Interpreting Age Effects of Human Fetal Brain from Spontaneous fMRI using Deep 3D Convolutional Neural Networks

Understanding human fetal neurodevelopment is of great clinical importance as abnormal development is linked to adverse neuropsychiatric outcomes after birth. Recent advances in functional Magnetic Resonance Imaging (fMRI) have provided new insight into development of the human brain before birth, but these studies have predominately focused on brain functional connectivity (i.e. Fisher z-score), which requires manual processing steps for feature extraction from fMRI images. Deep learning approaches (i.e., Convolutional Neural Networks) have achieved remarkable success on learning directly from image data, yet have not been applied on fetal fMRI for understanding fetal neurodevelopment. Here, we bridge this gap by applying a novel application of deep 3D CNN to fetal blood oxygen-level dependence (BOLD) resting-state fMRI data. Specifically, we test a supervised CNN framework as a data-driven approach to isolate variation in fMRI signals that relate to younger v.s. older fetal age groups. Based on the learned CNN, we further perform sensitivity analysis to identify brain regions in which changes in BOLD signal are strongly associated with fetal brain age. The findings demonstrate that deep CNNs are a promising approach for identifying spontaneous functional patterns in fetal brain activity that discriminate age groups. Further, we discovered that regions that most strongly differentiate groups are largely bilateral, share similar distribution in older and younger age groups, and are areas of heightened metabolic activity in early human development.Comment: 9 page

New perspective on the U(n) Wigner-Racah calculus. I. Vector coherent state theory and construction of Gel'fand bases

Using a vector coherent state theory, it is shown that the construction of Gel'fand bases for the unitary group is particularly simple. The very specific rules for construction of the states greatly facilitate the subsequent computation of matrix elements of the generators of the u(n) Lie algebra.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/48816/2/jav20i9p2241.pd

Rhizotoxicity of aluminate and polycationic aluminium at high pH

Although monomeric Al species are often toxic in acidic soils, the effects of the aluminate ion (Al(OH)4-) on roots grown in alkaline media are still unclear. Dilute, alkaline (pH 9.5) nutrient solutions were used to investigate the effects of Al(OH)4- on root growth of mungbean (Vigna radiata L.). Root growth was reduced by 13 % after 3 d growth in solutions with an Al(OH)4- activity of 16 μM and no detectable polycationic Al (Al13). This decrease in root growth was associated with the formation of lesions on the root tips (due to the rupturing of the epidermal and outer cortical cells) and a slight limitation to root hair growth (particularly on the lateral roots). When roots displaying these symptoms were transferred to fresh Al(OH)4- solutions for a further 12 h, no root tip lesions were observed and root hair growth on the lateral roots improved. The symptoms were similar to those induced by Al13 at concentrations as low as 0.50 μM Al which are below the detection limit of the ferron method. Thus, Al(OH)4- is considered to be non-toxic, with the observed reduction in root growth in solutions containing Al(OH)4- due to the gradual formation of toxic Al13 in the bulk nutrient solution resulting from the acidification of the alkaline nutrient solution by the plant roots

Automated brain masking of fetal functional MRI with open data

Fetal resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a critical new approach for characterizing brain development before birth. Despite the rapid and widespread growth of this approach, at present, we lack neuroimaging processing pipelines suited to address the unique challenges inherent in this data type. Here, we solve the most challenging processing step, rapid and accurate isolation of the fetal brain from surrounding tissue across thousands of non-stationary 3D brain volumes. Leveraging our library of 1,241 manually traced fetal fMRI images from 207 fetuses, we trained a Convolutional Neural Network (CNN) that achieved excellent performance across two held-out test sets from separate scanners and populations. Furthermore, we unite the auto-masking model with additional fMRI preprocessing steps from existing software and provide insight into our adaptation of each step. This work represents an initial advancement towards a fully comprehensive, open-source workflow, with openly shared code and data, for fetal functional MRI data preprocessing

Maternal stress during pregnancy alters fetal cortico-cerebellar connectivity in utero and increases child sleep problems after birth

Child sleep disorders are increasingly prevalent and understanding early predictors of sleep problems, starting in utero, may meaningfully guide future prevention efforts. Here, we investigated whether prenatal exposure to maternal psychological stress is associated with increased sleep problems in toddlers. We also examined whether fetal brain connectivity has direct or indirect influence on this putative association. Pregnant women underwent fetal resting-state functional connectivity MRI and completed questionnaires on stress, worry, and negative affect. At 3-year follow-up, 64 mothers reported on child sleep problems, and in the subset that have reached 5-year follow-up, actigraphy data (N = 25) has also been obtained. We observe that higher maternal prenatal stress is associated with increased toddler sleep concerns, with actigraphy sleep metrics, and with decreased fetal cerebellar-insular connectivity. Specific mediating effects were not identified for the fetal brain regions examined. The search for underlying mechanisms of the link between maternal prenatal stress and child sleep problems should be continued and extended to other brain areas

EC67-767 Soybean Irrigation in Nebraska

Extension Circular 67-767: Soybean Irrigation in Nebraska; general irrigation requirements, when to irrigate, how much water, water management and iron deficiencies, and water use and root zones