Neural Network Functional Interactions Mediate or Suppress WM-Emotional Behavior Relationships in Infants

BACKGROUND

Elucidating the neural basis of infant positive emotionality (PE) and negative emotionality (NE) can identify biomarkers of pathophysiological risk. Our goal was to determine how functional interactions among large-scale networks supporting emotional regulation influence WM microstructural-emotional behavior relationships in 3-month-old infants. We hypothesized that microstructural-emotional behavior relationships would be differentially mediated or suppressed by underlying resting-state functional connectivity (rsFC), particularly between Default Mode Network (DMN) and Central Executive Network (CEN) structures.

METHODS

The analytic sample comprised primary caregiver-infant dyads [52 infants (42% female, mean age at scan=15.10 weeks)], with infant neuroimaging and emotional behavior assessments at 3 months. Infant WM and rsFC were assessed by diffusion-weighted imaging/tractography and resting-state magnetic resonance imaging (MRI) during natural, non-sedated sleep. The Infant Behavior Questionnaire-R provided measures of infant PE and NE.

RESULTS

Following significant WM-emotional behavior relationships, multimodal analyses were performed using whole-brain voxelwise mediation. Results revealed that greater cingulum bundle volume was significantly associated with lower infant PE (ß = -0.263, p = 0.031); however, a pattern of lower rsFC between CEN and DMN structures suppressed this otherwise negative relationship. Greater uncinate fasciculus volume was significantly associated with lower infant NE (ß = -0.296, p = 0.022); however, lower orbitofrontal cortex (OFC)-amygdala rsFC, suppressed this otherwise negative relationship, while greater OFC-CEN rsFC mediated this relationship.

CONCLUSIONS

Functional interactions among neural networks have an important influence on WM microstructural-emotional behavior relationships in infancy. These relationships can elucidate neural mechanisms contributing to future behavioral and emotional problems in childhood.

Patterns of Infant Amygdala Connectivity Mediate the Impact of High Caregiver Affect on Reducing Infant Smiling: Discovery and Replication

BACKGROUND

Behavioral research indicates that caregiver mood disorders and emotional instability in the early months following childbirth are associated with lower positive emotionality and higher negative emotionality in infants, but the neural mechanisms remain understudied.

METHODS

Using resting-state functional connectivity as a measure of the functional architecture of the early infant brain, we aimed to determine the extent to which connectivity between the amygdala, a key region supporting emotional learning and perception, and large-scale neural networks mediated the association between caregiver affect and anxiety and early infant negative emotionality and positive emotionality. Two samples of infants (first sample: n = 58; second sample: n = 31) 3 months of age underwent magnetic resonance imaging during natural sleep.

RESULTS

During infancy, greater resting-state functional connectivity between the amygdala and the salience network and, to a lesser extent, lower amygdala and executive control network resting-state functional connectivity mediated the effect of greater caregiver postpartum depression and trait anxiety on reducing infant smiling (familywise error-corrected p < .05). Furthermore, results from the first sample were replicated in the second, independent sample, to a greater extent for caregiver depression than for caregiver anxiety.

CONCLUSIONS

We provide evidence of early objective neural markers that can help identify infants who are more likely to be at risk from, versus those who might be protected against, the deleterious effects of caregiver depression and anxiety and reduced positive emotionality.

Maternal depression during pregnancy alters infant subcortical and midbrain volumes

BACKGROUND

Maternal depression in pregnancy increases the risk for adverse neurodevelopmental outcomes in the offspring. The reason for this is unknown, however, one plausible mechanism may include the impact of maternal antenatal depression on infant brain. Nevertheless, relatively few studies have examined the brain anatomy of infants born to clinically diagnosed mothers.

METHODS

A legacy magnetic resonance imaging (MRI) dataset was used to compare regional brain volumes in 3-to-6-month-old infants born to women with a clinically confirmed diagnosis of major depressive disorder (MDD) during pregnancy (n = 31) and a reference sample of infants born to women without a current or past psychiatric diagnosis (n = 33). A method designed for analysis of low-resolution scans enabled examination of subcortical and midbrain regions previously found to be sensitive to the parent-child environment.

RESULTS

Compared with infants of non-depressed mothers, infants exposed to maternal antenatal depression had significantly larger subcortical grey matter volumes and smaller midbrain volumes. There was no association between gestational medication exposure and the infant regional brain volumes examined in our sample.

LIMITATIONS

Our scanning approach did not allow for an examination of fine-grained structural differences, and without repeated measures of brain volume, it is unknown whether the direction of reported associations are dependent on developmental stage.

CONCLUSIONS

Maternal antenatal depression is associated with an alteration in infant brain anatomy in early postnatal life; and that this is not accounted for by medication exposure. However, our study cannot address whether anatomical differences impact on future outcomes of the offspring.

Infant Cognitive Scores Prediction with Multi-stream Attention-Based Temporal Path Signature Features

There is stunning rapid development of human brains in the first year of life. Some studies have revealed the tight connection between cognition skills and cortical morphology in this period. Nonetheless, it is still a great challenge to predict cognitive scores using brain morphological features, given issues like small sample size and missing data in longitudinal studies. In this work, for the first time, we introduce the path signature method to explore hidden analytical and geometric properties of longitudinal cortical morphology features. A novel BrainPSNet is proposed with a differentiable temporal path signature layer to produce informative representations of different time points and various temporal granules. Further, a two-stream neural network is included to combine groups of raw features and path signature features for predicting the cognitive score. More importantly, considering different influences of each brain region on the cognitive function, we design a learning-based attention mask generator to automatically weight regions correspondingly. Experiments are conducted on an in-house longitudinal dataset. By comparing with several recent algorithms, the proposed method achieves the state-of-the-art performance. The relationship between morphological features and cognitive abilities is also analyzed.

A Deep Spatial Context Guided Framework for Infant Brain Subcortical Segmentation

Accurate subcortical segmentation of infant brain magnetic resonance (MR) images is crucial for studying early subcortical structural growth patterns and related diseases diagnosis. However, dynamic intensity changes, low tissue contrast, and small subcortical size of infant brain MR images make subcortical segmentation a challenging task. In this paper, we propose a spatial context guided, coarse-to-fine deep convolutional neural network (CNN) based framework for accurate infant subcortical segmentation. At the coarse stage, we propose a signed distance map (SDM) learning UNet (SDM-UNet) to predict SDMs from the original multi-modal images, including T1w, T2w, and T1w/T2w images. By doing this, the spatial context information, including the relative position information across different structures and the shape information of the segmented structures contained in the ground-truth SDMs, is used for supervising the SDM-UNet to remedy the bad influence from the low tissue contrast in infant brain MR images and generate high-quality SDMs. To improve the robustness to outliers, a Correntropy based loss is introduced in SDM-UNet to penalize the difference between the ground-truth SDMs and predicted SDMs in training. At the fine stage, the predicted SDMs, which contains spatial context information of subcortical structures, are combined with the multi-modal images, and then fed into a multi-source and multi-path UNet (M2-UNet) for delivering refined segmentation. We validate our method on an infant brain MR image dataset with 24 scans by evaluating the Dice ratio between our segmentation and the manual delineation. Compared to four state-of-the-art methods, our method consistently achieves better performances in both qualitative and quantitative evaluations.

Developmental score of the infant brain: characterizing diffusion MRI in term- and preterm-born infants

Large-scale longitudinal neuroimaging studies of the infant brain allow us to map the spatiotemporal development of the brain in its early phase. While the postmenstrual age (PMA) is commonly used as a time index to analyze longitudinal MRI data, the nonlinear relationship between PMA and MRI data imposes challenges for downstream analyses. We propose a mathematical model that provides a Developmental Score (DevS) as a data-driven time index to characterize the brain development based on MRI features. 319 diffusion tensor imaging (DTI) datasets were collected from 87 term-born and 66 preterm-born infants at multiple visits, which were automatically segmented based on the JHU neonatal atlas. The mean diffusivity (MD) and fractional anisotropy (FA) in 126 brain parcels were used in the model to derive DevS. We demonstrate that transforming the time index from PMA to DevS improves the linearity of the longitudinal changes in MD and FA in both gray and white matter structures. More importantly, regional developmental differences in DTI metrics between preterm- and term-born infants were identified more clearly using DevS, e.g. 79 structures showed significantly different regression patterns in MD between preterm- and term-born infants, compared to only 27 structures that showed group differences using PMA as the index. Therefore, the DevS model facilitates linear analyses of DTI metrics in the infant brain, and provides a useful tool to characterize altered brain development due to preterm-birth.

FRNET: FLATTENED RESIDUAL NETWORK FOR INFANT MRI SKULL STRIPPING

Skull stripping for brain MR images is a basic segmentation task. Although many methods have been proposed, most of them focused mainly on the adult MR images. Skull stripping for infant MR images is more challenging due to the small size and dynamic intensity changes of brain tissues during the early ages. In this paper, we propose a novel CNN based framework to robustly extract brain region from infant MR image without any human assistance. Specifically, we propose a simplified but more robust flattened residual network architecture (FRnet). We also introduce a new boundary loss function to highlight ambiguous and low contrast regions between brain and non-brain regions. To make the whole framework more robust to MR images with different imaging quality, we further introduce an artifact simulator for data augmentation. We have trained and tested our proposed framework on a large dataset (N=343), covering newborns to 48-month-olds, and obtained performance better than the state-of-the-art methods in all age groups.

White matter connectomes at birth accurately predict cognitive abilities at age 2

Cognitive ability is an important predictor of mental health outcomes that is influenced by neurodevelopment. Evidence suggests that the foundational wiring of the human brain is in place by birth, and that the white matter (WM) connectome supports developing brain function. It is unknown, however, how the WM connectome at birth supports emergent cognition. In this study, a deep learning model was trained using cross-validation to classify full-term infants (n = 75) as scoring above or below the median at age 2 using WM connectomes generated from diffusion weighted magnetic resonance images at birth. Results from this model were used to predict individual cognitive scores. We additionally identified WM connections important for classification. The model was also evaluated in a separate set of preterm infants (n = 37) scanned at term-age equivalent. Findings revealed that WM connectomes at birth predicted 2-year cognitive score group with high accuracy in both full-term (89.5%) and preterm (83.8%) infants. Scores predicted by the model were strongly correlated with actual scores (r = 0.98 for full-term and r = 0.96 for preterm). Connections within the frontal lobe, and between the frontal lobe and other brain areas were found to be important for classification. This work suggests that WM connectomes at birth can accurately predict a child's 2-year cognitive group and individual score in full-term and preterm infants. The WM connectome at birth appears to be a useful neuroimaging biomarker of subsequent cognitive development that deserves further study.

Exploring folding patterns of infant cerebral cortex based on multi-view curvature features: Methods and applications

The highly convoluted cortical folding of the human brain is intriguingly complex and variable across individuals. Exploring the underlying representative patterns of cortical folding is of great importance for many neuroimaging studies. At term birth, all major cortical folds are established and are minimally affected by the complicated postnatal environments; hence, neonates are the ideal candidates for exploring early postnatal cortical folding patterns, which yet remain largely unexplored. In this paper, we propose a novel method for exploring the representative regional folding patterns of infant brains. Specifically, first, multi-view curvature features are constructed to comprehensively characterize the complex characteristics of cortical folding. Second, for each view of curvature features, a similarity matrix is computed to measure the similarity of cortical folding in a specific region between any pair of subjects. Next, a similarity network fusion method is adopted to nonlinearly and adaptively fuse all the similarity matrices into a single one for retaining both shared and complementary similarity information of the multiple characteristics of cortical folding. Finally, based on the fused similarity matrix and a hierarchical affinity propagation clustering approach, all subjects are automatically grouped into several clusters to obtain the representative folding patterns. To show the applications, we have applied the proposed method to a large-scale dataset with 595 normal neonates and discovered representative folding patterns in several cortical regions, i.e., the superior temporal gyrus (STG), inferior frontal gyrus (IFG), precuneus, and cingulate cortex. Meanwhile, we have revealed sex difference in STG, IFG, and cingulate cortex, as well as hemispheric asymmetries in STG and cingulate cortex in terms of cortical folding patterns. Moreover, we have also validated the proposed method on a public adult dataset, i.e., the Human Connectome Project (HCP), and revealed that certain major cortical folding patterns of adults are largely established at term birth.

Associations of age and sex with brain volumes and asymmetry in 2-5-week-old infants

Information on normal brain structure and development facilitates the recognition of abnormal developmental trajectories and thus needs to be studied in more detail. We imaged 68 healthy infants aged 2–5 weeks with high-resolution structural MRI (magnetic resonance imaging) and investigated hemispheric asymmetry as well as the associations of various total and lobar brain volumes with infant age and sex. We found similar hemispheric asymmetry in both sexes, seen as larger volumes of the right temporal lobe, and of the left parietal and occipital lobes. The degree of asymmetry did not vary with age. Regardless of controlling for gestational age, gray and white matter had different age-related growth patterns. This is a reflection of gray matter growth being greater in the first years, while white matter growth extends into early adulthood. Sex-dependent differences were seen in gray matter as larger regional absolute volumes in males and as larger regional relative volumes in females. Our results are in line with previous studies and expand our understanding of infant brain development.

Computational neuroanatomy of baby brains: A review

The first postnatal years are an exceptionally dynamic and critical period of structural, functional and connectivity development of the human brain. The increasing availability of non-invasive infant brain MR images provides unprecedented opportunities for accurate and reliable charting of dynamic early brain developmental trajectories in understanding normative and aberrant growth. However, infant brain MR images typically exhibit reduced tissue contrast (especially around 6 months of age), large within-tissue intensity variations, and regionally-heterogeneous, dynamic changes, in comparison with adult brain MR images. Consequently, the existing computational tools developed typically for adult brains are not suitable for infant brain MR image processing. To address these challenges, many infant-tailored computational methods have been proposed for computational neuroanatomy of infant brains. In this review paper, we provide a comprehensive review of the state-of-the-art computational methods for infant brain MRI processing and analysis, which have advanced our understanding of early postnatal brain development. We also summarize publically available infant-dedicated resources, including MRI datasets, computational tools, grand challenges, and brain atlases. Finally, we discuss the limitations in current research and suggest potential future research directions.

Prenatal stress alters amygdala functional connectivity in preterm neonates

Exposure to prenatal and early-life stress results in alterations in neural connectivity and an increased risk for neuropsychiatric disorders. In particular, alterations in amygdala connectivity have emerged as a common effect across several recent studies. However, the impact of prenatal stress exposure on the functional organization of the amygdala has yet to be explored in the prematurely-born, a population at high risk for neuropsychiatric disorders. We test the hypothesis that preterm birth and prenatal exposure to maternal stress alter functional connectivity of the amygdala using two independent cohorts. The first cohort is used to establish the effects of preterm birth and consists of 12 very preterm neonates and 25 term controls, all without prenatal stress exposure. The second is analyzed to establish the effects of prenatal stress exposure and consists of 16 extremely preterm neonates with prenatal stress exposure and 10 extremely preterm neonates with no known prenatal stress exposure. Standard resting-state functional magnetic resonance imaging and seed connectivity methods are used. When compared to term controls, very preterm neonates show significantly reduced connectivity between the amygdala and the thalamus, the hypothalamus, the brainstem, and the insula (p < 0.05). Similarly, when compared to extremely preterm neonates without exposure to prenatal stress, extremely preterm neonates with exposure to prenatal stress show significantly less connectivity between the left amygdala and the thalamus, the hypothalamus, and the peristriate cortex (p < 0.05). Exploratory analysis of the combined cohorts suggests additive effects of prenatal stress on alterations in amygdala connectivity associated with preterm birth. Functional connectivity from the amygdala to other subcortical regions is decreased in preterm neonates compared to term controls. In addition, these data, for the first time, suggest that prenatal stress exposure amplifies these decreases.