Estimating Static and Dynamic Brain Networks by Kulback-Leibler Divergence from fMRI Data

Date

2021-01-01

Author

Degirmendereli, Gonul Gunal
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

103
views

0
downloads

Representing brain activities by networks is very crucial to understand various cognitive states. This study proposes a novel method to estimate static and dynamic brain networks using Kulback-Leibler divergence. The suggested brain networks are based on the probability distributions of voxel intensity values measured by functional Magnetic Resonance Images (fMRI) recorded while the subjects perform a predefined cognitive task, called complex problem solving. We investigate the validity of the estimated brain networks by modeling and analyzing the different phases of complex problem solving process of human brain, namely planning and execution phases. The suggested computational network model is tested by a classification schema using Support Vector Machines. We observe that the network models can successfully discriminate the planning and execution phases of complex problem solving process with more than 90% accuracy, when the estimated dynamic networks, extracted from the fMRI data, are classified by Support Vector Machines.

URI

https://hdl.handle.net/11511/91976

DOI

https://doi.org/10.1109/icpr48806.2021.9413047

Conference Name

25th International Conference on Pattern Recognition (ICPR)

Collections

Department of Computer Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Learning transferability of cognitive tasks by graph generation for brain decoding Coşkun, Bilgin; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2021-12-10) Brain decoding involves analyzing the cognitive states of human brain by using some statistical techniques in order to understand the relations among the cognitive states, based on neuroimaging data. A very powerful tool to acquire the brain data is functional magnetic resonance images (fMRI), which generates three-dimensional brain volume at each time instant, while a subject performs a cognitive task involving social activities, emotion processing, game playing, memory etc. However, it is very difficult a...
Encoding Multi-Resolution Brain Networks Using Unsupervised Deep Learning Rahnama, Arash; Alchihabi, Abdullah; Gupta, Vijay; Antsaklis, Panos J.; Yarman Vural, Fatoş Tunay (2017-10-25) The main goal of this study is to extract a set of brain networks in multiple time-resolutions to analyze the connectivity patterns among the anatomic regions for a given cognitive task. We suggest a deep architecture which learns the natural groupings of the connectivity patterns of human brain in multiple time-resolutions. The suggested architecture is tested on task data set of Human Connectome Project (HCP) where we extract multi-resolution networks, each of which corresponds to a cognitive task. At the...
In vivo brain sampling using a microextraction probe reveals metabolic changes in rodents after deep brain stimulation Reyes-Garcés, Nathaly; Diwan, Mustansir; Boyacı, Ezel; Gómez-Ríos, German A.; Bojko, Barbara; Nobrega, José N.; Bambico, Francis R.; Hamani, Clement; Pawliszyn, Janusz (American Chemical Society (ACS), 2019-08-06) Brain metabolomics is an emerging field that complements the more traditional approaches of neuroscience. However, typical brain metabolomics workflows require that animals be sacrificed and tend to involve tedious sample preparation steps. Microdialysis, the standard technique to study brain metabolites in vivo, is encumbered by significant limitations -in the analysis of hydrophobic metabolites, which are prone to adsorption losses on microdialysis equipment. An alternative sampling method suitable for in...
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...
Estimating and reshaping human intention via human robot interaction DURDU, AKİF; Erkmen, İsmet; Erkmen, Aydan Müşerref (2016-01-01) Human robot interaction (HRI) is studied in two important research areas, intention estimation and intention reshaping. Although there are many studies in the literature that define human intention, new research examines the reshaping of human intentions by using robots in HRI. In this paper, 2 different robot movements are tested in a real environment in order to reshape current human intention. The hidden Markov model (HMM) is used to estimate human intention in our intelligent robotic system. The algorit...

Citation Formats

G. G. Degirmendereli and F. T. Yarman Vural, “Estimating Static and Dynamic Brain Networks by Kulback-Leibler Divergence from fMRI Data,” presented at the 25th International Conference on Pattern Recognition (ICPR), ELECTR NETWORK, 2021, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/91976.