A tract-specific approach to assessing white matter in preterm infants

FetDTIAlign: A deep learning framework for affine and deformable registration of fetal brain dMRI

Diffusion MRI (dMRI) offers unique insights into the microstructure of fetal brain tissue in utero. Longitudinal and cross-sectional studies of fetal dMRI have the potential to reveal subtle but crucial changes associated with normal and abnormal neurodevelopment. However, these studies depend on precise spatial alignment of data across scans and subjects, which is particularly challenging in fetal imaging due to the low data quality, rapid brain development, and limited anatomical landmarks for accurate registration. Existing registration methods, primarily developed for superior-quality adult data, are not well-suited for addressing these complexities. To bridge this gap, we introduce FetDTIAlign, a deep learning approach tailored to fetal brain dMRI, enabling accurate affine and deformable registration. FetDTIAlign integrates a novel dual-encoder architecture and iterative feature-based inference, effectively minimizing the impact of noise and low resolution to achieve accurate alignment. Additionally, it strategically employs different network configurations and domain-specific image features at each registration stage, addressing the unique challenges of affine and deformable registration, enhancing both robustness and accuracy. We validated FetDTIAlign on a dataset covering gestational ages centered between 23 and 36 weeks, encompassing 60 white matter tracts. For all age groups, FetDTIAlign consistently showed superior anatomical correspondence and the best visual alignment in both affine and deformable registration, outperforming two classical optimization-based methods and a deep learning-based pipeline. Further validation on external data from the Developing Human Connectome Project demonstrated the generalizability of our method to data collected with different acquisition protocols. Our results show the feasibility of using deep learning for fetal brain dMRI registration, providing a more accurate and reliable alternative to classical techniques. By enabling precise cross-subject and tract-specific analyses, FetDTIAlign paves the way for new discoveries in early brain development. The code is available at https://gitlab.com/blibli/fetdtialign.

Methodological considerations on diffusion MRI tractography in infants aged 0-2 years: a scoping review

Diffusion MRI (dMRI) enables studying the complex architectural organization of the brain's white matter (WM) through virtual reconstruction of WM fiber tracts (tractography). Despite the anticipated clinical importance of applying tractography to study structural connectivity and tract development during the critical period of rapid infant brain maturation, detailed descriptions on how to approach tractography in young infants are limited. Over the past two decades, tractography from infant dMRI has mainly been applied in research settings and focused on diffusion tensor imaging (DTI). Only few studies used techniques superior to DTI in terms of disentangling information on the brain's organizational complexity, including crossing fibers. While more advanced techniques may enhance our understanding of the intricate processes of normal and abnormal brain development and extensive knowledge has been gained from application on adult scans, their applicability in infants has remained underexplored. This may partially be due to the higher technical requirements versus the need to limit scan time in young infants. We review various previously described methodological practices for tractography in the infant brain (0-2 years-of-age) and provide recommendations to optimize advanced tractography approaches to enable more accurate reconstructions of the brain WM's complexity. IMPACT: Diffusion tensor imaging is the technique most frequently used for fiber tracking in the developing infant brain but is limited in capability to disentangle the complex white matter organization. Advanced tractography techniques allow for reconstruction of crossing fiber bundles to better reflect the brain's complex organization. Yet, they pose practical and technical challenges in the fast developing young infant's brain. Methods on how to approach advanced tractography in the young infant's brain have hardly been described. Based on a literature review, recommendations are provided to optimize tractography for the developing infant brain, aiming to advance early diagnosis and neuroprotective strategies.

Multi-view fusion of diffusion MRI microstructural models: a preterm birth study

Objective

High Angular Resolution Diffusion Imaging (HARDI) models have emerged as a valuable tool for investigating microstructure with a higher degree of detail than standard diffusion Magnetic Resonance Imaging (dMRI). In this study, we explored the potential of multiple advanced microstructural diffusion models for investigating preterm birth in order to identify non-invasive markers of altered white matter development.

Approach

Rather than focusing on a single MRI modality, we studied on a compound of HARDI techniques in 46 preterm babies studied on a 3T scanner at term-equivalent age and in 23 control neonates born at term. Furthermore, we investigated discriminative patterns of preterm birth using multiple analysis methods, drawn from two only seemingly divergent modeling goals, namely inference and prediction. We thus resorted to (i) a traditional univariate voxel-wise inferential method, as the Tract-Based Spatial Statistics (TBSS) approach; (ii) a univariate predictive approach, as the Support Vector Machine (SVM) classification; and (iii) a multivariate predictive Canonical Correlation Analysis (CCA).

Main results

The TBSS analysis revealed significant differences between preterm and term cohorts in several white matter areas for multiple HARDI features. SVM classification on skeletonized HARDI measures yielded satisfactory accuracy, particularly for highly informative parameters about fiber directionality. Assessment of the degree of overlap between the two methods in voting for the most discriminating features exhibited a good, though parameter-dependent, rate of agreement. Finally, CCA identified joint changes precisely for those measures exhibiting less correspondence between TBSS and SVM.

Significance

Our results suggest that a data-driven intramodal imaging approach is crucial for gathering deep and complementary information. The main contribution of this methodological outline is to thoroughly investigate prematurity-related white matter changes through different inquiry focuses, with a view to addressing this issue, both aiming toward mechanistic insight and optimizing predictive accuracy.

Brain tissue segmentation in neurosurgery: a systematic analysis for quantitative tractography approaches

Diffusion magnetic resonance imaging (dMRI) is a cutting-edge imaging method that provides a macro-scale in vivo map of the white matter pathways in the brain. The measurement of brain microstructure and the enhancement of tractography rely heavily on dMRI tissue segmentation. Anatomical MRI technique (e.g., T1- and T2-weighted imaging) is the most widely used method for segmentation in dMRI. In comparison to anatomical MRI, dMRI suffers from higher image distortions, lower image quality, and making inter-modality registration more difficult. The dMRI tractography study of brain connectivity has become a major part of the neuroimaging landscape in recent years. In this research, we provide a high-level overview of the methods used to segment several brain tissues types, including grey and white matter and cerebrospinal fluid, to enable quantitative studies of structural connectivity in the brain in health and illness. In the first part of our review, we discuss the three main phases in the quantitative analysis of tractography, which are correction, segmentation, and quantification. Methodological possibilities are described for each phase, along with their popularity and potential benefits and drawbacks. After that, we will look at research that used quantitative tractography approaches to examine the white and grey matter of the brain, with an emphasis on neurodevelopment, ageing, neurological illnesses, mental disorders, and neurosurgery as possible applications. Even though there have been substantial advancements in methodological technology and the spectrum of applications, there is still no consensus regarding the "optimal" approach in the quantitative analysis of tractography. As a result, researchers should tread carefully when interpreting the findings of quantitative analysis of tractography.

Lateralization of major white matter tracts during infancy is time-varying and tract-specific

Lateralization patterns are a major structural feature of brain white matter and have been investigated as a neural architecture that indicates and supports the specialization of cognitive processing and observed behaviors, e.g. language skills. Many neurodevelopmental disorders have been associated with atypical lateralization, reinforcing the need for careful measurement and study of this structural characteristic. Unfortunately, there is little consensus on the direction and magnitude of lateralization in major white matter tracts during the first months and years of life-the period of most rapid postnatal brain growth and cognitive maturation. In addition, no studies have examined white matter lateralization in a longitudinal pediatric sample-preventing confirmation of if and how white matter lateralization changes over time. Using a densely sampled longitudinal data set from neurotypical infants aged 0-6 months, we aim to (i) chart trajectories of white matter lateralization in 9 major tracts and (ii) link variable findings from cross-sectional studies of white matter lateralization in early infancy. We show that patterns of lateralization are time-varying and tract-specific and that differences in lateralization results during this period may reflect the dynamic nature of lateralization through development, which can be missed in cross-sectional studies.

Preterm birth and neonatal white matter microstructure in in-vivo reconstructed fiber tracts among audiovisual integration brain regions

Individuals born preterm are at risk of developing a variety of sequelae. Audiovisual integration (AVI) has received little attention despite its facilitating role in the development of socio-cognitive abilities. The present study assessed the association between prematurity and in-vivo reconstructed fiber bundles among brain regions relevant for AVI. We retrieved data from 63 preterm neonates enrolled in the Developing Human Connectome Project (http://www.developingconnectome.org/) and matched them with 63 term-born neonates from the same study by means of propensity score matching. We performed probabilistic tractography, DTI and NODDI analysis on the traced fibers. We found that specific DTI and NODDI metrics are significantly associated with prematurity in neonates matched for postmenstrual age at scan. We investigated the spatial overlap and developmental order of the reconstructed tractograms between preterm and full-term neonates. Permutation-based analysis revealed significant differences in dice similarity coefficients and developmental order between preterm and full term neonates at the group level. Contrarily, no group differences in the amount of interindividual variability of DTI and NODDI metrics were observed. We conclude that microstructural detriment in the reconstructed fiber bundles along with developmental and morphological differences are likely to contribute to disadvantages in AVI in preterm individuals.

Quality standards and recommendations for research in music and neuroplasticity

Research on how music influences brain plasticity has gained momentum in recent years. Considering, however, the nonuniform methodological standards implemented, the findings end up being nonreplicable and less generalizable. To address the need for a standardized baseline of research quality, we gathered all the studies in the music and neuroplasticity field in 2019 and appraised their methodological rigor systematically and critically. The aim was to provide a preliminary and, at the minimum, acceptable quality threshold-and, ipso facto, suggested recommendations-whereupon further discussion and development may take place. Quality appraisal was performed on 89 articles by three independent raters, following a standardized scoring system. The raters' scoring was cross-referenced following an inter-rater reliability measure, and further studied by performing multiple ratings comparisons and matrix analyses. The results for methodological quality were at a quite good level (quantitative articles: mean = 0.737, SD = 0.084; qualitative articles: mean = 0.677, SD = 0.144), following a moderate but statistically significant level of agreement between the raters (W = 0.44, χ² = 117.249, p = 0.020). We conclude that the standards for implementation and reporting are of high quality; however, certain improvements are needed to reach the stringent levels presumed for such an influential interdisciplinary scientific field.

Harmonization of Multi-Center Diffusion Tensor Tractography in Neonates with Congenital Heart Disease: Optimizing Post-Processing and Application of ComBat

Advanced brain imaging of neonatal macrostructure and microstructure, which has prognosticating importance, is more frequently being incorporated into multi-center trials of neonatal neuroprotection. Multicenter neuroimaging studies, designed to overcome small sample sized clinical cohorts, are essential but lead to increased technical variability. Few harmonization techniques have been developed for neonatal brain microstructural (diffusion tensor) analysis. The work presented here aims to remedy two common problems that exist with the current state of the art approaches: 1) variance in scanner and protocol in data collection can limit the researcher's ability to harmonize data acquired under different conditions or using different clinical populations. 2) The general lack of objective guidelines for dealing with anatomically abnormal anatomy and pathology. Often, subjects are excluded due to subjective criteria, or due to pathology that could be informative to the final analysis, leading to the loss of reproducibility and statistical power. This proves to be a barrier in the analysis of large multi-center studies and is a particularly salient problem given the relative scarcity of neonatal imaging data. We provide an objective, data-driven, and semi-automated neonatal processing pipeline designed to harmonize compartmentalized variant data acquired under different parameters. This is done by first implementing a search space reduction step of extracting the along-tract diffusivity values along each tract of interest, rather than performing whole-brain harmonization. This is followed by a data-driven outlier detection step, with the purpose of removing unwanted noise and outliers from the final harmonization. We then use an empirical Bayes harmonization algorithm performed at the along-tract level, with the output being a lower dimensional space but still spatially informative. After applying our pipeline to this large multi-site dataset of neonates and infants with congenital heart disease (n= 398 subjects recruited across 4 centers, with a total of n=763 MRI pre-operative/post-operative time points), we show that infants with single ventricle cardiac physiology demonstrate greater white matter microstructural alterations compared to infants with bi-ventricular heart disease, supporting what has previously been shown in literature. Our method is an open-source pipeline for delineating white matter tracts in subject space but provides the necessary modular components for performing atlas space analysis. As such, we validate and introduce Diffusion Imaging of Neonates by Group Organization (DINGO), a high-level, semi-automated framework that can facilitate harmonization of subject-space tractography generated from diffusion tensor imaging acquired across varying scanners, institutions, and clinical populations. Datasets acquired using varying protocols or cohorts are compartmentalized into subsets, where a cohort-specific template is generated, allowing for the propagation of the tractography mask set with higher spatial specificity. Taken together, this pipeline can reduce multi-scanner technical variability which can confound important biological variability in relation to neonatal brain microstructure.

General factors of white matter microstructure from DTI and NODDI in the developing brain

Preterm birth is closely associated with diffuse white matter dysmaturation inferred from diffusion MRI and neurocognitive impairment in childhood. Diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) are distinct dMRI modalities, yet metrics derived from these two methods share variance across tracts. This raises the hypothesis that dimensionality reduction approaches may provide efficient whole-brain estimates of white matter microstructure that capture (dys)maturational processes. To investigate the optimal model for accurate classification of generalised white matter dysmaturation in preterm infants we assessed variation in DTI and NODDI metrics across 16 major white matter tracts using principal component analysis and structural equation modelling, in 79 term and 141 preterm infants at term equivalent age. We used logistic regression models to evaluate performances of single-metric and multimodality general factor frameworks for efficient classification of preterm infants based on variation in white matter microstructure. Single-metric general factors from DTI and NODDI capture substantial shared variance (41.8-72.5%) across 16 white matter tracts, and two multimodality factors captured 93.9% of variance shared between DTI and NODDI metrics themselves. General factors associate with preterm birth and a single model that includes all seven DTI and NODDI metrics provides the most accurate prediction of microstructural variations associated with preterm birth. This suggests that despite global covariance of dMRI metrics in neonates, each metric represents information about specific (and additive) aspects of the underlying microstructure that differ in preterm compared to term subjects.

Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: A review

Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo reconstruction of the brain's white matter connections at macro scale. It provides an important tool for quantitative mapping of the brain's structural connectivity using measures of connectivity or tissue microstructure. Over the last two decades, the study of brain connectivity using dMRI tractography has played a prominent role in the neuroimaging research landscape. In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain's structural connectivity in health and disease. We focus on two types of quantitative analyses of tractography, including: 1) tract-specific analysis that refers to research that is typically hypothesis-driven and studies particular anatomical fiber tracts, and 2) connectome-based analysis that refers to research that is more data-driven and generally studies the structural connectivity of the entire brain. We first provide a review of methodology involved in three main processing steps that are common across most approaches for quantitative analysis of tractography, including methods for tractography correction, segmentation and quantification. For each step, we aim to describe methodological choices, their popularity, and potential pros and cons. We then review studies that have used quantitative tractography approaches to study the brain's white matter, focusing on applications in neurodevelopment, aging, neurological disorders, mental disorders, and neurosurgery. We conclude that, while there have been considerable advancements in methodological technologies and breadth of applications, there nevertheless remains no consensus about the "best" methodology in quantitative analysis of tractography, and researchers should remain cautious when interpreting results in research and clinical applications.

OUP accepted manuscript

Very preterm children are more likely to exhibit difficulties in socio-emotional processing than their term-born peers. Emerging socio-emotional problems may be partly due to alterations in limbic system development associated with infants’ early transition to extrauterine life. The amygdala is a key structure in this system and plays a critical role in various aspects of socio-emotional development, including emotion regulation. The current study tested the hypothesis that amygdala resting-state functional connectivity at term-equivalent age would be associated with socio-emotional outcomes in childhood. Participants were 129 very preterm infants (<33 weeks' gestation) who underwent resting-state functional MRI at term and received a neurodevelopmental assessment at 4–7 years (median = 4.64). Using the left and right amygdalae as seed regions, we investigated associations between whole-brain seed-based functional connectivity and three socio-emotional outcome factors which were derived using exploratory factor analysis (Emotion Moderation, Social Function and Empathy), controlling for sex, neonatal sickness, post-menstrual age at scan and social risk. Childhood Emotion Moderation scores were significantly associated with neonatal resting-state functional connectivity of the right amygdala with right parahippocampal gyrus and right middle occipital gyrus, as well as with functional connectivity of the left amygdala with the right thalamus. No significant associations were found between amygdalar resting-state functional connectivity and either Social Function or Empathy scores. The current findings show that amygdalar functional connectivity assessed at term is associated with later socio-emotional outcomes in very preterm children.

Not all voxels are created equal: Reducing estimation bias in regional NODDI metrics using tissue-weighted means

Neurite orientation dispersion and density imaging (NODDI) estimates microstructural properties of brain tissue relating to the organisation and processing capacity of neurites, which are essential elements for neuronal communication. Descriptive statistics of NODDI tissue metrics are commonly analyzed in regions-of-interest (ROI) to identify brain-phenotype associations. Here, the conventional method to calculate the ROI mean weights all voxels equally. However, this produces biased estimates in the presence of CSF partial volume. This study introduces the tissue-weighted mean, which calculates the mean NODDI metric across the tissue within an ROI, utilising the tissue fraction estimate from NODDI to reduce estimation bias. We demonstrate the proposed mean in a study of white matter abnormalities in young onset Alzheimer's disease (YOAD). Results show the conventional mean induces significant bias that correlates with CSF partial volume, primarily affecting periventricular regions and more so in YOAD subjects than in healthy controls. Due to the differential extent of bias between healthy controls and YOAD subjects, the conventional mean under- or over-estimated the effect size for group differences in many ROIs. This demonstrates the importance of using the correct estimation procedure when inferring group differences in studies where the extent of CSF partial volume differs between groups. These findings are robust across different acquisition and processing conditions. Bias persists in ROIs at higher image resolution, as demonstrated using data obtained from the third phase of the Alzheimer's disease neuroimaging initiative (ADNI); and when performing ROI analysis in template space. This suggests that conventional ROI means of NODDI metrics are biased estimates under most contemporary experimental conditions, the correction of which requires the proposed tissue-weighted mean. The tissue-weighted mean produces accurate estimates of ROI means and group differences when ROIs contain voxels with CSF partial volume. In addition to NODDI, the technique can be applied to other multi-compartment models that account for CSF partial volume, such as the free water elimination method. We expect the technique to help generate new insights into normal and abnormal variation in tissue microstructure of regions typically confounded by CSF partial volume, such as those in individuals with larger ventricles due to atrophy associated with neurodegenerative disease.

Neonatal White Matter Microstructure and Emotional Development during the Preschool Years in Children Who Were Born Very Preterm

Children born very preterm (<33 weeks of gestation) are at a higher risk of developing socio-emotional difficulties compared with those born at term. In this longitudinal study, we tested the hypothesis that diffusion characteristics of white matter (WM) tracts implicated in socio-emotional processing assessed in the neonatal period are associated with socio-emotional development in 151 very preterm children previously enrolled into the Evaluation of Preterm Imaging study (EudraCT 2009-011602-42). All children underwent diffusion tensor imaging at term-equivalent age and fractional anisotropy (FA) was quantified in the uncinate fasciculus (UF), inferior fronto-occipital fasciculus (IFOF), inferior longitudinal fasciculus (ILF), and superior longitudinal fasciculus (SLF). Children's socio-emotional development was evaluated at preschool age (median = 4.63 years). Exploratory factor analysis conducted on the outcome variables revealed a three-factor structure, with latent constructs summarized as: "emotion moderation," "social function," and "empathy." Results of linear regression analyses, adjusting for full-scale IQ and clinical and socio-demographic variables, showed an association between lower FA in the right UF and higher "emotion moderation" scores (β = -0.280; p < 0.001), which was mainly driven by negative affectivity scores (β = -0.281; p = 0.001). Results further showed an association between higher full-scale IQ and better social functioning (β = -0.334, p < 0.001). Girls had higher empathy scores than boys (β = -0.341, p = 0.006). These findings suggest that early alterations of diffusion characteristics of the UF could represent a biological substrate underlying the link between very preterm birth and emotional dysregulation in childhood and beyond.

Multi-Channel 4D Parametrized Atlas of Macro- and Microstructural Neonatal Brain Development

Structural (also known as anatomical) and diffusion MRI provide complimentary anatomical and microstructural characterization of early brain maturation. However, the existing models of the developing brain in time include only either structural or diffusion MRI channels. Furthermore, there is a lack of tools for combined analysis of structural and diffusion MRI in the same reference space. In this work, we propose a methodology to generate a multi-channel (MC) continuous spatio-temporal parametrized atlas of the brain development that combines multiple MRI-derived parameters in the same anatomical space during 37-44 weeks of postmenstrual age range. We co-align structural and diffusion MRI of 170 normal term subjects from the developing Human Connectomme Project using MC registration driven by both T2-weighted and orientation distribution functions channels and fit the Gompertz model to the signals and spatial transformations in time. The resulting atlas consists of 14 spatio-temporal microstructural indices and two parcellation maps delineating white matter tracts and neonatal transient structures. In order to demonstrate applicability of the atlas for quantitative region-specific studies, a comparison analysis of 140 term and 40 preterm subjects scanned at the term-equivalent age is performed using different MRI-derived microstructural indices in the atlas reference space for multiple white matter regions, including the transient compartments. The atlas and software will be available after publication of the article.

Tract-specific analysis and neurocognitive functioning in sickle cell patients without history of overt stroke

INTRODUCTION

Sickle cell disease (SCD) is a hereditary blood disorder in which the oxygen-carrying hemoglobin molecule in red blood cells is abnormal. SCD patients are at increased risks for strokes and neurocognitive deficit, even though neurovascular screening and treatments have lowered the rate of overt strokes. Tract-specific analysis (TSA) is a statistical method to evaluate microstructural WM damage in neurodegenerative disorders, using diffusion tensor imaging (DTI).

METHODS

We utilized TSA and compared 11 major brain WM tracts between SCD patients with no history of overt stroke, anemic controls, and healthy controls. We additionally examined the relationship between the most commonly used DTI metric of WM tracts and neurocognitive performance in the SCD patients and healthy controls.

RESULTS

Disruption of WM microstructure orientation-dependent metrics for the SCD patients was found in the genu of the corpus callosum (CC), cortico-spinal tract, inferior fronto-occipital fasciculus, right inferior longitudinal fasciculus, superior longitudinal fasciculus, and left uncinate fasciculus. Neurocognitive performance indicated slower processing speed and lower response inhibition skills in SCD patients compared to controls. TSA abnormalities in the CC were significantly associated with measures of processing speed, working memory, and executive functions.

CONCLUSION

Decreased DTI-derived metrics were observed on six tracts in chronically anemic patients, regardless of anemia subtype, while two tracks with decreased measures were unique to SCD patients. Patients with WMHs had more significant FA abnormalities. Decreased FA values in the CC significantly correlated with all nine neurocognitive tests, suggesting a critical importance for CC in core neurocognitive processes.

The cingulum in very preterm infants relates to language and social-emotional impairment at 2 years of term-equivalent age

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Maturation of specific WM tracts in preterm individuals differs from those of term controls.

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The elastic net logistic regression model was used to identify altered white matter tracts in the preterm brain.

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The alteration of the cingulum in the preterm at near-term correlate with neurodevelopmental scores at 18–22 months of age.

Background

Relative to full-term infants, very preterm infants exhibit disrupted white matter (WM) maturation and problems related to development, including motor, cognitive, social-emotional, and receptive and expressive language processing.

Objective

The present study aimed to determine whether regional abnormalities in the WM microstructure of very preterm infants, as defined relative to those of full-term infants at a near-term age, are associated with neurodevelopmental outcomes at the age of 18–22 months.

Methods

We prospectively enrolled 89 very preterm infants (birth weight < 1500 g) and 43 normal full-term control infants born between 2016 and 2018. All infants underwent a structural brain magnetic resonance imaging scan at near-term age. The diffusion tensor imaging (DTI) metrics of the whole-brain WM tracts were extracted based on the neonatal probabilistic WM pathway. The elastic net logistic regression model was used to identify altered WM tracts in the preterm brain. We evaluated the associations between the altered WM microstructure at near-term age and motor, cognitive, social-emotional, and receptive and expressive language developments at 18–22 months of age, as measured using the Bayley Scales of Infant Development, Third Edition.

Results

We found that the elastic net logistic regression model could classify preterm and full-term neonates with an accuracy of 87.9% (corrected p < 0.008) using the DTI metrics in the pathway of interest with a 10% threshold level. The fractional anisotropy (FA) values of the body and splenium of the corpus callosum, middle cerebellar peduncle, left and right uncinate fasciculi, and right portion of the pathway between the premotor and primary motor cortices (premotor-PMC), as well as the mean axial diffusivity (AD) values of the left cingulum, were identified as contributive features for classification. Increased adjusted AD values in the left cingulum pathway were significantly correlated with language scores after false discovery rate (FDR) correction (r = 0.217, p = 0.043). The expressive language and social-emotional composite scores showed a significant positive correlation with the AD values in the left cingulum pathway (r = 0.226 [p = 0.036] and r = 0.31 [p = 0.003], respectively) after FDR correction.

Conclusion

Our approach suggests that the cingulum pathways of very preterm infants differ from those of full-term infants and significantly contribute to the prediction of the subsequent development of the language and social-emotional domains. This finding could improve our understanding of how specific neural substrates influence neurodevelopment at later ages, and individual risk prediction, thus helping to inform early intervention strategies that address developmental delay.

White Matter Development from Birth to 6 Years of Age: A Longitudinal Study

Human white matter development in the first years of life is rapid, setting the foundation for later development. Microstructural properties of white matter are linked to many behavioral and psychiatric outcomes; however, little is known about when in development individual differences in white matter microstructure are established. The aim of the current study is to characterize longitudinal development of white matter microstructure from birth through 6 years to determine when in development individual differences are established. Two hundred and twenty-four children underwent diffusion-weighted imaging after birth and at 1, 2, 4, and 6 years. Diffusion tensor imaging data were computed for 20 white matter tracts (9 left-right corresponding tracts and 2 commissural tracts), with tract-based measures of fractional anisotropy and axial and radial diffusivity. Microstructural maturation between birth and 1 year are much greater than subsequent changes. Further, by 1 year, individual differences in tract average values are consistently predictive of the respective 6-year values, explaining, on average, 40% of the variance in 6-year microstructure. Results provide further evidence of the importance of the first year of life with regard to white matter development, with potential implications for informing early intervention efforts that target specific sensitive periods.

Maternal Prenatal Stress Is Associated With Altered Uncinate Fasciculus Microstructure in Premature Neonates

BACKGROUND

Maternal prenatal stress exposure (PNSE) increases risk for adverse psychiatric and behavioral outcomes in offspring. The biological basis for this elevated risk is poorly understood but may involve alterations to the neurodevelopmental trajectory of white matter tracts within the limbic system, particularly the uncinate fasciculus. Additionally, preterm birth is associated with both impaired white matter development and adverse developmental outcomes. In this study we hypothesized that higher maternal PNSE was associated with altered uncinate fasciculus microstructure in offspring.

METHODS

In this study, 251 preterm infants (132 male, 119 female) (median gestational age = 30.29 weeks [range, 23.57-32.86 weeks]) underwent brain magnetic resonance imaging including diffusion-weighted imaging around term-equivalent age (median = 42.43 weeks [range, 37.86-45.71 weeks]). Measures of white matter microstructure were calculated for the uncinate fasciculus and the inferior longitudinal fasciculus, a control tract that we hypothesized was not associated with maternal PNSE. Multiple regressions were used to investigate the relationship among maternal trait anxiety scores, stressful life events, and white matter microstructure indices in the neonatal brain.

RESULTS

Adjusting for gestational age at birth, postmenstrual age at scan, maternal age, socioeconomic status, sex, and number of days on parenteral nutrition, higher stressful life events scores were associated with higher axial diffusivity (β = .177, q = .007), radial diffusivity (β = .133, q = .026), and mean diffusivity (β = .149, q = .012) in the left uncinate fasciculus, and higher axial diffusivity (β = .142, q = .026) in the right uncinate fasciculus.

CONCLUSIONS

These findings suggest that PNSE is associated with altered development of specific frontolimbic pathways in preterm neonates as early as term-equivalent age.

Top ten discoveries of the year: Neurodevelopmental disorders

Developmental brain disorders, a highly heterogeneous group of disorders with a prevalence of around 3% of worldwide population, represent a growing medical challenge. They are characterized by impaired neurodevelopmental processes leading to deficits in cognition, social interaction, behavior and motor functioning as a result of abnormal development of brain. This can include developmental brain dysfunction, which can manifest as neuropsychiatric problems or impaired motor function, learning, language or non-verbal communication. Several of these phenotypes can often co-exist in the same patient and characterize the same disorder. Here I discuss some contributions in 2019 that are shaking our basic understanding of the pathogenesis of neurodevelopmental disorders. Recent developments in sophisticated in-utero imaging diagnostic tools have raised the possibility of imaging the fetal human brain growth, providing insights into the developing anatomy and improving diagnostics but also allowing a better understanding of antenatal pathology. On the other hand, advances in our understanding of the pathogenetic mechanisms reveal a remarkably complex molecular neuropathology involving a myriad of genetic architectures and regulatory elements that will help establish more rigorous genotype-phenotype correlations.

Music enhances structural maturation of emotional processing neural pathways in very preterm infants

Prematurity disrupts brain maturation by exposing the developing brain to different noxious stimuli present in the neonatal intensive care unit (NICU) and depriving it from meaningful sensory inputs during a critical period of brain development, leading to later neurodevelopmental impairments. Musicotherapy in the NICU environment has been proposed to promote sensory stimulation, relevant for activity-dependent brain plasticity, but its impact on brain structural maturation is unknown. Neuroimaging studies have demonstrated that music listening triggers neural substrates implied in socio-emotional processing and, thus, it might influence networks formed early in development and known to be affected by prematurity. Using multi-modal MRI, we aimed to evaluate the impact of a specially composed music intervention during NICU stay on preterm infant's brain structure maturation. 30 preterm newborns (out of which 15 were exposed to music during NICU stay and 15 without music intervention) and 15 full-term newborns underwent an MRI examination at term-equivalent age, comprising diffusion tensor imaging (DTI), used to evaluate white matter maturation using both region-of-interest and seed-based tractography approaches, as well as a T2-weighted image, used to perform amygdala volumetric analysis. Overall, WM microstructural maturity measured through DTI metrics was reduced in preterm infants receiving the standard-of-care in comparison to full-term newborns, whereas preterm infants exposed to the music intervention demonstrated significantly improved white matter maturation in acoustic radiations, external capsule/claustrum/extreme capsule and uncinate fasciculus, as well as larger amygdala volumes, in comparison to preterm infants with standard-of-care. These results suggest a structural maturational effect of the proposed music intervention on premature infants' auditory and emotional processing neural pathways during a key period of brain development.

Fixel-based analysis of the preterm brain: Disentangling bundle-specific white matter microstructural and macrostructural changes in relation to clinical risk factors

Diffusion MRI (dMRI) studies using the tensor model have identified abnormal white matter development associated with perinatal risk factors in preterm infants studied at term equivalent age (TEA). However, this model is an oversimplification of the underlying neuroanatomy. Fixel-based analysis (FBA) is a novel quantitative framework, which identifies microstructural and macrostructural changes in individual fibre populations within voxels containing crossing fibres. The aim of this study was to apply FBA to investigate the relationship between fixel-based measures of apparent fibre density (FD), fibre bundle cross-section (FC), and fibre density and cross-section (FDC) and perinatal risk factors in preterm infants at TEA. We studied 50 infants (28 male) born at 24.0-32.9 (median 30.4) weeks gestational age (GA) and imaged at 38.6-47.1 (median 42.1) weeks postmenstrual age (PMA). dMRI data were acquired in non-collinear directions with b-value 2500 s/mm2 on a 3 Tesla system sited on the neonatal intensive care unit. FBA was performed to assess the relationship between FD, FC, FDC and PMA at scan, GA at birth, days on mechanical ventilation, days on total parenteral nutrition (TPN), birthweight z-score, and sex. FBA reveals fibre population-specific alterations in FD, FC and FDC associated with clinical risk factors. FD was positively correlated with GA at birth and was negatively correlated with number of days requiring ventilation. FC was positively correlated with GA at birth, birthweight z-scores and was higher in males. FC was negatively correlated with number of days on ventilation and days on TPN. FDC was positively correlated with GA at birth and birthweight z-scores, negatively correlated with days on ventilation and days on TPN and higher in males. We demonstrate that these relationships are fibre-specific even within regions of crossing fibres. These results show that aberrant white matter development involves both microstructural changes and macrostructural alterations.

Corticobulbar Tract Injury, Oromotor Impairment and Language Plasticity in Adolescents Born Preterm

Children born preterm are at risk of impairments in oromotor control, with implications for early feeding and speech development. In this study, we aimed to identify (a) neuroanatomical markers of persistent oromotor deficits using diffusion-weighted imaging (DWI) tractography and (b) evidence of compensatory neuroplasticity using functional MRI (fMRI) during a language production task. In a cross-sectional study of 36 adolescents born very preterm (<33 weeks' gestation) we identified persistent difficulties in oromotor control in 31% of cases, but no clinical diagnoses of speech-sound disorder (e.g., dysarthria, dyspraxia). We used DWI-tractography to examine the microstructure (fractional anisotropy, FA) of the corticospinal and corticobulbar tracts. Compared to the unimpaired group, the oromotor-impaired group showed (i) reduced FA within the dorsal portion of the left corticobulbar tract (containing fibres associated with movements of the lips, tongue, and larynx) and (ii) greater recruitment of right hemisphere language regions on fMRI. We conclude that, despite the development of apparently normal everyday speech, early injury to the corticobulbar tract leads to persistent subclinical problems with voluntary control of the face, lips, jaw, and tongue. Furthermore, we speculate that early speech problems may be ameliorated by cerebral plasticity - in particular, recruitment of right hemisphere language areas.

A framework for multi-component analysis of diffusion MRI data over the neonatal period

We describe a framework for creating a time-resolved group average template of the developing brain using advanced multi-shell high angular resolution diffusion imaging data, for use in group voxel or fixel-wise analysis, atlas-building, and related applications. This relies on the recently proposed multi-shell multi-tissue constrained spherical deconvolution (MSMT-CSD) technique. We decompose the signal into one isotropic component and two anisotropic components, with response functions estimated from cerebrospinal fluid and white matter in the youngest and oldest participant groups, respectively. We build an orientationally-resolved template of those tissue components from data acquired from 113 babies between 33 and 44 weeks postmenstrual age, imaged as part of the Developing Human Connectome Project. These data were split into weekly groups, and registered to the corresponding group average templates using a previously-proposed non-linear diffeomorphic registration framework, designed to align orientation density functions (ODF). This framework was extended to allow the use of the multiple contrasts provided by the multi-tissue decomposition, and shown to provide superior alignment. Finally, the weekly templates were registered to the same common template to facilitate investigations into the evolution of the different components as a function of age. The resulting multi-tissue atlas provides insights into brain development and accompanying changes in microstructure, and forms the basis for future longitudinal investigations into healthy and pathological white matter maturation.

Characterisation of brain volume and microstructure at term-equivalent age in infants born across the gestational age spectrum

BACKGROUND

Risk of morbidity differs between very preterm (VP; <32 weeks' gestational age (GA)), moderate preterm (MP; 32-33 weeks' GA), late preterm (LP; 34-36 weeks' GA), and full-term (FT; ≥37 weeks' GA) infants. However, brain structure at term-equivalent age (TEA; 38-44 weeks) remains to be characterised in all clinically important GA groups. We aimed to compare global and regional brain volumes, and regional white matter microstructure, between VP, MP, LP and FT groups at TEA, in order to establish the magnitude and anatomical locations of between-group differences.

METHODS

Structural images from 328 infants (91 VP, 63 MP, 104 LP and 70 FT) were segmented into white matter, cortical grey matter, cerebrospinal fluid (CSF), subcortical grey matter, brainstem and cerebellum. Global tissue volumes were analysed, and additionally, cortical grey matter and white matter volumes were analysed at the regional level using voxel-based morphometry. Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD) images from 361 infants (92 VP, 69 MP, 120 LP and 80 FT) were analysed using Tract-Based Spatial Statistics. Statistical analyses involved examining the overall effect of GA group on global volumes (using linear regressions) and regional volumes and microstructure (using non-parametric permutation testing), as well performing post-hoc comparisons between the GA sub-groups.

RESULTS

On global analysis, cerebrospinal fluid (CSF) volume was larger in all preterm sub-groups compared with the FT group. On regional analysis, volume was smaller in parts of the temporal cortical grey matter, and parts of the temporal white matter and corpus callosum, in all preterm sub-groups compared with the FT group. FA was lower, and RD and MD were higher in voxels located in much of the white matter in all preterm sub-groups compared with the FT group. The anatomical locations of group differences were similar for each preterm vs. FT comparison, but the magnitude and spatial extent of group differences was largest for the VP, followed by the MP, and then the LP comparison. Comparing within the preterm groups, the VP sub-group had smaller frontal and temporal grey and white matter volume, and lower FA and higher MD and RD within voxels in the approximate location of the corpus callosum compared with the MP sub-group. There were few volume and microstructural differences between the MP and LP sub-groups.

CONCLUSION

All preterm sub-groups had atypical brain volume and microstructure at TEA when compared with a FT group, particularly for the CSF, temporal grey and white matter, and corpus callosum. In general, the groups followed a gradient, where the differences were most pronounced for the VP group, less pronounced for the MP group, and least pronounced for the LP group. The VP sub-group was particularly vulnerable compared with the MP and LP sub-groups.

White matter organization in developmental coordination disorder: A pilot study exploring the added value of constrained spherical deconvolution

Previous studies of white matter organization in sensorimotor tracts in developmental coordination disorder (DCD) have adopted diffusion tensor imaging (DTI), a method unable to reconcile pathways with ‘crossing fibres’. In response to limitations of the commonly adopted DTI approach, the present study employed a framework that can reconcile the ‘crossing fibre’ problem (i.e., constrained spherical deconvolution- CSD) to characterize white matter tissue organization of sensorimotor tracts in young adults with DCD. Participants were 19 healthy adults aged 18–46: 7 met diagnostic criteria for DCD (4 females) and 12 were controls (3 females). All underwent high angular diffusion MRI. After preprocessing, the left and right corticospinal tracts (CST) and superior longitudinal fasciculi (SLF) were delineated and all tracts were then generated using both CSD and DTI tractography respectively. Based on the CSD model, individuals with DCD demonstrated significantly decreased mean apparent fibre density (AFD) in the left SLF relative to controls (with large effect size, Cohen's d = 1.32) and a trend for decreased tract volume of the right SLF (with medium-large effect size, Cohen's d = 0.73). No differences in SLF microstructure were found between groups using DTI, nor were differences in CST microstructure observed across groups regardless of hemisphere or diffusion model. Our data are consistent with the view that motor impairment characteristic of DCD may be subserved by white matter abnormalities in sensorimotor tracts, specifically the left and right SLF. Our data further highlight the benefits of higher order diffusion MRI (e.g. CSD) relative to DTI for clarifying earlier inconsistencies in reports speaking to white matter organization in DCD, and its contribution to poor motor skill in DCD.

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All previous diffusion studies of white matter in DCD have employed a tensor model

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We employed a non-tensor model to characterize microstructure in adults with DCD

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The non-tensor model showed atypical white matter organization in the SLF in DCD

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The tensor model failed to detect microstructural group differences for any tract

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Motor impairment characteristic of DCD may be subserved by white matter abnormalities

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We need to move beyond the tensor model in characterizing white matter in DCD

An anatomically curated fiber clustering white matter atlas for consistent white matter tract parcellation across the lifespan

This work presents an anatomically curated white matter atlas to enable consistent white matter tract parcellation across different populations. Leveraging a well-established computational pipeline for fiber clustering, we create a tract-based white matter atlas including information from 100 subjects. A novel anatomical annotation method is proposed that leverages population-based brain anatomical information and expert neuroanatomical knowledge to annotate and categorize the fiber clusters. A total of 256 white matter structures are annotated in the proposed atlas, which provides one of the most comprehensive tract-based white matter atlases covering the entire brain to date. These structures are composed of 58 deep white matter tracts including major long range association and projection tracts, commissural tracts, and tracts related to the brainstem and cerebellar connections, plus 198 short and medium range superficial fiber clusters organized into 16 categories according to the brain lobes they connect. Potential false positive connections are annotated in the atlas to enable their exclusion from analysis or visualization. In addition, the proposed atlas allows for a whole brain white matter parcellation into 800 fiber clusters to enable whole brain connectivity analyses. The atlas and related computational tools are open-source and publicly available. We evaluate the proposed atlas using a testing dataset of 584 diffusion MRI scans from multiple independently acquired populations, across genders, the lifespan (1 day-82 years), and different health conditions (healthy control, neuropsychiatric disorders, and brain tumor patients). Experimental results show successful white matter parcellation across subjects from different populations acquired on multiple scanners, irrespective of age, gender or disease indications. Over 99% of the fiber tracts annotated in the atlas were detected in all subjects on average. One advantage in terms of robustness is that the tract-based pipeline does not require any cortical or subcortical segmentations, which can have limited success in young children and patients with brain tumors or other structural lesions. We believe this is the first demonstration of consistent automated white matter tract parcellation across the full lifespan from birth to advanced age.

Tract-Specific Group Analysis in Fetal Cohorts Using in utero Diffusion Tensor Imaging

Diffusion tensor imaging (DTI) based group analysis has helped uncover the impact of white matter injuries in a wide range of studies involving subjects from preterm neonates to adults. The application of these methods to fetal cohorts, however, has been hampered by the challenging nature of in utero fetal DTI caused by unconstrained fetal motion, limited scan times, and limited signal-to-noise ratio. We present a framework that addresses these issues to systematically evaluate group differences in fetal cohorts. A motion-robust DTI computation approach with a new unbiased DTI template construction method is unified with kernel-regression in age and tensor-specific registration to normalize DTI volumes in an unbiased space. A robust statistical approach is used to map region-specific group differences to the medial representation of the tracts of interest. The proposed approach was applied and showed, for the first time, differences in local white matter fractional anisotropy based on in utero DTI of fetuses with congenital heart disease and age-matched healthy controls. This paper suggests the need for fetal-specific pipelines to be used for DTI-based group analysis involving fetal cohorts.

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.

Suprathreshold fiber cluster statistics: Leveraging white matter geometry to enhance tractography statistical analysis

This work presents a suprathreshold fiber cluster (STFC) method that leverages the whole brain fiber geometry to enhance statistical group difference analyses. The proposed method consists of 1) a well-established study-specific data-driven tractography parcellation to obtain white matter tract parcels and 2) a newly proposed nonparametric, permutation-test-based STFC method to identify significant differences between study populations. The basic idea of our method is that a white matter parcel's neighborhood (nearby parcels with similar white matter anatomy) can support the parcel's statistical significance when correcting for multiple comparisons. We propose an adaptive parcel neighborhood strategy to allow suprathreshold fiber cluster formation that is robust to anatomically varying inter-parcel distances. The method is demonstrated by application to a multi-shell diffusion MRI dataset from 59 individuals, including 30 attention deficit hyperactivity disorder patients and 29 healthy controls. Evaluations are conducted using both synthetic and in-vivo data. The results indicate that the STFC method gives greater sensitivity in finding group differences in white matter tract parcels compared to several traditional multiple comparison correction methods.