FUNCTIONAL NETWORKS OF ANATOMIC BRAIN REGIONS

Date

2014-08-20

Author

Velioglu, Burak
Aksan, Emre
Onal, Itir
Firat, Orhan
Ozay, Mete
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

190
views

0
downloads

In this study, we propose a new approach to construct a two-level functional brain network. The nodes of the first-level network are the voxels of the functional Magnetic Resonance Images (tMRI) recorded during an object recognition task. The nodes of the network at the second-level are the anatomic regions of the brain. The arcs of the first level are estimated by a linear regression equation for the meshes formed around each voxel. Neighbors of each voxel are determined by using a functional similarity metric. The node degree distributions of the voxel-level functional brain network are then used to estimate the node attributes and arc weights between the nodes of anatomic regions at the second level. The region-level functional brain network is then used to analyze the relationship among the anatomic regions of the brain during a cognitive process. Our results indicate that, although the neighborhood is defined functionally, voxels tend to make connections within the anatomic regions. Therefore, it can be deduced that nearby voxels work coherently during the cognitive task compared to the voxels apart from each other.

Subject Keywords

fMRI, Functional brain network, MVPA

URI

https://hdl.handle.net/11511/54730

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...
Improvement of temporal resolution of fMRI data for brain decoding Varol, Emel; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2022-2-10) In this study, we aim to increase the accuracy of the mapping between the states of the brain and problem-solving phases namely planning and execution. To create a computational model to generate the mapping, an fMRI dataset obtained from subjects solving the Tower of London problem has been used. fMRI data is suitable for this problem as it provides regional and time-varying changes in brain metabolism. However, developing the model using fMRI data is not trivial. Generally, fMRI data has a very large feat...
Functional Mesh Learning for Pattern Analysis of Cognitive Processes Firat, Orhan; Ozay, Mete; Onal, Itir; GİLLAM, İLKE; Yarman Vural, Fatoş Tunay (2013-07-18) We propose a statistical learning model for classifying cognitive processes based on distributed patterns of neural activation in the brain, acquired via functional magnetic resonance imaging (fMRI). In the proposed learning machine, local meshes are formed around each voxel. The distance between voxels in the mesh is determined by using functional neighborhood concept. In order to define functional neighborhood, the similarities between the time series recorded for voxels are measured and functional connec...
Comparison of partial directed coherence and dynamic bayesian network approach for brain effective connectivity modeling using fMRI Öğe, Oğuzhan Can; Ulusoy, İlkay; Department of Electrical and Electronics Engineering (2019) Two of the approaches attempting to model brain effective connectivity are compared. These methods are Partial Directed Coherence (PDC) and Dynamic Bayesian Network (DBN). PDC is based on linear and deterministic signal modelling. It is derived from the Granger Causality approach and underpinned by the Multivariate Auto Regressive (MVAR) model. On the other hand, DBN is based on probabilistic signal modelling, which gives DBN the ability of detecting nonlinear interactions between signals unlike all the oth...
Analyzing Complex Problem Solving by Dynamic Brain Networks Alchihabi, Abdullah; Ekmekci, Ömer; Kivilcim, Baran B.; Newman, Sharlene D.; Yarman Vural, Fatos T. (2021-12-01) Complex problem solving is a high level cognitive task of the human brain, which has been studied over the last decade. Tower of London (TOL) is a game that has been widely used to study complex problem solving. In this paper, we aim to explore the underlying cognitive network structure among anatomical regions of complex problem solving and its subtasks, namely planning and execution. A new computational model for estimating a brain network at each time instant of fMRI recordings is proposed. The suggested...

Citation Formats

B. Velioglu, E. Aksan, I. Onal, O. Firat, M. Ozay, and F. T. Yarman Vural, “FUNCTIONAL NETWORKS OF ANATOMIC BRAIN REGIONS,” 2014, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/54730.