Dissociative symptoms in traumatized individuals are common, debilitating, and costly; however, little is known about how its biological mechanisms interact with PTSD treatment. Traumatic dissociation broadly encompasses a range of distinct, yet clinically interrelated symptoms: depersonalization, derealization, amnesia, numbing, flashbacks, passive influence phenomena, and identity disturbances. Either alone or in various combinations, these symptoms serve as diagnostic criteria and commonly associated features across multiple psychiatric disorders. Traumatic dissociation is also associated with significant personal and societal burden. Traumatized individuals with dissociative symptoms typically have co-occurring psychiatric conditions, high rates of self-destructive behaviors and suicidality, and are disproportionate treatment utilizers. In addition, they are at increased risk for attrition, non-response and relapse following treatment interventions. Despite the significant and disabling nature of traumatic dissociative symptoms, little is known about the neurobiology of these processes and targeted interventions do not exist. PTSD treatment studies have neither looked at neural intermediate phenotypes of dissociation, nor how these are associated with psychophysiological and digital phenotypes. Compared to clinical symptom measures, these biological and in-the-moment digital markers of dissociation may more robustly map onto the underlying core aspects of the disorder differentiating dissociation subtypes following childhood and adult trauma. We propose to build upon our prior Exploratory R21 to now capture longitudinal multimodal phenotype data related to dissociation, pre-, post- and during PTSD treatment modalities that include empirically-derived, exposure-based components. The goals of this study will be 1) to understand the differential biomarkers that map onto dissociative symptoms, and 2) to understand how these biomarkers may best predict trajectory of response to empirically based standard-of-care treatments. For each of these Aims, we will collect Neuroimaging, Physiology, and Digital Phenotyping data, applying computational modeling with multimodal data to provide machine-learning based, unbiased predictive models of dissociative intermediate phenotypes at baseline and longitudinally. This naturalistic study will allow us to map the biology of dissociation, and importantly, the change in dissociative symptoms and underlying biomarkers over time, using naturalistic evidenced-based treatment for PTSD in 130 treatment-seeking patients with PTSD, and a range of dissociative symptoms. Successful completion of these Aims will provide a novel and powerful understanding of the biological markers of dissociation subtypes following trauma exposure, and will identify biological mechanisms for understanding and treating PTSD with dissociation.