Maternal Dietary Intake of Omega-3 Fatty Acids Correlates Positively with Regional Brain Volumes in 1-Month-Old Term Infants

Maternal Childhood Abuse Versus Neglect Associated with Differential Patterns of Infant Brain Development

Severity of maternal childhood maltreatment has been associated with lower infant grey matter volume and amygdala volume during the first two years of life. A developing literature argues that effects of threat (abuse) and of deprivation (neglect) should be assessed separately because these distinct aspects of adversity may have different impacts on developmental outcomes. However, distinct effects of threat versus deprivation have not been assessed in relation to intergenerational effects of child maltreatment. The objective of this study was to separately assess the links of maternal childhood abuse and neglect with infant grey matter volume (GMV), white matter volume (WMV), amygdala and hippocampal volume. Participants included 57 mother-infant dyads. Mothers were assessed for childhood abuse and neglect using the Adverse Childhood Experiences (ACE) questionnaire in a sample enriched for childhood maltreatment. Between 4 and 24 months (M age = 12.28 months, SD = 5.99), under natural sleep, infants completed an MRI using a 3.0 T Siemens scanner. GMV, WMV, amygdala and hippocampal volumes were extracted via automated segmentation. Maternal history of neglect, but not abuse, was associated with lower infant GMV. Maternal history of abuse, but not neglect, interacted with age such that abuse was associated with smaller infant amygdala volume at older ages. Results are consistent with a threat versus deprivation framework, in which threat impacts limbic regions central to the stress response, whereas deprivation impacts areas more central to cognitive function. Further studies are needed to identify mechanisms contributing to these differential intergenerational associations of threat versus deprivation.

Negative versus withdrawn maternal behavior: Differential associations with infant gray and white matter during the first 2 years of life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 Withdrawn versus negative parenting may represent dimensionalized indices of early deprivation versus early threat, but no studies have assessed neural correlates of withdrawn versus negative parenting in infancy.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.

Shaping the risk for late-life neurodegenerative disease: A systematic review on prenatal risk factors for Alzheimer's disease-related volumetric brain biomarkers

Environmental exposures including toxins and nutrition may hamper the developing brain in utero, limiting the brain's reserve capacity and increasing the risk for Alzheimer's disease (AD). The purpose of this systematic review is to summarize all currently available evidence for the association between prenatal exposures and AD-related volumetric brain biomarkers. We systematically searched MEDLINE and Embase for studies in humans reporting on associations between prenatal exposure(s) and AD-related volumetric brain biomarkers, including whole brain volume (WBV), hippocampal volume (HV) and/or temporal lobe volume (TLV) measured with structural magnetic resonance imaging (PROSPERO; CRD42020169317). Risk of bias was assessed using the Newcastle Ottawa Scale. We identified 79 eligible studies (search date: August 30th, 2020; Ntotal=24,784; median age 10.7 years) reporting on WBV (N = 38), HV (N = 63) and/or TLV (N = 5) in exposure categories alcohol (N = 30), smoking (N = 7), illicit drugs (N = 14), mental health problems (N = 7), diet (N = 8), disease, treatment and physiology (N = 10), infections (N = 6) and environmental exposures (N = 3). Overall risk of bias was low. Prenatal exposure to alcohol, opioids, cocaine, nutrient shortage, placental dysfunction and maternal anemia was associated with smaller brain volumes. We conclude that the prenatal environment is important in shaping the risk for late-life neurodegenerative disease.

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.

Perspective: Darwinian Applications to Nutrition-The Value of Evolutionary Insights to Teachers and Students

Evolutionary biology informs us that the living world is a product of evolution, guided by the Darwinian mechanism of natural selection. This recognition has been fruitfully employed in a number of issues in health and nutrition sciences; however, it has not been incorporated into education. Nutrition and dietetics students generally learn very little or nothing on the subject of evolution, despite the fact that evolution is the process by which our genetically determined physiological traits and needs were shaped. In the present Perspective article, 3 examples of topics (inflammatory diseases, nutrition transition, and food intolerance) that can benefit from evolutionary information and reasoning are given, with relevant lines of research and inquiry provided throughout. It is argued that the application of evolutionary science to these and other areas of nutrition education can facilitate a deeper and more coherent teaching and learning experience. By recognizing and reframing nutrition as an aspect and discipline of biology, grounded in the fundamental principle of adaptation, revelatory light is shed on physiological states and responses, contentious and unresolved issues, genomic, epigenomic, and microbiomic features, and optimal nutrient status and intakes.

Increased Breastfeeding Proportion Is Associated with Improved Gross Motor Skills at 3-5 Years of Age: A Pilot Study

Breastmilk provides key nutrients and bio-active factors that contribute to infant neurodevelopment. Optimizing maternal nutrition could provide further benefit to psychomotor outcomes. Our observational cohort pilot study aims to determine if breastfeeding extent and breastmilk nutrients correlate with psychomotor outcomes at school age. The breastfeeding proportion at 3 months of age and neurodevelopmental outcomes at 3-5 years of age were recorded for 33 typically developing newborns born after uncomplicated pregnancies. The association between categorical breastfeeding proportion and neurodevelopmental outcome scores was determined for the cohort using a Spearman correlation with and without the inclusion of parental factors. Vitamin E and carotenoid levels were determined in breastmilk samples from 14 of the mothers. After the inclusion of parental education and income as covariates, motor skill scores positively correlated with breastmilk contents of α-tocopherol (Spearman coefficient 0.88, p-value = 0.02), translutein (0.98, p-value = 0.0007), total lutein (0.92, p-value = 0.01), and zeaxanthin (0.93, p-value = 0.0068). Problem solving skills negatively correlated with the levels of the RSR enantiomer of α-tocopherol (-0.86, p-value = 0.03). Overall, higher exposure to breastfeeding was associated with improved gross motor and problem-solving skills at 3-5 years of age. The potential of α-tocopherol, lutein, and zeaxanthin intake to provide neurodevelopmental benefit is worthy of further investigation.

Study protocol: retrospectively mining multisite clinical data to presymptomatically predict seizure onset for individual patients with Sturge-Weber

INTRODUCTION

Secondary analysis of hospital-hosted clinical data can save time and cost compared with prospective clinical trials for neuroimaging biomarker development. We present such a study for Sturge-Weber syndrome (SWS), a rare neurovascular disorder that affects 1 in 20 000-50 000 newborns. Children with SWS are at risk for developing neurocognitive deficit by school age. A critical period for early intervention is before 2 years of age, but early diagnostic and prognostic biomarkers are lacking. We aim to retrospectively mine clinical data for SWS at two national centres to develop presymptomatic biomarkers.

METHODS AND ANALYSIS

We will retrospectively collect clinical, MRI and neurocognitive outcome data for patients with SWS who underwent brain MRI before 2 years of age at two national SWS care centres. Expert review of clinical records and MRI quality control will be used to refine the cohort. The merged multisite data will be used to develop algorithms for abnormality detection, lesion-symptom mapping to identify neural substrate and machine learning to predict individual outcomes (presence or absence of seizures) by 2 years of age. Presymptomatic treatment in 0-2 years and before seizure onset may delay or prevent the onset of seizures by 2 years of age, and thereby improve neurocognitive outcomes. The proposed work, if successful, will be one of the largest and most comprehensive multisite databases for the presymptomatic phase of this rare disease.

ETHICS AND DISSEMINATION

This study involves human participants and was approved by Boston Children's Hospital Institutional Review Board: IRB-P00014482 and IRB-P00025916 Johns Hopkins School of Medicine Institutional Review Board: NA_00043846. Participants gave informed consent to participate in the study before taking part. The Institutional Review Boards at Kennedy Krieger Institute and Boston Children's Hospital approval have been obtained at each site to retrospectively study this data. Results will be disseminated by presentations, publication and sharing of algorithms generated.

Validation of an abbreviated food frequency questionnaire for estimating DHA intake of pregnant women in the United States

Docosahexaenoic acid (DHA) intake was estimated in pregnant women between 12- and 20-weeks' gestation using the National Cancer Institute's (NCI) Diet History Questionnaire-II (DHQ-II) and a 7-question screener designed to capture DHA intake (DHA Food Frequency Questionnaire, DHA-FFQ). Results from both methods were compared to red blood cell phospholipid DHA (RBC-DHA) weight percent of total fatty acids. DHA intake from the DHA-FFQ was more highly correlated with RBC-DHA (r_s=0.528) than the DHQ-II (r_s=0.352). Moreover, the DHA-FFQ allowed us to obtain reliable intake data from 1355 of 1400 participants. The DHQ-II provided reliable intake for only 847 of 1400, because many participants only partially completed it and it was not validated for Hispanic participants. Maternal age, parity, and socioeconomic status (SES) were also significant predictors of RBC-DHA. When included with estimated intake from the DHA-FFQ, the model accounted for 36% of the variation in RBC-DHA.

A Role for Data Science in Precision Nutrition and Early Brain Development

Multimodal brain magnetic resonance imaging (MRI) can provide biomarkers of early influences on neurodevelopment such as nutrition, environmental and genetic factors. As the exposure to early influences can be separated from neurodevelopmental outcomes by many months or years, MRI markers can serve as an important intermediate outcome in multivariate analyses of neurodevelopmental determinants. Key to the success of such work are recent advances in data science as well as the growth of relevant data resources. Multimodal MRI assessment of neurodevelopment can be supplemented with other biomarkers of neurodevelopment such as electroencephalograms, magnetoencephalogram, and non-imaging biomarkers. This review focuses on how maternal nutrition impacts infant brain development, with three purposes: (1) to summarize the current knowledge about how nutrition in stages of pregnancy and breastfeeding impact infant brain development; (2) to discuss multimodal MRI and other measures of early neurodevelopment; and (3) to discuss potential opportunities for data science and artificial intelligence to advance precision nutrition. We hope this review can facilitate the collaborative march toward precision nutrition during pregnancy and the first year of life.

Integrated Lipidomic and Transcriptomic Analysis Reveals Lipid Metabolism in Foxtail Millet (Setaria italica)

Foxtail millet (Setaria italica) as the main traditional crop in China, is rich in many kinds of high quality fatty acids (FAs). In this study, Ultra-high performance liquid chromatography-time-of-flight-tandem mass spectrometer (UHPLC-Q-TOF-MS/MS) was used to determine the lipids of JG35 and JG39. A total of 2,633 lipid molecules and 31 lipid subclasses were identified, mainly including thirteen kinds of glycerophospholipids (GP), eleven kinds of glycerolipids (GL), four kinds of sphingolipids (SP), two kinds of fatty acyls (FA) and one kind of sterol (ST). Among them JG35 had higher contents of diacylglycerols (DG) and ceramides (Cer), while triacylglycerols, phosphatidyl ethanolamine, phosphatidic acid, sterol, fatty acyls and pardiolipin (TG, PE, PA, ST, FA and CL) were higher in JG39. Meantime, the correlation analysis of lipidomics and transcriptomics was used to map the main differential lipid metabolism pathways of foxtail millet. The results shown that a differentially expressed genes (DEGs) of FATA/B for the synthesis of FA was highly expressed in JG35, and the related genes for the synthesis DG (ACCase, KAS, HAD, KCS, LACS and GAPT), TG (DGAT and PDAT) and CL (CLS) were highly expressed in JG39. The results of this study will provide a theoretical basis for the future study of lipidomics, improvement of lipid quality directionally and breeding of idiosyncratic quality varieties in foxtail millet.

Multi-channel attention-fusion neural network for brain age estimation: Accuracy, generality, and interpretation with 16,705 healthy MRIs across lifespan

Brain age estimated by machine learning from T1-weighted magnetic resonance images (T1w MRIs) can reveal how brain disorders alter brain aging and can help in the early detection of such disorders. A fundamental step is to build an accurate age estimator from healthy brain MRIs. We focus on this step, and propose a framework to improve the accuracy, generality, and interpretation of age estimation in healthy brain MRIs. For accuracy, we used one of the largest sample sizes (N = 16,705). For each subject, our proposed algorithm first explicitly splits the T1w image, which has been commonly treated as a single-channel 3D image in other studies, into two 3D image channels representing contrast and morphometry information. We further proposed a "fusion-with-attention" deep learning convolutional neural network (FiA-Net) to learn how to best fuse the contrast and morphometry image channels. FiA-Net recognizes varying contributions across image channels at different brain anatomy and different feature layers. In contrast, multi-channel fusion does not exist for brain age estimation, and is mostly attention-free in other medical image analysis tasks (e.g., image synthesis, or segmentation), where treating channels equally may not be optimal. For generality, we used lifespan data 0-97 years of age for real-world utility; and we thoroughly tested FiA-Net for multi-site and multi-scanner generality by two phases of cross-validations in discovery and replication data, compared to most other studies with only one phase of cross-validation. For interpretation, we directly measured each artificial neuron's correlation with the chronological age, compared to other studies looking at the saliency of features where salient features may or may not predict age. Overall, FiA-Net achieved a mean absolute error (MAE) of 3.00 years and Pearson correlation r=0.9840 with known chronological ages in healthy brain MRIs 0-97 years of age, comparing favorably with state-of-the-art algorithms and studies for accuracy and generality across sites and datasets. We also provided interpretations on how different artificial neurons and real neuroanatomy contribute to the age estimation.

Importance of EPA and DHA Blood Levels in Brain Structure and Function

Brain structure and function depend on a constant and sufficient supply with eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) by blood. Blood levels of EPA and DHA reflect dietary intake and other variables and are preferably assessed as percentage in erythrocytes with a well-documented and standardized analytical method (HS-Omega-3 Index^®). Every human being has an Omega-3 Index between 2 and 20%, with an optimum of 8–11%. Compared to an optimal Omega-3 Index, a lower Omega-3 Index was associated with increased risk for total mortality and ischemic stroke, reduced brain volume, impaired cognition, accelerated progression to dementia, psychiatric diseases, compromises of complex brain functions, and other brain issues in epidemiologic studies. Most intervention trials, and their meta-analyses considered EPA and DHA as drugs with good bioavailability, a design tending to produce meaningful results in populations characterized by low baseline blood levels (e.g., in major depression), but otherwise responsible for many neutral results and substantial confusion. When trial results were evaluated using blood levels of EPA and DHA measured, effects were larger than comparing EPA and DHA to placebo groups, and paralleled epidemiologic findings. This indicates future trial design, and suggests a targeted use EPA and DHA, based on the Omega-3 Index.

Effect of Maternal Docosahexaenoic Acid (DHA) Supplementation on Offspring Neurodevelopment at 12 Months in India: A Randomized Controlled Trial

Intake of dietary docosahexaenoic acid (DHA 22:6n-3) is very low among Indian pregnant women. Maternal supplementation during pregnancy and lactation may benefit offspring neurodevelopment. We conducted a double-blind, randomized, placebo-controlled trial to test the effectiveness of supplementing pregnant Indian women (singleton gestation) from ≤20 weeks through 6 months postpartum with 400 mg/d algal DHA compared to placebo on neurodevelopment of their offspring at 12 months. Of 3379 women screened, 1131 were found eligible; 957 were randomized. The primary outcome was infant neurodevelopment at 12 months, assessed using the Development Assessment Scale for Indian Infants (DASII). Both groups were well balanced on sociodemographic variables at baseline. More than 72% of women took >90% of their assigned treatment. Twenty-five serious adverse events (SAEs), none related to the intervention, (DHA group = 16; placebo = 9) were noted. Of 902 live births, 878 were followed up to 12 months; the DASII was administered to 863 infants. At 12 months, the mean development quotient (DQ) scores in the DHA and placebo groups were not statistically significant (96.6 ± 12.2 vs. 97.1 ± 13.0, p = 0.60). Supplementing mothers through pregnancy and lactation with 400 mg/d DHA did not impact offspring neurodevelopment at 12 months of age in this setting.

Mining multi-site clinical data to develop machine learning MRI biomarkers: application to neonatal hypoxic ischemic encephalopathy

BACKGROUND

Secondary and retrospective use of hospital-hosted clinical data provides a time- and cost-efficient alternative to prospective clinical trials for biomarker development. This study aims to create a retrospective clinical dataset of Magnetic Resonance Images (MRI) and clinical records of neonatal hypoxic ischemic encephalopathy (HIE), from which clinically-relevant analytic algorithms can be developed for MRI-based HIE lesion detection and outcome prediction.

METHODS

This retrospective study will use clinical registries and big data informatics tools to build a multi-site dataset that contains structural and diffusion MRI, clinical information including hospital course, short-term outcomes (during infancy), and long-term outcomes (~ 2 years of age) for at least 300 patients from multiple hospitals.

DISCUSSION

Within machine learning frameworks, we will test whether the quantified deviation from our recently-developed normative brain atlases can detect abnormal regions and predict outcomes for individual patients as accurately as, or even more accurately, than human experts. Trial Registration Not applicable. This study protocol mines existing clinical data thus does not meet the ICMJE definition of a clinical trial that requires registration.