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CEREBRA: A 3-D Visualization Tool for Brain Network Extracted from fMRI Data
Date
2016-08-20
Author
Nasır, Barış
Yarman Vural, Fatoş Tunay
Metadata
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Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
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In this paper, we introduce a new tool, CEREBRA, to visualize the 3D network of human brain, extracted from the fMRI data. The tool aims to analyze the brain connectivity by representing the selected voxels as the nodes of the network. The edge weights among the voxels are estimated by considering the relationships among the voxel time series. The tool enables the researchers to observe the active brain regions and the interactions among them by using graph theoretic measures, such as, the edge weight and node degree distributions. CEREBRA provides an interactive interface with basic display and editing options for the researchers to study their hypotheses about the connectivity of the brain network. CEREBRA interactively simplifies the network by selecting the active voxels and the most correlated edge weights. The researchers may remove the voxels and edges by using local and global thresholds selected on the window. The built-in graph reduction algorithms are then eliminate the irrelevant regions, voxels and edges and display various properties of the network. The toolbox is capable of space-time representation of the voxel time series and estimated arc weights by using the animated heat maps.
Subject Keywords
Data visualization
,
Image edge detection
,
Time series analysis
,
Three-dimensional displays
,
Image color analysis
,
Correlation
,
Data mining
URI
https://hdl.handle.net/11511/54420
Collections
Department of Computer Engineering, Conference / Seminar
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Nasır, Barış; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2017)
In this thesis, we introduce a new tool, CEREBRA, for visualizing 3D network of human brain, extracted from the functional magnetic resonance imaging (fMRI) data. The tool aims to visualize the selected voxels as the nodes of the network and the edge weights are estimated by modeling the relationships among the voxel time series as a set of linear regression equations. This way, researchers can analyze the active brain regions/voxels and observe the interactions among them by analyzing the edge weights and ...
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B. Nasır and F. T. Yarman Vural, “CEREBRA: A 3-D Visualization Tool for Brain Network Extracted from fMRI Data,” 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/54420.