ACNP 59th Annual Meeting: Poster Session II

<span title="">2020</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="" style="color: black;">Neuropsychopharmacology</a> </i> &nbsp;
The structure of human neural circuits is critical for neurocognitive processes that maintain healthy emotional function. 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. However, limited work to date has investigated how changes in brain structure over time relate to changes in posttraumatic symptoms. Further, prior research has largely used
more &raquo; ... approaches which do not take into account shared variability between brain measures (i.e., noting that brain gray and white matter likely covary). Therefore, the present study utilized longitudinal multimodal magnetic resonance imaging (MRI) to investigate potential changes in structural covariance networks. 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. 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. 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. 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. 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. Data fusion was completed using linked independent components analysis (LICA) of the above brain structure features. A high dimensionality was estimated (d = 119) to better separate participant-specific noise from signal data. LICA returns a participant-specific value for the loading of each component, with each component representing shared variance across the feature modalities. For the 54 participants with longitudinal data, intraclass correlation coefficients (ICC) were obtained to evaluate the stability of the observed components. Preliminary analyses also assessed the relationship
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="">doi:10.1038/s41386-020-00891-6</a> <a target="_blank" rel="external noopener" href="">pmid:33279935</a> <a target="_blank" rel="external noopener" href="">pmcid:PMC7735200</a> <a target="_blank" rel="external noopener" href="">fatcat:5nfeakhcfnhpnjyuvli34vyxxq</a> </span>
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