A computational model of the brain for decoding mental states from FMRI images

Download

index.pdf

Date

2019

Author

Alkan, Sarper

Metadata

Show full item record

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

Item Usage Stats

224
views

141
downloads

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 based on voxel selection is proven to be an effective approach, we argue that an alternative approach exists, which resembles the processing hieararchy of the human brain for the processing and the representation of the mental states. In this study, we propose a hierarchical brain model for brain decoding. The hierarchical model we propose first clusters a brain image into sets of voxels where the voxels that have a highly correlated activity with each other fall into the same set, which we call supervoxels. Using the supervoxels, we aim to capture the nervous activity from specialized brain regions, which are assumed to process a distinct aspect of a given stimulus or mental task such as processing color, texture, or shape of a given visual object. Then, we combine the brain activity represented by each supervoxel using a method that we call Brain Region Ensembles (BRE) in order to decode mental states from fMRI images. Our analyses on multiple fMRI datasets show that the BRE is much better suited to the classification of mental states from fMRI images than classical voxel selection methodology. Additionally, we show that BRE can be used for the specification of brain regions that are relevant to the experimental tasks or stimuli when the aim is to identify the regions that have discriminative activity with respect to two different mental states.

Subject Keywords

Brain., MVPA, fMRI, Brain Decoding, Clustering, Classifier Ensembles

URI

http://etd.lib.metu.edu.tr/upload/12624645/index.pdf#page=7&zoom=auto,-214,57
https://hdl.handle.net/11511/45216

Collections

Graduate School of Informatics, Thesis

Suggestions

OpenMETU
Core

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...
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...
An Information Theoretic Approach to Classify Cognitive States Using fMRI Onal, Itir; Ozay, Mete; Firat, Orhan; GİLLAM, İLKE; Yarman Vural, Fatoş Tunay (2013-11-13) 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 inform...
Analysis and classification of spelling paradigm EEG data and an attempt for optimization of channels used Yıldırım, Asil; Halıcı, Uğur; Department of Electrical and Electronics Engineering (2010) Brain Computer Interfaces (BCIs) are systems developed in order to control devices by using only brain signals. In BCI systems, different mental activities to be performed by the users are associated with different actions on the device to be controlled. Spelling Paradigm is a BCI application which aims to construct the words by finding letters using P300 signals recorded via channel electrodes attached to the diverse points of the scalp. Reducing the letter detection error rates and increasing the speed of...
A model on building and modifying the stimulus action association in the brain Beyinde Uyaran Hareket Ilişkisinin Oluşmasi ve Uyarlanmasina Dair Bir Model Ercelik, Emec; Elibol, Rahmi; Şengör, Neslihan Serap (2015-06-19) © 2015 IEEE.It is expected that building computational models of neural structures taking part in generating cognitive processes and emotions would not only help us understanding the brain but also give us clues to diagnose and develop treatment for neurological disorders and diseases. In this work, a computational model of cognitive task, goal directed behavior is considered. The cortex-basal ganglia-thalamus loop which is known to be effective in goal directed behavior has been modeled. In the model, the ...

Citation Formats

S. Alkan, “A computational model of the brain for decoding mental states from FMRI images,” Thesis (Ph.D.) -- Graduate School of Informatics. Cognitive Sciences., Middle East Technical University, 2019.