# Precision mapping of individualized executive networks in youth

> **NIH NIH R37** · UNIVERSITY OF MINNESOTA · 2021 · $818,531

## Abstract

ABSTRACT
 Executive function (EF) improves dramatically during childhood and adolescence, and failures of EF
are associated with both a broad range of negative outcomes and diverse mental illnesses. The brain circuits
responsible for EF are spatially distributed, and include the fronto-parietal, cingulo-opercular, and salience
systems. These networks have typically been studied using standardized network atlases, which assume a
straightforward mapping between structural and functional neuroanatomy across individuals. However,
multiple independent efforts in adults using precision functional mapping techniques have recently
demonstrated that there is marked inter-individual variation in functional topography, which is defined as the
spatial distribution of functional networks on the cortex. The over-arching hypothesis of this proposal is that
individual variation in functional topography is a critical determinant of EF in youth. Our collaborative team
recently published the first report of individualized functional networks in children using a cross-sectional
sample (Cui et al., Neuron 2020). In this proposal, we will build upon this initial work by replicating and
generalizing this finding using two large cross-sectional datasets with high-resolution imaging: the Healthy
Brain Network (HBN; n=5,000) and Human Connectome Project: Development (HCP-D, n=1,300). Critically,
we will also leverage the unprecedented resources of the Adolescent Brain and Cognitive Development Study
(ABCD, n=11,572) to delineate within-subject change in personalized networks. In this proposal, we will first
harmonize these massive data resources using advanced techniques originally developed for statistical
genomics (Aim 1). Next, we will describe how personalized networks evolve with age (Aim 2) and predict EF
(Aim 3). Finally, we will use machine learning tools to discover how the functional topography of personalized
executive networks predict dimensions of psychopathology in a data-driven manner (Exploratory Aim 4).
Throughout, we will adhere to best practices of open science to maximize reproducibility, and ensure that all
processed data, code, and results are openly shared with the neuroscience community. Together, this
research will establish that functional topography is essential for understanding EF, and will motivate trials of
personalized neuromodulatory therapies.

## Key facts

- **NIH application ID:** 10178201
- **Project number:** 1R37MH125829-01
- **Recipient organization:** UNIVERSITY OF MINNESOTA
- **Principal Investigator:** Damien A Fair
- **Activity code:** R37 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $818,531
- **Award type:** 1
- **Project period:** 2021-07-01 → 2026-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10178201

## Citation

> US National Institutes of Health, RePORTER application 10178201, Precision mapping of individualized executive networks in youth (1R37MH125829-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10178201. Licensed CC0.

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