces

2018 ◽  
Author(s):  
Stefano Vassanelli
Keyword(s):  
Brain Function ◽  
Large Scale ◽  
Ionic Channels ◽  
Patch Clamp Technique ◽  
Advantages And Disadvantages ◽  
Optogenetic Stimulation ◽  
Physical Interfaces ◽  
The Brain

Establishing direct communication with the brain through physical interfaces is a fundamental strategy to investigate brain function. Starting with the patch-clamp technique in the seventies, neuroscience has moved from detailed characterization of ionic channels to the analysis of single neurons and, more recently, microcircuits in brain neuronal networks. Development of new biohybrid probes with electrodes for recording and stimulating neurons in the living animal is a natural consequence of this trend. The recent introduction of optogenetic stimulation and advanced high-resolution large-scale electrical recording approaches demonstrates this need. Brain implants for real-time neurophysiology are also opening new avenues for neuroprosthetics to restore brain function after injury or in neurological disorders. This chapter provides an overview on existing and emergent neurophysiology technologies with particular focus on those intended to interface neuronal microcircuits in vivo. Chemical, electrical, and optogenetic-based interfaces are presented, with an analysis of advantages and disadvantages of the different technical approaches.

scholarly journals Entropy and the Brain: An Overview

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 917 ◽  
Author(s):  
Soheil Keshmiri
Keyword(s):  
Information Processing ◽  
Brain Function ◽  
Conscious Experience ◽  
Brain Networks ◽  
Fundamental Property ◽  
Future Research ◽  
Processing Capacity ◽  
Information Processing Capacity ◽  
Ageing Brain ◽  
The Brain

Entropy is a powerful tool for quantification of the brain function and its information processing capacity. This is evident in its broad domain of applications that range from functional interactivity between the brain regions to quantification of the state of consciousness. A number of previous reviews summarized the use of entropic measures in neuroscience. However, these studies either focused on the overall use of nonlinear analytical methodologies for quantification of the brain activity or their contents pertained to a particular area of neuroscientific research. The present study aims at complementing these previous reviews in two ways. First, by covering the literature that specifically makes use of entropy for studying the brain function. Second, by highlighting the three fields of research in which the use of entropy has yielded highly promising results: the (altered) state of consciousness, the ageing brain, and the quantification of the brain networks’ information processing. In so doing, the present overview identifies that the use of entropic measures for the study of consciousness and its (altered) states led the field to substantially advance the previous findings. Moreover, it realizes that the use of these measures for the study of the ageing brain resulted in significant insights on various ways that the process of ageing may affect the dynamics and information processing capacity of the brain. It further reveals that their utilization for analysis of the brain regional interactivity formed a bridge between the previous two research areas, thereby providing further evidence in support of their results. It concludes by highlighting some potential considerations that may help future research to refine the use of entropic measures for the study of brain complexity and its function. The present study helps realize that (despite their seemingly differing lines of inquiry) the study of consciousness, the ageing brain, and the brain networks’ information processing are highly interrelated. Specifically, it identifies that the complexity, as quantified by entropy, is a fundamental property of conscious experience, which also plays a vital role in the brain’s capacity for adaptation and therefore whose loss by ageing constitutes a basis for diseases and disorders. Interestingly, these two perspectives neatly come together through the association of entropy and the brain capacity for information processing.

scholarly journals Finding Community Modules of Brain Networks Based on PSO with Uniform Design

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Jie Zhang ◽  
Lingkai Tang ◽  
Bo Liao ◽  
Xiaoshu Zhu ◽  
Fang-Xiang Wu
Keyword(s):  
Brain Function ◽  
Function Analysis ◽  
Uniform Design ◽  
Brain Networks ◽  
Complex Structures ◽  
Novel Method ◽  
The Difference ◽  
Living Organisms ◽  
Neural Unit ◽  
The Brain

The brain has the most complex structures and functions in living organisms, and brain networks can provide us an effective way for brain function analysis and brain disease detection. In brain networks, there exist some important neural unit modules, which contain many meaningful biological insights. It is appealing to find the neural unit modules and obtain their affiliations. In this study, we present a novel method by integrating the uniform design into the particle swarm optimization to find community modules of brain networks, abbreviated as UPSO. The difference between UPSO and the existing ones lies in that UPSO is presented first for detecting community modules. Several brain networks generated from functional MRI for studying autism are used to verify the proposed algorithm. Experimental results obtained on these brain networks demonstrate that UPSO can find community modules efficiently and outperforms the other competing methods in terms of modularity and conductance. Additionally, the comparison of UPSO and PSO also shows that the uniform design plays an important role in improving the performance of UPSO.

scholarly journals Molecular dynamics of neuronal information transfer

Neuroforum ◽  
2018 ◽  
Vol 24 (2) ◽  
pp. A73-A84
Author(s):  
Martin Heine ◽  
Arthur Bikbaev
Keyword(s):  
Molecular Dynamics ◽  
Brain Function ◽  
Information Transfer ◽  
Postsynaptic Membrane ◽  
Synaptic Activity ◽  
Optical Methods ◽  
Stochastic Motion ◽  
Lateral Dynamics ◽  
Health And Disease ◽  
The Brain

Abstract A detailed analysis of synapses as connecting elements between neurons is of central importance to understand the brain’s cognitive performance and its constraints. Nowadays, state-of-the-art optical methods make possible to localize individual molecules in a living cell. In particular, the dynamics of molecular composition can be evaluated in smallest neuronal compartments, such as pre- and postsynaptic membrane. The monitoring of the distribution of receptors, ion channels, and adhesion molecules over time revealed their continuous stochastic motion. This is surprising, since the synapses are considered as accumulation sites anchoring these molecules. The direct manipulation of the lateral dynamics of glutamate receptors, in combination with classical electrophysiological approaches, demonstrated that such molecular dynamics is necessary for the induction of synaptic plasticity and, in turn, is influenced by synaptic activity. Therefore, the molecular dynamics requires further studies in the context of the brain function in health and disease.

Box-Scan: A Novel 3DXRD Method for Studies of Recrystallization and Grain Growth

2012 ◽  
Vol 715-716 ◽  
pp. 518-520 ◽  
Author(s):  
Allan Lyckegaard ◽  
Henning Friis Poulsen ◽  
Wolfgang Ludwig ◽  
Richard W. Fonda ◽  
Erik M. Lauridsen
Keyword(s):  
Spatial Resolution ◽  
Near Field ◽  
Polycrystalline Materials ◽  
High Temporal Resolution ◽  
Far Field ◽  
X Ray Diffraction ◽  
The Individual ◽  
Individual Grains ◽  
Non Destructive

Within the last decade a number of x-ray diffraction methods have been presented for non-destructive 3D characterization of polycrystalline materials. 3DXRD [1] and Diffraction Contrast Tomography [2,3,4] are examples of such methods providing full spatial and crystallographic information of the individual grains. Both methods rely on specially designed high-resolution near-field detectors for acquire the shape of the illuminated grains, and therefore the spatial resolution is for both methods limited by the resolution of the detector, currently ~2 micrometers. Applying these methods using conventional far-field detectors provides information on centre of mass, crystallographic orientation and stress state of the individual grains [5], at the expense of high spatial resolution. However, far-field detectors have much higher efficiency than near-field detectors, and as such are suitable for dynamic studies requiring high temporal resolution and set-ups involving bulky sample environments (e.g. furnaces, stress-rigs etc.)

scholarly journals Differential Effects of Simulated Cortical Network Lesions on Synchrony and EEG Complexity

2019 ◽  
Vol 29 (04) ◽  
pp. 1850024 ◽  
Author(s):  
Antonio José Ibáñez-Molina ◽  
Sergio Iglesias-Parro ◽  
Javier Escudero
Keyword(s):  
Brain Function ◽  
Brain Activity ◽  
Spatial Location ◽  
Sample Entropy ◽  
Different Types ◽  
Kuramoto Oscillators ◽  
The Relationship ◽  
Electroencephalogram Eeg ◽  
The Brain

Brain function has been proposed to arise as a result of the coordinated activity between distributed brain areas. An important issue in the study of brain activity is the characterization of the synchrony among these areas and the resulting complexity of the system. However, the variety of ways to define and, hence, measure brain synchrony and complexity has sometimes led to inconsistent results. Here, we study the relationship between synchrony and commonly used complexity estimators of electroencephalogram (EEG) activity and we explore how simulated lesions in anatomically based cortical networks would affect key functional measures of activity. We explored this question using different types of neural network lesions while the brain dynamics was modeled with a time-delayed set of 66 Kuramoto oscillators. Each oscillator modeled a region of the cortex (node), and the connectivity and spatial location between different areas informed the creation of a network structure (edges). Each type of lesion consisted on successive lesions of nodes or edges during the simulation of the neural dynamics. For each type of lesion, we measured the synchrony among oscillators and three complexity estimators (Higuchi’s Fractal Dimension, Sample Entropy and Lempel-Ziv Complexity) of the simulated EEGs. We found a general negative correlation between EEG complexity metrics and synchrony but Sample Entropy and Lempel-Ziv showed a positive correlation with synchrony when the edges of the network were deleted. This suggests an intricate relationship between synchrony of the system and its estimated complexity. Hence, complexity seems to depend on the multiple states of interaction between the oscillators of the system. Our results can contribute to the interpretation of the functional meaning of EEG complexity.

Lessons From the Neural Bases of Speech and Voice

2011 ◽  
Vol 21 (1) ◽  
pp. 5-14
Author(s):  
Christy L. Ludlow
Keyword(s):  
Brain Networks ◽  
Neural Substrates ◽  
Voice Behavior ◽  
Therapeutic Approaches ◽  
Neural Bases ◽  
Sound Foundation ◽  
The Brain ◽  
Normal Speech

The premise of this article is that increased understanding of the brain bases for normal speech and voice behavior will provide a sound foundation for developing therapeutic approaches to establish or re-establish these functions. The neural substrates involved in speech/voice behaviors, the types of muscle patterning for speech and voice, the brain networks involved and their regulation, and how they can be externally modulated for improving function will be addressed.

Characterization of Ischemic Stroke in CT Images using Image Processing

2017 ◽  
Vol 7 (9) ◽  
pp. 18
Author(s):  
Amal Alzain ◽  
Suhaib Alameen ◽  
Rani Elmaki ◽  
Mohamed E. M. Gar-Elnabi
Keyword(s):  
Ischemic Stroke ◽  
White Matter ◽  
Classification Accuracy ◽  
Ct Images ◽  
Gray Level ◽  
Level Variation ◽  
Interactive Data ◽  
The Brain ◽  
Brain Tissues

This study concern to characterize the brain tissues to ischemic stroke, gray matter, white matter and CSF using texture analysisto extract classification features from CT images. The First Order Statistic techniques included sevenfeatures. To find the gray level variation in CT images it complements the FOS features extracted from CT images withgray level in pixels and estimate the variation of thesubpatterns. analyzing the image with Interactive Data Language IDL software to measure the grey level of images. The results show that the Gray Level variation and   features give classification accuracy of ischemic stroke 97.6%, gray matter95.2%, white matter 97.3% and the CSF classification accuracy 98.0%. The overall classification accuracy of brain tissues 97.0%.These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new CT images with the appropriate brain tissues names.

Frontier concept of rabi chip for intelligent humanoid

2018 ◽  
Vol 1 (2) ◽  
Author(s):  
Preecha Yupapin ◽  
Amiri I. S. ◽  
Ali J. ◽  
Ponsuwancharoen N. ◽  
Youplao P.
Keyword(s):  
Human Brain ◽  
Brain Function ◽  
Rabi Oscillation ◽  
Liquid Core ◽  
Brain Surface ◽  
Dipole Oscillation ◽  
On Chip ◽  
The Brain ◽  
Robotic Applications ◽  
Atom System

The sequence of the human brain can be configured by the originated strongly coupling fields to a pair of the ionic substances(bio-cells) within the microtubules. From which the dipole oscillation begins and transports by the strong trapped force, which is known as a tweezer. The tweezers are the trapped polaritons, which are the electrical charges with information. They will be collected on the brain surface and transport via the liquid core guide wave, which is the mixture of blood content and water. The oscillation frequency is called the Rabi frequency, is formed by the two-level atom system. Our aim will manipulate the Rabi oscillation by an on-chip device, where the quantum outputs may help to form the realistic human brain function for humanoid robotic applications.

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