A machine learning approach to automated structural network analysis: application to neonatal encephalopathy.
Neonatal encephalopathy represents a heterogeneous group of conditions associated with life-long developmental disabilities and neurological deficits. Clinical measures and current anatomic brain imaging remain inadequate predictors of outcome in children with neonatal encephalopathy. Some studies h...
Guardado en:
| Autores principales: | , , , , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Public Library of Science (PLoS)
2013
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/46aa067d2a7042f69db5a7795c1a8256 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| id |
oai:doaj.org-article:46aa067d2a7042f69db5a7795c1a8256 |
|---|---|
| record_format |
dspace |
| spelling |
oai:doaj.org-article:46aa067d2a7042f69db5a7795c1a82562021-11-18T08:44:56ZA machine learning approach to automated structural network analysis: application to neonatal encephalopathy.1932-620310.1371/journal.pone.0078824https://doaj.org/article/46aa067d2a7042f69db5a7795c1a82562013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24282501/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203Neonatal encephalopathy represents a heterogeneous group of conditions associated with life-long developmental disabilities and neurological deficits. Clinical measures and current anatomic brain imaging remain inadequate predictors of outcome in children with neonatal encephalopathy. Some studies have suggested that brain development and, therefore, brain connectivity may be altered in the subgroup of patients who subsequently go on to develop clinically significant neurological abnormalities. Large-scale structural brain connectivity networks constructed using diffusion tractography have been posited to reflect organizational differences in white matter architecture at the mesoscale, and thus offer a unique tool for characterizing brain development in patients with neonatal encephalopathy. In this manuscript we use diffusion tractography to construct structural networks for a cohort of patients with neonatal encephalopathy. We systematically map these networks to a high-dimensional space and then apply standard machine learning algorithms to predict neurological outcome in the cohort. Using nested cross-validation we demonstrate high prediction accuracy that is both statistically significant and robust over a broad range of thresholds. Our algorithm offers a novel tool to evaluate neonates at risk for developing neurological deficit. The described approach can be applied to any brain pathology that affects structural connectivity.Etay ZivOlga TymofiyevaDonna M FerrieroA James BarkovichChris P HessDuan XuPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 11, p e78824 (2013) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
Medicine R Science Q |
| spellingShingle |
Medicine R Science Q Etay Ziv Olga Tymofiyeva Donna M Ferriero A James Barkovich Chris P Hess Duan Xu A machine learning approach to automated structural network analysis: application to neonatal encephalopathy. |
| description |
Neonatal encephalopathy represents a heterogeneous group of conditions associated with life-long developmental disabilities and neurological deficits. Clinical measures and current anatomic brain imaging remain inadequate predictors of outcome in children with neonatal encephalopathy. Some studies have suggested that brain development and, therefore, brain connectivity may be altered in the subgroup of patients who subsequently go on to develop clinically significant neurological abnormalities. Large-scale structural brain connectivity networks constructed using diffusion tractography have been posited to reflect organizational differences in white matter architecture at the mesoscale, and thus offer a unique tool for characterizing brain development in patients with neonatal encephalopathy. In this manuscript we use diffusion tractography to construct structural networks for a cohort of patients with neonatal encephalopathy. We systematically map these networks to a high-dimensional space and then apply standard machine learning algorithms to predict neurological outcome in the cohort. Using nested cross-validation we demonstrate high prediction accuracy that is both statistically significant and robust over a broad range of thresholds. Our algorithm offers a novel tool to evaluate neonates at risk for developing neurological deficit. The described approach can be applied to any brain pathology that affects structural connectivity. |
| format |
article |
| author |
Etay Ziv Olga Tymofiyeva Donna M Ferriero A James Barkovich Chris P Hess Duan Xu |
| author_facet |
Etay Ziv Olga Tymofiyeva Donna M Ferriero A James Barkovich Chris P Hess Duan Xu |
| author_sort |
Etay Ziv |
| title |
A machine learning approach to automated structural network analysis: application to neonatal encephalopathy. |
| title_short |
A machine learning approach to automated structural network analysis: application to neonatal encephalopathy. |
| title_full |
A machine learning approach to automated structural network analysis: application to neonatal encephalopathy. |
| title_fullStr |
A machine learning approach to automated structural network analysis: application to neonatal encephalopathy. |
| title_full_unstemmed |
A machine learning approach to automated structural network analysis: application to neonatal encephalopathy. |
| title_sort |
machine learning approach to automated structural network analysis: application to neonatal encephalopathy. |
| publisher |
Public Library of Science (PLoS) |
| publishDate |
2013 |
| url |
https://doaj.org/article/46aa067d2a7042f69db5a7795c1a8256 |
| work_keys_str_mv |
AT etayziv amachinelearningapproachtoautomatedstructuralnetworkanalysisapplicationtoneonatalencephalopathy AT olgatymofiyeva amachinelearningapproachtoautomatedstructuralnetworkanalysisapplicationtoneonatalencephalopathy AT donnamferriero amachinelearningapproachtoautomatedstructuralnetworkanalysisapplicationtoneonatalencephalopathy AT ajamesbarkovich amachinelearningapproachtoautomatedstructuralnetworkanalysisapplicationtoneonatalencephalopathy AT chrisphess amachinelearningapproachtoautomatedstructuralnetworkanalysisapplicationtoneonatalencephalopathy AT duanxu amachinelearningapproachtoautomatedstructuralnetworkanalysisapplicationtoneonatalencephalopathy AT etayziv machinelearningapproachtoautomatedstructuralnetworkanalysisapplicationtoneonatalencephalopathy AT olgatymofiyeva machinelearningapproachtoautomatedstructuralnetworkanalysisapplicationtoneonatalencephalopathy AT donnamferriero machinelearningapproachtoautomatedstructuralnetworkanalysisapplicationtoneonatalencephalopathy AT ajamesbarkovich machinelearningapproachtoautomatedstructuralnetworkanalysisapplicationtoneonatalencephalopathy AT chrisphess machinelearningapproachtoautomatedstructuralnetworkanalysisapplicationtoneonatalencephalopathy AT duanxu machinelearningapproachtoautomatedstructuralnetworkanalysisapplicationtoneonatalencephalopathy |
| _version_ |
1718421380077191168 |