Reduced gyrification in fetal growth restriction with prenatal magnetic resonance images.

Placental-related fetal growth restriction, resulting from placental dysfunction, impacts 3-5% of pregnancies and is linked to elevated risk of adverse neurodevelopmental outcomes. In response, the fetus employs a mechanism known as brain-sparing, redirecting blood flow to the cerebral circuit, for adequate supply to the brain. In this study we aimed to quantitatively evaluate disparities in gyrification and brain volumes among fetal growth restriction, small for gestational age and appropriate-for gestational-age fetuses. Additionally, we compared fetal growth restriction fetuses with and without brain-sparing. The study encompassed 106 fetuses: 35 fetal growth restriction (14 with and 21 without brain-sparing), 8 small for gestational age, and 63 appropriate for gestational age. Gyrification, supratentorial, and infratentorial brain volumes were automatically computed from T2-weighted magnetic resonance images, following semi-automatic brain segmentation. Fetal growth restriction fetuses exhibited significantly reduced gyrification and brain volumes compared to appropriate for gestational age (P < 0.001). Small for gestational age fetuses displayed significantly reduced gyrification (P = 0.038) and smaller supratentorial volume (P < 0.001) compared to appropriate for gestational age. Moreover, fetal growth restriction fetuses with BS demonstrated reduced gyrification compared to those without BS (P = 0.04), with no significant differences observed in brain volumes. These findings demonstrate that brain development is affected in fetuses with fetal growth restriction, more severely than in small for gestational age, and support the concept that vasodilatation of the fetal middle cerebral artery reflects more severe hypoxemia, affecting brain development.

Fetal MRI-Based Body and Adiposity Quantification for Small for Gestational Age Perinatal Risk Stratification.

BACKGROUND: Small for gestational age (SGA) fetuses are at risk for perinatal adverse outcomes. Fetal body composition reflects the fetal nutrition status and hold promise as potential prognostic indicator. MRI quantification of fetal anthropometrics may enhance SGA risk stratification. HYPOTHESIS: Smaller, leaner fetuses are malnourished and will experience unfavorable outcomes. STUDY TYPE: Prospective. POPULATION: 40 SGA fetuses, 26 (61.9%) females: 10/40 (25%) had obstetric interventions due to non-reassuring fetal status (NRFS), and 17/40 (42.5%) experienced adverse neonatal events (CANO). Participants underwent MRI between gestational ages 30 + 2 and 37 + 2. FIELD STRENGTH/SEQUENCE: 3-T, True Fast Imaging with Steady State Free Precession (TruFISP) and T1 -weighted two-point Dixon (T1 W Dixon) sequences. ASSESSMENT: Total body volume (TBV), fat signal fraction (FSF), and the fat-to-body volumes ratio (FBVR) were extracted from TruFISP and T1 W Dixon images, and computed from automatic fetal body and subcutaneous fat segmentations by deep learning. Subjects were followed until hospital discharge, and obstetric interventions and neonatal adverse events were recorded. STATISTICAL TESTS: Univariate and multivariate logistic regressions for the association between TBV, FBVR, and FSF and interventions for NRFS and CANO. Fisher's exact test was used to measure the association between sonographic FGR criteria and perinatal outcomes. Sensitivity, specificity, positive and negative predictive values, and accuracy were calculated. A P-value <0.05 was considered statistically significant. RESULTS: FBVR (odds ratio [OR] 0.39, 95% confidence interval [CI] 0.2-0.76) and FSF (OR 0.95, CI 0.91-0.99) were linked with NRFS interventions. Furthermore, TBV (OR 0.69, CI 0.56-0.86) and FSF (OR 0.96, CI 0.93-0.99) were linked to CANO. The FBVR sensitivity/specificity for obstetric interventions was 85.7%/87.5%, and the TBV sensitivity/specificity for CANO was 82.35%/86.4%. The sonographic criteria sensitivity/specificity for obstetric interventions was 100%/33.3% and insignificant for CANO (P = 0.145). DATA CONCLUSION: Reduced TBV and FBVR may be associated with higher rates of obstetric interventions for NRFS and CANO. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 5.

Automatic Quantification of Normal Brain Gyrification Patterns and Changes in Fetuses with Polymicrogyria and Lissencephaly Based on MRI.

BACKGROUND AND PURPOSE: The current imaging assessment of fetal brain gyrification is performed qualitatively and subjectively using sonography and MR imaging. A few previous studies have suggested methods for quantification of fetal gyrification based on 3D reconstructed MR imaging, which requires unique data and is time-consuming. In this study, we aimed to develop an automatic pipeline for gyrification assessment based on routinely acquired fetal 2D MR imaging data, to quantify normal changes with gestation, and to measure differences in fetuses with lissencephaly and polymicrogyria compared with controls. MATERIALS AND METHODS: We included coronal T2-weighted MR imaging data of 162 fetuses retrospectively collected from 2 clinical sites: 134 controls, 12 with lissencephaly, 13 with polymicrogyria, and 3 with suspected lissencephaly based on sonography, yet with normal MR imaging diagnoses. Following brain segmentation, 5 gyrification parameters were calculated separately for each hemisphere on the basis of the area and ratio between the contours of the cerebrum and its convex hull. Seven machine learning classifiers were evaluated to differentiate control fetuses and fetuses with lissencephaly or polymicrogyria. RESULTS: In control fetuses, all parameters changed significantly with gestational age (P < .05). Compared with controls, fetuses with lissencephaly showed significant reductions in all gyrification parameters (P ≤ .02). Similarly, significant reductions were detected for fetuses with polymicrogyria in several parameters (P ≤ .001). The 3 suspected fetuses showed normal gyrification values, supporting the MR imaging diagnosis. An XGBoost-linear algorithm achieved the best results for classification between fetuses with lissencephaly and control fetuses (n = 32), with an area under the curve of 0.90 and a recall of 0.83. Similarly, a random forest classifier showed the best performance for classification of fetuses with polymicrogyria and control fetuses (n = 33), with an area under the curve of 0.84 and a recall of 0.62. CONCLUSIONS: This study presents a pipeline for automatic quantification of fetal brain gyrification and provides normal developmental curves from a large cohort. Our method significantly differentiated fetuses with lissencephaly and polymicrogyria, demonstrating lower gyrification values. The method can aid radiologic assessment, highlight fetuses at risk, and may improve early identification of fetuses with cortical malformations.

Neuronal Activity in the Sciatic Nerve Is Accompanied by Immediate Cytoskeletal Changes.

Mechanical events and alterations in neuronal morphology that accompany neuronal activity have been observed for decades. However, no clear neurophysiological role, nor an agreed molecular mechanism relating these events to the electrochemical process, has been found. Here we hypothesized that intense, yet physiological, electrical activity in neurons triggers cytoskeletal depolymerization. We excited the sciatic nerve of anesthetized mice with repetitive electric pulses (5, 10, and 100 Hz) for 1 and 2 min and immediately fixed the excised nerves. We then scanned the excised nerves with high-resolution transmission electron microscopy, and quantified cytoskeletal changes in the resulting micrographs. We demonstrate that excitation with a stimulation frequency that is within the physiological regime is accompanied by a significant reduction in the density of cytoskeletal proteins relative to the baseline values recorded in control nerves. After 10 Hz stimulation with durations of 1 and 2 min, neurofilaments density dropped to 55.8 and 51.1% of the baseline median values, respectively. In the same experiments, microtubules density dropped to 23.7 and 38.5% of the baseline median values, respectively. These changes were also accompanied by a reduction in the cytoskeleton-to-cytoplasm contrast that we attribute to the presence of depolymerized electron-dense molecules in the lumen. Thus, we demonstrate with an in vivo model a link between electrical activity and immediate cytoskeleton rearrangement at the nano-scale. We suggest that this cytoskeletal plasticity reduces cellular stiffness and allows cellular homeostasis, maintenance of neuronal morphology and that it facilitates in later stages growth of the neuronal projections.

Automatic linear measurements of the fetal brain on MRI with deep neural networks.

PURPOSE: Timely, accurate and reliable assessment of fetal brain development is essential to reduce short and long-term risks to fetus and mother. Fetal MRI is increasingly used for fetal brain assessment. Three key biometric linear measurements important for fetal brain evaluation are cerebral biparietal diameter (CBD), bone biparietal diameter (BBD), and trans-cerebellum diameter (TCD), obtained manually by expert radiologists on reference slices, which is time consuming and prone to human error. The aim of this study was to develop a fully automatic method computing the CBD, BBD and TCD measurements from fetal brain MRI. METHODS: The input is fetal brain MRI volumes which may include the fetal body and the mother's abdomen. The outputs are the measurement values and reference slices on which the measurements were computed. The method, which follows the manual measurements principle, consists of five stages: (1) computation of a region of interest that includes the fetal brain with an anisotropic 3D U-Net classifier; (2) reference slice selection with a convolutional neural network; (3) slice-wise fetal brain structures segmentation with a multi-class U-Net classifier; (4) computation of the fetal brain midsagittal line and fetal brain orientation, and; (5) computation of the measurements. RESULTS: Experimental results on 214 volumes for CBD, BBD and TCD measurements yielded a mean [Formula: see text] difference of 1.55 mm, 1.45 mm and 1.23 mm, respectively, and a Bland-Altman 95% confidence interval ([Formula: see text] of 3.92 mm, 3.98 mm and 2.25 mm, respectively. These results are similar to the manual inter-observer variability, and are consistent across gestational ages and brain conditions. CONCLUSIONS: The proposed automatic method for computing biometric linear measurements of the fetal brain from MR imaging achieves human-level performance. It has the potential of being a useful method for the assessment of fetal brain biometry in normal and pathological cases, and of improving routine clinical practice.