Multi-Site Clinical Data to Power MRI Biomarker of Neonatal Brain Injury

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Abstract This project aims to release our recently gathered existing clinically-acquired data for neonatal hypoxic ischemic encephalopathy (HIE). HIE affects 1-5/1000 term-born neonates and is a major cause of early-childhood mortality and morbidity. Neonatal brain magnetic resonance imaging (MRI) is acquired routinely for the clinical care of HIE. Neonatal brain MRI is expected to reveal 3D neuroanatomic mechanisms of adverse outcomes so that we can design new treatments specifically target those mechanisms. Neonatal brain MRI also carries hope to identify those neonates who are at risk to develop adverse outcomes later in life, so that early intervention program can target those at-risk neonates for maximum benefit. Despite MRI's vital role in caring for HIE, the current norm in clinical practice is to read MRI visually by expert neuroradiologist or neurologist. Expert reads, however, has many limitations – subjective, qualitative, insufficient to reveal mechanisms, and inadequate to predict outcomes. Objective and quantitative analysis of MRI is possible with the rise of artificial intelligence (AI) in medical and neuroimaging informatics. A major limitation, however, is the lack of publicly-available data on HIE. Our project aims to fill this gap, by archiving and releasing our clinically-acquired, large-scale (N=231), and multi-site (2 hospitals) data on HIE. Our data was acquired partly funded by NIH R01 (2012-2017) and foundations (2016-2020). Our data is comprehensive, including clinical data elements (from both mothers and neonates), neonatal brain MRI (structural and diffusion sequences), expert-consensus annotation of lesion regions in neonatal brain MRI, NICU outcome (death/survival, length of stay), and 2-year-old neurocognitive outcomes (normal/adverse, yes/no for development dealy, yes/no for the hearing/visual/motor impairment, and yes/no for cerebral palsy). Our data is also representative, coming from patients with different racial/ethnicity groups, in patients with a wide range of outcomes, from different MRI scanners (Siemens 3T or GE 1.5T), with different imaging protocols, and MRI scanned on different days of life. We will also derive new data from existing data. The anonymized (de-identified) data will be released to the NCBI dbGaP platform with the “Controlled Access” option, requiring IRB and data use agreement (DUA). The derived data will be released to dbGaP with the “Open Access” option, freely downloadable without any approval. Both release options are consistent with other clinical and MRI data that have already been released on dbGaP. We hope this first comprehensive data will boost future collaborative efforts for AI to automatically identify HIE lesions in MRI, and for AI to accurately predict HIE outcome integrating clinical and MRI information.