Mesh Learning for Object Classification using fMRI Measurements

Ekmekci, Ömer
Ozay, Mete
Oztekin, Ilke
Oztekin, Uygar
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, after an image preprocessing step, the voxel intensity values are fed to a classifier to train and recognize the underlying cognitive process. Sometimes, the fMRI data is further processed for de-noising or feature selection where techniques, such as Generalized Linear Model (GLM), Independent Component Analysis (ICA) or Principal Component Analysis are employed. Although these techniques are proved to be useful in MVPA, they do not model the spatial connectivity among the voxels.


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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...
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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...
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Firat, Orhan; Ozay, Mete; Onal, Itir; GİLLAM, İLKE; Yarman Vural, Fatoş Tunay (2013-07-18)
We propose a statistical learning model for classifying cognitive processes based on distributed patterns of neural activation in the brain, acquired via functional magnetic resonance imaging (fMRI). In the proposed learning machine, local meshes are formed around each voxel. The distance between voxels in the mesh is determined by using functional neighborhood concept. In order to define functional neighborhood, the similarities between the time series recorded for voxels are measured and functional connec...
Classification of fMRI Data by Using Clustering
Moğultay, Hazal; Yarman Vural, Fatoş Tunay (2015-05-19)
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 bra...
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
Ö. Ekmekci, M. Ozay, I. Oztekin, İ. GİLLAM, and U. Oztekin, “Mesh Learning for Object Classification using fMRI Measurements,” 2013, Accessed: 00, 2020. [Online]. Available: