A Prospective Multi-Institutional Study Comparing the Brain Development in the Third Trimester between Opioid-Exposed and Nonexposed Fetuses Using Advanced Fetal MR Imaging Techniques

Yun, Hyuk Jin; Nagaraj, Usha D.; Grant, P. Ellen; Merhar, Stephanie L.; Ou, Xiawei; Lin, Weili; Acheson, Ashley; Grewen, Karen; Kline-Fath, Beth M.; Im, Kiho

doi:10.3174/ajnr.a8101

Cited by 1 publication

(1 citation statement)

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“…We used a previously developed pipeline for fetal MRI pre-processing and inner cortical plate surface extraction (Yun et al, 2021(Yun et al, , 2024. The brain region was masked using our in-house tool based on a 2D U-Net model for each MRI stack (Hong et al, 2021).…”

Section: Fetal Mri Pre-processing and Surface Reconstructionmentioning

confidence: 99%

The role of cortical structural variance in deep learning-based prediction of fetal brain age

Kwon,

You,

Yun

et al. 2024

Front. Neurosci.

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BackgroundDeep-learning-based brain age estimation using magnetic resonance imaging data has been proposed to identify abnormalities in brain development and the risk of adverse developmental outcomes in the fetal brain. Although saliency and attention activation maps have been used to understand the contribution of different brain regions in determining brain age, there has been no attempt to explain the influence of shape-related cortical structural features on the variance of predicted fetal brain age.MethodsWe examined the association between the predicted brain age difference (PAD: predicted brain age–chronological age) from our convolution neural networks-based model and global and regional cortical structural measures, such as cortical volume, surface area, curvature, gyrification index, and folding depth, using regression analysis.ResultsOur results showed that global brain volume and surface area were positively correlated with PAD. Additionally, higher cortical surface curvature and folding depth led to a significant increase in PAD in specific regions, including the perisylvian areas, where dramatic agerelated changes in folding structures were observed in the late second trimester. Furthermore, PAD decreased with disorganized sulcal area patterns, suggesting that the interrelated arrangement and areal patterning of the sulcal folds also significantly affected the prediction of fetal brain age.ConclusionThese results allow us to better understand the variance in deep learning-based fetal brain age and provide insight into the mechanism of the fetal brain age prediction model.

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Section: Fetal Mri Pre-processing and Surface Reconstructionmentioning

confidence: 99%