In diffusion magnetic resonance imaging, or dMRI, an incomplete field of view (FOV) means that part of the brain is missing from the scan. This can seriously affect analyses of white matter connectivity, including tractography, which maps the pathways of nerve fiber bundles across the brain. Although previous studies have used deep generative models to estimate or “impute” the missing regions, it is still unclear how to best use additional information from paired multi-modality data, such as combining dMRI with structural T1-weighted (T1w) MRI, to improve the quality of reconstruction and support downstream analyses.

To address this, the researchers developed a new framework that imputes missing dMRI regions by integrating diffusion features from the acquired portion of the scan with information about the complete brain anatomical structure derived from paired imaging data. The idea is that using anatomical guidance from other modalities can improve how the missing diffusion signals are reconstructed. They tested the framework on two cohorts from different sites, including a total of 96 participants, and compared it with a baseline method that treated T1w and dMRI information equally without specifically leveraging their complementary roles.

The proposed framework significantly improved imputation quality, as measured by the angular correlation coefficient, and improved the accuracy of downstream tractography, as measured by the Dice score. These results suggest that carefully integrating paired multi-modality data leads to more accurate reconstruction of incomplete dMRI scans. By improving whole-brain tractography, this approach may reduce uncertainty in analyses of white matter bundles, particularly those relevant to neurodegenerative diseases.

Fig. 1.

Visualization (left) and histogram (right) of 103 real cases of dMRI scans with incomplete FOV that failed quality assurance. In the left figure, horizontal regions indicate the distribution of the incomplete part of FOV with an estimated position of a brain mask. The total cutoff distance from the incomplete FOV to the top of the brain is estimated using a corresponding and registered T1w image. Its histogram is presented in the right figure.

Diffusion magnetic resonance imaging data collected across different studies often vary because of differences in scanners, software, and acquisition protocols, which can introduce unwanted technical effects that interfere with biological analysis. This is especially challenging in large multi site studies of Alzheimer’s disease, where both technical variation and true disease related changes are present. In this study, the authors developed a harmonization approach that learns low dimensional representations of brain structural connectivity that are insensitive to imaging site, scanner type, and acquisition settings, while still preserving information related to cognitive status. They used a conditional variational autoencoder, a type of deep learning model that compresses data into a latent space while controlling which information is retained or removed. The model was trained on diffusion imaging data from 6956 individuals across 9 cohorts and 38 imaging sites, totaling nearly 12,000 scans, including participants with normal cognition, mild cognitive impairment, and Alzheimer’s disease. The learned representations successfully removed statistically significant site related effects across 12 brain network connectivity measures and improved the accuracy of predicting cognitive diagnosis from 68 percent to 73 percent. The method showed consistent performance across multiple data configurations, demonstrating that representation learning can effectively reduce scanner related confounding while strengthening biologically meaningful signals in large neuroimaging studies.

Fig.Ěý1

A total of 11,927 connectomes are pooled together across 9 cohorts and 35 unique imaging acquisitions (for a total of 38 “sites”). (a) If we naively combine all data and create t-SNE plots, there are obvious clusters driven by site. We compare cognitive diagnosis differences in one network connectivity measure, brain modularity, computed from these uncorrected connectomes. (b) Site-wise differences drive the detected differences and result in false findings. (c) In comparison, the t-SNE features of our learned representation have decreased site information, and the (d) resulting modularity values are harmonized and provide clear, significant (p<0.001) trends with cognitive diagnosis.

            Diffusion-weighted MRI (dMRI) is a powerful brain imaging technique that helps scientists study how nerve fibers, or white matter, are organized and connected in the brain. This method allows researchers to map the brain’s “connectome”—a network-like model that shows how different regions communicate. However, the accuracy of these maps can be affected by the shape and size of the 3D pixels, called voxels, used in the scans. When voxels are not perfect cubes (a condition called anisotropy), they can distort measurements of brain structure, but the full extent of this effect hasn’t been well understood.

In this study, we explored how anisotropic voxels influence both the fine details of brain tissue (microstructural measures like fractional anisotropy and mean diffusivity) and larger white matter features (such as bundle volume, length, and surface area). We analyzed brain scans from 44 participants in the Human Connectome Project, comparing data collected at different voxel resolutions. Using statistical tests, we examined how changing voxel shape affected key measurements of white matter structure and connectivity.

Our findings showed that even small changes in voxel shape caused significant differences in at least one microstructural and one bundle-related measure at every tested resolution. This means that voxel anisotropy can meaningfully alter how we interpret brain microstructure and tractography results. We also found that while certain detailed tissue measures could not be accurately restored through simple image upsampling, the consistency of larger white matter bundle measurements improved when data were resampled to 1 mm isotropic voxels.

In short, this study highlights how subtle differences in imaging resolution can affect the accuracy and reliability of brain connectivity studies, emphasizing the need for careful voxel selection and correction methods in diffusion MRI research.

Fig. 1.ĚýWe illustrate the range of voxels used for this experiment with tensor and tractogram representation. We see a bias in the tensor model toward the superior-inferior direction in the anisotropic voxels when compared to the isotropic sampling. The tractogram’s representation of the corpus callosum dramatically changes based on spatial sampling; the highly anisotropic voxels influence the tracking behavior to generate superior-inferior streamlines when the corpus callosum’s anatomy includes right-left whtie matter.

Diffusion magnetic resonance imaging (dMRI) is a non-invasive brain imaging technique that tracks the movement of water molecules in tissue over time. Because water movement is influenced by tiny cellular structures like membranes, axons, and myelin, dMRI provides a unique way to study the brain’s microstructure. One advanced dMRI method, called neurite orientation dispersion and density imaging (NODDI), models how brain cells and their connections are organized, giving detailed insights into tissue structure.

The early postnatal period is a time of rapid brain growth, including axonal growth, dendritic branching, and synapse formation. These processes change the brain’s microstructure in ways that NODDI can detect, making it a promising tool for studying early brain development. This review highlights recent studies using NODDI in infancy, showing how it can map typical developmental patterns, examine changes in preterm infants, and link microstructural properties to environmental factors and early behaviors.

While research is still limited—often with small sample sizes, narrow age ranges, and few longitudinal studies—initial findings suggest that NODDI can complement traditional diffusion measures and offer new insights into early neural development and brain plasticity. Continued use and refinement of NODDI in infants may help identify sensitive periods in brain development and improve understanding of emerging neurobehavioral traits.

Fig 1

NODDI model components and representative maps of NODDI parameters.ĚýA The brain microstructure is modeled as three compartments: free water (FW), intra-neurite, and extra-neurite spaces. The free water fraction (FWF), neurite density index (NDI), and orientation dispersion index (ODI) are derived from these compartments.ĚýB Representative axial slices from NODDI-derived maps for FWF, NDI, and ODI. Note: Figure adapted from Kraguljac et al. (), licensed under Creative Commons Attribution-NoDerivatives 4.0 International (CC BY-ND 4.0), available at 

The first annual International Society of Tractography (IST) debate, held in Corsica in 2024, focused on key challenges and controversies in tractography, a technique that maps brain connections using diffusion MRI. The debate centered on the provocative statement: “Tractography cannot give us anything we can’t get from an atlas template.” On one side, white matter atlas templates were highlighted as standardized, population-based maps of the brain that are especially useful for studying brain structure and making group comparisons. On the other side, subject-specific tractography was presented as a powerful approach that reconstructs the unique brain connections of each individual, allowing an in vivo “virtual dissection” of white matter pathways. This article introduces these concepts, summarizes the arguments for and against the statement, and, while acknowledging the strengths of both methods, underscores the unique value of tractography in advancing our understanding of individual brain connectivity.

Fig. 1

Schematic illustration of distinctions between atlas vs. subject-specific tractography. Atlas: volumetric or streamline-based atlases can be warped to an individual subject in a process called label/streamline propagation, enabling the identification and labeling of white matter pathways or regions. Subject-specific Tractography: bundle segmentation can be performed using a targeted approach (often using inclusion/exclusion regions of interest), or by generating a whole brain set of streamlines and subsequent processing to dissect and segment the streamlines of interest. We note that bundle dissection can be performed using manual regions of interest, or even making use of atlases to compare shape/location similarity when extracting and labelling subject-specific streamlines

The central nervous system [CNS], which includes the brain and spinal cord, is made up of white and gray matter and is responsible for sensory, motor, and cognitive functions. Advanced diffusion MRI (dMRI) techniques provide a way to study the structure of the CNS without invasive procedures, but most research has focused on either the brain or the spinal cord alone. In this study, we used a clinically practical dMRI protocol on a 3T scanner to examine the microstructure of both neurites and somas in the brain and spinal cord at the same time. The protocol allowed us to apply Diffusion Tensor Imaging (DTI), Standard Model Imaging (SMI), and Soma and Neurite Density Imaging (SANDI). This is the first time SMI and SANDI have been tested in the spinal cord, as well as in both the cord and brain simultaneously.

Our findings show that we were able to achieve high image quality, even with high diffusion weightings, and that the measurements from SMI and SANDI were reproducible in ways similar to DTI, with only a few exceptions. We also found biologically meaningful contrasts between and within white and gray matter regions. However, reproducibility and contrast were lower in the spinal cord compared to the brain, partly due to partial volume effects and image preprocessing challenges. Overall, this study introduces a unified method for imaging both the brain and spinal cord, opening new opportunities to study CNS diseases and identify biomarkers of structural integrity across the entire neuroaxis.

Fig. 1.

The brain and spinal cord are composed of highly organized white matter pathways, with varying neuronal densities, diameters, and orientations, as well as gray matter regions with varying cell densities and distributions. Created in BioRender.Ěý

Over the last few decades, diffusion MRI (dMRI) streamline tractography has become the main way to estimate white matter (WM) pathways—the brain’s wiring—while a person is alive. But a big limitation is that this method usually needs a special type of scan called high angular resolution diffusion imaging (HARDI), which can be hard to get during regular medical care. This means tractography is mostly used in research settings and with certain groups of patients, limiting its use in everyday clinical practice and for rare or underfunded diseases. Because of this, having a tractography method that works with common clinical scans would be very important. Such a method would need to perform flexible tractography, use only standard clinical imaging as input, and be openly available for anyone to use. In this study, we tested a new deep learning model that uses T1-weighted (T1w) MRI scans—common clinical images—to estimate brain pathways. We compared its performance with traditional dMRI-based tractography and atlas-based methods in healthy young people, older adults, and patients with epilepsy, depression, and brain cancer. In healthy young people, our deep learning model showed slightly more error than traditional tractography, but the difference was small and less than errors seen with atlas-based methods. We also found that the model could replicate some important findings from previous dMRI studies in the clinical groups, especially for long-range brain connections that atlas methods miss, but not in all cases. These results suggest that deep learning using T1w MRI shows promise for clinical tractography, especially compared to atlas-based methods, but still needs improvement and careful testing before it can be widely used in hospitals. Additionally, our findings raise new questions about how differences between dMRI and T1w MRI scans affect tractography results, and more research on this will help us better understand what brain features influence these measurements.

Fig 1

Tractograms (left view), right arcuate fasciculi (right view), left cinguli (left view), and cortical connectomes from traditional SD_STREAM tractography and the CoRNN method in a representative in-distribution HCP participant. Arrows denote visually appreciable differences between connectomes.

Brain scans can be used to estimate a person’s “brain age,” which may be older or younger than their actual age. A larger difference between brain age and actual age can provide early warning signs of neurodegenerative diseases like Alzheimer’s, potentially allowing for earlier diagnosis and prevention. One type of brain scan, called diffusion MRI (dMRI), is especially useful for this because it can detect very subtle changes in the brain’s structure that may happen before more obvious signs appear. However, dMRI captures both large-scale (macrostructural) and small-scale (microstructural) features of the brain, and it’s unclear whether current models for estimating brain age from dMRI are focusing on the small-scale changes that matter most for early detection.

To better isolate the microstructural information, this study developed a new approach that reduces the influence of macrostructural features by aligning all brain scans to a common reference template. The method was tested using imaging data from 13,398 people across 12 different datasets. The researchers compared this new microstructure-focused dMRI brain age model to several other models based on T1-weighted MRI, a common type of scan that primarily captures macrostructural features.

They found that the dMRI-based brain age and T1-based brain age showed different patterns depending on the stage of disease. For people who were transitioning from normal cognitive function to mild cognitive impairment (MCI), the dMRI brain age appeared older than the T1-based brain age. In contrast, for those already diagnosed with Alzheimer’s disease, the dMRI brain age appeared younger. Models based on T1-weighted MRI generally performed better at identifying who had Alzheimer’s, but the dMRI-based brain age may be more helpful in identifying early, subtle changes that happen before symptoms begin.

Fig 1

Brain age estimation frameworks have proven effective for using affinely aligned brain images to identify common patterns of aging, with deviations from these patterns likely indicating presence of abnormal neuropathologic processes. A common theme of existing brain age estimation methods has been using T1w MRI, denoted as “GM age” in the first row. Among them, there have been many innovations in network design, such as DeepBrainNet (DBN) (Bashyam et al., 2020) and the 3D convolutional neural network of TSAN (Cheng et al., 2021). T1w MRI lacks detail in white matter (WM). Here, we take the two most commonly used modalities for characterizing WM microstructure, fractional anisotropy (FA), and mean diffusivity (MD), and we evaluate brain age estimation in two contexts. First, we examine the direct substitution of FA and MD for T1w image, which we denote as “WM age affine” in the second row. A substantial amount of macrostructural differences is still present in WM age affine, notably ventricle enlargement. To isolate the microstructural changes, we apply non-rigid (deformable) registration into template space to mitigate the macrostructural changes and produce the “WM age nonrigid” in the third row. We explore the relative timing of changes in these brain age variants and their relative explainability in the context of mild cognitive impairment. Throughout the paper, we adhere to a consistent color scheme when visualizing results from different brain age estimates within the same plot to facilitate easier visual inspection. Specifically, we use red to represent GM ages, blue for WM age nonrigid, and purple for WM age affine.

Previous research has explored how the structure of white matter in the brain relates to academic skills like reading and math. Some studies have suggested that white matter—specifically, how it allows signals to travel through the brain—can predict a child’s academic abilities, while others have found no connection. However, studies that follow children over time (called longitudinal studies) have found that changes in white matter within the same child may be linked to learning progress.

This study aimed to replicate and expand on earlier findings by looking at how changes in a specific white matter pathway in the brain, called the left arcuate fasciculus, relate to reading development. The researchers followed 340 students from first through fourth grade, using diffusion MRI scans to measure white matter and tracking their reading and math scores over time. The results showed that year-to-year improvements in reading—but not math—were connected to changes in the left arcuate fasciculus. These findings offer more evidence that the brain’s white matter can change along with learning, and they underscore the value of long-term studies in understanding how children develop academic skills.

Fig 1

(A) Average estimated tract profiles for MD in the left arcuate fasciculus generated by the GAMM for four different quartiles of reading score change (reading state). Each color represents the magnitude of change relative to the average individual reading score. Shaded areas represent the standard errors of the predictions. (B) The estimated smoothing effect of time elapsed since the first study observation on average MD in the left arcuate. (C) Relationship between overall mean Woodcock–Johnson reading scores and MD in the left arcuate at each time point in the study.

Understanding how the brain changes as we age—especially in conditions like Alzheimer’s disease—is an important area of research. One part of the brain that hasn’t been studied as much is the white matter, which acts like a network of “wires” connecting different brain areas. This large study looked at how the structure of white matter relates to thinking and memory skills in older adults, including those with Alzheimer’s.

Researchers analyzed data from 9 separate studies, including nearly 4,500 adults aged 50 and older. Participants had brain scans using a technique called diffusion MRI (dMRI), along with memory and thinking tests over time. Most participants were cognitively healthy, while some had mild memory problems or Alzheimer’s dementia. The study focused on a specific feature of white matter called “free water” (FW), which can indicate damage or degeneration.

They found that higher levels of FW in white matter—especially in regions connected to memory like the cingulum and fornix—were strongly linked to worse memory and faster cognitive decline. These changes were even more noticeable in people who had other signs of Alzheimer’s, such as brain shrinkage, a genetic risk factor called APOE ε4, or positive tests for amyloid buildup (a hallmark of Alzheimer’s).

Overall, the study shows that changes in white matter—particularly FW—are an important piece of the puzzle in understanding memory loss and aging. These findings suggest that future brain studies should pay close attention to FW and highlight the importance of brain regions like the cingulum and fornix in Alzheimer’s-related decline.

Figure 1.  Cohort Characteristics and Data Harmonization

A, Participants were drawn from 9 well-established cohorts, including 3213 cognitively unimpaired (CU) individuals, 972 with mild cognitive impairment (MCI), and 282 with Alzheimer disease (AD) at baseline. B, The study also incorporated longitudinal data across 9208 cognitive sessions, spanning up to 13 years of follow-up. Longitudinal ComBat harmonization was applied to all imaging features to account for variability across imaging batches. C and D, Associations are shown between cingulum free-water (FW) and memory performance, using both raw (C) and harmonized (D) FW data, with points and lines color coded by imaging batch. Harmonized data were used across all analyses. ADNI indicates Alzheimer’s Disease Neuroimaging Initiative; BIOCARD, Biomarkers of Cognitive Decline Among Normal Adults; BLSA, Baltimore Longitudinal Study of Aging; MAP, Rush Memory and Aging Project; MARS, Minority Aging Research Study; NACC, National Alzheimer’s Coordinating Center; ROS, Religious Orders Study; VMAP, Vanderbilt Memory and Aging Project; WRAP, Wisconsin Registry for Alzheimer’s Prevention.