Population based spatio-temporal probabilistic modelling of fMRI data

Alowadi, Nahed (2018). Population based spatio-temporal probabilistic modelling of fMRI data. University of Birmingham. Ph.D.

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Abstract

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:	Thesis (Doctorates > Ph.D.)
Award Type:	Doctorates > Ph.D.
Supervisor(s):	Supervisor(s) Email ORCID Tino, Peter UNSPECIFIED UNSPECIFIED Shen, Yuan UNSPECIFIED UNSPECIFIED
Licence:
College/Faculty:	Colleges (2008 onwards) > College of Engineering & Physical Sciences
School or Department:	School of Computer Science
Funders:	Other
Other Funders:	King Abdulaziz University, Saudi Arabia
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
URI:	http://etheses.bham.ac.uk/id/eprint/8210

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