Learning transferability of cognitive tasks by graph generation for brain decoding

Download

Learning_Transferability_of_Cognitive_Tasks_by_Graph_Generation_for_Brain_Decoding.pdf

Date

2021-12-10

Author

Coşkun, Bilgin

Metadata

Show full item record

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

Item Usage Stats

268
views

107
downloads

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 and time-consuming to acquire data, which is statistically sufficient to train a deep neural network. An alternative to generate data is to reuse the available statistically similar data, so that the information of the available data can be transferred to relatively small datasets. In this thesis, we propose a pipeline method to create and verify a graph, which shows the relations among well-defined cognitive tasks, based on fMRI data. We measure the affinity between the cognitive tasks on an fMRI dataset gathered from multiple subjects using the adaptation of an existing end-to-end method which compares transferring performance between the cognitive tasks using learned latent representations. However, due to high variance between measurements on different subjects, the results vary greatly. In order to both verify and regulate the differences, we use the performance of the binary classifiers trained on imbalanced data. In the last step, we generate a graph, where the edges are the possible relations between the cognitive tasks which have the potential to improve transfer learning in other datasets.

Subject Keywords

fMRI, Deep learning, Cognitive state classification, Human Connectome Project, Transfer learning

URI

https://hdl.handle.net/11511/96227

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

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...
Analyzing Complex Problem Solving by Dynamic Brain Networks Alchihabi, Abdullah; Ekmekci, Ömer; Kivilcim, Baran B.; Newman, Sharlene D.; Yarman Vural, Fatos T. (2021-12-01) 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...
Hybrid statistical and machine learning modeling of cognitive neuroscience data Çakar, Serenay; Gökalp Yavuz, Fulya (2023-01-01) The nested data structure is prevalent for cognitive measure experiments due to repeatedly taken observations from different brain locations within subjects. The analysis methods used for this data type should consider the dependency structure among the repeated measurements. However, the dependency assumption is mainly ignored in the cognitive neuroscience data analysis literature. We consider both statistical, and machine learning methods extended to repeated data analysis and compare distinct algorithms ...
Exploring hand movement dynamics during the Simon task: a mouse tracking study. İkizoğlu, Hatice Buke; Çakır, Murat Perit; Department of Cognitive Sciences (2019) Understanding how the cognitive system processes information in real-time is one of the key concerns for researchers in experimental psychology and cognitive science. Several measures and software tools have been proposed to explore different aspects of cognitive processing phenomena. One of these methods, mouse-tracking, allows researchers to collect data about the dynamic unfolding of motor responses by recording the participants’ mouse movements during cognitive tasks. In the present study, we replicated...
EEG Classification based on Image Configuration in Social Anxiety Disorder Mokatren, Lubna Shibly; Ansari, Rashid; Cetin, Ahmet Enis; Leow, Alex D.; Ajilore, Olusola; Klumpp, Heide; Yarman Vural, Fatoş Tunay (2019-01-01) The problem of detecting the presence of Social Anxiety Disorder (SAD) using Electroencephalography (EEG) for classification has seen limited study and is addressed with a new approach that seeks to exploit the knowledge of EEG sensor spatial configuration. Two classification models, one which ignores the configuration (model 1) and one that exploits it with different interpolation methods (model 2), are studied. Performance of these two models is examined for analyzing 34 EEG data channels each consisting ...

Citation Formats

B. Coşkun, “Learning transferability of cognitive tasks by graph generation for brain decoding,” M.S. - Master of Science, Middle East Technical University, 2021.