Neonatal encephalopathy prediction of poor outcome with diffusion-weighted imaging connectome and fixel-based analysis.
Journal
Pediatric research
ISSN: 1530-0447
Titre abrégé: Pediatr Res
Pays: United States
ID NLM: 0100714
Informations de publication
Date de publication:
05 2022
05 2022
Historique:
received:
10
12
2020
accepted:
08
04
2021
revised:
01
04
2021
pubmed:
10
5
2021
medline:
18
6
2022
entrez:
9
5
2021
Statut:
ppublish
Résumé
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. 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. 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 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. 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.
Sections du résumé
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
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.
Identifiants
pubmed: 33966055
doi: 10.1038/s41390-021-01550-2
pii: 10.1038/s41390-021-01550-2
pmc: PMC9053106
mid: NIHMS1797053
doi:
Substances chimiques
Biomarkers
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
1505-1515Subventions
Organisme : NINDS NIH HHS
ID : R01 NS089659
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS114972
Pays : United States
Organisme : NINDS NIH HHS
ID : R01 NS117146
Pays : United States
Informations de copyright
© 2021. The Author(s), under exclusive licence to the International Pediatric Research Foundation, Inc.
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