scholarly journals Exploring Functional Networks of the Brain Relating to Upper Extremity Motor Skill Using Graph Theory

2012 ◽  
Author(s):  
Dominic Nathan ◽  
Stephen Guastello ◽  
Robert Prost ◽  
Dean Jeutter
Keyword(s):  
Graph Theory ◽  
Upper Extremity ◽  
Motor Skill ◽  
Functional Networks ◽  
The Brain

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.

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 Effects of a Motor Imagery Task on Functional Brain Network Community Structure in Older Adults: Data from the Brain Networks and Mobility Function (B-NET) Study

2021 ◽  
Vol 11 (1) ◽  
pp. 118
Author(s):  
Blake R. Neyland ◽  
Christina E. Hugenschmidt ◽  
Robert G. Lyday ◽  
Jonathan H. Burdette ◽  
Laura D. Baker ◽  
...  
Keyword(s):  
Older Adults ◽  
Graph Theory ◽  
Community Structure ◽  
Resting State ◽  
Brain Networks ◽  
Brain Network ◽  
Functional Brain ◽  
Network Community ◽  
Functional Brain Networks ◽  
The Brain

Elucidating the neural correlates of mobility is critical given the increasing population of older adults and age-associated mobility disability. In the current study, we applied graph theory to cross-sectional data to characterize functional brain networks generated from functional magnetic resonance imaging data both at rest and during a motor imagery (MI) task. Our MI task is derived from the Mobility Assessment Tool–short form (MAT-sf), which predicts performance on a 400 m walk, and the Short Physical Performance Battery (SPPB). Participants (n = 157) were from the Brain Networks and Mobility (B-NET) Study (mean age = 76.1 ± 4.3; % female = 55.4; % African American = 8.3; mean years of education = 15.7 ± 2.5). We used community structure analyses to partition functional brain networks into communities, or subnetworks, of highly interconnected regions. Global brain network community structure decreased during the MI task when compared to the resting state. We also examined the community structure of the default mode network (DMN), sensorimotor network (SMN), and the dorsal attention network (DAN) across the study population. The DMN and SMN exhibited a task-driven decline in consistency across the group when comparing the MI task to the resting state. The DAN, however, displayed an increase in consistency during the MI task. To our knowledge, this is the first study to use graph theory and network community structure to characterize the effects of a MI task, such as the MAT-sf, on overall brain network organization in older adults.

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 Motor Skill Learning on the Ipsi-Lateral Upper Extremity to the Damaged Hemisphere in Stroke Patients

2019 ◽  
Vol 31 (4) ◽  
pp. 212-215
Author(s):  
Sung Min Son ◽  
Yoon Tae Hwang ◽  
Seok Hyun Nam ◽  
Yonghyun Kwon
Keyword(s):  
Upper Extremity ◽  
Motor Skill ◽  
Skill Learning ◽  
Motor Skill Learning ◽  
Stroke Patients

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.

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