Neonatal encephalopathy prediction of poor outcome with diffusion-weighted imaging connectome and fixel-based analysis.

BACKGROUND Better biomarkers of eventual outcome are needed for neonatal encephalopathy. To identify the most potent neonatal imaging marker associated with 2-year outcomes, we retrospectively performed diffusion-weighted imaging connectome (DWIC) and fixel-based analysis (FBA) on magnetic resonance imaging (MRI) obtained in the first 4 weeks of life in term neonatal encephalopathy newborns. METHODS Diffusion tractography was available in 15 out of 24 babies with MRI, five each with normal, abnormal motor outcome, or death. All 15 except one underwent hypothermia as initial treatment. In abnormal motor and death groups, DWIC found 19 white matter pathways with severely disrupted fiber orientation distributions. RESULTS Using random forest classification, these disruptions predicted the follow-up outcomes with 89-99% accuracy. These pathways showed reduced integrity in abnormal motor and death vs. normal tone groups (p < 10-6). Using ranked supervised multi-view canonical correlation and depicting just three of the five dimensions of the analysis, the abnormal motor and death were clearly differentiated from each other and the normal tone group. CONCLUSIONS This study suggests that a machine-learning model for prediction using early DWIC and FBA could be a possible way of developing biomarkers in large MRI datasets having clinical outcomes. IMPACT Early connectome and FBA of clinically acquired DWI provide a new noninvasive imaging tool to predict the long-term motor outcomes after birth, based on the severity of white matter injury. Disrupted white matter connectivity as a novel neonatal marker achieves high accuracy of 89-99% to predict 2-year motor outcomes using conventional machine-learning classification. The proposed neonatal marker may allow better prognostication that is important to elucidate neural repair mechanisms and evaluate treatment modalities in neonatal encephalopathy.