Analysis of 3D pathology samples using weakly supervised AI.

Human tissue, which is inherently three-dimensional (3D), is traditionally examined through standard-of-care histopathology as limited two-dimensional (2D) cross-sections that can insufficiently represent the tissue due to sampling bias. To holistically characterize histomorphology, 3D imaging modalities have been developed, but clinical translation is hampered by complex manual evaluation and lack of computational platforms to distill clinical insights from large, high-resolution datasets. We present TriPath, a deep-learning platform for processing tissue volumes and efficiently predicting clinical outcomes based on 3D morphological features. Recurrence risk-stratification models were trained on prostate cancer specimens imaged with open-top light-sheet microscopy or microcomputed tomography. By comprehensively capturing 3D morphologies, 3D volume-based prognostication achieves superior performance to traditional 2D slice-based approaches, including clinical/histopathological baselines from six certified genitourinary pathologists. Incorporating greater tissue volume improves prognostic performance and mitigates risk prediction variability from sampling bias, further emphasizing the value of capturing larger extents of heterogeneous morphology.

Spatial transcriptomics reveal neuron-astrocyte synergy in long-term memory.

Memory encodes past experiences, thereby enabling future plans. The basolateral amygdala is a centre of salience networks that underlie emotional experiences and thus has a key role in long-term fear memory formation1. Here we used spatial and single-cell transcriptomics to illuminate the cellular and molecular architecture of the role of the basolateral amygdala in long-term memory. We identified transcriptional signatures in subpopulations of neurons and astrocytes that were memory-specific and persisted for weeks. These transcriptional signatures implicate neuropeptide and BDNF signalling, MAPK and CREB activation, ubiquitination pathways, and synaptic connectivity as key components of long-term memory. Notably, upon long-term memory formation, a neuronal subpopulation defined by increased Penk and decreased Tac expression constituted the most prominent component of the memory engram of the basolateral amygdala. These transcriptional changes were observed both with single-cell RNA sequencing and with single-molecule spatial transcriptomics in intact slices, thereby providing a rich spatial map of a memory engram. The spatial data enabled us to determine that this neuronal subpopulation interacts with adjacent astrocytes, and functional experiments show that neurons require interactions with astrocytes to encode long-term memory.

Author Correction: Structure of human GABAB receptor in an inactive state.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Structure of human GABAB receptor in an inactive state.

The human GABAB receptor-a member of the class C family of G-protein-coupled receptors (GPCRs)-mediates inhibitory neurotransmission and has been implicated in epilepsy, pain and addiction1. A unique GPCR that is known to require heterodimerization for function2-6, the GABAB receptor has two subunits, GABAB1 and GABAB2, that are structurally homologous but perform distinct and complementary functions. GABAB1 recognizes orthosteric ligands7,8, while GABAB2 couples with G proteins9-14. Each subunit is characterized by an extracellular Venus flytrap (VFT) module, a descending peptide linker, a seven-helix transmembrane domain and a cytoplasmic tail15. Although the VFT heterodimer structure has been resolved16, the structure of the full-length receptor and its transmembrane signalling mechanism remain unknown. Here we present a near full-length structure of the GABAB receptor, captured in an inactive state by cryo-electron microscopy. Our structure reveals several ligands that preassociate with the receptor, including two large endogenous phospholipids that are embedded within the transmembrane domains to maintain receptor integrity and modulate receptor function. We also identify a previously unknown heterodimer interface between transmembrane helices 3 and 5 of both subunits, which serves as a signature of the inactive conformation. A unique 'intersubunit latch' within this transmembrane interface maintains the inactive state, and its disruption leads to constitutive receptor activity.

A cGAL-UAS bipartite expression toolkit for Caenorhabditis elegans sensory neurons.

Animals integrate sensory information from the environment and display various behaviors in response to external stimuli. In Caenorhabditis elegans hermaphrodites, 33 types of sensory neurons are responsible for chemosensation, olfaction, and mechanosensation. However, the functional roles of all sensory neurons have not been systematically studied due to the lack of facile genetic accessibility. A bipartite cGAL-UAS system has been previously developed to study tissue- or cell-specific functions in C. elegans. Here, we report a toolkit of new cGAL drivers that can facilitate the analysis of a vast majority of the 60 sensory neurons in C. elegans hermaphrodites. We generated 37 sensory neuronal cGAL drivers that drive cGAL expression by cell-specific regulatory sequences or intersection of two distinct regulatory regions with overlapping expression (split cGAL). Most cGAL-drivers exhibit expression in single types of cells. We also constructed 28 UAS effectors that allow expression of proteins to perturb or interrogate sensory neurons of choice. This cGAL-UAS sensory neuron toolkit provides a genetic platform to systematically study the functions of C. elegans sensory neurons.

A hybrid open-top light-sheet microscope for versatile multi-scale imaging of cleared tissues.

Light-sheet microscopy has emerged as the preferred means for high-throughput volumetric imaging of cleared tissues. However, there is a need for a flexible system that can address imaging applications with varied requirements in terms of resolution, sample size, tissue-clearing protocol, and transparent sample-holder material. Here, we present a 'hybrid' system that combines a unique non-orthogonal dual-objective and conventional (orthogonal) open-top light-sheet (OTLS) architecture for versatile multi-scale volumetric imaging. We demonstrate efficient screening and targeted sub-micrometer imaging of sparse axons within an intact, cleared mouse brain. The same system enables high-throughput automated imaging of multiple specimens, as spotlighted by a quantitative multi-scale analysis of brain metastases. Compared with existing academic and commercial light-sheet microscopy systems, our hybrid OTLS system provides a unique combination of versatility and performance necessary to satisfy the diverse requirements of a growing number of cleared-tissue imaging applications.

Ambient exposure to fine particulate matter with oxidative potential affects oxidative stress biomarkers in pregnancy.

Prenatal exposures to ambient particulate matter (PM2.5) from traffic may generate oxidative stress, and thus contribute to adverse birth outcomes. We investigated whether PM2.5 constituents from brake and tire wear affect levels of oxidative stress biomarkers (malondialdehyde (MDA), 8-hydroxy-2'-deoxyguanosine (8-OHdG)) using urine samples collected up to three times during pregnancy in 156 women recruited from antenatal clinics at the University of California Los Angeles. Land use regression models with co-kriging were employed to estimate average residential outdoor concentrations of black carbon (BC), PM2.5 mass, PM2.5 metal components, and three PM2.5 oxidative potential metrics during the 4-weeks prior to urine sample collection. 8-OHdG concentrations in mid-pregnancy increased by 24.8% (95% CI: 9.0, 42.8) and 14.3% (95% CI: 0.4%, 30.0%) per interquartile range (IQR) increase in PM2.5 mass and BC, respectively. The brake wear marker (barium) and the oxidative potential metrics were associated with increased MDA concentration in the 1st sample collected (10-17 gestational week), but 95% CIs included the null. Traffic-related air pollution contributed in early to mid-pregnancy to oxidative stress generation previously linked to adverse birth outcomes.

Axially swept open-top light-sheet microscopy for densely labeled clinical specimens.

Open-top light-sheet (OTLS) microscopy offers rapid 3D imaging of large optically cleared specimens. This enables nondestructive 3D pathology, which provides key advantages over conventional slide-based histology including comprehensive sampling without tissue sectioning/destruction and visualization of diagnostically important 3D structures. With 3D pathology, clinical specimens are often labeled with small-molecule stains that broadly target nucleic acids and proteins, mimicking conventional hematoxylin and eosin (H&E) dyes. Tight optical sectioning helps to minimize out-of-focus fluorescence for high-contrast imaging in these densely labeled tissues but has been challenging to achieve in OTLS systems due to trade-offs between optical sectioning and field of view. Here we present an OTLS microscope with voice-coil-based axial sweeping to circumvent this trade-off, achieving 2 µm axial resolution over a 750 × 375 µm field of view. We implement our design in a non-orthogonal dual-objective (NODO) architecture, which enables a 10-mm working distance with minimal sensitivity to refractive index mismatches, for high-contrast 3D imaging of clinical specimens.

Nondestructive 3D pathology with analysis of nuclear features for prostate cancer risk assessment.

Prostate cancer treatment decisions rely heavily on subjective visual interpretation [assigning Gleason patterns or International Society of Urological Pathology (ISUP) grade groups] of limited numbers of two-dimensional (2D) histology sections. Under this paradigm, interobserver variance is high, with ISUP grades not correlating well with outcome for individual patients, and this contributes to the over- and undertreatment of patients. Recent studies have demonstrated improved prognostication of prostate cancer outcomes based on computational analyses of glands and nuclei within 2D whole slide images. Our group has also shown that the computational analysis of three-dimensional (3D) glandular features, extracted from 3D pathology datasets of whole intact biopsies, can allow for improved recurrence prediction compared to corresponding 2D features. Here we seek to expand on these prior studies by exploring the prognostic value of 3D shape-based nuclear features in prostate cancer (e.g. nuclear size, sphericity). 3D pathology datasets were generated using open-top light-sheet (OTLS) microscopy of 102 cancer-containing biopsies extracted ex vivo from the prostatectomy specimens of 46 patients. A deep learning-based workflow was developed for 3D nuclear segmentation within the glandular epithelium versus stromal regions of the biopsies. 3D shape-based nuclear features were extracted, and a nested cross-validation scheme was used to train a supervised machine classifier based on 5-year biochemical recurrence (BCR) outcomes. Nuclear features of the glandular epithelium were found to be more prognostic than stromal cell nuclear features (area under the ROC curve [AUC] = 0.72 versus 0.63). 3D shape-based nuclear features of the glandular epithelium were also more strongly associated with the risk of BCR than analogous 2D features (AUC = 0.72 versus 0.62). The results of this preliminary investigation suggest that 3D shape-based nuclear features are associated with prostate cancer aggressiveness and could be of value for the development of decision-support tools. © 2023 The Pathological Society of Great Britain and Ireland.

Contributions of neighborhood physical and social environments to racial and ethnic disparities in birth outcomes in California: A mediation analysis.

BACKGROUND: Racially minoritized populations experience higher rates of adverse birth outcomes than White populations in the U.S. We estimated the mediating effect of neighborhood social and physical environments on disparities in adverse birth outcomes in California. METHOD: We used birthing parent's residential address for California live birth records from 2019 to estimate census block group Area Deprivation Index and census tract level measures of ambient fine particulate matter (PM2.5), drinking water contamination, tree canopy coverage, as a measure of greenspace, potential heat vulnerability, and noise. We performed mediation analysis to assess whether neighborhood factors explain racial/ethnic disparities in preterm birth (PTB) and term-birth low birth weight (TLBW) comparing Black, Latinx, and Asian with White births after controlling for individual-level factors. RESULTS: Black, Latinx, and Asian parents had PTB rates that were 67%, 36%, and 11% higher, and TLBW rates that were 150%, 38%, and 81% higher than Whites. Neighborhood deprivation contributed 7% (95% CI: 3%, 11%) to the Black-White and 9% (95% CI: 6%, 12%) to the Latinx-White disparity in PTB, and 8% (95% CI: 3%, 12%) of the Black-White and 9% (95% CI: 5%, 15%) of the Latinx-White disparity in TLBW. Drinking water contamination contributed 2% (95% CI: 1%, 4%) to the Latinx-White disparity in PTB. Lack of greenspace accounted for 7% (95% CI: 2%, 10%) of the Latinx-White PTB disparity and 7% (95% CI: 3%, 12%) of the Asian-White PTB disparity. PM2.5 contributed 11% (95% CI: 5%, 18%), drinking water contamination contributed 3% (95% CI: 1%, 7%), and potential heat vulnerability contributed 2% (95% CI: 1%, 3%) to the Latinx-White TLBW disparity. Lack of green space contributed 3% (95% CI: 1%, 6%) to the Asian-White TLBW disparity. CONCLUSIONS: Our study suggests social environments explain portions of Black/Latinx-White disparities while physical environments explain Latinx/Asian-White disparities in PTB and TLBW.

Symmetric activation and modulation of the human calcium-sensing receptor.

The human extracellular calcium-sensing (CaS) receptor controls plasma Ca2+ levels and contributes to nutrient-dependent maintenance and metabolism of diverse organs. Allosteric modulation of the CaS receptor corrects disorders of calcium homeostasis. Here, we report the cryogenic-electron microscopy reconstructions of a near-full-length CaS receptor in the absence and presence of allosteric modulators. Activation of the homodimeric CaS receptor requires a break in the transmembrane 6 (TM6) helix of each subunit, which facilitates the formation of a TM6-mediated homodimer interface and expansion of homodimer interactions. This transformation in TM6 occurs without a positive allosteric modulator. Two modulators with opposite functional roles bind to overlapping sites within the transmembrane domain through common interactions, acting to stabilize distinct rotamer conformations of key residues on the TM6 helix. The positive modulator reinforces TM6 distortion and maximizes subunit contact to enhance receptor activity, while the negative modulator strengthens an intact TM6 to dampen receptor function. In both active and inactive states, the receptor displays symmetrical transmembrane conformations that are consistent with its homodimeric assembly.

Visual Assessment of 2-Dimensional Levels Within 3-Dimensional Pathology Data Sets of Prostate Needle Biopsies Reveals Substantial Spatial Heterogeneity.

Prostate cancer prognostication largely relies on visual assessment of a few thinly sectioned biopsy specimens under a microscope to assign a Gleason grade group (GG). Unfortunately, the assigned GG is not always associated with a patient's outcome in part because of the limited sampling of spatially heterogeneous tumors achieved by 2-dimensional histopathology. In this study, open-top light-sheet microscopy was used to obtain 3-dimensional pathology data sets that were assessed by 4 human readers. Intrabiopsy variability was assessed by asking readers to perform Gleason grading of 5 different levels per biopsy for a total of 20 core needle biopsies (ie, 100 total images). Intrabiopsy variability (Cohen κ) was calculated as the worst pairwise agreement in GG between individual levels within each biopsy and found to be 0.34, 0.34, 0.38, and 0.43 for the 4 pathologists. These preliminary results reveal that even within a 1-mm-diameter needle core, GG based on 2-dimensional images can vary dramatically depending on the location within a biopsy being analyzed. We believe that morphologic assessment of whole biopsies in 3 dimension has the potential to enable more reliable and consistent tumor grading.

Artificial Intelligence-Triaged 3-Dimensional Pathology to Improve Detection of Esophageal Neoplasia While Reducing Pathologist Workloads.

Early detection of esophageal neoplasia via evaluation of endoscopic surveillance biopsies is the key to maximizing survival for patients with Barrett's esophagus, but it is hampered by the sampling limitations of conventional slide-based histopathology. Comprehensive evaluation of whole biopsies with 3-dimensional (3D) pathology may improve early detection of malignancies, but large 3D pathology data sets are tedious for pathologists to analyze. Here, we present a deep learning-based method to automatically identify the most critical 2-dimensional (2D) image sections within 3D pathology data sets for pathologists to review. Our method first generates a 3D heatmap of neoplastic risk for each biopsy, then classifies all 2D image sections within the 3D data set in order of neoplastic risk. In a clinical validation study, we diagnose esophageal biopsies with artificial intelligence-triaged 3D pathology (3 images per biopsy) vs standard slide-based histopathology (16 images per biopsy) and show that our method improves detection sensitivity while reducing pathologist workloads.

Management of Pulmonary Arterial Hypertension.

Pulmonary arterial hypertension (PAH) is a rare pulmonary vascular disease characterized by progressive pulmonary arterial remodeling, increased pulmonary vascular resistance, right ventricular dysfunction, and reduced survival. Effective therapies have been developed that target three pathobiologic pathways in PAH: nitric oxide, endothelin-1, and prostacyclin. Approved therapies for PAH include phosphodiesterase type-5 inhibitors, soluble guanylate cyclase stimulators, endothelin receptor antagonists, prostacyclin analogs, and prostacyclin receptor agonists. Management of PAH in the modern era incorporates multidimensional risk assessment to guide the use of these medications. For patients with PAH and without significant comorbidities, current guidelines recommend two oral medications (phosphodiesterase type-5 inhibitor and endothelin receptor antagonist) for low- and intermediate-risk patients, with triple therapy including a parenteral prostacyclin to be considered in those at high or intermediate-high risk. Combination therapy may be poorly tolerated and less effective in patients with PAH and cardiopulmonary comorbidities. Thus, a single-agent approach with individualized decisions to add-on other PAH therapies is recommended in older patients and those with significant comorbid conditions. Management of PAH is best performed in multidisciplinary teams located in experienced centers. Other core pillars of PAH management include supportive and adjunctive treatments including oxygen, diuretics, rehabilitation, and anticoagulation in certain patients. Patients with PAH who progress despite optimal treatment or who are refractory to best medical care should be referred for lung transplantation, if eligible. Despite considerable progress, PAH is often fatal and new therapies that reverse the disease and improve outcomes are desperately needed.

Real-time single-cell characterization of the eukaryotic transcription cycle reveals correlations between RNA initiation, elongation, and cleavage.

The eukaryotic transcription cycle consists of three main steps: initiation, elongation, and cleavage of the nascent RNA transcript. Although each of these steps can be regulated as well as coupled with each other, their in vivo dissection has remained challenging because available experimental readouts lack sufficient spatiotemporal resolution to separate the contributions from each of these steps. Here, we describe a novel application of Bayesian inference techniques to simultaneously infer the effective parameters of the transcription cycle in real time and at the single-cell level using a two-color MS2/PP7 reporter gene and the developing fruit fly embryo as a case study. Our method enables detailed investigations into cell-to-cell variability in transcription-cycle parameters as well as single-cell correlations between these parameters. These measurements, combined with theoretical modeling, suggest a substantial variability in the elongation rate of individual RNA polymerase molecules. We further illustrate the power of this technique by uncovering a novel mechanistic connection between RNA polymerase density and nascent RNA cleavage efficiency. Thus, our approach makes it possible to shed light on the regulatory mechanisms in play during each step of the transcription cycle in individual, living cells at high spatiotemporal resolution.

Artificial Intelligence (AI) for Lung Nodules, From the AJR Special Series on AI Applications.

Interest in artificial intelligence (AI) applications for lung nodules continues to grow among radiologists, particularly with the expanding eligibility criteria and clinical utilization of lung cancer screening CT. AI has been heavily investigated for detecting and characterizing lung nodules and for guiding prognostic assessment. AI tools have also been used for image postprocessing (e.g., rib suppression on radiography or vessel suppression on CT) and for noninterpretive aspects of reporting and workflow, including management of nodule follow-up. Despite growing interest in and rapid development of AI tools and FDA approval of AI tools for pulmonary nodule evaluation, integration into clinical practice has been limited. Challenges to clinical adoption have included concerns about generalizability, regulatory issues, technical hurdles in implementation, and human skepticism. Further validation of AI tools for clinical use and demonstration of benefit in terms of patient-oriented outcomes also are needed. This article provides an overview of potential applications of AI tools in the imaging evaluation of lung nodules and discusses the challenges faced by practices interested in clinical implementation of such tools.

Aerosol Oxidative Potential in the Greater Los Angeles Area: Source Apportionment and Associations with Socioeconomic Position.

Oxidative potential (OP) has been proposed as a possible integrated metric for particles smaller than 2.5 µm in diameter (PM2.5) to evaluate adverse health outcomes associated with particulate air pollution exposure. Here, we investigate how OP depends on sources and chemical composition and how OP varies by land use type and neighborhood socioeconomic position in the Los Angeles area. We measured OH formation (OPOH), dithiothreitol loss (OPDTT), black carbon, and 52 metals and elements for 54 total PM2.5 samples collected in September 2019 and February 2020. The Positive Matrix Factorization source apportionment model identified four sources contributing to volume-normalized OPOH: vehicular exhaust, brake and tire wear, soil and road dust, and mixed secondary and marine. Exhaust emissions contributed 42% of OPOH, followed by 21% from brake and tire wear. Similar results were observed for the OPDTT source apportionment. Furthermore, by linking measured PM2.5 and OP with census tract level socioeconomic and health outcome data provided by CalEnviroScreen, we found that the most disadvantaged neighborhoods were exposed to both the most toxic particles and the highest particle concentrations. OPOH exhibited the largest inverse social gradients, followed by OPDTT and PM2.5 mass. Finally, OPOH was the metric most strongly correlated with adverse health outcome indicators.

DENOISING SWEPT SOURCE OPTICAL COHERENCE TOMOGRAPHY VOLUMETRIC SCANS USING A DEEP LEARNING MODEL.

PURPOSE: To evaluate the use of a deep learning noise reduction model on swept source optical coherence tomography volumetric scans. METHODS: Three groups of images including single-line highly averaged foveal scans (averaged images), foveal B-scans from volumetric scans using no averaging (unaveraged images), and deep learning denoised versions of the latter (denoised images) were obtained. We evaluated the potential increase in the signal-to-noise ratio by evaluating the contrast-to-noise ratio of the resultant images and measured the multiscale structural similarity index to determine whether the unaveraged and denoised images held true in structure to the averaged images. We evaluated the practical effects of denoising on a popular metric of choroidal vascularity known as the choroidal vascularity index. RESULTS: Ten eyes of 10 subjects with a mean age of 31 years (range 24-64 years) were evaluated. The deep choroidal contrast-to-noise ratio mean values of the averaged and denoised image groups were similar (7.06 vs. 6.81, P = 0.75), and both groups had better maximum contrast-to-noise ratio mean values (27.65 and 46.34) than the unaveraged group (14.75; P = 0.001 and P < 0.001, respectively). The mean multiscale structural similarity index of the average-denoised images was significantly higher than the one from the averaged--unaveraged images (0.85 vs. 0.61, P < 0.001). Choroidal vascularity index values from averaged and denoised images were similar (71.81 vs. 71.16, P = 0.554). CONCLUSION: Using three different metrics, we demonstrated that the deep learning denoising model can produce high-quality images that emulate, and may exceed, the quality of highly averaged scans.

Structural basis for auxiliary subunit KCTD16 regulation of the GABAB receptor.

Metabotropic GABAB receptors mediate a significant fraction of inhibitory neurotransmission in the brain. Native GABAB receptor complexes contain the principal subunits GABAB1 and GABAB2, which form an obligate heterodimer, and auxiliary subunits, known as potassium channel tetramerization domain-containing proteins (KCTDs). KCTDs interact with GABAB receptors and modify the kinetics of GABAB receptor signaling. Little is known about the molecular mechanism governing the direct association and functional coupling of GABAB receptors with these auxiliary proteins. Here, we describe the high-resolution structure of the KCTD16 oligomerization domain in complex with part of the GABAB2 receptor. A single GABAB2 C-terminal peptide is bound to the interior of an open pentamer formed by the oligomerization domain of five KCTD16 subunits. Mutation of specific amino acids identified in the structure of the GABAB2-KCTD16 interface disrupted both the biochemical association and functional modulation of GABAB receptors and G protein-activated inwardly rectifying K+ channel (GIRK) channels. These interfacial residues are conserved among KCTDs, suggesting a common mode of KCTD interaction with GABAB receptors. Defining the binding interface of GABAB receptor and KCTD reveals a potential regulatory site for modulating GABAB-receptor function in the brain.

A Description of the Imaging Innovations for Placental Assessment in Response to Environmental Pollution Study.

OBJECTIVE: The aim of Placental Assessment in Response to Environmental Pollution Study (PARENTs) was to determine whether imaging of the placenta by novel multiparametric magnetic resonance imaging (MRI) techniques in early pregnancy could help predict adverse pregnancy outcomes (APOs) due to ischemic placental disease (IPD). Additionally, we sought to determine maternal characteristics and environmental risk factors that contribute to IPD and secondary APOs. STUDY DESIGN: Potential patients in their first trimester of pregnancy, who agreed to MRI of the placenta and measures of assessment of environmental pollution, were recruited into PARENTs, a prospective population-based cohort study. Participants were seen at three study visits during pregnancy and again at their delivery from 2015 to 2019. We collected data from interviews, chart abstractions, and imaging. Maternal biospecimens (serum, plasma, and urine) at antepartum study visits and delivery specimens (placenta, cord, and maternal blood) were collected, processed, and stored. The primary outcome was a composite of IPD, which included any of the following: placental abruption, hypertensive disease of pregnancy, fetal growth restriction, or a newborn of small for gestational age. RESULTS: In this pilot cohort, of the 190 patients who completed pregnancy to viable delivery, 50 (26%) developed IPD. Among demographic characteristics, having a history of prior IPD in multiparous women was associated with the development of IPD. In the multiple novel perfusion measurements taken of the in vivo placenta using MRI, decreased high placental blood flow (mL/100 g/min) in early pregnancy (between 14 and 16 weeks) was found to be significantly associated with the later development of IPD. CONCLUSION: Successful recruitment of the PARENTs prospective cohort demonstrated the feasibility and acceptability of the use of MRI in human pregnancy to study the placenta in vivo and at the same time collect environmental exposure data. Analysis is ongoing and we hope these methods will assist researchers in the design of prospective imaging studies of pregnancy. KEY POINTS: · MRI was acceptable and feasible for the study of the human placenta in vivo.. · Functional imaging of the placenta by MRI showed a significant decrease in high placental blood flow.. · Measures of environmental exposures are further being analyzed to predict IPD..