# The Development of Individual Differences in Adolescent Brain Structure and Risk

> **NIH NIH R01** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2021 · $286,650

## Abstract

Rescuing Missed Longitudinal MRI visits in the UNC Early Brain Development
Studies Database
PROJECT ABSTRACT
 In our ongoing R01 (MH123747-01A1) “The Development of Individual Differences in Adolescent Brain
Structure and Risk”) project, we aim to characterize the portion of individual differences in brain structure in the
early adolescent brain is already present in the earlier years of life. Early adolescence and puberty is a major
period of postnatal brain development, characterized by dynamic structural and functional brain maturation and
reorganization, and emerging risk for psychiatric disorders, though it is not known how this period of development
contributes to individual differences in brain structure and risk. The UNC Early Brain Development Study (EBDS)
is a unique and innovative longitudinal study that has followed children, enrolled prenatally, with imaging and
cognitive/behavioral assessments at birth, 1, 2, 4, 6, 8, and 10 years. 482 children from this cohort are now
reaching adolescence, and we are following these children at 12, 14, and 16 years of age via MRI, cognitive and
behavioral assessments, with a focus on the phenotypes of executive function, attention, and anxiety, consistent
with RDoC constructs important for psychiatric disorder risk. One particular aim is to investigate the use of
machine learning (ML) for the predictive analysis of early brain development to cognitive and behavioral
outcomes in adolescence and to risk for subsequent psychiatric disorders. Yet, most machine learning (ML)
algorithms applied to longitudinal data do not perform well (or at all) when data points are missing, as ML
methods need both complete data and large sample sizes. As longitudinal studies suffer commonly from
significant missing data at different time points due to acquisition failure as well as participant attrition, even a
rich database like the UNC EBDS is reduced to a significantly lower sample size by selecting only complete
datasets to apply predictive ML (less than a third of the datasets of EBDS data from age 1 – 10 years is complete).
 Here, we propose to rescue missing EBDS timepoints (at ages 1 - 10 yrs) of structural MR image data via
multi-modal, multi-timepoints image predictions. This image data imputation includes cross-modality image
generation (generating missing MRI data from existing MRI data at the same time), where available, as well as
multi-timepoints imputation of longitudinal data (generating missing MRI data from existing MRI data at different
time points). We will then apply our out-of-distribution model to provide additional information on the
appropriateness of the imputed data. Subsequently, the same image processing that was applied to the original
EBDS MRI data will be applied to the imputed/generated MRI data to compute missing information of
morphometric measures (regional volumes, cortical thickness, surface area, and white matter fiber tract
properties). This imputed data will be a highly signifi...

## Key facts

- **NIH application ID:** 10412438
- **Project number:** 3R01MH123747-01A1S1
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** JOHN Horace GILMORE
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $286,650
- **Award type:** 3
- **Project period:** 2021-04-01 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10412438

## Citation

> US National Institutes of Health, RePORTER application 10412438, The Development of Individual Differences in Adolescent Brain Structure and Risk (3R01MH123747-01A1S1). Retrieved via AI Analytics 2026-05-29 from https://api.ai-analytics.org/grant/nih/10412438. Licensed CC0.

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