Show/Hide Menu
Hide/Show Apps
Logout
Türkçe
Türkçe
Search
Search
Login
Login
OpenMETU
OpenMETU
About
About
Open Science Policy
Open Science Policy
Open Access Guideline
Open Access Guideline
Postgraduate Thesis Guideline
Postgraduate Thesis Guideline
Communities & Collections
Communities & Collections
Help
Help
Frequently Asked Questions
Frequently Asked Questions
Guides
Guides
Thesis submission
Thesis submission
MS without thesis term project submission
MS without thesis term project submission
Publication submission with DOI
Publication submission with DOI
Publication submission
Publication submission
Supporting Information
Supporting Information
General Information
General Information
Copyright, Embargo and License
Copyright, Embargo and License
Contact us
Contact us
Statistical disease detection with resting state functional magnetic resonance imaging
Download
index.pdf
Date
2017
Author
Öztürk, Ebru
Metadata
Show full item record
Item Usage Stats
285
views
111
downloads
Cite This
Most of the functional magnetic resonance imaging (fMRI) data are based on a particular task. The fMRI data are obtained while the subject performs a task. Yet, it's obvious that the brain is active even when the subject is not performing a task. Resting state fMRI (R-fMRI) is a comparatively new and popular technique for assessing regional interactions when a subject is not performing a task. This study focuses on classifying subjects as healthy or diseased with the diagnosis of schizophrenia by analyzing R-fMRI data. The resting state situation in the dataset of “UCLA Consortium for Neuropsychiatric Phonemics LA5c Study” is used to extract brain signals in the Region of Interest (ROI) analysis. The default mode network (DMN) ROIs were selected since the DMN is a perception depending on an interconnected set of areas displaying higher activity during rest than task related activity (Raichle and Snyder, 2007). Pre-processing of fMRI images is achieved with toolbox of Statistical Parametric Mapping version 8 (SPM8). ROI-based on brain signals are obtained from Functional Connectivity (CONN). After brain signals are obtained, the disease status is predicted by adjusting for the magnitude of brain signals, the demographic information’s of subjects such as gender and age. Logistic regression model, marginal model, random effect model and k-means clustering, hierarchical clustering and clustering genes with replications (CGR) followed by logistic regression approaches are conducted to classify the subjects in the UCLA data set by using R-Studio. Marginal model with smoking status and k-means clustering algorithm followed with logistic regression model excluding smoking status give best results.
Subject Keywords
Medical statistics.
,
Diagnostic imaging.
,
Mathematical models.
,
Magnetic resonance imaging.
URI
http://etd.lib.metu.edu.tr/upload/12621474/index.pdf
https://hdl.handle.net/11511/26717
Collections
Graduate School of Natural and Applied Sciences, Thesis
Suggestions
OpenMETU
Core
Improving the sub-cortical gm segmentation using evolutionary hierarchical region merging
Çiftçioğlu, Mustafa Ulaş; Gökçay, Didem; Department of Medical Informatics (2011)
Segmentation of sub-cortical Gray Matter (GM) structures in magnetic resonance brain images is crucial in clinic and research for many purposes such as early diagnosis of neurological diseases, guidance of surgical operations and longitudinal volumetric studies. Unfortunately, the algorithms that segment the brain into 3 tissues usually suffer from poor performance in the sub-cortical region. In order to increase the detection of sub-cortical GM structures, an evolutionary hierarchical region merging approa...
Coil sensitivity map calculation using biot-savart law at 3 tesla and parallel imaging in MRI
Esin, Yunus Emre; Alpaslan, Ferda Nur; Department of Computer Engineering (2017)
Coil spatial sensitivity map is considered as one of the most valuable data used in parallel magnetic resonance imaging (MRI) reconstruction. In this study, a novel sensitivity map extraction method is introduced for phased-array coils. Proposed technique uses Biot-Savart law with coil shape information and low-resolution phase image data to form sensitivity maps. The performance of this method has been tested in the parallel image reconstruction task using sensitivity encoding technique. In MRI, coil sensi...
Induced current magnetic resonance electrical impedance tomography (ICMREIT) with low frequency switching of gradient fields
Eroğlu, Hasan Hüseyin; Eyüboğlu, Behçet Murat; Department of Electrical and Electronics Engineering (2017)
In this thesis, it is aimed to investigate induced current magnetic resonance electrical impedance tomography (ICMREIT) starting from modeling and analysis to experimental validation. Forward and inverse problems of ICMREIT are formulated. A magnetic resonance imaging (MRI) pulse sequence is proposed for the realization of ICMREIT using the slice selection (z) gradient coil of MRI scanners. Considering the proposed MRI pulse sequence, relationship between the low frequency (LF) MR phase and the secondary ma...
RF Coil Design for MRI Applications in Inhomogeneous Main Magnetic Fields
Yılmaz, Ayşen; Eyueboglu, B. M. (2006-09-01)
Conventional Magnetic Resonance Imaging (MRI) techniques require homogeneous main magnetic fields. However, MRI applications that are executed in inhomogenous main magnetic fields have been developed in recent years. In this study, RF coil geometries are designed for MRI applications in inhomogeneous magnetic fields. Method of moments is used to obtain the current density distribution on a predefined surface that can produce a desired magnetic field, which is perpendicular to the given inhomogenous main mag...
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
IEEE
ACM
APA
CHICAGO
MLA
BibTeX
E. Öztürk, “Statistical disease detection with resting state functional magnetic resonance imaging,” M.S. - Master of Science, Middle East Technical University, 2017.