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.

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.

Predicting the future of neuroimaging predictive models in mental health

Predictive modeling using neuroimaging data has the potential to improve our understanding of the neurobiology underlying psychiatric disorders and putatively information interventions. Accordingly, there is a plethora of literature reviewing published studies, the mathematics underlying machine learning, and the best practices for using these approaches. As our knowledge of mental health and machine learning continue to evolve, we instead aim to look forward and "predict" topics that we believe will be important in current and future studies. Some of the most discussed topics in machine learning, such as bias and fairness, the handling of dirty data, and interpretable models, may be less familiar to the broader community using neuroimaging-based predictive modeling in psychiatry. In a similar vein, transdiagnostic research and targeting brain-based features for psychiatric intervention are modern topics in psychiatry that predictive models are well-suited to tackle. In this work, we target an audience who is a researcher familiar with the fundamental procedures of machine learning and who wishes to increase their knowledge of ongoing topics in the field. We aim to accelerate the utility and applications of neuroimaging-based predictive models for psychiatric research by highlighting and considering these topics. Furthermore, though not a focus, these ideas generalize to neuroimaging-based predictive modeling in other clinical neurosciences and predictive modeling with different data types (e.g., digital health data).

Intergenerational neuroimaging study: mother-infant functional connectivity similarity and the role of infant and maternal factors

Mother and infant neural and behavioral synchrony is important for infant development during the first years of life. Recent studies also suggest that neural risk markers associated with parental psychopathology may be transmitted across generations before symptoms emerge in offspring. There is limited understanding of how early similarity in brain functioning between 2 generations emerges. In the current study, using functional magnetic resonance imaging, we examined the functional connectivity (FC) similarity between mothers and newborns during the first 3 months after the infant's birth. We found that FC similarity between mothers and infants increased as infant age increased. Furthermore, we examined whether maternal factors such as maternal socioeconomic status and prenatal maternal depressive symptoms may influence individual differences in FC similarity. For the whole-brain level, lower maternal education levels were associated with greater FC similarity. In previous literature, lower maternal education levels were associated with suboptimal cognitive and socioemotional development. Greater FC similarity may reflect that the infants develop their FC similarity prematurely, which may suboptimally influence their developmental outcomes in later ages.

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.

(Un)common space in infant neuroimaging studies: A systematic review of infant templates

In neuroimaging, spatial normalization is an important step that maps an individual's brain onto a template brain permitting downstream statistical analyses. Yet, in infant neuroimaging, there remain several technical challenges that have prevented the establishment of a standardized template for spatial normalization. Thus, many different approaches are used in the literature. To quantify the popularity and variability of these approaches in infant neuroimaging studies, we performed a systematic review of infant magnetic resonance imaging (MRI) studies from 2000 to 2020. Here, we present results from 834 studies meeting inclusion criteria. Studies were classified into (a) processing data in single subject space, (b) using an off the shelf, or "off the shelf," template, (c) creating a study specific template, or (d) using a hybrid of these methods. We found that across the studies in the systematic review, single subject space was the most used (no common space). This was the most used common space for diffusion-weighted imaging and structural MRI studies while functional MRI studies preferred off the shelf atlases. We found a pattern such that more recently published studies are more commonly using off the shelf atlases. When considering special populations, preterm studies most used single subject space while, when no special populations were being analyzed, an off the shelf template was most common. The most used off the shelf templates were the UNC Infant Atlases (24%). Using a systematic review of infant neuroimaging studies, we highlight a lack of an established "standard" template brain in these studies.

Editorial: Prenatal Depressive Symptoms, Cortical Morphology, and Reward Sensitivity in Preschoolers

Studies drawing on data from the Growing Up in Singapore Towards Healthy Outcomes (GUSTO, https://www.gusto.sg) have provided unprecedented evidence for associations between prenatal maternal mental health symptoms and variations in offspring early brain structural and functional development.¹ Wei et al.² expand upon these studies by using data from GUSTO to test for both sex-specific effects of prenatal maternal depressive symptoms (pre-MDS) and to examine whether cortical development mediated the relationship between pre-MDS and child sensitivity to reward and punishment in preschoolers. The study found a fascinating sex-specific pattern. It showed that higher pre-MDS was associated with greater cortical surface area in boys and lower surface area in girls, specifically in areas of the prefrontal cortex, superior temporal gyrus, and superior parietal lobule. Regarding their hypothesized mediation model, their analysis found that superior parietal lobule surface area mediated the association between pre-MDS and sensitivity to reward in girls but not boys. In this editorial, I will discuss some of the implications, limitations, and future directions for this line of research.

How prenatal exposures shape the infant brain: Insights from infant neuroimaging studies

Brain development during the prenatal period is rapid and unparalleled by any other time during development. Biological systems undergoing rapid development are at higher risk for disorganizing influences. Therefore, certain prenatal exposures impact brain development, increasing risk for negative neurodevelopmental outcome. While prenatal exposures have been associated with cognitive and behavioral outcomes later in life, the underlying macroscopic brain pathways remain unclear. Here, we review magnetic resonance imaging (MRI) studies investigating the association between prenatal exposures and infant brain development focusing on prenatal exposures via maternal physical health factors, maternal mental health factors, and maternal drug and medication use. Further, we discuss the need for studies to consider multiple prenatal exposures in parallel and suggest future directions for this body of research.

The instability of functional connectomes across the first year of life

The uniqueness and stability of the adolescent and adult functional connectome has been demonstrated to be high (80-95 % identification) using connectome-based identification (ID) or "fingerprinting". However, it is unclear to what extent individuals exhibit similar distinctiveness and stability in infancy, a developmental period of rapid and unparalleled brain development. In this study, we examined connectome-based ID rates within and across the first year of life using a longitudinal infant dataset at 1.5 month and 9 months of age. We also calculated the test-retest reliability of individual connections across the first year of life using the intraclass correlation coefficient (ICC). Overall, we found substantially lower infant ID rates than have been reported in adult and adolescent populations. Within-session ID rates were moderate and significant (ID = 48.94-70.83 %). Between-session ID rates were very low and not significant, with task-to-task connectomes resulting in the highest between-session ID rate (ID = 26.6 %). Similarly, average edge-level test-retest reliability was higher within-session than between-session (mean within-session ICC = 0.17, mean between-session ICC = 0.10). These findings suggest a lack of uniqueness and stability in functional connectomes across the first year of life consistent with the unparalleled changes in brain functional organization during this critical period.

Maternal trait anxiety symptoms, frontolimbic resting-state functional connectivity, and cognitive development in infancy

Exposure to maternal anxiety symptoms during infancy has been associated with difficulties in development and greater risk for developing anxiety later in life. Although previous studies have examined associations between prenatal maternal distress, infant brain development, and developmental outcomes, it is still largely unclear if there are associations between postnatal anxiety, infant brain development, and cognitive development in infancy. In this study, we used resting-state functional magnetic resonance imaging to examine the association between maternal anxiety symptoms and resting-state functional connectivity in the first year of life. We also examine the association between frontolimbic functional connectivity and infant cognitive development. The sample consisted of 21 infants (mean age = 24.15 months, SD = 4.17) that were scanned during their natural sleep using. We test the associations between maternal trait anxiety symptoms and amygdala-anterior cingulate cortex (ACC) functional connectivity, a neural circuit implicated in early life stress exposure. We also test the associations between amygdala-ACC connectivity and cognitive development. We found a significant negative association between maternal trait anxiety symptoms and left amygdala-right ACC functional connectivity (p < .05, false discovery rate corrected). We found a significant negative association between left amygdala-right ACC functional connectivity and infant cognitive development (p < .05). These findings have potential implications for understanding the role of postpartum maternal anxiety symptoms in functional brain and cognitive development in infancy.

Childhood socioeconomic status is prospectively associated with surface morphometry in adulthood

Childhood socioeconomic status (SES) has been associated with brain cortex surface area in children. However, the extent to which childhood SES is prospectively associated with brain morphometry in adulthood is unclear. We tested whether childhood SES (income-to-needs ratio averaged across ages 9, 13, and 17) is prospectively associated with cortical surface morphometry in adulthood. Average childhood income-to-needs ratio had a positive, prospective association with cortical thickness in adulthood in the precentral gyrus, postcentral gyrus, and caudal middle frontal gyrus (p < .05, FWE corrected). Childhood income-to-needs ratio also had a positive, prospective association with cortical surface area in adulthood in multiple regions, including the rostral and caudal middle frontal gyri and superior frontal gyrus (p < .05, FWE corrected). Concurrent income-to-needs ratio (measured at age 24) was not associated with cortical thickness or surface area in adulthood. The results underscore the importance of addressing poverty in childhood for brain morphological development.

Associations between stress exposure and new mothers' brain responses to infant cry sounds

Exposure to severe stress has been linked to negative postpartum outcomes among new mothers including mood disorders and harsh parenting. Non-human animal studies show that stress exposure disrupts the normative adaptation of the maternal brain, thus identifying a neurobiological mechanism by which stress can lead to negative maternal outcomes. However, little is known about the impact of stress exposure on the maternal brain response to infant cues in human mothers. We examined the association of stress exposure with brain response to infant cries and maternal behaviors, in a socioeconomically diverse (low- and middle-income) sample of first-time mothers (N=53). Exposure to stress across socioeconomic, environmental, and psychosocial domains was associated with reduced brain response to infant cry sounds in several regions, including the right insula/inferior frontal gyrus and superior temporal gyrus. Reduced activation in these regions was further associated with lower maternal sensitivity observed during a mother-infant interaction. The findings demonstrate that higher levels of stress exposure may be associated with reduced brain response to an infant’s cry in regions that are important for emotional and social information processing, and that reduced brain responses may further be associated with increased difficulties in developing positive mother-infant relationships.

Prospective associations, longitudinal patterns of childhood socioeconomic status, and white matter organization in adulthood

The association between childhood socioeconomic status (SES) and brain development is an emerging area of research. The primary focus to date has been on SES and variations in gray matter structure with much less known about the relation between childhood SES and white matter structure. Using a longitudinal study of SES, with measures of income-to-needs ratio (INR) at age 9, 13, 17, and 24, we examined the prospective relationship between childhood SES (age 9 INR) and white matter organization in adulthood using diffusion tensor imaging. We also examined how changes in INR from childhood through young adulthood are associated with white matter organization in adult using a latent growth mixture model. Using tract-based spatial statistics (TBSS) we found that there is a significant prospective positive association between childhood INR and white matter organization in the bilateral uncinate fasciculus, bilateral cingulum bundle, bilateral superior longitudinal fasciculus, and corpus callosum (p < .05, FWE corrected). The probability that an individual was in the high-increasing INR profile across development compared with the low-increasing INR profile was positively associated with white matter organization in the bilateral uncinate fasciculus, left cingulum, and bilateral superior longitudinal fasciculus. The results of the current study have potential implications for interventions given that early childhood poverty may have long-lasting associations with white matter structure. Furthermore, trajectories of socioeconomic status during childhood are important-with individuals that belong to the latent profile that had high increases in INR having greater regional white matter organization in adulthood.

Maternal brain resting-state connectivity in the postpartum period

In the postpartum period, the maternal brain experiences both structural and functional plasticity. Although we have a growing understanding of the responses of the human maternal brain to infant stimuli, little is known about the intrinsic connectivity among those regions during the postpartum months. Resting-state functional connectivity (rsFC) provides a measure of the functional architecture of the brain based upon intrinsic functional connectivity (ie, the temporal correlation in blood oxygenation level dependent signal when the brain is not engaged in a specific task). In the present study, we used resting-state functional magnetic resonance imaging to examine how later postpartum months are associated with rsFC and maternal behaviours. We recruited a sample of 47 socioeconomically diverse first-time mothers with singleton pregnancies. Because the amygdala has been shown to play a critical role in maternal behaviours in the postpartum period, this was chosen as the seed for a seed-based correlation analysis. For the left amygdala, later postpartum months were associated with greater connectivity with the anterior cingulate gyrus, left nucleus accumbens, right caudate and left cerebellum (P < 0.05, false discovery rate corrected). Furthermore, in an exploratory analysis, we observed indications that rsFC between the left amygdala and left nucleus accumbens was positively associated with maternal structuring during a mother child-interaction. In addition, later postpartum months were associated with greater connectivity between the right amygdala and the bilateral caudate and right putamen. Overall, we provide evidence of relationships between postpartum months and rsFC in the regions involved in salience detection and regions involved in maternal motivation. Greater connectivity between the amygdala and nucleus accumbens may play a role in positive maternal behaviours.

Cortical thickness variation of the maternal brain in the first 6 months postpartum: associations with parental self-efficacy

The postpartum period is associated with structural and functional plasticity in brain regions involved in parenting. While one study identified an increase in gray matter volume during the first 4 months among new mothers, little is known regarding the relationship between cortical thickness across postpartum months and perceived adjustment to parenthood. In this study of 39 socioeconomically diverse first-time new mothers, we examined the relations among postpartum months, cortical thickness, and parental self-efficacy. We identified a positive association between postpartum months and cortical thickness in the prefrontal cortex including the superior frontal gyrus extending into the medial frontal and orbitofrontal gyri, in the lateral occipital gyrus extending into the inferior parietal and fusiform gyri, as well as in the caudal middle frontal and precentral gyri. The relationship between cortical thickness and parental self-efficacy was specific to the prefrontal regions. These findings contribute to our understanding of the maternal brain in the first 6 months postpartum and provide evidence of a relationship between brain structure and perceived adjustment to parenthood.

Hick-Hyman Law is Mediated by the Cognitive Control Network in the Brain

The Hick-Hyman law describes a linear increase in reaction time (RT) as a function of the information entropy of response selection, which is computed as the binary logarithm of the number of response alternatives. While numerous behavioral studies have provided evidence for the Hick-Hyman law, its neural underpinnings have rarely been examined and are still unclear. In this functional magnetic resonance imaging study, by utilizing a choice reaction time task to manipulate the entropy of response selection, we examined brain activity mediating the input and the output, as well as the connectivity between corresponding regions in human participants. Beyond confirming the Hick-Hyman law in RT performance, we found that activation of the cognitive control network (CCN) increased and activation of the default mode network (DMN) decreased, both as a function of entropy. However, only the CCN, but not the DMN, was involved in mediating the relationship between entropy and RT. The CCN was involved in both stages of uncertainty representation and response generation, while the DMN was mainly involved at the stage of uncertainty representation. These findings indicate that the CCN serves as a core entity underlying the Hick-Hyman law by coordinating uncertainty representation and response generation in the brain.

Gray matter volume of the anterior insular cortex and social networking

In human life, social context requires the engagement in complex interactions among individuals as the dynamics of social networks. The evolution of the brain as the neurological basis of the mind must be crucial in supporting social networking. Although the relationship between social networking and the amygdala, a small but core region for emotion processing, has been reported, other structures supporting sophisticated social interactions must be involved and need to be identified. In this study, we examined the relationship between morphology of the anterior insular cortex (AIC), a structure involved in basic and high-level cognition, and social networking. Two independent cohorts of individuals (New York group n = 50, Beijing group n = 100) were recruited. Structural magnetic resonance images were acquired and the social network index (SNI), a composite measure summarizing an individual's network diversity, size, and complexity, was measured. The association between morphological features of the AIC, in addition to amygdala, and the SNI was examined. Positive correlations between the measures of the volume as well as sulcal depth of the AIC and the SNI were found in both groups, while a significant positive correlation between the volume of the amygdala and the SNI was only found in the New York group. The converging results from the two groups suggest that the AIC supports network-level social interactions.

Family Income, Cumulative Risk Exposure, and White Matter Structure in Middle Childhood

Family income is associated with gray matter morphometry in children, but little is known about the relationship between family income and white matter structure. In this paper, using Tract-Based Spatial Statistics, a whole brain, voxel-wise approach, we examined the relationship between family income (assessed by income-to-needs ratio) and white matter organization in middle childhood (N = 27, M = 8.66 years). Results from a non-parametric, voxel-wise, multiple regression (threshold-free cluster enhancement, p < 0.05 FWE corrected) indicated that lower family income was associated with lower white matter organization [assessed by fractional anisotropy (FA)] for several clusters in white matter tracts involved in cognitive and emotional functions including fronto-limbic circuitry (uncinate fasciculus and cingulum bundle), association fibers (inferior longitudinal fasciculus, superior longitudinal fasciculus), and corticospinal tracts. Further, we examined the possibility that cumulative risk (CR) exposure might function as one of the potential pathways by which family income influences neural outcomes. Using multiple regressions, we found lower FA in portions of these tracts, including those found in the left cingulum bundle and left superior longitudinal fasciculus, was significantly related to greater exposure to CR (β = -0.47, p < 0.05 and β = -0.45, p < 0.05).

The Capacity of Cognitive Control Estimated from a Perceptual Decision Making Task

Cognitive control refers to the processes that permit selection and prioritization of information processing in different cognitive domains to reach the capacity-limited conscious mind. Although previous studies have suggested that the capacity of cognitive control itself is limited, a direct quantification of this capacity has not been attempted. In this behavioral study, we manipulated the information rate of cognitive control by parametrically varying both the uncertainty of stimul measured as information entropy and the exposure time of the stimuli. We used the relationship between the participants' response accuracy and the information rate of cognitive control (in bits per second, bps) in the model fitting to estimate the capacity of cognitive control. We found that the capacity of cognitive control was approximately 3 to 4 bps, demonstrating that cognitive control as a higher-level function has a remarkably low capacity. This quantification of the capacity of cognitive control may have significant theoretical and clinical implications.