The Adolescent Brain Cognitive Development (ABCD) Study is the largest U.S.-based neuroimaging initiative of adolescent brain maturation. Diffusion MRI (dMRI) provides unique insights into white matter organization, yet applying advanced processing pipelines and managing technical variability across scanning environments remains challenging at scale. To address these issues, we present ABCD-BIDS Community Collection (ABCC) release 3.1.0, including a curated resource of more than 24,000 fully processed ABCD dMRI datasets. ABCC provides fully processed images, nuanced image quality metrics, advanced microstructural measures, and person-specific bundle tractography. Evaluating these rich data revealed that measures of diffusion restriction and non-Gaussianity—in particular the intracellular volume fraction from NODDI and return-to-origin probability from MAP-MRI—were highly sensitive to neurodevelopment and robust to variation in image quality. Additionally, harmonization of microstructural features markedly improved the cross-vendor generalizability of developmental effects. Together, ABCC accelerates reproducible, rigorous research on adolescent white matter development.

Tractography based on diffusion-weighted MRI (dMRI) is the predominant in vivo method for mapping the brain's white matter. However, it is also one of the most computationally demanding steps in neuroimaging data analysis-requiring the generation and filtering of millions of streamlines per subject. Over the past decade, high-performance computing (HPC) and graphics processing units (GPUs) have reshaped the landscape of tractography. What once required hours or days per subject can now be performed in minutes or even seconds. This facilitates faster processing of large-scale studies and high-resolution datasets, supports real-time clinical applications, and enables more computationally demanding algorithms. This review surveys the recent wave of applications of HPC and GPUs to massively parallelize tractography. We discuss how these advances have accelerated tractography pipelines, identify common strategies for parallelization, and highlight opportunities where further parallelization could improve efficiency and accuracy.

Tractography is a key component of efforts to map brain connectivity. As a rapidly-evolving field of neuroscience, current tractography methods are diverse, often varying across research laboratories and different software pipelines. Therefore, it suffers from a lack of standardization leading to inconsistencies in results, which can limit reproducibility, and affect the robustness needed for research and clinical applications of these methods. Variability in data acquisition procedures, inconsistencies in spatial referencing schemes and implementations, and anatomical heterogeneity —at the individual level, across the lifespan, and across species— hinders comparative analyses. Additionally, the lack of consensus on best practices complicates the development of robust automated quality control pipelines and limits the clinical translation of tractography-based procedures. Establishing standardized protocols for acquisition, preprocessing, and tractography reconstruction are critical towards enabling reliable tract-specific analyses, facilitating cross-study harmonization, and supporting replicable large-scale population studies. The present article provides an overview of the current challenges in tractography standardization and identifies the key aspects that require standardization for reliable, reproducible, and robust tractography.

Abstract The infant brain undergoes rapid myelination that is critical for healthy brain function. This development has been characterized for gray and white matter independently, but the link between gray and white matter myelination remains unexplored. To close this knowledge gap, we evaluated two complementary myelin-sensitive imaging metrics: Large-scale (N = 273) T1w/T2w and quantitative (N = 21) R1 data. Automated software was employed to identify 26 white matter bundles and map their cortical terminations, before evaluating T1w/T2w and R1 development shortly after birth. Here we show that for both metrics mean values as well as developmental slopes are correlated across tissues. The synchrony of brain T1w/T2w is impacted by postmenstrual age and prematurity, whereas inter-individual differences in this synchrony predict motor outcomes at 17 − 25 months of age. As T1w/T2w and R1 are associated with myelin content, our results reveal an intricate relationship between gray and white matter myelination.

Tractometry uses diffusion-weighted magnetic resonance imaging (dMRI) to assess physical properties of brain connections. Here, we present an integrative ecosystem of software that performs all steps of tractometry: post-processing of dMRI data, delineation of major white matter pathways, and modeling of the tissue properties within them. This ecosystem also provides a set of interoperable and extensible tools for visualization and interpretation of the results that extract insights from these measurements. These include novel machine learning and statistical analysis methods adapted to the characteristic structure of tract-based data. We benchmark the performance of these statistical analysis methods in different datasets and analysis tasks, including hypothesis testing on group differences and predictive analysis of subject age. We also demonstrate that computational advances implemented in the software offer orders of magnitude of acceleration. Taken together, these open-source software tools-freely available at https://tractometry.org-provide a transformative environment for the analysis of dMRI data.

Reading is a cognitive skill that requires our brain to go through a myriad of changes during learning. While many studies have described how reading acquisition shapes children's brain function, less is known about the impact of reading on brain structure. Here we examined short-term causal effects of reading training on preschoolers' behavior and white matter structure. Forty-eight English-speaking preschoolers (4y10m to 6y2m) participated in a randomized controlled trial where they were randomly assigned to two training programs: the Letter training program was focused on key skills for reading (e.g., decoding and letter knowledge), while the Language training program strengthened oral language comprehension skills without exposure to text. Longitudinal behavioral data showed that only the Letter Training group increased letter knowledge and decoding skills after the two-week training. Diffusion MRI measures (FA and MD) of eighteen white matter pathways (including the left arcuate and the left inferior longitudinal fasciculus) did not reveal any statistically significant changes for either group despite high degrees of scan-rescan reliability across sessions. These findings suggest that a two-week reading training program can cause changes in preschoolers' letter knowledge and decoding abilities, without being accompanied by measurable changes in the diffusion properties of the major white matter pathways of the reading network. We conclude highlighting possible constraints (i.e., age, training onset and duration, cognitive profile) to reading-related white matter plasticity.

The infant brain undergoes rapid myelin growth that is critical for healthy brain function. This development has been characterized for gray and white matter independently, but the link between gray and white matter myelination remains unexplored. To close this knowledge gap, we processed large-scale (N=279) Developing Human Connectome Project data with automated software to identify 26 white matter bundles and map their cortical terminations, before evaluating T1w/T2w development in individuals. We found that mean T1w/T2w as well as the slope of T1w/T2w development are correlated across tissues. This synchrony of brain T1w/T2w is impacted by birth age and postnatal experience, whereas inter-individual differences in this synchrony predict motor outcomes at 17 - 25 months of age. As T1w/T2w is associated with myelin content, our results reveal an intricate relationship between gray and white matter myelination, highlighting the importance of conjointly evaluating both tissues.

IntroductionThe Human Connectome Project (HCP) has become a keystone dataset in human neuroscience, with a plethora of important applications in advancing brain imaging methods and an understanding of the human brain. We focused on tractometry of HCP diffusion-weighted MRI (dMRI) data.MethodsWe used an open-source software library (pyAFQ; https://yeatmanlab.github.io/pyAFQ) to perform probabilistic tractography and delineate the major white matter pathways in the HCP subjects that have a complete dMRI acquisition (n = 1,041). We used diffusion kurtosis imaging (DKI) to model white matter microstructure in each voxel of the white matter, and extracted tract profiles of DKI-derived tissue properties along the length of the tracts. We explored the empirical properties of the data: first, we assessed the heritability of DKI tissue properties using the known genetic linkage of the large number of twin pairs sampled in HCP. Second, we tested the ability of tractometry to serve as the basis for predictive models of individual characteristics (e.g., age, crystallized/fluid intelligence, reading ability, etc.), compared to local connectome features. To facilitate the exploration of the dataset we created a new web-based visualization tool and use this tool to visualize the data in the HCP tractometry dataset. Finally, we used the HCP dataset as a test-bed for a new technological innovation: the TRX file-format for representation of dMRI-based streamlines.ResultsWe released the processing outputs and tract profiles as a publicly available data resource through the AWS Open Data program's Open Neurodata repository. We found heritability as high as 0.9 for DKI-based metrics in some brain pathways. We also found that tractometry extracts as much useful information about individual differences as the local connectome method. We released a new web-based visualization tool for tractometry—“Tractoscope” (https://nrdg.github.io/tractoscope). We found that the TRX files require considerably less disk space-a crucial attribute for large datasets like HCP. In addition, TRX incorporates a specification for grouping streamlines, further simplifying tractometry analysis.

Cross-sectional studies have linked differences in white matter tissue properties to reading skills. However, past studies have reported a range of, sometimes conflicting, results. Some studies suggest that white matter properties act as individual-level traits predictive of reading skill, whereas others suggest that reading skill and white matter develop as a function of an individual’s educational experience. In the present study, we tested two hypotheses: a) that diffusion properties of the white matter reflect stable brain characteristics that relate to stable individual differences in reading ability or b) that white matter is a dynamic system, linked with learning over time. To answer these questions, we examined the relationship between white matter and reading in a five-year longitudinal dataset and a series of large-scale, single-observation, cross-sectional datasets (N = 14,249 total participants). We find that gains in reading skill correspond to longitudinal changes in the white matter. However, in the cross-sectional datasets, we find no evidence for the hypothesis that individual differences in white matter predict reading skill. These findings highlight the link between dynamic processes in the white matter and learning.

BACKGROUND:Sensory changes due to aging or disease can impact brain tissue. This study aims to investigate the link between glaucoma, a leading cause of blindness, and alterations in brain connections. METHODS:We analyzed diffusion MRI measurements of white matter tissue in a large group, consisting of 905 glaucoma patients (aged 49-80) and 5292 healthy individuals (aged 45-80) from the UK Biobank. Confounds due to group differences were mitigated by matching a sub-sample of controls to glaucoma subjects. We compared classification of glaucoma using convolutional neural networks (CNNs) focusing on the optic radiations, which are the primary visual connection to the cortex, against those analyzing non-visual brain connections. As a control, we evaluated the performance of regularized linear regression models. RESULTS:We showed that CNNs using information from the optic radiations exhibited higher accuracy in classifying subjects with glaucoma when contrasted with CNNs relying on information from non-visual brain connections. Regularized linear regression models were also tested, and showed significantly weaker classification performance. Additionally, the CNN was unable to generalize to the classification of age-group or of age-related macular degeneration. CONCLUSIONS:Our findings indicate a distinct and potentially non-linear signature of glaucoma in the tissue properties of optic radiations. This study enhances our understanding of how glaucoma affects brain tissue and opens avenues for further research into how diseases that affect sensory input may also affect brain aging.

Alpha is the strongest electrophysiological rhythm in awake humans at rest. Despite its predominance in the EEG signal, large variations can be observed in alpha properties during development, with an increase in alpha frequency over childhood and adulthood. Here, we tested the hypothesis that these changes in alpha rhythm are related to the maturation of visual white matter pathways. We capitalized on a large diffusion MRI (dMRI)-EEG dataset (dMRI n = 2,747, EEG n = 2,561) of children and adolescents of either sex (age range, 5 - 21 years old) and showed that maturation of the optic radiation speci fi cally accounts for developmental changes of alpha frequency. Behavioral analyses also con fi rmed that variations of alpha frequency are related to maturational changes in visual perception. The present fi ndings demonstrate the close link between developmental variations in white matter tissue properties, electrophysiological responses, and behavior.

Diffusion-weighted MRI (dMRI) provides a unique non-invasive view of human brain tissue properties. The present review article focuses on tractometry analysis methods that use dMRI to assess the properties of brain tissue within the long-range connections comprising brain networks. We focus specifically on the major white matter tracts that convey visual information. These connections are particularly important because vision provides rich information from the environment that supports a large range of daily life activities. Many of the diseases of the visual system are associated with advanced aging, and tractometry of the visual system is particularly important in the modern aging society. We provide an overview of the tractometry analysis pipeline, which includes a primer on dMRI data acquisition, voxelwise model fitting, tractography, recognition of white matter tracts, and calculation of tract tissue property profiles. We then review dMRI-based methods for analyzing visual white matter tracts: the optic nerve, optic tract, optic radiation, forceps major, and vertical occipital fasciculus. For each tract, we review background anatomical knowledge together with recent findings in tractometry studies on these tracts and their properties in relation to visual function and disease. Overall, we find that measurements of the brain's visual white matter are sensitive to a range of disorders and correlate with perceptual abilities. We highlight new and promising analysis methods, as well as some of the current barriers to progress toward integration of these methods into clinical practice. These barriers, such as variability in measurements between protocols and instruments, are targets for future development.

Large diffusion MRI datasets provide the statistical power necessary to model complex effects in the white matter. They also motivate the need for fully automatic algorithms for finding white matter bundles. One popular algorithm, Automated Fiber Quantification (AFQ), has been shown to be reliable for analyzing a suite of large bundles. Here, we demonstrate that this approach can be extended to a relatively small white matter bundle, the optic tract. We develop an automated method that finds a portion of this bundle automatically. We compare the automatically found optic tract to optic tracts previously found using an algorithm that requires expert intervention, and find high degree of overlap. While previous methods work well in high-quality data, we demonstrate that the novel method proposed here generalizes broadly in subjects from three different datasets with differing data quality and a broad age range. Finally, we describe how this approach could be easily extended to other small bundles.

The formation of myelin, the fatty sheath that insulates nerve fibers, is critical for healthy brain function. A fundamental open question is what impact being born has on myelin growth. To address this, we evaluated a large (n = 300) cross-sectional sample of newborns from the Developing Human Connectome Project (dHCP). First, we developed software for the automated identification of 20 white matter bundles in individual newborns that is well suited for large samples. Next, we fit linear models that quantify how T1w/T2w (a myelin-sensitive imaging contrast) changes over time at each point along the bundles. We found faster growth of T1w/T2w along the lengths of all bundles before birth than right after birth. Further, in a separate longitudinal sample of preterm infants (N = 34), we found lower T1w/T2w than in full-term peers measured at the same age. By applying the linear models fit on the cross-section sample to the longitudinal sample of preterm infants, we find that their delay in T1w/T2w growth is well explained by the amount of time they spent developing in utero and ex utero. These results suggest that white matter myelinates faster in utero than ex utero. The reduced rate of myelin growth after birth, in turn, explains lower myelin content in individuals born preterm and could account for long-term cognitive, neurological, and developmental consequences of preterm birth. We hypothesize that closely matching the environment of infants born preterm to what they would have experienced in the womb may reduce delays in myelin growth and hence improve developmental outcomes.

The white matter pathways that carry information from the foveal, macular, and peripheral visual fields have distinct biological properties. The optic radiations (OR) carry foveal and peripheral information from the thalamus to the primary visual cortex (V1) through adjacent but separate pathways in the white matter. Here, we perform white matter tractometry using pyAFQ 1 on a large sample of diffusion MRI (dMRI) data from the UK Biobank dataset (UKBB; N=6021; age 45-81). We use pyAFQ to characterize white matter tissue properties in parts of the OR that transmit information about the foveal, macular, and peripheral visual fields, and to characterize the changes in these tissue properties with age. We find that (1) independent of age there is higher fractional anisotropy, lower mean diffusivity, and higher mean kurtosis in the foveal and macular OR than in peripheral OR, consistent with denser, more organized nerve fiber populations in foveal/parafoveal pathways, and (2) age is associated with increased diffusivity and decreased anisotropy and kurtosis, consistent with decreased density and tissue organization with adulthood aging. However, anisotropy in foveal OR decreases faster with age than in peripheral OR, while diffusivity increases faster in peripheral OR, suggesting foveal/peri-foveal OR and peripheral OR differ in how they age.

The formation of myelin, the fatty sheath that insulates nerve fibers, is critical for healthy brain function. A fundamental open question is what is the impact of being born on myelin growth. To address this question, we evaluated a large (n=300) cross-sectional sample of newborns from the Developing Human Connectome Project (dHCP). First, we developed new software for the automated identification of 20 white matter bundles in individuals that is well-suited for large samples. Next, we fit linear models that quantify T1w/T2w, a myelin-sensitive imaging contrast, increases along bundles. We found faster growth of T1w/T2w along the lengths of all bundles before birth than right after birth. Further, in a separate longitudinal sample of preterm infants (N=34), we found lower T1w/T2w at term-equivalent age than in full-term peers. By applying the linear models fit on the cross-section sample to the longitudinal sample of preterm infants, we find that their delay in T1w/T2w growth is well explained by the amount of time preterm infants spend developing in utero and ex utero. These results suggest that being born slows the rate of myelin growths. This reduction in the rate of myelin growth at birth, in turn, explains lower myelin content in individuals born preterm, and could account for long-term cognitive, neurological, and developmental consequences of preterm birth. We hypothesize that closely matching the environment of infants born preterm to what they would have experienced in the womb may reduce delays in myelin growth and hence improve developmental outcomes.

Changes in sensory input with aging and disease affect brain tissue properties. To establish the link between glaucoma, the most prevalent cause of irreversible blindness, and changes in major brain connections, we characterized white matter tissue properties in diffusion MRI measurements in a large sample of subjects with glaucoma (N=905; age 49-80) and healthy controls (N=5,292; age 45-80) from the UK Biobank. Confounds due to group differences were mitigated by matching a sub-sample of controls to glaucoma subjects. A convolutional neural network (CNN) accurately classified whether a subject has glaucoma using information from the primary visual connection to cortex (the optic radiations, OR), but not from non-visual brain connections. On the other hand, regularized linear regression could not classify glaucoma, and the CNN did not generalize to classification of age-group or of age-related macular degeneration. This suggests a unique non-linear signature of glaucoma in OR tissue properties.

This tractography and registration mapping was created from the data that is in this PURL on the Stanford Digital Repository: https://searchworks.stanford.edu/view/yx282xq2090.The data were processed using the pyAFQ software (https://github.com/yeatmanlab/pyAFQ) and Dipy, to create tensor-based tracts. These were down-sampled by a factor of 1000, and stored in the trk file. The data were also registered to the MNI T2 template, using the SyN algorithm (Avants et al. 2008), implemented in Dipy. Both forward and backward transformation images were saved and are stored the nifti files.

We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets.

The biological properties of the foveal and peripheral visual pathways differ substantially. Here, we compared tissue properties of optic radiations (OR) carrying foveal and peripheral information to primary visual cortex (V1), measured with diffusion MRI (dMRI). We analyzed dMRI in two datasets: the Human Connectome Project (HCP; n=180; age 22-35) and the UK Biobank (UKB; n=7,088; age 45-81). In the HCP, OR was delineated using a three-dimensional atlas; parts of OR representing fovea and periphery were divided based on V1 fMRI responses close to OR endpoints. (2) In the UKB, OR was delineated using landmarks and divided using anatomically-based estimates of V1 responses close to OR endpoints. The dMRI signal was modeled using a kurtosis model, which provides information about tissue microstructure. Despite differences in data collection, population characteristics, and analysis methods, both datasets revealed higher fractional anisotropy, lower mean diffusivity, and higher mean kurtosis in the foveal OR than in peripheral OR, consistent with denser nerve fiber populations in foveal pathways. In further analysis of the UKB, we found that age is associated with increased diffusivity and decreased anisotropy and kurtosis, consistent with decreased density and tissue organization with aging. However, anisotropy in fovea decreases faster with age than in periphery, while diffusivity increases faster in periphery, suggesting foveal and peripheral OR differ in terms of aging.