Abnormal brain maturation in preterm neonates associated with adverse developmental outcomes

Background: Nutrition in early life plays a critical role in the growth and neurodevelopment of preterm neonates. However, whether early nutrition modified the association of white matter injury (WMI) with brain maturation and neurodevelopmental outcomes remains unclear. Methods: In this prospective cohort study, very preterm neonates were recruited from the NICU at BC Women’s Hospital. MRI and measures of NAA/choline were obtained. Energy intake was recorded over the first two weeks of life and the cohort was dichotomized. Neurodevelopmental outcomes were assessed at 4.5 years of age using WPPSI-III. Results: Neonates in the high lipid group had higher levels of NAA/choline in the basal ganglia. When accounting for confounders, this relationship was only significant in neonates without WMI (p=0.04). Overall, neonates with larger WMI volumes had lower IQ scores at 4.5 years (p<0.001). However, this relationship was attenuated in the high lipid group (p=0.002) relative to the lower lipid intake group. Conclusions: In this cohort, higher energy intake is associated with increased brain maturation. Similarly, neonates with large WMI had higher full-scale IQ if they received greater lipid intake in the neonatal period, suggesting that greater early lipid intake may contribute to blunting the deleterious effects of WMI on neurodevelopmental outcomes.

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Predicting age and clinical risk from the neonatal connectome

10.1101/2020.09.28.317180 ◽

2020 ◽

Author(s):

Yassine Taoudi-Benchekroun ◽

Daan Christiaens ◽

Irina Grigorescu ◽

Andreas Schuh ◽

Maximilian Pietsch ◽

...

Keyword(s):

Diffusion Mri ◽

Brain Connectivity ◽

Later Life ◽

Absolute Error ◽

Brain Maturation ◽

Preterm Neonates ◽

Efficient Treatment ◽

Human Connectome Project ◽

Maturation Index ◽

Atypical Development

AbstractThe development of perinatal brain connectivity underpins motor, cognitive and behavioural abilities in later life. With the rise of advanced imaging methods such as diffusion MRI, the study of brain connectivity has emerged as an important tool to understand subtle alterations associated with neurodevelopmental conditions. Brain connectivity derived from diffusion MRI is complex, multi-dimensional and noisy, and hence it can be challenging to interpret on an individual basis. Machine learning methods have proven to be a powerful tool to uncover hidden patterns in such data, thus opening an opportunity for early identification of atypical development and potentially more efficient treatment.In this work, we used Deep Neural Networks and Random Forests to predict neurodevelopmental characteristics from neonatal structural connectomes, in a large sample of neonates (N = 524) derived from the developing Human Connectome Project. We achieved a highly accurate prediction of post menstrual age (PMA) at scan on term-born infants (Mean absolute error (MAE) = 0.72 weeks, r = 0.83, p<<0.001). We also achieved good accuracy when predicting gestational age at birth on a cohort of term and preterm babies scanned at term equivalent age (MAE = 2.21 weeks, r = 0.82, p<<0.001). From our models of PMA at scan for infants born at term, we computed the brain maturation index (i.e. predicted minus actual age) of individual preterm neonates and found significant correlation of this index with motor outcome at 18 months corrected age. Our results suggest that the neural substrate for later neurological functioning is detectable within a few weeks after birth in the structural connectome.

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Quantitative assessment of myelination patterns in preterm neonates using T2-weighted MRI

Scientific Reports ◽

10.1038/s41598-019-49350-3 ◽

2019 ◽

Vol 9 (1) ◽

Author(s):

Siying Wang ◽

Christian Ledig ◽

Joseph V. Hajnal ◽

Serena J. Counsell ◽

Julia A. Schnabel ◽

...

Keyword(s):

Gestational Age ◽

Markov Random Fields ◽

Quantitative Assessment ◽

Mean Squared Error ◽

Developmental Process ◽

Growth Models ◽

Brain Structures ◽

Magnetic Resonance Images ◽

Brain Maturation ◽

Preterm Neonates

Abstract Myelination is considered to be an important developmental process during human brain maturation and closely correlated with gestational age. Quantitative assessment of the myelination status requires dedicated imaging, but the conventional T2-weighted scans routinely acquired during clinical imaging of neonates carry signatures that are thought to be associated with myelination. In this work, we develop a quatitative marker of progressing myelination for assessment preterm neonatal brain maturation based on novel automatic segmentation method for myelin-like signals on T2-weighted magnetic resonance images. Firstly we define a segmentation protocol for myelin-like signals. We then develop an expectation-maximization framework to obtain the automatic segmentations of myelin-like signals with explicit class for partial volume voxels whose locations are configured in relation to the composing pure tissues via second-order Markov random fields. The proposed segmentation achieves high Dice overlaps of 0.83 with manual annotations. The automatic segmentations are then used to track volumes of myelinated tissues in the regions of the central brain structures and brainstem. Finally, we construct a spatio-temporal growth models for myelin-like signals, which allows us to predict gestational age at scan in preterm infants with root mean squared error 1.41 weeks.

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An Automated Quiet Sleep Detection Approach in Preterm Infants as a Gateway to Assess Brain Maturation

International Journal of Neural Systems ◽

10.1142/s012906571750023x ◽

2017 ◽

Vol 27 (06) ◽

pp. 1750023 ◽

Cited By ~ 21

Author(s):

Anneleen Dereymaeker ◽

Kirubin Pillay ◽

Jan Vervisch ◽

Sabine Van Huffel ◽

Gunnar Naulaers ◽

...

Keyword(s):

Well Being ◽

Brain Maturation ◽

Preterm Neonates ◽

Sleep Stages ◽

Sleep State ◽

Quiet Sleep ◽

Postmenstrual Age ◽

And Performance ◽

Sensitivity Specificity ◽

Age Range

Sleep state development in preterm neonates can provide crucial information regarding functional brain maturation and give insight into neurological well being. However, visual labeling of sleep stages from EEG requires expertise and is very time consuming, prompting the need for an automated procedure. We present a robust method for automated detection of preterm sleep from EEG, over a wide postmenstrual age ([Formula: see text] age) range, focusing first on Quiet Sleep (QS) as an initial marker for sleep assessment. Our algorithm, CLuster-based Adaptive Sleep Staging (CLASS), detects QS if it remains relatively more discontinuous than non-QS over PMA. CLASS was optimized on a training set of 34 recordings aged 27–42 weeks PMA, and performance then assessed on a distinct test set of 55 recordings of the same age range. Results were compared to visual QS labeling from two independent raters (with inter-rater agreement [Formula: see text]), using Sensitivity, Specificity, Detection Factor ([Formula: see text] of visual QS periods correctly detected by CLASS) and Misclassification Factor ([Formula: see text] of CLASS-detected QS periods that are misclassified). CLASS performance proved optimal across recordings at 31–38 weeks (median [Formula: see text], median MF 0–0.25, median Sensitivity 0.93–1.0, and median Specificity 0.80–0.91 across this age range), with minimal misclassifications at 35–36 weeks (median [Formula: see text]). To illustrate the potential of CLASS in facilitating clinical research, normal maturational trends over PMA were derived from CLASS-estimated QS periods, visual QS estimates, and nonstate specific periods (containing QS and non-QS) in the EEG recording. CLASS QS trends agreed with those from visual QS, with both showing stronger correlations than nonstate specific trends. This highlights the benefit of automated QS detection for exploring brain maturation.

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