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Population based spatio-temporal probabilistic modelling of fMRI data

Alowadi, Nahed (2018)
Ph.D. thesis, University of Birmingham.

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High-dimensional functional magnetic resonance imaging (fMRI) data is characterized by complex spatial and temporal patterns related to neural activation. Mixture based Bayesian spatio-temporal modelling is able to extract spatiotemporal components representing distinct haemodyamic response and activation patterns. A recent development of such approach to fMRI data analysis is so-called spatially regularized mixture model of hidden process models (SMM-HPM). SMM-HPM can be used to reduce the four-dimensional fMRI data of a pre-determined region of interest (ROI) to a small number of spatio-temporal prototypes, sufficiently representing the spatio-temporal features of the underlying neural activation. Summary statistics derived from these features can be interpreted as quantification of (1) the spatial extent of sub-ROI activation patterns, (2) how fast the brain respond to external stimuli; and (3) the heterogeneity in single ROIs. This thesis aims to extend the single-subject SMM-HPM to a multi-subject SMM-HPM so that such features can be extracted at group-level, which would enable more robust conclusion to be drawn.

Type of Work:Ph.D. thesis.
Supervisor(s):Tino, Peter and Shen, Yuan
School/Faculty:Colleges (2008 onwards) > College of Engineering & Physical Sciences
Department:School of Computer Science
Additional Information:

Publications resulting from research:

Alowadi, N., Shen, Y. and Tino, P., 2016. Prototype-Based Spatio-Temporal Probabilistic Modelling of fMRI Data. In Advances in Self-Organizing Maps and Learning Vector Quantization (pp. 193-203). Springer, Cham.

Subjects:QA75 Electronic computers. Computer science
Institution:University of Birmingham
ID Code:8210
This unpublished thesis/dissertation is copyright of the author and/or third parties. The intellectual property rights of the author or third parties in respect of this work are as defined by The Copyright Designs and Patents Act 1988 or as modified by any successor legislation. Any use made of information contained in this thesis/dissertation must be in accordance with that legislation and must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the permission of the copyright holder.
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