New dimension reduction technique for brain decoding

Download
2015
Afrasiyabi, Arman
A new architecture for dimension reduction, analyzing and decoding the discriminative information, distributed in function Magnetic Resonance Imaging (fMRI) data, is proposed. This architecture called Sparse Temporal Mesh Model (STMM) which consists of three phases with a visualization tool. In phase A, a univariate voxel selection method, based on the assumption that voxels are independent, is used to select the informative voxels among the whole brain voxels. For this purpose, one of feature selection methods namely one way analysis of variance (ANOVA) or mutual information (MI) is employed. Then, in phase B, a multivariate voxel selection method, based on the multivariate form of the brain, known as recursive feature elimination (RFE) is employed. The last phase, phase C, contains two parts. In phase C.1, a local mesh with fix size around each voxel called seed voxel is formed. Next, the relationships, called arc weights, between the seed voxel and the neighbouring voxels are estimated. In phase C.2, ANOVA feature selection method is used to eliminate the unnecessary arc weights. Additionally, a visualization tool known as t Distributed Stochastic Neighbor Embedding (tSNE) is used to analyse the effect of each phase. The results indicate that STMM can successfully use for brain decoding purpose.

Suggestions

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...
Effect of Voxel Selection on Temporal Mesh Model for Brain Decoding
Afrasiyabi, Arman; Onal, Itir; Yarman Vural, Fatoş Tunay (2016-05-19)
In this study, we combine a voxel selection method with temporal mesh model to decode the discriminative information distributed in functional Magnetic Resonance Imaging (fMRI) data. We first employ one way Analysis of Variance (ANOVA) feature selection to select the most informative voxels. Then, we form meshes around selected voxels with their spatial and functional neighbors by employing the Mesh Model with Temporal Measurements (MM-TM). We estimate the arc weights of meshes, which represent the relation...
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...
New Pulse Shapes for CPM Signals
Doyuran, Enis; Tanık, Yalçın (2006-05-10)
A study on finding new pulse shapes for binary CPM (Continuous Phase Modulation) signals to achieve better spectral and detection performance is carried out. Under the constraint that the power spectral density stays below the GSM spectral envelope, pulse shape is optimized to minimize the error probability. Polynomials are utilized in representing pulse shapes. The performance depends on the degree of the polynomials and the pulse length (L). With 8(th) degree polynomials having 4 free parameters, optimiza...
Citation Formats
A. Afrasiyabi, “New dimension reduction technique for brain decoding,” M.S. - Master of Science, Middle East Technical University, 2015.