Poster No:
1025
Submission Type:
Abstract Submission
Authors:
Nathan Stevenson1, Kartik Iyer2, James Roberts3, Anton Tokariev4, Sampsa Vanhatalo5
Institutions:
1QIMR Berghofer, Brisbane, QLD, 2QIMR Berghofer, Brisbane, Queensland, 3QIMR Berghofer Medical Research Institute, Brisbane, QLD, 4University of Helsinki, Helsinki, Finland, 5University of Helsinki, Helsinki, Uusimaa
First Author:
Co-Author(s):
James Roberts
QIMR Berghofer Medical Research Institute
Brisbane, QLD
Introduction:
Early structural arealization of human cortex supports upcoming neurocognitive development. The corresponding arealization of cortical activity is poorly understood. We used cortical activity as measured by the electroencephalogram (EEG) to investigate regionally specific brain maturation in preterm infants.
Methods:
Serial recordings of high density EEG (128 electrodes) from a cohort of 136 preterm infants was analysed (EEG age range: 32-44 weeks PMA). The typical recording duration was 1h. Source space reconstruction converted sensor space data into a parcellation of 58 regions/sources (only 44 regions were used as 14 regions were deemed too distant from EEG sensors or provided poor age predictions). Machine learning predictions of functional brain age (FBA) were calculated and based on a bag of features extracted from 1h epochs of EEG combined using support vector regression to form a prediction. Feature selection (20 features were selected) and data augmentation were used to improve model fit. The FBA was trained on one parcel and then applied to all other parcels to determine relative age across the brain. Correlation analysis was performed between the relative age and three geometric axis (frontal-occipital, lateral, inferior-superior), as well as, an axis of sensorimotor-association rank to determine the presence of spatial gradients.
Results:
A significant gradient of maturation was observed along frontal-occipital (r = 0.61, 95%CI: 0.38-0.77) and lateral axes (r = -0.49, 95%CI: -0.69 to -0.23). The FBA estimated from frontal parcels was more mature than occipital parcels with a difference of 10 days (95%CI: 7-13 days) between frontal and occipital extrema. The FBA estimated from central parcels was more mature than temporal with a difference of 7 days (95%CI: 3-11 days) between central and temporal extrema. There were no significant gradients observed along the superior-inferior axis. There was a significant gradient of maturation along the SA axis (r = 0.398; 95%CI: 0.115 to 0.622) with association parcels more mature than sensorimotor. Frontal-occipital distance and sensorimotor-association rank were correlated (r = 0.717; 95%CI: 0.534 to 0.836). Gradients were unaltered by sex or neurodevelopmental outcome.
Conclusions:
When assessing maturation relative to parcels, rather than eventual adult activity, early brain activity is more mature in frontal regions with a significant frontal-occipital gradient. This gradient is more prominent when assessed against a sensorimotor-association rank.
Lifespan Development:
Normal Brain Development: Fetus to Adolescence 1
Novel Imaging Acquisition Methods:
EEG 2
Keywords:
Electroencephaolography (EEG)
Machine Learning
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
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Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
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Was this research conducted in the United States?
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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
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