In the beginning… Advances in fetal and infant Imaging

Functional magnetic resonance imaging (fMRI) preprocessing encompasses multiple steps aimed at cleansing and standardizing data prior to statistical analysis. Typically, researchers are faced with a few choices: create a customized workflow tailormade for their scanner acquisition protocol, or reuse an existing tool, many of which place the burden of dependency installation compatibility on the user. Furthermore, large, distributed data collection initiatives, such as the ABCD and HBCD studies, require reproducible and standardized data processing to ensure results are comparable across sites.

From this need came fMRIPrep, a preprocessing pipeline designed to provide an easily accessible, state-of-the-art interface that is robust across protocols and requires minimal user input, while providing easily interpretable and comprehensive output reporting. fMRIPrep established a model for developing standardized neuroimaging workflows that build on well-tested, existing tools. However, it was built for and its performance assessed on human adult MRIs, and does not account for the stark structural differences nor variance in tissue contrast across ages due to ongoing myelination. Because of this, we developed fMRIPrep-Infants, an adaptation of fMRIPrep across the entire infant spectrum, from newborns to two-year-olds.

Just as fMRIPrep revolutionized preprocessing for adult brains, fMRIPrep-Infants replicates its robust methodology, shares the 'glass-box philosophy' of scientific transparency, and minimizes misoperation with an easy-to-use interface. By adhering to the Brain Imaging Data Structure (BIDS), all available scans and necessary metadata can be easily collected, and used to create an adaptive workflow. Although fMRIPrep-Infants leverages much of the same underlying methodology as fMRIPrep, a few important deviations are taken to enhance performance.

Brain Extraction
fMRIPrep-Infants uses an atlas-based registration method for anatomical brain extraction, inspired by antsBrainExtraction. However, the infant’s T2-weighted (T2w) image is preferred for normalization to the target atlas over the T1-weighted (T1w) image, due to the increase in tissue contrast, most notably in the 0-8 month range. The default template used is the UNC 0-1-2 Infant atlas, which provides three atlases of distinct time points in infant development. By utilizing the participant’s age available via BIDS, a best match time point is selected as the target template. To ensure a consistent output space, the T2w image is coregistered to T1w space, as well as the brain mask.

Surface Reconstruction
Signal intensities in tissue exhibit variations between neonatal and adult brains. Consequently, finding the necessary contrast between gray and white matter, crucial for identifying cortical surfaces, varies depending on age. fMRIPrep-Infants provides three alternative methods to reconstruct cortical surfaces, each performant in a stage of development. With its utilization of the T2w over the T1w, Melbourne Children’s Regional Infant Brain Surface (M-CRIB-S) voxel-based parcellation excels in the early months (0-8). As the infant brain matures and myelination completes, T1w approaches become effective - Infant FreeSurfer can be used around 9-24 months. Upwards of 24 months, we have found FreeSurfer’s recon-all to perform reasonably well. These ranges are only recommendations; users can override and select the method they most prefer if desired.

Subcortical Structures Alignment
The compact overall anatomy of the infant brain generally results in a decreased signal-to-noise ratio (SNR) and an increased partial voluming, relative to an adult brain. These effects become evident during segmentation of subcortical brain areas, where a large amount of brain structures closely border each other in low resolution. Even if the segmentation is expert-validated, transforming these into a shared template space may result in misalignment. To remedy this, fMRIPrep-Infants incorporates a structure-by-structure alignment process, to protect from potential structure-specific distortions.

Offering maximal flexibility, fMRIPrep-Infants provides a containerized, easy-to-use interface to generate results that are minimally tied to a specific analysis, allowing seamless integration with other downstream tools or workflows. As part of the NiPreps community initiative, this open-source project is crafted with thorough documentation and actively encouraging contributions from the community. Its methods are continuously under evaluation to incorporate the latest advances in the field, and hopes to spark a community adoption similar to that of fMRIPrep’s.

Abbreviations: ABCD - Adolescent Brain Cognitive Development; HBCD - HEALthy Brain and Child Development 

Parcellation maps describe the cerebral cortex as sets of meaningful units, which are fundamental to study the brain structure and function. Infant brain parcellation is especially important for the developing brain, due to the dynamic and drastic developing features during infancy. I will introduce the set of infant-specific parcellation maps that we previously built using the high-resolution longitudinal brain imaging data from the Baby Connectome Project. We studied the parcellation according to the regionalization of cortical thickness and surface area respectively, and then a combined parcellation according to a multi-view scheme. We also built a fine-grained parcellation map using longitudinal scans of the infant brain rs-fMRI. I will also introduce some analyses regarding individual differences and dynamic developing properties that we performed during based on these parcellations. Our series of infant parcellation maps are publicly available at https://www.nitrc.org/projects/infantsurfatlas/ . 

Fetal period is characterised by rapid maturational transformations of the brain structure and function. It becomes increasingly appreciated that adverse perturbations during this critical time can have long-lasting consequences, including negative implications for higher cognition and states of consciousness. Recent advances in in-utero MRI technology enable for the first time non-invasive measurement of the living fetal brain. However, there are specific challenges for acquiring high-quality data from this population, such as an inherently low signal-to-noise ratio and unconstrained motion, which together necessitate implementation of tailored pre-processing and analyses methods.

In my talk I will describe the fetal MRI cross-sectional dataset of the developing Human Connectome Project (dHCP), the first-ever open-access multimodal (anatomical, diffusion and functional) fetal dataset, that incorporates optimised scanning protocol, motion-tolerant acquisition methods, robust motion-correction preprocessing pipelines, and advanced data infrastructure. The dataset consists of 297 scanning sessions from 273 typically developing individuals of postmenstrual age 21-38 weeks, complemented by rich socio-demographic and clinical information.

The released data ensure that the maximum potential can be uncovered from the data by researchers with various scientific backgrounds. To benefit developers of methods for image analysis and reconstruction, the dataset includes outputs from different stages of preprocessing, from raw to fully processed, that can be utilised to develop novel preprocessing methods and to provide a benchmark for their performance. To benefit modelers of neurodevelopment and population variability, the dataset incorporates advanced registration infrastructure, accompanied with state-of-the-art volumetric and surface atlases, thereby providing an opportunity to synthesise anatomical, diffusion and functional data across various stages of fetal development for group-level analyses.

The dHCP fetal dataset represents an important step towards promoting fetal MRI from its current status as a niche research field to its deserved and timely place in the community-wide effort to build a life-long connectome of the human brain. These rich observational data can empower detailed models of typical developmental trajectories at a key stage of human life and, in combination with the large open-access dHCP neonatal MRI resource, provide a normative reference for studies of pathophysiology and the effects of adverse perinatal factors, such as premature birth.
 

Over 100 million children worldwide show social deficits in the first year of life that develop into mental health or neurodevelopmental disorders between ages 2 to 8 years. In addition, over 250 million children worldwide live with at least one risk factor (e.g., neglect, domestic violence, parental psychiatric disorder) known to undermine social development. The fundamental architecture of the social brain is developed in infancy, and early deficits in social development are difficult to compensate for later in life. However, current techniques limit our ability to assess early differences and deficits in the infant’s social brain. This has limited our fundamental knowledge of the developing social brain in human infants and has delayed the development of new diagnostics and therapeutics targeting these crucial earliest years. The goal of the present study was to demonstrate the feasibility of using a novel fMRI paradigm to measure infants’ developing social responsiveness to the first social partner—the mother—at 6 months of age.

Our novel fMRI paradigm uses an established MRI protocol to scan infants during natural sleep and relies on the infant’s fully developed auditory responsiveness which is present by 6 months and well preserved under sleep. Twenty-four (15 males) typically developing 6-month-old (± 1 month) infants underwent scanning during natural sleep, listening to maternal voice, unfamiliar female voice, and speech-shaped noise (15-sec-long blocks; 7.5-sec inter-block intervals). We measured maternal cue responsiveness, defined as the infant’s fMRI response to maternal voice, compared to unfamiliar voice and speech-shaped noise. Unfamiliar voices were identified to be distinct from maternal voice on 512 features extracted by the Pyannote machine learning model. Speech-shaped noise consisted of white noise that was edited to match maternal voice on frequency and loudness. FMRI data processing was carried out using FEAT (FMRI Expert Analysis Tool) version 6.00, part of FSL. A total of 54 runs from 18 infants (11 males) passed quality assurance, showing distinct auditory activation and no excessive movement, and were included in the analysis. Voxel-wise whole-brain analyses examined the infant’s fMRI response to: (a) human (maternal and unfamiliar) voice compared to speech-shaped noise (Human Voice > Noise contrast), and (b) maternal voice compared to unfamiliar voice (Maternal > Unfamiliar Voice contrast), adjusting for infant sex, infant age (in weeks), and maternal age. Z-statistic images were thresholded using clusters determined by Z > 3.1 and a corrected cluster threshold of p = .05. Exploratory correlational analysis examined Pearson’s r between the infant’s fMRI responses to Maternal > Unfamiliar Voice contrast and concurrently administered behavioral measures of maternal anxiety (State-Trait Anxiety Inventory), maternal stress (Perceived Stress Scale), maternal depression (Edinburgh Postnatal Depression Scale), and infant negative affectivity (Infant Behavior Questionnaire-Revised, Very Short Form).

Compared to speech-shaped noise, human voice elicited increased activations in multiple cortical regions (all corrected p < .05) of the infant’s social brain, including the superior temporal gyrus (Z = 12.75), anterior cingulate gyrus (Z = 3.20), and temporoparietal junction (Z = 8.17). Compared to unfamiliar voice, maternal voice elicited increased activations in all aforementioned cortical regions, and additionally in key dopamine- and oxytocin-rich subcortical regions (all corrected p < .05), including the striatum (Z = 4.40), amygdala (Z = 3.79), and ventral diencephalon (encompassing the hypothalamus, ventral tegmental area/substantia nigra; Z = 3.58). Compared to maternal voice, unfamiliar voice did not elicit any additional activations in the infant’s brain. Exploratory correlational analyses suggested that maternal anxiety, stress, depression, and infant negative affectivity were negatively correlated with the infant’s preferential brain responses to maternal voice in several important social brain regions, including the striatum, amygdala, ventral diencephalon, and anterior cingulate gyrus (ps < .05).

Six-month-old infants show preferential brain responses to human voice (compared to speech-shaped noise) and voice of their first social partner (compared to unfamiliar voice). Our findings provide support for the feasibility of using fMRI to measure the developing brain’s responsiveness to socially salient cues at 6 months of age. Our findings also provide preliminary evidence that the infant’s preferential response to socially salient cues is negatively associated with maternal anxiety, stress, and depression, and infant’s negative affectivity. When extended to at-risk infants, this work has the potential to yield breakthroughs in identifying novel neural markers that can detect early differences and deficits in an infant’s developing social brain.