<p dir="ltr">Functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) via MRI are powerful, noninvasive methods for imaging of the human brain. Here, two studies are presented which explore algorithm development for the processing and analysis of fMRI and DTI-MRI data.</p><p dir="ltr">In the first study, brain functional connectivity was analyzed in a cohort of high school American football athletes over a single play season and compared against participants in non-collision high school sports. Football athletes underwent four resting-state functional magnetic resonance imaging sessions: once before (pre-season), twice during (in-season), and once 34–80 days after the contact activities play season ended (post-season). For each imaging session, functional connectomes (FCs) were computed for each athlete and compared across sessions using a metric reflecting the (self) similarity between two FCs. HAEs were monitored during all practices and games throughout the season using head-mounted sensors. Relative to the pre-season scan session, football athletes exhibited decreased FC self-similarity at the later in-season session, with apparent recovery of self-similarity by the time of the post-season session. In addition, both within and post-season self-similarity was correlated with cumulative exposure to head acceleration events. These results suggest that repetitive exposure to HAEs produces alterations in functional brain connectivity and highlight the necessity of collision-free recovery periods for football athletes.</p><p dir="ltr">In the second study, a method for harmonization of DTI-MRI data across sites was assessed. Pooling of data from multiple sites is limited by noise characteristics of individual scanners and their receive chain elements (e.g., coils, filters, algorithms), requiring careful consideration of methods to harmonize multisite data. Here, the ComBat data harmonization method was assessed on DTI-MRI data to determine if the harmonizing transformation produced by the algorithm could be transferred to harmonize new subject data from previously-observed sites without necessitating reharmonization of pre-existing data. Results indicated that this transferable ComBat methodology (T-ComBat) yielded reduced differences in fractional anisotropy and mean diffusivity across sites when compared with unharmonized data but did not fully reach the performance of ComBat applied to the entire dataset. Results of this study provide guidelines for circumstances (namely, the proportion of subjects one may wish to add to an existing dataset) under which T-ComBat may be effectively applied to harmonize new subject DTI-MRI data.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/25665759 |
| Date | 22 April 2024 |
| Creators | Bradley Jacob Fitzgerald (13783537) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/_b_ALGORITHM_DEVELOPMENT_FOR_FUNCTIONAL_MAGNETIC_RESONANCE_IMAGING_ANALYSIS_AND_DIFFUSION_TENSOR_IMAGING_DATA_HARMONIZATION_b_/25665759 |
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