Background Connectivity-Understanding the brain's functional organization
| dc.contributor.author | Holmes, Mikayla | |
| dc.contributor.supervisor | Miranda, Michelle F. | |
| dc.date.accessioned | 2023-06-22T16:21:58Z | |
| dc.date.available | 2023-06-22T16:21:58Z | |
| dc.date.copyright | 2023 | en_US |
| dc.date.issued | 2023-06-22 | |
| dc.degree.department | Department of Mathematics and Statistics | |
| dc.degree.level | Master of Science M.Sc. | en_US |
| dc.description.abstract | Task-state fMRI (tfMRI) and rest-state fMRI (rfMRI) surface data from the Human Connectome Project (HCP) was examined with the goal of better understanding the nature of background activation signatures and how they compare to the functional connectivity of a brain at rest. In this paper we use a hybrid---decomposition and seed-based---approach to calculate functional connectivity of both rfMRI data and the estimated residual data from a Bayesian spatiotemporal model. This model accounts for local and global spatial correlations within the brain by applying two levels of data decomposition methods. Moreover, long-memory temporal correlations are taken into account by using the Haar discrete wavelet transform. Modifications applied to the original spatiotemporal model that facilitate the use of surface and volumetric (whole-brain) data -- in the CIFTI file format -- are what make this analysis novel. Motor task data from the HCP is modelled, followed by an analysis of the residuals, which provide details regarding the brain's background functional connectivity. These residual connectivity patterns are assessed using a manual procedure and through studying the induced covariance matrix of the model's error term. When we compare these activation signatures to those found for the same subject at rest we found that regions within the subcortex displayed strong connections in both states. Regions associated with the default mode network also displayed statistically significant connectivity while the subject was at rest. In contrast, the pre-central ventral and mid-cingulate regions had strong functional patterns in the background activation signatures that were not present in the rest-state data. This modelling technique combined with a hybrid approach to assessing functional activation signatures provides valuable insights into the role background connections play in the brain. Moreover, it is easily adaptable which allows for this research to be extended across a variety of tasks and at a multi-subject level. | en_US |
| dc.description.scholarlevel | Graduate | en_US |
| dc.identifier.uri | http://hdl.handle.net/1828/15170 | |
| dc.language | English | eng |
| dc.language.iso | en | en_US |
| dc.rights | Available to the World Wide Web | en_US |
| dc.subject | fMRI | en_US |
| dc.subject | Bayesian regression | en_US |
| dc.subject | Background connectivity | en_US |
| dc.subject | Human Connectome Project | en_US |
| dc.subject | Functional connectivity | en_US |
| dc.title | Background Connectivity-Understanding the brain's functional organization | en_US |
| dc.type | Thesis | en_US |