The Effect of Connectivity on EEG Rhythms, Power Spectral Density and Coherence Among Coupled Neural Populations: Analysis With a Neural Mass Model

2008 ◽  
Vol 55 (1) ◽  
pp. 69-77 ◽  
Author(s):  
M. Zavaglia ◽  
L. Astolfi ◽  
F. Babiloni ◽  
M. Ursino
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Neural Mass Model ◽  
Eeg Rhythms ◽  
Mass Model ◽  
Power Spectral ◽  
Neural Mass ◽  
Neural Populations

scholarly journals A Neural Mass Model to Simulate Different Rhythms in a Cortical Region

2010 ◽  
Vol 2010 ◽  
pp. 1-8 ◽  
Author(s):  
M. Zavaglia ◽  
F. Cona ◽  
M. Ursino
Keyword(s):  
Long Range ◽  
Critical Role ◽  
Experimental Studies ◽  
Pyramidal Cells ◽  
Neural Mass Model ◽  
Cortical Region ◽  
Eeg Rhythms ◽  
Mass Model ◽  
Neural Mass ◽  
Neural Populations

An original neural mass model of a cortical region has been used to investigate the origin of EEG rhythms. The model consists of four interconnected neural populations: pyramidal cells, excitatory interneurons and inhibitory interneurons with slow and fast synaptic kinetics, and respectively. A new aspect, not present in previous versions, consists in the inclusion of a self-loop among interneurons. The connectivity parameters among neural populations have been changed in order to reproduce different EEG rhythms. Moreover, two cortical regions have been connected by using different typologies of long range connections. Results show that the model of a single cortical region is able to simulate the occurrence of multiple power spectral density (PSD) peaks; in particular the new inhibitory loop seems to have a critical role in the activation in gamma () band, in agreement with experimental studies. Moreover the effect of different kinds of connections between two regions has been investigated, suggesting that long range connections toward interneurons have a major impact than connections toward pyramidal cells. The model can be of value to gain a deeper insight into mechanisms involved in the generation of rhythms and to provide better understanding of cortical EEG spectra.

The Response of Rotating Machinery to External Random Vibration

1974 ◽  
Vol 96 (2) ◽  
pp. 477-489 ◽  
Author(s):  
J. M. Tessarzik ◽  
T. Chiang ◽  
R. H. Badgley
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
High Speed ◽  
Thrust Bearing ◽  
Dynamic Environment ◽  
Amplitude Distribution ◽  
Random Vibrations ◽  
Mass Model ◽  
Power Spectral ◽  
Rotor Axis

A high-speed turbogenerator employing gas-lubricated hydrodynamic journal and thrust bearings was subjected to external random vibrations for the purpose of assessing bearing performance in a dynamic environment. The pivoted-pad type journal bearings and the step-sector thrust bearing supported a turbine-driven rotor weighing approximately twenty-one pounds at a nominal operating speed of 36,000 rpm. The response amplitudes of both the rigid-supported and flexible-supported bearing pads, the gimballed thrust bearing, and the rotor relative to the machine casing were measured with capacitance type displacement probes. Random vibrations were applied by means of a large electrodynamic shaker at input levels ranging between 0.5 g (rms) and 1.5 g (rms). Vibrations were applied both along and perpendicular to the rotor axis. Response measurements were analyzed for amplitude distribution and power spectral density. Experimental results compare well with calculations of amplitude power spectral density made for the case where the vibrations were applied along the rotor axis. In this case, the rotor-bearing system was treated as a linear, three-mass model.

scholarly journals Comparing average network signals and neural mass signals in systems with low-synchrony

2017 ◽  
Author(s):  
P. Tewarie ◽  
A. Daffertshofer ◽  
B.W. van Dijk
Keyword(s):  
Good Description ◽  
Mean Field ◽  
Neuronal Loss ◽  
Neuronal Population ◽  
Neural Mass Model ◽  
Test Statistic ◽  
Mass Model ◽  
Power Spectral ◽  
Neural Mass ◽  
The Mean

1AbstractNeural mass models are accepted as efficient modelling techniques to model empirical observations such as disturbed oscillations or neuronal synchronization. Neural mass models are based on the mean-field assumption, i.e. they capture the mean-activity of a neuronal population. However, it is unclear if neural mass models still describe the mean activity of a neuronal population when the underlying neural network topology is not homogenous. Here, we test whether the mean activity of a neuronal population can be described by neural mass models when there is neuronal loss and when the connections in the network become sparse. To this end, we derive two neural mass models from a conductance based leaky integrate-and-firing (LIF) model. We then compared the power spectral densities of the mean activity of a network of inhibitory and excitatory LIF neurons with that of neural mass models by computing the Kolmogorov-Smirnov test statistic. Firstly, we found that when the number of neurons in a fully connected LIF-network is larger than 300, the neural mass model is a good description of the mean activity. Secondly, if the connection density in the LIF-network does not exceed a crtical value, this leads to desynchronization of neurons within the LIF-network and to failure of neural mass description. Therefore we conclude that neural mass models can be used for analysing empirical observations if the neuronal network of interest is large enough and when neurons in this system synchronize.

Realistically Coupled Neural Mass Models Can Generate EEG Rhythms

2007 ◽  
Vol 19 (2) ◽  
pp. 478-512 ◽  
Author(s):  
Roberto C. Sotero ◽  
Nelson J. Trujillo-Barreto ◽  
Yasser Iturria-Medina ◽  
Felix Carbonell ◽  
Juan C. Jimenez
Keyword(s):  
Differential Equations ◽  
Short Range ◽  
Imaging Techniques ◽  
Diffusion Imaging ◽  
Neural Mass Model ◽  
Connectivity Matrix ◽  
Eeg Rhythms ◽  
Mass Model ◽  
Neural Mass ◽  
Random Differential Equations

We study the generation of EEG rhythms by means of realistically coupled neural mass models. Previous neural mass models were used to model cortical voxels and the thalamus. Interactions between voxels of the same and other cortical areas and with the thalamus were taken into account. Voxels within the same cortical area were coupled (short-range connections) with both excitatory and inhibitory connections, while coupling between areas (long-range connections) was considered to be excitatory only. Short-range connection strengths were modeled by using a connectivity function depending on the distance between voxels. Coupling strength parameters between areas were defined from empirical anatomical data employing the information obtained from probabilistic paths, which were tracked by water diffusion imaging techniques and used to quantify white matter tracts in the brain. Each cortical voxel was then described by a set of 16 random differential equations, while the thalamus was described by a set of 12 random differential equations. Thus, for analyzing the neuronal dynamics emerging from the interaction of several areas, a large system of differential equations needs to be solved. The sparseness of the estimated anatomical connectivity matrix reduces the number of connection parameters substantially, making the solution of this system faster. Simulations of human brain rhythms were carried out in order to test the model. Physiologically plausible results were obtained based on this anatomically constrained neural mass model.

Interactions between two neural populations: A mechanism of chaos and oscillation in neural mass model

2011 ◽  
Vol 74 (6) ◽  
pp. 1026-1034 ◽  
Author(s):  
Gan Huang ◽  
Dingguo Zhang ◽  
Jiangjun Meng ◽  
Xiangyang Zhu
Keyword(s):  
Neural Mass Model ◽  
Mass Model ◽  
Neural Mass ◽  
Neural Populations

Fuzzy Modeling of Multi-Degree-of-Freedom Power Spectral Density Systems

2009 ◽  
Vol 2 (1) ◽  
pp. 40-47
Author(s):  
Montasser Tahat ◽  
Hussien Al-Wedyan ◽  
Kudret Demirli ◽  
Saad Mutasher
Keyword(s):  
Spectral Density ◽  
Power Spectral Density ◽  
Fuzzy Modeling ◽  
Degree Of Freedom ◽  
Power Spectral
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