Dynamic Neuromodulatory Network Control: A Multi-State Imaging Framework for Transdiagnostic Psychiatry

Over 1 billion people worldwide suffer from psychiatric disorders, yet treatment remains largely trial-and-error, with many patients cycling through multiple medications before finding effective relief. This reflects the need to develop mechanistic biomarkers to enhance or supplant current symptom-based diagnosis. Neuromodulatory subcortical systems (the source of noradrenaline, serotonin, dopamine, and acetylcholine) govern brain network function, are implicated across all psychiatric disorders, and are targets of most psychotropic medications. However, the small size and deep location of their source nuclei have left them critically understudied. My goals are to 1) characterize how these systems collectively control cortical networks across cognitive and affective states, and 2) determine whether these state-dependent patterns predict psychiatric symptoms and treatment response. I will use brainstem-optimized fMRI to map how neuromodulatory nuclei dynamically modulate cortical networks across naturalistic and task stimuli. Using transdiagnostic cohorts, I will test whether task-evoked patterns outperform resting-state in predicting symptoms and differential response to serotonergic versus dopaminergic antidepressants. By shifting focus from cortical regions to the ascending systems that regulate them, these findings could enable treatment selection based on (dys)function of the core circuits where treatments act, transitioning psychiatric care from trial-and-error towards precision medicine.