Zero-DeepSub: Zero-shot deep subspace reconstruction for rapid multiparametric quantitative MRI using 3D-QALAS.

PURPOSE: To develop and evaluate methods for (1) reconstructing 3D-quantification using an interleaved Look-Locker acquisition sequence with T2 preparation pulse (3D-QALAS) time-series images using a low-rank subspace method, which enables accurate and rapid T1 and T2 mapping, and (2) improving the fidelity of subspace QALAS by combining scan-specific deep-learning-based reconstruction and subspace modeling. THEORY AND METHODS: A low-rank subspace method for 3D-QALAS (i.e., subspace QALAS) and zero-shot deep-learning subspace method (i.e., Zero-DeepSub) were proposed for rapid and high fidelity T1 and T2 mapping and time-resolved imaging using 3D-QALAS. Using an ISMRM/NIST system phantom, the accuracy and reproducibility of the T1 and T2 maps estimated using the proposed methods were evaluated by comparing them with reference techniques. The reconstruction performance of the proposed subspace QALAS using Zero-DeepSub was evaluated in vivo and compared with conventional QALAS at high reduction factors of up to nine-fold. RESULTS: Phantom experiments showed that subspace QALAS had good linearity with respect to the reference methods while reducing biases and improving precision compared to conventional QALAS, especially for T2 maps. Moreover, in vivo results demonstrated that subspace QALAS had better g-factor maps and could reduce voxel blurring, noise, and artifacts compared to conventional QALAS and showed robust performance at up to nine-fold acceleration with Zero-DeepSub, which enabled whole-brain T1, T2, and PD mapping at 1 mm isotropic resolution within 2 min of scan time. CONCLUSION: The proposed subspace QALAS along with Zero-DeepSub enabled high fidelity and rapid whole-brain multiparametric quantification and time-resolved imaging.

A Prospective Multi-Institutional Study Comparing the Brain Development in the Third Trimester between Opioid-Exposed and Nonexposed Fetuses Using Advanced Fetal MR Imaging Techniques.

BACKGROUND AND PURPOSE: While the adverse neurodevelopmental effects of prenatal opioid exposure on infants and children in the United States are well described, the underlying causative mechanisms have yet to be fully understood. This study aims to compare quantitative volumetric and surface-based features of the fetal brain between opioid-exposed fetuses and unexposed controls by using advanced MR imaging processing techniques. MATERIALS AND METHODS: This is a multi-institutional IRB-approved study in which pregnant women with and without opioid use during the current pregnancy were prospectively recruited to undergo fetal MR imaging. A total of 14 opioid-exposed (31.4 ± 2.3 weeks of gestation) and 15 unexposed (31.4 ± 2.4 weeks) fetuses were included. Whole brain volume, cortical plate volume, surface area, sulcal depth, mean curvature, and gyrification index were computed as quantitative features by using our fetal brain MR imaging processing pipeline. RESULTS: After correcting for gestational age, fetal sex, maternal education, polysubstance use, high blood pressure, and MR imaging acquisition site, all of the global morphologic features were significantly lower in the opioid-exposed fetuses compared with the unexposed fetuses, including brain volume, cortical volume, cortical surface area, sulcal depth, cortical mean curvature, and gyrification index. In regional analysis, the opioid-exposed fetuses showed significantly decreased surface area and sulcal depth in the bilateral Sylvian fissures, central sulci, parieto-occipital fissures, temporal cortices, and frontal cortices. CONCLUSIONS: In this small cohort, prenatal opioid exposure was associated with altered fetal brain development in the third trimester. This adds to the growing body of literature demonstrating that prenatal opioid exposure affects the developing brain.

Sleep Spindle Generation Before and After Epilepsy Surgery: A Source Imaging Study in Children with Drug-Resistant Epilepsy.

INTRODUCTION: Literature lacks studies investigating the cortical generation of sleep spindles in drug-resistant epilepsy (DRE) and how they evolve after resection of the epileptogenic zone (EZ). Here, we examined sleep EEGs of children with focal DRE who became seizure-free after focal epilepsy surgery, and aimed to investigate the changes in the spindle generation before and after the surgery using low-density scalp EEG and electrical source imaging (ESI). METHODS: We analyzed N2-sleep EEGs from 19 children with DRE before and after surgery. We identified slow (8-12 Hz) and fast spindles (13-16 Hz), computed their spectral features and cortical generators through ESI and computed their distance from the EZ and irritative zone (IZ). We performed two-way ANOVA testing the effect of spindle type (slow vs. fast) and surgical phase (pre-surgery vs. post-surgery) on each feature. RESULTS: Power, frequency and cortical activation of slow spindles increased after surgery (p < 0.005), while this was not seen for fast spindles. Before surgery, the cortical generators of slow spindles were closer to the EZ (57.3 vs. 66.2 mm, p = 0.007) and IZ (41.3 vs. 55.5 mm, p = 0.02) than fast spindle generators. CONCLUSIONS: Our data indicate alterations in the EEG slow spindles after resective epilepsy surgery. Fast spindle generation on the contrary did not change after surgery. Although the study is limited by its retrospective nature, lack of healthy controls, and reduced cortical spatial sampling, our findings suggest a spatial relationship between the slow spindles and the epileptogenic generators.

Association of genetic and sulcal traits with executive function in congenital heart disease.

OBJECTIVE: Persons with congenital heart disease (CHD) are at increased risk of neurodevelopmental disabilities, including impairments to executive function. Sulcal pattern features correlate with executive function in adolescents with single-ventricle heart disease and tetralogy of Fallot. However, the interaction of sulcal pattern features with genetic and participant factors in predicting executive dysfunction is unknown. METHODS: We studied sulcal pattern features, participant factors, and genetic risk for executive function impairment in a cohort with multiple CHD types using stepwise linear regression and machine learning. RESULTS: Genetic factors, including predicted damaging de novo or rare inherited variants in neurodevelopmental disabilities risk genes, apolipoprotein E genotype, and principal components of sulcal pattern features were associated with executive function measures after adjusting for age at testing, sex, mother's education, and biventricular versus single-ventricle CHD in a linear regression model. Using regression trees and bootstrap validation, younger participant age and larger alterations in sulcal pattern features were consistently identified as important predictors of decreased cognitive flexibility with left hemisphere graph topology often selected as the most important predictor. Inclusion of both sulcal pattern and genetic factors improved model fit compared to either alone. INTERPRETATION: We conclude that sulcal measures remain important predictors of cognitive flexibility, and the model predicting executive outcomes is improved by inclusion of potential genetic sources of neurodevelopmental risk. If confirmed, measures of sulcal patterning may serve as early imaging biomarkers to identify those at heightened risk for future neurodevelopmental disabilities.

Acute Diffusion-Weighted Imaging Signaling Severe Periventricular Leukomalacia in Preterm Infants: Case Report and Review of Literature.

Introduction: Periventricular leukomalacia occurs in up to 25% of very preterm infants resulting in adverse neurodevelopmental outcomes. In its acute phase, periventricular leukomalacia is clinically silent. Although ultrasonography is widely available, its sensitivity in the early detection of periventricular leukomalacia is low. Case Report and Published Literature: We identified a preterm infant with early diffusion-weighted imaging changes that later evolved to periventricular leukomalacia. Thirty-two cases of abnormal diffusion-weighted imaging reliably heralding severe periventricular leukomalacia in the preterm infant have been published in the literature. Notable features include the following: (1) infants were more mature preterm infants (29-36 weeks' gestation); (2) findings were often serendipitous with benign clinical courses; (3) diffusion-weighted imaging changes only were evident in the first weeks of life with later evolution to more classical abnormalities on conventional magnetic resonance imaging (MRI) or ultrasonography. Conclusion: Diffusion-weighted imaging in the first week of life may be a reliable early marker of severe periventricular leukomalacia injury in more mature preterm infants.

SSL-QALAS: Self-Supervised Learning for rapid multiparameter estimation in quantitative MRI using 3D-QALAS.

PURPOSE: To develop and evaluate a method for rapid estimation of multiparametric T1 , T2 , proton density, and inversion efficiency maps from 3D-quantification using an interleaved Look-Locker acquisition sequence with T2 preparation pulse (3D-QALAS) measurements using self-supervised learning (SSL) without the need for an external dictionary. METHODS: An SSL-based QALAS mapping method (SSL-QALAS) was developed for rapid and dictionary-free estimation of multiparametric maps from 3D-QALAS measurements. The accuracy of the reconstructed quantitative maps using dictionary matching and SSL-QALAS was evaluated by comparing the estimated T1 and T2 values with those obtained from the reference methods on an International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology phantom. The SSL-QALAS and the dictionary-matching methods were also compared in vivo, and generalizability was evaluated by comparing the scan-specific, pre-trained, and transfer learning models. RESULTS: Phantom experiments showed that both the dictionary-matching and SSL-QALAS methods produced T1 and T2 estimates that had a strong linear agreement with the reference values in the International Society for Magnetic Resonance in Medicine/National Institute of Standards and Technology phantom. Further, SSL-QALAS showed similar performance with dictionary matching in reconstructing the T1 , T2 , proton density, and inversion efficiency maps on in vivo data. Rapid reconstruction of multiparametric maps was enabled by inferring the data using a pre-trained SSL-QALAS model within 10 s. Fast scan-specific tuning was also demonstrated by fine-tuning the pre-trained model with the target subject's data within 15 min. CONCLUSION: The proposed SSL-QALAS method enabled rapid reconstruction of multiparametric maps from 3D-QALAS measurements without an external dictionary or labeled ground-truth training data.

Negative versus withdrawn maternal behavior: Differential associations with infant gray and white matter during the first 2 years of life.

Distinct neural effects of threat versus deprivation emerge by childhood, but little data are available in infancy. Withdrawn versus negative parenting may represent dimensionalized indices of early deprivation versus early threat, but no studies have assessed neural correlates of withdrawn versus negative parenting in infancy. The objective of this study was to separately assess the links of maternal withdrawal and maternal negative/inappropriate interaction with infant gray matter volume (GMV), white matter volume (WMV), amygdala, and hippocampal volume. Participants included 57 mother-infant dyads. Withdrawn and negative/inappropriate aspects of maternal behavior were coded from the Still-Face Paradigm at four months infant age. Between 4 and 24 months (M age = 12.28 months, SD = 5.99), during natural sleep, infants completed an MRI using a 3.0 T Siemens scanner. GMV, WMV, amygdala, and hippocampal volumes were extracted via automated segmentation. Diffusion weighted imaging volumetric data were also generated for major white matter tracts. Maternal withdrawal was associated with lower infant GMV. Negative/inappropriate interaction was associated with lower overall WMV. Age did not moderate these effects. Maternal withdrawal was further associated with reduced right hippocampal volume at older ages. Exploratory analyses of white matter tracts found that negative/inappropriate maternal behavior was specifically associated with reduced volume in the ventral language network. Results suggest that quality of day-to-day parenting is related to infant brain volumes during the first two years of life, with distinct aspects of interaction associated with distinct neural effects.

BOston Neonatal Brain Injury Dataset for Hypoxic Ischemic Encephalopathy (BONBID-HIE): Part I. MRI and Manual Lesion Annotation.

Hypoxic ischemic encephalopathy (HIE) is a brain injury that occurs in 1 ~ 5/1000 term neonates. Accurate identification and segmentation of HIE-related lesions in neonatal brain magnetic resonance images (MRIs) is the first step toward predicting prognosis, identifying high-risk patients, and evaluating treatment effects. It will lead to a more accurate estimation of prognosis, a better understanding of neurological symptoms, and a timely prediction of response to therapy. We release the first public dataset containing neonatal brain diffusion MRI and expert annotation of lesions from 133 patients diagnosed with HIE. HIE-related lesions in brain MRI are often diffuse (i.e., multi-focal), and small (over half the patients in our data having lesions occupying <1% of brain volume). Segmentation for HIE MRI data is remarkably different from, and arguably more challenging than, other segmentation tasks such as brain tumors with focal and relatively large lesions. We hope that this dataset can help fuel the development of MRI lesion segmentation methods for HIE and small diffuse lesions in general.

Morphological Features of Language Regions in Individuals with Tuberous Sclerosis Complex.

A significant number of individuals with tuberous sclerosis complex (TSC) exhibit language difficulties. Here, we examined the language-related brain morphometry in 59 participants (7 participants with TSC and comorbid autism spectrum disorder (ASD) (TSC + ASD), 13 with TSC but no ASD (TSC-ASD), 10 with ASD-only (ASD), and 29 typically developing (TD) controls). A hemispheric asymmetry was noted in surface area and gray matter volume of several cortical language areas in TD, ASD, and TSC-ASD groups, but not in TSC + ASD group. TSC + ASD group demonstrated increased cortical thickness and curvature values in multiple language regions for both hemispheres, compared to other groups. After controlling for tuber load in the TSC groups, within-group differences stayed the same but the differences between TSC-ASD and TSC + ASD were no longer statistically significant. These preliminary findings suggest that comorbid ASD in TSC as well as tuber load in TSC is associated with changes in the morphometry of language regions. Future studies with larger sample sizes will be needed to confirm these findings.

Diffusion-Weighted Magnetic Resonance Imaging Demonstrates White Matter Alterations in Watershed Regions in Children With Moyamoya Without Stroke or Silent Infarct.

BACKGROUND: Moyamoya is a disease with progressive cerebral arterial stenosis leading to stroke and silent infarct. Diffusion-weighted magnetic resonance imaging (dMRI) studies show that adults with moyamoya have significantly lower fractional anisotropy (FA) and higher mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) compared with controls, which raises concern for unrecognized white matter injury. Children with moyamoya have significantly lower FA and higher MD in their white matter compared with controls. However, it is unknown which white matter tracts are affected in children with moyamoya. METHODS: We present a cohort of 15 children with moyamoya with 24 affected hemispheres without stroke or silent infarct compared with 25 controls. We analyzed dMRI data using unscented Kalman filter tractography and extracted major white matter pathways with a fiber clustering method. We compared the FA, MD, AD, and RD in each segmented white matter tract and combined white matter tracts found within the watershed region using analysis of variance. RESULTS: Age and sex were not significantly different between children with moyamoya and controls. Specific white matter tracts affected included inferior fronto-occipital fasciculus, inferior longitudinal fasciculus, superior longitudinal fasciculus, thalamofrontal, uncinate fasciculus, and arcuate fasciculus. Combined watershed region white matter tracts in children with moyamoya had significantly lower FA (-7.7% ± 3.2%, P = 0.02) and higher MD (4.8% ± 1.9%, P = 0.01) and RD (8.7% ± 2.8%, P = 0.002). CONCLUSIONS: Lower FA with higher MD and RD is concerning for unrecognized white matter injury. Affected tracts were located in watershed regions suggesting that the findings may be due to chronic hypoperfusion. These findings support the concern that children with moyamoya without overt stroke or silent infarction are sustaining ongoing injury to their white matter microstructure and provide practitioners with a noninvasive method of more accurately assessing disease burden in children with moyamoya.

NeSVoR: Implicit Neural Representation for Slice-to-Volume Reconstruction in MRI.

Reconstructing 3D MR volumes from multiple motion-corrupted stacks of 2D slices has shown promise in imaging of moving subjects, e. g., fetal MRI. However, existing slice-to-volume reconstruction methods are time-consuming, especially when a high-resolution volume is desired. Moreover, they are still vulnerable to severe subject motion and when image artifacts are present in acquired slices. In this work, we present NeSVoR, a resolution-agnostic slice-to-volume reconstruction method, which models the underlying volume as a continuous function of spatial coordinates with implicit neural representation. To improve robustness to subject motion and other image artifacts, we adopt a continuous and comprehensive slice acquisition model that takes into account rigid inter-slice motion, point spread function, and bias fields. NeSVoR also estimates pixel-wise and slice-wise variances of image noise and enables removal of outliers during reconstruction and visualization of uncertainty. Extensive experiments are performed on both simulated and in vivo data to evaluate the proposed method. Results show that NeSVoR achieves state-of-the-art reconstruction quality while providing two to ten-fold acceleration in reconstruction times over the state-of-the-art algorithms.

A high-resolution pediatric female whole-body numerical model with comparison to a male model.

Objective. Numerical models are central in designing and testing novel medical devices and in studying how different anatomical changes may affect physiology. Despite the numerous adult models available, there are only a few whole-body pediatric numerical models with significant limitations. In addition, there is a limited representation of both male and female biological sexes in the available pediatric models despite the fact that sex significantly affects body development, especially in a highly dynamic population. As a result, we developed Athena, a realistic female whole-body pediatric numerical model with high-resolution and anatomical detail.Approach. We segmented different body tissues through Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) images of a healthy 3.5 year-old female child using 3D Slicer. We validated the high anatomical accuracy segmentation through two experienced sub-specialty-certified neuro-radiologists and the inter and intra-operator variability of the segmentation results comparing sex differences in organ metrics with physiologic values. Finally, we compared Athena with Martin, a similar male model, showing differences in anatomy, organ metrics, and MRI dosimetric exposure.Main results. We segmented 267 tissue compartments, which included 50 brain tissue labels. The tissue metrics of Athena displayed no deviation from the literature value of healthy children. We show the variability of brain metrics in the male and female models. Finally, we offer an example of computing Specific Absorption Rate and Joule heating in a toddler/preschooler at 7 T MRI.Significance. This study introduces a female realistic high-resolution numerical model using MRI and CT scans of a 3.5 year-old female child, the use of which includes but is not limited to radiofrequency safety studies for medical devices (e.g. an implantable medical device safety in MRI), neurostimulation studies, and radiation dosimetry studies. This model will be open source and available on the Athinoula A. Martinos Center for Biomedical Imaging website.

Association of Potentially Damaging De Novo Gene Variants With Neurologic Outcomes in Congenital Heart Disease.

Importance: Neurodevelopmental disabilities are commonly associated with congenital heart disease (CHD), but medical and sociodemographic factors explain only one-third of the variance in outcomes. Objective: To examine whether potentially damaging de novo variants (dDNVs) in genes not previously linked to neurodevelopmental disability are associated with neurologic outcomes in CHD and, post hoc, whether some dDNVs or rare putative loss-of-function variants (pLOFs) in specific gene categories are associated with outcomes. Design, Setting, and Participants: This cross-sectional study was conducted from September 2017 to June 2020 in 8 US centers. Inclusion criteria were CHD, age 8 years or older, and available exome sequencing data. Individuals with pathogenic gene variants in known CHD- or neurodevelopment-related genes were excluded. Cases and controls were frequency-matched for CHD class, age group, and sex. Exposures: Heterozygous for (cases) or lacking (controls) dDNVs in genes not previously associated with neurodevelopmental disability. Participants were separately stratified as heterozygous or not heterozygous for dDNVs and/or pLOFs in 4 gene categories: chromatin modifying, constrained, high level of brain expression, and neurodevelopmental risk. Main Outcomes and Measures: Main outcomes were neurodevelopmental assessments of academic achievement, intelligence, fine motor skills, executive function, attention, memory, social cognition, language, adaptive functioning, and anxiety and depression, as well as 7 structural, diffusion, and functional brain magnetic resonance imaging metrics. Results: The study cohort included 221 participants in the post hoc analysis and 219 in the case-control analysis (109 cases [49.8%] and 110 controls [50.2%]). Of those 219 participants (median age, 15.0 years [IQR, 10.0-21.2 years]), 120 (54.8%) were male. Cases and controls had similar primary outcomes (reading composite, spelling, and math computation on the Wide Range Achievement Test, Fourth Edition) and secondary outcomes. dDNVs and/or pLOFs in chromatin-modifying genes were associated with lower mean (SD) verbal comprehension index scores (91.4 [20.4] vs 103.4 [17.8]; P = .01), Social Responsiveness Scale, Second Edition, scores (57.3 [17.2] vs 49.4 [11.2]; P = .03), and Wechsler Adult Intelligence Scale, Fourth Edition, working memory scores (73.8 [16.4] vs 97.2 [15.7]; P = .03), as well as higher likelihood of autism spectrum disorder (28.6% vs 5.2%; P = .01). dDNVs and/or pLOFs in constrained genes were associated with lower mean (SD) scores on the Wide Range Assessment of Memory and Learning, Second Edition (immediate story memory: 9.7 [3.7] vs 10.7 [3.0]; P = .03; immediate picture memory: 7.8 [3.1] vs 9.0 [2.9]; P = .008). Adults with dDNVs and/or pLOFs in genes with a high level of brain expression had greater Conners adult attention-deficit hyperactivity disorder rating scale scores (mean [SD], 55.5 [15.4] vs 46.6 [12.3]; P = .007). Conclusions and Relevance: The study findings suggest neurodevelopmental outcomes are not associated with dDNVs as a group but may be worse in individuals with dDNVs and/or pLOFs in some gene sets, such as chromatin-modifying genes. Future studies should confirm the importance of specific gene variants to brain function and structure.

Cross-Sectional Observational Study of Typical in utero Fetal Movements Using Machine Learning.

Early variations of fetal movements are the hallmark of a healthy developing central nervous system. However, there are no automatic methods to quantify the complex 3D motion of the developing fetus in utero. The aim of this prospective study was to use machine learning (ML) on in utero MRI to perform quantitative kinematic analysis of fetal limb movement, assessing the impact of maternal, placental, and fetal factors. In this cross-sectional, observational study, we used 76 sets of fetal (24-40 gestational weeks [GW]) blood oxygenation level-dependent (BOLD) MRI scans of 52 women (18-45 years old) during typical pregnancies. Pregnant women were scanned for 5-10 min while breathing room air (21% O2) and for 5-10 min while breathing 100% FiO2 in supine and/or lateral position. BOLD acquisition time was 20 min in total with effective temporal resolution approximately 3 s. To quantify upper and lower limb kinematics, we used a 3D convolutional neural network previously trained to track fetal key points (wrists, elbows, shoulders, ankles, knees, hips) on similar BOLD time series. Tracking was visually assessed, errors were manually corrected, and the absolute movement time (AMT) for each joint was calculated. To identify variables that had a significant association with AMT, we constructed a mixed-model ANOVA with interaction terms. Fetuses showed significantly longer duration of limb movements during maternal hyperoxia. We also found a significant centrifugal increase of AMT across limbs and significantly longer AMT of upper extremities <31 GW and longer AMT of lower extremities >35 GW. In conclusion, using ML we successfully quantified complex 3D fetal limb motion in utero and across gestation, showing maternal factors (hyperoxia) and fetal factors (gestational age, joint) that impact movement. Quantification of fetal motion on MRI is a potential new biomarker of fetal health and neuromuscular development.

Segmentation ability map: Interpret deep features for medical image segmentation.

Deep convolutional neural networks (CNNs) have been widely used for medical image segmentation. In most studies, only the output layer is exploited to compute the final segmentation results and the hidden representations of the deep learned features have not been well understood. In this paper, we propose a prototype segmentation (ProtoSeg) method to compute a binary segmentation map based on deep features. We measure the segmentation abilities of the features by computing the Dice between the feature segmentation map and ground-truth, named as the segmentation ability score (SA score for short). The corresponding SA score can quantify the segmentation abilities of deep features in different layers and units to understand the deep neural networks for segmentation. In addition, our method can provide a mean SA score which can give a performance estimation of the output on the test images without ground-truth. Finally, we use the proposed ProtoSeg method to compute the segmentation map directly on input images to further understand the segmentation ability of each input image. Results are presented on segmenting tumors in brain MRI, lesions in skin images, COVID-related abnormality in CT images, prostate segmentation in abdominal MRI, and pancreatic mass segmentation in CT images. Our method can provide new insights for interpreting and explainable AI systems for medical image segmentation. Our code is available on: https://github.com/shengfly/ProtoSeg.

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.

Maternal Childhood Maltreatment Is Associated With Lower Infant Gray Matter Volume and Amygdala Volume During the First Two Years of Life.

Background: Childhood maltreatment affects approximately 25% of the world's population. Importantly, the children of mothers who have been maltreated are at increased risk of behavioral problems. Thus, one important priority is to identify child neurobiological processes associated with maternal childhood maltreatment (MCM) that might contribute to such intergenerational transmission. This study assessed the impact of MCM on infant gray and white matter volumes and infant amygdala and hippocampal volumes during the first 2 years of life. Methods: Fifty-seven mothers with 4-month-old infants were assessed for MCM, using both the brief Adverse Childhood Experiences screening questionnaire and the more detailed Maltreatment and Abuse Chronology of Exposure scale. A total of 58% had experienced childhood maltreatment. Between 4 and 24 months (age in months: mean = 12.28, SD = 5.99), under natural sleep, infants completed a magnetic resonance imaging scan using a 3T Siemens scanner. Total brain volume, gray matter volume, white matter volume, and amygdala and hippocampal volumes were extracted via automated segmentation. Results: MCM on the Adverse Childhood Experiences and Maltreatment and Abuse Chronology of Exposure scales were associated with lower infant total brain volume and gray matter volume, with no moderation by infant age. However, infant age moderated the association between MCM and right amygdala volume, such that MCM was associated with lower volume at older ages. Conclusions: MCM is associated with alterations in infant brain volumes, calling for further identification of the prenatal and postnatal mechanisms contributing to such intergenerational transmission. Furthermore, the brief Adverse Childhood Experiences questionnaire predicted these alterations, suggesting the potential utility of early screening for infant risk.

Patterns of Neural Functional Connectivity in Infants at Familial Risk of Developmental Dyslexia.

Importance: Developmental dyslexia is a heritable learning disability affecting 7% to 10% of the general population and can have detrimental impacts on mental health and vocational potential. Individuals with dyslexia show altered functional organization of the language and reading neural networks; however, it remains unknown how early in life these neural network alterations might emerge. Objective: To determine whether the early emergence of large-scale neural functional connectivity (FC) underlying long-term language and reading development is altered in infants with a familial history of dyslexia (FHD). Design, Setting, and Participants: This cohort study included infants recruited at Boston Children's Hospital between May 2011 and February 2019. Participants underwent structural and resting-state functional magnetic resonance imaging in the Department of Radiology at Boston Children's Hospital. Infants with FHD were matched with infants without FHD based on age and sex. Data were analyzed from April 2019 to June 2021. Exposures: FHD was defined as having at least 1 first-degree relative with a dyslexia diagnosis or documented reading difficulties. Main Outcomes and Measures: Whole-brain FC patterns associated with 20 predefined cerebral regions important for long-term language and reading development were computed for each infant. Multivariate pattern analyses were applied to identify specific FC patterns that differentiated between infants with vs without FHD. For classification performance estimates, 99% CIs were calculated as the classification accuracy minus chance level. Results: A total of 98 infants (mean [SD] age, 8.5 [2.3] months; 51 [52.0%] girls) were analyzed, including 35 infants with FHD and 63 infants without FHD. Multivariate pattern analyses identified distinct FC patterns between infants with vs without FHD in the left fusiform gyrus (classification accuracy, 0.55 [99% CI, 0.046-0.062]; corrected P < .001; Cohen d = 0.76). Connections linking left fusiform gyrus to regions in the frontal and parietal language and attention networks were among the paths with the highest contributions to the classification performance. Conclusions and Relevance: These findings suggest that on the group level, FHD was associated with an early onset of atypical FC of regions important for subsequent word form recognition during reading acquisition. Longitudinal studies linking the atypical functional network and school-age reading abilities will be essential to further elucidate the ontogenetic mechanisms underlying the development of dyslexia.