Simulated FMRI toolbox

Download

index.pdf

Date

2009

Author

Türkay, Kemal Doğuş

Metadata

Show full item record

Item Usage Stats

46
views

26
downloads

In this thesis a simulated fMRI toolbox is developed in order to generate simulated data to compare and benchmark different functional magnetic resonance image analysis methods. This toolbox is capable of loading a high resolution anatomic brain volume, generating 4D fMRI data in the same data space with the anatomic image, and allowing the user to create block and event-related design paradigms. Common fMRI artifacts such as scanner drift, cardiac pulsation, habituation and task related or spontaneous head movement can be incorporated into the 4D fMRI data. Input to the toolbox is possible through MINC 2.0 file format, and output is provided in ANALYZE format. The major contribution of this toolbox is its facilitation of comparison of fMRI analysis methods by generating several different fMRI data under varying noise and experiment parameters.

Subject Keywords

Medical Informatics., Electronic computers.

URI

http://etd.lib.metu.edu.tr/upload/12611465/index.pdf
https://hdl.handle.net/11511/19176

Collections

Graduate School of Informatics, Thesis

Suggestions

OpenMETU
Core

RC Performance Evaluation of Interconnect Architecture Options Beyond the 10-nm Logic Node Kıncal, Serkan; Abraham, Mathew C.; Schuegraf, Klaus (Institute of Electrical and Electronics Engineers (IEEE), 2014-06-01) This paper summarizes the findings of an RC performance modeling approach for evaluating various material and architecture options by which interconnect wires are incorporated onto integrated circuits. For the present dual-damascene structure, the grain boundary and surface scattering modes are identified as the top contributors to resistance degradation, along with the cross-sectional area consumed by the liner/barrier layers. Self-forming barriers, a technology that provides direct Cu-insulator interfaces...
Comparison of rough multi layer perceptron and rough radial basis function networks using fuzzy attributes Vural, Hülya; Alpaslan, Ferda Nur; Department of Computer Engineering (2004) The hybridization of soft computing methods of Radial Basis Function (RBF) neural networks, Multi Layer Perceptron (MLP) neural networks with back-propagation learning, fuzzy sets and rough sets are studied in the scope of this thesis. Conventional MLP, conventional RBF, fuzzy MLP, fuzzy RBF, rough fuzzy MLP, and rough fuzzy RBF networks are compared. In the fuzzy neural networks implemented in this thesis, the input data and the desired outputs are given fuzzy membership values as the fuzzy properties أlow...
Further developments in the dynamic stiffness matrix (DSM) based direct damping identification method Özgen, Gökhan Osman (2005-01-01) Theoretical development of a dynamic stiffness matrix (DSM) based direct damping matrix identification method is revisited in this paper. This method was proposed to identify both the mechanism and spatial distribution of damping in dynamic structures as a matrix of general function of frequency. The objective of this paper, in addition to the review of the theoretical development, is to investigate some major issues regarding the feasibility of this method. The first issue investigated is how the errors in...
Transformation Electromagnetics Based Analysis of Waveguides With Random Rough or Periodic Grooved Surfaces Ozgun, Ozlem; Kuzuoğlu, Mustafa (Institute of Electrical and Electronics Engineers (IEEE), 2013-02-01) A computational model is introduced which employs transformation-based media to increase the computational performance of finite methods (such as finite element or finite difference methods) for analyzing waveguides with grooves or rough surfaces. Random behavior of the roughness is taken into account by utilizing the Monte Carlo technique, which is based on a set of random rough surfaces generated from Gaussian distribution. The main objective of the proposed approach is to create a single mesh, and to ana...
Comparison of two inference approaches in Gaussian graphical models Purutçuoğlu Gazi, Vilda; Wit, Ernst (Walter de Gruyter GmbH, 2017-04-01) Introduction: The Gaussian Graphical Model (GGM) is one of the well-known probabilistic models which is based on the conditional independency of nodes in the biological system. Here, we compare the estimates of the GGM parameters by the graphical lasso (glasso) method and the threshold gradient descent (TGD) algorithm.

Citation Formats

K. D. Türkay, “Simulated FMRI toolbox,” M.S. - Master of Science, Middle East Technical University, 2009.