Network-level Analysis
Network-level 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).
07:28
JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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Cited by 13 •
2021
We present a systems biology tool JUMPn to perform and visualize network analysis for quantitative proteomics data, with a detailed protocol including data pre-processing, co-expression clustering, pathway enrichment, and protein-protein interaction network analysis.
07:11
CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data
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Cited by 1 •
2023
We present CorrelationCalculator and Filigree, two tools for data-driven network construction and analysis of metabolomics data. CorrelationCalculator supports building a single interaction network of metabolites based on expression data, while Filigree allows building a differential network, followed by network clustering and enrichment analysis.
11:52
Analysis of LINE-1 Retrotransposition at the Single Nucleus Level
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2016
Here we employ FISH methodology to track LINE-1 retrotransposition at the single nuclei level in chromosome spreads of HepG2 cell lines stably expressing synthetic LINE-1.
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.
FAQs
These measures capture different properties of brain organization. Centrality identifies nodes that occupy influential or strategically important positions, modularity evaluates how strongly the network separates into groups, and path length estimates how many connection steps may link elements. Considering them together helps distinguish hubs, functional communities, and the efficiency of potential information flow.
An edge can represent an anatomical connection or a relationship in coordinated activity, and the distinction changes the interpretation of the network. Structural data describe physical organization, whereas functional data describe relationships in activity. Network-level analysis can compare these perspectives or integrate them with electrophysiological, neuroimaging, and anatomical evidence to examine how observed activity relates to underlying pathways.
Changing the scale of the nodes changes the question the analysis can address. Nodes may represent individual neurons, neural populations, or larger brain regions, allowing investigators to connect cellular activity with large-scale function. This multiscale perspective is useful for asking whether a network property appears locally, emerges across populations, or characterizes communication among distributed regions.
A typical workflow begins by selecting the neural elements and connection type, constructing a network from anatomical, electrophysiological, or neuroimaging data, and then calculating graph-theoretic measures. Researchers can compare those measures across conditions or groups to characterize organization and information flow. The resulting analysis converts complex connectivity data into interpretable system-level patterns.
Neuroscientists use Network-level Analysis when isolated measurements do not explain coordinated brain behavior. It can reveal communication pathways and functional networks, then support comparisons across learning, development, or neurological disease. Because the framework accommodates multiple data sources, investigators can relate changes in measured connectivity to broader differences in brain organization rather than treating each signal as an independent observation.
Network-level analysis supports comparative and predictive research by providing measures that can be examined across conditions or stages of change. Differences in centrality, modularity, or path length may help describe how organization shifts with learning, development, or neurological disease. These measures do not replace the underlying recordings or images; they provide a structured way to interpret them.