A Hierarchical representation and decoding of fMRI data by partitioning a brain network

Download

index.pdf

Date

2017

Author

Moğultay, Hazal

Metadata

Show full item record

Item Usage Stats

195
views

66
downloads

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 algorithm, called BrainParcel. As current literature tends to represent human brain as a graph, BrainParcel adopts this approach. The suggested algorithm partitions a brain network, called mesh network using a graph partitioning method. The supervoxels obtained at the output of BrainParcel form partitions of brain as an alternative to anatomical regions (AAL). Compared to AAL, supervoxels gather the linearly dependent voxels. As the next step, we form a mesh network among the supervoxels. Therefore, we represent fMRI data by two networks of different granularity. The first network is at voxel level, whereas the second is at supervoxel level. In order to test the representation power of this two level network, we suggest an ensemble learning architecture, called Cognitive Learner. The suggested ensemble learning method is used in brain decoding problem, where we classified the cognitive states. The results applied on an object recognition problem show that the suggested BrainParcel algorithm together with Cognitive Learner has a better representation power on brain decoding in terms of classification accuracy.

Subject Keywords

Brain, Brain, Magnetic resonance imaging., Brain, Neurosciences.

URI

http://etd.lib.metu.edu.tr/upload/12621399/index.pdf
https://hdl.handle.net/11511/26702

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

An Information theoretic representation of brain connectivity for cognitive state classification using functional magnetic resonance imaging Önal, Itır; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2013) In this study, a new method for analyzing and representing the discriminative information, distributed in functional Magnetic Resonance Imaging (fMRI) data, is proposed. For this purpose, a local mesh with varying size is formed around each voxel, called the seed voxel. The relationships among each seed voxel and its neighbors are estimated using a linear regression equation by minimizing the expectation of the squared error. This squared error coming from linear regression is used to calculate various info...
FUNCTIONAL NETWORKS OF ANATOMIC BRAIN REGIONS Velioglu, Burak; Aksan, Emre; Onal, Itir; Firat, Orhan; Ozay, Mete; Yarman Vural, Fatoş Tunay (2014-08-20) 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 me...
An Information Theoretic Approach to Classify Cognitive States Using fMRI Onal, Itir; Ozay, Mete; Firat, Orhan; GİLLAM, İLKE; Yarman Vural, Fatoş Tunay (2013-11-13) In this study, an information theoretic approach is proposed to model brain connectivity during a cognitive processing task, measured by functional Magnetic Resonance Imaging (fMRI). For this purpose, a local mesh of varying size is formed around each voxel. The arc weights of each mesh are estimated using a linear regression model by minimizing the squared error. Then, the optimal mesh size for each sample, that represents the information distribution in the brain, is estimated by minimizing various inform...
Analyzing effective connectivity of brain using fMRI data : DCM and PPI Mojtahedi, Sina; Halıcı, Uğur; Çiçek, Metehan; Department of Biomedical Engineering (2013) In neuroscience and biomedical engineering fields, one of the most important issues nowadays is finding a relationship between different brain regions when it is stimulated. Connectivity is an important research area in neuroscience which tries to determine the relationship between different brain region when the brain is stimulated externally or internally. Three main type of connectivity are discussed in this field: Anatomical, Functional and Effective connectivity. Importance of effective connectivity is...
Prediction of protein subcellular localization using global protein sequence feature Bozkurt, Burçin; Atalay, Mehmet Volkan; Department of Computer Engineering (2003) The problem of identifying genes in eukaryotic genomic sequences by computational methods has attracted considerable research attention in recent years. Many early approaches to the problem focused on prediction of individual functional elements and compositional properties of coding and non coding deoxyribonucleic acid (DNA) in entire eukaryotic gene structures. More recently, a number of approaches has been developed which integrate multiple types of information including structure, function and genetic p...

Citation Formats

H. Moğultay, “A Hierarchical representation and decoding of fMRI data by partitioning a brain network,” M.S. - Master of Science, Middle East Technical University, 2017.