Classification of fMRI Data by Using Clustering

Date

2015-05-19

Author

Moğultay, Hazal
Yarman Vural, Fatoş Tunay

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

52
views

0
downloads

Recognition of the the cognitive states by using functional Magnetic Rezonans Imaging (fMRI) data is a challenging problem that has been a focus of scientific research for a long time. In this study the effectiveness of clustering and the ensemble learning techniques on fMRI dataset is investigated and different paramaters are compared. Moreover, the performance of these techniques are tested on both raw voxel intensity values and meshes formed by multiple voxels. Clusters are compared to the functional brain regions, however higher performances are obtained when the number of clusters is higher than the number of functional brain regions.

Subject Keywords

fMRI, Clustering, Multi voxel pattern analysis (MVPA)

URI

https://hdl.handle.net/11511/55070

Collections

Department of Computer Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Mesh Learning for Object Classification using fMRI Measurements Ekmekci, Ömer; Ozay, Mete; Oztekin, Ilke; GİLLAM, İLKE; Oztekin, Uygar (2013-09-18) Machine learning algorithms have been widely used as reliable methods for modeling and classifying cognitive processes using functional Magnetic Resonance Imaging (fMRI) data. In this study, we aim to classify fMRI measurements recorded during an object recognition experiment. Previous studies focus on Multi Voxel Pattern Analysis (MVPA) which feeds a set of active voxels in a concatenated vector form to a machine learning algorithm to train and classify the cognitive processes. In most of the MVPA methods,...
Analyzing the Information Distribution in the fMRI Measurements by Estimating the Degree of Locality Onal, Itir; Ozay, Mete; Firat, Orhan; GİLLAM, İLKE; Yarman Vural, Fatoş Tunay (2013-07-07) In this study, we propose a new method for analyzing and representing the distribution of discriminative information for data acquired via functional Magnetic Resonance Imaging (fMRI). For this purpose, we form a spatially local mesh with varying size, around each voxel, called the seed voxel. The relationship among each seed voxel and its neighbors is estimated using a linear regression model by minimizing the square error. Then, we estimate the optimal mesh size that represents the connections among each ...
Representation of Cognitive Processes Using the Minimum Spanning Tree of Local Meshes Firat, Orhan; Ozay, Mete; Onal, Itir; GİLLAM, İLKE; Yarman Vural, Fatoş Tunay (2013-07-07) A new graphical model called Cognitive Process Graph (CPG) is proposed, for classifying cognitive processes based on neural activation patterns which are acquired via functional Magnetic Resonance Imaging (fMRI) in brain. In the CPG, first local meshes are formed around each voxel. Second, the relationships between a voxel and its neighbors in a local mesh, which are estimated by using a linear regression model, are used to form the edges among the voxels (graph nodes) in the CPG. Then, a minimum spanning t...
A computational model of the brain for decoding mental states from FMRI images Alkan, Sarper; Yarman Vural, Fatoş Tunay; Department of Cognitive Sciences (2019) Brain decoding from brain images obtained using functional magnetic resonance imaging (fMRI) techniques is an important task for the identification of mental states and illnesses as well as for the development of brain machine interfaces. The brain decoding methods that use multi-voxel pattern analysis that rely on the selection of voxels (volumetric pixels) that have relevant activity with respect to the experimental tasks or stimuli of the fMRI experiments are the most commonly used methods. While MVPA ba...
Classification of migraineurs using functional near infrared spectroscopy data Sayıta, Yusuf; Temizel, Alptekin; Akın, Ata; Department of Medical Informatics (2012) Classification of migraineur and healthy subjects using statistical pattern classifiers on functional Near Infrared Spectroscopy (NIRS) data is the main purpose of this study. Also a statistical comparison between trials that have different type of classifiers, classifier settings and feature sets is done. Features are extracted from raw light measurement data acquired with NIRS device, namely Niroxcope, during two separate previous studies, using Modified Beer-Lambert Law. After feature extraction, Naïve B...

Citation Formats

H. Moğultay and F. T. Yarman Vural, “Classification of fMRI Data by Using Clustering,” 2015, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/55070.