# The Roles of Inflammatory and Glutamatergic Processes in the Neurodevelopmental Mechanisms Underlying Adolescent Depression

> **NIH NIH K01** · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · 2021 · $41,086

## Abstract

ABSTRACT
Suicidal thoughts and behaviors (STBs) are growing more prevalent among adolescents; despite these
alarming trends, researchers have been hampered in their efforts to identify antecedents of STBs because of
the transient nature of suicidal impulses that are unlikely to be captured during a clinical or laboratory
assessment. Furthermore, research in this area has focused primarily on time-invariant factors (e.g., gender)
and self-disclosed information, which greatly limits our understanding of the neurobiological and psychosocial
mechanisms underlying the etiology and maintenance of STBs. Advances in real-time monitoring technology,
including mobile apps, provide an unprecedented opportunity to continuously measure key behaviors relevant
for understanding suicide risk (e.g., social interactions, sleep) outside of the laboratory for the purposes
generating digital phenotypes of STBs. Moreover, statistical approaches such as machine learning are ideal for
handling high-dimensional data across different constructs (e.g., clinical, digital, neurobiological) and are
increasingly being used in the context of improving prediction of STBs. Thus, the overarching goal of this
supplement is to collect and integrate digital phenotypes with neurobiological phenotypes in a machine
learning framework to identify multi-level factors associated with the etiology and maintenance of STBs in a
high-risk sample: depressed adolescents. Specifically, we will build on the existing infrastructure of the parent
grant—which focuses on characterizing the stress-related neurobiological trajectories using a multi-level
approach in a sample of depressed adolescents—by seeking to identify multi-level predictors and trajectories
of STBs in this high-risk sample and to compute deviations from normative phenotypes and trajectories
computed from a low-risk comparison group. We will use machine learning algorithms to identify the
constellation of factors that best predict likelihood of engaging in STBs by Time 3 among the depressed
adolescents (Aim 1); we will also identify the factors that best predict trajectories of STBs based on changes
from Time to Time 3 among the depressed adolescents (Aim 2); we will also test whether deviations from
normative phenotypes and trajectories (computed from data in the healthy controls) are better predictors of
STBs (Aim 3). In accordance with NOT-MH-19-026 (“Administrative Supplements for NIMH Grants to Expand
Suicide Research”), this approach addresses current barriers in our understanding of the mechanisms of
action underlying suicide risk by collecting ecologically valid measurements of suicide-relevant behaviors and
by fostering advanced statistical methods for multi-level and cross-construct integration.

## Key facts

- **NIH application ID:** 9933235
- **Project number:** 3K01MH117442-05S1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
- **Principal Investigator:** TIFFANY CHEING HO
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $41,086
- **Award type:** 3
- **Project period:** 2018-06-01 → 2022-10-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9933235

## Citation

> US National Institutes of Health, RePORTER application 9933235, The Roles of Inflammatory and Glutamatergic Processes in the Neurodevelopmental Mechanisms Underlying Adolescent Depression (3K01MH117442-05S1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/9933235. Licensed CC0.

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