Network Analysis
Network Analysis - Related Videos
12:09
Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy
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Cited by 35 •
2014
The Default Mode Network (DMN) in Temporal Lobe Epilepsy (TLE) is analyzed in the resting state of the brain using seed-based functional connectivity MRI (fcMRI).
02:26
Protein Networks
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2020
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions. These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
02:18
Network Covalent Solids
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2020
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds. To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
09:32
Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients
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Cited by 5 •
2016
This protocol describes a procedure to track the evolution of mesial network measures in temporal lobe epilepsy (TLE) patients. It is based on the combination of intracranial recordings with a novel numerical technique for data analysis. Specifically, we present a protocol for network analyses of foramen ovale recordings.
12:27
Piping Networks and Pressure Losses
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2023
Source: Alexander S Rattner, Department of Mechanical and Nuclear Engineering, The Pennsylvania State University, University Park, PA This experiment introduces the measurement and modeling of pressure losses in piping networks and internal flow systems. In such systems, frictional flow resistance from channel walls, fittings, and obstructions causes mechanical energy in the form of fluid pressure to be converted to heat. Engineering analyses are needed to size flow hardware to ensure...
FAQs
Connectivity describes how many relationships a node has, while centrality identifies nodes that occupy especially influential positions within the network. Community structure highlights groups of nodes with coordinated connections. Examining these measures together helps distinguish broadly connected components, potentially influential genes or proteins, and organized biological modules rather than relying on a single topological feature.
The interaction represented by each edge determines which biological relationships the network can illuminate. Gene regulation, protein binding, metabolic reactions, and ecological associations describe different kinds of system organization. Consequently, the same network approach can address distinct questions depending on whether researchers are examining regulation, molecular interaction, biochemical relationships, or associations within an ecosystem.
Topology places each component within the structure of its relationships, allowing researchers to examine patterns across the system rather than isolated measurements. This perspective can expose coordinated modules, influential components, and disease-associated pathways. It therefore supports interpretation of how individual genes, proteins, reactions, or ecological entities may contribute to broader system-level function.
A typical workflow begins by constructing a network from biologically relevant interaction data, with entities represented as nodes and their relationships as edges. Researchers then examine topology through connectivity, centrality, or community structure. Finally, they interpret the resulting patterns to generate hypotheses, compare biological systems, or relate network features to pathways, modules, or ecological associations.
Researchers can use this approach when they need to interpret relationships among biological components in the context of disease-associated pathways. By examining network position and organization, they may identify influential genes or proteins and recognize coordinated modules linked to disease. These patterns support hypothesis generation and help organize complex biological datasets around system-level relationships.
Because biological relationships can be represented quantitatively, researchers can examine and compare the organization of networks across systems. Differences in connectivity, centrality, or community structure may indicate changes in influential components, coordinated modules, or interaction patterns. This makes the method useful for comparing biological systems while preserving information about how their components are connected.