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

Date

2013-07-07

Author

Firat, Orhan
Ozay, Mete
Onal, Itir
GİLLAM, İLKE
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

147
views

0
downloads

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.

Subject Keywords

Functional connectivity, Brain activity, Networks, States, FMRI, Principles, Patterns, Graphs, Memory

URI

https://hdl.handle.net/11511/53170

Conference Name

35th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

Collections

Department of Computer Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

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,...
Effect of Voxel Selection on Temporal Mesh Model for Brain Decoding Afrasiyabi, Arman; Onal, Itir; Yarman Vural, Fatoş Tunay (2016-05-19) In this study, we combine a voxel selection method with temporal mesh model to decode the discriminative information distributed in functional Magnetic Resonance Imaging (fMRI) data. We first employ one way Analysis of Variance (ANOVA) feature selection to select the most informative voxels. Then, we form meshes around selected voxels with their spatial and functional neighbors by employing the Mesh Model with Temporal Measurements (MM-TM). We estimate the arc weights of meshes, which represent the relation...

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.