Mapping early brain-body interactions: associations of fetal heart rate variation with newborn brainstem, hypothalamic, and dorsal anterior cingulate cortex functional connectivity.

The autonomic nervous system (ANS) regulates the body's physiology, including cardiovascular function. As the ANS develops during the second to third trimester, fetal heart rate variability (HRV) increases while fetal heart rate (HR) decreases. In this way, fetal HR and HRV provide an index of fetal autonomic nervous system development and future neurobehavioral regulation. Fetal HR and HRV have been associated with child language ability and psychomotor development behavior in toddlerhood. However, their associations with post-birth autonomic brain systems, such as the brainstem, hypothalamus, and dorsal anterior cingulate cortex (dACC), have yet to be investigated even though brain pathways involved in autonomic regulation are well established in older individuals. We assessed whether fetal HR and HRV were associated with the brainstem, hypothalamic and dACC functional connectivity in newborns. Data were obtained from 60 pregnant individuals (ages 14-42) at 24-27 and 34-37 weeks gestation using a fetal actocardiograph to generate fetal HR and HRV. During natural sleep, their infants (38 males and 22 females) underwent a fMRI scan between 40-46 weeks of postmenstrual age. Our findings relate fetal heart indices to brainstem, hypothalamic, and dACC connectivity and reveal connections with widespread brain regions that may support behavioral and emotional regulation. We demonstrated the basic physiologic association between fetal HR indices and lower and higher order brain regions involved in regulatory processes. This work provides the foundation for future behavioral or physiological regulation research in fetuses and infants.Significance statement Fetal heart rate indices are quantifiable, developmental markers of the fetal autonomic nervous system. Variations in their trajectories can signal compromised neurodevelopmental outcomes. We assessed associations between fetal heart rate indices and early infant brain development to identify unique or common associations corresponding to autonomic nervous system maturation patterns. We found associations between fetal heart rate indices and infant brainstem, hypothalamic, and dACC connectivity-areas that support autonomic and behavioral regulatory functions. The study demonstrates that these associations between ANS and brain regions involved in autonomic regulation exist early in life. These findings are a first step to understanding how these brain connections form the basis of future regulatory development.

The effects of experience of discrimination and acculturation during pregnancy on the developing offspring brain.

The experience of ethnic, racial, and structural inequalities is increasingly recognized as detrimental to health, and early studies suggest that its experience in pregnant mothers may affect the developing fetus. We characterized discrimination and acculturation experiences in a predominantly Hispanic sample of pregnant adolescent women and assessed their association with functional connectivity in their neonate's brain. We collected self-report measures of acculturation, discrimination, maternal distress (i.e., perceived stress, childhood trauma, and depressive symptoms), and socioeconomic status in 165 women. Then, we performed a data-driven clustering of acculturation, discrimination, perceived stress, depressive symptoms, trauma, and socioeconomic status variables during pregnancy to determine whether discrimination or acculturation clustered into distinct factors. Discrimination and acculturation styles loaded onto different factors from perceived stress, depressive symptoms, trauma, and socioeconomic status, suggesting that they were distinct from other factors in our sample. We associated these data-driven maternal phenotypes (discrimination and acculturation styles) with measures of resting-state functional MRI connectivity of the infant amygdala (n = 38). Higher maternal report of assimilation was associated with weaker connectivity between their neonate's amygdala and bilateral fusiform gyrus. Maternal experience of discrimination was associated with weaker connectivity between the amygdala and prefrontal cortex and stronger connectivity between the amygdala and fusiform of their neonate. Cautiously, the results may suggest a similarity to self-contained studies with adults, noting that the experience of discrimination and acculturation may influence amygdala circuitry across generations. Further prospective studies are essential that consider a more diverse population of minoritized individuals and with a comprehensive assessment of ethnic, racial, and structural factors.

Network controllability of structural connectomes in the neonatal brain.

White matter connectivity supports diverse cognitive demands by efficiently constraining dynamic brain activity. This efficiency can be inferred from network controllability, which represents the ease with which the brain moves between distinct mental states based on white matter connectivity. However, it remains unclear how brain networks support diverse functions at birth, a time of rapid changes in connectivity. Here, we investigate the development of network controllability during the perinatal period and the effect of preterm birth in 521 neonates. We provide evidence that elements of controllability are exhibited in the infant's brain as early as the third trimester and develop rapidly across the perinatal period. Preterm birth disrupts the development of brain networks and altered the energy required to drive state transitions at different levels. In addition, controllability at birth is associated with cognitive ability at 18 months. Our results suggest network controllability develops rapidly during the perinatal period to support cognitive demands but could be altered by environmental impacts like preterm birth.

Intergenerational transmission of cognitive control capacity among children at risk for depression.

A maternal history of major depressive disorder (MDD) is a well-known risk factor for depression in offspring. However, the mechanism through which familial risk is transmitted remains unclear. Cognitive control alterations are common in MDD, and thus, the current study investigated whether altered control capacity is transmitted intergenerationally, and whether it then contributes to the developmental pathways through which depression is passed from mothers to children. We recruited children (N = 65) ages 4-10-years-old, of which 47.7 % (n = 31) reported a maternal history of MDD, and their biological mother (N = 65). Children performed a child-friendly Go/NoGo task while electroencephalography (EEG) data were recorded, and mothers performed a Flanker task. Children exhibited heightened sensitivity to error versus correct responses, which was characterized by an error-related negativity (ERN), error positivity (Pe) as well as prominent delta and frontal midline theta (FMT) oscillations. Interestingly, worse maternal performance on the Flanker task associated with an increased Go/NoGo error rate and a smaller ERN and Pe in children. However, there was no association between maternal or child control indices with child depression symptoms. Our results suggest a familial influence of cognitive control capacity in mother-child dyads, but it remains unclear whether this confers risk for depressive symptoms in children. Further research is necessary to determine whether alterations in cognitive control over time may influence symptom development in at-risk children.

Utilizing maternal prenatal cognition as a predictor of newborn brain measures of intellectual development.

Identifying reliable indicators of cognitive functioning prior to age five has been challenging. Prior studies have shown that maternal cognition, as indexed by intellectual quotient (IQ) and years of education, predict child intelligence at school age. We examined whether maternal full scale IQ, education, and inhibitory control (index of executive function) are associated with newborn brain measures and toddler language outcomes to assess potential indicators of early cognition. We hypothesized that maternal indices of cognition would be associated with brain areas implicated in intelligence in school-age children and adults in the newborn period. Thirty-seven pregnant women and their newborns underwent an MRI scan. T2-weighted images and surface-based morphometric analysis were used to compute local brain volumes in newborn infants. Maternal cognition indices were associated with local brain volumes for infants in the anterior and posterior cingulate, occipital lobe, and pre/postcentral gyrus - regions associated with IQ, executive function, or sensori-motor functions in children and adults. Maternal education and executive function, but not maternal intelligence, were associated with toddler language scores at 12 and 24 months. Newborn brain volumes did not predict language scores. Overall, the pre/postcentral gyrus and occipital lobe may be unique indicators of early intellectual development in the newborn period. Given that maternal executive function as measured by inhibitory control has robust associations with the newborn brain and is objective, brief, and easy to administer, it may be a useful predictor of early developmental and cognitive capacity for young children.

Maternal prenatal immune activation associated with brain tissue microstructure and metabolite concentrations in newborn infants.

Importance: Few translational human studies have assessed the association of prenatal maternal immune activation with altered brain development and psychiatric risk in newborn offspring. Objective: To identify the effects of maternal immune activation during the 2nd and 3rd trimesters of pregnancy on newborn brain metabolite concentrations, tissue microstructure, and longitudinal motor development. Design: Prospective longitudinal cohort study conducted from 2012 - 2017. Setting: Columbia University Irving Medical Center and Weill Cornell Medical College. Participants: 76 nulliparous pregnant women, aged 14 to 19 years, were recruited in their 2nd trimester, and their children were followed through 14 months of age. Exposure: Maternal immune activation indexed by maternal interleukin-6 and C-reactive protein in the 2nd and 3rd trimesters of pregnancy. Main Outcomes and Measures: The main outcomes included (1) newborn metabolite concentrations, measured as N-acetylaspartate, creatine, and choline using Magnetic Resonance Spectroscopy; (2) newborn fractional anisotropy and mean diffusivity measured using Diffusion Tensor Imaging; and (3) indices of motor development assessed prenatally and postnatally at ages 4- and 14-months. Results: Maternal interleukin-6 and C-reactive protein levels in the 2nd or 3rd trimester were significantly positively associated with the N-acetylaspartate, creatine, and choline concentrations in the putamen, thalamus, insula, and anterior limb of the internal capsule. Maternal interleukin-6 was associated with fractional anisotropy in the putamen, insula, thalamus, precuneus, and caudate, and with mean diffusivity in the inferior parietal and middle temporal gyrus. C-reactive protein was associated with fractional anisotropy in the thalamus, insula, and putamen. Regional commonalities were found across imaging modalities, though the direction of the associations differed by immune marker. In addition, a significant positive association was observed between offspring motor development and both maternal interleukin-6 and C-reactive protein (in both trimesters) prenatally and 4- and 14-months of age. Conclusions and Relevance: Using a healthy sample, these findings demonstrate that levels of maternal immune activation in mid- to late pregnancy associate with tissue characteristics in newborn brain regions primarily supporting motor integration/coordination and behavioral regulation and may lead to alterations in motor development.

Machine Learning and Prediction in Fetal, Infant, and Toddler Neuroimaging: A Review and Primer.

Predictive models in neuroimaging are increasingly designed with the intent to improve risk stratification and support interventional efforts in psychiatry. Many of these models have been developed in samples of children school-aged or older. Nevertheless, despite growing evidence that altered brain maturation during the fetal, infant, and toddler (FIT) period modulates risk for poor mental health outcomes in childhood, these models are rarely implemented in FIT samples. Applications of predictive modeling in children of these ages provide an opportunity to develop powerful tools for improved characterization of the neural mechanisms underlying development. To facilitate the broader use of predictive models in FIT neuroimaging, we present a brief primer and systematic review on the methods used in current predictive modeling FIT studies. Reflecting on current practices in more than 100 studies conducted over the past decade, we provide an overview of topics, modalities, and methods commonly used in the field and under-researched areas. We then outline ethical and future considerations for neuroimaging researchers interested in predicting health outcomes in early life, including researchers who may be relatively new to either advanced machine learning methods or using FIT data. Altogether, the last decade of FIT research in machine learning has provided a foundation for accelerating the prediction of early-life trajectories across the full spectrum of illness and health.

Brain-phenotype models fail for individuals who defy sample stereotypes.

Individual differences in brain functional organization track a range of traits, symptoms and behaviours1-12. So far, work modelling linear brain-phenotype relationships has assumed that a single such relationship generalizes across all individuals, but models do not work equally well in all participants13,14. A better understanding of in whom models fail and why is crucial to revealing robust, useful and unbiased brain-phenotype relationships. To this end, here we related brain activity to phenotype using predictive models-trained and tested on independent data to ensure generalizability15-and examined model failure. We applied this data-driven approach to a range of neurocognitive measures in a new, clinically and demographically heterogeneous dataset, with the results replicated in two independent, publicly available datasets16,17. Across all three datasets, we find that models reflect not unitary cognitive constructs, but rather neurocognitive scores intertwined with sociodemographic and clinical covariates; that is, models reflect stereotypical profiles, and fail when applied to individuals who defy them. Model failure is reliable, phenotype specific and generalizable across datasets. Together, these results highlight the pitfalls of a one-size-fits-all modelling approach and the effect of biased phenotypic measures18-20 on the interpretation and utility of resulting brain-phenotype models. We present a framework to address these issues so that such models may reveal the neural circuits that underlie specific phenotypes and ultimately identify individualized neural targets for clinical intervention.

An ode to fetal, infant, and toddler neuroimaging: Chronicling early clinical to research applications with MRI, and an introduction to an academic society connecting the field.

Fetal, infant, and toddler neuroimaging is commonly thought of as a development of modern times (last two decades). Yet, this field mobilized shortly after the discovery and implementation of MRI technology. Here, we provide a review of the parallel advancements in the fields of fetal, infant, and toddler neuroimaging, noting the shifts from clinical to research use, and the ongoing challenges in this fast-growing field. We chronicle the pioneering science of fetal, infant, and toddler neuroimaging, highlighting the early studies that set the stage for modern advances in imaging during this developmental period, and the large-scale multi-site efforts which ultimately led to the explosion of interest in the field today. Lastly, we consider the growing pains of the community and the need for an academic society that bridges expertise in developmental neuroscience, clinical science, as well as computational and biomedical engineering, to ensure special consideration of the vulnerable mother-offspring dyad (especially during pregnancy), data quality, and image processing tools that are created, rather than adapted, for the young brain.

Prenatal maternal infection and risk for autism in offspring: A meta-analysis.

While prenatal maternal infection has received attention as a preventable and treatable risk factor for autism, findings have been inconsistent. This paper presents the results of a meta-analysis to determine whether the weight of the evidence supports such an association. Studies with a categorical diagnosis of autism as the outcome and an assessment of its association with prenatal maternal infection or fever (or the data necessary to compute this association) were included. A total of 36 studies met these criteria. Two independent reviewers extracted data on study design, methods of assessment, type of infectious agent, site of infection, trimester of exposure, definition of autism, and effect size. Analyses demonstrated a statistically significant association of maternal infection/fever with autism in offspring (OR = 1.32; 95% CI = 1.20-1.46). Adjustment for evident publication bias slightly weakened this association. There was little variation in effect sizes across agent or site of infection. Small differences across trimester of exposure were not statistically significant. There was some evidence that recall bias associated with status on the outcome variable leads to differential misclassification of exposure status. Nonetheless, the overall association is only modestly reduced when studies potentially contaminated by such bias are removed. Although causality has not been firmly established, these findings suggest maternal infection during pregnancy confers an increase in risk for autism in offspring. Given the prevalence of this risk factor, it is possible that the incidence of autism would be reduced by 12%-17% if maternal infections could be prevented or safely treated in a timely manner. LAY SUMMARY: This study is a meta-analysis of the association of maternal infection during pregnancy and subsequent autism in offspring. In combining the results from 36 studies of this association we find that a significant relationship is present. The association does not vary much across the types of infections or when they occur during pregnancy. We conclude that the incidence of autism could be substantially reduced if maternal infections could be prevented or safely treated in a timely manner.

Transdiagnostic, Connectome-Based Prediction of Memory Constructs Across Psychiatric Disorders.

Memory deficits are observed in a range of psychiatric disorders, but it is unclear whether memory deficits arise from a shared brain correlate across disorders or from various dysfunctions unique to each disorder. Connectome-based predictive modeling is a computational method that captures individual differences in functional connectomes associated with behavioral phenotypes such as memory. We used publicly available task-based functional MRI data from patients with schizophrenia (n = 33), bipolar disorder (n = 34), attention deficit hyper-activity disorder (n = 32), and healthy controls (n = 73) to model the macroscale brain networks associated with working, short- and long-term memory. First, we use 10-fold and leave-group-out analyses to demonstrate that the same macroscale brain networks subserve memory across diagnostic groups and that individual differences in memory performance are related to individual differences within networks distributed throughout the brain, including the subcortex, default mode network, limbic network, and cerebellum. Next, we show that diagnostic groups are associated with significant differences in whole-brain functional connectivity that are distinct from the predictive models of memory. Finally, we show that models trained on the transdiagnostic sample generalize to novel, healthy participants (n = 515) from the Human Connectome Project. These results suggest that despite significant differences in whole-brain patterns of functional connectivity between diagnostic groups, the core macroscale brain networks that subserve memory are shared.

Association of Maternal Prepregnancy Body Mass Index With Fetal Growth and Neonatal Thalamic Brain Connectivity Among Adolescent and Young Women.

Importance: Higher maternal prepregnancy body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) is associated with adverse long-term outcomes for offspring, including obesity, poorer cognitive and social abilities, and increased risk of psychiatric disorders. Less clear is whether higher maternal BMI disrupts fetal growth and brain development. Objective: To investigate the association of maternal prepregnancy BMI with fetal growth and neonatal functional connectivity. Design, Setting, and Participants: This prospective longitudinal cohort study was conducted from 2012 to 2017. Participants included nulliparous pregnant adolescent and young adult women, aged 14 to 19 years who were recruited in the second trimester through Columbia University Irving Medical Center and Weill Cornell Medical College. Women received routine prenatal care and had no major health problems at the time of recruitment. Data were analyzed from January 2018 to March 2020. Exposures: Maternal prepregnancy BMI. Main Outcomes and Measures: The main outcomes were fetal growth, measured as estimated fetal weight, and neonatal functional connectivity, measured using magnetic resonance imaging. Prepregnancy BMI and fetal ultrasonographic measurements were obtained from electronic health record review. Resting-state brain imaging data were acquired in infants within the first month of postnatal life. Functional connectivity was measured using intrinsic functional distribution and seed-based methods. Results: Among 129 women recruited, 105 had ultrasonographic data from at least 2 points and were included in analyses. The mean (SD) age at delivery was 17.82 (1.31) years. Maternal prepregnancy BMI was positively associated with the slope of estimated fetal weight (ß = 0.668; 95% CI, 0.163 to 1.175; P = .01) but not with fetal head circumference (ß = -0.004; 95% CI, -0.024 to 0.016; P = .70). In a subsample of 45 infants with magnetic resonance imaging data, maternal prepregnancy BMI was positively correlated with global connectivity in the left thalamus. Using this thalamic region as a seed, higher maternal BMI was associated with greater local thalamic (both hemispheres) and lower frontothalamic connectivity. Conclusions and Relevance: These results suggest that maternal prepregnancy BMI was associated with the development of regulation of body weight and thalamic functional brain connectivity in offspring even during fetal development.

Associations between different dimensions of prenatal distress, neonatal hippocampal connectivity, and infant memory.

Prenatal maternal distress-an umbrella concept encompassing multiple negative psychological states including stress, anxiety, and depression-is a substantial prenatal exposure. Consistent across preclinical and human studies, the hippocampus displays alterations due to prenatal distress. Nevertheless, most prenatal distress studies do not focus on multiple dimensions of, have not examined hippocampal functional connectivity in association with, and do not consider observer-based functional outcomes related to distress. We investigated the effects of different dimensions of prenatal distress in pregnant adolescents, a population at high risk for distress, in association with neonatal hippocampal connectivity and infant memory. In pregnant adolescents (n = 42), we collected four measures of distress (perceived stress, depression, pregnancy-specific distress, and 24-h ambulatory salivary cortisol) during the 2nd and 3rd trimesters. Resting-state imaging data were acquired in their infants at 40-44 weeks post-menstrual age. Functional connectivity was measured from hippocampal seeds. Memory abilities were obtained at 4 months using the mobile conjugate reinforcement task. Shared across different dimensions of maternal distress, increased 3rd trimester maternal distress associated with weaker hippocampal-cingulate cortex connectivity and stronger hippocampal-temporal lobe connectivity. Perceived stress inversely correlated while hippocampal-cingulate cortex connectivity positively correlated with infant memory. Increased cortisol-collected during the 2nd, but not the 3rd, trimester-associated with weaker hippocampal-cingulate cortex connectivity and stronger hippocampal-temporal lobe connectivity. Different dimensions of prenatal maternal distress likely contribute shared and unique effects to shaping infant brain and behavior.

Executive and non-executive functions in low birthweight/preterm adolescents with differing temporal patterns of inattention.

OBJECTIVE: This study assesses whether low birthweight/preterm (LBW/PT) adolescents with persistent inattention (PIA) have neuropsychological deficits that distinguish them from adolescents with school age limited inattention (SAL) and those largely unaffected (UA). METHOD: Three latent classes (PIA, SAL, UA), derived from an earlier analysis of a LBW/PT birth cohort were compared on non-executive and executive functioning measures assessed at age 16. RESULTS: The PIA class displayed the poorest performance on executive functioning, which was exaggerated in the context of lower IQ. The PIA and the SAL classes had poorer performance on non-executive functioning relative to the UA class. Both types of functioning mediated the relationship of class to school service use and grade retention. CONCLUSION: Neuropsychological impairment characterizes children and adolescents with inattention problems. Problems in executive functioning characterize the subset whose inattention persists through adolescence. Subsequent research can examine the potential for remediating these deficits to address academic and social problems.

Prenatal socioeconomic status and social support are associated with neonatal brain morphology, toddler language and psychiatric symptoms.

Few studies have assessed the association of parental socioeconomic status (SES) with brain measures in neonates, at a time when exposure to the postnatal environment is minimal. Social support may buffer the adverse consequences of SES, and has been associated with better cognitive - emotional development in children. We studied the association of prenatal SES and social support with neonatal brain structure, and toddler cognition, and psychiatric symptoms. In a sample of 37 healthy neonates, we correlated a measure of SES and marital/partner status (an index of social support) with morphological features of the cerebral surface measured on high-resolution MRI scans between the 1st - 6th weeks of postnatal life. We then assessed how SES relates to cognitive and behavioral outcomes at age 24-months. We found that neonates born to mothers with lower SES had greater local volumes at the surface of the right occipital lobe, left temporal pole, and left inferior frontal and anterior cingulate regions. Partner status moderated the associations of SES on neonatal brain morphology. Lower SES was associated with poorer language scores and less severe ADHD and ODD symptoms. In summary, SES was associated with neonatal brain structure and language and behavioral outcomes at toddler age. Future studies with a greater sample size and longitudinal MRI scans will help to determine whether prenatal SES continues to relate to early brain development in the same or different brain regions.

The association of serologically documented maternal thyroid conditions during pregnancy with bipolar disorder in offspring.

OBJECTIVES: Higher rates of thyroid conditions are reported in individuals with bipolar disorder. However, no study to date has considered whether maternal thyroid conditions during pregnancy are associated with offspring risk of bipolar disorder, even though the fetus exclusively relies on maternal thyroid hormones through the early second trimester. We therefore examined the association between offspring bipolar disorder and serologically documented maternal thyroid conditions. METHODS: The study was based on a nested case-control design that utilized data from the Child Health and Development Study, a birth cohort that enrolled pregnant women from 1959 to 1966. Eighty-five cases with DSM-IV-TR were ascertained and matched to controls (1:2) by date of birth, sex, gestational timing of the serum draws, and residence in Alameda County the first year receiving treatment. Archived prenatal maternal serum drawn during early to mid-gestation was used to measure two thyroid hormones, free thyroxine (fT4) and thyroid stimulating hormone (TSH). Subclinical and clinical hypothyroxinemia, hypothyroidism, and hyperthyroidism were determined based on standard methods. RESULTS: Exposure to maternal hypothyroxinemia was associated with a five-fold increased risk of offspring bipolar disorder with psychotic features, but not without psychotic features. In stratified analysis, female offspring demonstrated increased risk for bipolar disorder with exposure to maternal hypothyroxinemia. No significant association was found between maternal hypothyroidism and offspring bipolar disorder. CONCLUSIONS: These findings suggest that prenatal thyroid hormone deficiency, particularly a thyroid condition marked by low levels of thyroxine, may be an important developmental mechanism related to the risk of bipolar disorder with psychotic features.

The Functional Brain Organization of an Individual Allows Prediction of Measures of Social Abilities Transdiagnostically in Autism and Attention-Deficit/Hyperactivity Disorder.

BACKGROUND: Autism spectrum disorder and attention-deficit/hyperactivity disorder (ADHD) are associated with complex changes as revealed by functional magnetic resonance imaging. To date, neuroimaging-based models are not able to characterize individuals with sufficient sensitivity and specificity. Further, although evidence shows that ADHD traits occur in individuals with autism spectrum disorder, and autism spectrum disorder traits in individuals with ADHD, the neurofunctional basis of the overlap is undefined. METHODS: Using individuals from the Autism Brain Imaging Data Exchange and ADHD-200, we apply a data-driven, subject-level approach, connectome-based predictive modeling, to resting-state functional magnetic resonance imaging data to identify brain-behavior associations that are predictive of symptom severity. We examine cross-diagnostic commonalities and differences. RESULTS: Using leave-one-subject-out and split-half analyses, we define networks that predict Social Responsiveness Scale, Autism Diagnostic Observation Schedule, and ADHD Rating Scale scores and confirm that these networks generalize to novel subjects. Networks share minimal overlap of edges (<2%) but some common regions of high hubness (Brodmann areas 10, 11, and 21, cerebellum, and thalamus). Further, predicted Social Responsiveness Scale scores for individuals with ADHD are linked to ADHD symptoms, supporting the hypothesis that brain organization relevant to autism spectrum disorder severity shares a component associated with attention in ADHD. Predictive connections and high-hubness regions are found within a wide range of brain areas and across conventional networks. CONCLUSIONS: An individual's functional connectivity profile contains information that supports dimensional, nonbinary classification in autism spectrum disorder and ADHD. Furthermore, we can determine disorder-specific and shared neurofunctional pathology using our method.