Brain states are widely distributed patterns of neural activity, a consequence of specific physiological conditions. Epidemiology demonstrates that early life adversity (ELA) followed by life-threatening fear increases vulnerability to various mental and physical disorders, possibly by interfering with natural brain states. Our understanding of natural brain states remains limited. Therefore, characterization of these brain states, their changes after ELA and subsequent responses to threat, will be paramount to enabling precision medicine targeting neuromodulatory systems to improve clinical outcomes. In order to define brain states accurately under each condition and determine endogenous neuromodulation, measurements must be made brain-wide, longitudinally, and with high spatial resolution. In the F99 phase of this proposed research Taylor Uselman will continue developing statistical methods and computational software to analyze images from his established longitudinal manganese-enhanced MRI (MEMRI) protocol. In completed experiments, he acquired and processed MEMRI data with and without ELA before and after exposure to acute threat. In new experiments, he is developing structural equation modeling to test whether sample covariance between segments in MEMRI images is a meaningful measure of functional brain architecture. He will then explore data-driven computational algorithms to characterize brain states and underlying networks in all his longitudinal MEMRI datasets. He will also explore the influence of the noradrenergic system on brain states, using viral transduction for chemogenetic activation of the locus coeruleus followed by computational analyses of images acquired by his longitudinal MEMRI protocol. By fulfilling this aim, Uselman will gain expertise in experimental design, neuroimaging, computational neuroscience, and exogenous neuromodulation technologies. In the K00 phase, Uselman will investigate control of pathological brain states via neuromodulation technology. Based on his skills and results acquired in Aim 1, he will develop control theory algorithms toward identification of targets for exogenous modulation of brain states occurring in pathological conditions. He will then develop neuromodulation technology to influence identified targets for therapeutic intervention and assess intervention efficacy by imaging. The proposed work will yield innovative strategies to enable the identification and characterization of brain states after ELA, sophisticated open-access software, and a generalizable imaging protocol. Ultimately, application of these advanced methods to ELA’s effect on brain states will provide a deeper understanding of increased vulnerabilities after ELA to mental disorders. An additional outcome of this fellowship will be the preparation of a talented young investigator for a future productive career in brain science.