Symptom Profiles and Neural Mechanisms in Posttraumatic Psychopathology

Traditional psychiatric classification systems conceptualize mental disorders as discrete, categorical constructs. However, despite extensive efforts, no disorder-specific biological markers have been reliably identified. Instead, findings consistently reveal overlapping genetic, neural, and physiological patterns across various psychiatric conditions, calling into question whether traditional diagnostic categories reflect distinct diseases or arbitrary classifications imposed upon a continuous spectrum of distress. These limitations become particularly pronounced in the study of trauma-related psychopathology, where substantial symptom overlap, diagnostic heterogeneity, and high comorbidity complicate both clinical practice and scientific understanding.

This dissertation presents an alternative approach that shifts the focus away from discrete categorical diagnoses and toward transdiagnostic symptom dimensions, which may more accurately capture the underlying biological and psychological processes of psychopathology. The first empirical study presented in this dissertation applies a datadriven clustering approach to identify distinct symptom profiles in a large sample of treatment-seeking veterans, revealing meaningful symptom-based subgroups that transcend traditional diagnostic boundaries. The second empirical study builds upon this by examining neural markers of reward processing deficits, a key transdiagnostic feature linked to posttraumatic psychopathology, using event-related potentials (ERPs). Together, these findings illustrate the limitations inherent in disorder-based classification systems, particularly in trauma research, and underscore the promise of transdiagnostic, symptom-focused research to uncover more precise and clinically relevant markers of mental health conditions. By highlighting symptom dimensions rather than traditional diagnoses, this dissertation contributes to the ongoing effort to move psychiatric research toward frameworks that better reflect clincial complexity, enhancing both scientific understanding and patient-centered care.