# HCP-2.0: Ascertaining Network Mechanisms and Analytics of Emotional Dysfunction (HARMONY)

> **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2024 · $778,117

## Abstract

In response to NIMH Strategic priorities “to map the connectomes for mental illness, harness the power of data”
and “develop computational approaches” and the Notice of Special Interest (NOT-MH-21-175) regarding the
“Use of Human Connectome Data for Secondary Analysis” we leverage data across four Connectomes Related
to Human Disease (CRHD) projects, as well as the Human Connectome Project (HCP) Aging and Development
Lifespan and the Adolescent Brain Cognitive Development (ABCD) studies to address a major challenge in our
field. Specifically, we use novel computational approaches to identify cohesive symptom/cognitive dimensions
and subtypes across the continuum of anxious misery disorders in relation to the natural heterogeneity of brain
network alterations. This proposal capitalizes on an established record of collaboration between independent
CRHD projects and the HCP data core. Multimodal magnetic resonance imaging (MRI), clinical and cognitive
data will be integrated for 2,187 people, including 531 adults and 150 adolescent patients with anxious misery
disorders, and 1,506 matched healthy people. Cutting edge HCP and UK Biobank processing streams will
centrally process data to derive harmonized multimodal imaging features or phenotypes (IDPs) across datasets,
extracted from resting state functional MRI, task-derived functional MRI, structural MRI and diffusion imaging
data for use in analyses and for dissemination with the scientific community (Aim 1). Using a novel group
regularized canonical correlation analysis (GRCCA), we will evaluate the covariation between IDPs and clinical
and cognitive measures to identify brain network-symptom/cognitive dimensions of anxious misery across
adolescents and adults (Aim 2). In tandem, we will use the unsupervised Uniform Manifold Approximation and
Projection (UMAP) with Density-based Spatial Clustering of Applications with Noise (DBSCAN) applied to identify
anxious misery subtypes (Aim 3) distinguished by brain network IDP profiles and symptom and cognitive
measures. Both data-driven analysis approaches will be applied to determine similarities of brain network-
symptom/cognitive dimensions and subtypes across adolescent and adults and their influence on antidepressant
treatment outcomes. Our preliminary data suggest these methods will advance our understanding of the links
between brain network dysfunction and specific psychopathology across age and in relation to antidepressant
response well beyond DSM diagnoses.
Public Health Significance: Successful completion of this project will deconstruct and validate the natural
heterogeneity of brain circuit alterations underlying transdiagnostic anxious misery disorders. The resulting brain-
clinical phenotypes will yield a robust set of dimensional and subtype targets for future clinical and mechanistic
investigations of heterogeneity in anxious misery disorders across the lifespan. These phenotypes can also be
used to inform precision medicine approaches t...

## Key facts

- **NIH application ID:** 10930150
- **Project number:** 5R01MH132962-02
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Janine Diane Bijsterbosch
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $778,117
- **Award type:** 5
- **Project period:** 2023-09-15 → 2025-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10930150

## Citation

> US National Institutes of Health, RePORTER application 10930150, HCP-2.0: Ascertaining Network Mechanisms and Analytics of Emotional Dysfunction (HARMONY) (5R01MH132962-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10930150. Licensed CC0.

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