Quantitative evaluation of brain development using anatomical MRI and diffusion tensor imaging

A systematic review of (semi-)automatic quality control of T1-weighted MRI scans

PURPOSE

Artifacts in magnetic resonance imaging (MRI) scans degrade image quality and thus negatively affect the outcome measures of clinical and research scanning. Considering the time-consuming and subjective nature of visual quality control (QC), multiple (semi-)automatic QC algorithms have been developed. This systematic review presents an overview of the available (semi-)automatic QC algorithms and software packages designed for raw, structural T1-weighted (T1w) MRI datasets. The objective of this review was to identify the differences among these algorithms in terms of their features of interest, performance, and benchmarks.

METHODS

We queried PubMed, EMBASE (Ovid), and Web of Science databases on the fifth of January 2023, and cross-checked reference lists of retrieved papers. Bias assessment was performed using PROBAST (Prediction model Risk Of Bias ASsessment Tool).

RESULTS

A total of 18 distinct algorithms were identified, demonstrating significant variations in methods, features, datasets, and benchmarks. The algorithms were categorized into rule-based, classical machine learning-based, and deep learning-based approaches. Numerous unique features were defined, which can be roughly divided into features capturing entropy, contrast, and normative measures.

CONCLUSION

Due to dataset-specific optimization, it is challenging to draw broad conclusions about comparative performance. Additionally, large variations exist in the used datasets and benchmarks, further hindering direct algorithm comparison. The findings emphasize the need for standardization and comparative studies for advancing QC in MR imaging. Efforts should focus on identifying a dataset-independent measure as well as algorithm-independent methods for assessing the relative performance of different approaches.

Quantification of Diffusion Magnetic Resonance Imaging for Prognostic Prediction of Neonatal Hypoxic-Ischemic Encephalopathy

Neonatal hypoxic-ischemic encephalopathy (HIE) is the leading cause of acquired neonatal brain injury with the risk of developing serious neurological sequelae and death. An accurate and robust prediction of short- and long-term outcomes may provide clinicians and families with fundamental evidence for their decision-making, the design of treatment strategies, and the discussion of developmental intervention plans after discharge. Diffusion tensor imaging (DTI) is one of the most powerful neuroimaging tools with which to predict the prognosis of neonatal HIE by providing microscopic features that cannot be assessed by conventional magnetic resonance imaging (MRI). DTI provides various scalar measures that represent the properties of the tissue, such as fractional anisotropy (FA) and mean diffusivity (MD). Since the characteristics of the diffusion of water molecules represented by these measures are affected by the microscopic cellular and extracellular environment, such as the orientation of structural components and cell density, they are often used to study the normal developmental trajectory of the brain and as indicators of various tissue damage, including HIE-related pathologies, such as cytotoxic edema, vascular edema, inflammation, cell death, and Wallerian degeneration. Previous studies have demonstrated widespread alteration in DTI measurements in severe cases of HIE and more localized changes in neonates with mild-to-moderate HIE. In an attempt to establish cutoff values to predict the occurrence of neurological sequelae, MD and FA measurements in the corpus callosum, thalamus, basal ganglia, corticospinal tract, and frontal white matter have proven to have an excellent ability to predict severe neurological outcomes. In addition, a recent study has suggested that a data-driven, unbiased approach using machine learning techniques on features obtained from whole-brain image quantification may accurately predict the prognosis of HIE, including for mild-to-moderate cases. Further efforts are needed to overcome current challenges, such as MRI infrastructure, diffusion modeling methods, and data harmonization for clinical application. In addition, external validation of predictive models is essential for clinical application of DTI to prognostication.

Fast and functionally specific cortical thickness changes induced by visual stimulation

Structural characteristics of the human brain serve as important markers of brain development, aging, disease progression, and neural plasticity. They are considered stable properties, changing slowly over time. Multiple recent studies reported that structural brain changes measured with magnetic resonance imaging (MRI) may occur much faster than previously thought, within hours or even minutes. The mechanisms behind such fast changes remain unclear, with hemodynamics as one possible explanation. Here we investigated the functional specificity of cortical thickness changes induced by a flickering checkerboard and compared them to blood oxygenation level-dependent (BOLD) functional MRI activity. We found that checkerboard stimulation led to a significant thickness increase, which was driven by an expansion at the gray-white matter boundary, functionally specific to V1, confined to the retinotopic representation of the checkerboard stimulus, and amounted to 1.3% or 0.022 mm. Although functional specificity and the effect size of these changes were comparable to those of the BOLD signal in V1, thickness effects were substantially weaker in V3. Furthermore, a comparison of predicted and measured thickness changes for different stimulus timings suggested a slow increase of thickness over time, speaking against a hemodynamic explanation. Altogether, our findings suggest that visual stimulation can induce structural gray matter enlargement measurable with MRI.

Application of diffusion kurtosis imaging in neonatal brain development

Background

Deviations from the regular pattern of growth and development could lead to early childhood diseases, suggesting the importance of evaluating early brain development. Through this study, we aimed to explore the changing patterns of white matter and gray matter during neonatal brain development using diffusion kurtosis imaging (DKI).

Materials and methods

In total, 42 full-term neonates (within 28 days of birth) underwent conventional brain magnetic resonance imaging (MRI) and DKI. The DKI metrics (including kurtosis parameters and diffusion parameters) of white matter and deep gray matter were measured. DKI metrics from the different regions of interest (ROIs) were evaluated using the Kruskal-Wallis test and Bonferroni method. Spearman rank correlation analysis of the DKI metrics was conducted, and the age at the time of brain MRI acquisition was calculated. The subjects were divided into three groups according to their age at the time of brain MRI acquisition: the first group, neonates aged ≤7 days; the second group, neonates aged 8-14 days; and the third group, neonates aged 15-28 days. The rate of change in DKI metrics relative to the first group was computed.

Results

The mean kurtosis (MK), axial kurtosis (Ka), radial kurtosis (Kr), and fractional anisotropy (FA) values showed positive correlations, whereas mean diffusion (MD), axial diffusion (Da), and radial diffusion (Dr) values showed negative correlations with the age at the time of brain MRI acquisition. The absolute correlation coefficients between MK values of almost all ROIs (except genu of the corpus callosum and frontal white matter) and the age at the time of brain MRI acquisition were greater than other metrics. The kurtosis parameters and FA values of central white matter were significantly higher than that of peripheral white matter, whereas the MD and Dr values were significantly lower than that of peripheral white matter. The MK value of the posterior limb of the internal capsule was the highest among the white matter areas. The FA value of the splenium of the corpus callosum was significantly higher than that of the other white matter areas. The kurtosis parameters and FA values of globus pallidus and thalamus were significantly higher than those of the caudate nucleus and putamen, whereas the Da and Dr values of globus pallidus and thalamus were significantly lower than those of the caudate nucleus and putamen. The relative change rates of kurtosis parameters and FA values of all ROIs were greater than those of MD, Da, and Dr values. The amplitude of MK values of almost all ROIs (except for the genu of the corpus callosum and central white matter of the centrum semiovale level) was greater than that of other metrics. The relative change rates of the Kr values of most ROIs were greater than those of the Ka value, and the relative change rates of the Dr values of most ROIs were greater than those of the Da value.

Conclusion

DKI parameters showed potential advantages in detecting the changes in brain microstructure during neonatal brain development.

Brain structural alterations in young girls with Rett syndrome: A voxel-based morphometry and tract-based spatial statistics study

Rett syndrome (RTT) is a neurodevelopmental disorder caused by loss-of-function variants in the MECP2 gene, currently with no cure. Neuroimaging is an important tool for obtaining non-invasive structural and functional information about the in vivo brain. Multiple approaches to magnetic resonance imaging (MRI) scans have been utilized effectively in RTT patients to understand the possible pathological basis. This study combined developmental evaluations with clinical severity, T1-weighted imaging, and diffusion tensor imaging, aiming to explore the structural alterations in cohorts of young girls with RTT, idiopathic autism spectrum disorder (ASD), or typical development. Voxel-based morphometry (VBM) was used to determine the voxel-wised volumetric characteristics of gray matter, while tract-based spatial statistics (SPSS) was used to obtain voxel-wised properties of white matter. Finally, a correlation analysis between the brain structural alterations and the clinical evaluations was performed. In the RTT group, VBM revealed decreased gray matter volume in the insula, frontal cortex, calcarine, and limbic/paralimbic regions; TBSS demonstrated decreased fractional anisotropy (FA) and increased mean diffusivity (MD) mainly in the corpus callosum and other projection and association fibers such as superior longitudinal fasciculus and corona radiata. The social impairment quotient and clinical severity were associated with these morphometric alterations. This monogenic study with an early stage of RTT may provide some valuable guidance for understanding the disease pathogenesis. At the same time, the pediatric-adjusted analytic pipelines for VBM and TBSS were introduced for significant improvement over classical approaches for MRI scans in children.

Age-related topographic map of magnetic resonance diffusion metrics in neonatal brains

Accelerated maturation of brain parenchyma close to term-equivalent age leads to rapid changes in diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) metrics of neonatal brains, which can complicate the evaluation and interpretation of these scans. In this study, we characterized the topography of age-related evolution of diffusion metrics in neonatal brains. We included 565 neonates who had MRI between 0 and 3 months of age, with no structural or signal abnormality-including 162 who had DTI scans. We analyzed the age-related changes of apparent diffusion coefficient (ADC) values throughout brain and DTI metrics (fractional anisotropy [FA] and mean diffusivity [MD]) along white matter (WM) tracts. Rate of change in ADC, FA, and MD values across 5 mm cubic voxels was calculated. There was significant reduction of ADC and MD values and increase of FA with increasing gestational age (GA) throughout neonates' brain, with the highest temporal rates in subcortical WM, corticospinal tract, cerebellar WM, and vermis. GA at birth had significant effect on ADC values in convexity cortex and corpus callosum as well as FA/MD values in corpus callosum, after correcting for GA at scan. We developed online interactive atlases depicting age-specific normative values of ADC (ages 34-46 weeks), and FA/MD (35-41 weeks). Our results show a rapid decrease in diffusivity metrics of cerebral/cerebellar WM and vermis in the first few weeks of neonatal age, likely attributable to myelination. In addition, prematurity and low GA at birth may result in lasting delay in corpus callosum myelination and cerebral cortex cellularity.

Quantitative Structural Brain Magnetic Resonance Imaging Analyses: Methodological Overview and Application to Rett Syndrome

Congenital genetic disorders often present with neurological manifestations such as neurodevelopmental disorders, motor developmental retardation, epilepsy, and involuntary movement. Through qualitative morphometric evaluation of neuroimaging studies, remarkable structural abnormalities, such as lissencephaly, polymicrogyria, white matter lesions, and cortical tubers, have been identified in these disorders, while no structural abnormalities were identified in clinical settings in a large population. Recent advances in data analysis programs have led to significant progress in the quantitative analysis of anatomical structural magnetic resonance imaging (MRI) and diffusion-weighted MRI tractography, and these approaches have been used to investigate psychological and congenital genetic disorders. Evaluation of morphometric brain characteristics may contribute to the identification of neuroimaging biomarkers for early diagnosis and response evaluation in patients with congenital genetic diseases. This mini-review focuses on the methodologies and attempts employed to study Rett syndrome using quantitative structural brain MRI analyses, including voxel- and surface-based morphometry and diffusion-weighted MRI tractography. The mini-review aims to deepen our understanding of how neuroimaging studies are used to examine congenital genetic disorders.

Fiber tracing and microstructural characterization among audiovisual integration brain regions in neonates compared with young adults

Audiovisual integration has been related with cognitive-processing and behavioral advantages, as well as with various socio-cognitive disorders. While some studies have identified brain regions instantiating this ability shortly after birth, little is known about the structural pathways connecting them. The goal of the present study was to reconstruct fiber tracts linking AVI regions in the newborn in-vivo brain and assess their adult-likeness by comparing them with analogous fiber tracts of young adults. We performed probabilistic tractography and compared connective probabilities between a sample of term-born neonates (N = 311; the Developing Human Connectome Project (dHCP, http://www.developingconnectome.org) and young adults (N = 311 The Human Connectome Project; https://www.humanconnectome.org/) by means of a classification algorithm. Furthermore, we computed Dice coefficients to assess between-group spatial similarity of the reconstructed fibers and used diffusion metrics to characterize neonates' AVI brain network in terms of microstructural properties, interhemispheric differences and the association with perinatal covariates and biological sex. Overall, our results indicate that the AVI fiber bundles were successfully reconstructed in a vast majority of neonates, similarly to adults. Connective probability distributional similarities and spatial overlaps of AVI fibers between the two groups differed across the reconstructed fibers. There was a rank-order correspondence of the fibers' connective strengths across the groups. Additionally, the study revealed patterns of diffusion metrics in line with early white matter developmental trajectories and a developmental advantage for females. Altogether, these findings deliver evidence of meaningful structural connections among AVI regions in the newborn in-vivo brain.

Diffusion Tensor Based White Matter Tract Atlases for Pediatric Populations

Diffusion Tensor Imaging (DTI) is a non-invasive neuroimaging method that has become the most widely employed MRI modality for investigations of white matter fiber pathways. DTI has proven especially valuable for improving our understanding of normative white matter maturation across the life span and has also been used to index clinical pathology and cognitive function. Despite its increasing popularity, especially in pediatric research, the majority of existing studies examining infant white matter maturation depend on regional or white matter skeleton-based approaches. These methods generally lack the sensitivity and spatial specificity of more advanced functional analysis options that provide information about microstructural properties of white matter along fiber bundles. DTI studies of early postnatal brain development show that profound microstructural and maturational changes take place during the first two years of life. The pattern and rate of these changes vary greatly throughout the brain during this time compared to the rest of the life span. For this reason, appropriate image processing of infant MR imaging requires the use of age-specific reference atlases. This article provides an overview of the pre-processing, atlas building, and the fiber tractography procedures used to generate two atlas resources, one for neonates and one for 1- to 2-year-old populations. Via the UNC-NAMIC DTI Fiber Analysis Framework, our pediatric atlases provide the computational templates necessary for the fully automatic analysis of infant DTI data. To the best of our knowledge, these atlases are the first comprehensive population diffusion fiber atlases in early pediatric ages that are publicly available.

Multimodal Neuroimaging in Rett Syndrome With MECP2 Mutation

Rett syndrome (RTT) is a rare neurodevelopmental disorder characterized by severe cognitive, social, and physical impairments resulting from de novo mutations in the X-chromosomal methyl-CpG binding protein gene 2 (MECP2). While there is still no cure for RTT, exploring up-to date neurofunctional diagnostic markers, discovering new potential therapeutic targets, and searching for novel drug efficacy evaluation indicators are fundamental. Multiple neuroimaging studies on brain structure and function have been carried out in RTT-linked gene mutation carriers to unravel disease-specific imaging features and explore genotype-phenotype associations. Here, we reviewed the neuroimaging literature on this disorder. MRI morphologic studies have shown global atrophy of gray matter (GM) and white matter (WM) and regional variations in brain maturation. Diffusion tensor imaging (DTI) studies have demonstrated reduced fractional anisotropy (FA) in left peripheral WM areas, left major WM tracts, and cingulum bilaterally, and WM microstructural/network topology changes have been further found to be correlated with behavioral abnormalities in RTT. Cerebral blood perfusion imaging studies using single-photon emission CT (SPECT) or PET have evidenced a decreased global cerebral blood flow (CBF), particularly in prefrontal and temporoparietal areas, while magnetic resonance spectroscopy (MRS) and PET studies have contributed to unraveling metabolic alterations in patients with RTT. The results obtained from the available reports confirm that multimodal neuroimaging can provide new insights into a complex interplay between genes, neurotransmitter pathway abnormalities, disease-related behaviors, and clinical severity. However, common limitations related to the available studies include small sample sizes and hypothesis-based and region-specific approaches. We, therefore, conclude that this field is still in its early development phase and that multimodal/multisequence studies with improved post-processing technologies as well as combined PET–MRI approaches are urgently needed to further explore RTT brain alterations.

A diffusion MRI-based spatiotemporal continuum of the embryonic mouse brain for probing gene-neuroanatomy connections

We established an ultra high-resolution diffusion MRI atlas of the embryonic mouse brains from E10.5 to E15.5, which characterizes the continuous changes of brain morphology and microstructures at mesoscopic scale. By integrating gene-expression data into the spatiotemporal continuum, we can navigate the evolving landscape of gene expression and neuroanatomy across both spatial and temporal dimensions to visualize their interactions in normal and abnormal embryonic brain development. We also identified regional clusters with distinct developmental trajectories and identified gene-expression profiles that matched to these regional domains. The diffusion MRI–based continuum of the embryonic brain and the computational techniques presented in this study offer a valuable tool for systematic study of the genetic control of brain development.

The embryonic mouse brain undergoes drastic changes in establishing basic anatomical compartments and laying out major axonal connections of the developing brain. Correlating anatomical changes with gene-expression patterns is an essential step toward understanding the mechanisms regulating brain development. Traditionally, this is done in a cross-sectional manner, but the dynamic nature of development calls for probing gene–neuroanatomy interactions in a combined spatiotemporal domain. Here, we present a four-dimensional (4D) spatiotemporal continuum of the embryonic mouse brain from E10.5 to E15.5 reconstructed from diffusion magnetic resonance microscopy (dMRM) data. This study achieved unprecedented high-definition dMRM at 30- to 35-µm isotropic resolution, and together with computational neuroanatomy techniques, we revealed both morphological and microscopic changes in the developing brain. We transformed selected gene-expression data to this continuum and correlated them with the dMRM-based neuroanatomical changes in embryonic brains. Within the continuum, we identified distinct developmental modes comprising regional clusters that shared developmental trajectories and similar gene-expression profiles. Our results demonstrate how this 4D continuum can be used to examine spatiotemporal gene–neuroanatomical interactions by connecting upstream genetic events with anatomical changes that emerge later in development. This approach would be useful for large-scale analysis of the cooperative roles of key genes in shaping the developing brain.

Nature and nurture shape structural connectivity in the face processing brain network

Face processing is a key ability facilitating social cognition. Only a few studies explored how nature and nurture shape face processing ontogeny at the behavioral and neural level. Also, very little is known about the contributions of nature and nurture to the establishment of white matter fibers supporting this specific human ability. The main purpose of this study was to assess genetic and environmental influences on white matter bundles connecting atlas-defined and functionally-defined face-responsive areas in the brain. Diffusion weighted images from 408 twins (monozygotic = 264, dizygotic = 144) were obtained from the WU-Minn Human Connectome Project. Fractional anisotropy - a widely used measure of fiber quality - of seven white matter tracts in the face network and ten global white matter tracts was analyzed by means of Structural Equation Modeling for twin data. Results revealed small and moderate genetic effects on face network fiber quality in addition to their shared variance with global brain white matter integrity. Furthermore, a theoretically expected common latent factor accounted for limited genetic and larger environmental variance in multiple face network fibers. The findings suggest that both genetic and environmental factors explain individual differences in fiber quality within the face network, as compared with much larger genetic effects on global brain white matter quality. In addition to heritability, individual-specific environmental influences on the face processing brain network are large, a finding that suggests to connect nature and nurture views on this remarkably specific human ability.

Current landscape and future perspectives in preclinical MR and PET imaging of brain metastasis

Brain metastasis (BM) is a major cause of cancer patient morbidity. Clinical magnetic resonance imaging (MRI) and positron emission tomography (PET) represent important resources to assess tumor progression and treatment responses. In preclinical research, anatomical MRI and to some extent functional MRI have frequently been used to assess tumor progression. In contrast, PET has only to a limited extent been used in animal BM research. A considerable culprit is that results from most preclinical studies have shown little impact on the implementation of new treatment strategies in the clinic. This emphasizes the need for the development of robust, high-quality preclinical imaging strategies with potential for clinical translation. This review focuses on advanced preclinical MRI and PET imaging methods for BM, describing their applications in the context of what has been done in the clinic. The strengths and shortcomings of each technology are presented, and recommendations for future directions in the development of the individual imaging modalities are suggested. Finally, we highlight recent developments in quantitative MRI and PET, the use of radiomics and multimodal imaging, and the need for a standardization of imaging technologies and protocols between preclinical centers.

More than meets the eye: Longitudinal visual system neurodevelopment in very preterm children and anophthalmia

Anophthalmia, characterized by the absence of an eye(s), is a rare major birth defect with a relatively unexplored neuroanatomy. Longitudinal comparison of white matter development in an anophthalmic (AC) very preterm (VPT) child with both binocular VPT and full-term (FT) children provides unique insights into early neurodevelopment of the visual system. VPT-born neonates (<32wks gestational age), including the infant with unilateral anophthalmia, underwent neuroimaging every two years from birth until 8 years. DTI images (N = 168) of the optic radiation (OR) and a control track, the posterior limb of the internal capsule (PLIC), were analysed. The diameter of the optic nerves (ON) were analysed using T1-weighted images. Significant group differences in FA and AD were found bilaterally in the OR and PLIC. This extends the literature on altered white matter development in VPT children, being the first longitudinal study showing stable group differences across the 4, 6 and 8 year timepoints. AC showed greater deficits in FA and AD bilaterally, but recovered towards VPT group means from 4 to 8 years-of-age. Complete lack of binocular input would be responsible for these early deficits; compensatory mechanisms may facilitate structural improvement over time. AC's ON exhibited significant atrophy ipsilateral to the anophthalmic eye. Functionally, AC displayed normal visual acuity and form perception, but naso-temporal bias in motion perception. Following these groups and AC longitudinally enabled novel understanding of the joint influence of monocular vision and VPT birth on neurodevelopment.

Imaging Developmental and Interventional Plasticity Following Perinatal Stroke

Perinatal stroke occurs around the time of birth and leads to lifelong neurological disabilities including hemiparetic cerebral palsy. Magnetic resonance imaging (MRI) has revolutionized our understanding of developmental neuroplasticity following early injury, quantifying volumetric, structural, functional, and metabolic compensatory changes after perinatal stroke. Such techniques can also be used to investigate how the brain responds to treatment (interventional neuroplasticity). Here, we review the current state of knowledge of how established and emerging neuroimaging modalities are informing neuroplasticity models in children with perinatal stroke. Specifically, we review structural imaging characterizing lesion characteristics and volumetrics, diffusion tensor imaging investigating white matter tracts and networks, task-based functional MRI for localizing function, resting state functional imaging for characterizing functional connectomes, and spectroscopy examining neurometabolic changes. Key challenges and exciting avenues for future investigations are also considered.

Diffusion Weighted and Diffusion Tensor MRI in Pediatric Neuroimaging Including Connectomics: Principles and Applications

Diffusion weighted MRI (DWI) including diffusion tensor imaging (DTI) are unique imaging techniques that render qualitative and quantitative information of the central nervous system white matter (WM) ultrastructure. It uses the Brownian movement of water molecules to probe tissue microstructure. It is a noninvasive method, with superb sensitivity to the differential mobility of water molecules within various components of the brain without the necessity to inject contrast agents. By sampling the 3 dimensional shape, direction and magnitude of the water diffusion, DWI/DTI generates unique tissue contrasts that can be used to study the axonal WM organization of the central nervous system. Its application allows to study the normal and anomalous brain development including connectivity, as well as a multitude of WM diseases. This article discusses/summarizes the principles of DWI/DTI and its applications in pediatric neuroscience research.

Magnitude and timing of major white matter tract maturation from infancy through adolescence with NODDI

White matter maturation is a nonlinear and heterogeneous phenomenon characterized by axonal packing, increased axon caliber, and a prolonged period of myelination. While current in vivo diffusion MRI (dMRI) methods, like diffusion tensor imaging (DTI), have successfully characterized the gross structure of major white matter tracts, these measures lack the specificity required to unravel the distinct processes that contribute to microstructural development. Neurite orientation dispersion and density imaging (NODDI) is a dMRI approach that probes tissue compartments and provides biologically meaningful measures that quantify neurite density index (NDI) and orientation dispersion index (ODI). The purpose of this study was to characterize the magnitude and timing of major white matter tract maturation with NODDI from infancy through adolescence in a cross-sectional cohort of 104 subjects (0.6–18.8 years). To probe the regional nature of white matter development, we use an along-tract approach that partitions tracts to enable more fine-grained analysis. Major white matter tracts showed exponential age-related changes in NDI with distinct maturational patterns. Overall, analyses revealed callosal fibers developed before association fibers. Our along-tract analyses elucidate spatially varying patterns of maturation with NDI that are distinct from those obtained with DTI. ODI was not significantly associated with age in the majority of tracts. Our results support the conclusion that white matter tract maturation is heterochronous process and, furthermore, we demonstrate regional variability in the developmental timing within major white matter tracts. Together, these results help to disentangle the distinct processes that contribute to and more specifically define the time course of white matter maturation.

Structural neuroimaging

Characterizing the neuroanatomical correlates of brain development is essential in understanding brain-behavior relationships and neurodevelopmental disorders. Advances in brain MRI acquisition protocols and image processing techniques have made it possible to detect and track with great precision anatomical brain development and pediatric neurologic disorders. In this chapter, we provide a brief overview of the modern neuroimaging techniques for pediatric brain development and review key normal brain development studies. Characteristic disorders affecting neurodevelopment in childhood, such as prematurity, attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), epilepsy, and brain cancer, and key neuroanatomical findings are described and then reviewed. Large datasets of typically developing children and children with various neurodevelopmental conditions are now being acquired to help provide the biomarkers of such impairments. While there are still several challenges in imaging brain structures specific to the pediatric populations, such as subject cooperation and tissues contrast variability, considerable imaging research is now being devoted to solving these problems and improving pediatric data analysis.

Fractional Anisotropy of Thalamic Nuclei Is Associated With Verticality Misperception After Extra-Thalamic Stroke

Verticality misperception after stroke is a frequent neurological deficit that leads to postural imbalance and a higher risk of falls. The posterior thalamic nuclei are described to be involved with verticality perception, but it is unknown if extra-thalamic lesions can have the same effect via diaschisis and degeneration of thalamic nuclei. We investigated the relationship between thalamic fractional anisotropy (FA, a proxy of structural integrity), and verticality perception, in patients after stroke with diverse encephalic extra-thalamic lesions. We included 11 first time post-stroke patients with extra-thalamic primary lesions, and compared their region-based FA to a group of 25 age-matched healthy controls. For the patient sample, correlation and regression analyses evaluated the relationship between thalamic nuclei FA and error of postural vertical (PV) and haptic vertical (HV) in the roll (PVroll/HVroll) and pitch planes (PVpitch/HVpitch). Relative to controls, patients showed decreased FA of anterior, ventral anterior, ventral posterior lateral, dorsal, and pulvinar thalamic nuclei, despite the primary lesions being extra-thalamic. We found a significant correlation between HVroll, and FA in the anterior and dorsal nuclei, and PVroll with FA in the anterior nucleus. FA in the anterior, ventral anterior, ventral posterior lateral, dorsal and pulvinar nuclei predicted PV, and FA in the ventral anterior, ventral posterior lateral and dorsal nuclei predicted HV. While prior studies indicate that primary lesions of the thalamus can result in verticality misperception, here we present evidence supporting that secondary degeneration of thalamic nuclei via diaschisis can also be associated with verticality misperception after stroke.

Quantification of DTI in the Pediatric Spinal Cord: Application to Clinical Evaluation in a Healthy Patient Population

BACKGROUND AND PURPOSE

The purpose of the study is to characterize diffusion tensor imaging indices in the developing spinal cord, evaluating differences based on age and cord region. Describing the progression of DTI indices in the pediatric cord increases our understanding of spinal cord development.

MATERIALS AND METHODS

A retrospective analysis was performed on DTI acquired in 121 pediatric patients (mean, 8.6 years; range, 0.3-18.0 years) at Monroe Carell Jr. Children's Hospital at Vanderbilt from 2017 to 2018. Diffusion-weighted images (15 directions; b = 750 s/mm²; slice thickness, 5 mm; in-plane resolution, 1.0 × 1.0 mm²) were acquired on a 3T scanner in the cervicothoracic and/or thoracolumbar cord. Manual whole-cord segmentation was performed. Images were masked and further segmented into cervical, upper thoracic, thoracolumbar, and conus regions. Analyses of covariance were performed for each DTI-derived index to investigate how age affects diffusion across cord regions, and 95% confidence intervals were calculated across age for each derived index and region. Post hoc testing was performed to analyze regional differences.

RESULTS

Analyses of covariance revealed significant correlations of age with axial diffusivity, mean diffusivity, and fractional anisotropy (all, P < .001). There were also significant differences among cord regions for axial diffusivity, radial diffusivity, mean diffusivity, and fractional anisotropy (all, P < .001).

CONCLUSIONS

This research demonstrates that diffusion evolves in the pediatric spinal cord during development, dependent on both cord region and the diffusion index of interest. Future research could investigate how diffusion may be affected by common pediatric spinal pathologies.

A Multi-Atlas Label Fusion Tool for Neonatal Brain MRI Parcellation and Quantification

Structure-by-structure analysis, in which the brain magnetic resonance imaging (MRI) is parcellated based on its anatomical units, is widely used to investigate chronological changes in morphology or signal intensity during normal development, as well as to identify the alterations seen in various diseases or conditions. The multi-atlas label fusion (MALF) method is considered a highly accurate parcellation approach, and anticipated for clinical application to quantitatively evaluate early developmental processes. However, the current MALF methods, which are designed for neonatal brain segmentations, are not widely available. In this study, we developed a T1-weighted, neonatal, multi-atlas repository and integrated it into the MALF-based brain segmentation tools in the cloud-based platform, MRICloud. The cloud platform ensures users instant access to the advanced MALF tool for neonatal brains, with no software or installation requirements for the client. The Web platform by braingps.mricloud.org will eliminate the dependence on a particular operating system (eg, Windows, Macintosh, or Linux) and the requirement for high computational performance of the user's computers. The MALF-based, fully automated, image parcellation could achieve excellent agreement with manual parcellation, and the whole and regional brain volumes quantified through this method demonstrated developmental trajectories comparable to those from a previous publication. This solution will make the latest MALF tools readily available to users, with minimum barriers, and will expedite and accelerate advancements in developmental neuroscience research, neonatology, and pediatric neuroradiology.

A review of diffusion MRI of typical white matter development from early childhood to young adulthood

Understanding typical, healthy brain development provides a baseline from which to detect and characterize brain anomalies associated with various neurological or psychiatric disorders and diseases. Diffusion MRI is well suited to study white matter development, as it can virtually extract individual tracts and yield parameters that may reflect alterations in the underlying neural micro-structure (e.g. myelination, axon density, fiber coherence), though it is limited by its lack of specificity and other methodological concerns. This review summarizes the last decade of diffusion imaging studies of healthy white matter development spanning childhood to early adulthood (4-35 years). Conclusions about anatomical location, rates, and timing of white matter development with age are discussed, as well as the influence of image acquisition, analysis, age range/sample size, and statistical model. Despite methodological variability between studies, some consistent findings have emerged from the literature. Specifically, diffusion studies of neurodevelopment overwhelmingly demonstrate regionally varying increases of fractional anisotropy and decreases of mean diffusivity during childhood and adolescence, some of which continue into adulthood. While most studies use linear fits to model age-related changes, studies with sufficient sample sizes and age range provide clear evidence that white matter development (as indicated by diffusion) is non-linear. Several studies further suggest that maturation in association tracts with frontal-temporal connections continues later than commissural and projection tracts. The emerging contributions of more advanced diffusion methods are also discussed, as they may reveal new aspects of white matter development. Although non-specific, diffusion changes may reflect increases of myelination, axonal packing, and/or coherence with age that may be associated with changes in cognition.

Fetal brain growth portrayed by a spatiotemporal diffusion tensor MRI atlas computed from in utero images

Altered structural fetal brain development has been linked to neuro-developmental disorders. These structural alterations can be potentially detected in utero using diffusion tensor imaging (DTI). However, acquisition and reconstruction of in utero fetal brain DTI remains challenging. Until now, motion-robust DTI methods have been employed for reconstruction of in utero fetal DTIs. However, due to the unconstrained fetal motion and permissible in utero acquisition times, these methods yielded limited success and have typically resulted in noisy DTIs. Consequently, atlases and methods that could enable groupwise studies, multi-modality imaging, and computer-aided diagnosis from in utero DTIs have not yet been developed. This paper presents the first DTI atlas of the fetal brain computed from in utero diffusion-weighted images. For this purpose an algorithm for computing an unbiased spatiotemporal DTI atlas, which integrates kernel-regression in age with a diffeomorphic tensor-to-tensor registration of motion-corrected and reconstructed individual fetal brain DTIs, was developed. Our new algorithm was applied to a set of 67 fetal DTI scans acquired from healthy fetuses each scanned at a gestational age between 21 and 39 weeks. The neurodevelopmental trends in the fetal brain, characterized by the atlas, were qualitatively and quantitatively compared with the observations reported in prior ex vivo and in utero studies, and with results from imaging gestational-age equivalent preterm infants. Our major findings revealed early presence of limbic fiber bundles, followed by the appearance and maturation of projection pathways (characterized by an age related increase in FA) during late 2nd and early 3rd trimesters. During the 3rd trimester association fiber bundles become evident. In parallel with the appearance and maturation of fiber bundles, from 21 to 39 gestational weeks gradual disappearance of the radial coherence of the telencephalic wall was qualitatively identified. These results and analyses show that our DTI atlas of the fetal brain is useful for reliable detection of major neuronal fiber bundle pathways and for characterization of the fetal brain reorganization that occurs in utero. The atlas can also serve as a useful resource for detection of normal and abnormal fetal brain development in utero.

Age-specific gray and white matter DTI atlas for human brain at 33, 36 and 39 postmenstrual weeks

During the 3rd trimester, dramatic structural changes take place in the human brain, underlying the neural circuit formation. The survival rate of premature infants has increased significantly in recent years. The large morphological differences of the preterm brain at 33 or 36 postmenstrual weeks (PMW) from the brain at 40PMW (full term) make it necessary to establish age-specific atlases for preterm brains. In this study, with high quality (1.5 × 1.5 × 1.6 mm3 imaging resolution) diffusion tensor imaging (DTI) data obtained from 84 healthy preterm and term-born neonates, we established age-specific preterm and term-born brain templates and atlases at 33, 36 and 39PMW. Age-specific DTI templates include a single-subject template, a population-averaged template with linear transformation and a population-averaged template with nonlinear transformation. Each of the age-specific DTI atlases includes comprehensive labeling of 126 major gray matter (GM) and white matter (WM) structures, specifically 52 cerebral cortical structures, 40 cerebral WM structures, 22 brainstem and cerebellar structures and 12 subcortical GM structures. From 33 to 39 PMW, dramatic morphological changes of delineated individual neural structures such as ganglionic eminence and uncinate fasciculus were revealed. The evaluation based on measurements of Dice ratio and L1 error suggested reliable and reproducible automated labels from the age-matched atlases compared to labels from manual delineation. Applying these atlases to automatically and effectively delineate microstructural changes of major WM tracts during the 3rd trimester was demonstrated. The established age-specific DTI templates and atlases of 33, 36 and 39 PMW brains may be used for not only understanding normal functional and structural maturational processes but also detecting biomarkers of neural disorders in the preterm brains.

Neural histology and neurogenesis of the human fetal and infant brain

The human brain develops slowly and over a long period of time which lasts for almost three decades. This enables good spatio-temporal resolution of histogenetic and neurogenetic events as well as an appropriate and clinically relevant timing of these events. In order to successfully apply in vivo neuroimaging data, in analyzing both the normal brain development and the neurodevelopmental origin of major neurological and mental disorders, it is important to correlate these neuroimaging data with the existing data on morphogenetic, histogenetic and neurogenetic events. Furthermore, when performing such correlation, the genetic, genomic, and molecular biology data on phenotypic specification of developing brain regions, areas and neurons should also be included. In this review, we focus on early developmental periods (form 8 postconceptional weeks to the second postnatal year) and describe the microstructural organization and neural circuitry elements of the fetal and early postnatal human cerebrum.

Early Diagnosis of Spastic Cerebral Palsy in Infants with Periventricular White Matter Injury Using Diffusion Tensor Imaging

BACKGROUND AND PURPOSE

Periventricular white matter injury is the common cause of spastic cerebral palsy. However, the early diagnosis of spastic cerebral palsy still remains a challenge. Our aim was to investigate whether infants with periventricular white matter injury with bilateral spastic cerebral palsy have unique lesions different from those in infants without cerebral palsy and to evaluate the efficiency of DTI in the early diagnosis of spastic cerebral palsy.

MATERIALS AND METHODS

Infants with periventricular white matter injury and controls underwent MR imaging at 6-18 months of age. Fractional anisotropy was calculated from DTI. Cerebral palsy was diagnosed by 24-30 months of age. Subjects were divided into 3 groups: infants with periventricular white matter injury with bilateral spastic cerebral palsy, infants with periventricular white matter injury without cerebral palsy, and controls. Tract-Based Spatial Statistics and Automated Fiber Quantification were used to investigate intergroup differences. Receiver operating characteristic curves were used to assess the diagnostic accuracy of spastic cerebral palsy. Correlations between motor function scores and fractional anisotropy were evaluated along white matter tracts.

RESULTS

There were 20, 19, and 33 subjects in periventricular white matter injury with spastic cerebral palsy, periventricular white matter injury without cerebral palsy, and control groups, respectively. Decreased fractional anisotropy in the corticospinal tract was only observed in infants with periventricular white matter injury with spastic cerebral palsy, whereas decreased fractional anisotropy in the posterior thalamic radiation and genu and splenium of the corpus callosum was seen in both periventricular white matter injury subgroups. Fractional anisotropy in the corticospinal tract at the internal capsule level was effective in differentiating infants with periventricular white matter injury with spastic cerebral palsy from those without cerebral palsy by a threshold of 0.53, and it had strong correlations with motor function scores.

CONCLUSIONS

Corticospinal tract lesions play a crucial role in motor impairment related to spastic cerebral palsy in infants with periventricular white matter injury. Fractional anisotropy in the corticospinal tract at the internal capsule level could aid in the early diagnosis of spastic cerebral palsy with high diagnostic accuracy.

Bihemispheric alterations in myelination in children following unilateral perinatal stroke

Background

Stroke is a leading cause of perinatal brain injury with variable outcomes including cerebral palsy and epilepsy. The biological processes that underlie these heterogeneous outcomes are poorly understood. Alterations in developmental myelination are recognized as a major determinant of outcome in preterm brain injury but have not been explored in perinatal stroke. We aimed to characterize myelination in hemiparetic children after arterial perinatal stroke, hypothesizing that ipsilesional myelination would be impaired, the degree of which would correlate with poor outcome.

Methods

Retrospective, controlled cohort study. Participants were identified through the Alberta Perinatal Stroke Project (APSP), a population-based research cohort (n > 400). Inclusion criteria were: 1) MRI-confirmed, unilateral arterial perinatal stroke, 2) T1-weighted MRI after 6 months of age, 3) absence of other neurological disorders, 4) neurological outcome that included at least one of the following tests - Pediatric Stroke Outcome Measure (PSOM), Assisting Hand Assessment (AHA), Melbourne Assessment (MA), neuropsychological evaluation (NPE), and robotic sensorimotor measurements. FreeSurfer software measured hemispheric asymmetry in myelination intensity (primary outcome). A second method using ImageJ software validated the detection of myelination asymmetry. A repeated measures ANOVA was used to compare perilesional, ipsilesional remote, and contralesional homologous region myelination between stroke cases and typically developing controls. Myelination metrics were compared to clinical outcome measures (t-test, Pearson's correlation).

Results

Twenty youth with arterial stroke (mean age: 13.4 ± 4.2yo) and 27 typically developing controls (mean age: 12.5 ± 3.7yo) were studied in FreeSurfer. Participants with stroke demonstrated lower myelination in the ipsilesional hemisphere (p < 0.0001). Myelination in perilesional regions had lower intensity compared to ipsilesional remote areas (p < .00001) and contralesional homologous areas (p < 0.00001). Ipsilesional remote regions had decreased myelination compared to homologous regions on the contralesional hemisphere (p = 0.016). Contralesional myelination was decreased compared to controls (p < 0.00001). Myelination metrics were not strongly associated with clinical motor, robotic sensorimotor, or neuropsychological outcomes though some complex tests requiring speeded responses had moderate effect sizes.

Conclusion

Myelination of apparently uninjured brain in both the ipsilesional and contralesional hemispheres is decreased after perinatal stroke. Differences appear to radiate outward from the lesion. Further study is needed to determine clinical significance.

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Myelination is altered in the lesioned hemisphere after perinatal stroke.

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The uninjured, contralesional hemisphere also demonstrates differences in myelination.

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Simple software can estimate MRI myelination abnormalities in children with perinatal brain injury.

Automated processing pipeline for neonatal diffusion MRI in the developing Human Connectome Project

The developing Human Connectome Project is set to create and make available to the scientific community a 4-dimensional map of functional and structural cerebral connectivity from 20 to 44 weeks post-menstrual age, to allow exploration of the genetic and environmental influences on brain development, and the relation between connectivity and neurocognitive function. A large set of multi-modal MRI data from fetuses and newborn infants is currently being acquired, along with genetic, clinical and developmental information. In this overview, we describe the neonatal diffusion MRI (dMRI) image processing pipeline and the structural connectivity aspect of the project. Neonatal dMRI data poses specific challenges, and standard analysis techniques used for adult data are not directly applicable. We have developed a processing pipeline that deals directly with neonatal-specific issues, such as severe motion and motion-related artefacts, small brain sizes, high brain water content and reduced anisotropy. This pipeline allows automated analysis of in-vivo dMRI data, probes tissue microstructure, reconstructs a number of major white matter tracts, and includes an automated quality control framework that identifies processing issues or inconsistencies. We here describe the pipeline and present an exemplar analysis of data from 140 infants imaged at 38–44 weeks post-menstrual age.

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A comprehensive and automated pipeline to consistently analyse neonatal dMRI data.

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Optimised motion and distortions correction to address newborn specific challenges.

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The automated QC framework allows to detect issues and to quantify data quality.

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Automated white matter segmentation allows to extract tract-specific masks.

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Preliminary data analysis of 140 infants imaged at 38–44 weeks post-menstrual age.

Computational neuroanatomy of baby brains: A review

The first postnatal years are an exceptionally dynamic and critical period of structural, functional and connectivity development of the human brain. The increasing availability of non-invasive infant brain MR images provides unprecedented opportunities for accurate and reliable charting of dynamic early brain developmental trajectories in understanding normative and aberrant growth. However, infant brain MR images typically exhibit reduced tissue contrast (especially around 6 months of age), large within-tissue intensity variations, and regionally-heterogeneous, dynamic changes, in comparison with adult brain MR images. Consequently, the existing computational tools developed typically for adult brains are not suitable for infant brain MR image processing. To address these challenges, many infant-tailored computational methods have been proposed for computational neuroanatomy of infant brains. In this review paper, we provide a comprehensive review of the state-of-the-art computational methods for infant brain MRI processing and analysis, which have advanced our understanding of early postnatal brain development. We also summarize publically available infant-dedicated resources, including MRI datasets, computational tools, grand challenges, and brain atlases. Finally, we discuss the limitations in current research and suggest potential future research directions.

Longitudinally and inter-site consistent multi-atlas based parcellation of brain anatomy using harmonized atlases

As longitudinal and multi-site studies become increasingly frequent in neuroimaging, maintaining longitudinal and inter-scanner consistency of brain parcellation has become a major challenge due to variation in scanner models and/or image acquisition protocols across scanners and sites. We present a new automated segmentation method specifically designed to achieve a consistent parcellation of anatomical brain structures in such heterogeneous datasets. Our method combines a site-specific atlas creation strategy with a state-of-the-art multi-atlas anatomical label fusion framework. Site-specific atlases are computed such that they preserve image intensity characteristics of each site's scanner and acquisition protocol, while atlas pairs share anatomical labels in a way consistent with inter-scanner acquisition variations. This harmonization of atlases improves inter-study and longitudinal consistency of segmentations in the subsequent consensus labeling step. We tested this approach on a large sample of older adults from the Baltimore Longitudinal Study of Aging (BLSA) who had longitudinal scans acquired using two scanners that vary with respect to vendor and image acquisition protocol. We compared the proposed method to standard multi-atlas segmentation for both cross-sectional and longitudinal analyses. The harmonization significantly reduced scanner-related differences in the age trends of ROI volumes, improved longitudinal consistency of segmentations, and resulted in higher across-scanner intra-class correlations, particularly in the white matter.

Derivatizations of Sgc8-c aptamer to prepare metallic radiopharmaceuticals as imaging diagnostic agents: Syntheses, isolations, and physicochemical characterizations

Aptamers, oligonucleotides with the capability to bind to a target through non-covalent bonds with high affinity and specificity, have a great number of advantages as scaffold to prepare molecular imaging agents. In this sense, we have performed post-SELEX modifications of a truncated aptamer, Sgc8-c, which bind to protein tyrosine kinase 7 to obtain a specific molecular targeting probe for in vivo diagnosis and in vivo therapy. Herein, we describe the synthetic efforts to prepare conjugates between Sgc8-c and different metallic ions chelator moieties in short times, high purities, and adequate yields. The selected chelator moieties, derived from 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid, 2-benzyl-1,4,7-triazacyclononane-1,4,7-triacetic acid, and 6-hydrazinonicotinic acid, were covalently attached at the 5'-aptamer position yielding the expected products which were stable in aqueous solution up to 75°C and in typical aptamer storage conditions at least for 30 days.

Prenatal methamphetamine exposure is associated with reduced subcortical volumes in neonates

OBJECTIVES

Prenatal exposure to methamphetamine is associated with a range of neuropsychological, behavioural and cognitive deficits. A small number of imaging studies suggests that these may be mediated by neurostructural changes, including reduced volumes of specific brain regions. This study investigated potential volumetric changes in the brains of neonates with prenatal methamphetamine exposure. To our knowledge no previous studies have examined methamphetamine effects on regional brain volumes at this age.

STUDY DESIGN

Mothers were recruited antenatally and interviewed regarding methamphetamine use during pregnancy. Mothers in the exposure group reported using methamphetamine≥twice/month during pregnancy; control infants had no exposure to methamphetamine or other drugs and minimal exposure to alcohol. MRI scans were performed in the first postnatal month, following which anatomical images were processed using FreeSurfer. Subcortical and cerebellar regions were manually segmented and their volumes determined using FreeView. Pearson correlations were used to analyse potential associations between methamphetamine exposure and regional volumes. The associations between methamphetamine exposure and regional volumes were then examined adjusting for potential confounding variables.

RESULTS

Methamphetamine exposure was associated with reduced left and right caudate and thalamus volumes. The association in the right caudate remained significant following adjustment for potential confounding variables.

CONCLUSIONS

Our findings showing reduced caudate and thalamus volumes in neonates with prenatal methamphetamine exposure are consistent with previous findings in older exposed children, and demonstrate that these changes are already detectable in neonates. Continuing research is warranted to examine whether reduced subcortical volumes are predictive of cognitive, behavioural and affective impairment in older children.

Mapping orbitofrontal-limbic maturation in non-human primates: A longitudinal magnetic resonance imaging study

Brain development involves spatiotemporally complex microstructural changes. A number of neuropsychiatric disorders are linked to the neural processes of development and aging. Thus, it is important to understanding the typical developmental patterns of various brain structures, which will help to define critical periods of vulnerability for neural maturation, as well as anatomical mechanisms of brain structure-related neuropathology. In this study, we used magnetic resonance imaging to assess development of the orbitofrontal cortex, cingulate cortex, amygdala, and hippocampus in a non-human primate species, the common marmoset (Callithrix jacchus). We collected a total of 114 T2-weighted and 91 diffusion-weighted scans from 23 animals from infancy to early adulthood. Quantitative and qualitative evaluation of age-related brain growth patterns showed non-linear structural developmental changes in all measured brain regions, consistent with reported human data. Overall, robust volumetric growth was observed from 1 to 3 months of age (from infancy to the early juvenile period). This rapid brain growth was associated with the largest decrease in mean, axial, and radial diffusivities of diffusion tensor imaging in all brain regions, suggesting an increase in the number and size of cells, dendrites, and spines during this period. After this developmental period, the volume of various brain regions steadily increased until adolescence (7-13 months of age, depending on the region). Further, structural connectivity derived from tractography data in various brain regions continuously changed from infancy to adolescence, suggesting that the increase in brain volume was related to continued axonal myelination during adolescence. Thereafter, the volume of the cortical regions decreased considerably, while there was no change in subcortical regions. Familial factors, rather than sex, contributed the development of the front-limbic brain regions. Overall, this study provides further data on the factors and timing important for normal brain development, and suggest that the common marmoset is a useful animal model for human neural development.

Microstructural Alterations and Oligodendrocyte Dysmaturation in White Matter After Cardiopulmonary Bypass in a Juvenile Porcine Model

Background

Newly developed white matter (WM) injury is common after cardiopulmonary bypass (CPB) in severe/complex congenital heart disease. Fractional anisotropy (FA) allows measurement of macroscopic organization of WM pathology but has rarely been applied after CPB. The aims of our animal study were to define CPB‐induced FA alterations and to determine correlations between these changes and cellular events after congenital heart disease surgery.

Methods and Results

Normal porcine WM development was first assessed between 3 and 7 weeks of age: 3‐week‐old piglets were randomly assigned to 1 of 3 CPB‐induced insults. FA was analyzed in 31 WM structures. WM oligodendrocytes, astrocytes, and microglia were assessed immunohistologically. Normal porcine WM development resembles human WM development in early infancy. We found region‐specific WM vulnerability to insults associated with CPB. FA changes after CPB were also insult dependent. Within various WM areas, WM within the frontal cortex was susceptible, suggesting that FA in the frontal cortex should be a biomarker for WM injury after CPB. FA increases occur parallel to cellular processes of WM maturation during normal development; however, they are altered following surgery. CPB‐induced oligodendrocyte dysmaturation, astrogliosis, and microglial expansion affect these changes. FA enabled capturing CPB‐induced cellular events 4 weeks postoperatively. Regions most resilient to CPB‐induced FA reduction were those that maintained mature oligodendrocytes.

Conclusions

Reducing alterations of oligodendrocyte development in the frontal cortex can be both a metric and a goal to improve neurodevelopmental impairment in the congenital heart disease population. Studies using this model can provide important data needed to better interpret human imaging studies.

Macroanatomical Landmarks Featuring Junctions of Major Sulci and Fissures and Scalp Landmarks Based on the International 10-10 System for Analyzing Lateral Cortical Development of Infants

The topographic relationships between the macroanatomical structure of the lateral cortex, including sulci and fissures, and anatomical landmarks on the external surface of the head are known to be consistent. This allows the coregistration of EEG electrodes or functional near-infrared spectroscopy over the scalp with underlying cortical regions. However, limited information is available as to whether the topographic relationships are maintained in rapidly developing infants, whose brains and heads exhibit drastic growth. We used MRIs of infants ranging in age from 3 to 22 months old, and identified 20 macroanatomical landmarks, featuring the junctions of major sulci and fissures, as well as cranial landmarks and virtually determined positions of the international 10-20 and 10-10 systems. A Procrustes analysis revealed developmental trends in changes of shape in both the cortex and head. An analysis of Euclidian distances between selected pairs of cortical landmarks at standard stereotactic coordinates showed anterior shifts of the relative positions of the premotor and parietal cortices with age. Finally, cortical landmark positions and their spatial variability were compared with 10-10 landmark positions. The results indicate that variability in the distribution of each macroanatomical landmark was much smaller than the pitch of the 10-10 landmarks. This study demonstrates that the scalp-based 10-10 system serves as a good frame of reference in infants not only for assessing the development of the macroanatomy of the lateral cortical structure, but also for functional studies of cortical development using transcranial modalities such as EEG and fNIRS.

CerebroMatic: A Versatile Toolbox for Spline-Based MRI Template Creation

Brain image spatial normalization and tissue segmentation rely on prior tissue probability maps. Appropriately selecting these tissue maps becomes particularly important when investigating "unusual" populations, such as young children or elderly subjects. When creating such priors, the disadvantage of applying more deformation must be weighed against the benefit of achieving a crisper image. We have previously suggested that statistically modeling demographic variables, instead of simply averaging images, is advantageous. Both aspects (more vs. less deformation and modeling vs. averaging) were explored here. We used imaging data from 1914 subjects, aged 13 months to 75 years, and employed multivariate adaptive regression splines to model the effects of age, field strength, gender, and data quality. Within the spm/cat12 framework, we compared an affine-only with a low- and a high-dimensional warping approach. As expected, more deformation on the individual level results in lower group dissimilarity. Consequently, effects of age in particular are less apparent in the resulting tissue maps when using a more extensive deformation scheme. Using statistically-described parameters, high-quality tissue probability maps could be generated for the whole age range; they are consistently closer to a gold standard than conventionally-generated priors based on 25, 50, or 100 subjects. Distinct effects of field strength, gender, and data quality were seen. We conclude that an extensive matching for generating tissue priors may model much of the variability inherent in the dataset which is then not contained in the resulting priors. Further, the statistical description of relevant parameters (using regression splines) allows for the generation of high-quality tissue probability maps while controlling for known confounds. The resulting CerebroMatic toolbox is available for download at http://irc.cchmc.org/software/cerebromatic.php.

Mapping the critical gestational age at birth that alters brain development in preterm-born infants using multi-modal MRI

Preterm birth adversely affects postnatal brain development. In order to investigate the critical gestational age at birth (GAB) that alters the developmental trajectory of gray and white matter structures in the brain, we investigated diffusion tensor and quantitative T2 mapping data in 43 term-born and 43 preterm-born infants. A novel multivariate linear model-the change point model, was applied to detect change points in fractional anisotropy, mean diffusivity, and T2 relaxation time. Change points captured the "critical" GAB value associated with a change in the linear relation between GAB and MRI measures. The analysis was performed in 126 regions across the whole brain using an atlas-based image quantification approach to investigate the spatial pattern of the critical GAB. Our results demonstrate that the critical GABs are region- and modality-specific, generally following a central-to-peripheral and bottom-to-top order of structural development. This study may offer unique insights into the postnatal neurological development associated with differential degrees of preterm birth.

Diffusion Tensor Imaging Detects Occult Cerebellar Injury in Severe Neonatal Hypoxic-Ischemic Encephalopathy

BACKGROUND

Despite the benefits of whole-body hypothermia therapy, many infants with hypoxic-ischemic encephalopathy (HIE) die or have significant long-term neurodevelopmental impairment. Prospectively identifying neonates at risk of poor outcome is essential but not straightforward. The cerebellum is not classically considered to be a brain region vulnerable to hypoxic-ischemic insults; recent literature suggests, however, that the cerebellum may be involved in neonatal HIE. In this study, we aimed to assess the microstructural integrity of cerebellar and linked supratentorial structures in neonates with HIE compared to neurologically healthy neonatal controls.

METHODS

In this prospective cohort study, we performed a quantitative diffusion tensor imaging (DTI) analysis of the structural pathways of connectivity, which may be affected in neonatal cerebellar injury by measuring fractional anisotropy (FA) and mean diffusivity (MD) within the superior, middle, and inferior cerebellar peduncles, dentate nuclei, and thalami. All magnetic resonance imaging (MRI) studies were grouped into 4 categories of severity based on a qualitative evaluation of conventional and advanced MRI sequences. Multivariable linear regression analysis of cerebellar scalars of patients and controls was performed, controlling for gestational age, age at the time of MRI, and HIE severity. Spearman rank correlation was performed to correlate DTI scalars of the cerebellum and thalami.

RESULTS

Fifty-seven (23 females, 40%) neonates with HIE and 12 (6 females, 50%) neonatal controls were included. There were 8 patients (14%) in HIE severity groups 3 and 4 (injury of the basal ganglia/thalamus and/or cortex). Based on a qualitative analysis of conventional and DTI images, no patients had evidence of cerebellar injury. No significant differences between patients and controls were found in the FA and MD scalars. However, FA values of the middle cerebellar peduncles (0.294 vs. 0.380, p < 0.001) and MD values of the superior cerebellar peduncles (0.920 vs. 1.007 × 10-3 mm/s2, p = 0.001) were significantly lower in patients with evidence of moderate or severe injury on MRI (categories 3 and 4) than in controls. In patients, cerebellar DTI scalars correlated positively with DTI scalars within the thalami.

CONCLUSION

Our results suggest that infants with moderate-to-severe HIE may have occult injury of cerebellar white-matter tracts, which is not detectable by the qualitative analysis of neuroimaging data alone. Cerebellar DTI scalars correlate with thalamic measures, highlighting that cerebellar injury is unlikely to occur in isolation and may reflect the severity of HIE. The impact of concomitant cerebellar injury in HIE on long-term neurodevelopmental outcome warrants further study.

Development of new PTK7-targeting aptamer-fluorescent and -radiolabelled probes for evaluation as molecular imaging agents: Lymphoma and melanoma in vivo proof of concept

Aptamers are single-stranded oligonucleotides that recognize molecular targets with high affinity and specificity. Aptamer that selectively bind to the protein tyrosine kinase-7 (PTK7) receptor, overexpressed on many cancers, has been labelled as probes for molecular imaging of cancer. Two new PTK7-targeting aptamer probes were developed by coupling frameworks from the fluorescent dye AlexaFluor647 or the 6-hydrazinonicotinamide (HYNIC) chelator-labelled to ^99mTc. The derivatizations via a 5'-aminohexyl terminal linker were done at room temperature and under mild buffer conditions. Physicochemical and biological controls for both imaging agents were performed verifying the integrity of the aptamer-conjugates by HPLC. Recognition of melanoma (B16F1) and lymphoma (A20) mouse cell lines by the aptamer was studied using cell binding, flow cytometry and confocal microscopy. Finally, in vivo imaging studies in tumour-bearing mice were performed. The new probes were able to bind to melanoma and lymphoma cell lines in vitro, the in vivo imaging in tumour-bearing mice showed different uptake behaviours showing for the fluorescent conjugate good uptake by B cell lymphoma while the radiolabelled conjugate did not display tumour uptake due to its high extravascular distribution, and both showed rapid clearance properties in tumour-bearing mice.

Structural Brain Network Reorganization and Social Cognition Related to Adverse Perinatal Condition from Infancy to Early Adolescence

Adverse conditions during fetal life have been associated to both structural and functional changes in neurodevelopment from the neonatal period to adolescence. In this study, connectomics was used to assess the evolution of brain networks from infancy to early adolescence. Brain network reorganization over time in subjects who had suffered adverse perinatal conditions is characterized and related to neurodevelopment and cognition. Three cohorts of prematurely born infants and children (between 28 and 35 weeks of gestational age), including individuals with a birth weight appropriated for gestational age and with intrauterine growth restriction (IUGR), were evaluated at 1, 6, and 10 years of age, respectively. A common developmental trajectory of brain networks was identified in both control and IUGR groups: network efficiencies of the fractional anisotropy (FA)-weighted and normalized connectomes increase with age, which can be related to maturation and myelination of fiber connections while the number of connections decreases, which can be associated to an axonal pruning process and reorganization. Comparing subjects with or without IUGR, a similar pattern of network differences between groups was observed in the three developmental stages, mainly characterized by IUGR group having reduced brain network efficiencies in binary and FA-weighted connectomes and increased efficiencies in the connectome normalized by its total connection strength (FA). Associations between brain networks and neurobehavioral impairments were also evaluated showing a relationship between different network metrics and specific social cognition-related scores, as well as a higher risk of inattention/hyperactivity and/or executive functional disorders in IUGR children.

Automatic Segmentation of Hippocampus for Longitudinal Infant Brain MR Image Sequence by Spatial-Temporal Hypergraph Learning

Accurate segmentation of infant hippocampus from Magnetic Resonance (MR) images is one of the key steps for the investigation of early brain development and neurological disorders. Since the manual delineation of anatomical structures is time-consuming and irreproducible, a number of automatic segmentation methods have been proposed, such as multi-atlas patch-based label fusion methods. However, the hippocampus during the first year of life undergoes dynamic appearance, tissue contrast and structural changes, which pose substantial challenges to the existing label fusion methods. In addition, most of the existing label fusion methods generally segment target images at each time-point independently, which is likely to result in inconsistent hippocampus segmentation results along different time-points. In this paper, we treat a longitudinal image sequence as a whole, and propose a spatial-temporal hypergraph based model to jointly segment infant hippocampi from all time-points. Specifically, in building the spatial-temporal hypergraph, (1) the atlas-to-target relationship and (2) the spatial/temporal neighborhood information within the target image sequence are encoded as two categories of hyperedges. Then, the infant hippocampus segmentation from the whole image sequence is formulated as a semi-supervised label propagation model using the proposed hypergraph. We evaluate our method in segmenting infant hippocampi from T1-weighted brain MR images acquired at the age of 2 weeks, 3 months, 6 months, 9 months, and 12 months. Experimental results demonstrate that, by leveraging spatial-temporal information, our method achieves better performance in both segmentation accuracy and consistency over the state-of-the-art multi-atlas label fusion methods.

Cerebral Reorganization after Hemispherectomy: A DTI Study

BACKGROUND AND PURPOSE

Hemispherectomy is a neurosurgical procedure to treat children with intractable seizures. Postsurgical improvement of cognitive and behavioral functions is observed in children after hemispherectomy suggesting plastic reorganization of the brain. Our aim was to characterize changes in DTI scalars in WM tracts of the remaining hemisphere in children after hemispherectomy, assess the associations between WM DTI scalars and age at the operation and time since the operation, and evaluate the changes in GM fractional anisotropy values in patients compared with controls.

MATERIALS AND METHODS

Patients with congenital or acquired neurologic diseases who required hemispherectomy and had high-quality postsurgical DTI data available were included in this study. Atlas- and voxel-based analyses of DTI raw data of the remaining hemisphere were performed. Fractional anisotropy and mean, axial, and radial diffusivity values were calculated for WM and GM regions. A linear regression model was used for correlation between DTI scalars and age at and time since the operation.

RESULTS

Nineteen patients after hemispherectomy and 21 controls were included. In patients, a decrease in fractional anisotropy and axial diffusivity values and an increase in mean diffusivity and radial diffusivity values of WM regions were observed compared with controls (P < .05, corrected for multiple comparisons). In patients with acquired pathologies, time since the operation had a significant positive correlation with white matter fractional anisotropy values. In all patients, an increase in cortical GM fractional anisotropy values was found compared with controls (P < .05).

CONCLUSIONS

Changes in DTI metrics likely reflect Wallerian and/or transneuronal degeneration of the WM tracts within the remaining hemisphere. In patients with acquired pathologies, postsurgical fractional anisotropy values correlated positively with elapsed time since the operation, suggesting a higher ability to recover compared with patients with congenital pathologies leading to hemispherectomy.

The impact of quality assurance assessment on diffusion tensor imaging outcomes in a large-scale population-based cohort

BACKGROUND

Diffusion tensor imaging (DTI) is applied in investigation of brain biomarkers for neurodevelopmental and neurodegenerative disorders. However, the quality of DTI measurements, like other neuroimaging techniques, is susceptible to several confounding factors (e.g., motion, eddy currents), which have only recently come under scrutiny. These confounds are especially relevant in adolescent samples where data quality may be compromised in ways that confound interpretation of maturation parameters. The current study aims to leverage DTI data from the Philadelphia Neurodevelopmental Cohort (PNC), a sample of 1601 youths with ages of 8-21 who underwent neuroimaging, to: 1) establish quality assurance (QA) metrics for the automatic identification of poor DTI image quality; 2) examine the performance of these QA measures in an external validation sample; 3) document the influence of data quality on developmental patterns of typical DTI metrics.

METHODS

All diffusion-weighted images were acquired on the same scanner. Visual QA was performed on all subjects completing DTI; images were manually categorized as Poor, Good, or Excellent. Four image quality metrics were automatically computed and used to predict manual QA status: Mean voxel intensity outlier count (MEANVOX), Maximum voxel intensity outlier count (MAXVOX), mean relative motion (MOTION) and temporal signal-to-noise ratio (TSNR). Classification accuracy for each metric was calculated as the area under the receiver-operating characteristic curve (AUC). A threshold was generated for each measure that best differentiated visual QA status and applied in a validation sample. The effects of data quality on sensitivity to expected age effects in this developmental sample were then investigated using the traditional MRI diffusion metrics: fractional anisotropy (FA) and mean diffusivity (MD). Finally, our method of QA is compared with DTIPrep.

RESULTS

TSNR (AUC=0.94) best differentiated Poor data from Good and Excellent data. MAXVOX (AUC=0.88) best differentiated Good from Excellent DTI data. At the optimal threshold, 88% of Poor data and 91% Good/Excellent data were correctly identified. Use of these thresholds on a validation dataset (n=374) indicated high accuracy. In the validation sample 83% of Poor data and 94% of Excellent data was identified using thresholds derived from the training sample. Both FA and MD were affected by the inclusion of poor data in an analysis of an age, sex and race matched comparison sample. In addition, we show that the inclusion of poor data results in significant attenuation of the correlation between diffusion metrics (FA and MD) and age during a critical neurodevelopmental period. We find higher correspondence between our QA method and DTIPrep for Poor data, but we find our method to be more robust for apparently high-quality images.

CONCLUSION

Automated QA of DTI can facilitate large-scale, high-throughput quality assurance by reliably identifying both scanner and subject induced imaging artifacts. The results present a practical example of the confounding effects of artifacts on DTI analysis in a large population-based sample, and suggest that estimates of data quality should not only be reported but also accounted for in data analysis, especially in studies of development.

Probabilistic maps of the white matter tracts with known associated functions on the neonatal brain atlas: Application to evaluate longitudinal developmental trajectories in term-born and preterm-born infants

Diffusion tensor imaging (DTI) has been widely used to investigate the development of the neonatal and infant brain, and deviations related to various diseases or medical conditions like preterm birth. In this study, we created a probabilistic map of fiber pathways with known associated functions, on a published neonatal multimodal atlas. The pathways-of-interest include the superficial white matter (SWM) fibers just beneath the specific cytoarchitectonically defined cortical areas, which were difficult to evaluate with existing DTI analysis methods. The Jülich cytoarchitectonic atlas was applied to define cortical areas related to specific brain functions, and the Dynamic Programming (DP) method was applied to delineate the white matter pathways traversing through the SWM. Probabilistic maps were created for pathways related to motor, somatosensory, auditory, visual, and limbic functions, as well as major white matter tracts, such as the corpus callosum, the inferior fronto-occipital fasciculus, and the middle cerebellar peduncle, by delineating these structures in eleven healthy term-born neonates. In order to characterize maturation-related changes in diffusivity measures of these pathways, the probabilistic maps were then applied to DTIs of 49 healthy infants who were longitudinally scanned at three time-points, approximately five weeks apart. First, we investigated the normal developmental pattern based on 19 term-born infants. Next, we analyzed 30 preterm-born infants to identify developmental patterns related to preterm birth. Last, we investigated the difference in diffusion measures between these groups to evaluate the effects of preterm birth on the development of these functional pathways. Term-born and preterm-born infants both demonstrated a time-dependent decrease in diffusivity, indicating postnatal maturation in these pathways, with laterality seen in the corticospinal tract and the optic radiation. The comparison between term- and preterm-born infants indicated higher diffusivity in the preterm-born infants than in the term-born infants in three of these pathways: the body of the corpus callosum; the left inferior longitudinal fasciculus; and the pathway connecting the left primary/secondary visual cortices and the motion-sensitive area in the occipitotemporal visual cortex (V5/MT+). Probabilistic maps provided an opportunity to investigate developmental changes of each white matter pathway. Whether alterations in white matter pathways can predict functional outcomes will be further investigated in a follow-up study.