# Infant Functional Connectome Fingerprinting based on Deep Learning

> **NIH NIH R21** · UNIV OF NORTH CAROLINA CHAPEL HILL · 2021 · $155,500

## Abstract

Project Abstract
Functional connectome fingerprinting is to discover the reliable and robust individualized functional connectivity
patterns that are capable of accurately distinguishing one individual from others, like the “fingerprint”. To date,
the fingerprinting capability of functional connectome has been widely observed from older children to
adolescents to adults. Meanwhile, the most contributive functional connections for fingerprinting are consistently
identified as the most predictive ones for cognitive performance. However, functional connectome fingerprinting
during infancy featuring the most dynamic postnatal brain development remains uninvestigated, which is
essential for understanding the early individual-level intrinsic patterns of functional organization, the relationship
of inter-individual distinguishability with distinct behavioral phenotypes, as well as aberrant patterns associated
with prenatal drug exposure. Two major obstacles prevent from investigation of infant functional connectome
fingerprint: 1) there exist significant challenges in precisely processing infant neuroimages, which typically exhibit
extremely low contrast, dynamic imaging appearance, morphological and functional changes; 2) conventional
methods for functional connectome fingerprinting simply use the linearly-transformed, low-order functional
connectivity features and are thus unable to separate the intrinsically-entangled identity-related individualized
information and age-related developmental information in infant brains. To fill critical gaps in both methodology
and knowledge, this project aims to develop an innovative dedicated deep learning model for infant functional
connectome fingerprinting, thus addressing three fundamental questions in neurodevelopment: 1) whether the
individualized functional connectome fingerprint exists during early brain development; 2) which functional
connections contribute more to fingerprinting during infancy; 3) what is the association of infant functional
connectome fingerprint with cognitive performance and adverse prenatal drug exposure. Our team is well
positioned to conduct this project, as we have extensive experiences in developing infant-dedicated
computational tools and deep learning techniques and have acquired multiple longitudinal infant datasets
involving both typically developing infants and infants with prenatal drug exposure. Two specific aims are
proposed. In Aim 1, we will develop a deep neural network model for infant functional connectome fingerprinting.
Specifically, to boost the discriminative capability of the functional connectivity features, we will develop a triplet
autoencoder model to map these features into a new feature space with high-order discriminative information.
To restrain the interference from the developmental information, we will disentangle the latent variables from the
triple autoencoder into identity-code, age-code, and noise-code, and meanwhile design multiple specific losse...

## Key facts

- **NIH application ID:** 10288361
- **Project number:** 1R21MH127544-01
- **Recipient organization:** UNIV OF NORTH CAROLINA CHAPEL HILL
- **Principal Investigator:** Gang Li
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $155,500
- **Award type:** 1
- **Project period:** 2021-09-01 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10288361

## Citation

> US National Institutes of Health, RePORTER application 10288361, Infant Functional Connectome Fingerprinting based on Deep Learning (1R21MH127544-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10288361. Licensed CC0.

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