Bioinformed idiographic symptom networks to identify neurobiological predictors of affective-state transitions in bipolar disorder.

Bipolar disorder remains an understudied psychiatric condition. Research has been focused on neurobiological mechanisms with the hope of identifying unique markers distinguishing bipolar disorder from similar conditions (i.e., major depressive disorder and schizophrenia), in addition to neurocognitive mechanisms driving affective state transitions. While these investigations continue to gain popularity, the current literature does not present a strong account for a neurobiological cause of affective state transitions and document significant heterogeneity between individuals. We argue that current difficulties with identifying neurobiological associations of affective state transitions can be targeted by incorporating idiographic symptom network analyses, a statistical and methodological tool more commonly used within the behavioral psychopathology literature, into the neurobiological study of bipolar disorder. Idiographic symptom networks allow the modeling of temporal relationships between symptoms and behavior at a finer temporal resolution compared to standard longitudinal analyses. As such, collecting many within-subject neurological measures samples alongside ecological momentary assessments indexing transient mood and cognitive functioning can provide an opportunity to identify potential neurobiological drivers of bipolar disorder symptoms and affective state transitions. The perspective explores current methodological designs, their associated strengths and limitations, in addition to the clinical utility with adopting idiographic network analyses within clinical neuroscience research.