Asymmetry in white matter is believed to give rise to the brain's capacity for specialized processing and is involved in the lateralization of various cognitive processes, such as language and visuo-spatial reasoning. Although studies of white matter asymmetry have been previously documented, they have often been constrained by limited age ranges, sample sizes, or the scope of the tracts and structural features examined. While normative lifespan charts for brain structures are emerging, comprehensive charts detailing white matter asymmetries across numerous pathways and diverse structural measures have been notably absent. This study addresses this gap by leveraging a large-scale dataset of 35,120 typically developing and aging individuals, ranging from 0 to 100 years of age, from 50 primary neuroimaging studies. We generated comprehensive lifespan trajectories for 30 lateralized association and projection white matter tracts, examining six distinct microstructural and macrostructural features of these pathways. Our findings reveal that: (1) asymmetries are widespread across the brain's white matter and are present in all 30 pathways; (2) for a given pathway, the degree and direction of asymmetry differ between features of tissue microstructure and pathway macrostructure; (3) asymmetries vary across and within pathway types (association and projection tracts); and (4) these asymmetries are not static, following unique trajectories across the lifespan, with distinct changes during development, and a general trend of becoming more asymmetric with increasing age (particularly in later adulthood) across pathways. This study represents the most extensive characterization of white matter asymmetry across the lifespan to date, charting how lateralization patterns emerge, mature, and change throughout life. It provides a foundational resource for understanding the principles of white matter organization from early to late life, its relation to functional specialization and inter-individual variability, and offers a key reference for interpreting deviations during healthy development and aging as well as those associated with clinical populations.

Brain charts, or normative models of quantitative neuroimaging measures, can identify trajectories of brain development and abnormalities in groups and individuals by leveraging large populations. Recent work has extended these brain charts to model microstructural and macrostructural features of white matter. Assessments of variance for these brain charts are necessary to determine whether the models being used for these data are stable. We implement an analytic approach to characterize variability of the parameters in previously released brain charts created using the generalized additive models for location, scale, and shape (GAMLSS) framework. Additionally, we empirically validate the accuracy of each analytic model through a comparison to a bootstrapping approach from 0.2 to 90 years of age. We find that across all models, the analytic coefficient of variation (COV) remains below 5% for ages greater than 0.25 years, with the maximum empirical observed COV reaching 7% at 0.2 years of age. Further, the empirical assessment shows high agreement with the analytic assessment, with COV estimates averaged across the lifespan for all models having a Pearson correlation coefficient of 0.776 and a mean difference of 4 x 10-4. Both methods exhibit volume and surface area as the features with the largest average COV for the majority of tracts. However, the analytic assessment yields axial diffusivity as the feature most frequently having the smallest COV, whereas the corresponding feature for the empirical assessment is average length. These results suggest that the analytic approach overestimates model stability for WM brain charts when the COV is low and that the validation method is suitable for assessing whether GAMLSS models are unstable.

Dense longitudinal neuroimaging (DLN) is a precision neuroimaging approach that samples frequently throughout a targeted window of time to characterize individual trajectories of brain change. The approach enables the answering of fundamental questions about the underlying mechanisms of cognitive development and learning. However, acquiring DLN data presents practical challenges that go beyond those associated with acquiring conventional longitudinal data. In this paper, we discuss key practical considerations for designing and executing DLN studies, especially with respect to collecting data from children and other populations with limited scan tolerance. We provide ten simple rules for study design, measurement, recruitment and retention, and compliance to assist research teams seeking to conduct DLN studies.

The first year of formal schooling is a year of foundational reading and math learning, and individual differences emerging within this single year predict academic achievement decades later. Yet, how brain changes throughout this critical year relate to individual differences in reading and math learning remains uncharacterized. In this pre-registered study ( https://osf.io/97ybe ), we acquired monthly both behavioral assessments of reading- and math-learning, and diffusion-weighted MRI scans to measure white matter microstructure, across the first-grade year. Behavioral learning trajectories follow either a sigmoid for reading or an inverted-U for math. Month-to-month microstructural changes in the right middle longitudinal fasciculus predicted corresponding changes in math performance, but not in reading. Findings highlight white matter microstructure as a dynamic substrate of early math learning, and reveal a more general principle: rapid changes in white-matter microstructure during the foundational learning window may be associated with distinct academic domains. Funding:R01 HD114489.

Longitudinal measurements of brain structure and function are critical for understanding how humans change over time. Traditional longitudinal approaches sample sparsely across large windows of time to estimate coarse, long-term brain changes. This review showcases insights from dense longitudinal neuroimaging (DLN), an emerging approach that samples densely across relatively short windows of time to precisely estimate individual trajectories of brain change. DLN measures multiple samples from individuals throughout critical periods of rapid change. It allows precise estimates of nonlinear trajectories to advance a mechanistic understanding of brain change. Novel findings from this approach are improving our understanding of human cognition, such as the role of the motor system in visual development and learning.

Normative reference charts are widely used in healthcare, especially for assessing the development of individuals by benchmarking anatomic and physiological features against population trajectories across the lifespan. Recent work has extended this concept to gray matter morphology in the brain, but no such reference framework currently exists for white matter (WM) even though WM constitutes the essential substrate for neuronal communication and large-scale network integration. Here, we present the first comprehensive WM brain charts, which describe how microstructural and macrostructural features of WM evolve across the lifespan, by leveraging over 26,199 diffusion MRI scans from 42 harmonized studies. Using generalized additive models for location, scale, and shape (GAMLSS), we estimate age- and sex-stratified trajectories for 72 individual white matter pathways, quantifying both tract-specific microstructural and morphometric features. We demonstrate that these WM brain charts enable four important applications: (1) defining normative trajectories of WM maturation and decline across distinct pathways, (2) identifying previously uncharacterized developmental milestones and spatial gradients of tract maturation, (3) detecting individualized deviations from normative patterns with clinical relevance across multiple neurological disorders, and (4) facilitating standardized, cross-study centile scoring of new datasets. By establishing a unified, interpretable reference framework for WM structure, these brain charts provide a foundational metric for research and clinical neuroscience. The accompanying open-access trajectories, centile scoring tools, and harmonization methods facilitate precise mapping of WM development, aging, and pathology across diverse populations. We release the brain charts and provide an out-of-sample alignment process as a Docker image: https://zenodo.org/records/15367426.

A hallmark of preschool and kindergarten classrooms are manual production tasks, including handwriting, drawing, building, and other arts and crafts activities produced manually with the hands. This chapter explores the potential mechanisms through which early manual production activities lay a solid foundation to support learning throughout the lifespan. First, we present behavioral research that explores the use of production tasks in preschool and kindergarten classrooms as early precursors to writing in the later elementary school years when writing becomes a critical element of lessons across multiple academic domains. Second, we present neuroimaging research on the brain correlates of handwriting and drawing in pre-reading children and literate adults that, together, suggest that these production tasks are supported by sensorimotor brain connections in childhood that develop into the sensorimotor brain connections observed in adulthood. Finally, we discuss the foundational role of production tasks in early childhood by synthesizing the behavioral and neuroimaging research, concluding that manual production tasks may leave behind sensorimotor brain changes that lead to better learning in the future. We end by discussing the need for more research on the effects of production activities for early writing development and other long-term educational outcomes.

Data standardization promotes a common framework through which researchers can utilize others’ data and is one of the leading methods neuroimaging researchers use to share and replicate findings. As of today, standardizing datasets requires technical expertise such as coding and knowledge of file formats. We present ezBIDS, a tool for converting neuroimaging data and associated metadata to the Brain Imaging Data Structure (BIDS) standard. ezBIDS contains four major features: (1) No installation or programming requirements. (2) Handling of both imaging and task events data and metadata. (3) Semi-automated inference and guidance for adherence to BIDS. (4) Multiple data management options: download BIDS data to local system, or transfer to OpenNeuro.org or to brainlife.io. In sum, ezBIDS requires neither coding proficiency nor knowledge of BIDS, and is the first BIDS tool to offer guided standardization, support for task events conversion, and interoperability with OpenNeuro.org and brainlife.io.

Human learning is a complex phenomenon that varies greatly among individuals and is related to the microstructure of major white matter tracts in several learning domains, yet the impact of the existing myelination of white matter tracts on future learning outcomes remains unclear. We employed a machine-learning model selection framework to evaluate whether existing microstructure might predict individual differences in the potential for learning a sensorimotor task, and further, if the mapping between the microstructure of major white matter tracts and learning was selective for learning outcomes. We used diffusion tractography to measure the mean fractional anisotropy (FA) of white matter tracts in 60 adult participants who then underwent training and subsequent testing to evaluate learning. During training, participants practiced drawing a set of 40 novel symbols repeatedly using a digital writing tablet. We measured drawing learning as the slope of draw duration over the practice session and visual recognition learning as the performance accuracy in an old/new 2-AFC recognition task. Results demonstrated that the microstructure of major white matter tracts selectively predicted learning outcomes, with left hemisphere pArc and SLF 3 tracts predicting drawing learning and the left hemisphere MDLFspl predicting visual recognition learning. These results were replicated in a repeat, held-out data set and supported with complementary analyses. Overall, results suggest that individual differences in the microstructure of human white matter tracts may be selectively related to future learning outcomes and open avenues of inquiry concerning the impact of existing tract myelination in the potential for learning. Significance statement:A selective mapping between tract microstructure and future learning has been demonstrated in the murine model and, to our knowledge, has not yet been demonstrated in humans. We employed a data-driven approach that identified only two tracts, the two most posterior segments of the arcuate fasciculus in the left hemisphere, to predict learning a sensorimotor task (drawing symbols) and this prediction model did not transfer to other learning outcomes (visual symbol recognition). Results suggest that individual differences in learning may be selectively related to the tissue properties of major white matter tracts in the human brain.

Ensuring that children learn to produce symbols by hand is a central aim of preschool educators because symbol production is fundamental to future learning activities in elementary school, such as writing letters to practice spelling and writing digits to practice math. The current work provides an analysis of symbol production in typically developing preschool children to assist educators in preparing students for success in elementary school. 37 preschool children completed a short battery of standard assessments followed by 6 weeks of practice producing letters or digits. A classification scheme was used to measure legibility and confusability for each symbol produced by each child for each practice week. At the beginning of the 6-week period, preschool children produced some symbols well (letters: E, H, I, O, T, digits: 1, 0) and productions of Z were by far the least recognizable and often resembled S. During the 6-week period, productions of H, R, S, Y, and Z improved more than other symbols. Finally, visual-motor, literacy, and phonological skills interacted to predict production learning during the 6-week period, even after controlling for pre-practice production ability, age, and sex: scores on each assessment positively predicted production learning but only when scores on the other two assessments were low. Efforts to ensure that children enter elementary school with strong symbol production abilities may benefit from allotting more attention to some letters relative to others and, furthermore, that visual-motor, literacy, and phonological skills may be early predictors of preschool production learning.

Neuroscience research has expanded dramatically over the past 30 years by advancing standardization and tool development to support rigor and transparency. Consequently, the complexity of the data pipeline has also increased, hindering access to FAIR data analysis to portions of the worldwide research community. brainlife.io was developed to reduce these burdens and democratize modern neuroscience research across institutions and career levels. Using community software and hardware infrastructure, the platform provides open-source data standardization, management, visualization, and processing and simplifies the data pipeline. brainlife.io automatically tracks the provenance history of thousands of data objects, supporting simplicity, efficiency, and transparency in neuroscience research. Here brainlife.io's technology and data services are described and evaluated for validity, reliability, reproducibility, replicability, and scientific utility. Using data from 4 modalities and 3,200 participants, we demonstrate that brainlife.io's services produce outputs that adhere to best practices in modern neuroscience research.

The hippocampus is a complex brain structure composed of subfields that each have distinct cellular organizations. While the volume of hippocampal subfields displays age-related changes that have been associated with inference and memory functions, the degree to which the cellular organization within each subfield is related to these functions throughout development is not well understood. We employed an explicit model testing approach to characterize the development of tissue microstructure and its relationship to performance on 2 inference tasks, one that required memory (memory-based inference) and one that required only perceptually available information (perception-based inference). We found that each subfield had a unique relationship with age in terms of its cellular organization. While the subiculum (SUB) displayed a linear relationship with age, the dentate gyrus (DG), cornu ammonis field 1 (CA1), and cornu ammonis subfields 2 and 3 (combined; CA2/3) displayed nonlinear trajectories that interacted with sex in CA2/3. We found that the DG was related to memory-based inference performance and that the SUB was related to perception-based inference; neither relationship interacted with age. Results are consistent with the idea that cellular organization within hippocampal subfields might undergo distinct developmental trajectories that support inference and memory performance throughout development.

Rectified noise floor is a challenging problem for high-resolution diffusion MRI, which cannot be tackled by current denoising methods. We propose a simple deep learning method for correcting noise floor in diffusion MRI. The method is based on 1D CNN model and works on voxel-wise time courses. Therefore, even one dataset of the brain has sufficient number of samples for training, which is a big advantage for practical application. Both simulation and in vivo results show that the method is robust in mitigating the noise floor artifact and restore the true values of diffusion metrics.

Handwriting instruction in preschool and kindergarten often includes copying letters of the alphabet, a visual-motor task that facilitates letter processing in early reading development. This chapter explores the mechanisms through which early handwriting instruction affects letter processing in early reading development. First, we present behavioral research that explores how the visual-motor nature of handwriting leads to changes in visual letter processing. Second, we discuss the effects of handwriting on early reading development in terms of how handwriting changes the brain in ways that facilitate letter processing in pre-reading children. Finally, we present neuroimaging research that explores how the brain supports handwriting in pre-reading children but also in adult populations to capture handwriting in late reading development. We end by discussing the need for more research on the effects of handwriting instruction methods to support early reading development and other long-term educational outcomes.

Diffusion weighted imaging (DWI) with multiple, high b-values is critical for extracting tissue microstructure measurements; however, high b-value DWI images contain high noise levels that can overwhelm the signal of interest and bias microstructural measurements. Here, we propose a simple denoising method that can be applied to any dataset, provided a low-noise, single-subject dataset is acquired using the same DWI sequence. The denoising method uses a one-dimensional convolutional neural network (1D-CNN) and deep learning to learn from a low-noise dataset, voxel-by-voxel. The trained model can then be applied to high-noise datasets from other subjects. We validated the 1D-CNN denoising method by first demonstrating that 1D-CNN denoising resulted in DWI images that were more similar to the noise-free ground truth than comparable denoising methods, e.g., MP-PCA, using simulated DWI data. Using the same DWI acquisition but reconstructed with two common reconstruction methods, i.e. SENSE1 and sum-of-square, to generate a pair of low-noise and high-noise datasets, we then demonstrated that 1D-CNN denoising of high-noise DWI data collected from human subjects showed promising results in three domains: DWI images, diffusion metrics, and tractography. In particular, the denoised images were very similar to a low-noise reference image of that subject, more than the similarity between repeated low-noise images (i.e. computational reproducibility). Finally, we demonstrated the use of the 1D-CNN method in two practical examples to reduce noise from parallel imaging and simultaneous multi-slice acquisition. We conclude that the 1D-CNN denoising method is a simple, effective denoising method for DWI images that overcomes some of the limitations of current state-of-the-art denoising methods, such as the need for a large number of training subjects and the need to account for the rectified noise floor.

The degree of interaction between the ventral and dorsal visual streams has been discussed in multiple scientific domains for decades. Recently, several white matter tracts that directly connect cortical regions associated with the dorsal and ventral streams have become possible to study due to advancements in automated and reproducible methods. The developmental trajectory of this set of tracts, here referred to as the posterior vertical pathway (PVP), has yet to be described. We propose an input-driven model of white matter development and provide evidence for the model by focusing on the development of the PVP. We used reproducible, cloud-computing methods and diffusion imaging from adults and children (ages 5–8 years) to compare PVP development to that of tracts within the ventral and dorsal pathways. PVP microstructure was more adult-like than dorsal stream microstructure, but less adult-like than ventral stream microstructure. Additionally, PVP microstructure was more similar to the microstructure of the ventral than the dorsal stream and was predicted by performance on a perceptual task in children. Overall, results suggest a potential role for the PVP in the development of the dorsal visual stream that may be related to its ability to facilitate interactions between ventral and dorsal streams during learning. Our results are consistent with the proposed model, suggesting that the microstructural development of major white matter pathways is related, at least in part, to the propagation of sensory information within the visual system.

The posterior vertical pathway (PVP) is a set of white matter tracts that directly connect cortical regions associated with the dorsal and ventral visual streams. By traveling between action-oriented regions in the dorsal stream and perception-oriented regions in the ventral stream, white matter tracts in the PVP may be particularly important for performing tasks that link action and perception. Here, we tested whether the tissue properties of PVP white matter tracts predict learning to draw novel symbols, a motor task strongly associated with visual perception processing. Using advanced diffusion tractography, we measured fractional anisotropy (FA) in the PVP tracts (i.e., TPC, pArc, MdLF-SPL, MdLF-Ang) as well as major white matter tracts within the dorsal (i.e., SLF1and2, SLF3) and ventral streams (i.e., ILF, IFOF) in 30 adult participants before they were trained on drawing novel symbols. Training consisted of 1600 symbol drawing trials, evenly distributed over the course of 4 days. We measured learning as the trial-to-trial change in symbol drawing duration. Learning occurred most rapidly during the first day of training, with significantly less learning in the following training days. Pre-training FA in the PVP tracts that travel between the dorsal and ventral streams predicted learning; pre-training FA in tracts that travel within the dorsal and ventral streams did not significantly predict learning. Overall, our results suggest a key role for the PVP in learning that may be related to its ability to facilitate interactions between dorsal and ventral streams during action.

The ventral and dorsal visual streams process visual information for different purposes though it is clear that these two streams interact. Recent evidence shows that several white matter tracts directly connect regions associated with ventral and dorsal visual streams. Together, these white matter tracts constitute the posterior vertical pathway (PVP). As of today, we know little about PVP development and even less about its development in relation to ventral and dorsal streams. We propose a model that posits that the development of PVP white matter is related to the flow of neural activity from the ventral visual stream and to the dorsal visual stream. We characterized the development of PVP tracts in a cross-sectional sample of 31 children (4.5-8.5 years old) and 13 adults (18-22 years old) using diffusion-MRI and ensemble tractography. We measured fractional anisotropy (FA) in dorsal (i.e., SLF1and2 and SLF3) and ventral (i.e., ILF, IFOF) streams as well as the four vertical white matter tracts that constitute the PVP (i.e., TPC, pArc, MdLF-SPL, MdLF-Ang). We found that PVP microstructure was more adult-like than the microstructure of the dorsal stream tracts, suggesting that PVP white matter develops earlier than dorsal stream white matter. PVP microstructure was more similar to the microstructure of the ventral than the dorsal stream, suggesting that PVP development follows ventral stream development more closely than it follows dorsal stream development. Finally, PVP microstructure was predicted by performance on a perceptual task in children, suggesting that PVP development is related to developing visual perceptual skills. Overall, results support our model of white matter development and suggest a key role for the PVP in the development of the dorsal visual stream that may be related to its ability to facilitate interactions between ventral and dorsal streams during visual perception.

Letter production through handwriting creates visual experiences that may be important for the development of visual letter perception. We sought to better understand the neural responses to different visual percepts created during handwriting at different levels of experience. Three groups of participants, younger children, older children, and adults, ranging in age from 4.5 to 22 years old, were presented with dynamic and static presentations of their own handwritten letters, static presentations of an age-matched control's handwritten letters, and typeface letters during fMRI. First, data from each group were analyzed through a series of contrasts designed to highlight neural systems that were most sensitive to each visual experience in each age group. We found that younger children recruited ventral-temporal cortex during perception and this response was associated with the variability present in handwritten forms. Older children and adults also recruited ventral-temporal cortex; this response, however, was significant for typed letter forms but not variability. The adult response to typed letters was more distributed than in the children, including ventral-temporal, parietal, and frontal motor cortices. The adult response was also significant for one's own handwritten letters in left parietal cortex. Second, we compared responses among age groups. Compared to older children, younger children demonstrated a greater fusiform response associated with handwritten form variability. When compared to adults, younger children demonstrated a greater response to this variability in left parietal cortex. Our results suggest that the visual perception of the variability present in handwritten forms that occurs during handwriting may contribute to developmental changes in the neural systems that support letter perception.