Supervised and unsupervised models of brain networks for brain decoding

Download

index.pdf

Date

2018

Author

Alchihabi, Abdullah

Metadata

Show full item record

Item Usage Stats

184
views

205
downloads

In this thesis, we propose computational network models for human brain. The models are estimated from fMRI measurements, recorded while subjects perform a set of cognitive tasks. We employ supervised and unsupervised machine learning techniques to represent high level cognitive tasks of human brain by dynamic networks. In the first part of this thesis, we propose an unsupervised multi-resolution brain network model. First, we decompose the signal into multiple sub-bands using Wavelet transform and estimate a set of local meshes at each sub-band. Then, we use stacked denoising auto-encoders to learn low-dimensional connectivity patterns from constructed mesh networks. Finally, learned connectivity patterns are concatenated across different frequency sub-bands and clustered using a hierarchical clustering method. Results show that our proposed model successfully decodes the cognitive states of Human Connectome Project, yielding high rand and adjusted rand indices. In the second part of this thesis, we propose a supervised dynamic brain network model to decode the cognitive subtasks of complex problem solving. First, the raw fMRI images are passed through a preprocessing pipeline that decreases their spatial resolution while increasing their temporal resolution. Then, dynamic functional brain networks are constructed using neural networks. Constructed networks successfully distinguish the phases of complex problem solving. Finally, we analyze the network properties of constructed brain networks to identify potential hubs and clusters of densely connected anatomic regions during planning and execution subtasks. Results show that there are more potential hubs during planning and that clusters are more strongly connected in planning compared to execution.

Subject Keywords

Computational neuroscience., Magnetic resonance imaging., Problem solving., Neurosciences.

URI

http://etd.lib.metu.edu.tr/upload/12622601/index.pdf
https://hdl.handle.net/11511/27578

Collections

Graduate School of Natural and Applied Sciences, Thesis

Suggestions

OpenMETU
Core

A Hierarchical representation and decoding of fMRI data by partitioning a brain network Moğultay, Hazal; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2017) In this study, we propose a hierarchical network representation of human brain extracted from fMRI data. This representation consists of two levels. In the first level, we form a network among the voxels, smallest building block of fMRI data. In the second level, we define a set of supervoxels by partitioning the first level network into a set of subgraphs, which are assu med to represent homogeneous brain regions with respect to a predefined criteria. For this purpose, we develop a novel brain parcellation...
Representation of human brain by mesh networks Önal Ertuğrul, Itır; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2017) In this thesis, we propose novel representations to extract discriminative information in functional Magnetic Resonance Imaging (fMRI) data for cognitive state and gender classification. First, we model the local relationship among a set of fMRI time series within a neighborhood by considering temporal information obtained from all measurements in time series. The estimated local relationships, called Mesh Arc Descriptors (MADs), are employed to represent information in fMRI data. Second, we adapt encoding ...
2D/3D human pose estimation using deep convolutional neural nets Kocabaş, Muhammed; Akbaş, Emre; Department of Computer Engineering (2019) In this thesis, we propose algorithms to estimate 2D/3D human pose from single view images. In the first part of the thesis, we present MultiPoseNet, a novel bottom-up multiperson pose estimation architecture that combines a multi-task model with a novel assignment method. MultiPoseNet can jointly handle person detection, keypoint detection, person segmentation and pose estimation problems. The novel assignment method is implemented by the Pose Residual Network (PRN) which receives keypoint and person detec...
CEREBRA: a 3-D visualization and processing tool for brain network extracted from fMRI data Nasır, Barış; Yarman Vural, Fatoş Tunay; Department of Computer Engineering (2017) In this thesis, we introduce a new tool, CEREBRA, for visualizing 3D network of human brain, extracted from the functional magnetic resonance imaging (fMRI) data. The tool aims to visualize the selected voxels as the nodes of the network and the edge weights are estimated by modeling the relationships among the voxel time series as a set of linear regression equations. This way, researchers can analyze the active brain regions/voxels and observe the interactions among them by analyzing the edge weights and ...
Representing temporal knowledge in connectionist expert systems Alpaslan, Ferda Nur (1996-09-27) This paper introduces a new temporal neural networks model which can be used in connectionist expert systems. Also, a Variation of backpropagation algorithm, called the temporal feedforward backpropagation algorithm is introduced as a method for training the neural network. The algorithm was tested using training examples extracted from a medical expert system. A series of experiments were carried out using the temporal model and the temporal backpropagation algorithm. The experiments indicated that the alg...

Citation Formats

A. Alchihabi, “Supervised and unsupervised models of brain networks for brain decoding,” M.S. - Master of Science, Middle East Technical University, 2018.