# Remote computational phenotyping of behavioral and affective dynamics in major depression

> **NIH NIH R01** · YALE UNIVERSITY · 2024 · $747,595

## Abstract

PROJECT SUMMARY / ABSTRACT
Major depression is a highly debilitating disorder affecting over 300 million people worldwide. Treatment
assignment can involve a lengthy trial-and-error process complicated by symptom heterogeneity. The
Research Domain Criteria (RDoC) matrix provides a framework for investigating psychiatric disorders that
integrates across multiple levels of analysis. Depressive symptoms are closely linked to the RDoC Positive
Valence Systems (PVS) domain, but it is unknown how PVS constructs relate to common depressive
symptoms including low mood, anhedonia, and apathy. Computational probes of behavioral and affective
dynamics show great promise as a means of ‘computationally phenotyping’ individuals and providing a way to
validate PVS constructs in relation to symptom heterogeneity. The ubiquity of smartphones makes them an
ideal platform for remote testing. We propose to collect longitudinal data using smartphones for three ‘gamified’
tasks that measure risky decision making, probabilistic reinforcement learning, and reward-effort trade-offs and
concurrent fluctuations in affective state. We will establish the reliability of remotely collected computational
assays of behavioral and affective dynamics for understanding heterogeneity in depressive symptoms. We will
first test a community sample (n=200) both in the lab and remotely by smartphone to verify that behavioral and
affective computational parameters have the same relationship to depressive symptoms (low mood,
anhedonia, and apathy) in both environments (Aim 1). We will then recruit a large sample of patients with
moderate depressive symptoms (n=400) and test them remotely using smartphones for up to 12 months (Aim
2). We will test whether behavioral and affective computational parameters are related to changes in
depressive symptoms over time. We will also use data-driven recurrent neural network approaches to identify
additional features of our data related to depressive symptoms. Finally, we will collect MRI scans and in-lab
data in a subsample of patients (n=200) from Aim 2 and ask whether reward sensitivity and reward prediction
error, features of all three tasks, map onto consistent neural circuitry and depressive symptoms (Aim 3). We
will test for a mapping between depression subtypes defined by brain network connectivity, behavioral and
affective computational parameters, and depressive symptoms. Using computational models, we can bridge
between levels of circuits, behavior, and self-report, and test for a mapping onto heterogeneity in symptoms,
enhancing our understanding of RDoC constructs and paving the way for more effective and timely
interventions to treat depression.

## Key facts

- **NIH application ID:** 10893402
- **Project number:** 5R01MH124110-05
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Robb Brooks Rutledge
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $747,595
- **Award type:** 5
- **Project period:** 2020-09-01 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10893402

## Citation

> US National Institutes of Health, RePORTER application 10893402, Remote computational phenotyping of behavioral and affective dynamics in major depression (5R01MH124110-05). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10893402. Licensed CC0.

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