Representation of Cognitive Processes Using the Minimum Spanning Tree of Local Meshes

2013-07-07
Firat, Orhan
Ozay, Mete
Onal, Itir
GİLLAM, İLKE
Yarman Vural, Fatoş Tunay
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 tree (MST) of the CPG which spans all the voxels in the region of interest is computed. The arc weights of the MST are used to represent the underlying cognitive processes. The proposed method reduces the curse of dimensionality problem that is caused by very large dimension of the feature space of the fMRI measurements, compared to number of instances. Finally, the arc weights computed over the path of the MST called MST-Features (MST-F) are used to train a statistical learning machine.
35th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

Suggestions

Functional Mesh Learning for Pattern Analysis of Cognitive Processes
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...
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...
A Sparse Temporal Mesh Model for Brain Decoding
Afrasiyabi, Arman; Onal, Itir; Yarman Vural, Fatoş Tunay (2016-08-23)
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...
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,...
AN INFORMATION THEORETIC REPRESENTATION OF HUMAN BRAIN FOR DECODING MENTAL STATES OF COMPLEX PROBLEM SOLVING
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...
Citation Formats
O. Firat, M. Ozay, I. Onal, İ. GİLLAM, and F. T. Yarman Vural, “Representation of Cognitive Processes Using the Minimum Spanning Tree of Local Meshes,” presented at the 35th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC), Osaka, Japan, 2013, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/53170.