HARMONY: A large-scale harmonized neuroimaging dataset for research on anxious misery disorders

Abstract

Patterns of brain circuit dysfunction underlying depression and anxiety have been increasingly characterized, including dimensional and subtype variation. A key challenge is determining how such patterns generalize across populations and measurement frameworks. Here, we introduce HARMONY, a harmonized multimodal neuroimaging dataset supporting large-scale investigation of brain-behavior associations across symptom-defined dimensions. HARMONY integrates four Human Connectome Project-style Connectomes Related to Human Disease cohorts spanning adolescence to later adulthood and capturing anxious misery symptoms. The resource combines standardized HCP-style preprocessing, quality control, imaging-derived phenotypes, and harmonized symptom measures into a clinically enriched public dataset. Proof-of-concept analyses using HARMONY showed that pooling heterogeneous cohorts increased statistical power for detecting associations between imaging-derived phenotypes and anhedonia and depression severity. Effect sizes remained modest, consistent with symptom-based measures across heterogeneous samples. Functional imaging-derived phenotypes showed the strongest multivariate predictive performance. In summary, HARMONY provides a large multi-cohort resource for reproducible mental health neuroimaging research.