Functional networks of the brain: from connectivity restoration to dynamic integration

2020 ◽  
Author(s):  
Aleksandr E. Hramov ◽  
Nikita S. Frolov ◽  
Vladimir A. Maksimenko ◽  
Semen A. Kurkin ◽  
Viktor B. Kazantsev ◽  
...  
Keyword(s):  
Functional Networks ◽  
Connectivity Restoration ◽  
Dynamic Integration ◽  
The Brain

Functional networks of the brain: from connectivity restoration to dynamic integration

2020 ◽  
Author(s):  
Aleksandr E. Hramov ◽  
Nikita S. Frolov ◽  
Vladimir A. Maksimenko ◽  
Semen A. Kurkin ◽  
Viktor B. Kazantsev ◽  
...  
Keyword(s):  
Functional Networks ◽  
Connectivity Restoration ◽  
Dynamic Integration ◽  
The Brain

scholarly journals Small world in a seismic network: the California case

2008 ◽  
Vol 15 (3) ◽  
pp. 389-395 ◽  
Author(s):  
A. Jiménez ◽  
K. F. Tiampo ◽  
A. M. Posadas
Keyword(s):  
Complex Networks ◽  
Recent Work ◽  
Brain Activity ◽  
Social Systems ◽  
Small World ◽  
Functional Networks ◽  
Topological Properties ◽  
Southern California Region ◽  
Seismic Area ◽  
The Brain

Abstract. Recent work has shown that disparate systems can be described as complex networks i.e. assemblies of nodes and links with nontrivial topological properties. Examples include technological, biological and social systems. Among them, earthquakes have been studied from this perspective. In the present work, we divide the Southern California region into cells of 0.1°, and calculate the correlation of activity between them to create functional networks for that seismic area, in the same way that the brain activity is studied from the complex network perspective. We found that the network shows small world features.

scholarly journals Unsupervised seizure localisation with attention-based graph neural networks

2020 ◽  
Author(s):  
Daniele Grattarola ◽  
Lorenzo Livi ◽  
Cesare Alippi ◽  
Richard Wennberg ◽  
Taufik Valiante
Keyword(s):  
Neural Networks ◽  
Brain Activity ◽  
Functional Networks ◽  
Seizure Onset ◽  
The Past ◽  
Seizure Onset Zone ◽  
Brain States ◽  
Graph Neural Networks ◽  
Intracranial Electroencephalography ◽  
The Brain

Abstract Graph neural networks (GNNs) and the attention mechanism are two of the most significant advances in artificial intelligence methods over the past few years. The former are neural networks able to process graph-structured data, while the latter learns to selectively focus on those parts of the input that are more relevant for the task at hand. In this paper, we propose a methodology for seizure localisation which combines the two approaches. Our method is composed of several blocks. First, we represent brain states in a compact way by computing functional networks from intracranial electroencephalography recordings, using metrics to quantify the coupling between the activity of different brain areas. Then, we train a GNN to correctly distinguish between functional networks associated with interictal and ictal phases. The GNN is equipped with an attention-based layer which automatically learns to identify those regions of the brain (associated with individual electrodes) that are most important for a correct classification. The localisation of these regions is fully unsupervised, meaning that it does not use any prior information regarding the seizure onset zone. We report results both for human patients and for simulators of brain activity. We show that the regions of interest identified by the GNN strongly correlate with the localisation of the seizure onset zone reported by electroencephalographers. We also show that our GNN exhibits uncertainty on those patients for which the clinical localisation was also unsuccessful, highlighting the robustness of the proposed approach.

scholarly journals Disrupted Topological Organization of Functional Networks in Asymptomatic Carotid Plaque Without Significant Carotid Stenosis: A Resting-State fMRI Study

2021 ◽  
Vol 15 ◽  
Author(s):  
Jia Tuo ◽  
Wei He ◽  
Shuai Yang ◽  
Lihui Liu ◽  
Xiaojuan Liu ◽  
...  
Keyword(s):  
Resting State ◽  
Carotid Plaque ◽  
Functional Network ◽  
Middle Temporal Gyrus ◽  
Functional Networks ◽  
Middle Temporal ◽  
Brain Functional Network ◽  
Asymptomatic Patients ◽  
The Brain ◽  
Left Middle

Purpose: Previous studies have found that there are significant changes in functional network properties for patients with moderate to severe carotid artery stenosis. Our study aimed to explore the topology properties of brain functional network in asymptomatic patients with carotid plaque without significant stenosis.Methods: A total of 61 asymptomatic patients with carotid plaque (mean age 61.79 ± 7.35 years) and 25 healthy control subjects (HC; 58.12 ± 6.79 years) were recruited. General data collection, carotid ultrasound examination and resting state functional magnetic resonance imaging were performed on all subjects. Graph-theory was applied to examine the differences in the brain functional network topological properties between two groups.Results: In the plaque group, Eloc(P = 0.03), γ (P = 0.01), and σ (P = 0.01) were significantly higher than in the HC group. The degree centrality of left middle frontal gyrus and the nodal efficiency of left middle frontal gyrus and right inferior parietal angular gyrus were significantly higher in the plaque group than in HC. The degree centrality and betweenness centrality of right middle temporal gyrus, as well as the nodal efficiency of right middle temporal gyrus, were significantly lower in the plaque group than in HC.Conclusions: The brain functional networks of patients with carotid plaques differ from those of healthy controls. Asymptomatic patients with carotid plaques exhibit increased local and global connectivity, which may reflect subtle reorganizations in response to early brain damage.

scholarly journals Loss of integration of brain networks after sleep deprivation relates to the worsening of cognitive functions

2020 ◽  
Author(s):  
Pesoli Matteo ◽  
Rucco Rosaria ◽  
Liparoti Marianna ◽  
Lardone Anna ◽  
D’Aurizio Giula ◽  
...  
Keyword(s):  
Sleep Deprivation ◽  
Resting State ◽  
Large Scale ◽  
Brain Networks ◽  
Brain Network ◽  
Cognitive Tasks ◽  
Functional Networks ◽  
Long Sleep ◽  
Before And After ◽  
The Brain

AbstractThe topology of brain networks changes according to environmental demands and can be described within the framework of graph theory. We hypothesized that 24-hours long sleep deprivation (SD) causes functional rearrangements of the brain topology so as to impair optimal communication, and that such rearrangements relate to the performance in specific cognitive tasks, namely the ones specifically requiring attention. Thirty-two young men underwent resting-state MEG recording and assessments of attention and switching abilities before and after SD. We found loss of integration of brain network and a worsening of attention but not of switching abilities. These results show that brain network changes due to SD affect switching abilities, worsened attention and induce large-scale rearrangements in the functional networks.

scholarly journals Neuronal Graphs: A Graph Theory Primer for Microscopic, Functional Networks of Neurons Recorded by Calcium Imaging

2021 ◽  
Vol 15 ◽  
Author(s):  
Carl J. Nelson ◽  
Stephen Bonner
Keyword(s):  
Graph Theory ◽  
Fluorescence Microscopy ◽  
Calcium Imaging ◽  
Neural Mechanisms ◽  
Fmri Data ◽  
Functional Networks ◽  
Large Bank ◽  
Fundamental Structure ◽  
Formal Study ◽  
The Brain

Connected networks are a fundamental structure of neurobiology. Understanding these networks will help us elucidate the neural mechanisms of computation. Mathematically speaking these networks are “graphs”—structures containing objects that are connected. In neuroscience, the objects could be regions of the brain, e.g., fMRI data, or be individual neurons, e.g., calcium imaging with fluorescence microscopy. The formal study of graphs, graph theory, can provide neuroscientists with a large bank of algorithms for exploring networks. Graph theory has already been applied in a variety of ways to fMRI data but, more recently, has begun to be applied at the scales of neurons, e.g., from functional calcium imaging. In this primer we explain the basics of graph theory and relate them to features of microscopic functional networks of neurons from calcium imaging—neuronal graphs. We explore recent examples of graph theory applied to calcium imaging and we highlight some areas where researchers new to the field could go awry.

scholarly journals Functionnectome: a framework to analyse the contribution of brain circuits to fMRI

2021 ◽  
Author(s):  
Victor Nozais ◽  
Stephanie Forkel ◽  
Chris Foulon ◽  
Laurent Petit ◽  
Michel Thiebaut de Schotten
Keyword(s):  
White Matter ◽  
Functional Neuroimaging ◽  
Brain Regions ◽  
Functional Networks ◽  
Language Functions ◽  
Brain Circuits ◽  
Novel Method ◽  
Joint Contribution ◽  
The Relationship ◽  
The Brain

Abstract In recent years, the field of functional neuroimaging has moved from a pure localisationist approach of isolated functional brain regions to a more integrated view of those regions within functional networks. The methods used to investigate such networks, however, rely on local signals in grey matter and are limited in identifying anatomical circuitries supporting the interaction between brain regions. Mapping the brain circuits mediating the functional signal between brain regions would propel forward our understanding of the brain’s functional signatures and dysfunctions. We developed a novel method to unravel the relationship between brain circuits and functions: The Functionnectome. The Functionectome combines the functional signal from fMRI with the anatomy of white matter brain circuits to unlock and chart the first maps of functional white matter. To showcase the versatility of this new method, we provide the first functional white matter maps revealing the joint contribution of connected areas to motor, working memory, and language functions. The Functionnectome comes with an open source companion software and opens new avenues into studying functional networks by applying the method to already existing dataset and beyond task fMRI.

scholarly journals The Neural Marketplace: I. General Formalism and Linear Theory

2014 ◽  
Author(s):  
Sarah N Lewis ◽  
Kenneth Daniel Harris
Keyword(s):  
Network Performance ◽  
Neuronal Development ◽  
Free Market ◽  
Learning Rule ◽  
Adult Brain ◽  
Self Organization ◽  
Functional Networks ◽  
Parallel Search ◽  
Supply Networks ◽  
The Brain

We propose a mathematical theory for how networks of neurons in the brain self-organize into functional networks, similarly to the self-organization of supply networks in a free-market economy. The theory is inspired by recent experimental results showing how information about changes to output synapses can travel backward along axons to affect a neuron's inputs. In neuronal development, competition for such ``retroaxonal'' signals determines which neurons live and which die. We suggest that in adults, an analogous form of competition occurs between neurons, to supply their targets with appropriate information in exchange for a ``payment'' returned to them backward along the axon. We review experimental evidence suggesting that neurotrophins may constitute such a signaling pathway in the adult brain. We construct a mathematical model, in which a small number of ``consumer'' neurons receive explicit fast error signals while a larger number of ``producer'' neurons compete to supply them with information, guided by retroaxonal signals from the consumers and from each other. We define a loss function to measure network performance and define the ``worth'' of a producer to be the increase in loss that would result if that neuron were to fall silent. We show how slow retroaxonal signals can allow producers to estimate their worth, and how these estimates allow the network to perform a form of parallel search over multiple producer cells. We validate our approximations and demonstrate the proposed learning rule using simulations.

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