BIBSNet: A Deep Learning Baby Image Brain Segmentation Network for MRI Scans.

Objectives: Brain segmentation of infant magnetic resonance (MR) images is vitally important in studying developmental mental health and disease. The infant brain undergoes many changes throughout the first years of postnatal life, making tissue segmentation difficult for most existing algorithms. Here, we introduce a deep neural network BIBSNet (Baby and Infant Brain Segmentation Neural Network), an open-source, community-driven model that relies on data augmentation and a large sample size of manually annotated images to facilitate the production of robust and generalizable brain segmentations. Experimental Design: Included in model training and testing were MR brain images on 84 participants with an age range of 0-8 months (median postmenstrual ages of 13.57 months). Using manually annotated real and synthetic segmentation images, the model was trained using a 10-fold cross-validation procedure. Testing occurred on MRI data processed with the DCAN labs infant-ABCD-BIDS processing pipeline using segmentations produced from gold standard manual annotation, joint-label fusion (JLF), and BIBSNet to assess model performance. Principal Observations: Using group analyses, results suggest that cortical metrics produced using BIBSNet segmentations outperforms JLF segmentations. Additionally, when analyzing individual differences, BIBSNet segmentations perform even better. Conclusions: BIBSNet segmentation shows marked improvement over JLF segmentations across all age groups analyzed. The BIBSNet model is 600x faster compared to JLF and can be easily included in other processing pipelines.

Resting-state functional connectivity identifies individuals and predicts age in 8-to-26-month-olds.

Resting-state functional connectivity (rsFC) measured with fMRI has been used to characterize functional brain maturation in typically and atypically developing children and adults. However, its reliability and utility for predicting development in infants and toddlers is less well understood. Here, we use fMRI data from the Baby Connectome Project study to measure the reliability and uniqueness of rsFC in infants and toddlers and predict age in this sample (8-to-26 months old; n = 170). We observed medium reliability for within-session infant rsFC in our sample, and found that individual infant and toddler's connectomes were sufficiently distinct for successful functional connectome fingerprinting. Next, we trained and tested support vector regression models to predict age-at-scan with rsFC. Models successfully predicted novel infants' age within ± 3.6 months error and a prediction R2 = .51. To characterize the anatomy of predictive networks, we grouped connections into 11 infant-specific resting-state functional networks defined in a data-driven manner. We found that connections between regions of the same network-i.e. within-network connections-predicted age significantly better than between-network connections. Looking ahead, these findings can help characterize changes in functional brain organization in infancy and toddlerhood and inform work predicting developmental outcome measures in this age range.

Real-time motion monitoring improves functional MRI data quality in infants.

Imaging the infant brain with MRI has improved our understanding of early neurodevelopment. However, head motion during MRI acquisition is detrimental to both functional and structural MRI scan quality. Though infants are typically scanned while asleep, they commonly exhibit motion during scanning causing data loss. Our group has shown that providing MRI technicians with real-time motion estimates via Framewise Integrated Real-Time MRI Monitoring (FIRMM) software helps obtain high-quality, low motion fMRI data. By estimating head motion in real time and displaying motion metrics to the MR technician during an fMRI scan, FIRMM can improve scanning efficiency. Here, we compared average framewise displacement (FD), a proxy for head motion, and the amount of usable fMRI data (FD ≤ 0.2 mm) in infants scanned with (n = 407) and without FIRMM (n = 295). Using a mixed-effects model, we found that the addition of FIRMM to current state-of-the-art infant scanning protocols significantly increased the amount of usable fMRI data acquired per infant, demonstrating its value for research and clinical infant neuroimaging.

Examining the factor structure and discriminative utility of the Infant Behavior Questionnaire-Revised in infant siblings of autistic children.

Using the Infant Behavior Questionnaire-Revised in a longitudinal sample of infant siblings of autistic children (HR; n = 427, 171 female, 83.4% White) and a comparison group of low-risk controls (LR, n = 200, 86 female, 81.5% White), collected between 2007 and 2017, this study identified an invariant factor structure of temperament traits across groups at 6 and 12 months. Second, after partitioning the groups by familial risk and diagnostic outcome at 24 months, results reveal an endophenotypic pattern of Positive Emotionality at both 6 and 12 months, (HR-autism spectrum disorder [ASD] < HR-no-ASD < LR). Third, increased 'Duration of Orienting' at 12 months was associated with lower scores on the 24-month developmental outcomes in HR infants. These findings may augment efforts for early identification of ASD.

Caregivers' estimate of early childhood developmental status in rural Uganda: a cross-sectional study.

OBJECTIVE: To characterise developmental milestones among young children living in rural communities in Uganda. DESIGN: Cross-sectional study. SETTING: Iganga-Mayuge Health and Demographic Surveillance Site in rural eastern Uganda. PARTICIPANTS: A total of 720 caregivers of children aged 3-4 years old from a health and demographic surveillance site in rural eastern Uganda were recruited into this study. Caregivers reported on their child's developmental skills and behaviours using the 10-item Early Childhood Development Index (ECDI) developed by UNICEF. Childhood development was characterised based on the ECDI's four domains: literacy-numeracy, learning/cognition, physical and socioemotional development. As an exploratory analysis, we implemented a hierarchical agglomerative cluster analysis to identify homogenous subgroups of children based on the features assessed. The cluster analysis was performed to identify potential subgroups of children who may be at risk of developmental problems. RESULTS: Between November 2017 and June 2018, 720 caregivers of children aged 3-4 years completed the ECDI. The proportions of children at risk of delay in each domain were as follows: literacy-numeracy: 75% (n=538); socioemotional development: 22% (n=157); physical: 3% (n=22); and cognitive: 4% (n=32). The cluster analysis revealed a three-cluster solution that included 93% of children assigned to a low-risk group, 4% assigned to a moderate-risk group and 3% assigned to a high-risk group characterised by low scores in almost all domains. CONCLUSION: The findings suggest that a high proportion of children in rural eastern Uganda demonstrate poor literacy-numeracy skills. These results underscore the need to improve population-based screening and intervention efforts to improve early childhood developmental outcomes, particularly in literacy and socioemotional domains, in low-income and middle-income countries such as Uganda.

Decomposing environmental unpredictability in forecasting adolescent and young adult development: A two-sample study.

To illuminate which features of an unpredictable environment early in life best forecast adolescent and adult functioning, data from two longitudinal studies were examined. After decomposing a composite unpredictability construct found to predict later development, results of both studies revealed that paternal transitions predicted outcomes more consistently and strongly than did residential or occupational changes across the first 5 years of a child's life. These results derive from analyses of the NICHD Study of Early Child Care and Youth Development, which included diverse families from 10 different sites in the United States, and from the Minnesota Longitudinal Study of Risk and Adaptation, whose participants came from one site, were disproportionately economically disadvantaged, and were enrolled 15 years earlier than the NICHD Study sample. The finding that results from both studies are consistent with evolutionary, life history thinking regarding the importance of males in children's lives makes this general, cross-study replication noteworthy.

The impact of stress at different life stages on physical health and the buffering effects of maternal sensitivity.

OBJECTIVE: Many studies indicate that early life stress leads to negative health outcomes in adulthood, and some suggest that high-quality parenting might buffer these effects. Most prior research, however, has relied on cross-sectional retrospective reports of stress and parenting. Our study tests how coder-rated stress and parenting quality assessed at different life stages predict adult health outcomes in a prospective, longitudinal study. METHOD: Participants were 163 individuals in the Minnesota Longitudinal Study of Risk and Adaptation studied since birth. Physical health was assessed at age 32 with body mass index, self-reports of symptoms and illnesses experienced, and self-ratings of overall physical health. Stress was assessed by coder-rated interviews involving participants or their mothers at 16 time points partitioned into 5 life stages: early childhood, middle childhood, adolescence, young adulthood, and at age 32 (when health was assessed). Parenting quality was measured by coder ratings of each mother's provision of sensitive, responsive support at 7 time points between birth and age 13. RESULTS: Early childhood, adolescent, and concurrent stress predicted adult health outcomes at age 32. Early childhood and adolescent stress, and adolescent and concurrent stress, both showed a "dual-risk" pattern, such that experiencing higher stress at both of these life stages predicted the worst health outcomes. Higher maternal sensitivity, however, buffered these deleterious effects. CONCLUSION: Our prospective data reveal that early childhood and adolescence are important developmental periods during which stress is influential for adult physical health. However, parenting interventions that promote greater sensitivity may help children in high-stress environments avoid negative adult health outcomes. (PsycINFO Database Record

Secure Infant-Mother Attachment Buffers the Effect of Early-Life Stress on Age of Menarche.

Prior research indicates that being reared in stressful environments is associated with earlier onset of menarche in girls. In this research, we examined (a) whether these effects are driven by exposure to certain dimensions of stress (harshness or unpredictability) during the first 5 years of life and (b) whether the negative effects of stress on the timing of menarche are buffered by secure infant-mother attachment. Results revealed that (a) exposure to greater harshness (but not unpredictability) during the first 5 years of life predicted earlier menarche and (b) secure infant-mother attachment buffered girls from this effect of harsh environments. By connecting attachment research to its evolutionary foundations, these results illuminate how environmental stressors and relationships early in life jointly affect pubertal timing.

Cognitive adaptations to stressful environments: When childhood adversity enhances adult executive function.

Can growing up in a stressful childhood environment enhance certain cognitive functions? Drawing participants from higher-income and lower-income backgrounds, we tested how adults who grew up in harsh or unpredictable environments fared on 2 types of executive function tasks: inhibition and shifting. People who experienced unpredictable childhoods performed worse at inhibition (overriding dominant responses), but performed better at shifting (efficiently switching between different tasks). This finding is consistent with the notion that shifting, but not inhibition, is especially useful in unpredictable environments. Importantly, differences in executive function between people who experienced unpredictable versus predictable childhoods emerged only when they were tested in uncertain contexts. This catalyst suggests that some individual differences related to early life experience are manifested under conditions of uncertainty in adulthood. Viewed as a whole, these findings indicate that adverse childhood environments do not universally impair mental functioning, but can actually enhance specific types of cognitive performance in the face of uncertainty.

Evolution, stress, and sensitive periods: the influence of unpredictability in early versus late childhood on sex and risky behavior.

According to a recent evolutionary life history model of development proposed by Ellis, Figueredo, Brumbach, and Schlomer (2009), growing up in harsh versus unpredictable environments should have unique effects on life history strategies in adulthood. Using data from the Minnesota Longitudinal Study of Risk and Adaptation, we tested how harshness and unpredictability experienced in early childhood (age 0-5) versus in later childhood (age 6-16) uniquely predicted sexual and risky behavior at age 23. Findings showed that the strongest predictor of both sexual and risky behavior was an unpredictable environment between ages 0 and 5. Individuals exposed to more unpredictable, rapidly changing environments during the first 5 years of life displayed a faster life history strategy at age 23 by having more sexual partners, engaging in more aggressive and delinquent behaviors, and being more likely to be associated with criminal activities. In contrast, exposure to either harsh environments or experiencing unpredictability in later childhood (age 6-16) was, for the most part, not significantly related to these outcomes at age 23. Viewed together, these findings show that unpredictable rather than merely harsh childhood environments exert unique effects on risky behavior later in life consistent with a faster life history strategy. The findings also suggest that there is a developmentally sensitive period for assessing environmental unpredictability during the first 5 years of life.