Analyzing Complex Problem Solving by Dynamic Brain Networks

Download

fninf-15-670052.pdf

Date

2021-12-01

Author

Alchihabi, Abdullah
Ekmekci, Ömer
Kivilcim, Baran B.
Newman, Sharlene D.
Yarman Vural, Fatos T.

Metadata

Show full item record

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

Item Usage Stats

159
views

54
downloads

Complex problem solving is a high level cognitive task of the human brain, which has been studied over the last decade. Tower of London (TOL) is a game that has been widely used to study complex problem solving. In this paper, we aim to explore the underlying cognitive network structure among anatomical regions of complex problem solving and its subtasks, namely planning and execution. A new computational model for estimating a brain network at each time instant of fMRI recordings is proposed. The suggested method models the brain network as an Artificial Neural Network, where the weights correspond to the relationships among the brain anatomic regions. The first step of the model is preprocessing that manages to decrease the spatial redundancy while increasing the temporal resolution of the fMRI recordings. Then, dynamic brain networks are estimated using the preprocessed fMRI signal to train the Artificial Neural Network. The properties of the estimated brain networks are studied in order to identify regions of interest, such as hubs and subgroups of densely connected brain regions. The representation power of the suggested brain network is shown by decoding the planning and execution subtasks of complex problem solving. Our findings are consistent with the previous results of experimental psychology. Furthermore, it is observed that there are more hubs during the planning phase compared to the execution phase, and the clusters are more strongly connected during planning compared to execution.

Subject Keywords

fMRI, machine learning, brain networks, tower of London (TOL), complex problem solving, TASK-PERFORMANCE, TOWER, LONDON, FMRI, CONNECTIVITY, ACTIVATION, PATTERNS, VERSION, MEMORY, STATES

URI

https://hdl.handle.net/11511/95993

Journal

FRONTIERS IN NEUROINFORMATICS

DOI

https://doi.org/10.3389/fninf.2021.670052

Collections

Department of Computer Engineering, Article

Suggestions

OpenMETU
Core

Improvement of temporal resolution of fMRI data for brain decoding Varol, Emel; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2022-2-10) In this study, we aim to increase the accuracy of the mapping between the states of the brain and problem-solving phases namely planning and execution. To create a computational model to generate the mapping, an fMRI dataset obtained from subjects solving the Tower of London problem has been used. fMRI data is suitable for this problem as it provides regional and time-varying changes in brain metabolism. However, developing the model using fMRI data is not trivial. Generally, fMRI data has a very large feat...
On the Entropy of Brain Anatomic Regions for Complex Problem Solving Degirmendereli, Gonul Gunal; Newman, Sharlene D.; Yarman Vural, Fatoş Tunay (2019-01-01) In this paper, we aim to measure the information content of brain anatomic regions using the functional magnetic resonance images (fMRI) recorded during a complex problem solving (CPS) task. We, also, analyze the brain regions, activated in different phases of the problem solving process. Previous studies have widely used machine learning approaches to examine the active anatomic regions for cognitive states of human subjects based on their fMRI data. This study proposes an information theoretic method for ...
Modeling and Decoding Complex Problem Solving Process by Artificial Neural Networks Akan, Adil Kaan; Kivilcim, Baran Baris; Akbaş, Emre; Newman, Sharlene D.; Yarman Vural, Fatoş Tunay (2019-01-01) It is hypothesized that the process of complex problem solving in human brain consists of two basic phases, namely, planning and execution. In this study, we propose a computational model in order to verify this hypothesis. For this purpose, we develop a holistic approach for decoding the planning and execution phases of complex problem solving, using the functional magnetic resonance imaging data (fMRI), recorded when the subjects play the Tower of London (TOL) game. In the first step of the proposed stud...
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...

Citation Formats

A. Alchihabi, Ö. Ekmekci, B. B. Kivilcim, S. D. Newman, and F. T. Yarman Vural, “Analyzing Complex Problem Solving by Dynamic Brain Networks,” FRONTIERS IN NEUROINFORMATICS, vol. 15, pp. 0–0, 2021, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/95993.