A shock flash breaking out of a dusty red supergiant.

Shock-breakout emission is light that arises when a shockwave, generated by the core-collapse explosion of a massive star, passes through its outer envelope. Hitherto, the earliest detection of such a signal was at several hours after the explosion1, although a few others had been reported2-7. The temporal evolution of early light curves should provide insights into the shock propagation, including explosion asymmetry and environment in the vicinity, but this has been hampered by the lack of multiwavelength observations. Here we report the instant multiband observations of a type II supernova (SN 2023ixf) in the galaxy M101 (at a distance of 6.85 ± 0.15 Mpc; ref. 8), beginning at about 1.4 h after the explosion. The exploding star was a red supergiant with a radius of about 440 solar radii. The light curves evolved rapidly, on timescales of 1-2 h, and appeared unusually fainter and redder than predicted by the models9-11 within the first few hours, which we attribute to an optically thick dust shell before it was disrupted by the shockwave. We infer that the breakout and perhaps the distribution of the surrounding dust were not spherically symmetric.

Multifaceted atlases of the human brain in its infancy.

Brain atlases are spatial references for integrating, processing, and analyzing brain features gathered from different individuals, sources, and scales. Here we introduce a collection of joint surface-volume atlases that chart postnatal development of the human brain in a spatiotemporally dense manner from two weeks to two years of age. Our month-specific atlases chart normative patterns and capture key traits of early brain development and are therefore conducive to identifying aberrations from normal developmental trajectories. These atlases will enhance our understanding of early structural and functional development by facilitating the mapping of diverse features of the infant brain to a common reference frame for precise multifaceted quantification of cortical and subcortical changes.

Mapping developmental regionalization and patterns of cortical surface area from 29 post-menstrual weeks to 2 years of age.

Surface area of the human cerebral cortex expands extremely dynamically and regionally heterogeneously from the third trimester of pregnancy to 2 y of age, reflecting the spatial heterogeneity of the underlying microstructural and functional development of the cerebral cortex. However, little is known about the developmental patterns and regionalization of cortical surface area during this critical stage, due to the lack of high-quality imaging data and accurate computational tools for pediatric brain MRI data. To fill this critical knowledge gap, by leveraging 1,037 high-quality MRI scans with the age between 29 post-menstrual weeks and 24 mo from 735 pediatric subjects in two complementary datasets, i.e., the Baby Connectome Project (BCP) and the developing Human Connectome Project (dHCP), and state-of-the-art dedicated image-processing tools, we unprecedentedly parcellate the cerebral cortex into a set of distinct subdivisions purely according to the developmental patterns of the cortical surface. Our discovered developmentally distinct subdivisions correspond well to structurally and functionally meaningful regions and reveal spatially contiguous, hierarchical, and bilaterally symmetric patterns of early cortical surface expansion. We also show that high-order association subdivisions, where cortical folds emerge later during prenatal stages, undergo more dramatic cortical surface expansion during infancy, compared with the central regions, especially the sensorimotor and insula cortices, thus forming a distinct central-pole division in early cortical surface expansion. These results provide an important reference for exploring and understanding dynamic early brain development in health and disease.

Mapping Genetic Topography of Cortical Thickness and Surface Area in Neonatal Brains.

Adult twin neuroimaging studies have revealed that cortical thickness (CT) and surface area (SA) are differentially influenced by genetic information, leading to their spatially distinct genetic patterning and topography. However, the postnatal origins of the genetic topography of CT and SA remain unclear, given the dramatic cortical development from neonates to adults. To fill this critical gap, this study unprecedentedly explored how genetic information differentially regulates the spatial topography of CT and SA in the neonatal brain by leveraging brain magnetic resonance (MR) images from 202 twin neonates with minimal influence by the complicated postnatal environmental factors. We capitalized on infant-dedicated computational tools and a data-driven spectral clustering method to parcellate the cerebral cortex into a set of distinct regions purely according to the genetic correlation of cortical vertices in terms of CT and SA, respectively, and accordingly created the first genetically informed cortical parcellation maps of neonatal brains. Both genetic parcellation maps exhibit bilaterally symmetric and hierarchical patterns, but distinct spatial layouts. For CT, regions with closer genetic relationships demonstrate an anterior-posterior (A-P) division, while for SA, regions with greater genetic proximity are typically within the same lobe. Certain genetically informed regions exhibit strong similarities between neonates and adults, with the most striking similarities in the medial surface in terms of SA, despite their overall substantial differences in genetic parcellation maps. These results greatly advance our understanding of the development of genetic influences on the spatial patterning of cortical morphology.SIGNIFICANCE STATEMENT Genetic influences on cortical thickness (CT) and surface area (SA) are complex and could evolve throughout the lifespan. However, studies revealing distinct genetic topography of CT and SA have been limited to adults. Using brain structural magnetic resonance (MR) images of twins, we unprecedentedly discovered the distinct genetically-informed parcellation maps of CT and SA in neonatal brains, respectively. Each genetic parcellation map comprises a distinct spatial layout of cortical regions, where vertices within the same region share high genetic correlation. These genetic parcellation maps of CT and SA of neonates largely differ from those of adults, despite their highly remarkable similarities in the medial cortex of SA. These discoveries provide important insights into the genetic organization of the early cerebral cortex development.

Urinary metabolite concentrations of phthalate and plasticizers in infancy and childhood in the UNC baby connectome project.

INTRODUCTION: Existing evidence suggests that exposure to phthalates is higher among younger age groups. However, limited knowledge exists on how phthalate exposure, as well as exposure to replacement plasticizers, di(isononyl) cyclohexane-1,2-dicarboxylate (DINCH) and di-2-ethylhexyl terephthalate (DEHTP), change from infancy through early childhood. METHODS: Urine samples were collected across the first 5 years of life from typically developing infants and young children enrolled between 2017 and 2020 in the longitudinal UNC Baby Connectome Project. From 438 urine samples among 187 participants, we quantified concentrations of monobutyl phthalate (MnBP), mono-3-carboxypropyl phthalate (MCPP), monoisobutyl phthalate (MiBP), monoethyl phthalate (MEP), monobenzyl phthalate (MBzP), and metabolites of di(2-ethylhexyl) phthalate (DEHP), diisonoyl phthalate (DiNP), DINCH and DEHTP. Specific gravity (SG) adjusted metabolite and molar sum concentrations were compared across age groups. Intraclass correlation coefficients (ICCs) were calculated among 122 participants with multiple urine specimens (373 samples). RESULTS: Most phthalate metabolites showed high detection frequencies (>80% of samples). Replacement plasticizers DINCH (58-60%) and DEHTP (>97%) were also commonly found. DiNP metabolites were less frequently detected (<10%). For some metabolites, SG-adjusted concentrations were inversely associated with age, with the highest concentrations found in the first year of life. ICCs revealed low to moderate reliability in metabolite measurements (ρ = 0.10-0.48) suggesting a high degree of within-individual variation in exposure among this age group. The first 6 months (compared to remaining age groups) showed an increased ratio of carboxylated metabolites of DEHP and DEHTP, compared to other common metabolites, but no clear age trends for DINCH metabolite ratios were observed. CONCLUSION: Metabolites of phthalates and replacements plasticizers were widely detected in infancy and early childhood, with the highest concentrations observed in the first year of life for several metabolites. Higher proportions of carboxylated metabolites of DEHP and DEHTP in younger age groups indicate potential differences in metabolism during infancy.

Existence of Functional Connectome Fingerprint during Infancy and Its Stability over Months.

The functional connectome fingerprint is a cluster of individualized brain functional connectivity patterns that are capable of distinguishing one individual from others. Although its existence has been demonstrated in adolescents and adults, whether such individualized patterns exist during infancy is barely investigated despite its importance in identifying the origin of the intrinsic connectome patterns that potentially mirror distinct behavioral phenotypes. To fill this knowledge gap, capitalizing on a longitudinal high-resolution structural and resting-state functional MRI dataset with 104 human infants (53 females) with 806 longitudinal scans (age, 16-876 d) and infant-specific functional parcellation maps, we observe that the brain functional connectome fingerprint may exist since infancy and keeps stable over months during early brain development. Specifically, we achieve an ∼78% individual identification rate by using ∼5% selected functional connections, compared with the best identification rate of 60% without connection selection. The frontoparietal networks recognized as the most contributive networks in adult functional connectome fingerprinting retain their superiority in infants despite being widely acknowledged as rapidly developing systems during childhood. The existence and stability of the functional connectome fingerprint are further validated on adjacent age groups. Moreover, we show that the infant frontoparietal networks can reach similar accuracy in predicting individual early learning composite scores as the whole-brain connectome, again resembling the observations in adults and highlighting the relevance of functional connectome fingerprint to cognitive performance. For the first time, these results suggest that each individual may retain a unique and stable marker of functional connectome during early brain development.SIGNIFICANCE STATEMENT Functional connectome fingerprinting during infancy featuring rapid brain development remains almost uninvestigated even though it is essential for understanding the early individual-level intrinsic pattern of functional organization and its relationship with distinct behavioral phenotypes. With an infant-tailored functional connection selection and validation strategy, we strive to provide the delineation of the infant functional connectome fingerprint by examining its existence, stability, and relationship with early cognitive performance. We observe that the brain functional connectome fingerprint may exist since early infancy and remains stable over months during the first 2 years. The identified key contributive functional connections and networks for fingerprinting are also verified to be highly predictive for cognitive score prediction, which reveals the association between infant connectome fingerprint and cognitive performance.

An attention-based context-informed deep framework for infant brain subcortical segmentation.

Precise segmentation of subcortical structures from infant brain magnetic resonance (MR) images plays an essential role in studying early subcortical structural and functional developmental patterns and diagnosis of related brain disorders. However, due to the dynamic appearance changes, low tissue contrast, and tiny subcortical size in infant brain MR images, infant subcortical segmentation is a challenging task. In this paper, we propose a context-guided, attention-based, coarse-to-fine deep framework to precisely segment the infant subcortical structures. At the coarse stage, we aim to directly predict the signed distance maps (SDMs) from multi-modal intensity images, including T1w, T2w, and the ratio of T1w and T2w images, with an SDM-Unet, which can leverage the spatial context information, including the structural position information and the shape information of the target structure, to generate high-quality SDMs. At the fine stage, the predicted SDMs, which encode spatial-context information of each subcortical structure, are integrated with the multi-modal intensity images as the input to a multi-source and multi-path attention Unet (M2A-Unet) for achieving refined segmentation. Both the 3D spatial and channel attention blocks are added to guide the M2A-Unet to focus more on the important subregions and channels. We additionally incorporate the inner and outer subcortical boundaries as extra labels to help precisely estimate the ambiguous boundaries. We validate our method on an infant MR image dataset and on an unrelated neonatal MR image dataset. Compared to eleven state-of-the-art methods, the proposed framework consistently achieves higher segmentation accuracy in both qualitative and quantitative evaluations of infant MR images and also exhibits good generalizability in the neonatal dataset.

Improving motion robustness of 3D MR fingerprinting with a fat navigator.

PURPOSE: To develop a 3D MR fingerprinting (MRF) method in combination with fat navigators to improve its motion robustness for neuroimaging. METHODS: A rapid fat navigator was developed using the stack-of-spirals acquisition and non-Cartesian spiral GRAPPA. The fat navigator module was implemented in the 3D MRF sequence with high scan efficiency. The developed method was first validated in phantoms and five healthy subjects with intentional head motion. The method was further applied to infants with neonatal opioid withdrawal symptoms. The 3D MRF scans with fat navigators acquired with and without acceleration along the partition-encoding direction were both examined in the study. RESULTS: Both phantom and in vivo results demonstrated that the added fat navigator modules did not influence the quantification accuracy in MRF. In combination with non-Cartesian spiral GRAPPA, a rapid fat navigator sampling with whole-brain coverage was achieved in ˜0.5 s at 3T, reducing its sensitivity to potential motion. Based on the motion waveforms extracted from fat navigators, the motion robustness of the 3D MRF was largely improved. With the proposed method, the motion-corrupted MRF datasets yielded T1 and T2 maps with significantly reduced artifacts and high correlations with measurements from the reference motion-free MRF scans. CONCLUSION: We developed a 3D MRF method coupled with rapid fat navigators to improve its motion robustness for quantitative neuroimaging. Our results demonstrate that (1) accurate tissue quantification was preserved with the fat navigator modules and (2) the motion robustness for quantitative tissue mapping was largely improved with the developed method.

Altered neural flexibility in children with attention-deficit/hyperactivity disorder.

Attention-deficit/hyperactivity disorder (ADHD) is one of the most common neurodevelopmental disorders of childhood, and is often characterized by altered executive functioning. Executive function has been found to be supported by flexibility in dynamic brain reconfiguration. Thus, we applied multilayer community detection to resting-state fMRI data in 180 children with ADHD and 180 typically developing children (TDC) to identify alterations in dynamic brain reconfiguration in children with ADHD. We specifically evaluated MR derived neural flexibility, which is thought to underlie cognitive flexibility, or the ability to selectively switch between mental processes. Significantly decreased neural flexibility was observed in the ADHD group at both the whole brain (raw p = 0.0005) and sub-network levels (p < 0.05, FDR corrected), particularly for the default mode network, attention-related networks, executive function-related networks, and primary networks. Furthermore, the subjects with ADHD who received medication exhibited significantly increased neural flexibility (p = 0.025, FDR corrected) when compared to subjects with ADHD who were medication naïve, and their neural flexibility was not statistically different from the TDC group (p = 0.74, FDR corrected). Finally, regional neural flexibility was capable of differentiating ADHD from TDC (Accuracy: 77% for tenfold cross-validation, 74.46% for independent test) and of predicting ADHD severity using clinical measures of symptom severity (R2: 0.2794 for tenfold cross-validation, 0.156 for independent test). In conclusion, the present study found that neural flexibility is altered in children with ADHD and demonstrated the potential clinical utility of neural flexibility to identify children with ADHD, as well as to monitor treatment responses and disease severity.

MRI Findings in Third-Trimester Opioid-Exposed Fetuses, With Focus on Brain Measurements: A Prospective Multicenter Case-Control Study.

BACKGROUND. The opioid epidemic has profoundly affected infants born in the United States, as in utero opioid exposure increases the risk of cognitive and behavioral problems in childhood. Scarce literature has evaluated prenatal brain development in fetuses with opioid exposure in utero (hereafter opioid-exposed fetuses). OBJECTIVE. The purpose of this study is to compare opioid-exposed fetuses and fetuses without opioid exposure (hereafter unexposed fetuses) in terms of 2D biometric measurements of the brain and additional pregnancy-related assessments on fetal MRI. METHODS. This prospective case-control study included patients in the third trimester of pregnancy who underwent investigational fetal MRI at one of three U.S. academic medical centers from July 1, 2020, through December 31, 2021. Fetuses were classified as opioid exposed or unexposed in utero. Fourteen 2D biometric measurements of the fetal brain were manually assessed and used to derive four indexes. Measurements and indexes were compared between the two groups by use of multivariable linear regression models, which were adjusted for gestational age (GA), fetal sex, and nicotine exposure. Additional pregnancy-related findings on MRI were evaluated. RESULTS. The study included 65 women (mean age, 29.0 ± 5.5 [SD] years). A total of 28 fetuses (mean GA at the time of MRI, 32.2 ± 2.5 weeks) were opioid-exposed, and 37 fetuses (mean GA at the time of MRI, 31.9 ± 2.7 weeks) were unexposed. In the adjusted models, seven measurements were smaller (p < .05) in opioid-exposed fetuses than in unexposed fetuses: cerebral frontooccipital diameter (93.8 ± 7.4 vs 95.0 ± 8.6 mm), bone biparietal diameter (79.0 ± 6.0 vs 80.3 ± 7.1 mm), brain biparietal diameter (72.9 ± 7.7 vs 74.1 ± 8.6 mm), corpus callosum length (37.7 ± 4.0 vs 39.4 ± 3.7 mm), vermis height (18.2 ± 2.7 vs 18.8 ± 2.6 mm), anteroposterior pons measurement (11.6 ± 1.4 vs 12.1 ± 1.4 mm), and transverse cerebellar diameter (40.4 ± 5.1 vs 41.4 ± 6.0 mm). In addition, in the adjusted model, the frontoocccipital index was larger (p = .02) in opioid-exposed fetuses (0.04 ± 0.02) than in unexposed fetuses (0.04 ± 0.02). Remaining measures and indexes were not significantly different between the two groups (p > .05). Fetal motion, cervical length, and deepest vertical pocket of amniotic fluid were not significantly different (p > .05) between groups. Opioid-exposed fetuses, compared with unexposed fetuses, showed higher frequencies of both breech position (21% vs 3%, p = .03) and increased amniotic fluid volume (29% vs 8%, p = .04). CONCLUSION. Fetuses with opioid exposure in utero had a smaller brain size and altered fetal physiology. CLINICAL IMPACT. The findings provide insight into the impact of prenatal opioid exposure on fetal brain development.

Summertime Urban Ammonia Emissions May Be Substantially Underestimated in Beijing, China.

Ammonia (NH3) is critical to the nitrogen cycle and PM2.5 formation, yet a great deal of uncertainty exists in its urban emission quantifications. Model-underestimated NH3 concentrations have been reported for cities, yet few studies have provided an explanation. Here, we explore reasons for severe WRF-Chem model underestimations of NH3 concentrations in Beijing in August 2018, including simulated gas-particle partitioning, meteorology, regional transport, and emissions, using spatially refined (3 km resolution) NH3 emission estimates in the agricultural sector for Beijing-Tianjin-Hebei and in the traffic sector for Beijing. We find that simulated NH3 concentrations are significantly lower than ground-based and satellite observations during August in Beijing, while wintertime underestimations are much more moderate. Further analyses and sensitivity experiments show that such discrepancies cannot be attributed to factors other than biases in NH3 emissions. Using site measurements as constraints, we estimate that both agricultural and non-agricultural NH3 emission totals in Beijing shall increase by ∼5 times to match the observations. Future research should be performed to allocate underestimations to urban fertilizer, power, traffic, or residential sources. Dense and regular urban NH3 observations are necessary to constrain and validate bottom-up inventories and NHx simulation.

Validating HONO as an Intermediate Tracer of the External Cycling of Reactive Nitrogen in the Background Atmosphere.

In the urban atmosphere, nitrogen oxide (NOx═NO + NO2)-related reactions dominate the formation of nitrous acid (HONO). Here, we validated an external cycling route of HONO and NOx, i.e., formation of HONO resulting from precursors other than NOx, in the background atmosphere. A chemical budget closure experiment of HONO and NOx was conducted at a background site on the Tibetan Plateau and provided direct evidence of the external cycling. An external daytime HONO source of 100 pptv h-1 was determined. Both soil emissions and photolysis of nitrate on ambient surfaces constituted likely candidate mechanisms characterizing this external source. The external source dominated the chemical production of NOx with HONO as an intermediate tracer. The OH production was doubled as a result of the external cycling. A high HONO/NOx ratio (0.31 ± 0.06) during the daytime was deduced as a sufficient condition for the external cycling. Literature review suggested the prevalence of high HONO/NOx ratios in various background environments, e.g., polar regions, pristine mountains, and forests. Our analysis validates the prevalence of external cycling in general background atmosphere and highlights the promotional role of external cycling regarding the atmospheric oxidative capacity.

Hydrogel-based miR-192 delivery inhibits the development of hepatocellular carcinoma by suppressing the GSK3ß/Wnt/ß-catenin pathway.

Hepatocellular carcinoma (HCC) is a primary liver cancer characterized by high invasiveness, metastasis, and poor prognosis, which lacks effective treatments. Although the role of miR-192 in HCC development has been recognized, the underlying molecular mechanism is still poorly understood. This study aimed to explore the impact of mir-192 on HCC and its potential as a therapeutic strategy. Wound healing assay, Transwell assay, CCK-8 assay, and flow cytometry were performed to detect the impact of miR-192 on HCC cell metastasis, invasion, proliferation, and apoptosis, respectively. q-PCR and western blot were applied to measure the relative mRNA and protein expression of the GSK3ß/Wnt/ß-catenin pathway in miR-192-overexpressing cell lines. Immunofluorescence was carried out to detect the nuclear translocation of ß-catenin. starBase website and dual luciferase reporter assay were used to verify the interaction between miR-192 and the target gene WNT10B 3'-untranslated region (3'-UTR) of the Wnt pathway. In addition, we developed algin/polyethyleneimine@miR-192 (AG/PEI@miR-192) nanohydrogel for in vivo delivery of miR-192-agomir. The results revealed that overexpressed miR-192 reduced the expression of HCC cell surface markers CD90, EpCAM, and CD133. Moreover, miR-192 overexpression inhibited HCC cell metastasis, invasion, and proliferation, promoted cell apoptosis, and reduced GSK3ß/Wnt/ß-catenin pathway expression. Additionally, AG/PEI@miR-192 exhibited good drug release and tumor inhibition. In conclusion, our study suggested that miR-192 inhibits HCC development by suppressing the GSK3ß/Wnt/ß-catenin pathway and proposed a promising hydrogel-based miR-192 delivery approach to hinder tumor growth.

The emergence of a functionally flexible brain during early infancy.

Adult brains are functionally flexible, a unique characteristic that is thought to contribute to cognitive flexibility. While tools to assess cognitive flexibility during early infancy are lacking, we aimed to assess the spatiotemporal developmental features of "neural flexibility" during the first 2 y of life. Fifty-two typically developing children 0 to 2 y old were longitudinally imaged up to seven times during natural sleep using resting-state functional MRI. Using a sliding window approach, MR-derived neural flexibility, a quantitative measure of the frequency at which brain regions change their allegiance from one functional module to another during a given time period, was used to evaluate the temporal emergence of neural flexibility during early infancy. Results showed that neural flexibility of whole brain, motor, and high-order brain functional networks/regions increased significantly with age, while visual regions exhibited a temporally stable pattern, suggesting spatially and temporally nonuniform developmental features of neural flexibility. Additionally, the neural flexibility of the primary visual network at 3 mo of age was significantly and negatively associated with cognitive ability evaluated at 5/6 y of age. The "flexible club," comprising brain regions with neural flexibility significantly higher than whole-brain neural flexibility, were consistent with brain regions known to govern cognitive flexibility in adults and exhibited unique characteristics when compared to the functional hub and diverse club regions. Thus, MR-derived neural flexibility has the potential to reveal the underlying neural substrates for developing a cognitively flexible brain during early infancy.

Longitudinal Prediction of Postnatal Brain Magnetic Resonance Images via a Metamorphic Generative Adversarial Network.

Missing scans are inevitable in longitudinal studies due to either subject dropouts or failed scans. In this paper, we propose a deep learning framework to predict missing scans from acquired scans, catering to longitudinal infant studies. Prediction of infant brain MRI is challenging owing to the rapid contrast and structural changes particularly during the first year of life. We introduce a trustworthy metamorphic generative adversarial network (MGAN) for translating infant brain MRI from one time-point to another. MGAN has three key features: (i) Image translation leveraging spatial and frequency information for detail-preserving mapping; (ii) Quality-guided learning strategy that focuses attention on challenging regions. (iii) Multi-scale hybrid loss function that improves translation of image contents. Experimental results indicate that MGAN outperforms existing GANs by accurately predicting both tissue contrasts and anatomical details.

Deep attentive spatio-temporal feature learning for automatic resting-state fMRI denoising.

Resting-state functional magnetic resonance imaging (rs-fMRI) is a non-invasive functional neuroimaging modality that has been widely used to investigate functional connectomes in the brain. Since noise and artifacts generated by non-neuronal physiological activities are predominant in raw rs-fMRI data, effective noise removal is one of the most important preprocessing steps prior to any subsequent analysis. For rs-fMRI denoising, a common trend is to decompose rs-fMRI data into multiple components and then regress out noise-related components. Therefore, various machine learning techniques have been used in such analyses with predefined procedures and manually engineered features. However, the lack of a universal definition of a noise-related source or artifact complicates manual feature engineering. Manual feature selection can result in the failure to capture unknown types of noise. Furthermore, the possibility that the hand-crafted features will only work for the broader population (e.g., healthy adults) but not for "outliers" (e.g., infants or subjects that belong to a disease cohort) is quite high. In practice, we have limited knowledge of which features should be extracted; thus, multi-classifier assembly must be implemented to improve performance, although this process is quite time-consuming. However, in real rs-fMRI applications, fast and accurate automatic identification of noise-related components on different datasets is critical. To solve this problem, we propose a novel, automatic, and end-to-end deep learning framework dedicated to noise-related component identification via a faster and more effective multi-layer feature extraction strategy that learns deeply embedded spatio-temporal features of the components. In this study, we achieved remarkable performance on various rs-fMRI datasets, including multiple adult rs-fMRI datasets from different rs-fMRI studies and an infant rs-fMRI dataset, which is quite heterogeneous and differs from that of adults. Our proposed framework also dramatically increases the noise detection speed owing to its inherent ability for deep learning (< 1s for single-component classification). It can be easily integrated into any preprocessing pipeline, even those that do not use standard procedures but depend on alternative toolboxes.

A 4D infant brain volumetric atlas based on the UNC/UMN baby connectome project (BCP) cohort.

Spatiotemporal (four-dimensional) infant-dedicated brain atlases are essential for neuroimaging analysis of early dynamic brain development. However, due to the substantial technical challenges in the acquisition and processing of infant brain MR images, 4D atlases densely covering the dynamic brain development during infancy are still scarce. Few existing ones generally have fuzzy tissue contrast and low spatiotemporal resolution, leading to degraded accuracy of atlas-based normalization and subsequent analyses. To address this issue, in this paper, we construct a 4D structural MRI atlas for infant brains based on the UNC/UMN Baby Connectome Project (BCP) dataset, which features a high spatial resolution, extensive age-range coverage, and densely sampled time points. Specifically, 542 longitudinal T1w and T2w scans from 240 typically developing infants up to 26-month of age were utilized for our atlas construction. To improve the co-registration accuracy of the infant brain images, which typically exhibit dynamic appearance with low tissue contrast, we employed the state-of-the-art registration method and leveraged our generated reliable brain tissue probability maps in addition to the intensity images to improve the alignment of individual images. To achieve consistent region labeling on both infant and adult brain images for facilitating region-based analysis across ages, we mapped the widely used Desikan cortical parcellation onto our atlas by following an age-decreasing mapping manner. Meanwhile, the typical subcortical structures were manually delineated to facilitate the studies related to the subcortex. Compared with the existing infant brain atlases, our 4D atlas has much higher spatiotemporal resolution and preserves more structural details, and thus can boost accuracy in neurodevelopmental analysis during infancy.

Prospective motion correction and automatic segmentation of penetrating arteries in phase contrast MRI at 7 T.

PURPOSE: To develop a prospective motion correction (MC) method for phase contrast (PC) MRI of penetrating arteries (PAs) in centrum semiovale at 7 T and to evaluate its performance using automatic PA segmentation. METHODS: Head motion was monitored and corrected during the scan based on fat navigator images. Two convolutional neural networks (CNN) were developed to automatically segment PAs and exclude surface vessels. Real-life scans with MC and without MC (NoMC) were performed to evaluate the MC performance. Motion score was calculated from the ranges of translational and rotational motion parameters. MC versus NoMC pairs with similar motion scores during MC and NoMC scans were compared. Data corrupted by motion were reacquired to further improve PA visualization. RESULTS: PA counts (NPA ) and PC and magnitude contrasts (MgC) relative to neighboring tissue were significantly correlated with motion score and were higher in MC than NoMC images at motion scores above 0.5-0.8 mm. Data reacquisition further increased PC but had no significant effect on NPA and MgC. CNNs had higher sensitivity and Dice similarity coefficient for detecting PAs than a threshold-based method. CONCLUSIONS: Prospective MC can improve the count and contrast of segmented PAs in the presence of severe motion. CNN-based PA segmentation has improved performance in delineating PAs than the threshold-based method.

Laparoscopic Triple-tourniquet Constriction: A Convenient Way for Minimizing Blood Loss during Myomectomy.

STUDY OBJECTIVE: Although a pericervical tourniquet helped reduce blood loss in myomectomy [1], a technique of triple tourniquets was more influential in occluding the uterine vessel networks [2,3]. This video demonstrates the procedures of laparoscopic triple-tourniquet constriction with the number 1 suture around the uterine isthmic portion and bilateral infundibulopelvic ligaments [4] in a case of robotic myomectomy. DESIGN: A step-by-step, narrated video demonstration. SETTING: A university hospital. INTERVENTIONS: Robotic myomectomy was scheduled for a patient with menorrhagia. Magnetic resonance imaging revealed 8 uterine myomas; the maximal one was 9.1 × 8.4 × 8.6 cm in dimension. Our robotic settings included 3 ports: fenestrated bipolar in the left lower quadrant, spatula or mega needle holder in the right lower quadrant, and an umbilical glove port accessible for lens and assisted instruments. A number 1 Monocryl (Ethicon, Bridgewater, NJ) was introduced from the suprapubic area extracorporeally; then, the needle penetrated through bilateral avascular zones of broad ligaments at the isthmic level and with a sliding tie made anteriorly to the uterus. The isthmic tourniquet-we also named it as the hangman's tourniquet-was tightened by manually tensioning the suture extracorporeally and pushing down the knot intracorporeally. Bilateral infundibulopelvic tourniquets were placed by using sliding ties of 1-0 Monocryl as well. With the total occlusion of uterine vessel networks, the uterus should retain only minimal blood flow. During the enucleation of uterine myomas, the tourniquet may loosen because of newly developed, unoccupied space with increasing bleeding; therefore, the tourniquet should be tightened up regularly throughout the surgery. After the repair of all the uterine wounds, we removed the 3 tourniquets. CONCLUSION: The convenient and adjustable triple-tourniquet constriction is a safe and feasible laparoscopic technique to block the vessel networks temporally in uterine-preserving surgery.

Developmental topography of cortical thickness during infancy.

During the first 2 postnatal years, cortical thickness of the human brain develops dynamically and spatially heterogeneously and likely peaks between 1 and 2 y of age. The striking development renders this period critical for later cognitive outcomes and vulnerable to early neurodevelopmental disorders. However, due to the difficulties in longitudinal infant brain MRI acquisition and processing, our knowledge still remains limited on the dynamic changes, peak age, and spatial heterogeneities of cortical thickness during infancy. To fill this knowledge gap, in this study, we discover the developmental regionalization of cortical thickness, i.e., developmentally distinct regions, each of which is composed of a set of codeveloping cortical vertices, for better understanding of the spatiotemporal heterogeneities of cortical thickness development. We leverage an infant-dedicated computational pipeline, an advanced multivariate analysis method (i.e., nonnegative matrix factorization), and a densely sampled longitudinal dataset with 210 serial MRI scans from 43 healthy infants, with each infant being scheduled to have 7 longitudinal scans at around 1, 3, 6, 9, 12, 18, and 24 mo of age. Our results suggest that, during the first 2 y, the whole-brain average cortical thickness increases rapidly and reaches a plateau at about 14 mo of age and then decreases at a slow pace thereafter. More importantly, each discovered region is structurally and functionally meaningful and exhibits a distinctive developmental pattern, with several regions peaking at varied ages while others keep increasing in the first 2 postnatal years. Our findings provide valuable references and insights for early brain development.