Over the last 20 years, there has been increasing research on the negative effects of discrimination on the mental and physical health of people of color. As mental health has an important relationship with the functional connectivity of brain networks, it is vital to further understand this. One way to measure functional brain connectivity is by observing the activity of the brain’s resting state networks (RSN) while a participant is at rest. Previous studies investigating connectivity have demonstrated a relationship between altered connectivity of RSNs and neuropsychiatric disorders, including Alzheimer’s disease, depression, and anxiety. The RSN of interest in this analysis is the salience network (SN). This network, anchored in the anterior insula and dorsal anterior cingulate cortex, is involved in the responses to “salient” stimuli that are infrequent in space or time, compete for an individual’s attention, and are surprising or emotionally engaging, such as an act of discrimination. The aim of this study was to use a seed-based correlation analysis to examine the relationship between perceived discrimination and the functional connectivity of the SN in black and white participants and to evaluate the differences in SN functional connectivity between black and white participants. Resting state functional connectivity was measured by using the functional magnetic resonance imaging (fMRI) data collected from 18 healthy older adults partaking in two different studies investigating aging, cognition, and the accompanying changes in neuroanatomy. The Analysis of Functional NeuroImages (AFNI) software was used to examine the correlations in activation in the primary nodes of the SN with activation in clusters in the other primary nodes. Perceived discrimination was measured using the Experiences of Discrimination Scale (EOD), a self-report measuring the frequency of instances of discrimination and the perceived reason behind the discrimination. Preliminary results from this analysis demonstrated that black participants, when compared to the white participants, demonstrated greater functional connectivity between the left and right insula and decreased functional connectivity between the right anterior cingulate cortex and the right insula. Black participants demonstrated a positive association between perceived overall discrimination and functional connectivity between the right and left insula and a negative association between perceived overall discrimination and functional connectivity between the right anterior cingulate cortex and the left insula. The white participants demonstrated a negative association between perceived overall discrimination and functional connectivity between the left and the right insula. Considering the inability for these results to survive correction for multiple comparisons, a larger sample size is necessary to obtain true statistical significance. Although existing research has implicated functional connectivity changes in the regions of the salience network in populations experiencing social exclusion, anxiety, and depression, further analyses are necessary to expand the limited research available regarding the effects of overall and race-based discrimination on the resting state functional connectivity of neural networks involved in emotional processing.

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Data acquisition for Magnetic Resonance Imaging (MRI) is usually expensive and time-consuming. Multi-site study enables pooling more data with less cost. However, the reliability of multi-site study is not guaranteed since the data acquired from different sites always introduces site related variations. Further, these variation can not be fully resolved even using the same imaging protocols. In this thesis, we propose a seed-based image processing and statistical analyzing pipeline which mitigates the variations brought by sites to a statistically insignificant level. We collect data from a same group of subjects on two different scanners where each subject undergoes two imaging session on each site. Seed-based correlations of BOLD timeseries are used to access the connectivity between the human brain regions and seed region. The results imply that images collected from the four visits generate similar results of seed-based connectivity. The variance brought by site-related factors, machine, visit and interaction are proved to be insignificant by ANOVA test. Moreover, principle component analysis (PCA) are performed in a manner that data are reconstructed where subject identifiability is maximized. It is shown that reconstructed data introduces less variance from interaction of machine and visit.

Young football players are hypothesized to experience damage to the brain and brain function from repeated subconcussive head acceleration events (HAEs) during practices and games. Such damage may cause delayed cognitive and mental problems. Resting state fMRI (rs-fMRI) is an effective non-invasive method to detect alterations in brain functional connectivity. Seed-based rsfMRI analysis using the central node of the default mode network (DMN) as the seed is a common approach to measuring intrinsic changes of the DMN, accepted as a key network in brain function. Seed-based rs-fMRI analysis of the DMN was used to explore how age, HAE intensity, and HAE counts influence brain connectivity in youth athletes (ages 12-18). Middle school and high school football players and peer controls were studied using rs-fMRI before and after one season of competition. An identifiability matrix was generated from the seed-based connectivity matrix, allowing measurement of similarity between pre-season and post-season functional connectivity. The consistency of seed-based brain functional connectivity we observed across the season of play for players has no statistically significant difference from controls. The identifiability matrix exhibited no relation to the number and magnitude of any subset of HAEs experienced which rejected our hypothesis. Another finding is that high school football players exhibited the largest percentage increase in identification from middle school football players in the somatomotor network over other resting-state networks.