An Information theoretic representation of brain connectivity for cognitive state classification using functional magnetic resonance imaging

Download
2013
Önal, Itır
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 information theoretic criteria. Then, the optimal mesh size, which represents the connections among a voxel and its neighbors, is estimated by minimizing these information theoretic criteria with respect to mesh size. The optimal mesh size is used to represent the degree of connectivity such that if the optimal mesh size is small, then the voxel is assumed to be connected with a small number of neighbors. On the other hand, high optimal mesh size indicates that voxels are massively connected. The proposed method shows that the local mesh size with the highest discriminative power depends on the participants, samples in the experiment, and voxels. The results indicate that the local mesh model with optimal mesh size can successfully represent discriminative information.

Suggestions

A Hybrid geo-activity recommendation system using advanced feature combination and semantic activity similarity
Sattari, Masoud; Toroslu, İsmail Hakkı; 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...
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...
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...
An fMRI segmentation method under markov random fields for brain decoding
Aksan, Emre; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2015)
In this study, a specially tailored segmentation method for partitioning the fMRI data into a set of "homogenous" regions with respect to a predefined cost function is proposed. The proposed method, referred as f-MRF, employs univariate and multivariate fMRI data analysis techniques under Markov Random Fields to estimate the segments by resolving a mixture density. The univariate approach helps identifying activation pattern of a voxel independently from other voxels. In order to capture local interactions ...
Representation of human brain by mesh networks
Önal Ertuğrul, Itır; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2017)
In this thesis, we propose novel representations to extract discriminative information in functional Magnetic Resonance Imaging (fMRI) data for cognitive state and gender classification. First, we model the local relationship among a set of fMRI time series within a neighborhood by considering temporal information obtained from all measurements in time series. The estimated local relationships, called Mesh Arc Descriptors (MADs), are employed to represent information in fMRI data. Second, we adapt encoding ...
Citation Formats
I. Önal, “An Information theoretic representation of brain connectivity for cognitive state classification using functional magnetic resonance imaging,” M.S. - Master of Science, Middle East Technical University, 2013.