Investigating mother-child inter-brain synchrony in a naturalistic paradigm: A functional near infrared spectroscopy (fNIRS) hyperscanning study.

Successful social interactions between mothers and children are hypothesised to play a significant role in a child's social, cognitive and language development. Earlier research has confirmed, through structured experimental paradigms, that these interactions could be underpinned by coordinated neural activity. Nevertheless, the extent of neural synchrony during real-life, ecologically valid interactions between mothers and their children remains largely unexplored. In this study, we investigated mother-child inter-brain synchrony using a naturalistic free-play paradigm. We also examined the relationship between neural synchrony, verbal communication patterns and personality traits to further understand the underpinnings of brain synchrony. Twelve children aged between 3 and 5 years old and their mothers participated in this study. Neural synchrony in mother-child dyads were measured bilaterally over frontal and temporal areas using functional Near Infra-red Spectroscopy (fNIRS) whilst the dyads were asked to play with child-friendly toys together (interactive condition) and separately (independent condition). Communication patterns were captured via video recordings and conversational turns were coded. Compared to the independent condition, mother-child dyads showed increased neural synchrony in the interactive condition across the prefrontal cortex and temporo-parietal junction. There was no significant relationship found between neural synchrony and turn-taking and between neural synchrony and the personality traits of each member of the dyad. Overall, we demonstrate the feasibility of measuring inter-brain synchrony between mothers and children in a naturalistic environment. These findings can inform future study designs to assess inter-brain synchrony between parents and pre-lingual children and/or children with communication needs.

Methods and Software to Analyze Gene-Environment Interactions under a Case-Mother-Control-Mother Design with Partially Missing Child Genotype.

INTRODUCTION: The case-mother-control-mother design allows to study fetal and maternal genetic factors together with environmental exposures on early life outcomes. Mendelian constraints and conditional independence between child genotype and environmental factors enabled semiparametric likelihood methods to estimate logistic models with greater efficiency than standard logistic regression. Difficulties in child genotype collection require methods handling missing child genotype. METHODS: We review a stratified retrospective likelihood and two semiparametric likelihood approaches: a prospective one and a modified retrospective one, the latter either modeling the maternal genotype as a function of covariates or leaving their joint distribution unspecified (robust version). We also review software implementing these modeling alternatives, compare their statistical properties in a simulation study, and illustrate their application, focusing on gene-environment interactions and partially missing child genotype. RESULTS: The robust retrospective likelihood provides generally unbiased estimates, with standard errors only slightly larger than when modeling maternal genotype based on exposure. The prospective likelihood encounters maximization problems. In the application to the association of small-for-gestational-age babies with CYP2E1 and drinking water disinfection by-products, the retrospective likelihood allowed a full array of covariates, while the prospective likelihood was limited to few covariates. CONCLUSION: We recommend the robust version of the modified retrospective likelihood.

Efficient inference of parent-of-origin effect using case-control mother-child genotype data.

Parent-of-origin effect plays an important role in mammal development and disorder. Case-control mother-child pair genotype data can be used to detect parent-of-origin effect and is often convenient to collect in practice. Most existing methods for assessing parent-of-origin effect do not incorporate any covariates, which may be required to control for confounding factors. We propose to model the parent-of-origin effect through a logistic regression model, with predictors including maternal and child genotypes, parental origins, and covariates. The parental origins may not be fully inferred from genotypes of a target genetic marker, so we propose to use genotypes of markers tightly linked to the target marker to increase inference efficiency. A robust statistical inference procedure is developed based on a modified profile log-likelihood in a retrospective way. A computationally feasible expectation-maximization algorithm is devised to estimate all unknown parameters involved in the modified profile log-likelihood. This algorithm differs from the conventional expectation-maximization algorithm in the sense that it is based on a modified instead of the original profile log-likelihood function. The convergence of the algorithm is established under some mild regularity conditions. This expectation-maximization algorithm also allows convenient handling of missing child genotypes. Large sample properties, including weak consistency, asymptotic normality, and asymptotic efficiency, are established for the proposed estimator under some mild regularity conditions. Finite sample properties are evaluated through extensive simulation studies and the application to a real dataset.

Analysis of parent-of-origin effects for secondary phenotypes using case-control mother-child pair data.

The detection of parent-of-origin effects (POEs) has become a research focus in genetic association studies since POEs play an important role in explaining the heritability of many complex human disorders. Genetic association studies are commonly conducted based on case-control designs. Case-control genetic association studies often collect additional information on secondary phenotypes other than the case-control status. Various statistical methods have been proposed to analyze the secondary phenotypes, but no methods are specifically tailored for identifying POEs of offspring genes on the secondary phenotypes. The parental origin information may not be determined unambiguously using the genotypes of the test locus for some families, and ignoring such families would lose considerable information. In this article, we focus on case-control mother-child pair design that has been widely used for studying human early life growth and development, and propose a robust and efficient retrospective likelihood method to detect POEs for the secondary phenotypes using multilocus genotypes. The proposed method fully utilizes the information from multilocus genotypes, Hardy-Weinberg equilibrium (HWE), Mendelian inheritance law, and conditional independence between child genotype and maternal covariate given maternal genotype. Large sample properties, including consistency and asymptotic normality, are established for our proposed statistical method. The finite sample performance of our method are demonstrated through extensive simulation studies and application to the Danish National Birth Cohort data.

Covariate adjusted inference of parent-of-origin effects using case-control mother-child paired multilocus genotype data.

It is of great interest to identify parent-of-origin effects (POEs) since POEs play an important role in many human heritable disorders and human early life growth and development. POE is sometimes referred to as imprinting effect in the literature. Compared with the standard logistic regression analyses, retrospective likelihood-based statistical methods are more powerful in identifying POEs when data are collected from related individuals retrospectively. However, none of existing retrospective-based methods can appropriately incorporate covariates that should be adjusted for if they are confounding factors. In this paper, a novel semiparametric statistical method, M-HAP, is developed to detect POEs by fully exploring available information from multilocus genotypes of case-control mother-child pairs and covariates. Some large sample properties are established for M-HAP. Finite sample properties of M-HAP are illustrated by extensive simulation studies and real data applications to the Jerusalem Perinatal Study and the Danish National Birth Cohort study, which confirm the desired superiority of M-HAP over some existing methods. M-HAP has been implemented in the updated R package CCMO.

Placental dysfunction: health status, nutritional status and mineral profile of a mother-child pair.

OBJECTIVE: The aim: the analysis of the PD, pregnancy, the labor, the research on peculiarities of the development and health status of breast-fed children, who are born to mothers with PD, by means of analyzing the mother-child pair's nutritional status and mineral homeostasis. PATIENTS AND METHODS: Materials and methods: At the 1 stage, an analysis of the PD frequency, the pregnancy, the labor was conducted during 5 years. At the 2 stage, 188 mother-child pairs were examined: 84.04% women had PD and 15.96% didn't have it. The research included the analysis of the anamnestic data, maternal nutritional status, general clinical study, assessment of the physical, psychomotor level of the child's development, study of the elemental profile. RESULTS: Results: High frequency of complications in pregnancy and labor was observed in cases when women had PD, due to the imbalance in the "mother-placenta-fetus" system. The results' analysis showed an increased level of Zn (1.437%), K (10.147%), and Ca (83.900%) in hair; an increased level of K (82.818%), Cr (0.274%), and Na (3.611%) in breast milk of women with PD. Children born to mothers with PD had a significantly increased level of Cr (0.92%), S (0.578%) and P (0.169%), Na (0.107%), Ca (56.041%), and Zn (7.149%). CONCLUSION: Conclusions: PD has a negative impact on the pregnancy and labor and may be one of the factors causing the mineral imbalance of breast-fed infant.

Prevalence and socio-demographic factors associated with double and triple burden of malnutrition among mother-child pairs in India: Findings from a nationally representative survey (NFHS-5).

Introduction: Improper consumption of food leads to various forms of malnutrition such as undernutrition, overnutrition and micronutrient deficiency. The coexistence of various malnutrition forms in the same household is a public health concern in developing countries. Very little research has been done on exploring the burden and risk factors associated with double (DBM) and triple burden of malnutrition (TBM). Methods: Secondary analysis was done using data from India's National Family Health Survey-5 (NFHS-5), 2019-21. Mothers and under-five children were paired at the household level as a unit of analysis. DBM and TBM were interpreted from children's height-for-age, weight-for-height, weight-for-age and anaemia status and mothers' body-mass-index. Stratification and clustering in the sample design were accounted for during the analysis in STATA v14.2. DBM and TBM were summarized as weighted proportions with 95 % confidence interval (CI) and the risk factors associated were reported as adjusted odds ratio (aOR) with 95%CI using mixed effects logistic regression. Results: We included 167,380 mother-child pairs for analysis. In India, the prevalence of DBM was 7.7 % (95 % CI: 7.5-7.9 %) and TBM was 5.1 % (5.0-5.3 %) at the household level among mother-child dyads. Mothers' age, age at first birth, educational levels, current breastfeeding habits, mode of delivery, child's age, gender, twin birth, birth weight, geographical region, residence, caste and religion, and wealth index were associated with both DBM and TBM. Conclusion: Both DBM and TBM are public health concerns in India. Thus, scaling-up of health intervention, effective implementation of nutritional programmes and life-course approach are needed to control malnutrition.

Comparison of the PCB serum levels among mother-child pairs in areas of Eastern Japan and Central Taiwan.

Polychlorinated biphenyls (PCBs) have been prohibited for two decades in Japan and Taiwan. The aim of this study was to compare the PCB congeners in maternal and cord serum between two countries. Our study subjects were 248 and 100 mother-child pairs in Japan and Taiwan. The measured levels of 23 serum PCB congeners between two countries were analyzed using gas chromatography-electron capture negative ionization quadrupole mass spectrometry (GC-NICI-qMS). The statistical comparisons were conducted by Student's t-test and principal component analysis with further stratification by maternal age and parity. The maternal total PCBs levels in Japan (426 ± 244 pg/g wet wt) were significantly higher than those in Taiwan (254 ± 155 pg/g wet wt), and the similar results were found in cord total PCBs levels (97 ± 76 and 58 ± 87 pg/g wet wt). It showed different distributions of PCB congeners between two countries. Whether in maternal or cord serum, the CB138, CB153 and CB180 were the highest detectable congeners whether in Japan or Taiwan. And, the CB66, CB99, CB206 and CB209 were only detected in maternal serum of Taiwan. The women of advanced maternal age had higher levels of PCB congeners, especially in Taiwan, and the primiparous women had higher levels of PCB congeners in two countries. In summary, the PCB congeners in Japan's mother-child pairs were with higher levels and different distributions when compared to those in Taiwan, and the maternal age and parity were important factors associated with the PCB levels.

Gut Bacteria Shared by Children and Their Mothers Associate with Developmental Level and Social Deficits in Autism Spectrum Disorder.

The gut microbiota of autism spectrum disorder (ASD) children differs from that of children without ASD. The maternal gut microbiota impacts offspring gut microbiota. However, the relationship between the development of ASD and gut bacteria shared between children and their mothers remains elusive. Our study recruited 76 children with ASD and 47 age- and gender-matched children with typical development (TD), as well as the mothers of both groups, and investigated their gut microbiota using amplicon sequence variants (ASVs). The gut microbiota of ASD children was altered compared with that of children with TD, while no significant alterations were found in their mothers. We established 30 gut bacterial coabundance groups (CAGs) and found the relative abundances of CAG15 and CAG16 significantly decreased in ASD children. CAG15 showed a positive correlation with developmental level. The proportion of ASD children who shared either one of the two Lachnospiraceae ASVs from CAG15 with their mothers was significantly lower than that of children with TD. Moreover, we found that CAG12, CAG13, and CAG18 negatively correlated with the severity of social deficits in ASD children. ASD children who shared any one of the four (two Ruminococcaceae, one Lachnospiraceae, and one Collinsella) ASVs in CAG13 and CAG18 with their mothers showed a lower level of social deficits than ASD children that did not share those with their mothers. These data demonstrate that these shared gut bacteria in ASD children are associated with their developmental level and social deficits. This work provides a new direction toward understanding the role of the gut microbiota in the pathogenesis and development of ASD. (This study has been registered in the Chinese Clinical Trial Registry under number ChiCTR-RPC-16008139.)IMPORTANCE Gut microbiota may contribute to the pathogenesis and development of autism spectrum disorder. The maternal gut microbiota influences offspring gut microbial structure and composition. However, the relationship between the clinical symptoms of autism spectrum disorder and the gut bacteria shared between children and their mothers is not yet known. In our study, the gut microbiota of children with autism spectrum disorder differed from that of children with typical development, but there were no differences in the gut microbiota of their mothers. More importantly, gut bacteria shared between children with autism spectrum disorder and their mothers were related to developmental disabilities and social deficits. Thus, our study suggests that these shared gut bacteria may play an important role in the development of autism spectrum disorder. This provides a new direction for future studies aiming to explore the role of the gut microbiota in autism spectrum disorder.

Correlation of Gut Microbiome Between ASD Children and Mothers and Potential Biomarkers for Risk Assessment.

Variation of maternal gut microbiota may increase the risk of autism spectrum disorders (ASDs) in offspring. Animal studies have indicated that maternal gut microbiota is related to neurodevelopmental abnormalities in mouse offspring, while it is unclear whether there is a correlation between gut microbiota of ASD children and their mothers. We examined the relationships between gut microbiome profiles of ASD children and those of their mothers, and evaluated the clinical discriminatory power of discovered bacterial biomarkers. Gut microbiome was profiled and evaluated by 16S ribosomal RNA gene sequencing in stool samples of 59 mother-child pairs of ASD children and 30 matched mother-child pairs of healthy children. Significant differences were observed in the gut microbiome composition between ASD and healthy children in our Chinese cohort. Several unique bacterial biomarkers, such as Alcaligenaceae and Acinetobacter, were identified. Mothers of ASD children had more Proteobacteria, Alphaproteobacteria, Moraxellaceae, and Acinetobacter than mothers of healthy children. There was a clear correlation between gut microbiome profiles of children and their mothers; however, children with ASD still had unique bacterial biomarkers, such as Alcaligenaceae, Enterobacteriaceae, and Clostridium. Candidate biomarkers discovered in this study had remarkable discriminatory power. The identified patterns of mother-child gut microbiome profiles may be important for assessing risks during the early stage and planning of personalized treatment and prevention of ASD via microbiota modulation.

Correlation of Gut Microbiome Between ASD Children and Mothers and Potential Biomarkers for Risk Assessment / 基因组蛋白质组与生物信息学报·英文版

Variation of maternal gut microbiota may increase the risk of autism spectrum disorders (ASDs) in offspring. Animal studies have indicated that maternal gut microbiota is related to neurodevelopmental abnormalities in mouse offspring, while it is unclear whether there is a correlation between gut microbiota of ASD children and their mothers. We examined the relationships between gut microbiome profiles of ASD children and those of their mothers, and evaluated the clinical discriminatory power of discovered bacterial biomarkers. Gut microbiome was profiled and evaluated by 16S ribosomal RNA gene sequencing in stool samples of 59 mother-child pairs of ASD children and 30 matched mother-child pairs of healthy children. Significant differences were observed in the gut microbiome composition between ASD and healthy children in our Chinese cohort. Several unique bacterial biomarkers, such as Alcaligenaceae and Acinetobacter, were identified. Mothers of ASD children had more Proteobacteria, Alphaproteobacteria, Moraxellaceae, and Acinetobacter than mothers of healthy children. There was a clear correlation between gut microbiome profiles of children and their mothers; however, children with ASD still had unique bacterial biomarkers, such as Alcaligenaceae, Enterobacteriaceae, and Clostridium. Candidate biomarkers discovered in this study had remarkable discriminatory power. The identified patterns of mother-child gut microbiome profiles may be important for assessing risks during the early stage and planning of personalized treatment and prevention of ASD via microbiota modulation.