# Supplemental: Neural and cognitive propensity for trait rumination in adolescents: a multimodal study with ecological momentary assessments and MRI

> **NIH NIH R01** · MCLEAN HOSPITAL · 2024 · $269,872

## Abstract

Abstract
Rumination is a transdiagnostic risk factor for developing depression and anxiety in
adolescents. Rumination reflects compromised inhibitory control capability and is fundamentally
a maladaptive psychological process in response to stressors. Understanding the neural
correlates of rumination and inhibitory control enriches our knowledge on the neural and
cognitive propensity for rumination, thereby informing the design of effective clinical programs
for the prevention and treatment of adolescent mental health issues. To achieve the objectives
of identifying neural features associated with trait rumination and inhibitory control capabilities,
the proposed project will analyze the multimodal data from the parent R01 including anatomical
MRI, resting state fMRI, the severity and temporal dynamics of trait rumination measured via
Ecological Momentary Assessments (EMA), and performance profiles from a Sustained
Attention to Response Task (SART). Voxelwise-based morphometry (VBM) will be used to
obtain voxelwise estimate of gray matter density. Algorithms for calculating the Amplitudes of
Low Frequency Fluctuation (ALFF) from the resting state fMRI data will be utilized to obtain
voxelwise ALFF maps. Resting State Functional Connectivity (RSFC) analyses will be
conducted using Regions of Interest (ROI) including subgenual prefrontal cortex, left amygdala
and Posterior Cingulate Cortex (PCC). Voxelwise maps of gray matter density, ALFF and RSFC
will be used for linear regression analyses with EMA and SART metrics as regressors to identify
significant clusters after false discovery rate correction. Based on existing knowledge on the
neurobiology of rumination and inhibitory control, we hypothesize trait rumination to be
significantly associated with the gray matter density of the Dorsal Lateral Prefrontal Cortex
(DLPFC), the ALFF values of the medial prefrontal cortex, amygdala, hippocampus and
subgenual cingulate cortex, as well as the RSFC between the subgenual prefrontal cortex and
Default Mode Network (DMN); we also hypothesize SART commission errors (incorrect
response to “no-go” trials) and frequencies of task-unrelated thoughts during SART probe trials
to be associated with the gray matter density at DLPFC and dorsal anterior cingulate cortex as
well as the RSFC between DMN, frontal-parietal network and salience network. Finally, the MRI
metrics of the significant clusters identified above will be entered into Machine Learning (ML)
processes to predict depression severity as measured by the Center for Epidemiological Studies
Depression (CESD) scale scores. The ML processes include data-driven feature selection and
cross-validation steps to quantitatively evaluate the predictive power of these neural features.

## Key facts

- **NIH application ID:** 11004551
- **Project number:** 3R01AT011002-04S1
- **Recipient organization:** MCLEAN HOSPITAL
- **Principal Investigator:** Diane Joss
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $269,872
- **Award type:** 3
- **Project period:** 2021-06-15 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11004551

## Citation

> US National Institutes of Health, RePORTER application 11004551, Supplemental: Neural and cognitive propensity for trait rumination in adolescents: a multimodal study with ecological momentary assessments and MRI (3R01AT011002-04S1). Retrieved via AI Analytics 2026-06-01 from https://api.ai-analytics.org/grant/nih/11004551. Licensed CC0.

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