Diffusion-tensor-imaging 1-year-old and 2-year-old infant brain atlases with comprehensive gray and white matter labels.

Human infancy is marked by fastest postnatal brain structural changes. It also coincides with the onset of many neurodevelopmental disorders. Atlas-based automated structure labeling has been widely used for analyzing various neuroimaging data. However, the relatively large and nonlinear neuroanatomical differences between infant and adult brains can lead to significant offsets of the labeled structures in infant brains when adult brain atlas is used. Age-specific 1- and 2-year-old brain atlases covering all major gray and white matter (GM and WM) structures with diffusion tensor imaging (DTI) and structural MRI are critical for precision medicine for infant population yet have not been established. In this study, high-quality DTI and structural MRI data were obtained from 50 healthy children to build up three-dimensional age-specific 1- and 2-year-old brain templates and atlases. Age-specific templates include a single-subject template as well as two population-averaged templates from linear and nonlinear transformation, respectively. Each age-specific atlas consists of 124 comprehensively labeled major GM and WM structures, including 52 cerebral cortical, 10 deep GM, 40 WM, and 22 brainstem and cerebellar structures. When combined with appropriate registration methods, the established atlases can be used for highly accurate automatic labeling of any given infant brain MRI. We demonstrated that one can automatically and effectively delineate deep WM microstructural development from 3 to 38 months by using these age-specific atlases. These established 1- and 2-year-old infant brain DTI atlases can advance our understanding of typical brain development and serve as clinical anatomical references for brain disorders during infancy.

Lateralization of dorsal fiber tract targeting Broca's area concurs with language skills during development.

Language is bounded to the left hemisphere in the adult brain and the functional lateralization can already be observed early during development. Here we investigate whether this is paralleled by a lateralization of the white matter structural language network. We analyze the strength and microstructural properties of language-related fiber tracts connecting temporal and frontal cortices with a separation of two dorsal tracts, one targeting the posterior Broca's area (BA44) and one targeting the precentral gyrus (BA6). In a large sample of young children (3-6 years), we demonstrate that, in contrast to the BA6-targeting tract, the microstructural asymmetry of the BA44-targeting fiber tract significantly correlates locally with different aspects of development. While the asymmetry in its anterior segment reflects age, the asymmetry in its posterior segment is associated with the children's language skills. These findings demonstrate a fine-grained structure-to-function mapping in the lateralized network and go beyond our current view of language-related human brain maturation.

Affinity of structural white matter tracts between infant and adult pig.

BACKGROUND: The piglet brain has been increasingly used as an excellent surrogate for investigation of pediatric neurodevelopment, nutrition, and traumatic brain injuries. This study intends to establish a piglet brain's structural connectivity model and compare it with the adult pig, enhancing its application for structurally guided functional analysis. METHODS: In this study, diffusion-weighted (DW)-MRI data from piglets (n=11, 3-week-old) was used to establish piglet model and compare with adult pigs. We employed a data-driven independent component analysis (ICA) method to derive piglet-specific tracts. Pearson correlations and Kullback-Leibler (KL) divergences was employed to identify common tracts and unique tracts for piglet. Common tracts were then used in a blueprint connectome study to highlight differences in regions of interest (ROI). RESULTS: The data-driven approach applied to piglet brains revealed 17 common tracts, showing high similarity with adult pigs' white matter (WM) tracts, and identified 3 tracts unique to piglets and 10 negative marker tracts. Additionally, the study highlighted notable differences in 3 ROIs associated with blueprint connectome. COMPARING WITH EXISTING METHODS: This study marks a significant shift from surface-based to voxel-based methodologies in analyzing pig brain structural connectivity and generating connectome blueprints. Additionally, it sheds light on the use of the piglet model for developmental studies, offering new perspectives in this area. CONCLUSION: This study established a piglet brain tract model and conducts a comparative analysis of adult pig's and piglet's structural connectivity. These findings underscore the potential use of the piglet brain model in employing piglet model for developmental studies.

Normative volumes and relaxation times at 3T during brain development.

While research has unveiled and quantified brain markers of abnormal neurodevelopment, clinicians still work with qualitative metrics for MRI brain investigation. The purpose of the current article is to bridge the knowledge gap between case-control cohort studies and individual patient care. Here, we provide a unique dataset of seventy-three 3-to-17 years-old healthy subjects acquired with a 6-minute MRI protocol encompassing T1 and T2 relaxation quantitative sequence that can be readily implemented in the clinical setting; MP2RAGE for T1 mapping and the prototype sequence GRAPPATINI for T2 mapping. White matter and grey matter volumes were automatically quantified. We further provide normative developmental curves based on these two imaging sequences; T1, T2 and volume normative ranges with respect to age were computed, for each ROI of a pediatric brain atlas. This open-source dataset provides normative values allowing to position individual patients acquired with the same protocol on the brain maturation curve and as such provides potentially useful quantitative biomarkers facilitating precise and personalized care.

Early motor outcomes in infants with critical congenital heart disease are related to neonatal brain development and brain injury.

AIM: To assess the relationship between neonatal brain development and injury with early motor outcomes in infants with critical congenital heart disease (CCHD). METHOD: Neonatal brain magnetic resonance imaging was performed after open-heart surgery with cardiopulmonary bypass. Cortical grey matter (CGM), unmyelinated white matter, and cerebellar volumes, as well as white matter motor tract fractional anisotropy and mean diffusivity were assessed. White matter injury (WMI) and arterial ischaemic stroke (AIS) with corticospinal tract (CST) involvement were scored. Associations with motor outcomes at 3, 9, and 18 months were corrected for repeated cardiac surgery. RESULTS: Fifty-one infants (31 males, 20 females) were included prospectively. Median age at neonatal surgery and postoperative brain magnetic resonance imaging was 7 days (interquartile range [IQR] 5-11d) and 15 days (IQR 12-21d) respectively. Smaller CGM and cerebellar volumes were associated with lower fine motor scores at 9 months (CGM regression coefficient=0.51, 95% confidence interval [CI]=0.15-0.86; cerebellum regression coefficient=3.08, 95% CI=1.07-5.09) and 18 months (cerebellum regression coefficient=2.08, 95% CI=0.47-5.12). The fractional anisotropy and mean diffusivity of white matter motor tracts were not related with motor scores. WMI was related to lower gross motor scores at 9 months (mean difference -0.8SD, 95% CI=-1.5 to -0.2). AIS with CST involvement increased the risk of gross motor problems and muscle tone abnormalities. Cerebral palsy (n=3) was preceded by severe ischaemic brain injury. INTERPRETATION: Neonatal brain development and injury are associated with fewer favourable early motor outcomes in infants with CCHD.

Early life factors and white matter microstructure in children with overweight and obesity: The ActiveBrains project.

BACKGROUND & AIMS: Exposure to a suboptimal environment during the fetal and early infancy period's results in long-term consequences for brain morphology and function. We investigated the associations of early life factors such as anthropometric neonatal data (i.e., birth length, birth weight and birth head circumference) and breastfeeding practices (i.e., exclusive and any breastfeeding) with white matter (WM) microstructure, and ii) we tested whether WM tracts related to early life factors are associated with academic performance in children with overweight/obesity. METHODS: 96 overweight/obese children (10.03 ± 1.16 years; 38.7% girls) were included from the ActiveBrains Project. WM microstructure indicators used were fractional anisotropy (FA) and mean diffusivity (MD), derived from Diffusion Tensor Imaging. Academic performance was evaluated with the Battery III Woodcock-Muñoz Tests of Achievement. Regression models were used to examine the associations of the early life factors with tract-specific FA and MD, as well as its association with academic performance. RESULTS: Head circumference at birth was positively associated with FA of the inferior fronto-occipital fasciculus tract (0.441; p = 0.005), as well as negatively associated with MD of the cingulate gyrus part of cingulum (-0.470; p = 0.006), corticospinal (-0.457; p = 0.005) and superior thalamic radiation tract (-0.476; p = 0.001). Association of birth weight, birth length and exclusive breastfeeding with WM microstructure did not remain significant after false discovery rate correction. None tract related to birth head circumference was associated with academic performance (all p > 0.05). CONCLUSIONS: Our results highlighted the importance of the perinatal growth in WM microstructure later in life, although its possible academic implications remain inconclusive.

Longitudinal brain atlases of early developing cynomolgus macaques from birth to 48 months of age.

Longitudinal brain imaging atlases with densely sampled time-points and ancillary anatomical information are of fundamental importance in studying early developmental characteristics of human and non-human primate brains during infancy, which feature extremely dynamic imaging appearance, brain shape and size. However, for non-human primates, which are highly valuable animal models for understanding human brains, the existing brain atlases are mainly developed based on adults or adolescents, denoting a notable lack of temporally densely-sampled atlases covering the dynamic early brain development. To fill this critical gap, in this paper, we construct a comprehensive set of longitudinal brain atlases and associated tissue probability maps (gray matter, white matter, and cerebrospinal fluid) with totally 12 time-points from birth to 4 years of age (i.e., 1, 2, 3, 4, 5, 6, 9, 12, 18, 24, 36, and 48 months of age) based on 175 longitudinal structural MRI scans from 39 typically-developing cynomolgus macaques, by leveraging state-of-the-art computational techniques tailored for early developing brains. Furthermore, to facilitate region-based analysis using our atlases, we also provide two popular hierarchy parcellations, i.e., cortical hierarchy maps (6 levels) and subcortical hierarchy maps (6 levels), on our longitudinal macaque brain atlases. These early developing atlases, which have the densest time-points during infancy (to the best of our knowledge), will greatly facilitate the studies of macaque brain development.

Mapping white matter maturational processes and degrees on neonates by diffusion kurtosis imaging with multiparametric analysis.

White matter maturation has been characterized by diffusion tensor (DT) metrics. However, maturational processes and degrees are not fully investigated due to limitations of univariate approaches and limited specificity/sensitivity. Diffusion kurtosis imaging (DKI) provides kurtosis tensor (KT) and white matter tract integrity (WMTI) metrics, besides DT metrics. Therefore, we tried to investigate performances of DKI with the multiparametric analysis in characterizing white matter maturation. Developmental changes in metrics were investigated by using tract-based spatial statistics and the region of interest analysis on 50 neonates with postmenstrual age (PMA) from 37.43 to 43.57 weeks. Changes in metrics were combined into various patterns to reveal different maturational processes. Mahalanobis distance based on DT metrics (DM,DT ) and that combing DT and KT metrics (DM,DT-KT ) were computed, separately. Performances of DM,DT-KT and DM,DT were compared in revealing correlations with PMA and the neurobehavioral score. Compared with DT metrics, WMTI metrics demonstrated additional changing patterns. Furthermore, variations of DM,DT-KT across regions were in agreement with the maturational sequence. Additionally, DM,DT-KT demonstrated stronger negative correlations with PMA and the neurobehavioral score in more regions than DM,DT . Results suggest that DKI with the multiparametric analysis benefits the understanding of white matter maturational processes and degrees on neonates.

Nutritional Intake, White Matter Integrity, and Neurodevelopment in Extremely Preterm Born Infants.

BACKGROUND: Determining optimal nutritional regimens in extremely preterm infants remains challenging. This study aimed to evaluate the effect of a new nutritional regimen and individual macronutrient intake on white matter integrity and neurodevelopmental outcome. METHODS: Two retrospective cohorts of extremely preterm infants (gestational age < 28 weeks) were included. Cohort B (n = 79) received a new nutritional regimen, with more rapidly increased, higher protein intake compared to cohort A (n = 99). Individual protein, lipid, and caloric intakes were calculated for the first 28 postnatal days. Diffusion tensor imaging was performed at term-equivalent age, and cognitive and motor development were evaluated at 2 years corrected age (CA) (Bayley-III-NL) and 5.9 years chronological age (WPPSI-III-NL, MABC-2-NL). RESULTS: Compared to cohort A, infants in cohort B had significantly higher protein intake (3.4 g/kg/day vs. 2.7 g/kg/day) and higher fractional anisotropy (FA) in several white matter tracts but lower motor scores at 2 years CA (mean (SD) 103 (12) vs. 109 (12)). Higher protein intake was associated with higher FA and lower motor scores at 2 years CA (B = -6.7, p = 0.001). However, motor scores at 2 years CA were still within the normal range and differences were not sustained at 5.9 years. There were no significant associations with lipid or caloric intake. CONCLUSION: In extremely preterm born infants, postnatal protein intake seems important for white matter development but does not necessarily improve long-term cognitive and motor development.

Corticospinal neuron subpopulation-specific developmental genes prospectively indicate mature segmentally specific axon projection targeting.

For precise motor control, distinct subpopulations of corticospinal neurons (CSN) must extend axons to distinct spinal segments, from proximal targets in the brainstem and cervical cord to distal targets in thoracic and lumbar spinal segments. We find that developing CSN subpopulations exhibit striking axon targeting specificity in spinal white matter, which establishes the foundation for durable specificity of adult corticospinal circuitry. Employing developmental retrograde and anterograde labeling, and their distinct neocortical locations, we purified developing CSN subpopulations using fluorescence-activated cell sorting to identify genes differentially expressed between bulbar-cervical and thoracolumbar-projecting CSN subpopulations at critical developmental times. These segmentally distinct CSN subpopulations are molecularly distinct from the earliest stages of axon extension, enabling prospective identification even before eventual axon targeting decisions are evident in the spinal cord. This molecular delineation extends beyond simple spatial separation of these subpopulations in the cortex. Together, these results identify candidate molecular controls over segmentally specific corticospinal axon projection targeting.

Development of the visual white matter pathways mediates development of electrophysiological responses in visual cortex.

The latency of neural responses in the visual cortex changes systematically across the lifespan. Here, we test the hypothesis that development of visual white matter pathways mediates maturational changes in the latency of visual signals. Thirty-eight children participated in a cross-sectional study including diffusion magnetic resonance imaging (MRI) and magnetoencephalography (MEG) sessions. During the MEG acquisition, participants performed a lexical decision and a fixation task on words presented at varying levels of contrast and noise. For all stimuli and tasks, early evoked fields were observed around 100 ms after stimulus onset (M100), with slower and lower amplitude responses for low as compared to high contrast stimuli. The optic radiations and optic tracts were identified in each individual's brain based on diffusion MRI tractography. The diffusion properties of the optic radiations predicted M100 responses, especially for high contrast stimuli. Higher optic radiation fractional anisotropy (FA) values were associated with faster and larger M100 responses. Over this developmental window, the M100 responses to high contrast stimuli became faster with age and the optic radiation FA mediated this effect. These findings suggest that the maturation of the optic radiations over childhood accounts for individual variations observed in the developmental trajectory of visual cortex responses.

Brain Atrophy and White Matter Damage Linked to Peripheral Bioenergetic Deficits in the Neurodegenerative Disease FXTAS.

Fragile X-associated tremor/ataxia syndrome (FXTAS) is a neurodegenerative disorder affecting subjects (premutation carriers) with a 55-200 CGG-trinucleotide expansion in the 5'UTR of the fragile X mental retardation 1 gene (FMR1) typically after age 50. As both the presence of white matter hyperintensities (WMHs) and atrophied gray matter on magnetic resonance imaging (MRI) are linked to age-dependent decline in cognition, here we tested whether MRI outcomes (WMH volume (WMHV) and brain volume) were correlated with mitochondrial bioenergetics from peripheral blood monocytic cells in 87 carriers with and without FXTAS. As a parameter assessing cumulative damage, WMHV was correlated to both FXTAS stages and age, and brain volume discriminated between carriers and non-carriers. Similarly, mitochondrial mass and ATP production showed an age-dependent decline across all participants, but in contrast to WMHV, only FADH2-linked ATP production was significantly reduced in carriers vs. non-carriers. In carriers, WMHV negatively correlated with ATP production sustained by glucose-glutamine and FADH2-linked substrates, whereas brain volume was positively associated with the latter and mitochondrial mass. The observed correlations between peripheral mitochondrial bioenergetics and MRI findings-and the lack of correlations with FXTAS diagnosis/stages-may stem from early brain bioenergetic deficits even before overt FXTAS symptoms and/or imaging findings.

The longitudinal relationship between BOLD signal variability changes and white matter maturation during early childhood.

Intra-individual transient temporal fluctuations in brain signal, as measured by fMRI blood oxygenation level dependent (BOLD) variability, is increasingly considered an important signal rather than measurement noise. Evidence from computational and cognitive neuroscience suggests that signal variability is a good proxy-measure of brain functional integrity and information processing capacity. Here, we sought to explore across-participant and longitudinal relationships between BOLD variability, age, and white matter structure in early childhood. We measured standard deviation of BOLD signal, total white matter volume, global fractional anisotropy (FA) and mean diffusivity (MD) during passive movie viewing in a sample of healthy children (aged 2-8 years; N = 83). We investigated how age and white matter development related to changes in BOLD variability both across- and within-participants. Our across-participant analyses using behavioural partial least squares (bPLS) revealed that the influence of age and white matter maturation on BOLD variability was highly interrelated. BOLD variability increased in widespread frontal, temporal and parietal regions, and decreased in the hippocampus and parahippocampal gyrus with age and white matter development. Our longitudinal analyses using linear mixed effects modelling revealed significant associations between BOLD variability, age and white matter microstructure. Analyses using artificial neural networks demonstrated that BOLD variability and white matter micro and macro-structure at earlier ages were strong predictors of BOLD variability at later ages. By characterizing the across-participant and longitudinal features of the association between BOLD variability and white matter micro- and macrostructure in early childhood, our results provide a novel perspective to understand structure-function relationships in the developing brain.

Inter-individual variability in structural brain development from late childhood to young adulthood.

A fundamental task in neuroscience is to characterize the brain's developmental course. While replicable group-level models of structural brain development from childhood to adulthood have recently been identified, we have yet to quantify and understand individual differences in structural brain development. The present study examined inter-individual variability and sex differences in changes in brain structure, as assessed by anatomical MRI, across ages 8.0-26.0 years in 269 participants (149 females) with three time points of data (807 scans), drawn from three longitudinal datasets collected in the Netherlands, Norway, and USA. We further investigated the relationship between overall brain size and developmental changes, as well as how females and males differed in change variability across development. There was considerable inter-individual variability in the magnitude of changes observed for all examined brain measures. The majority of individuals demonstrated decreases in total gray matter volume, cortex volume, mean cortical thickness, and white matter surface area in mid-adolescence, with more variability present during the transition into adolescence and the transition into early adulthood. While most individuals demonstrated increases in white matter volume in early adolescence, this shifted to a majority demonstrating stability starting in mid-to-late adolescence. We observed sex differences in these patterns, and also an association between the size of an individual's brain structure and the overall rate of change for the structure. The present study provides new insight as to the amount of individual variance in changes in structural morphometrics from late childhood to early adulthood in order to obtain a more nuanced picture of brain development. The observed individual- and sex-differences in brain changes also highlight the importance of further studying individual variation in developmental patterns in healthy, at-risk, and clinical populations.

Interaction between Preterm White Matter Injury and Childhood Thalamic Growth.

OBJECTIVE: The purpose of this study was to determine how preterm white matter injury (WMI) and long-term thalamic growth interact to predict 8-year neurodevelopmental outcomes. METHODS: A prospective cohort of 114 children born at 24 to 32 weeks' gestational age (GA) underwent structural and diffusion tensor magnetic resonance imaging early in life (median 32 weeks), at term-equivalent age and at 8 years. Manual segmentation of neonatal WMI was performed on T1-weighted images and thalamic volumes were obtained using the MAGeT brain segmentation pipeline. Cognitive, motor, and visual-motor outcomes were evaluated at 8 years of age. Multivariable regression was used to examine the relationship among neonatal WMI volume, school-age thalamic volume, and neurodevelopmental outcomes. RESULTS: School-age thalamic volumes were predicted by neonatal thalamic growth rate, GA, sex, and neonatal WMI volume (p < 0.0001). After accounting for total cerebral volume, WMI volume remained associated with school-age thalamic volume (ß = -0.31, p = 0.005). In thalamocortical tracts, fractional anisotropy (FA) at term-equivalent age interacted with early WMI volume to predict school-age thalamic volumes (all p < 0.02). School-age thalamic volumes and neonatal WMI interacted to predict full-scale IQ (p = 0.002) and adverse motor scores among those with significant WMI (p = 0.01). Visual-motor scores were predicted by thalamic volumes (p = 0.04). INTERPRETATION: In very preterm-born children, neonatal thalamic growth and WMI volume predict school-age thalamic volumes. The emergence at term of an interaction between FA and WMI to impact school-age thalamic volume indicates dysmaturation as a mechanism of thalamic growth failure. Cognition is predicted by the interaction of WMI and thalamic growth, highlighting the need to consider multiple dimensions of brain injury in these children. ANN NEUROL 2021;90:584-594.

Diffusion MRI of the infant brain reveals unique asymmetry patterns during the first-half-year of development.

The human brain demonstrates anatomical and functional lateralization/asymmetry between the left and right hemispheres, and such asymmetry is known to start from the early age of life. However, how the asymmetry changes with brain development during infancy remained unknown. In this study, we aimed to systematically investigate the spatiotemporal pattern of brain asymmetry in healthy preterm-born infants during the first-half-year of development, using high angular resolution diffusion MRI. Sixty-five healthy preterm-born infants (gestational age between 25.3-36.6 weeks) were scanned with postmenstrual age (PMA) ranging from term-equivalent age (TEA) to 6-months. At the regional level, we performed a region-of-interest-based analysis by segmenting the brain into 63 symmetrical pairs of regions, based on which the laterality index was assessed and correlated with PMA. At the voxel level, we performed a fixel-based analysis of each fiber component between the native and left-right flipped data, separately in TEA-1 month, 1-3 months, and 3-6 months groups. The infant brains demonstrated extensive regions with structural asymmetry during their first half-of-year of life. A distinct central-peripheral asymmetry pattern was observed in mean diffusivity, namely, leftward lateralization in the neocortex and rightward asymmetry in the deep brain regions. Besides, the posterior brain demonstrated a higher lateralization index compared with the anterior brain in all metrics, which is congruent with the brain developmental pattern from caudal to rostral. Regionally, language processing regions showed a rightward asymmetry, while visuospatial processing regions exhibited leftward lateralization in fractional anisotropy, fibre density, and fibre cross-section measurements, and most white matter regions were lateralized to the left in these measurements. The laterality index of several regions (12 out 63) demonstrated significant developmental changes in mean diffusivity. At the fixel level, the fiber cross-section of inferior fronto-occipital fasciculus showed significant leftward asymmetry and the extent of asymmetry increased with PMA. In summary, the results revealed unique spatiotemporal patterns of macro- and micro-structural asymmetry in early life, which dynamically changed with age. These findings may contribute to the understanding of brain development during infancy.

Maturity of gray matter structures and white matter connectomes, and their relationship with psychiatric symptoms in youth.

Brain predicted age difference, or BrainPAD, compares chronological age to an age estimate derived by applying machine learning (ML) to MRI brain data. BrainPAD studies in youth have been relatively limited, often using only a single MRI modality or a single ML algorithm. Here, we use multimodal MRI with a stacked ensemble ML approach that iteratively applies several ML algorithms (AutoML). Eligible participants in the Healthy Brain Network (N = 489) were split into training and test sets. Morphometry estimates, white matter connectomes, or both were entered into AutoML to develop BrainPAD models. The best model was then applied to a held-out evaluation dataset, and associations with psychometrics were estimated. Models using morphometry and connectomes together had a mean absolute error of 1.18 years, outperforming models using a single MRI modality. Lower BrainPAD values were associated with more symptoms on the CBCL (pcorr  = .012) and lower functioning on the Children's Global Assessment Scale (pcorr  = .012). Higher BrainPAD values were associated with better performance on the Flanker task (pcorr  = .008). Brain age prediction was more accurate using ComBat-harmonized brain data (MAE = 0.26). Associations with psychometric measures remained consistent after ComBat harmonization, though only the association with CGAS reached statistical significance in the reduced sample. Our findings suggest that BrainPAD scores derived from unharmonized multimodal MRI data using an ensemble ML approach may offer a clinically relevant indicator of psychiatric and cognitive functioning in youth.

Cognitive and White Matter Microstructure Development in Congenital Hypothyroidism and Familial Thyroid Disorders.

CONTEXT: Children with congenital hypothyroidism (CH) are at risk for suboptimal neurodevelopment. OBJECTIVES: To evaluate neurocognitive function and white matter microstructure in children with permanent or transient CH and to correlate these findings with disease severity. DESIGN, PARTICIPANTS AND METHODS: A retrospective and prospective observational study was conducted in 39 children with permanent or transient CH, and in 39 healthy children. Cognitive function was assessed by Wechsler Intelligence Scale, Fourth Edition, and by other tests; the white matter microstructure was investigated by 3 Tesla magnetic resonance imaging. RESULTS: Children with permanent CH have lower cognitive scores at a median age of 9.5 years than those with transient CH and controls. An IQ score between 71 and 84 was found in 28.6% of permanent CH and of <70 (P = 0.06) in 10.7%. The Processing Speed Index (PSI; P = 0.004), sustained visual attention (P = 0.02), reading speed (P = 0.0001), written calculations (P = 0.002), and numerical knowledge (P = 0.0001) were significantly lower than controls. Children born to mothers with Hashimoto's thyroiditis have significantly lower IQ values (P = 0.02), Working Memory Index (P = 0.03), and PSI (P = 0.02). Significantly lower IQ and Verbal Comprehension Index values were found in children with a family history of thyroid disorders (P = 0.004 and P = 0.009, respectively). In children with permanent CH, significant correlations between abnormalities in white matter microstructural, clinical, and cognitive measures were documented. CONCLUSIONS: These findings indicate that children with CH are at risk of neurocognitive impairment and white matter abnormalities despite timely and adequate treatment. The association between offspring cognitive vulnerability and maternal thyroid disorders requires careful consideration.

Wiring and plumbing: Oligodendrocyte precursors and angiogenesis in the oligovascular niche.

For efficient stroke recovery, the entire neurovascular unit must be repaired. A recent study underscores this concept by highlighting the importance of cellular crosstalk for white mater remodeling. In developing brains and in brains injured by hypoxia, interactions between oligodendrocyte precursors and endothelium play an essential role for physiological and compensatory angiogenesis. Further studies are warranted to build on these emerging findings in the oligovascular niche in order to identify novel therapeutic targets for stroke and other CNS diseases.