Reproductive Deficits Induced by Prenatal Antimüllerian Hormone Exposure Require Androgen Receptor in Kisspeptin Cells.

Polycystic ovary syndrome (PCOS) is a common reproductive disorder characterized by elevated androgens and antimüllerian hormone (AMH). These hormones remain elevated throughout pregnancy, and potential effects of hormone exposure on offspring from women with PCOS remain largely unexplored. Expanding on recent reports of prenatal AMH exposure in mice, we have fully characterized the reproductive consequences of prenatal AMH (pAMH) exposure throughout the lifespan of first- and second-generation offspring of both sexes. We also sought to elucidate mechanisms underlying pAMH-induced reproductive effects. There is a known reciprocal relationship between AMH and androgens, and in PCOS and PCOS-like animal models, androgen feedback is dysregulated at the level of the hypothalamus. Kisspeptin neurons express androgen receptors and play a critical role in sexual development and function. We therefore hypothesized that pAMH-induced reproductive phenotypes would be mediated by androgen signaling at the level of kisspeptin cells. We tested the pAMH model in kisspeptin-specific androgen receptor knockout (KARKO) mice and found that virtually all pAMH-induced phenotypes assayed are eliminated in KARKO offspring compared to littermate controls. By demonstrating the necessity of androgen receptor in kisspeptin cells to induce pAMH phenotypes, we have advanced understanding of the interactions between AMH and androgens in the context of prenatal exposure, which could have significant implications for children of women with PCOS.

A comparison of machine learning classifiers for pediatric epilepsy using resting-state functional MRI latency data.

Epilepsy affects 1 in 150 children under the age of 10 and is the most common chronic pediatric neurological condition; poor seizure control can irreversibly disrupt normal brain development. The present study compared the ability of different machine learning algorithms trained with resting-state functional MRI (rfMRI) latency data to detect epilepsy. Preoperative rfMRI and anatomical MRI scans were obtained for 63 patients with epilepsy and 259 healthy controls. The normal distribution of latency z-scores from the epilepsy and healthy control cohorts were analyzed for overlap in 36 seed regions. In these seed regions, overlap between the study cohorts ranged from 0.44-0.58. Machine learning features were extracted from latency z-score maps using principal component analysis. Extreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Random Forest algorithms were trained with these features. Area under the receiver operating characteristics curve (AUC), accuracy, sensitivity, specificity and F1-scores were used to evaluate model performance. The XGBoost model outperformed all other models with a test AUC of 0.79, accuracy of 74%, specificity of 73%, and a sensitivity of 77%. The Random Forest model performed comparably to XGBoost across multiple metrics, but it had a test sensitivity of 31%. The SVM model did not perform >70% in any of the test metrics. The XGBoost model had the highest sensitivity and accuracy for the detection of epilepsy. Development of machine learning algorithms trained with rfMRI latency data could provide an adjunctive method for the diagnosis and evaluation of epilepsy with the goal of enabling timely and appropriate care for patients.

Convolutional Neural Networks for Pediatric Refractory Epilepsy Classification Using Resting-State Functional Magnetic Resonance Imaging.

OBJECTIVE: This study aims to evaluate the performance of convolutional neural networks (CNNs) trained with resting-state functional magnetic resonance imaging (rfMRI) latency data in the classification of patients with pediatric epilepsy from healthy controls. METHODS: Preoperative rfMRI and anatomic magnetic resonance imaging scans were obtained from 63 pediatric patients with refractory epilepsy and 259 pediatric healthy controls. Latency maps of the temporal difference between rfMRI and the global mean signal were calculated using voxel-wise cross-covariance. Healthy control and epilepsy latency z score maps were pseudorandomized and partitioned into training data (60%), validation data (20%), and test data (20%). Healthy control individuals and patients with epilepsy were labeled as negative and positive, respectively. CNN models were then trained with the designated training data. Model hyperparameters were evaluated with a grid-search method. The model with the highest sensitivity was evaluated using unseen test data. Accuracy, sensitivity, specificity, F1 score, and area under the receiver operating characteristic curve were used to evaluate the ability of the model to classify epilepsy in the test data set. RESULTS: The model with the highest validation sensitivity correctly classified 74% of unseen test patients with 85% sensitivity, 71% specificity, F1 score of 0.56, and an area under the receiver operating characteristic curve of 0.86. CONCLUSIONS: Using rfMRI latency data, we trained a CNN model to classify patients with pediatric epilepsy from healthy controls with good performance. CNN could serve as an adjunct in the diagnosis of pediatric epilepsy. Identification of pediatric epilepsy earlier in the disease course could decrease time to referral to specialized epilepsy centers and thus improve prognosis in this population.

Cap-Based Transcranial Optical Tomography in an Awake Infant.

Although Blood Oxygenation Level Dependent (BOLD) functional MRI (fMRI) is widely used to examine brain function in adults, the need for general anesthesia limits its practical utility in infants and small children. Functional Near-Infrared Spectroscopy - Diffuse Optical Tomography (fNIRS-DOT) imaging promises to be an alternative brain network imaging technique. Yet current versions of continuous-wave fNIRS-DOT systems are restricted to the cortical surface measurements and do not probe deep structures that are frequently injured especially in premature infants. Herein we report a transcranial near infrared optical imaging system, called Cap-based Transcranial Optical Tomography (CTOT) able to image whole brain hemodynamic activity with 3 seconds of data acquisition time. We show the system is capable of whole brain oxygenation mapping in an awake child, and that tomographically reconstructed static CTOT-derived oxy- and deoxygenated blood volumes are spatially correlated with the time-averaged BOLD fMRI volumes. By removing time bottlenecks in the current system, dynamic CTOT mapping should be possible, which would then enable evaluation of functional connectivity in awake infants.

Role of resting state MRI temporal latency in refractory pediatric extratemporal epilepsy lateralization.

BACKGROUND: Pediatric epilepsy affects 0.5-1% of children, with 10-30% of these children refractory to medical anticonvulsant therapy and potentially requiring surgical intervention. Analysis of resting state functional MRI (rsMRI) signal temporal differences (latency) has been proposed to study the pathological cognitive processes. PURPOSE/HYPOTHESIS: To quantitatively and qualitatively analyze the correlation of rsMRI signal latency to pediatric refractory extratemporal epilepsy seizure foci lateralization. STUDY TYPE: Retrospective review. POPULATION: With Institutional Review Board approval, rsMRI and anatomical MRI scans were obtained from 38 registered pediatric epilepsy surgery patients from Washington University and 259 healthy control patients from the ADHD-200 dataset. FIELD STRENGTH/SEQUENCE: 3 T echo planar imaging (EPI) blood oxygenation level-dependent (BOLD) sequence. ASSESSMENT: The images were transformed to pediatric atlases in Talairach space. Preoperative voxelwise latency maps were generated with parabolic interpolation of the rsMRI signal lateness or earliness when compared with the global mean signal (GMS) using cross-covariance analysis. STATISTICAL TESTS: Latency z-score maps were created for each epilepsy patient by voxelwise calculation using healthy control mean and standard deviation maps. Voxelwise hypothesis testing was performed via multiple comparisons corrected (false discovery and familywise error rate) and uncorrected methods to determine significantly late and early voxels. Significantly late and/or early voxels were counted for the right and left hemisphere separately. The hemisphere with the greater proportion of significantly late and/or early voxels was hypothesized to contain the seizure focus. Preoperative rsMRI latency analysis hypotheses were compared with postoperative seizure foci lateralization determined by resection images. RESULTS: Preoperative rsMRI latency analysis correctly identified seizure foci lateralization of 64-85% of postoperative epilepsy resections with the proposed methods. RsMRI latency lateralization analysis was 77-100% sensitive and 58-79% specific. In some patients, qualitative analysis yielded preoperative rsMRI latency patterns specific to procedure performed. DATA CONCLUSION: Preoperative rsMRI signal latency of pediatric epilepsy patients was correlated with seizure foci lateralization. J. Magn. Reson. Imaging 2019;49:1347-1355.