# Linking brain activity during naturalistic tasks to individual phenotypes on the depression spectrum

> **NIH NIH R00** · DARTMOUTH COLLEGE · 2022 · $249,000

## Abstract

PROJECT SUMMARY / ABSTRACT
Rather than a dichotomy between health and pathology, many mental illnesses—especially depression and other
mood disorders—are best conceptualized as the far end of a phenotypic spectrum, suggesting that
characterizing individual differences in brain function and behavior will help further our understanding of disease.
Existing work suggests that fMRI has the potential to predict individual behaviors from brain function, yet progress
has been hindered by an overreliance on group studies (i.e., patients versus controls) and limited paradigms
(i.e., either highly controlled tasks that risk being artificial, or at the other extreme, resting state, which is entirely
unconstrained and difficult to interpret). Naturalistic fMRI, in which subjects do complex, engaging tasks such as
watching films or listening to stories, offers an alternative that more closely mimics real-world cognition and may
allow researchers to extract richer, more meaningful information from a single individual’s scan. As such, these
paradigms are promising candidates for brain “stress tests” that would elicit patterns of brain activity that predict
present or future behaviors. The specific aims of this project are: (1) to leverage existing large-scale datasets
to develop methods to predict phenotypes from naturalistic fMRI data; (2) to design and conduct an fMRI study
using targeted film stimuli to draw out individual variability of interest, specifically in traits related to depression;
and (3) to extend the newly developed paradigms and analyses to a longitudinal study of a population at risk for
depression and/or other mood disorders. Several innovative approaches to data analysis will be investigated.
The central hypothesis is that brain activity evoked by these paradigms will vary across individuals in a
continuous, multidimensional space that covaries with phenotype strength, that these relationships will be strong
enough to predict phenotypes in unseen individuals, and that modified (e.g., attenuated) versions of patterns
associated with illness will be detectable via these paradigms in those at risk before the emergence of symptoms.
The long-term goal of the PI is to become an independent NIH-funded faculty member at a research-intensive
university, with a research program exploring the basic cognitive neuroscience of individual differences in
personality and cognition, as well as developing translational applications for psychiatry. To reach this goal, the
training objectives for this award are to enhance the PI’s skills in the following areas: (1) applying machine
learning techniques to predict individual-subject behavior from fMRI data; (2) conducting neuroimaging and
behavioral research on depression and mood disorders with clinical and at-risk populations; and (3) gaining
professional skills essential for a successful independent research career. The environment in which the career
development will take place is the Intramural Research Prog...

## Key facts

- **NIH application ID:** 10415111
- **Project number:** 5R00MH120257-04
- **Recipient organization:** DARTMOUTH COLLEGE
- **Principal Investigator:** Emily Suzanne Finn
- **Activity code:** R00 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $249,000
- **Award type:** 5
- **Project period:** 2020-07-01 → 2023-09-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10415111

## Citation

> US National Institutes of Health, RePORTER application 10415111, Linking brain activity during naturalistic tasks to individual phenotypes on the depression spectrum (5R00MH120257-04). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10415111. Licensed CC0.

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