Advancing Our Understanding of Structure and Function in the Brain: Developing Novel Approaches for Network Inference and Emergent Phenomena

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Original Research
20 August 2019
Characterization of Visuomotor/Imaginary Movements in EEG: An Information Theory and Complex Network Approach
Roman Baravalle
3 more and 
Fernando Montani
Network interconnectivity. (A,B) Show the network averaged values of the interconnectivity considering 109 subjects when performing the visuomotor task for the 64-channel EEG considering the different oscillation bands alpha 1 and alpha 2. (C,D) Are the same as (A,B) but considering the imagined task. We consider D = 6 and τ = 1. Gamma 1 oscillation band corresponds to [31, 41)Hz and gamma 2 oscillation band to [41, 50)Hz.

Imagined activities could actually be a cognitive basis for creative thinking. However, it is still unknown how they might be related with the architecture of the brain. A recent study has proved the relevance of the imagined activity when investigating neuronal diseases by comparing variations in the neuronal activity of patients with brain diseases and healthy subjects. One important aspect of the scientific methodologies focused on neuronal diseases is therefore to provide a trustable methodology that could allow us to distinguish between realized and imagined activities in the brain. The electroencephalogram is the result of synchronized action of the cerebrum, and our end is portraying the network dynamics through the neuronal responses when the subjects perform visuomotor and specific imaginary assignments. We use a subtle information theoretical approach accounting for the time causality of the signal and the closeness centrality of the different nodes. More specifically we perform estimations of the probability distribution of the data associated to each node using the Bandt and Pompe approach to account for the causality of the electroencephalographic signals. We calculate the Jensen-Shannon distance across different nodes, and then we quantify how fast the information flow would be through a given node to other nodes computing the closeness centrality. We perform a statistical analysis to compare the closeness centrality considering the different rhythmic oscillation bands for each node taking into account imagined and visuomotor tasks. Our discoveries stress the pertinence of the alpha band while performing and distinguishing the specific imaginary or visuomotor assignments.

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Methods
13 March 2019

Connectivity and biophysical processes determine the functionality of neuronal networks. We, therefore, developed a real-time framework, called Neural Interactome1,2, to simultaneously visualize and interact with the structure and dynamics of such networks. Neural Interactome is a cross-platform framework, which combines graph visualization with the simulation of neural dynamics, or experimentally recorded multi neural time series, to allow application of stimuli to neurons to examine network responses. In addition, Neural Interactome supports structural changes, such as disconnection of neurons from the network (ablation feature). Neural dynamics can be explored on a single neuron level (using a zoom feature), back in time (using a review feature), and recorded (using presets feature). The development of the Neural Interactome was guided by generic concepts to be applicable to neuronal networks with different neural connectivity and dynamics. We implement the framework using a model of the nervous system of Caenorhabditis elegans (C. elegans) nematode, a model organism with resolved connectome and neural dynamics. We show that Neural Interactome assists in studying neural response patterns associated with locomotion and other stimuli. In particular, we demonstrate how stimulation and ablation help in identifying neurons that shape particular dynamics. We examine scenarios that were experimentally studied, such as touch response circuit, and explore new scenarios that did not undergo elaborate experimental studies.

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23 citations
Original Research
07 February 2019
Analysis of Structure and Dynamics in Three-Neuron Motifs
Patrick Krauss
3 more and 
Claus Metzner
Unique labeling of motif classes. Possible entries in the 3 × 3 weight matrix of a motif are −1 (blue), 0 (white), and +1 (red). Shown are all possible permutations of topologically equivalent motifs for two arbitrary chosen cases (A,B). Each motif class is assigned a unique label (green numbers), as described in the Methods section.

Recurrent neural networks can produce ongoing state-to-state transitions without any driving inputs, and the dynamical properties of these transitions are determined by the neuronal connection strengths. Due to non-linearity, it is not clear how strongly the system dynamics is affected by discrete local changes in the connection structure, such as the removal, addition, or sign-switching of individual connections. Moreover, there are no suitable metrics to quantify structural and dynamical differences between two given networks with arbitrarily indexed neurons. In this work, we present such permutation-invariant metrics and apply them to motifs of three binary neurons with discrete ternary connection strengths, an important class of building blocks in biological networks. Using multidimensional scaling, we then study the similarity relations between all 3,411 topologically distinct motifs with regard to structure and dynamics, revealing a strong clustering and various symmetries. As expected, the structural and dynamical distance between pairs of motifs show a significant positive correlation. Strikingly, however, the key parameter controlling motif dynamics turns out to be the ratio of excitatory to inhibitory connections.

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33 citations
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Frontiers in Physics

Advancing Understanding of Biological and Nanostructured Materials Through Atomistic Simulations
Edited by Maksudbek YUSUPOV, Yuantao Zhang, Golibjon Berdiyorov, Umedjon Khalilov
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23 May 2025
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