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.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 02 Jul 2026.

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