An Information Theoretic Approach to Classify Cognitive States Using fMRI

Onal, Itir
Ozay, Mete
Firat, Orhan
Yarman Vural, Fatoş Tunay
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 information criteria which employ the mean square error of linear regression model. The estimated mesh size shows the degree of locality or degree of connectivity of the voxels for the underlying cognitive process.


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...
A Sparse Temporal Mesh Model for Brain Decoding
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One of the major drawbacks of brain decoding from the functional magnetic resonance images (fMRI) is the very high dimension of feature space which consists of thousands of voxels in sequence of brain volumes, recorded during a cognitive stimulus. In this study, we propose a new architecture, called Sparse Temporal Mesh Model (STMM), which reduces the dimension of the feature space by combining the voxel selection methods with the mesh learning method. We, first, select the "most discriminative" voxels usin...
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...
Gunal Degirmendereli, Gonul; Yarman Vural, Fatoş Tunay; Department of Cognitive Sciences (2022-2)
In this thesis, we propose an information theoretic method for the representation of human brain activity to decode mental states of a high-order cognitive process, complex problem solving (CPS) using functional magnetic resonance images. First, we aim to identify the active regions and represent underlying cognitive states by measuring the information content of anatomical regions for expert and novice problem solvers during the main phases of problem solving, namely planning and execution. Based on Shann...
A temporal neural network model for constructing connectionist expert system knowledge bases
Alpaslan, Ferda Nur (Elsevier BV, 1996-04-01)
This paper introduces a temporal feedforward neural network model that can be applied to a number of neural network application areas, including connectionist expert systems. The neural network model has a multi-layer structure, i.e. the number of layers is not limited. Also, the model has the flexibility of defining output nodes in any layer. This is especially important for connectionist expert system applications.
Citation Formats
I. Onal, M. Ozay, O. Firat, İ. GİLLAM, and F. T. Yarman Vural, “An Information Theoretic Approach to Classify Cognitive States Using fMRI,” 2013, Accessed: 00, 2020. [Online]. Available: