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
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Zusammenfassung: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 that abnormal network properties may be useful as discriminative features and have exploited machine learning models for early diagnosis in a variety of neurological conditions. However, analogous studies in neonates are rare and with limited significant findings. In this paper, we propose an artificial neural network (ANN) framework for early prediction of cognitive deficits in very preterm infants based on functional connectome data from resting state fMRI. Specifically, we conducted feature selection via stacked sparse autoencoder and outcome prediction via support vector machine (SVM). The proposed ANN model was unsupervised learned using brain connectome data from 884 subjects in autism brain imaging data exchange database and SVM was cross-validated on 28 very preterm infants (born at 23–31 weeks of gestation and without brain injury; scanned at term-equivalent postmenstrual age). Using 90 regions of interests, we found that the ANN model applied to functional connectome data from very premature infants can predict cognitive outcome at 2 years of corrected age with an accuracy of 70.6% and area under receiver operating characteristic curve of 0.76. We also noted that several frontal lobe and somatosensory regions, significantly contributed to prediction of cognitive deficits 2 years later. Our work can be considered as a proof of concept for utilizing ANN models on functional connectome data to capture the individual variability inherent in the developing brains of preterm infants. The full potential of ANN will be realized and more robust conclusions drawn when applied to much larger neuroimaging datasets, as we plan to do. •Stacked sparse autoencoder for high-level connectome feature learning•Artificial neural network on functional connectome data for outcome prediction•Prediction of cognitive deficits in very preterm infants•Exploration of functional brain connections that most contribute to prediction
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2213-1582
2213-1582
DOI:10.1016/j.nicl.2018.01.032