# Ecologically‐Valid Emotion Signatures Enhance Mood Disorder Diagnostics

## TL;DR

This study introduces a new method using brain activity during naturalistic movie watching to improve the diagnosis of mood disorders like depression and bipolar disorder.

## Contribution

The novel Divergent Emotional Functional Networks (DEFN) approach uses naturalistic emotion induction and machine learning to enhance mood disorder diagnostics.

## Key findings

- DEFN-based models achieved 70.33% accuracy for MDD and 75.18% for BD, outperforming baseline models.
- DEFN showed strong reproducibility across age and sex, supporting its robustness.
- Emotion-specific functional patterns were identified with 83.99% accuracy in healthy individuals.

## Abstract

Mood disorders, including Major Depressive Disorder (MDD) and Bipolar Disorder (BD), are highly prevalent conditions. These disorders are characterized by persistent emotional dysregulation and substantial functional impairments. Despite extensive neuroimaging research, reliable neurofunctional markers remains elusive. To address this gap, we propose a novel approach that utilizes Divergent Emotional Functional Networks (DEFN), derived from functional magnetic resonance imaging (fMRI) in naturalistic contexts.By integrating naturalistic emotion induction, dynamic functional connectivity (dFC), and machine learning, we identified emotion‐specific functional patterns in healthy individuals with an accuracy of 83.99%. The DEFN was subsequently validated in clinical datasets, including a multi‐site MDD cohort (Hiroshima University: MDDs = 63, HCs = 111; University of Tokyo: MDDs = 62, HCs = 96) and an independently BD cohort (BDs = 59, HCs = 50). Using static functional connectivity (sFC) and nested 10‐fold cross‐validation, DEFN‐based models (MDD: 70.33%, BD: 75.18%) significantly outperformed baseline models in classifying patients and HCs (MDD: 70.33% vs. 57.58%; BD: 75.18% vs. 63.18%). Additionally, DEFN demonstrates highly reproducibility across age and sex, supporting the robustness of DEFN model. In conclusion, the DEFN approach presents a promising, reproducible, and clinically relevant neural marker for diagnosing, offering potential for more effective and timely interventions.

This study identifies ecologically‐valid Divergent Emotional Functional Networks (DEFN), derived from dynamic functional connectivity during naturalistic movie watching. The DEFN reliably enhances diagnostic accuracy for mood disorders, including major depressive and bipolar disorders, demonstrating strong reproducibility across demographic factors and offering a promising framework for emotion‐specific neural characterization in clinical applications.