Novel Functional Magnetic Resonance Imaging Analysis Approaches for Investigations of the Dynamics of Resting-State Functional Connectivity

Spontaneous fluctuations of blood-oxygenation level-dependent functional magnetic resonance imaging (BOLD fMRI) signals are highly synchronous between brain regions that serve similar functions. This provides a means to investigate functional networks of the human brain; however, most data analysis techniques assume functional connections are constant over time. This is problematic when studying brain processes associated with aging or neurological disease, where functional connections may become highly variable. Proposed methods of examining moment-to-moment changes in the strength of functional connections over an imaging session (so called dynamic connectivity) are not well established, and there are several pitfalls in current analysis approaches. In this thesis, novel analysis frameworks are developed to address several issues associated with dynamic resting-state fMRI analysis techniques. These techniques are then used to analyze the dynamics of functional connectivity within long-range and local brain networks. Specifically, a hierarchical observation modeling approach is proposed to permit statistical inference of the presence of dynamic connectivity at any point in time. Also, a sliding-window regional homogeneity approach is developed to examine the dynamics of local functional connectivity, to gain even further insight into the global functional organization of the human brain. Finally, the proposed methods are used in a study to determine resting-state local and long-range connectivity changes related to healthy aging, and further, how these changes demonstrate that age changes the proportion of time the brain occupies certain functional states. The studies in this thesis greatly further our understanding of the functional architecture of the human brain, in terms of how local and long-distance interactions are organized both in space and time. This thesis also helps establish a framework for dynamic resting-state fMRI analysis with consistency and reliability.

Name thesis:degree_name

Doctor of Philosophy (PhD)

Discipline thesis:degree_discipline

Biomedical Engineering

Grantor dc:publisher.institution

Graduate Studies

Year dc:date.issued

2018

Author dc:creator

• Sojoudi, Alireza

Advisor dc:contributor.advisor

• Goodyear, Bradley G.

Committee members dc:contributor.committeemember

• Smith, Michael Richard
• Dukelow, Sean P.
• MacIntosh, Bradley
• MacMaster, Frank P.
• Sotero Díaz, Roberto

• functional MRI
• dynamic functional connectivity
• hierarchical observation modeling
• regional homogeneity
• Bayesian inference

Statement dc:rights

• University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.

Language dc:language.iso

eng

DOI

http://dx.doi.org/10.11575/PRISM/32843

OAI identifier oai:identifier

oai:ucalgary.scholaris.ca:1880/107663