John E. Richards

White matter (WM) changes occur throughout the lifespan at a different rate for each developmental period. We aggregated 10879 structural MRIs and 6186 diffusion-weighted MRIs from participants between 2 weeks to 100 years of age. Age-related changes in gray matter and WM partial volumes and microstructural WM properties, both brain-wide and on 29 reconstructed tracts, were investigated as a function of biological sex and hemisphere, when appropriate. We investigated the curve fit that would best explain age-related differences by fitting linear, cubic, quadratic, and exponential models to macro and microstructural WM properties. Following the first steep increase in WM volume during infancy and childhood, the rate of development slows down in adulthood and decreases with aging. Similarly, microstructural properties of WM, particularly fractional anisotropy (FA) and mean diffusivity (MD), follow independent rates of change across the lifespan. The overall increase in FA and decrease in MD are modulated by demographic factors, such as the participant’s age, and show different hemispheric asymmetries in some association tracts reconstructed via probabilistic tractography. All changes in WM macro and microstructure seem to follow nonlinear trajectories, which also differ based on the considered metric. Exponential changes occurred for the WM volume and FA and MD values in the first five years of life. Collectively, these results provide novel insight into how changes in different metrics of WM occur when a lifespan approach is considered.

The current study examined patterns of event‐related potential (ERP) responses during a face processing task in groups of preschoolers uniquely impacted by autism spectrum disorder (ASD), including (1) children with ASD; (2) children with fragile X syndrome (FXS); (3) children with familial risk for ASD, but without a diagnosis (i.e., ASIBs); and (4) a low‐risk control (LRC) group. Children with FXS have a high incidence of ASD diagnoses, but there have been no studies of the ERP response to faces in children with FXS and little work focused on children with ASD who have cognitive impairment. The current study examined children's ERP responses to faces and houses in four groups: LRC (N = 28, age = 5.2 years), ASIB (N = 23, age = 5.5 years), FXS (N = 19, age = 5.82 years), and ASD (N = 23, age = 5.5 years). The FXS and ASD groups were characterized by the presence of cognitive impairment. Pictures of upright and inverted faces and houses were presented while recording EEG with a 128‐channel system. The N170 occurred at about 200 ms post stimulus onset, was largest on the posterior‐lateral electrodes, and was larger for faces than houses. The P1 and N170 ERP components were larger for the FXS group than for the other three groups. The N170 ERP amplitude for the ASD and ASIB groups was smaller than both the LRC and FXS groups, and the LRC and FXS groups had the largest N170 responses on the right side. No difference was found in N170 latency between groups. The similarity of the ASD and ASIB responses suggest a common genetic or environmental origin of the reduced response. Although children with FXS have a high incidence of ASD outcomes, they differed from ASD and ASIB children in this study. Specifically, the children with FXS were hyperresponsive to all stimulus types while the ASD and ASIB groups showed attenuated responses for specific stimuli.

Appropriate head models for cortical source analysis were investigated and applied to source analyses examining the neural bases of the face-sensitive N290 event-related potential (ERP) component in infants at high risk for autism spectrum disorder (ASD). This included infant siblings of children with ASD (ASIBs) and infants with fragile X syndrome (FXS). First, alternative head models for use with ASIBs and FXS were investigated. Head models created from the infant’s own MRI were examined in relation to five head models based on average MRI templates. The results of the head model comparison identified group-specific (i.e., ASIB or FXS) head models created from a large collection of structural MRIs as the best substitution for the head model created from the participant’s own structural MRI. Second, the cortical source analysis was completed on N290 data collected from a previous study to investigate brain areas associated with face sensitive ERP responses. Participants’ own MRIs were used for head models when available, and the group-specific head model was used when the participants’ own MRIs were not available. The results provide evidence for unique patterns of neural activation during face processing across infants at high and low risk for ASD and across etiologically distinct high-risk groups. All infants demonstrated greater activation to faces than toys in brain areas most associated with specialized face processing. Infants with FXS displayed higher levels of activation to faces across all areas analyzed, while ASIBs show more muted levels of activation. Overall, the results of the current study demonstrate the importance of group-specific head models for accurate cortical source analysis in infants at high risk for ASD. This also allows for further research on early distinctions in brain function based on risk status.

Diffuse optical tomography (DOT) uses near-infrared light spectroscopy (NIRS) to indirectly measure neural activities by estimating changes in cerebral hemoglobin concentration.^1,2 Conventional NIRS instruments provide multiple pairs of source and detector optodes that are placed on the scalp to form measurement channels that detect hemoglobin changes in the brain. High-density DOT (HD-DOT) instruments provide better spatial resolution using overlapping channels with multiple source–detector separation distances.³ For both types of instruments, characterizing DOT sensitivity profiles is important for determining the extent to which a given channel can detect hemoglobin changes in the underlying brain tissues and localizing the specific brain regions being sampled.⁴

Near-infrared spectroscopy (NIRS) is a noninvasive technique that uses scalp-placed sensors to measure cerebral hemoglobin concentration. Functional NIRS (fNIRS) is a noninvasive neuroimaging technique that measures event-evoked changes in cerebral blood oxygenation. A key advantage of the technology is its ease of use with participants of different ages. Hence, NIRS enables researchers to track neurodevelopment from infancy to adulthood.^{1 – 3}

Diffuse optical tomography (DOT) uses near-infrared light spectroscopy (NIRS) to measure changes in cerebral hemoglobin concentration.^{1 – 3} DOT does not provide anatomical information about the location of the hemodynamic signal. Spatial scalp projection can be implemented to interrogate the brain region(s) underlying the scalp sensor (i.e., optodes) locations.⁴ An alternative approach is to generate a forward model of DOT sensitivity using photon propagation simulations.⁵ The forward model can then guide DOT image reconstruction to recover the brain locations of hemoglobin concentration changes.^6,7 There are considerable brain structural changes during infancy through childhood and adulthood.^8,9 Accurate models of DOT sensitivity must account for age-related changes in the head.¹⁰

Significance: Diffuse optical tomography (DOT) uses near-infrared light spectroscopy (NIRS) to measure changes in cerebral hemoglobin concentration. Anatomical interpretations of NIRS data requires accurate descriptions of the cranio-cerebral relations and DOT sensitivity to the underlying cortical structures. Such information is limited for pediatric populations because they undergo rapid head and brain development. Aim: The present study aimed to investigate age-related differences in scalp-to-cortex distance and mapping between scalp locations and cortical regions of interest (ROIs) among infants (2 weeks to 24 months with narrow age bins), children (4 and 12 years) and adults (20 to 24 years). Approach: We used spatial scalp projection and photon propagation simulation methods with age-matched realistic head models based on MRIs. Results: There were age-group differences in the scalp-to-cortex distances in infancy. The developmental increase was magnified in children and adults. There were systematic age-related differences in the probabilistic mappings between scalp locations and cortical ROIs. Conclusions: Our findings have important implications in the design of sensor placement and making anatomical interpretations in NIRS and fNIRS research. Age-appropriate realistic head models should be used to provide anatomical guidance for standalone DOT data in infants.

Electroencephalographic (EEG) source reconstruction is a powerful approach that helps to unmix scalp signals, mitigates volume conduction issues, and allows anatomical localization of brain activity. Algorithms used to estimate cortical sources require an anatomical model of the head and the brain, generally reconstructed using magnetic resonance imaging (MRI). When such scans are unavailable, a population average can be used for adults, but no average surface template is available for cortical source imaging in infants. To address this issue, this paper introduces a new series of 12 anatomical models for subjects between zero and 24 months of age. These templates are built from MRI averages and volumetric boundary element method segmentation of head tissues available as part of the Neurodevelopmental MRI Database. Surfaces separating the pia mater, the gray matter, and the white matter were estimated using the Infant FreeSurfer pipeline. The surface of the skin as well as the outer and inner skull surfaces were extracted using a cube marching algorithm followed by Laplacian smoothing and mesh decimation. We post-processed these meshes to correct topological errors and ensure watertight meshes. The use of these templates for source reconstruction is demonstrated and validated using 100 high-density EEG recordings in 7-month-old infants. Hopefully, these templates will support future studies based on EEG source reconstruction and functional connectivity in healthy infants as well as in clinical pediatric populations. Particularly, they should make EEG-based neuroimaging more feasible in longitudinal neurodevelopmental studies where it may not be possible to scan infants at multiple time points. Graphical Highlights: Twelve surface templates for infants in the 0-2 years old range are proposed These templates can be used for EEG source reconstruction using existing toolboxes A relatively modest impact of age differences was found in this age range Correlation analysis confirms increasing source differences with age differences Sources reconstructed with infants versus adult templates significantly differ

Children living in low‐resource settings are at risk for failing to reach their developmental potential. While the behavioral outcomes of growing up in such settings are well‐known, the neural mechanisms underpinning poor outcomes have not been well elucidated, particularly in the context of low‐ and middle‐income countries. In this study, we measure brain metabolic responses to social and nonsocial stimuli in a cohort of 6‐ and 36‐month‐old Bangladeshi children. Study participants in both cohorts lived in an urban slum and were exposed to a broad range of adversity early in life including extreme poverty, malnutrition, recurrent infections, and low maternal education. We observed brain regions that responded selectively to social stimuli in both ages indicating that these specialized brain responses are online from an early age. We additionally show that the magnitude of the socially selective response is related to maternal education, maternal stress, and the caregiving environment. Ultimately our results suggest that a variety of psychosocial hazards have a measurable relationship with the developing social brain.