Results: ICCs across the components were generally high with 59 of the components demonstrating ICCs of 0.99, but another 44 components showed relatively low ICCs ( CS−: F1,93 = 122.95, p 0.25) and attenuated responses during extinction on Day 2 (differential response on Day 1 vs 2: F1,92 = 78.27, p 0.25). Analyses of the relationships between changes in component loadings and changes in unidimensional outcome data are planned pending the release of 6-month psychometric data. Preliminary analyses also assessed the relationship between unidimensional posttraumatic syndromes (e.g., PTSD, depression, and anxiety) and component loadings. For the 54 participants with longitudinal data, intraclass correlation coefficients (ICC) were obtained to evaluate the stability of the observed components. LICA returns a participant-specific value for the loading of each component, with each component representing shared variance across the feature modalities. A high dimensionality was estimated (d = 119) to better separate participant-specific noise from signal data. ![]() Data fusion was completed using linked independent components analysis (LICA) of the above brain structure features. Diffusion tensor imaging was completed to obtain measures of fractional anisotropy (FA), mean diffusivity (MD), and mode of the diffusion tensor (MO) of the white matter skeleton. Structural MRI processing through a combination of FMRIPPREP and FSLVBM was completed to obtain measures of gray-matter volume (GMV), cortical thickness (CT), and pial surface area (PSA) to index gray matter properties. Following quality control and removal of participants missing scan modalities, n = 248 participants were included in a multimodal data fusion analysis with n = 54 scanned at both timepoints. In the current analysis, an initial dataset of 363 participants completed MRI scans within a) ~2-weeks, b) ~6-months, or c) both ~2-weeks and ~6-months post-trauma. Methods: Participants were recruited from emergency departments following trauma exposure (primarily motor vehicle collisions) as part of the AURORA Study, a multisite longitudinal investigation of posttraumatic syndromes. ![]() Therefore, the present study utilized longitudinal multimodal magnetic resonance imaging (MRI) to investigate potential changes in structural covariance networks. ![]() Further, prior research has largely used unimodal approaches which do not take into account shared variability between brain measures (i.e., noting that brain gray and white matter likely covary). However, limited work to date has investigated how changes in brain structure over time relate to changes in posttraumatic symptoms. Prior human and animal model research suggests that trauma/stress exposure can trigger maladaptive processes that may lead to brain structure changes that contribute to posttraumatic dysfunction. Structural Covariance Network Stability Over Time After Trauma Exposure: Preliminary Assessment of Longitudinal Multimodal MRI Data From the Aurora Study Nathaniel Harnett*, Timothy Ely, Sanne van Rooij, Katherine Finegold, Lauren LeBois, Vishnu Murty, Tanja Jovanovic, Kerry Ressler, Jennifer Stevens Harvard Medical School/McLean Hospital, Belmont, Massachusetts, United Statesīackground: The structure of human neural circuits is critical for neurocognitive processes that maintain healthy emotional function.
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