Factors Affecting Resting-State Functional Connectivity Across Three Intrinsically Connected Networks in Traumatic Brain Injury
| dc.contributor.advisor | Marquez de la Plata, Carlos | en |
| dc.contributor.committeeMember | Cullum, C. Munro | en |
| dc.contributor.committeeMember | Krawczyk, Daniel | en |
| dc.contributor.committeeMember | Aslan, Sina | en |
| dc.contributor.committeeMember | Spence, Jeffrey | en |
| dc.creator | Bosworth, Christopher Charles | en |
| dc.date.accessioned | 2016-09-01T19:35:27Z | |
| dc.date.available | 2016-09-01T19:35:27Z | |
| dc.date.created | 2014-08 | |
| dc.date.issued | 2014-07-28 | |
| dc.date.submitted | August 2014 | |
| dc.date.updated | 2016-09-01T19:10:09Z | |
| dc.description.abstract | This study investigated the factors that influence post-TBI functional connectivity within three intrinsically connected networks; the default mode network (DMN), central executive network (CEN), and salience network (SN). The aim was to develop a predictive model for each network, based off a combination of cognitive performance, brain volumetric factors, aging/demographic factors, and TBI-related factors. A secondary aim was to examine the relationship between the SN and the anticorrelation (i.e., between-network BOLD signal correlation) between the DMN and CEN. Participants (n=63) sustained a mild-to-moderate TBI within six-months of participating in the study. They completed a cognitive assessment battery consisting of measures of executive functioning, language, memory, reasoning, and intelligence estimates. They also underwent structural MRI, resting-state fMRI, and completed mood symptom questionnaires. A seed-based, resting-state functional connectivity analysis was conducted for the DMN, CEN, and SN. Measures of brain volumetrics were calculated from the structural MRI. Stepwise multiple linear regressions using cognitive factors, demographic and injury factors, functional outcomes, brain volumetric factors, and symptoms of depression were preformed in order to develop predictive models of DMN, CEN, and SN functional connectivity. A Pearson correlation was used to examine the relationship between SN functional connectivity and DMN/CEN anticorrelation. The predictive model for the DMN accounted for approximately 50% of the variance within the network, and was comprised of factors which included TBI severity, age at assessment, volumetric factors, and cognitive factors (including attention and abstract verbal reasoning). The predictive model for the CEN accounted for 37% of the network’s variance, and was comprised solely of cognitive factors, including verbal ability, attention, and inhibition. The SN model accounted for 45% of the variance, and was comprised of factors that included gender, functional outcomes, volumetric factors, and cognitive factors (including attention and cognitive switching). The functional connectivity within the SN had a trending positive correlation with the degree of anticorrelation between the DMN and CEN. These results not only reveal the factors that contribute to functional connectivity, but they also highlight the differences between networks, including that the DMN may be more sensitive to volumetric changes and TBI severity than the CEN or SN. | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.oclc | 957676333 | |
| dc.identifier.uri | https://hdl.handle.net/2152.5/3599 | |
| dc.language.iso | en | en |
| dc.subject | Brain | en |
| dc.subject | Brain Injuries | en |
| dc.subject | Magnetic Resonance Imaging | en |
| dc.title | Factors Affecting Resting-State Functional Connectivity Across Three Intrinsically Connected Networks in Traumatic Brain Injury | en |
| dc.type | Thesis | en |
| dc.type.material | text | en |
| thesis.degree.department | Graduate School of Biomedical Sciences | en |
| thesis.degree.discipline | Clinical Psychology | en |
| thesis.degree.grantor | UT Southwestern Medical Center | en |
| thesis.degree.level | Doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |