Infant FreeSurfer: An automated segmentation and surface extraction pipeline for T1-weighted neuroimaging data of infants 0-2 years

Impact of antenatal corticosteroids on subcortical volumes in preterm infants at term-equivalent age: A retrospective observational study

OBJECTIVE

Antenatal corticosteroids (ACS) is a well-established treatment for women at risk of preterm birth that improves neonatal outcomes. However, several concerns have been raised regarding the potential long-term adverse effects of ACS on the offspring's developing brain. Here we investigated the association between ACS and subcortical segmental volumes in preterm infants at term-equivalent age.

STUDY DESIGN

This retrospective observational study was conducted using the clinical data of preterm singleton infants born between 220/7 and 336/7 gestational weeks at Nagoya University Hospital in 2014-2020. Subcortical volumes of the bilateral thalami, caudate nuclei, putamens, pallidums, hippocampi, amygdalae, and nuclei accumbens were evaluated using an automated segmentation tool, Infant FreeSurfer, and compared between neonates exposed to a single course of ACS (n = 46) and those who were not (n = 13) by multiple linear regression analysis (covariates: postmenstrual age at magnetic resonance imaging, infant sex, and gestational age at birth). We compared each subcortical volume stratified by gestational age at birth (<28 vs. ≥28 gestational weeks).

RESULTS

Multivariate analyses revealed significantly smaller volumes in the bilateral amygdalae (left, p < 0.03; right, p < 0.03) and caudate nuclei (left, p < 0.03; right, p = 0.04) in neonates with ACS. Significantly smaller volumes in these regions were observed only in neonates born at 28 weeks of gestation or later.

CONCLUSIONS

ACS was associated with smaller volumes of the bilateral amygdalae and caudate nuclei at term-equivalent age. This association was observed exclusively in infants born at 28 weeks of gestation or later.

Baby Open Brains: An Open-Source Repository of Infant Brain Segmentations

Reproducibility of neuroimaging research on infant brain development remains limited due to highly variable protocols and processing approaches. Progress towards reproducible pipelines is limited by a lack of benchmarks such as gold standard brain segmentations. Addressing this core limitation, we constructed the Baby Open Brains (BOBs) Repository, an open source resource comprising manually curated and expert-reviewed infant brain segmentations. Markers and expert reviewers manually segmented anatomical MRI data from 71 infant imaging visits across 51 participants, using both T1w and T2w images per visit. Anatomical images showed dramatic differences in myelination and intensities across the 1 to 9 month age range, emphasizing the need for densely sampled gold standard manual segmentations in these ages. The BOBs repository is publicly available through the Masonic Institute for the Developing Brain (MIDB) Open Data Initiative, which links S3 storage, Datalad for version control, and BrainBox for visualization. This repository represents an open-source paradigm, where new additions and changes can be added, enabling a community-driven resource that will improve over time and extend into new ages and protocols. These manual segmentations and the ongoing repository provide a benchmark for evaluating and improving pipelines dependent upon segmentations in the youngest populations. As such, this repository provides a vitally needed foundation for early-life large-scale studies such as HBCD.

Lateralization of activation within the superior temporal gyrus during speech perception in sleeping infants is associated with subsequent language skills in kindergarten: A passive listening task-fMRI study

Brain asymmetries are hypothesized to reduce functional duplication and thus have evolutionary advantages. The goal of this study was to examine whether early brain lateralization contributes to skill development within the speech-language domain. To achieve this goal, 25 infants (2-13 months old) underwent behavioral language examination and fMRI during sleep while listening to forward and backward speech, and then were assessed on various language skills at 55-69 months old. We observed that infant functional lateralization of the superior temporal gyrus (STG) for forward > backward speech was associated with phonological, vocabulary, and expressive language skills 4 to 5 years later. However, we failed to observe that infant language skills or the anatomical lateralization of STG were related to subsequent language skills. Overall, our findings suggest that infant functional lateralization of STG for speech perception may scaffold subsequent language acquisition, supporting the hypothesis that functional hemisphere asymmetries are advantageous.

Functional and structural maturation of auditory cortex from 2 months to 2 years old

BACKGROUND

In school-age children, the myelination of the auditory radiation thalamocortical pathway is associated with the latency of auditory evoked responses, with the myelination of thalamocortical axons facilitating the rapid propagation of acoustic information. Little is known regarding this auditory system function-structure association in infants and toddlers.

METHODS AND PARTICIPANTS

The present study tested the hypothesis that maturation of auditory radiation white-matter microstructure (e.g., fractional anisotropy (FA); measured using diffusion-weighted MRI) is associated with the latency of the infant auditory response (the P2m response, measured using magnetoencephalography, MEG) in a cross-sectional (N = 47, 2 to 24 months, 19 females) as well as longitudinal cohort (N = 18, 2 to 29 months, 8 females) of typically developing infants and toddlers. Of 18 longitudinal infants, 2 infants had data from 3 timepoints and 16 infants had data from 2 timepoints.

RESULTS

In the cross-sectional sample, non-linear maturation of P2m latency and auditory radiation diffusion measures were observed. Auditory radiation diffusion accounted for significant variance in P2m latency, even after removing the variance associated with age in both P2m latency and auditory radiation diffusion measures. In the longitudinal sample, latency and FA associations could be observed at the level of a single child.

CONCLUSIONS

Findings provide strong support for the hypothesis that an increase in thalamocortical neural conduction velocity, due to increased axon diameter and/or myelin maturation, contributes to a decrease in the infant P2m auditory evoked response latency.

SIGNIFICANCE

Infant multimodal brain imaging identifies brain mechanisms contributing to the rapid changes in neural circuit activity during the first two years of life.

Subcortical Change and Neurohabilitation Treatment Adherence Effects in Extremely Preterm Children

Extremely preterm birth entails an increased risk for multimorbidity and the prevalence of developmental deficits because this risk is negatively correlated to the number of gestation weeks. This work evaluated subcortical volume changes in children born extremely preterm who received Katona neurohabilitation, as well as the effects of subcortical volume and treatment adherence on their three-year-old neurodevelopment outcomes. Fifteen extremely preterm-born participants were treated from two months to two years old and followed up until past three years of age. The participants received Katona neurohabilitation, which provides vestibular and proprioceptive stimulation and promotes movement integration through the early, intensive practice of human-specific elementary movements. Subcortical brain volumes from magnetic resonance images were obtained at the beginning and after treatment. Also, treatment adherence to Katona neurohabilitation and neurodevelopment outcomes were measured. The results showed that absolute subcortical volumes increased after treatment; however, when adjusted by intracranial volume, these volumes decreased. Subcortical function inhibition allows cortical control and increased connectivity, which may explain decreased adjusted volume. Regression analyses showed that after-treatment hippocampal volumes had a discrete predictive value. However, treatment adherence showed a clear effect on mental and psychomotor neurodevelopment. Thus, the effectiveness of Katona neurohabilitation is constrained by treatment adherence.

A brain subcortical segmentation tool based on anatomy attentional fusion network for developing macaques

Magnetic Resonance Imaging (MRI) plays a pivotal role in the accurate measurement of brain subcortical structures in macaques, which is crucial for unraveling the complexities of brain structure and function, thereby enhancing our understanding of neurodegenerative diseases and brain development. However, due to significant differences in brain size, structure, and imaging characteristics between humans and macaques, computational tools developed for human neuroimaging studies often encounter obstacles when applied to macaques. In this context, we propose an Anatomy Attentional Fusion Network (AAF-Net), which integrates multimodal MRI data with anatomical constraints in a multi-scale framework to address the challenges posed by the dynamic development, regional heterogeneity, and age-related size variations of the juvenile macaque brain, thus achieving precise subcortical segmentation. Specifically, we generate a Signed Distance Map (SDM) based on the initial rough segmentation of the subcortical region by a network as an anatomical constraint, providing comprehensive information on positions, structures, and morphology. Then we construct AAF-Net to fully fuse the SDM anatomical constraints and multimodal images for refined segmentation. To thoroughly evaluate the performance of our proposed tool, over 700 macaque MRIs from 19 datasets were used in this study. Specifically, we employed two manually labeled longitudinal macaque datasets to develop the tool and complete four-fold cross-validations. Furthermore, we incorporated various external datasets to demonstrate the proposed tool's generalization capabilities and promise in brain development research. We have made this tool available as an open-source resource at https://github.com/TaoZhong11/Macaque_subcortical_segmentation for direct application.

Neonatal amygdala volumes, procedural pain and the association with social-emotional development in children born very preterm

Very preterm birth (< 32 weeks' gestational age) is associated with later social and emotional impairments, which may result from enhanced vulnerability of the limbic system during this period of heightened vulnerability. Evidence suggests that early procedural pain may be a key moderator of early brain networks. In a prospective cohort study, neonates born very preterm (< 30 weeks' gestation) underwent MRI scanning at term-equivalent age (TEA) and clinical data were collected (mechanical ventilation, analgesics, sedatives). Procedural pain was operationalized as the number of skin breaking procedures. Amygdala volumes were automatically extracted. The Strengths and Difficulties questionnaire was used to assess social-emotional outcomes at 5 years of age (mean age 67.5 months). General linear models were employed to examine the association between neonatal amygdala volumes and social-emotional outcomes and the timing and amount of procedural pain exposure (early within the first weeks of life to TEA) as a moderator, adjusting for biological sex, gestational age, 5-year assessment age, days of mechanical ventilation and total cerebral volumes. A total of 42 preterm infants participated. Right amygdala volumes at TEA were associated with prosocial behaviour at age 5 (B = -0.010, p = 0.005). Procedural pain was found to moderate the relationship between right amygdala volumes in the neonatal period and conduct problems at 5 years, such that early skin breaking procedures experienced within the first few weeks of life strengthened the association between right amygdala volumes and conduct problems (B = 0.005, p = 0.047). Late skin breaking procedures, experienced near TEA, also strengthened the association between right amygdala volumes and conduct problems (B = 0.004, p = 0.048).

White matter connections of human ventral temporal cortex are organized by cytoarchitecture, eccentricity, and category-selectivity from birth

Category-selective regions in ventral temporal cortex (VTC) have a consistent anatomical organization, which is hypothesized to be scaffolded by white matter connections. However, it is unknown how white matter connections are organized from birth. Here, we scanned newborn to 6-month-old infants and adults and used a data-driven approach to determine the organization of the white matter connections of VTC. We find that white matter connections are organized by cytoarchitecture, eccentricity, and category from birth. Connectivity profiles of functional regions in the same cytoarchitectonic area are similar from birth and develop in parallel, with decreases in endpoint connectivity to lateral occipital, and parietal, and somatosensory cortex, and increases to lateral prefrontal cortex. Additionally, connections between VTC and early visual cortex are organized topographically by eccentricity bands and predict eccentricity biases in VTC. These data have important implications for theories of cortical functional development and open new possibilities for understanding typical and atypical white matter development.

Identification and treatment of surgically-remediable causes of infantile epileptic spasms syndrome

INTRODUCTION

Infantile epileptic spasms syndrome (IESS) is a common developmental and epileptic encephalopathy with poor long-term outcomes. A substantial proportion of patients with IESS have a potentially surgically remediable etiology. Despite this, epilepsy surgery is underutilized in this patient group. Some surgically remediable etiologies, such as focal cortical dysplasia and malformation of cortical development with oligodendroglial hyperplasia in epilepsy (MOGHE), are under-diagnosed in infants and young children. Even when a surgically remediable etiology is recognised, for example, tuberous sclerosis or focal encephalomalacia, epilepsy surgery may be delayed or not considered due to diffuse EEG changes, unclear surgical boundaries, or concerns about operating in this age group.

AREAS COVERED

In this review, the authors discuss the common surgically remediable etiologies of IESS, their clinical and EEG features, and the imaging techniques that can aid in their diagnosis. They then describe the surgical approaches used in this patient group, and the beneficial impact that early epilepsy surgery can have on developing brain networks.

EXPERT OPINION

Epilepsy surgery remains underutilized even when a potentially surgically remediable cause is recognized. Overcoming the barriers that result in under-recognition of surgical candidates and underutilization of epilepsy surgery in IESS will improve long-term seizure and developmental outcomes.

Longitudinal trajectories of brain development from infancy to school age and their relationship to literacy development

Reading is one of the most complex skills that we utilize daily, and it involves the early development and interaction of various lower-level subskills, including phonological processing and oral language. These subskills recruit brain structures, which begin to develop long before the skill manifests and exhibit rapid development during infancy. However, how longitudinal trajectories of early brain development in these structures supports long-term acquisition of literacy subskills and subsequent reading is unclear. Children underwent structural and diffusion MRI scanning at multiple timepoints between infancy and second grade and were tested for literacy subskills in preschool and decoding and word reading in early elementary school. We developed and implemented a reproducible pipeline to generate longitudinal trajectories of early brain development to examine associations between these trajectories and literacy (sub)skills. Furthermore, we examined whether familial risk of reading difficulty and a child's home literacy environment, two common literacy-related covariates, influenced those trajectories. Results showed that individual differences in curve features (e.g., intercepts and slopes) for longitudinal trajectories of volumetric, surface-based, and white matter organization measures in left-hemispheric reading-related regions and tracts were linked directly to phonological processing and indirectly to second-grade decoding and word reading skills via phonological processing. Altogether, these findings suggest that the brain bases of phonological processing, previously identified as the strongest behavioral predictor of reading and decoding skills, may already begin to develop early in infancy but undergo further refinement between birth and preschool. The present study underscores the importance of considering academic skill acquisition from the very beginning of life.

Consortium Profile: The Methylation, Imaging and NeuroDevelopment (MIND) Consortium

Epigenetic processes, such as DNA methylation, show potential as biological markers and mechanisms underlying gene-environment interplay in the prediction of mental health and other brain-based phenotypes. However, little is known about how peripheral epigenetic patterns relate to individual differences in the brain itself. An increasingly popular approach to address this is by combining epigenetic and neuroimaging data; yet, research in this area is almost entirely comprised of cross-sectional studies in adults. To bridge this gap, we established the Methylation, Imaging and NeuroDevelopment (MIND) Consortium, which aims to bring a developmental focus to the emerging field of Neuroimaging Epigenetics by (i) promoting collaborative, adequately powered developmental research via multi-cohort analyses; (ii) increasing scientific rigor through the establishment of shared pipelines and open science practices; and (iii) advancing our understanding of DNA methylation-brain dynamics at different developmental periods (from birth to emerging adulthood), by leveraging data from prospective, longitudinal pediatric studies. MIND currently integrates 15 cohorts worldwide, comprising (repeated) measures of DNA methylation in peripheral tissues (blood, buccal cells, and saliva) and neuroimaging by magnetic resonance imaging across up to five time points over a period of up to 21 years (Npooled DNAm = 11,299; Npooled neuroimaging = 10,133; Npooled combined = 4,914). By triangulating associations across multiple developmental time points and study types, we hope to generate new insights into the dynamic relationships between peripheral DNA methylation and the brain, and how these ultimately relate to neurodevelopmental and psychiatric phenotypes.

Functional and structural maturation of auditory cortex from 2 months to 2 years old

In school-age children, the myelination of the auditory radiation thalamocortical pathway is associated with the latency of auditory evoked responses, with the myelination of thalamocortical axons facilitating the rapid propagation of acoustic information. Little is known regarding this auditory system function-structure association in infants and toddlers. The present study tested the hypothesis that maturation of auditory radiation white-matter microstructure (e.g., fractional anisotropy (FA); measured using diffusion-weighted MRI) is associated with the latency of the infant auditory response (P2m measured using magnetoencephalography, MEG) in a cross-sectional (2 to 24 months) as well as longitudinal cohort (2 to 29 months) of typically developing infants and toddlers. In the cross-sectional sample, non-linear maturation of P2m latency and auditory radiation diffusion measures were observed. After removing the variance associated with age in both P2m latency and auditory radiation diffusion measures, auditory radiation still accounted for significant variance in P2m latency. In the longitudinal sample, latency and FA associations could be observed at the level of a single child. Findings provide strong support for a contribution of auditory radiation white matter to rapid cortical auditory encoding processes in infants.

Why did my seizures start now? Influences of lesion connectivity and genetic etiology on age at seizure onset in focal epilepsy

OBJECTIVE

Patients with focal, lesional epilepsy present with seizures at variable ages. Larger lesion size and overlap with sensorimotor or default mode network (DMN) have been associated with younger age at seizure onset in cohorts with mixed types of focal cortical dysplasia (FCD). Here, we studied determinants of age at seizure onset in patients with bottom-of-sulcus dysplasia (BOSD), a discrete type of FCD with highly localized epileptogenicity.

METHODS

Eighty-four patients (77% operated) with BOSD were studied. Demographic, histopathologic, and genetic findings were recorded. BOSD volume and anatomical, primary versus association, rostral versus caudal, and functional network locations were determined. Normative functional connectivity analyses were performed using each BOSD as a region of interest in resting-state functional magnetic resonance imaging data of healthy children. Variables were correlated with age at seizure onset.

RESULTS

Median age at seizure onset was 5.4 (interquartile range = 2-7.9) years. Of 50 tested patients, 22 had somatic and nine had germline pathogenic mammalian target of rapamycin (mTOR) pathway variants. Younger age at seizure onset was associated with greater BOSD volume (p = .002), presence of a germline pathogenic variant (p = .04), DMN overlap (p = .04), and increased functional connectivity with the DMN (p < .05, false discovery rate corrected). Location within sensorimotor cortex and networks was not associated with younger age at seizure onset in our relatively small but homogenous cohort.

SIGNIFICANCE

Greater lesion size, pathogenic mTOR pathway germline variants, and DMN connectivity are associated with younger age at seizure onset in small FCD. Our findings strengthen the suggested role of DMN connectivity in the onset of FCD-related focal epilepsy and reveal novel contributions of genetic etiology.

A regional brain volume-based age prediction model for neonates and the derived brain maturation index

OBJECTIVE

To develop a postmenstrual age (PMA) prediction model based on segmentation volume and to evaluate the brain maturation index using the proposed model.

METHODS

Neonatal brain MRIs without clinical illness or structural abnormalities were collected from four datasets from the Developing Human Connectome Project, the Catholic University of Korea, Hammersmith Hospital (HS), and Dankook University Hospital (DU). T1- and T2-weighted images were used to train a brain segmentation model. Another model to predict the PMA of neonates based on segmentation data was developed. Accuracy was assessed using mean absolute error (MAE), root mean square error (RMSE), and mean error (ME). The brain maturation index was calculated as the difference between the PMA predicted by the model and the true PMA, and its correlation with postnatal age was analyzed.

RESULTS

A total of 247 neonates (mean gestation age 37 ± 4 weeks; range 24-42 weeks) were included. Thirty-one features were extracted from each neonate and the three most contributing features for PMA prediction were the right lateral ventricle, left caudate, and corpus callosum. The predicted and true PMA were positively correlated (coefficient = 0.88, p < .001). MAE, RMSE, and ME of the external dataset of HS and DU were 1.57 and 1.33, 1.79 and 1.37, and 0.37 and 0.06 weeks, respectively. The brain maturation index negatively correlated with postnatal age (coefficient =  - 0.24, p < .001).

CONCLUSION

A model that calculates the regional brain volume can predict the PMA of neonates, which can then be utilized to show the brain maturation degree.

CLINICAL RELEVANCE STATEMENT

A brain maturity index based on regional volume of neonate's brain can be used to measure brain maturation degree, which can help identify the status of early brain development.

KEY POINTS

• Neonatal brain MRI segmentation model could be used to assess neonatal brain maturation status. • A postmenstrual age (PMA) prediction model was developed based on a neonatal brain MRI segmentation model. • The brain maturation index, derived from the PMA prediction model, enabled the estimation of the neonatal brain maturation status.

An end-to-end infant brain parcellation pipeline

Objective

Accurate infant brain parcellation is crucial for understanding early brain development; however, it is challenging due to the inherent low tissue contrast, high noise, and severe partial volume effects in infant magnetic resonance images (MRIs). The aim of this study was to develop an end-to-end pipeline that enabled accurate parcellation of infant brain MRIs.

Methods

We proposed an end-to-end pipeline that employs a two-stage global-to-local approach for accurate parcellation of infant brain MRIs. Specifically, in the global regions of interest (ROIs) localization stage, a combination of transformer and convolution operations was employed to capture both global spatial features and fine texture features, enabling an approximate localization of the ROIs across the whole brain. In the local ROIs refinement stage, leveraging the position priors from the first stage along with the raw MRIs, the boundaries o the ROIs are refined for a more accurate parcellation.

Results

We utilized the Dice ratio to evaluate the accuracy of parcellation results. Results on 263 subjects from National Database for Autism Research (NDAR), Baby Connectome Project (BCP) and Cross-site datasets demonstrated the better accuracy and robustness of our method than other competing methods.

Conclusion

Our end-to-end pipeline may be capable of accurately parcellating 6-month-old infant brain MRIs.

BOOSTING SKULL-STRIPPING PERFORMANCE FOR PEDIATRIC BRAIN IMAGES

Skull-stripping is the removal of background and non-brain anatomical features from brain images. While many skull-stripping tools exist, few target pediatric populations. With the emergence of multi-institutional pediatric data acquisition efforts to broaden the understanding of perinatal brain development, it is essential to develop robust and well-tested tools ready for the relevant data processing. However, the broad range of neuroanatomical variation in the developing brain, combined with additional challenges such as high motion levels, as well as shoulder and chest signal in the images, leaves many adult-specific tools ill-suited for pediatric skull-stripping. Building on an existing framework for robust and accurate skull-stripping, we propose developmental SynthStrip (d-SynthStrip), a skull-stripping model tailored to pediatric images. This framework exposes networks to highly variable images synthesized from label maps. Our model substantially outperforms pediatric baselines across scan types and age cohorts. In addition, the <1-minute runtime of our tool compares favorably to the fastest baselines. We distribute our model at https://w3id.org/synthstrip.

Intrauterine exposure to chorioamnionitis and neuroanatomical alterations at term-equivalent age in preterm infants

PURPOSE

Infants born to mothers with chorioamnionitis (CAM) are at increased risk of developing adverse neurodevelopmental disorders in later life. However, clinical magnetic resonance imaging (MRI) studies examining brain injuries and neuroanatomical alterations attributed to CAM have yielded inconsistent results. We aimed to determine whether exposure to histological CAM in utero leads to brain injuries and alterations in the neuroanatomy of preterm infants using 3.0- Tesla MRI at term-equivalent age.

METHODS

A total of 58 preterm infants born before 34 weeks of gestation at Nagoya University Hospital between 2010 and 2018 were eligible for this study (CAM group, n = 21; non-CAM group, n = 37). Brain injuries and abnormalities were assessed using the Kidokoro Global Brain Abnormality Scoring system. Gray matter, white matter, and subcortical gray matter (thalamus, caudate nucleus, putamen, pallidum, hippocampus, amygdala, and nucleus accumbens) volumes were evaluated using segmentation tools (SPM12 and Infant FreeSurfer).

RESULTS

The Kidokoro scores for each category and severity in the CAM group were comparable to those observed in the non-CAM group. White matter volume was significantly smaller in the CAM group after adjusting for covariates (postmenstrual age at MRI, infant sex, and gestational age) (p = 0.007), whereas gray matter volume was not significantly different. Multiple linear regression analyses revealed significantly smaller volumes in the bilateral pallidums (right, p = 0.045; left, p = 0.038) and nucleus accumbens (right, p = 0.030; left, p = 0.004) after adjusting for covariates.

CONCLUSIONS

Preterm infants born to mothers with histological CAM showed smaller volumes in white matter, pallidum, and nucleus accumbens at term-equivalent age.

Distinct alterations in white matter properties and organization related to maternal treatment initiation in neonates exposed to HIV but uninfected

HIV exposed-uninfected (HEU) infants and children are at risk of developmental delays as compared to HIV uninfected unexposed (HUU) populations. The effects of exposure to in utero HIV and ART regimens on the HEU the developing brain are not well understood. In a cohort of 2-week-old newborns, we used diffusion tensor imaging (DTI) tractography and graph theory to examine the influence of HIV and ART exposure in utero on neonate white matter integrity and organisation. The cohort included HEU infants born to mothers who started ART before conception (HEUpre) and after conception (HEUpost), as well as HUU infants from the same community. We investigated HIV exposure and ART duration group differences in DTI metrics (fractional anisotropy (FA) and mean diffusivity (MD)) and graph measures across white matter. We found increased MD in white matter connections involving the thalamus and limbic system in the HEUpre group compared to HUU. We further identified reduced nodal efficiency in the basal ganglia. Within the HEUpost group, we observed reduced FA in cortical-subcortical and cerebellar connections as well as decreased transitivity in the hindbrain area compared to HUU. Overall, our analysis demonstrated distinct alterations in white matter integrity related to the timing of maternal ART initiation that influence regional brain network properties.

Clinical-functional correlation with brain volumetry in severe perinatal asphyxia: a case report

BACKGROUND

Hypoxic-ischemic encephalopathy (HIE) appears in neurological conditions where some brain areas are likely to be injured, such as deep grey matter, basal ganglia area, and white matter subcortical periventricular áreas. Moreover, modeling these brain areas in a newborn is challenging due to significant variability in the intensities associated with HIE conditions. This paper aims to evaluate functional measurements and 3D machine learning models of a given HIE case by correlating the affected brain areas with the pathophysiology and clinical neurodevelopmental.

CASE PRESENTATION

A comprehensive analysis of a term infant with perinatal asphyxia using longitudinal 3D brain information from Machine Learning Models is presented. The clinical analysis revealed the perinatal asphyxia diagnosis with APGAR <5 at 5 and 10 minutes, umbilical arterial pH of 7.0 BE of -21.2 mmol / L), neonatal seizures, and invasive ventilation mechanics. Therapeutic interventions: physical, occupational, and language neurodevelopmental therapies. Epilepsy treatment: vagus nerve stimulation, levetiracetam, and phenobarbital. Furthermore, the 3D analysis showed how the volume decreases due to age, exhibiting an increasing asymmetry between hemispheres. The results of the basal ganglia area showed that thalamus asymmetry, caudate, and putamen increase over time while globus pallidus decreases.

CLINICAL OUTCOMES

spastic cerebral palsy, microcephaly, treatment-refractory epilepsy.

CONCLUSIONS

Slight changes in the basal ganglia and cerebellum require 3D volumetry for detection, as standard MRI examinations cannot fully reveal their complex shape variations. Quantifying these subtle neurodevelopmental changes helps in understanding their clinical implications. Besides, neurophysiological evaluations can boost neuroplasticity in children with neurological sequelae by stimulating new neuronal connections.

Early, low-dose hydrocortisone and near-term brain connectivity in extremely preterm infants

BACKGROUND

Postnatal steroids are used to prevent bronchopulmonary dysplasia in extremely preterm infants but may have adverse effects on brain development. We assessed connectivity metrics of major cerebral and cerebellar white matter pathways at near-term gestational age among infants who did or did not receive a standardized regimen of hydrocortisone during the first 10 days of life.

METHODS

Retrospective cohort study.

PARTICIPANTS

Infants born <28 weeks: Protocol group (n = 33) received at least 50% and not more than 150% of an intended standard dose of 0.5 mg/kg hydrocortisone twice daily for 7 days, then 0.5 mg/kg per day for 3 days; Non-Protocol group (n = 22), did not receive protocol hydrocortisone or completed <50% of the protocol dose. We assessed group differences in near-term diffusion MRI mean fractional anisotropy (FA) and mean diffusivity (MD) across the corticospinal tract, inferior longitudinal fasciculus, corpus callosum and superior cerebellar peduncle.

RESULTS

Groups were comparable in gestational age, post-menstrual age at scan, medical complications, bronchopulmonary dysplasia, and necrotizing enterocolitis. No significant large effect group differences were identified in mean FA or MD in any cerebral or cerebellar tract.

CONCLUSION(S)

Low dose, early, postnatal hydrocortisone was not associated with significant differences in white matter tract microstructure at near-term gestational age.

IMPACT

This study compared brain microstructural connectivity as a primary outcome among extremely preterm infants who did or did not receive early postnatal hydrocortisone. Low dose hydrocortisone in the first 10 days of life was not associated with significant differences in white matter microstructure in major cerebral and cerebellar pathways. Hydrocortisone did not have a significant effect on early brain white matter circuits.

BOOSTING SKULL-STRIPPING PERFORMANCE FOR PEDIATRIC BRAIN IMAGES

Skull-stripping is the removal of background and non-brain anatomical features from brain images. While many skull-stripping tools exist, few target pediatric populations. With the emergence of multi-institutional pediatric data acquisition efforts to broaden the understanding of perinatal brain development, it is essential to develop robust and well-tested tools ready for the relevant data processing. However, the broad range of neuroanatomical variation in the developing brain, combined with additional challenges such as high motion levels, as well as shoulder and chest signal in the images, leaves many adult-specific tools ill-suited for pediatric skull-stripping. Building on an existing framework for robust and accurate skull-stripping, we propose developmental SynthStrip (d-SynthStrip), a skull-stripping model tailored to pediatric images. This framework exposes networks to highly variable images synthesized from label maps. Our model substantially outperforms pediatric baselines across scan types and age cohorts. In addition, the <1-minute runtime of our tool compares favorably to the fastest baselines. We distribute our model at https://w3id.org/synthstrip.

Altered resting-state functional connectivity in newborns with hypoxic ischemic encephalopathy assessed using high-density functional near-infrared spectroscopy

Hypoxic-ischemic encephalopathy (HIE) results from a lack of oxygen to the brain during the perinatal period. HIE can lead to mortality and various acute and long-term morbidities. Improved bedside monitoring methods are needed to identify biomarkers of brain health. Functional near-infrared spectroscopy (fNIRS) can assess resting-state functional connectivity (RSFC) at the bedside. We acquired resting-state fNIRS data from 21 neonates with HIE (postmenstrual age [PMA] = 39.96), in 19 neonates the scans were acquired post-therapeutic hypothermia (TH), and from 20 term-born healthy newborns (PMA = 39.93). Twelve HIE neonates also underwent resting-state functional magnetic resonance imaging (fMRI) post-TH. RSFC was calculated as correlation coefficients amongst the time courses for fNIRS and fMRI data, respectively. The fNIRS and fMRI RSFC maps were comparable. RSFC patterns were then measured with graph theory metrics and compared between HIE infants and healthy controls. HIE newborns showed significantly increased clustering coefficients, network efficiency and modularity compared to controls. Using a support vector machine algorithm, RSFC features demonstrated good performance in classifying the HIE and healthy newborns in separate groups. Our results indicate the utility of fNIRS-connectivity patterns as potential biomarkers for HIE and fNIRS as a new bedside tool for newborns with HIE.

Sex- and age-based differences in fetal and early childhood hippocampus maturation: a cross-sectional and longitudinal analysis

The hippocampus, essential for cognitive and affective processes, develops exponentially with differential trajectories seen in girls and boys, yet less is known about its development during early fetal life until early childhood. In a cross-sectional and longitudinal study, we examined the sex-, age-, and laterality-related developmental trajectories of hippocampal volumes in fetuses, infants, and toddlers associated with age. Third trimester fetuses (27-38 weeks' gestational age), newborns (0-4 weeks' postnatal age), infants (5-50 weeks' postnatal age), and toddlers (2-3 years postnatal age) were scanned with magnetic resonance imaging. A total of 133 datasets (62 female, postmenstrual age [weeks] M = 69.38, SD = 51.39, range = 27.6-195.3) were processed using semiautomatic segmentation methods. Hippocampal volumes increased exponentially during the third trimester and the first year of life, beginning to slow at approximately 2 years. Overall, boys had larger hippocampal volumes than girls. Lateralization differences were evident, with left hippocampal growth beginning to plateau sooner than the right. This period of rapid growth from the third trimester, continuing through the first year of life, may support the development of cognitive and affective function during this period.

Distinct alterations in white matter properties and organization related to maternal treatment initiation in neonates exposed to HIV but uninfected

HIV exposed-uninfected (HEU) infants and children are at risk of developmental delays as compared to uninfected unexposed (HUU) populations. The effects of exposure to in utero HIV and ART regimens on the HEU the developing brain are not well understood. In a cohort of 2-week-old newborns, we used diffusion tensor imaging (DTI) tractography and graph theory to examine the influence of HIV and ART exposure in utero on neonate white matter integrity and organisation. The cohort included HEU infants born to mothers who started ART before conception (HEUpre) and after conception (HEUpost), as well as HUU infants from the same community. We investigated HIV exposure and ART duration group differences in DTI metrics (fractional anisotropy (FA) and mean diffusivity (MD)) and graph measures across white matter. We found increased MD in white matter connections involving the thalamus and limbic system in the HEUpre group compared to HUU. We further identified reduced nodal efficiency in the basal ganglia. Within the HEUpost group, we observed reduced FA in cortical-subcortical and cerebellar connections as well as decreased transitivity in the hindbrain area compared to HUU. Overall, our analysis demonstrated distinct alterations in white matter integrity related to the timing of maternal ART initiation that influence regional brain network properties.

A global multicohort study to map subcortical brain development and cognition in infancy and early childhood

The human brain grows quickly during infancy and early childhood, but factors influencing brain maturation in this period remain poorly understood. To address this gap, we harmonized data from eight diverse cohorts, creating one of the largest pediatric neuroimaging datasets to date focused on birth to 6 years of age. We mapped the developmental trajectory of intracranial and subcortical volumes in ∼2,000 children and studied how sociodemographic factors and adverse birth outcomes influence brain structure and cognition. The amygdala was the first subcortical volume to mature, whereas the thalamus exhibited protracted development. Males had larger brain volumes than females, and children born preterm or with low birthweight showed catch-up growth with age. Socioeconomic factors exerted region- and time-specific effects. Regarding cognition, males scored lower than females; preterm birth affected all developmental areas tested, and socioeconomic factors affected visual reception and receptive language. Brain-cognition correlations revealed region-specific associations.

Pediatric disorders of consciousness: Considerations, controversies, and caveats

Pediatric disorders of consciousness (PedDOC) encompass conditions that may occur following very severe traumatic or other forms of acquired brain injury sustained during childhood. As in adults, PedDOC is described as a disturbance of awareness and/or responsiveness. PedDOC is a complex condition that requires specialized care, infrastructures, and technologies. PedDOC poses many challenges to healthcare providers and caregivers during recovery and throughout development. In this commentary, we intend to highlight some considerations, controversies, and caveats on the diagnosis, prognosis and treatment of PedDOC.

Concurrent and prospective associations between infant frontoparietal and default mode network connectivity and negative affectivity

Emotion dysregulation is linked to differences in frontoparietal (FPN) and default mode (DMN) brain network functioning. These differences may be identifiable early in development. Temperamental negative affectivity has been identified as a precursor to later emotion dysregulation, though the underlying neurodevelopmental mechanism is unknown. The present study explores concurrent and prospective associations between FPN and DMN connectivity in infants and measures of negative affectivity. 72 infants underwent 5.03-13.28 min of resting state fMRI during natural sleep (M±SD age=4.90 ± 0.84 weeks; 54% male; usable data=9.92 ± 2.15 min). FPN and DMN intra- and internetwork connectivity were computed using adult network assignments. Crying was obtained from both parent-report and day-long audio recordings. Temperamental negative affectivity was obtained from a parent-report questionnaire. In this preregistered study, based on analyses conducted with a subset of this data (N = 32), we hypothesized that greater functional connectivity within and between FPN and DMN would be associated with greater negative affectivity. In the full sample we did not find support for these hypotheses. Instead, greater DMN intranetwork connectivity at age one month was associated with lower concurrent parent-reported crying and temperamental negative affectivity at age six months (ßs>-0.35, ps<.025), but not crying at age six months. DMN intranetwork connectivity was also negatively associated with internalizing symptoms at age eighteen-months (ß=-0.58, p = .012). FPN intra- and internetwork connectivity was not associated with negative affectivity measures after accounting for covariates. This work furthers a neurodevelopmental model of emotion dysregulation by suggesting that infant functional connectivity at rest is associated with later emotional functioning.

Subcortical brain volumes in neonatal hypoxic-ischemic encephalopathy

BACKGROUND

Despite treatment with therapeutic hypothermia, hypoxic-ischemic encephalopathy (HIE) is associated with adverse developmental outcomes, suggesting the involvement of subcortical structures including the thalamus and basal ganglia, which may be vulnerable to perinatal asphyxia, particularly during the acute period. The aims were: (1) to examine subcortical macrostructure in neonates with HIE compared to age- and sex-matched healthy neonates within the first week of life; (2) to determine whether subcortical brain volumes are associated with HIE severity.

METHODS

Neonates (n = 56; HIE: n = 28; Healthy newborns from the Developing Human Connectome Project: n = 28) were scanned with MRI within the first week of life. Subcortical volumes were automatically extracted from T1-weighted images. General linear models assessed between-group differences in subcortical volumes, adjusting for sex, gestational age, postmenstrual age, and total cerebral volumes. Within-group analyses evaluated the association between subcortical volumes and HIE severity.

RESULTS

Neonates with HIE had smaller bilateral thalamic, basal ganglia and right hippocampal and cerebellar volumes compared to controls (all, p < 0.02). Within the HIE group, mild HIE severity was associated with smaller volumes of the left and right basal ganglia (both, p < 0.007) and the left hippocampus and thalamus (both, p < 0.04).

CONCLUSIONS

Findings suggest that, despite advances in neonatal care, HIE is associated with significant alterations in subcortical brain macrostructure.

IMPACT

Compared to their healthy counterparts, infants with HIE demonstrate significant alterations in subcortical brain macrostructure on MRI acquired as early as 4 days after birth. Smaller subcortical volumes impacting sensory and motor regions, including the thalamus, basal ganglia, and cerebellum, were seen in infants with HIE. Mild and moderate HIE were associated with smaller subcortical volumes.

Developmental atlas of phase-amplitude coupling between physiologic high-frequency oscillations and slow waves

We investigated the developmental changes in high-frequency oscillation (HFO) and Modulation Index (MI) - the coupling measure between HFO and slow-wave phase. We generated normative brain atlases, using subdural EEG signals from 8251 nonepileptic electrode sites in 114 patients (ages 1.0-41.5 years) who achieved seizure control following resective epilepsy surgery. We observed a higher MI in the occipital lobe across all ages, and occipital MI increased notably during early childhood. The cortical areas exhibiting MI co-growth were connected via the vertical occipital fasciculi and posterior callosal fibers. While occipital HFO rate showed no significant age-association, the temporal, frontal, and parietal lobes exhibited an age-inversed HFO rate. Assessment of 1006 seizure onset sites revealed that z-score normalized MI and HFO rate were higher at seizure onset versus nonepileptic electrode sites. We have publicly shared our intracranial EEG data to enable investigators to validate MI and HFO-centric presurgical evaluations to identify the epileptogenic zone.

Volumetric differences of thalamic nuclei in children with trisomy 21

OBJECTIVES

Histological studies have shown alterations of thalamic nuclei in patients with Down syndrome (DS). The correlation of these changes on MRI (magnetic resonance imaging) is unclear. Therefore, this study investigates volumetric differences of thalamic nuclei in children with DS compared to controls.

METHODS

Patients were retrospectively identified between 01/2000 and 10/2021. Patient inclusion criteria were: (1) 0-18 years of age, (2) diagnosis of DS, and (3) availability of a brain MRI without parenchymal injury and a non-motion-degraded volumetric T1-weighted sequence. Whole thalamus and thalamic nuclei (n = 25) volumes were analyzed bilaterally relative to the total brain volume (TBV). Two-sided t-tests were used to evaluate differences between groups. Differences were considered significant if the adjusted p-value was <0.05 after correction for multiple hypothesis testing using the Holm-Bonferroni method.

RESULTS

21 children with DS (11 females, 52.4%, mean age: 8.6 ± 4.3 years) and 63 age- and sex-matched controls (32 females, 50.8%, 8.6 ± 4.3 years) were studied using automated volumetric segmentation. Significantly smaller ratios were found for nine thalamic nuclei and the whole thalamus on the right and five thalamic nuclei on the left. TBV was significantly smaller in patients with DS (p < 0.001). No significant differences were found between the groups for age and sex.

CONCLUSIONS

In this exploratory volumetric analysis of the thalamus and thalamic nuclei, we observed statistically significant volumetric changes in children with DS. Our findings confirm prior neuroimaging and histological studies and extend the range of involved thalamic nuclei in pediatric DS.

Brain Growth Charts for Quantitative Analysis of Pediatric Clinical Brain MRI Scans with Limited Imaging Pathology

Background Clinically acquired brain MRI scans represent a valuable but underused resource for investigating neurodevelopment due to their technical heterogeneity and lack of appropriate controls. These barriers have curtailed retrospective studies of clinical brain MRI scans compared with more costly prospectively acquired research-quality brain MRI scans. Purpose To provide a benchmark for neuroanatomic variability in clinically acquired brain MRI scans with limited imaging pathology (SLIPs) and to evaluate if growth charts from curated clinical MRI scans differed from research-quality MRI scans or were influenced by clinical indication for the scan. Materials and Methods In this secondary analysis of preexisting data, clinical brain MRI SLIPs from an urban pediatric health care system (individuals aged ≤22 years) were scanned across nine 3.0-T MRI scanners. The curation process included manual review of signed radiology reports and automated and manual quality review of images without gross pathology. Global and regional volumetric imaging phenotypes were measured using two image segmentation pipelines, and clinical brain growth charts were quantitatively compared with charts derived from a large set of research controls in the same age range by means of Pearson correlation and age at peak volume. Results The curated clinical data set included 532 patients (277 male; median age, 10 years [IQR, 5-14 years]; age range, 28 days after birth to 22 years) scanned between 2005 and 2020. Clinical brain growth charts were highly correlated with growth charts derived from research data sets (22 studies, 8346 individuals [4947 male]; age range, 152 days after birth to 22 years) in terms of normative developmental trajectories predicted by the models (median r = 0.979). Conclusion The clinical indication of the scans did not significantly bias the output of clinical brain charts. Brain growth charts derived from clinical controls with limited imaging pathology were highly correlated with brain charts from research controls, suggesting the potential of curated clinical MRI scans to supplement research data sets. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Ertl-Wagner and Pai in this issue.

SurfFlow: A Flow-Based Approach for Rapid and Accurate Cortical Surface Reconstruction from Infant Brain MRI

The infant brain undergoes rapid changes in volume, shape, and structural organization during the first postnatal year. Accurate cortical surface reconstruction (CSR) is essential for understanding rapid changes in cortical morphometry during early brain development. However, existing CSR methods, designed for adult brain MRI, fall short in reconstructing cortical surfaces from infant MRI, owing to the poor tissue contrasts, partial volume effects, and rapid changes in cortical folding patterns. Here, we introduce an infant-centric CSR method in light of these challenges. Our method, SurfFlow, utilizes three seamlessly connected deformation blocks to sequentially deform an initial template mesh to target cortical surfaces. Remarkably, our method can rapidly reconstruct a high-resolution cortical surface mesh with 360k vertices in approximately one second. Performance evaluation based on an MRI dataset of infants 0 to 12 months of age indicates that SurfFlow significantly reduces geometric errors and substantially improves mesh regularity compared with state-of-the-art deep learning approaches.

White matter tract microstructure, macrostructure, and associated cortical gray matter morphology across the lifespan

Characterizing how, when and where the human brain changes across the lifespan is fundamental to our understanding of developmental processes of childhood and adolescence, degenerative processes of aging, and divergence from normal patterns in disease and disorders. We aimed to provide detailed descriptions of white matter pathways across the lifespan by thoroughly characterizing white matter microstructure, white matter macrostructure, and morphology of the cortex associated with white matter pathways. We analyzed 4 large, high-quality, publicly-available datasets comprising 2789 total imaging sessions, and participants ranging from 0 to 100 years old, using advanced tractography and diffusion modeling. We first find that all microstructural, macrostructural, and cortical features of white matter bundles show unique lifespan trajectories, with rates and timing of development and degradation that vary across pathways - describing differences between types of pathways and locations in the brain, and developmental milestones of maturation of each feature. Second, we show cross-sectional relationships between different features that may help elucidate biological changes occurring during different stages of the lifespan. Third, we show unique trajectories of age-associations across features. Finally, we find that age associations during development are strongly related to those during aging. Overall, this study reports normative data for several features of white matter pathways of the human brain that will be useful for studying normal and abnormal white matter development and degeneration.

Self-supervised learning with application for infant cerebellum segmentation and analysis

Accurate tissue segmentation is critical to characterize early cerebellar development in the first two postnatal years. However, challenges in tissue segmentation arising from tightly-folded cortex, low and dynamic tissue contrast, and large inter-site data heterogeneity have limited our understanding of early cerebellar development. In this paper, we propose an accurate self-supervised learning framework for infant cerebellum segmentation. We validate its accuracy using 358 subjects from three datasets. Our results suggest the first six months exhibit the most rapid and dynamic changes, with gray matter (GM) playing a dominant role in cerebellar growth over white matter (WM). We also find both GM and WM volumes are larger in males than females, and GM and WM volumes are larger in autistic males than neurotypical males. Application of our method to a larger population will fuel more cerebellar studies, ultimately advancing our comprehension of its structure and function in neurotypical and disordered development.

A Graph Convolutional Network Based on Univariate Neurodegeneration Biomarker for Alzheimer's Disease Diagnosis

OBJECTIVE

Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease that is not easily detectable in the early stage. This study proposed an efficient method of applying a graph convolutional network (GCN) on the early prediction of AD.

METHODS

We proposed a univariate neurodegeneration biomarker (UNB) based GCN semi-supervised classification framework. We generated UNB by comparing the similarity of individual morphological atrophy pattern and the atrophy pattern of [Formula: see text] AD group according to the brain morphological abnormalities induced by AD. For the GCN semi-supervised classification model, we took the UNBs of individuals as the features of nodes and constructed the weight of edges according to the similarity of phenotypic information between individuals, which explored the essential features of individuals through spectral graph convolution. The attention module was constructed and embedded into the GCN framework, which may refine the input morphological features to highlight the main impact of AD on the cerebral cortex and weaken the instability caused by individual diversities, thereby identifying the significant ROIs affected by AD and improving the classification accuracy.

RESULTS

We tested the UNB-GCN framework on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The estimated minimum sample sizes were 156, 349 and 423 for the longitudinal [Formula: see text] AD, [Formula: see text] mild cognitive impairment (MCI) and [Formula: see text] cognitively unimpaired (CU) groups, respectively. And the proposed UNB-GCN framework combined with the attention module can effectively improve the classification performance with 93.90% classification accuracy for AD vs. CU and 82.05% for AD vs. MCI on the validation set.

CONCLUSION

The proposed UNB measures were superior to the conventional volume measures in describing the AD-induced cerebral cortex morphological changes. And the UNB-GCN framework combined with attention module may effectively improve the classification performance between MCI subjects and AD patients. Clinical and Translational Impact Statement: This study aims to predict the early AD patients, so as to help clinicians develop effective interventions to delay the deterioration of AD symptoms.

Effect of subject-specific head morphometry on specific absorption rate estimates in parallel-transmit MRI at 7 T

PURPOSE

To assess the accuracy of morphing an established reference electromagnetic head model to a subject-specific morphometry for the estimation of specific absorption rate (SAR) in 7T parallel-transmit (pTx) MRI.

METHODS

Synthetic T1 -weighted MR images were created from three high-resolution open-source electromagnetic head voxel models. The accuracy of morphing a "reference" (multimodal image-based detailed anatomical [MIDA]) electromagnetic model into a different subject's native space (Duke and Ella) was compared. Both linear and nonlinear registration methods were evaluated. Maximum 10-g averaged SAR was estimated for circularly polarized mode and for 5000 random RF shim sets in an eight-channel transmit head coil, and comparison made between the morphed MIDA electromagnetic models and the native Duke and Ella electromagnetic models, respectively.

RESULTS

The averaged error in maximum 10-g averaged SAR estimation across pTx MRI shim sets between the MIDA and the Duke target model was reduced from 17.5% with only rigid-body registration, to 11.8% when affine linear registration was used, and further reduced to 10.7% when nonlinear registration was used. The corresponding figures for the Ella model were 16.7%, 11.2%, and 10.1%.

CONCLUSION

We found that morphometry accounts for up to half of the subject-specific differences in pTx SAR. Both linear and nonlinear morphing of an electromagnetic model into a target subject improved SAR agreement by better matching head size, morphometry, and position. However, differences remained, likely arising from details in tissue composition estimation. Thus, the uncertainty of the head morphometry and tissue composition may need to be considered separately to achieve personalized SAR estimation.

Machine Learning and Prediction in Fetal, Infant, and Toddler Neuroimaging: A Review and Primer

Predictive models in neuroimaging are increasingly designed with the intent to improve risk stratification and support interventional efforts in psychiatry. Many of these models have been developed in samples of children school-aged or older. Nevertheless, despite growing evidence that altered brain maturation during the fetal, infant, and toddler (FIT) period modulates risk for poor mental health outcomes in childhood, these models are rarely implemented in FIT samples. Applications of predictive modeling in children of these ages provide an opportunity to develop powerful tools for improved characterization of the neural mechanisms underlying development. To facilitate the broader use of predictive models in FIT neuroimaging, we present a brief primer and systematic review on the methods used in current predictive modeling FIT studies. Reflecting on current practices in more than 100 studies conducted over the past decade, we provide an overview of topics, modalities, and methods commonly used in the field and under-researched areas. We then outline ethical and future considerations for neuroimaging researchers interested in predicting health outcomes in early life, including researchers who may be relatively new to either advanced machine learning methods or using FIT data. Altogether, the last decade of FIT research in machine learning has provided a foundation for accelerating the prediction of early-life trajectories across the full spectrum of illness and health.

An attention-based context-informed deep framework for infant brain subcortical segmentation

Precise segmentation of subcortical structures from infant brain magnetic resonance (MR) images plays an essential role in studying early subcortical structural and functional developmental patterns and diagnosis of related brain disorders. However, due to the dynamic appearance changes, low tissue contrast, and tiny subcortical size in infant brain MR images, infant subcortical segmentation is a challenging task. In this paper, we propose a context-guided, attention-based, coarse-to-fine deep framework to precisely segment the infant subcortical structures. At the coarse stage, we aim to directly predict the signed distance maps (SDMs) from multi-modal intensity images, including T1w, T2w, and the ratio of T1w and T2w images, with an SDM-Unet, which can leverage the spatial context information, including the structural position information and the shape information of the target structure, to generate high-quality SDMs. At the fine stage, the predicted SDMs, which encode spatial-context information of each subcortical structure, are integrated with the multi-modal intensity images as the input to a multi-source and multi-path attention Unet (M2A-Unet) for achieving refined segmentation. Both the 3D spatial and channel attention blocks are added to guide the M2A-Unet to focus more on the important subregions and channels. We additionally incorporate the inner and outer subcortical boundaries as extra labels to help precisely estimate the ambiguous boundaries. We validate our method on an infant MR image dataset and on an unrelated neonatal MR image dataset. Compared to eleven state-of-the-art methods, the proposed framework consistently achieves higher segmentation accuracy in both qualitative and quantitative evaluations of infant MR images and also exhibits good generalizability in the neonatal dataset.

Impact of Cerebellar Injury on Neurodevelopmental Outcomes in Preterm Infants With Cerebral Palsy

OBJECTIVE

We aimed to analyze brain imaging findings and neurodevelopmental outcomes of preterm infants diagnosed with cerebral palsy.

DESIGN

Brain magnetic resonance imaging of preterm infants born between 23 and 32 wks' gestation and diagnosed with cerebral palsy at 2 yrs of corrected age were evaluated. Brain lesions were categorized as periventricular leukomalacia, intraventricular hemorrhage, and cerebellar hemorrhage and graded by the severity. Neurodevelopmental outcomes were assessed using the Bayley Scales of Infant and Toddler Development, Third Edition, at 18-24 mos corrected age, and the Korean Ages and Stages Questionnaire at 18 and 24 mos of corrected age.

RESULTS

Cerebral palsy was found in 38 children (6.1%) among 618 survivors. Cerebellar injury of high-grade cerebellar hemorrhage and/or atrophy accounted for 25%. Among patients with supratentorial lesions, those having cerebellar injury showed significantly lower scores on each Korean Ages and Stages Questionnaire domain except gross motor than patients without cerebellar injury. They also revealed a high proportion of patients below the cutoff value of Korean Ages and Stages Questionnaire in language, fine motor, and problem-solving domains ( P < 0.05) and lower Bayley Scales of Infant and Toddler Development, Third Edition, language composite scores ( P = 0.038).

CONCLUSIONS

Poor neurodevelopmental outcomes other than motor function were associated with cerebellar injury. Evaluation of the cerebellum may help predict functional outcomes of patients with cerebral palsy.

Aluminum Thin Film Nanostructure Traces in Pediatric EEG Net for MRI and CT Artifact Reduction

Magnetic resonance imaging (MRI) and continuous electroencephalogram (EEG) monitoring are essential in the clinical management of neonatal seizures. EEG electrodes, however, can significantly degrade the image quality of both MRI and CT due to substantial metallic artifacts and distortions. Thus, we developed a novel thin film trace EEG net ("NeoNet") for improved MRI and CT image quality without compromising the EEG signal quality. The aluminum thin film traces were fabricated with an ultra-high-aspect ratio (up to 17,000:1, with dimensions 30 nm × 50.8 cm × 100 µm), resulting in a low density for reducing CT artifacts and a low conductivity for reducing MRI artifacts. We also used numerical simulation to investigate the effects of EEG nets on the B1 transmit field distortion in 3 T MRI. Specifically, the simulations predicted a 65% and 138% B1 transmit field distortion higher for the commercially available copper-based EEG net ("CuNet", with and without current limiting resistors, respectively) than with NeoNet. Additionally, two board-certified neuroradiologists, blinded to the presence or absence of NeoNet, compared the image quality of MRI images obtained in an adult and two children with and without the NeoNet device and found no significant difference in the degree of artifact or image distortion. Additionally, the use of NeoNet did not cause either: (i) CT scan artifacts or (ii) impact the quality of EEG recording. Finally, MRI safety testing confirmed a maximum temperature rise associated with the NeoNet device in a child head-phantom to be 0.84 °C after 30 min of high-power scanning, which is within the acceptance criteria for the temperature for 1 h of normal operating mode scanning as per the FDA guidelines. Therefore, the proposed NeoNet device has the potential to allow for concurrent EEG acquisition and MRI or CT scanning without significant image artifacts, facilitating clinical care and EEG/fMRI pediatric research.

iBEAT V2.0: a multisite-applicable, deep learning-based pipeline for infant cerebral cortical surface reconstruction

The human cerebral cortex undergoes dramatic and critical development during early postnatal stages. Benefiting from advances in neuroimaging, many infant brain magnetic resonance imaging (MRI) datasets have been collected from multiple imaging sites with different scanners and imaging protocols for the investigation of normal and abnormal early brain development. However, it is extremely challenging to precisely process and quantify infant brain development with these multisite imaging data because infant brain MRI scans exhibit (a) extremely low and dynamic tissue contrast caused by ongoing myelination and maturation and (b) inter-site data heterogeneity resulting from the use of diverse imaging protocols/scanners. Consequently, existing computational tools and pipelines typically perform poorly on infant MRI data. To address these challenges, we propose a robust, multisite-applicable, infant-tailored computational pipeline that leverages powerful deep learning techniques. The main functionality of the proposed pipeline includes preprocessing, brain skull stripping, tissue segmentation, topology correction, cortical surface reconstruction and measurement. Our pipeline can handle both T1w and T2w structural infant brain MR images well in a wide age range (from birth to 6 years of age) and is effective for different imaging protocols/scanners, despite being trained only on the data from the Baby Connectome Project. Extensive comparisons with existing methods on multisite, multimodal and multi-age datasets demonstrate superior effectiveness, accuracy and robustness of our pipeline. We have maintained a website, iBEAT Cloud, for users to process their images with our pipeline ( http://www.ibeat.cloud ), which has successfully processed over 16,000 infant MRI scans from more than 100 institutions with various imaging protocols/scanners.

A high-resolution pediatric female whole-body numerical model with comparison to a male model

Objective. Numerical models are central in designing and testing novel medical devices and in studying how different anatomical changes may affect physiology. Despite the numerous adult models available, there are only a few whole-body pediatric numerical models with significant limitations. In addition, there is a limited representation of both male and female biological sexes in the available pediatric models despite the fact that sex significantly affects body development, especially in a highly dynamic population. As a result, we developed Athena, a realistic female whole-body pediatric numerical model with high-resolution and anatomical detail.Approach. We segmented different body tissues through Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) images of a healthy 3.5 year-old female child using 3D Slicer. We validated the high anatomical accuracy segmentation through two experienced sub-specialty-certified neuro-radiologists and the inter and intra-operator variability of the segmentation results comparing sex differences in organ metrics with physiologic values. Finally, we compared Athena with Martin, a similar male model, showing differences in anatomy, organ metrics, and MRI dosimetric exposure.Main results. We segmented 267 tissue compartments, which included 50 brain tissue labels. The tissue metrics of Athena displayed no deviation from the literature value of healthy children. We show the variability of brain metrics in the male and female models. Finally, we offer an example of computing Specific Absorption Rate and Joule heating in a toddler/preschooler at 7 T MRI.Significance. This study introduces a female realistic high-resolution numerical model using MRI and CT scans of a 3.5 year-old female child, the use of which includes but is not limited to radiofrequency safety studies for medical devices (e.g. an implantable medical device safety in MRI), neurostimulation studies, and radiation dosimetry studies. This model will be open source and available on the Athinoula A. Martinos Center for Biomedical Imaging website.

Postnatal maternal distress, infant subcortical brain macrostructure and emotional regulation

BACKGROUND

Maternal distress is associated with an increased risk for adverse emotional development in infants, including difficulties with emotion regulation. Prenatal maternal distress has been associated with alterations in infant brain development. However, less is known about these associations with postnatal maternal distress, despite this being an important modifiable risk factor that can promote healthy brain development and emotional outcomes in infants.

METHODS & RESULTS

Infants underwent magnetic resonance imaging (MRI) and mothers completed standardized questionnaires concerning their levels of perceived distress 2-5 months postpartum. Infant emotion regulation was assessed at 8-11 months via maternal report. When examining the associations between maternal distress and infant macrostructure, maternal anxiety was associated with infant right pallidum volumes. Increased display of negative emotions at 8-11 months of age was associated with smaller hippocampal volumes and this association was stronger in girls than boys.

CONCLUSION

Findings suggest that postnatal maternal distress may be associated with early infant brain development and emphasize the importance of maternal mental health, supporting previous work. Furthermore, macrostructural properties of infant subcortical structures may be further investigated as potential biomarkers to identify infants at risk of adverse emotional outcomes.

Brain Age Prediction in Developing Childhood with Multimodal Magnetic Resonance Images

It is well known that brain development is very fast and complex in the early childhood with age-based neurological and physiological changes of brain structure and function. The brain maturity is an important indicator for evaluating the normal development of children. In this paper, we propose a multimodal regression framework to combine the features from structural magnetic resonance imaging (sMRI) and diffusion tensor imaging (DTI) data for age prediction of children. First, three types of features are extracted from sMRI and DTI data. Second, we propose to combine the sparse coding and Q-Learning for feature selection from each modality. Finally, the ensemble regression is performed by random forest based on proximity measures to fuse multimodal features for age prediction. The proposed method is evaluated on 212 participants, including 76 young children less than 2 years old and 136 children aged from 2-15 years old recruited from Shanghai Children's Hospital. The results show that integrating multimodal features has achieved the highest accuracies with the root mean squared error (RMSE) of 0.208 years and mean absolute error (MAE) of 0.150 years for age prediction of young children (0-2), and RMSE of 1.666 years and MAE of 1.087 years for older children (2-15). We have shown that the selected features by Q-Learning can consistently improve the prediction accuracy. The comparison of prediction results demonstrates that the proposed method performs better than other competing methods.

Distinct Thalamic and Frontal Neuroanatomical Substrates in Children with Familial vs. Non-Familial Attention-Deficit/Hyperactivity Disorder (ADHD)

Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent, inheritable, and heterogeneous neurodevelopmental disorder. Children with a family history of ADHD are at elevated risk of having ADHD and persisting its symptoms into adulthood. The objective of this study was to investigate the influence of having or not having positive family risk factor in the neuroanatomy of the brain in children with ADHD. Cortical thickness-, surface area-, and volume-based measures were extracted and compared in a total of 606 participants, including 132, 165, and 309 in groups of familial ADHD (ADHD-F), non-familial ADHD (ADHD-NF), and typically developed children, respectively. Compared to controls, ADHD probands showed significantly reduced gray matter surface area in the left cuneus. Among the ADHD subgroups, ADHD-F showed significantly increased gray matter volume in the right thalamus and significantly thinner cortical thickness in the right pars orbitalis. Among ADHD-F, an increased volume of the right thalamus was significantly correlated with a reduced DSM-oriented t-score for ADHD problems. The findings of this study may suggest that a positive family history of ADHD is associated with the structural abnormalities in the thalamus and inferior frontal gyrus; these anatomical abnormalities may significantly contribute to the emergence of ADHD symptoms.

Neurodevelopmental outcomes and volumetric analysis of brain in preterm infants with isolated cerebellar hemorrhage

Background

Cerebellar hemorrhage (CBH) is a major form of cerebellar injury in preterm infants. We aimed to investigate the risk factors and neurodevelopmental outcomes of isolated CBH and performed volumetric analysis at term-equivalent age.

Methods

This single-centered nested case-control study included 26 preterm infants with isolated CBH and 52 infants without isolated CBH and any significant supratentorial injury.

Results

Isolated CBH was associated with PCO2 fluctuation within 72 h after birth (adjusted odds ratio 1.007, 95% confidence interval 1.000–1.014). The composite score in the motor domain of the Bayley Scales of Infant and Toddler Development at 24 month of corrected age was lower in the punctate isolated CBH group than that in the control group (85.3 vs. 94.5, P = 0.023). Preterm infants with isolated CBH had smaller cerebellum and pons at term-equivalent age compared to the control group. Isolated CBH with adverse neurodevelopment had a smaller ventral diencephalon and midbrain compared to isolated CBH without adverse neurodevelopmental outcomes.

Conclusions

In preterm infants, isolated CBH with punctate lesions were associated with abnormal motor development at 24 months of corrected age. Isolated CBH accompanied by a smaller ventral diencephalon and midbrain at term equivalent had adverse neurodevelopmental outcomes.

Home language and literacy environment and its relationship to socioeconomic status and white matter structure in infancy

The home language and literacy environment (HLLE) in infancy has been associated with subsequent pre-literacy skill development and HLLE at preschool-age has been shown to correlate with white matter organization in tracts that subserve pre-reading and reading skills. Furthermore, childhood socioeconomic status (SES) has been linked with both HLLE and white matter organization. It is important to understand whether the relationships between environmental factors such as HLLE and SES and white matter organization can be detected as early as infancy, as this period is characterized by rapid brain development that may make white matter pathways particularly susceptible to these early experiences. Here, we hypothesized that HLLE (1) relates to white matter organization in pre-reading and reading-related tracts in infants, and (2) mediates a link between SES and white matter organization. To test these hypotheses, infants (mean age: 8.6 ± 2.3 months, N = 38) underwent diffusion-weighted imaging MRI during natural sleep. Image processing was performed with an infant-specific pipeline and fractional anisotropy (FA) was estimated from the arcuate fasciculus (AF) and superior longitudinal fasciculus (SLF) bilaterally using the baby automated fiber quantification method. HLLE was measured with the Reading subscale of the StimQ (StimQ-Reading) and SES was measured with years of maternal education. Self-reported maternal reading ability was also quantified and applied to our statistical models as a proxy for confounding genetic effects. StimQ-Reading positively correlated with FA in left AF and to maternal education, but did not mediate the relationship between them. Taken together, these findings underscore the importance of considering HLLE from the start of life and may inform novel prevention and intervention strategies to support developing infants during a period of heightened brain plasticity.

SynthStrip: skull-stripping for any brain image

The removal of non-brain signal from magnetic resonance imaging (MRI) data, known as skull-stripping, is an integral component of many neuroimage analysis streams. Despite their abundance, popular classical skull-stripping methods are usually tailored to images with specific acquisition properties, namely near-isotropic resolution and T1-weighted (T1w) MRI contrast, which are prevalent in research settings. As a result, existing tools tend to adapt poorly to other image types, such as stacks of thick slices acquired with fast spin-echo (FSE) MRI that are common in the clinic. While learning-based approaches for brain extraction have gained traction in recent years, these methods face a similar burden, as they are only effective for image types seen during the training procedure. To achieve robust skull-stripping across a landscape of imaging protocols, we introduce SynthStrip, a rapid, learning-based brain-extraction tool. By leveraging anatomical segmentations to generate an entirely synthetic training dataset with anatomies, intensity distributions, and artifacts that far exceed the realistic range of medical images, SynthStrip learns to successfully generalize to a variety of real acquired brain images, removing the need for training data with target contrasts. We demonstrate the efficacy of SynthStrip for a diverse set of image acquisitions and resolutions across subject populations, ranging from newborn to adult. We show substantial improvements in accuracy over popular skull-stripping baselines - all with a single trained model. Our method and labeled evaluation data are available at https://w3id.org/synthstrip.