Variation in Neurodegeneration-Linked Brain Regions in Young Adult APOE E4 Carriers With Spina Bifida.

The traditional categorical diagnostic system that emerged in DSM-3() has been consistently criticized for ignoring dimensional features of psychopathology, as well as the extent to which individual diagnoses overlap with each other, as well as with normal variation(). To address these limitations, and to better approximate neurobiological systems, the NIMH Research Domain framework was developed(). This framework posited 5, and later 6, dimensions on which neuropsychiatric systems could be mapped across scale: Negative Valence Systems, Positive Valence Systems, Cognitive Systems, Social Processes, Arousal and Regulatory Systems, and Sensorimotor Systems(). Substantial effort has been expended to measure functions in individual domains(). One challenge to the more widespread application of the RDoC framework is the lack of documentation of dimensional measurements in clinical settings(). In an effort to enable dimensional analysis of narrative clinical documentation, we previously developed and validated a simple natural language processing approach that employs lists of terms and synonyms corresponding to each domain, and measures the extent to which such terms are reflected in notes(). We showed that this approach had utility in a range of contexts – predicting longitudinal clinical outcomes including suicide(), correlating with postmortem neuropathology(), and enabling the first genomewide association studies of RDoC domains(). The development of large language models enables far more powerful approaches to document summarization and concept extraction, with a range of applications in medicine(). We hypothesized that these models could estimate RDoC-like dimensional measures of psychopathology at scale from electronic health records, overcoming some of the limitations of prior natural language processing strategies. This approach would enable large-scale investigation of RDoC-like phenotypes across health systems, biobanks, and other large longitudinal data sets. To test this possibility, we drew on a large cohort of children and adolescents evaluated in a psychiatric emergency room(). We did not aim to develop clinically-deployable predictors, but rather to use clinical outcomes to examine predictive validity for the resulting scores as compared to previous methods.