# Harnessing Network Science to Personalize Scalable Interventions for Adolescent Depression

> **NIH NIH DP5** · STATE UNIVERSITY NEW YORK STONY BROOK · 2020 · $71,935

## Abstract

Project Summary/Abstract
Major depression (MD) is the leading cause of disability in youth, with a global economic burden of >$210
billion annually. However, up to 70% of youth with MD do not receive services. Even among those who do
access treatment, 30-65% fail to respond, demonstrating a need for more potent, accessible interventions. A
challenge underlying limited treatment potency is MD's heterogeneity: An MD diagnosis reflects >1400
symptom combinations, creating a need for treatments matched to personal clinical need. Separately, low
treatment accessibility stems from the structure of existing interventions. Most span many weeks and are
designed for delivery by highly trained clinicians , making them difficult to scale. This proposal aims to address
the need for accessible, potent youth MD interventions by integrating methods and findings from previously
separate areas: single-session intervention (SSI) research and network science. In a meta-analysis of 50
randomized trials, the investigator has found that SSIs can reduce diverse youth psychiatric problems,
including MD. The investigator also found that a web-based SSI teaching growth mindset (the belief that
personal traits are malleable) reduced depression and anxiety in high-symptom youth across 9 months.
Thus, well-targeted SSIs can yield lasting benefits—but given MD's heterogeneity, there is a need for tools
that can match youth to SSIs optimized for personal symptom structures. The proposed project harnesses
computational advances from the network approach to psychopathology, which views psychiatric disorders as
causal interactions between symptoms, to evaluate such a tool. The first goal is to establish a new method of
characterizing MD symptom structures; the second is to test parameters from these structures as predictors of
response to two SSIs targeting distinct MD features (behavioral vs. cognitive symptoms). Specifically, Aim 1 is
to establish guidelines for computing personalized symptom networks using experience sampling method
(ESM) data from youth with MD collected 7x/day for 3 weeks (N=50, ages 11-16; 147 time-points each). This
will include a comparison of two leading approaches for computing network parameters, such as outward
centrality (the degree to which a symptom prospectively predicts other symptoms). Aim 2 is to test network
parameters as SSI outcome predictors among youth with MD (N=180). Youth will be randomized to a
behavioral activation (BA) SSI (adapted from evidence-based BA SSIs); the mindset SSI noted above; or a
control SSI. Network parameters will be tested as predictors of SSI response. For instance, youth with stronger
centrality on a behavioral symptom (e.g. withdrawal from pleasurable activities) may respond more favorably to
the BA SSI, and youth with stronger centrality on a cognitive symptom (e.g. hopelessness) to the mindset SSI.
Results may identify a novel means of matching youth to targeted MD SSIs by personal need. The project will
also in...

## Key facts

- **NIH application ID:** 10126161
- **Project number:** 3DP5OD028123-02S1
- **Recipient organization:** STATE UNIVERSITY NEW YORK STONY BROOK
- **Principal Investigator:** Jessica Lee Schleider
- **Activity code:** DP5 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $71,935
- **Award type:** 3
- **Project period:** 2019-09-16 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10126161

## Citation

> US National Institutes of Health, RePORTER application 10126161, Harnessing Network Science to Personalize Scalable Interventions for Adolescent Depression (3DP5OD028123-02S1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10126161. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*