scholarly journals State-Dependent Synchrony and Functional Connectivity in the Primary and Secondary Whisker Somatosensory Cortices

2021 ◽  
Vol 15 ◽  
Author(s):  
Mohamed Khateb ◽  
Jackie Schiller ◽  
Yitzhak Schiller
Keyword(s):  
Functional Connectivity ◽  
Network Connectivity ◽  
Small World ◽  
Sensory Coding ◽  
Small World Networks ◽  
Cell Level ◽  
State Dependent ◽  
Somatosensory Cortices ◽  
Whisker Stimulation ◽  
Sensory Activation

Synchronized activity plays an important role in sensory coding and memory and is a hallmark of functional network connectivity. However, the effect of sensory activation on synchronization and cortical functional connectivity is largely unknown. In this study, we investigated the effect of whisker activation on synchronization and functional connectivity of the primary (wS1) and secondary (wS2) whisker somatosensory cortices at the single-cell level. The results showed that during the spontaneous pre-stimulus state, neurons tended to be functionally connected with nearby neurons which shared similar tuning characteristics. Whisker activation using either ramp-and-hold stimulation or artificial whisking against sandpaper has significantly reduced the average overall pairwise synchronization and functional connectivity within the wS1 barrel and wS2 cortices. Whisker stimulation disconnected approximately a third of neuronal pairs that were functionally connected during the unstimulated state. Nearby neurons with congruent tuning properties were more likely to remain functionally connected during whisker activation. The findings of this study indicated that cortical somatosensory networks are organized in non-random small world networks composed of neurons sharing relatively similar tuning properties. Sensory whisker activation intensifies these properties and further subdivides the cortical network into smaller more functionally uniform subnetworks, which possibly serve to increase the computational capacity of the network.

scholarly journals Lynn_etal_MathSkills_CPM_Preprint_V1_05July2021

2021 ◽  
Author(s):  
Andrew Lynn ◽  
Eric D. Wilkey ◽  
Gavin Price
Keyword(s):  
Functional Connectivity ◽  
Predictive Modeling ◽  
Brain Connectivity ◽  
Network Connectivity ◽  
Brain Regions ◽  
Math Skills ◽  
Whole Brain ◽  
Building Models ◽  
State Dependent ◽  
Brain Behavior

The human brain comprises multiple canonical networks, several of which are distributed across frontal, parietal, and temporooccipital regions. Studies report both positive and negative correlations between children’s math skills and the strength of functional connectivity among these regions during math-related tasks and at rest. Yet, it is unclear how the relation between children’s math skills and functional connectivity map onto patterns of distributed whole-brain connectivity, canonical network connectivity, and whether these relations are consistent across different task-states. We used connectome-based predictive modeling to test whether functional connectivity during number comparison and at rest predicts children’s math skills (N=31, Mage=9.21years) using distributed whole-brain connections versus connections among canonical networks. We found that weaker connectivity distributed across the whole brain and weaker connectivity between key math-related brain regions in specific canonical networks predicts better math skills in childhood. The specific connections predicting math skills, and whether they were distributed or mapped onto canonical networks, varied between tasks, suggesting that state-dependent rather than trait-level functional network architectures support children’s math skills. Furthermore, the current predictive modeling approach moves beyond brain-behavior correlations and toward building models of brain connectivity that may eventually aid in predicting future math skills.

Studies on the signal amplification in weighted and unweighted small-world networks

2017 ◽  
Vol 31 (04) ◽  
pp. 1750021
Author(s):  
Yang Gao ◽  
Jianjun Wang ◽  
Fuqiu Ma
Keyword(s):  
Coupling Strength ◽  
Gaussian White Noise ◽  
Network Connectivity ◽  
Small World ◽  
Weak Signal ◽  
Small World Networks ◽  
Weighted Networks ◽  
Resonance Behavior ◽  
Optimal Values ◽  
Temporal Periodicity

Weighted and unweighted networks composed of coupled bistable oscillators with small-world topology are investigated under the co-presence of a weak signal and multiplicative Gaussian white noise. As the noise intensity is adjusted to one or two optimal values, the temporal periodicity of the output of the system reaches the maximum, indicating the occurrence of stochastic resonance (SR) or stochastic bi-resonance (SBR). The resonance behavior is strongly-dependent on the coupling strength in both networks. At a weak coupling, SR more likely takes place; whereas at a strong coupling, SBR is prone to occur. Compared with unweighted networks, the span of coupling strength for SBR is narrower in weighted networks. In addition, the weak signal cannot be amplified so effectively in the weighted networks as in the unweighted networks, attributing to the weakening effect of the link weight on the coupling between oscillators and the heterogeneity of the whole network connectivity caused by the weight distribution.

scholarly journals Small-World Networks and Functional Connectivity in Alzheimer's Disease

2006 ◽  
Vol 17 (1) ◽  
pp. 92-99 ◽  
Author(s):  
C. Stam ◽  
B. Jones ◽  
G Nolte ◽  
M Breakspear ◽  
P. Scheltens
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Small World ◽  
Small World Networks

scholarly journals Loss of ‘Small-World’ Networks in Alzheimer's Disease: Graph Analysis of fMRI Resting-State Functional Connectivity

PLoS ONE ◽  
2010 ◽  
Vol 5 (11) ◽  
pp. e13788 ◽  
Author(s):  
Ernesto J. Sanz-Arigita ◽  
Menno M. Schoonheim ◽  
Jessica S. Damoiseaux ◽  
Serge A. R. B. Rombouts ◽  
Erik Maris ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Functional Connectivity ◽  
Resting State ◽  
Small World ◽  
Graph Analysis ◽  
Small World Networks ◽  
Resting State Functional Connectivity

scholarly journals On the Distributions of Subgraph Centralities in Complex Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Faxu Li ◽  
Liang Wei ◽  
Haixing Zhao ◽  
Feng Hu
Keyword(s):  
Complex Networks ◽  
Probability Distributions ◽  
Network Connectivity ◽  
Network Models ◽  
Small World ◽  
Small World Networks ◽  
Power Law Distribution ◽  
Scale Free ◽  
Scale Free Networks ◽  
Complex Network Models

Subgraph centrality measure characterizes the participation of each node in all subgraphs in a network. Smaller subgraphs are given more weight than large ones, which makes this measure appropriate for characterizing network motifs. This measure is better in being able to discriminate the nodes of a network than alternate measures. In this paper, the important issue of subgraph centrality distributions is investigated through theory-guided extensive numerical simulations, for three typical complex network models, namely, the ER random-graph networks, WS small-world networks, and BA scale-free networks. It is found that these three very different types of complex networks share some common features, particularly that the subgraph centrality distributions in increasing order are all insensitive to the network connectivity characteristics, and also found that the probability distributions of subgraph centrality of the ER and of the WS models both follow the gamma distribution, and the BA scale-free networks exhibit a power-law distribution with an exponential cutoff.

scholarly journals Heritability of “small-world” networks in the brain: A graph theoretical analysis of resting-state EEG functional connectivity

2008 ◽  
Vol 29 (12) ◽  
pp. 1368-1378 ◽  
Author(s):  
Dirk J. A. Smit ◽  
Cornelis J. Stam ◽  
Danielle Posthuma ◽  
Dorret I. Boomsma ◽  
Eco J. C. de Geus
Keyword(s):  
Functional Connectivity ◽  
Theoretical Analysis ◽  
Resting State ◽  
Small World ◽  
Small World Networks ◽  
Graph Theoretical Analysis ◽  
Eeg Functional Connectivity ◽  
The Brain

Small-world networks and disturbed functional connectivity in schizophrenia

2006 ◽  
Vol 87 (1-3) ◽  
pp. 60-66 ◽  
Author(s):  
Sifis Micheloyannis ◽  
Ellie Pachou ◽  
Cornelis Jan Stam ◽  
Michael Breakspear ◽  
Panagiotis Bitsios ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Small World ◽  
Small World Networks

Networks

2018 ◽  
Author(s):  
Stefan Thurner ◽  
Rudolf Hanel ◽  
Peter Klimekl
Keyword(s):  
Complex Systems ◽  
Complex Networks ◽  
Phase Diagrams ◽  
Community Detection ◽  
Network Science ◽  
Small World ◽  
Small World Networks ◽  
Multilayer Networks ◽  
Basic Vocabulary

Understanding the interactions between the components of a system is key to understanding it. In complex systems, interactions are usually not uniform, not isotropic and not homogeneous: each interaction can be specific between elements.Networks are a tool for keeping track of who is interacting with whom, at what strength, when, and in what way. Networks are essential for understanding of the co-evolution and phase diagrams of complex systems. Here we provide a self-contained introduction to the field of network science. We introduce ways of representing and handle networks mathematically and introduce the basic vocabulary and definitions. The notions of random- and complex networks are reviewed as well as the notions of small world networks, simple preferentially grown networks, community detection, and generalized multilayer networks.

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