# Neural Substrate of Outcomes after Neonatal Hypoxic Ischemic Encephalopathy

> **NIH NIH R21** · BOSTON CHILDREN'S HOSPITAL · 2022 · $278,453

## Abstract

Abstract
Neonatal brain injury caused by hypoxic ischemic encephalopathy (HIE) affects 1-5/1000 term-born infants. HIE
often causes adverse outcomes, defined as death or cognitive Bayley Scales of Infant Development (cBSID)<85
by 2 years. Current therapeutic trials are testing whether late, deeper, and/or longer versions of the standard
therapeutic hypothermia could further reduce the adverse 2-year outcome. However, therapeutic innovation is
slow and inconclusive, because the exact neuroanatomic substrate (i.e., the anatomy that underlies outcomes)
is unknown and there is a lack of a reliable tool to predict 2-year adverse outcomes after HIE. Understanding the
neural substrate and hence prediction of the adverse 2-year outcome can: (a) before therapy in the neonatal
stage, identify those patients at high risk to develop adverse 2-year outcomes, so our therapeutic trials can focus
on those high-risk patients and avoid unnecessary therapies to low-risk patients; and (b) right after therapy, to
evaluate therapeutic effects early (in the neonatal stage), avoiding the wait until 2 years to observe the outcome
and therefore expediting therapeutic innovations. Brain magnetic resonance imaging (MRI) is being explored by
NIH Neonatal Research Network (NRN) as a potential biomarker to quantify neural substrate and predict the 2-
year adverse outcome. However, expert interpretation and scoring systems, the current norm, only consider a
selected set of brain regions (e.g., thalamus, basal ganglia, internal capsules, and others usually along the
corticospinal tract), and only consider MRI metrics, not optimally combining MRI with clinical variables. The
recent rise of novel lesion-symptom mapping (LSM) technologies allows us to conduct the first study to quantify
voxel-, region-, and fiber-wise neural substrate throughout the brain, in a much more comprehensive and
multivariate manner than the NRN scoring systems. Our preliminary results using the first-generation of LSM
(voxel-wise LSM, or V-LSM) have led to novel findings of new regions and left hemisphere dominance in the
neural substrate that were previously not considered in the NRN scoring system. In this R21, our Aim 1 will
further use the second-generation LSM (i.e., multivariate LSM, or M-LSM) and the third-generation LSM (i.e.,
connectome LSM, or C-LSM) to more comprehensively explore neural substrate underlying 2-year adverse HIE
outcome. Our Aim 2 will propose a novel machine learning and deep learning algorithm, termed predictive LSM
or P-LSM, to compare and combine the findings from three generations of LSM, and apply this novel algorithm
to predict individual outcomes in HIE patients, without and with adding clinical variables (Aims 2a and 2b,
respectively). Our overall hypothesis is that quantitative and thorough characterization of the neural substrate in
our to-be-developed machine learning and deep learning framework can predict 2-year adverse HIE outcomes
more accurately than the current ...

## Key facts

- **NIH application ID:** 10452978
- **Project number:** 1R21NS121735-01A1
- **Recipient organization:** BOSTON CHILDREN'S HOSPITAL
- **Principal Investigator:** Yangming Ou
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $278,453
- **Award type:** 1
- **Project period:** 2022-03-01 → 2024-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10452978

## Citation

> US National Institutes of Health, RePORTER application 10452978, Neural Substrate of Outcomes after Neonatal Hypoxic Ischemic Encephalopathy (1R21NS121735-01A1). Retrieved via MORVS 2026-10-26 from https://api.morvs.ai/grant/nih/10452978. Licensed CC0.

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