The Art, Science, and Secrets of Scanning Young Children

Modality-level obstacles and initiatives to improve representation in fetal, infant, and toddler neuroimaging research samples

Fetal, infant, and toddler (FIT) neuroimaging researchers study early brain development to gain insights into neurodevelopmental processes and identify early markers of neurobiological vulnerabilities to target for intervention. However, the field has historically excluded people from global majority countries and from marginalized communities in FIT neuroimaging research. Inclusive and representative samples are essential for generalizing findings across neuroimaging modalities, such as magnetic resonance imaging, magnetoencephalography, electroencephalography, functional near-infrared spectroscopy, and cranial ultrasonography. These FIT neuroimaging techniques pose unique and overlapping challenges to equitable representation in research through sampling bias, technical constraints, limited accessibility, and insufficient resources. The present article adds to the conversation around the need to improve inclusivity by highlighting modality-specific historical and current obstacles and ongoing initiatives. We conclude by discussing tangible solutions that transcend individual modalities, ultimately providing recommendations to promote equitable FIT neuroscience.

Atypical early neural responses to native and non-native language in infants at high likelihood for developing autism

BACKGROUND

Language difficulties are common in autism spectrum disorder (ASD), a neurodevelopmental condition characterized by impairments in social communication as well as restricted and repetitive behaviors. Amongst infant siblings of children with an ASD diagnosis - who are at higher likelihood for developing ASD - a high proportion also show difficulties and delays in language acquisition.

METHODS

In this study, we used functional magnetic resonance imaging (fMRI) to examine differences in language processing in 9-month-old infants at high (HL) and typical (TL) familial likelihood for ASD. Infants were presented with native (English) and novel (Japanese) speech while sleeping naturally in the scanner. Whole-brain and a priori region-of-interest analyses were conducted to evaluate neural differences in language processing based on likelihood group and language condition.

RESULTS

HL infants showed attenuated responses to speech in general, particularly in left temporal language areas, as well as a lack of neural discrimination between the native and novel languages compared to the TL group. Importantly, we also demonstrate that HL infants show distinctly atypical patterns of lateralization for speech processing, particularly during native speech processing, suggesting a failure to left-lateralize.

LIMITATIONS

The sample size, particularly for the TL group, is relatively modest because of the challenges inherent to collecting auditory stimulus-evoked data from sleeping participants, as well as retention and follow-up difficulties posed by the COVID-19 pandemic. The groups were not matched on some demographic variables, but the present findings held even after accounting for these differences.

CONCLUSIONS

To our knowledge, this is the first fMRI study to directly measure autism-associated atypicalities in native language uptake during infancy. These findings provide a better understanding of the neurodevelopmental underpinnings of language delay in ASD, which is a prerequisite step for developing earlier and more effective interventions for autistic children and HL siblings who experience language impairments.

Atypical Neural Responses to Native and Non-Native Language in Infants at High Likelihood for Developing Autism

Background

Language difficulties are common in autism spectrum disorder (ASD), a neurodevelopmental condition characterized by impairments in social communication as well as restricted and repetitive behaviors. Amongst infant siblings of children with an ASD diagnosis - who are at higher likelihood for developing ASD - a high proportion also show difficulties and delays in language acquisition.

Methods

In this study, we used functional magnetic resonance imaging (fMRI) to examine atypicalities associated with language processing in 9-month-old infants at high (HL) and typical (TL) familial likelihood for ASD. Infants were presented with native (English) and novel (Japanese) speech while sleeping naturally in the scanner. Whole-brain and a priori region-of-interest analyses were conducted to evaluate neural differences in language processing based on likelihood group and language condition.

Results

HL infants showed attenuated responses to speech in general, particularly in left temporal language areas, as well as a lack of neural discrimination between the native and novel languages compared to the TL group. Importantly, we also demonstrate that HL infants show distinctly atypical patterns of greater rightward lateralization for speech processing. Limitations: The sample size, particularly for the TL group, is relatively modest due to the challenges inherent to collecting auditory stimulus-evoked data from sleeping participants, as well as retention and follow-up difficulties posed by the COVID-19 pandemic. Additionally, the groups were not matched on some demographic variables; however, the present findings held even after accounting for these differences.

Conclusions

To our knowledge, this is the first fMRI study to directly measure autism-associated atypicalities in native language uptake during infancy. These findings provide a better understanding of the neurodevelopmental underpinnings of language delay in ASD, which is a prerequisite step for developing earlier and more effective interventions for autistic children and HL siblings who experience language impairments.

Quantifying brain development in the HEALthy Brain and Child Development (HBCD) Study: The magnetic resonance imaging and spectroscopy protocol

The HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. The acquisition of multimodal magnetic resonance-based brain development data is central to the study's core protocol. However, application of Magnetic Resonance Imaging (MRI) methods in this population is complicated by technical challenges and difficulties of imaging in early life. Overcoming these challenges requires an innovative and harmonized approach, combining age-appropriate acquisition protocols together with specialized pediatric neuroimaging strategies. The HBCD MRI Working Group aimed to establish a core acquisition protocol for all 27 HBCD Study recruitment sites to measure brain structure, function, microstructure, and metabolites. Acquisition parameters of individual modalities have been matched across MRI scanner platforms for harmonized acquisitions and state-of-the-art technologies are employed to enable faster and motion-robust imaging. Here, we provide an overview of the HBCD MRI protocol, including decisions of individual modalities and preliminary data. The result will be an unparalleled resource for examining early neurodevelopment which enables the larger scientific community to assess normative trajectories from birth through childhood and to examine the genetic, biological, and environmental factors that help shape the developing brain.

Gray matter based spatial statistics framework in the 1-month brain: insights into gray matter microstructure in infancy

The neurodevelopmental epoch from fetal stages to early life embodies a critical window of peak growth and plasticity in which differences believed to be associated with many neurodevelopmental and psychiatric disorders first emerge. Obtaining a detailed understanding of the developmental trajectories of the cortical gray matter microstructure is necessary to characterize differential patterns of neurodevelopment that may subserve future intellectual, behavioral, and psychiatric challenges. The neurite orientation dispersion density imaging (NODDI) Gray-Matter Based Spatial Statistics (GBSS) framework leverages information from the NODDI model to enable sensitive characterization of the gray matter microstructure while limiting partial volume contamination and misregistration errors between images collected in different spaces. However, limited contrast of the underdeveloped brain poses challenges for implementing this framework with infant diffusion MRI (dMRI) data. In this work, we aim to examine the development of cortical microstructure in infants. We utilize the NODDI GBSS framework and propose refinements to the original framework that aim to improve the delineation and characterization of gray matter in the infant brain. Taking this approach, we cross-sectionally investigate age relationships in the developing gray matter microstructural organization in infants within the first month of life and reveal widespread relationships with the gray matter architecture.

Neural Correlates of Novelty-Evoked Distress in 4-Month-Old Infants: A Synthetic Cohort Study

BACKGROUND

Observational assessments of infant temperament have provided unparalleled insight into prediction of risk for social anxiety. However, it is challenging to administer and score these assessments alongside high-quality infant neuroimaging data. In the current study, we aimed to identify infant resting-state functional connectivity associated with both parent report and observed behavioral estimates of infant novelty-evoked distress.

METHODS

Using data from the OIT (Origins of Infant Temperament) study, which includes deep phenotyping of infant temperament, we identified parent-report measures that were associated with observed novelty-evoked distress. These parent-report measures were then summarized into a composite score used for imaging analysis. Our infant magnetic resonance imaging sample was a synthetic cohort, harmonizing data from 2 functional magnetic resonance imaging studies of 4-month-old infants (OIT and BCP [Baby Connectome Project]; n = 101), both of which included measures of parent-reported temperament. Brain-behavior associations were evaluated using enrichment, a statistical approach that quantifies the clustering of brain-behavior associations within network pairs.

RESULTS

Results demonstrated that parent-report composites of novelty-evoked distress were significantly associated with 3 network pairs: dorsal attention-salience/ventral attention, dorsal attention-default mode, and dorsal attention-control. These network pairs demonstrated negative associations with novelty-evoked distress, indicating that less connectivity between these network pairs was associated with greater novelty-evoked distress. Additional analyses demonstrated that dorsal attention-control network connectivity was associated with observed novelty-evoked distress in the OIT sample (n = 38).

CONCLUSIONS

Overall, this work is broadly consistent with existing work and implicates dorsal attention network connectivity in novelty-evoked distress. This study provides novel data on the neural basis of infant novelty-evoked distress.

Individual patterns of functional connectivity in neonates as revealed by surface-based Bayesian modeling

Resting-state functional connectivity is a widely used approach to study the functional brain network organization during early brain development. However, the estimation of functional connectivity networks in individual infants has been rather elusive due to the unique challenges involved with functional magnetic resonance imaging (fMRI) data from young populations. Here, we use fMRI data from the developing Human Connectome Project (dHCP) database to characterize individual variability in a large cohort of term-born infants (N = 289) using a novel data-driven Bayesian framework. To enhance alignment across individuals, the analysis was conducted exclusively on the cortical surface, employing surface-based registration guided by age-matched neonatal atlases. Using 10 minutes of resting-state fMRI data, we successfully estimated subject-level maps for fourteen brain networks/subnetworks along with individual functional parcellation maps that revealed differences between subjects. We also found a significant relationship between age and mean connectivity strength in all brain regions, including previously unreported findings in higher-order networks. These results illustrate the advantages of surface-based methods and Bayesian statistical approaches in uncovering individual variability within very young populations.

Development of human visual cortical function: A scoping review of task- and naturalistic-fMRI studies through the interactive specialization and maturational frameworks

Overarching theories such as the interactive specialization and maturational frameworks have been proposed to describe human functional brain development. However, these frameworks have not yet been systematically examined across the fMRI literature. Visual processing is one of the most well-studied fields in neuroimaging, and research in this area has recently expanded to include naturalistic paradigms that facilitate study in younger age ranges, allowing for an in-depth critical appraisal of these frameworks across childhood. To this end, we conducted a scoping review of 94 developmental visual fMRI studies, including both traditional experimental task and naturalistic studies, across multiple sub-domains (early visual processing, category-specific higher order processing, naturalistic visual processing). We found that across domains, many studies reported progressive development, but few studies describe regressive or emergent changes necessary to fit the maturational or interactive specialization frameworks. Our findings suggest a need for the expansion of developmental frameworks and clearer reporting of both progressive and regressive changes, along with well-powered, longitudinal studies.

Current perspectives on perinatal mental health and neurobehavioral development: focus on regulation, coregulation and self-regulation

PURPOSE OF REVIEW

Perinatal mental health research provides an important perspective on neurobehavioral development. Here, we aim to review the association of maternal perinatal health with offspring neurodevelopment, providing an update on (self-)regulation problems, hypothesized mechanistic pathways, progress and challenges, and implications for mental health.

RECENT FINDINGS

(1) Meta-analyses confirm that maternal perinatal mental distress is associated with (self-)regulation problems which constitute cognitive, behavioral, and affective social-emotional problems, while exposure to positive parental mental health has a positive impact. However, effect sizes are small. (2) Hypothesized mechanistic pathways underlying this association are complex. Interactive and compensatory mechanisms across developmental time are neglected topics. (3) Progress has been made in multiexposure studies. However, challenges remain and these are shared by clinical, translational and public health sciences. (4) From a mental healthcare perspective, a multidisciplinary and system level approach employing developmentally-sensitive measures and timely treatment of (self-)regulation and coregulation problems in a dyadic caregiver-child and family level approach seems needed. The existing evidence-base is sparse.

SUMMARY

During the perinatal period, addressing vulnerable contexts and building resilient systems may promote neurobehavioral development. A pluralistic approach to research, taking a multidisciplinary approach to theoretical models and empirical investigation needs to be fostered.

Human lower leg muscles grow asynchronously

Muscle volume must increase substantially during childhood growth to generate the power required to propel the growing body. One unresolved but fundamental question about childhood muscle growth is whether muscles grow at equal rates; that is, if muscles grow in synchrony with each other. In this study, we used magnetic resonance imaging (MRI) and advances in artificial intelligence methods (deep learning) for medical image segmentation to investigate whether human lower leg muscles grow in synchrony. Muscle volumes were measured in 10 lower leg muscles in 208 typically developing children (eight infants aged less than 3 months and 200 children aged 5 to 15 years). We tested the hypothesis that human lower leg muscles grow synchronously by investigating whether the volume of individual lower leg muscles, expressed as a proportion of total lower leg muscle volume, remains constant with age. There were substantial age-related changes in the relative volume of most muscles in both boys and girls (p < 0.001). This was most evident between birth and five years of age but was still evident after five years. The medial gastrocnemius and soleus muscles, the largest muscles in infancy, grew faster than other muscles in the first five years. The findings demonstrate that muscles in the human lower leg grow asynchronously. This finding may assist early detection of atypical growth and allow targeted muscle-specific interventions to improve the quality of life, particularly for children with neuromotor conditions such as cerebral palsy.

Automated motion artifact detection in early pediatric diffusion MRI using a convolutional neural network

Diffusion MRI (dMRI) is a widely used method to investigate the microstructure of the brain. Quality control (QC) of dMRI data is an important processing step that is performed prior to analysis using models such as diffusion tensor imaging (DTI) or neurite orientation dispersion and density imaging (NODDI). When processing dMRI data from infants and young children, where intra-scan motion is common, the identification and removal of motion artifacts is of the utmost importance. Manual QC of dMRI data is (1) time-consuming due to the large number of diffusion directions, (2) expensive, and (3) prone to subjective errors and observer variability. Prior techniques for automated dMRI QC have mostly been limited to adults or school-age children. Here, we propose a deep learning-based motion artifact detection tool for dMRI data acquired from infants and toddlers. The proposed framework uses a simple three-dimensional convolutional neural network (3DCNN) trained and tested on an early pediatric dataset of 2,276 dMRI volumes from 121 exams acquired at 1 month and 24 months of age. An average classification accuracy of 95% was achieved following four-fold cross-validation. A second dataset with different acquisition parameters and ages ranging from 2-36 months (consisting of 2,349 dMRI volumes from 26 exams) was used to test network generalizability, achieving 98% classification accuracy. Finally, to demonstrate the importance of motion artifact volume removal in a dMRI processing pipeline, the dMRI data were fit to the DTI and NODDI models and the parameter maps were compared with and without motion artifact removal.

Three-dimensional skeletal muscle architecture in the lower legs of living human infants

Little is known about the skeletal muscle architecture of living humans at birth. In this study, we used magnetic resonance imaging (MRI) to measure the volumes of ten muscle groups in the lower legs of eight human infants aged less than three months. We then combined MRI and diffusion tensor imaging (DTI) to provide detailed, high-resolution reconstructions and measurements of moment arms, fascicle lengths, physiological cross-sectional areas (PCSAs), pennation angles and diffusion parameters of the medial (MG) and lateral gastrocnemius (LG) muscles. On average, the total lower leg muscle volume was 29.2 cm³. The largest muscle was the soleus muscle with a mean volume of 6.5 cm³. Compared to the LG muscles, the MG muscles had, on average, greater volumes (by ∼35%) and greater PCSAs (by ∼63%) but similar ankle-to-knee moment arm ratios (∼0.1 difference), fascicle lengths (∼5.7 mm difference) and pennation angles (∼2.7° difference). The MG data were compared with data previously collected from adults. The MG muscles of adults had, on average, a 63-fold greater volume, a 36-fold greater PCSA, and 1.7-fold greater fascicle length. This study demonstrates the feasibility of using MRI and DTI to reconstruct the three-dimensional architecture of skeletal muscles in living human infants. It is shown that, between infancy and adulthood, MG muscle fascicles grow primarily in cross-section rather than in length.

Applications of advanced diffusion MRI in early brain development: a comprehensive review

Brain development follows a protracted developmental timeline with foundational processes of neurodevelopment occurring from the third trimester of gestation into the first decade of life. Defining structural maturational patterns of early brain development is a critical step in detecting divergent developmental trajectories associated with neurodevelopmental and psychiatric disorders that arise later in life. While considerable advancements have already been made in diffusion magnetic resonance imaging (dMRI) for pediatric research over the past three decades, the field of neurodevelopment is still in its infancy with remarkable scientific and clinical potential. This comprehensive review evaluates the application, findings, and limitations of advanced dMRI methods beyond diffusion tensor imaging, including diffusion kurtosis imaging (DKI), constrained spherical deconvolution (CSD), neurite orientation dispersion and density imaging (NODDI) and composite hindered and restricted model of diffusion (CHARMED) to quantify the rapid and dynamic changes supporting the underlying microstructural architectural foundations of the brain in early life.