Amir Ansari , Kirubin Pillay , Emad Arasteh , Anneleen Dereymaeker , Gabriela Schmidt Mellado , Katrien Jansen , Anderson M. Winkler , Gunnar Naulaers , Aomesh Bhatt , Sabine Van Huffel , Caroline Hartley , Maarten De Vos , Rebeccah Slater , Luke Baxter
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引用次数: 0
Abstract
Objective
Electroencephalography (EEG) can be used to estimate neonates’ biological brain age. Discrepancies between postmenstrual age and brain age, termed the brain age gap, can potentially quantify maturational deviation. Existing brain age EEG models are not well suited to clinical cot-side use for estimating neonates’ brain age gap due to their dependency on relatively large data and pre-processing requirements.
Methods
We trained a deep learning model on resting state EEG data from preterm neonates with normal neurodevelopmental Bayley Scale of Infant and Toddler Development (BSID) outcomes, using substantially reduced data requirements. We subsequently tested this model in two independent datasets from two clinical sites.
Results
In both test datasets, using only 20 min of resting-state EEG activity from a single channel, the model generated accurate age predictions: mean absolute error = 1.03 weeks (p-value = 0.0001) and 0.98 weeks (p-value = 0.0001). In one test dataset, where 9-month follow-up BSID outcomes were available, the average neonatal brain age gap in the severe abnormal outcome group was significantly larger than that of the normal outcome group: difference in mean brain age gap = 0.50 weeks (p-value = 0.04).
Conclusions
These findings demonstrate that the deep learning model generalises to independent datasets from two clinical sites, and that the model’s brain age gap magnitudes differ between neonates with normal and severe abnormal follow-up neurodevelopmental outcomes.
Significance
The magnitude of neonates’ brain age gap, estimated using only 20 min of resting state EEG data from a single channel, can encode information of clinical neurodevelopmental value.
期刊介绍:
As of January 1999, The journal Electroencephalography and Clinical Neurophysiology, and its two sections Electromyography and Motor Control and Evoked Potentials have amalgamated to become this journal - Clinical Neurophysiology.
Clinical Neurophysiology is the official journal of the International Federation of Clinical Neurophysiology, the Brazilian Society of Clinical Neurophysiology, the Czech Society of Clinical Neurophysiology, the Italian Clinical Neurophysiology Society and the International Society of Intraoperative Neurophysiology.The journal is dedicated to fostering research and disseminating information on all aspects of both normal and abnormal functioning of the nervous system. The key aim of the publication is to disseminate scholarly reports on the pathophysiology underlying diseases of the central and peripheral nervous system of human patients. Clinical trials that use neurophysiological measures to document change are encouraged, as are manuscripts reporting data on integrated neuroimaging of central nervous function including, but not limited to, functional MRI, MEG, EEG, PET and other neuroimaging modalities.