Neural Substrate of Outcomes after Neonatal Hypoxic Ischemic Encephalopathy

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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 NRN scores.