¹The University of Hong Kong, Department of Psychology, SRT: AI, Society & Social Dynamics, Hong Kong, Hong Kong, SAR of China

Background: Pharmacological functional MRI (pharmaco‑fMRI) has greatly advanced understanding of the neural bases of cognitive and emotional processes and enabled mechanistically informed intervention studies. However, progress is hampered by the lack of precise neuromarkers for specific cognitive–affective processes and by limited evaluation of treatments under ecologically valid, real‑world conditions.

Aims & Objectives: This work introduces and applies a precision pharmaco‑imaging approach that integrates pharmacological challenges with machine‑learning–based neural decoding of fMRI data, transcriptomics, and naturalistic experimental designs to characterize and target neuropeptide systems, focusing on angiotensin II and oxytocin.

Method: A series of pharmaco‑fMRI studies combined: (1) transcriptomic mapping of neuropeptide receptor distributions; (2) resting‑state and task‑based pharmaco‑fMRI with angiotensin II type‑1 receptor (AT1R) blockade using losartan; and (3) pharmacological fMRI with oxytocin during naturalistic social and non‑social fear paradigms.

Results: Transcriptomic analyses showed a specific distribution of the angiotensin II receptor in human brain regions implicated in fear, arousal, motivation, and learning. Resting‑state pharmaco‑fMRI with losartan confirmed target engagement in receptor‑rich regions, and task‑based experiments demonstrated enhanced reward‑based learning, with machine‑learning decoding indicating sharpened neural reward‑prediction error signals. Two complementary studies combining oxytocin with naturalistic paradigms capitalized on a recently developed neuromarkers for fear under naturalsitic conditions (CAFE) and revealed that oxytocin selectively reduces the neural signature of fear in social - but not non‑social - contexts.

Discussion & Conclusions: These studies illustrate how precision pharmaco‑fMRI, leveraging transcriptomics, advanced neural decoding, and naturalistic designs, can yield mechanistically specific neuromarkers, accelerate target validation, and support the development of focused, hypothesis‑driven clinical trials for mental disorders.