The contributions of intelligence and executive function to behaviour problems in school‐age children born very preterm

Dai, Darren W T; Franke, Nike; Wouldes, Trecia; Brown, Gavin; Tottman, Anna C; Harding, Jane E.

doi:10.1111/apa.15763

Cited by 8 publications

(8 citation statements)

References 22 publications

(46 reference statements)

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“…Clinical decision making regarding the provision of developmental care interventions (Burke, 2018) using deep learning-based predictions of infant brain age would benefit from advancing the prognostic validity of the brain age delta metric. For example, demonstrating associations between the metric and additional follow-up outcome metrics, such as executive function (Dai et al, 2021), would improve validity. Additionally, understanding the association between the metric and contemporaneous structural (e.g.…”

Section: Discussionmentioning

confidence: 99%

Brain age as an estimator of neurodevelopmental outcome: A deep learning approach for neonatal cot-side monitoring

Ansari

Pillay

Baxter

et al. 2023

Preprint

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The preterm neonate can experience stressors that affect the rate of brain maturation and lead to long-term neurodevelopmental deficits. However, some neonates who are born early follow normal developmental trajectories. Extraction of data from electroencephalography (EEG) signals can be used to calculate the neonate's brain age which can be compared to their true age. Discrepancies between true age and brain age (the brain age delta) can then be used to quantify maturational deviation, which has been shown to correlate with long-term abnormal neurodevelopmental outcomes. Nevertheless, current brain age models that are based on traditional analytical techniques are less suited to clinical cot-side monitoring due to their dependency on long-duration EEG recordings, the need to record activity across multiple EEG channels, and the manual calculation of predefined EEG features which is time-consuming and may not fully capture the wealth of information in the EEG signal. In this study, we propose an alternative deep-learning approach to determine brain age, which operates directly on the EEG, using a Convolutional Neural Network (CNN) block based on the Inception architecture (called Sinc). Using this deep-learning approach on a dataset of preterm infants with normal neurodevelopmental outcomes (where we assume brain age = postmenstrual age), we can calculate infant brain age with a Mean Absolute Error (MAE) of 0.78 weeks (equivalent to a brain age estimation error for the infant within +/- 5.5 days of their true age). Importantly, this level of accuracy can be achieved by recording only 20 minutes of EEG activity from a single channel. This compares favourably to the degree of accuracy that can be achieved using traditional methods that require long duration recordings (typically >2 hours of EEG activity) recorded from a higher density 8-electrode montage (MAE = 0.73 weeks). Importantly, the deep learning model's brain age deltas also distinguish between neonates with normal and severely abnormal outcomes (Normal MAE = 0.71 weeks, severely abnormal MAE = 1.27 weeks, p=0.02, one-way ANOVA), making it highly suited for potential clinical applications. Lastly, in an independent dataset collected at an independent site, we demonstrate the model's generalisability in age prediction, as accurate age predictions were also observed (MAE of 0.97 weeks).

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Section: Discussionmentioning

confidence: 99%

Brain age as an estimator of neurodevelopmental outcome: A deep learning approach for neonatal cot-side monitoring

Ansari

Pillay

Baxter

et al. 2023

Preprint

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“…Because of the reduced fluid intake, the change in nutrition also reduced carbohydrate intake. Paediatric, neurodevelopmental and neuroimaging outcomes in these children have been reported previously 23,32–34 …”

Section: Introductionmentioning

confidence: 87%

“…Detailed descriptions of the full study protocol have been published previously. 23,[32][33][34]36 Of the 201 participants available for follow-up, 128 underwent the visual assessment.…”

Section: Methodsmentioning

confidence: 99%

“…Families were traced and all surviving participants were invited to attend an assessment at 7 years ± 6 months corrected age between December 2012 and March 2016. Detailed descriptions of the full study protocol have been published previously 23,32–34,36 . Of the 201 participants available for follow‐up, 128 underwent the visual assessment.…”

Section: Methodsmentioning

confidence: 99%

“…Paediatric, neurodevelopmental and neuroimaging outcomes in these children have been reported previously. 23,[32][33][34]…”

Section: Introductionmentioning

confidence: 99%

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Associations between neonatal nutrition and visual outcomes in 7‐year‐old children born very preterm

Kulmaganbetov,

Leung,

Alsweiler

et al. 2023

Ophthalmic Physiologic Optic

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PurposeThere is uncertainty about the effect of increased neonatal protein intake on neurodevelopmental outcomes following preterm birth. The aim of this study was to assess the effect of a change in neonatal nutrition protocol at a major tertiary neonatal intensive care unit intended to increase protein intake on ophthalmic and visual development in school‐age children born very preterm.MethodsThe study cohort comprised children (n = 128) with birthweight <1500 g or gestational age < 30 weeks born at Auckland City Hospital before (OldPro group, n = 55) and after (NewPro group, n = 73) a reformulation of parenteral nutrition that resulted in increased total protein intake during the first postnatal week and decreased carbohydrate, total parenteral fluid and sodium intake. Clinical and psychophysical vision assessments were completed at 7 years' corrected age, including visual acuity, global motion perception (a measure of dorsal stream function), stereoacuity, ocular motility and ocular health. Composite measures of favourable overall visual, binocular and functional visual outcomes along with individual vision measures were compared between the groups using logistic and linear regression models.ResultsFavourable overall visual outcome did not differ between the two groups. However, global motion perception was better in the NewPro group (p = 0.04), whereas the OldPro group were more likely to have favourable binocular visual outcomes (60% vs. 36%, p = 0.02) and passing stereoacuity (p = 0.02).ConclusionsThese results indicate subtle but complex associations between early neonatal nutrition after very preterm birth and visual development at school age.

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Resting state electroencephalographic brain activity in neonates can predict age and is indicative of neurodevelopmental outcome

Ansari,

Pillay,

Arasteh

et al. 2024

Clinical Neurophysiology

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The contributions of intelligence and executive function to behaviour problems in school‐age children born very preterm

Cited by 8 publications

References 22 publications

Brain age as an estimator of neurodevelopmental outcome: A deep learning approach for neonatal cot-side monitoring

Brain age as an estimator of neurodevelopmental outcome: A deep learning approach for neonatal cot-side monitoring

Associations between neonatal nutrition and visual outcomes in 7‐year‐old children born very preterm

Resting state electroencephalographic brain activity in neonates can predict age and is indicative of neurodevelopmental outcome

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