Sexual trauma leaves wounds that science cannot see, yet survivors live with them every day. Traditional tools rely on words or self-reports, often forcing survivors to “bleed in silence” when their pain is doubted or dismissed. Trauma, however, is not one-dimensional. It disrupts multiple brain networks and produces states fear, vigilance, detachment that cannot be captured by words alone. This creates the need for approaches that reveal trauma’s complexity in ways that are both objective and interpretable. We propose a framework that combines fMRI, EEG, and interpretable self-supervised AI (DINO) to uncover hidden patterns of trauma in the brain. Instead of producing abstract scans or opaque predictions, the system will generate exploratory measures of trauma response that support therapists’ understanding while guiding future research. These measures will be presented through a simple dashboard that summarizes three indices (TPI, DI, RBS) alongside heatmaps and plain language notes. By turning complex data into clear, anonymized session snapshots, the dashboard provides researchers with a practical output that can be compared across participants and refined in future work