Neonatal White Matter Maturation Is Associated With Infant Language Development

Background: While neonates have no sophisticated language skills, the neural basis for acquiring this function is assumed to already be present at birth. Receptive language is measurable by 6 months of age and meaningful speech production by 10-18 months of age. Fiber tracts supporting language processing include the corpus callosum (CC), which plays a key role in the hemispheric lateralization of language; the left arcuate fasciculus (AF), which is associated with syntactic processing; and the right AF, which plays a role in prosody and semantics. We examined if neonatal maturation of these fiber tracts is associated with receptive language development at 12 months of age. Methods: Diffusion-weighted imaging (DWI) was performed in 86 infants at 26.6 ± 12.2 days post-birth. Receptive language was assessed via the MacArthur-Bates Communicative Development Inventory at 12 months of age. Tract-based fractional anisotropy (FA) was determined using the NA-MIC atlas-based fiber analysis toolkit. Associations between neonatal regional FA, adjusted for gestational age at birth and age at scan, and language development at 12 months of age were tested using ANOVA models. Results: After multiple comparisons correction, higher neonatal FA was positively associated with receptive language at 12 months of age within the genu (p < 0.001), rostrum (p < 0.001), and tapetum (p < 0.001) of the CC and the left fronto-parietal AF (p = 0.008). No significant clusters were found in the right AF. Conclusion: Microstructural development of the CC and the AF in the newborn is associated with receptive language at 12 months of age, demonstrating that interindividual variation in white matter microstructure is relevant for later language development, and indicating that the neural foundation for language processing is laid well ahead of the majority of language acquisition. This suggests that some origins of impaired language development may lie in the intrauterine and potentially neonatal period of life. Understanding how interindividual differences in neonatal brain maturity relate to the acquisition of function, particularly during early development when the brain is in an unparalleled window of plasticity, is key to identifying opportunities for harnessing neuroplasticity in health and disease

A novel maturation index based on neonatal diffusion tensor imaging reflects typical perinatal white matter development in humans

Human birth presents an abrupt transition from intrauterine to extrauterine life. Here we introduce a novel Maturation Index (MI) that considers the relative importance of gestational age at birth and postnatal age at scan in a General Linear Model. The MI is then applied to Diffusion Tensor Imaging (DTI) in newborns for characterizing typical white matter development in neonates. DTI was performed cross-sectionally in 47 neonates (gestational age at birth=39.1±1.6 weeks [GA], postnatal age at scan=25.5±12.2days [SA]). Radial diffusivity (RD), axial diffusivity (AD) and fractional anisotropy (FA) along 27 white matter fiber tracts were considered. The MI was used to characterize inflection in maturation at the time of birth using GLM estimated rates of change before and after birth. It is proposed that the sign (positive versus negative) of MI reflects the period of greatest maturation rate. Two general patterns emerged from the MI analysis. First, RD and AD (but not FA) had positive MI on average across the whole brain (average MIAD=0.31±0.42, average MIRD=0.22±0.34). Second, significant regions of negative MI in RD and FA (but not AD) were observed in the inferior corticospinal regions, areas known to myelinate early. Observations using the proposed method are consistent with proposed models of the white matter maturation process in which pre-myelination is described by changes in AD and RD due to oligodendrocyte proliferation while true myelination is characterized by changes in RD and FA due to myelin formation

MAPSeg: Unified Unsupervised Domain Adaptation for Heterogeneous Medical Image Segmentation Based on 3D Masked Autoencoding and Pseudo-Labeling

Robust segmentation is critical for deriving quantitative measures from large-scale, multi-center, and longitudinal medical scans. Manually annotating medical scans, however, is expensive and labor-intensive and may not always be available in every domain. Unsupervised domain adaptation (UDA) is a well-studied technique that alleviates this label-scarcity problem by leveraging available labels from another domain. In this study, we introduce Masked Autoencoding and Pseudo-Labeling Segmentation (MAPSeg), a $\textbf{unified}$ UDA framework with great versatility and superior performance for heterogeneous and volumetric medical image segmentation. To the best of our knowledge, this is the first study that systematically reviews and develops a framework to tackle four different domain shifts in medical image segmentation. More importantly, MAPSeg is the first framework that can be applied to $\textbf{centralized}$, $\textbf{federated}$, and $\textbf{test-time}$ UDA while maintaining comparable performance. We compare MAPSeg with previous state-of-the-art methods on a private infant brain MRI dataset and a public cardiac CT-MRI dataset, and MAPSeg outperforms others by a large margin (10.5 Dice improvement on the private MRI dataset and 5.7 on the public CT-MRI dataset). MAPSeg poses great practical value and can be applied to real-world problems. GitHub: https://github.com/XuzheZ/MAPSeg/.Comment: CVPR 2024 camera-ready (8 pages, 3 figures) with the supplemental materials (5 pages, 4 figures). Xuzhe Zhang and Yuhao Wu are co-first authors. Andrew F. Laine and Yun Wang are co-senior supervising author

Prospective association of maternal psychosocial stress in pregnancy with newborn hippocampal volume and implications for infant social-emotional development

AbstractMaternal psychosocial stress during pregnancy can impact the developing fetal brain and influence offspring mental health. In this context, animal studies have identified the hippocampus and amygdala as key brain regions of interest, however, evidence in humans is sparse. We, therefore, examined the associations between maternal prenatal psychosocial stress, newborn hippocampal and amygdala volumes, and child social-emotional development.In a sample of 86 mother-child dyads, maternal perceived stress was assessed serially in early, mid and late pregnancy. Following birth, newborn (aged 5–64 postnatal days, mean: 25.8 ± 12.9) hippocampal and amygdala volume was assessed using structural magnetic resonance imaging. Infant social-emotional developmental milestones were assessed at 6- and 12-months age using the Bayley-III.After adjusting for covariates, maternal perceived stress during pregnancy was inversely associated with newborn left hippocampal volume (β = −0.26, p = .019), but not with right hippocampal (β = −0.170, p = .121) or bilateral amygdala volumes (ps > .5). Furthermore, newborn left hippocampal volume was positively associated with infant social-emotional development across the first year of postnatal life (B = 0.01, p = .011). Maternal perceived stress was indirectly associated with infant social-emotional development via newborn left hippocampal volume (B = −0.34, 95% CIBC [-0.97, −0.01]), suggesting mediation.This study provides prospective evidence in humans linking maternal psychosocial stress in pregnancy with newborn hippocampal volume and subsequent infant social-emotional development across the first year of life. These findings highlight the importance of maternal psychosocial state during pregnancy as a target amenable to interventions to prevent or attenuate its potentially unfavorable neural and behavioral consequences in the offspring.</p

Early development of negative and positive affect: Implications for ADHD symptomatology across three birth cohorts

High levels of early emotionality (of either negative or positive valence) are hypothesized to be important precursors to early psychopathology, with attention-deficit/hyperactivity disorder (ADHD) a prime early target. The positive and negative affect domains are prime examples of Research Domain Criteria (RDoC) concepts that may enrich a multilevel mechanistic map of psychopathology risk. Utilizing both variable-centered and person-centered approaches, the current study examined whether levels and trajectories of infant negative and positive emotionality, considered either in isolation or together, predicted children's ADHD symptoms at 4 to 8 years of age. In variable-centered analyses, higher levels of infant negative affect (at as early as 3 months of age) were associated with childhood ADHD symptoms. Findings for positive affect failed to reach statistical threshold. Results from person-centered trajectory analyses suggest that additional information is gained by simultaneously considering the trajectories of positive and negative emotionality. Specifically, only when exhibiting moderate, stable or low levels of positive affect did negative affect and its trajectory relate to child ADHD symptoms. These findings add to a growing literature that suggests that infant negative emotionality is a promising early life marker of future ADHD risk and suggest secondarily that moderation by positive affectivity warrants more consideration.</p

The Function Biomedical Informatics Research Network Data Repository

The Function Biomedical Informatics Research Network (FBIRN) developed methods and tools for conducting multi-scanner functional magnetic resonance imaging (fMRI) studies. Method and tool development were based on two major goals: 1) to assess the major sources of variation in fMRI studies conducted across scanners, including instrumentation, acquisition protocols, challenge tasks, and analysis methods, and 2) to provide a distributed network infrastructure and an associated federated database to host and query large, multi-site, fMRI and clinical datasets. In the process of achieving these goals the FBIRN test bed generated several multi-scanner brain imaging data sets to be shared with the wider scientific community via the BIRN Data Repository (BDR). The FBIRN Phase 1 dataset consists of a traveling subject study of 5 healthy subjects, each scanned on 10 different 1.5 to 4 Tesla scanners. The FBIRN Phase 2 and Phase 3 datasets consist of subjects with schizophrenia or schizoaffective disorder along with healthy comparison subjects scanned at multiple sites. In this paper, we provide concise descriptions of FBIRN’s multi-scanner brain imaging data sets and details about the BIRN Data Repository instance of the Human Imaging Database (HID) used to publicly share the data

A multi-scanner study of subcortical brain volume abnormalities in schizophrenia

Schizophrenia patients show significant subcortical brain abnormalities. We examined these abnormalities using automated image analysis software and provide effect size estimates for prospective multi-scanner schizophrenia studies. Subcortical and intracranial volumes were obtained using FreeSurfer 5.0.0 from high-resolution structural imaging scans from 186 schizophrenia patients (mean age±SD=38.9±11.6, 78% males) and 176 demographically similar controls (mean age±SD=37.5±11.2, 72% males). Scans were acquired from seven 3-Tesla scanners. Univariate mixed model regression analyses compared between-group volume differences. Weighted mean effect sizes (and number of subjects needed for 80% power at α=0.05) were computed based on the individual single site studies as well as on the overall multi-site study. Schizophrenia patients have significantly smaller intracranial, amygdala, and hippocampus volumes and larger lateral ventricle, putamen and pallidum volumes compared with healthy volunteers. Weighted mean effect sizes based on single site studies were generally larger than effect sizes computed based on analysis of the overall multi-site sample. Prospectively collected structural imaging data can be combined across sites to increase statistical power for meaningful group comparisons. Even when using similar scan protocols at each scanner, some between-site variance remains. The multi-scanner effect sizes provided by this study should help in the design of future multi-scanner schizophrenia imaging studies