Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Simplification and Visualization of Brain Network Extracted from fMRI Data Using CEREBRA
Date
2016-08-23
Author
Nasır, Barış
Yarman Vural, Fatoş Tunay
Metadata
Show full item record
This work is licensed under a
Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
.
Item Usage Stats
152
views
0
downloads
Cite This
In this paper, we introduce graph simplification capabilities of a new tool, CEREBRA, which is used to visualize the 3D network of human brain, extracted from the fMRI data. The nodes of the network are defined as the voxels with the attributes corresponding to the intensity values changing by time and the coordinates in three dimensional Euclidean space. The arc weights are estimated by modeling the relationships among the voxel activation records. We aim to help researchers to reveal the underlying brain state by examining the active regions of the brain and observe the interactions among them. Although the tool provides many features for displaying the fMRI data as a dynamical network, in this study, we have mainly focused on two main features. The first one is the unique graph simplification module that allows users to eliminate redundant edges according to some weighted similarity criterion. The second one is visualizing the output of the external algorithms for voxel selection, clustering or network representation of fMRI data. Thus, users are able to display, analyze and further process the output of their own algorithms.
URI
https://hdl.handle.net/11511/55076
Collections
Department of Computer Engineering, Conference / Seminar
Suggestions
OpenMETU
Core
A Hierarchical representation and decoding of fMRI data by partitioning a brain network
Moğultay, Hazal; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2017)
In this study, we propose a hierarchical network representation of human brain extracted from fMRI data. This representation consists of two levels. In the first level, we form a network among the voxels, smallest building block of fMRI data. In the second level, we define a set of supervoxels by partitioning the first level network into a set of subgraphs, which are assu med to represent homogeneous brain regions with respect to a predefined criteria. For this purpose, we develop a novel brain parcellation...
CEREBRA: A 3-D Visualization Tool for Brain Network Extracted from fMRI Data
Nasır, Barış; Yarman Vural, Fatoş Tunay (2016-08-20)
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 n...
Representation of Cognitive Processes Using the Minimum Spanning Tree of Local Meshes
Firat, Orhan; Ozay, Mete; Onal, Itir; GİLLAM, İLKE; Yarman Vural, Fatoş Tunay (2013-07-07)
A new graphical model called Cognitive Process Graph (CPG) is proposed, for classifying cognitive processes based on neural activation patterns which are acquired via functional Magnetic Resonance Imaging (fMRI) in brain. In the CPG, first local meshes are formed around each voxel. Second, the relationships between a voxel and its neighbors in a local mesh, which are estimated by using a linear regression model, are used to form the edges among the voxels (graph nodes) in the CPG. Then, a minimum spanning t...
Neural networks with piecewise constant argument and impact activation
Yılmaz, Enes; Akhmet, Marat; Department of Scientific Computing (2011)
This dissertation addresses the new models in mathematical neuroscience: artificial neural networks, which have many similarities with the structure of human brain and the functions of cells by electronic circuits. The networks have been investigated due to their extensive applications in classification of patterns, associative memories, image processing, artificial intelligence, signal processing and optimization problems. These applications depend crucially on the dynamical behaviors of the networks. In t...
Quantification and localisation of damage in beam-like structures by using artificial neural networks with experimental validation
Şahin, Melin (Elsevier BV, 2003-12-01)
This paper presents a damage detection algorithm using a combination of global (changes in natural frequencies) and local (curvature mode shapes) vibration-based analysis data as input in artificial neural networks (ANNs) for location and severity prediction of damage in beam-like structures. A finite element analysis tool has been used to obtain the dynamic characteristics of intact and damaged cantilever steel beams for the first three natural modes. Different damage scenarios have been introduced by redu...
Citation Formats
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
B. Nasır and F. T. Yarman Vural, “Simplification and Visualization of Brain Network Extracted from fMRI Data Using CEREBRA,” 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/55076.