Brain Development in Fetuses of Mothers with Diabetes: A Case-Control MR Imaging Study

Denison, Fiona C.; Macnaught, Gillian; Semple, Scott; Terris, Gaynor; Walker, Jane; Anblagan, Devasuda; Serag, Ahmed; Reynolds, Rebecca M.; Boardman, James P.

doi:10.3174/ajnr.a5118

Cited by 17 publications

(8 citation statements)

References 45 publications

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“…However, fetal neuroimaging is an emerging technique for exposure studies as it can examine brain development in utero [76]. To date, only prenatal lead [77], maternal BMI [78], gestational diabetes [79], and maternal distress [80,81] exposures have been investigated with fetal neuroimaging, leaving this line of research largely unexplored.…”

Section: Future Directionsmentioning

confidence: 99%

How prenatal exposures shape the infant brain: Insights from infant neuroimaging studies

Dufford

Spann

Scheinost

2021

Neuroscience & Biobehavioral Reviews

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Section: Future Directionsmentioning

confidence: 99%

How prenatal exposures shape the infant brain: Insights from infant neuroimaging studies

Dufford

Spann

Scheinost

2021

Neuroscience & Biobehavioral Reviews

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“…From a research perspective, the method can be used at the population level to examine associations between various genetic or environmental exposures and fetal brain development. For example, by using automated volume calculations from our normative population, we demonstrate that in our data, there are no significant effects of maternal diabetes, 19 fetal sex, 20 or maternal age 21 on the relationship between GA and total intracranial volume. The lack of an effect of fetal sex on brain volume may seem surprising, given recent 22 and prior 20 results demonstrating sexual dimorphisms in brain volume subregions, including total brain volume.…”

Section: Discussionmentioning

confidence: 58%

Development of Gestational Age–Based Fetal Brain and Intracranial Volume Reference Norms Using Deep Learning

Tran

Nedelec

Weiß

et al. 2022

AJNR Am J Neuroradiol

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BACKGROUND AND PURPOSE: Fetal brain MR imaging interpretations are subjective and require subspecialty expertise. We aimed to develop a deep learning algorithm for automatically measuring intracranial and brain volumes of fetal brain MRIs across gestational ages. MATERIALS AND METHODS:This retrospective study included 246 patients with singleton pregnancies at 19-38 weeks gestation. A 3D U-Net was trained to segment the intracranial contents of 2D fetal brain MRIs in the axial, coronal, and sagittal planes. An additional 3D U-Net was trained to segment the brain from the output of the first model. Models were tested on MRIs of 10 patients (28 planes) via Dice coefficients and volume comparison with manual reference segmentations. Trained U-Nets were applied to 200 additional MRIs to develop normative reference intracranial and brain volumes across gestational ages and then to 9 pathologic fetal brains.RESULTS: Fetal intracranial and brain compartments were automatically segmented in a mean of 6.8 (SD, 1.2) seconds with median Dices score of 0.95 and 0.90, respectively (interquartile ranges, 0.91-0.96/0.89-0.91) on the test set. Correlation with manual volume measurements was high (Pearson r ¼ 0.996, P , .001). Normative samples of intracranial and brain volumes across gestational ages were developed. Eight of 9 pathologic fetal intracranial volumes were automatically predicted to be .2 SDs from this age-specific reference mean. There were no effects of fetal sex, maternal diabetes, or maternal age on intracranial or brain volumes across gestational ages.CONCLUSIONS: Deep learning techniques can quickly and accurately quantify intracranial and brain volumes on clinical fetal brain MRIs and identify abnormal volumes on the basis of a normative reference standard.

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Section: Future Directionsmentioning

confidence: 99%

How Prenatal Exposures Shape the Infant Brain: Insights from Infant Neuroimaging Studies

Dufford

Spann

Scheinost

2021

Preprint

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Brain development during the prenatal period is rapid and unparalleled by any other time during development. Biological systems undergoing rapid development are at higher risk for disorganizing influences. Therefore, certain prenatal exposures impact brain development, increasing risk for negative neurodevelopmental outcome. While prenatal exposures have been associated with cognitive and behavioral outcomes later in life, the underlying macroscopic brain pathways remain unclear. Here, we review studies investigating the association between prenatal exposures and infant brain development focusing on prenatal exposures via maternal physical health factors, maternal mental health factors, and maternal drug and medication use. Further, we discuss the need for studies to consider multiple prenatal exposures in parallel and suggest future directions for this body of research.

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Brain Development in Fetuses of Mothers with Diabetes: A Case-Control MR Imaging Study

Abstract: BACKGROUND AND PURPOSE: Offspring exposed to maternal diabetes are at increased risk of neurocognitive impairment, but its origins are unknown. With MR imaging, we investigated the feasibility of comprehensive assessment of brain metabolism ( 1 H-MRS),

Cited by 17 publications

References 45 publications

How prenatal exposures shape the infant brain: Insights from infant neuroimaging studies

How prenatal exposures shape the infant brain: Insights from infant neuroimaging studies

Development of Gestational Age–Based Fetal Brain and Intracranial Volume Reference Norms Using Deep Learning

How Prenatal Exposures Shape the Infant Brain: Insights from Infant Neuroimaging Studies

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