| Summary: | Functional Magnetic Resonance Imaging using the Blood Oxygenation Level Dependent (BOLD) contrast allows the brain's neural activity to be measured indirectly. This BOLD signal contains a wealth of information including changes in brain activity and functional connectivity (FC). FC is a measure of how correlated spatially separate brain regions are with each other. The work in this thesis is primarily concerned with novel methods of analysing the BOLD signal, in particular to give new measures of FC. A particular problem with typical measures of FC is that they assume that the networks are large scale and distributed, and that they originate from low frequency, static oscillations. It is clear from the way that we interact with the world that these assumptions are wrong, requiring a dynamic approach to investigate FC and the origins of what might be driving this. Here, a method combining short window correlation analysis and paradigm free mapping, a technique to detect spontaneous BOLD events without prior knowledge of their timings, is used to study the dynamic nature of these networks. It is further shown that these networks are at least in part driven by spontaneous activity, and that the rate of this spontaneous activity can be modulated by a task. These spontaneous events are then combined with network masks and temporal Independent Component Analysis to decompose these large scale networks into smaller sub-networks. Finally, the effects of spontaneous BOLD events on attention and task performance during a visual task is evaluated, highlighting how different brain regions that are not associated with a task can distract the subject's attention. It is shown that BOLD events that relate to a specific task use highly focal specific regions of the brain, confirming the spatial specificity of brain regions to a particular function.
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