Sarah Shultz

Infant functional and structural magnetic resonance imaging (MRI) is increasingly used in research to identify early emerging biomarkers of neurodevelopmental conditions. Although these methods hold promise for advancing discovery, pediatric MRI research protocols, which often require infants to be scanned during natural sleep, can present a range of challenges for infants and their caregivers. Yet little is known about caregivers’ experiences participating in non-clinically indicated MRI studies with their infants. This study explored caregivers’ motivations for participating in infant MRI research, their experiences during participation, and their suggestions for improving infant MRI protocols. Three semi-structured focus groups were conducted with caregivers of 0-6-month-old infants who participated in a longitudinal MRI study. Thematic analysis revealed that caregivers were primarily motivated by contributing to science and helping other families. Many experienced performance anxieties while attempting to help their infants fall asleep for a scan, but supportive communication from research staff helped reduce these concerns. Caregivers recommended flexible scheduling, clearer procedural expectations, and pre-visit materials to improve preparedness. These findings underscore the importance of family-centered communication in infant neuroimaging protocols.

Early infancy is characterized by rapid, nonlinear reorganization of the functional connectome, yet its dominant growth modes and relationship to short-timescale brain dynamics remain unclear. Using a densely sampled longitudinal multimodal dataset collected across the first six postnatal months, we estimated days-scale trajectories of whole-brain functional connectivity growth and visually guided behaviors. We identify a growth-mode transition in the functional connectome at approximately 2-4 months, marked by a shift from predominantly local strengthening toward increasingly distributed integration. This developmental reorganization was preceded by changes in the fluctuation asymmetry ratio (FLARe), a measure of asymmetry in recurring short-timescale co-fluctuation states. Across individuals, FLARe predicted subsequent change rates of functional connectivity and visually guided behaviors. These findings identify short-timescale fluctuation asymmetry as a previously unrecognized prospective signal of later days-scale connectome and behavioral change, and provide a cross-timescale framework for linking functional brain dynamics unfolding over seconds to developmental change emerging over weeks to months.

This study explores the nonlinear developmental trajectories of brain networks in neurotypically developing infants during their first six months. Using a longitudinal dataset of 137 resting-state functional MRI scans from 74 infants, we analyzed five spatial metrics across 13 intrinsic connectivity networks, including motor, visual, subcortical, and prefrontal networks. A cubic model was specifically employed to capture distinct linear and non-linear trends in the networks' developmental patterns, allowing for the identification of significant differences in cubic, quadratic, and linear slope parameters across networks. This model choice was driven by the need to examine how specific non-linear components (e.g., inflection points and acceleration rates) uniquely characterize each network's trajectory, which a generalized approach might smooth out without pinpointing such network-specific features. Notably, the subcortical network exhibited a distinct cubic growth pattern, while secondary motor and visual networks showed pronounced quadratic variations, suggesting network-specific shifts in spatial organization and connectivity. These findings highlight the unique maturation timelines and interactions between functional systems, such as early sensory-motor coordination and later cognitive integration. The results underscore the importance of network-specific growth patterns, providing deeper insights into how infant brain networks evolve and interact to support emerging cognitive and behavioral functions.

The first months after a baby's birth encompass the most rapid period of postnatal change in the human lifespan, but temporally precise trajectories of white matter maturation in this period remain largely uncharted. Using densely sampled diffusion tensor images from predominantly term-born infants, we measured non-linear growth and growth rate trajectories of diffusion tensor imaging metrics from birth to 6 months. Overall white matter growth rates at birth were approximately 6 times faster than at 6 months, and less mature tracts developed the fastest. When matched on chronological age, shorter gestation infants had less mature white matter at birth but faster growth rates than their longer gestation peers; however, growth trajectories were highly similar when corrected for gestational age. These trajectories provide novel insight into the complex and dynamic processes underlying white matter development during the first six postnatal months, which can inform the study of neurodevelopmental disorders beginning in infancy.

Nonlinearity is a hallmark of brain complexity at multiple scales. However, existing functional magnetic resonance imaging (fMRI) functional connectivity studies typically utilize linear methods. Therefore, links between nonlinear fMRI connectivity patterns and the development of the human brain during critical periods such as infancy remain unclear. To address this gap in knowledge, we developed a data-driven approach to capture brain intrinsic connectivity networks from explicitly nonlinear resting-state fMRI connectivity and profiled their developmental associations in a cohort of typically developing human infants. We identified neurobiologically structured nonlinear fMRI connectivity patterns during early postnatal life, indicating that macroscopic brain ensembles systematically participate in nonlinear relationships at birth. Furthermore, we found that linear and explicitly nonlinear network counterparts are linked to partially overlapping but complementary developmental profiles during this period of rapid brain maturation, with the explicitly nonlinear approach unveiling insights into the development of networks that have been associated with sensorimotor capacities, default mode processes, executive functioning, language production, and stimulus saliency. Our study marks the first comprehensive developmental investigation of whole-brain nonlinear fMRI networks in human infants and deepens contemporary perspectives on neuroimaging data discovery by emphasizing the informational richness of nonlinear relationships at fMRI scales of observation.

The unique and intricate pattern of human cortical folding is rooted in fetal neurodevelopmental processes and can now be comprehensively quantified by new neuroimaging-derived measures of sulcal complexity. Here, we provide the first genetic maps of human sulcal complexity. Beginning with large effects of rare variants, we survey nine different neurogenetic syndromes ( n =615), detecting visible changes in sulcal complexity on a shared axis of sulcal change coupled to the prenatal timing of sulcation. Turning to common genetic variants, we use genome-wide association studies of complexity scores for 40 sulci in the UK Biobank ( n ∼29,000) to (i) resolve variable heritability across sulci, (ii) reveal both local and remote shared genetic effects with cortical morphology, and (iii) identify complexity-associated genes and their embedding in brain maps of prenatal gene expression. These reference genetic maps uncover multiple new mechanistic pathways for cortical morphogenesis in health and disease.

Converging evidence suggests that understanding the human brain requires more than just examining pairwise functional brain interactions. The human brain is a complex, nonlinear system, and focusing solely on linear pairwise functional connectivity often overlooks important nonlinear and higher-order interactions. Infancy is a critical period marked by significant brain development that could contribute to future learning, health, and life success. Exploring higher-order functional relationships in the brain can provide insight into brain function and development. To the best of our knowledge, there is no existing research on multiway, multiscale brain network interactions in infants. In this study, we comprehensively investigate the interactions among brain intrinsic connectivity networks (ICNs), including both pairwise (pair-FNC) and triple relationships (tri-FNC). We focused on a dataset of typically developing infants scanned during the first six months of life-a critical period for brain maturation. In total, 71 infants (aged 4-179 days) contributed 126 scans (76 from 41 males, 50 from 30 females). Our results revealed significant hierarchical, multiway, multiscale brain functional network interactions in the infant brain. These findings suggest that tri-FNC provide additional insights beyond what pairwise interactions reveal during early brain development. The tri-FNC predominantly involve the default mode, sensorimotor, visual, limbic, language, salience, and central executive domains. Notably, these triplet networks align with the classical triple network model of the human brain, which includes the default mode network, the salience network, and the central executive network. This suggests that the brain network system might already be initially established during the first six months of infancy. We also found that pair-FNC were less effective at detecting these networks. The present study suggests that exploring tri-FNC can offer additional insights beyond pair-FNC by capturing higher-order nonlinear interactions, potentially yielding more reliable biomarkers to characterize developmental trajectories.

The early postnatal period is crucial for brain development and understanding neurodevelopmental disorders. This study examines spatial brain network development in early infancy, a less-explored area. Using independent component analysis on longitudinal resting-state functional magnetic resonance imaging data from 74 neurotypical infants, we examined how the spatial organization of brain networks evolves from birth to 6 months. Our findings show significant age-related changes in spatial characteristics. Network-averaged spatial similarity, reflecting alignment between individual and group-level network maps, increased with age. Concurrently, network engagement range, representing voxel intensity fluctuation within networks, decreased, suggesting a consolidation process where voxel contributions became more uniform. Network strength, calculated as the average of all the significant voxel intensities in the network, indicating the degree of involvement in the specific functional network, increased across age in networks such as the frontal-medial prefrontal cortex and visual networks. We found that network size and network center of mass (illustrating spatial distribution alterations of brain networks) increased in the temporal network. These findings fill a gap in infant neuroimaging by spatially characterizing early functional network development. Quantifying changes in topology, size, and similarity offers a framework for understanding early brain maturation and identifying atypical trajectories.

Registering infant brain images is challenging, as the infant brain undergoes rapid changes in size, shape and tissue contrast in the first months of life. Diffusion tensor images (DTI) have relatively consistent tissue properties over the course of infancy compared to commonly used T1 or T2-weighted images, presenting great potential for infant brain registration. Moreover, groupwise registration using intermediate templates can reduce deformation and bias introduced by predefined atlases, but most methods use scalar (e.g., fractional anisotropy) images, which lack the microstructural orientation information in tensor images that can help differentiate brain structures and further improve infant image registration accuracy. Here, we propose an intermediate subgroup tensor template-based groupwise (IST-G tensor) registration approach to align infant tensor images to a sample-specific common space. First, tensor images are clustered into more homogenous subgroups using Louvain clustering based on image similarity. Within each subgroup, tensor images are aligned using DTI-toolkit to generate subgroup tensor templates, which are subsequently aligned to a sample-specific common space. Results show that our approach significantly improved registration accuracy both globally and locally compared to standard tensor-based and fractional anisotropy-based approaches. Clustering based on image similarity yielded significantly higher registration accuracy than no clustering and performed comparably to clustering by chronological age. By leveraging the consistency of features in tensor maps across early infancy and reducing deformation through intermediate subgroup tensor templates, our IST-G tensor registration framework facilitates more accurate alignment of longitudinal infant brain tensor images.

The first months after a baby’s birth encompass the most rapid period of postnatal change in the human lifespan, but longitudinal trajectories of white matter maturation in this period remain uncharted. Using densely sampled diffusion tensor images collected longitudinally at a mean rate of 1 scan per 1.55 days, we measured non-linear growth and growth rate trajectories of major white matter tracts from birth to 6 months. Growth rates at birth were 6 to 11 times faster than at 6 months, with tracts less mature at birth developing fastest. When matched on chronological age, shorter gestation infants had less mature white matter at birth but faster growth rates than their longer gestation peers; however, growth trajectories were highly similar when corrected for gestational age. This is the first study to estimate white matter trajectories using dense sampling in the first 6 post-natal months, which can inform the study of neurodevelopmental disorders beginning in infancy.