Early prediction of cognitive deficits in very preterm infants using functional connectome data in an artificial neural network framework

Investigation of the brain's functional connectome can improve our understanding of how an individual brain's organizational changes influence cognitive function and could result in improved individual risk stratification. Brain connectome studies in adults and older children have shown th...

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Veröffentlicht in:	NeuroImage clinical Jg. 18; S. 290 - 297
Hauptverfasser:	He, Lili, Li, Hailong, Holland, Scott K., Yuan, Weihong, Altaye, Mekibib, Parikh, Nehal A.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Netherlands Elsevier Inc 01.01.2018 Elsevier
Schlagworte:	Artificial neural network Brain - diagnostic imaging Brain - physiopathology Cognition Disorders - diagnosis Cognitive deficit Connectome Female Functional MRI Gestational Age Humans Image Processing, Computer-Assisted Infant Infant, Extremely Premature Magnetic Resonance Imaging Male Neural Networks, Computer Neuropsychological Tests Oxygen - blood Radiology Regular Stacked sparse autoencoder Support Vector Machine Very preterm infants Very preterm infants Functional MRI Artificial neural network Support vector machine Stacked sparse autoencoder Cognitive deficit
ISSN:	2213-1582, 2213-1582
Online-Zugang:	Volltext
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