Axon diameter and myelin thickness affect the conduction velocity of action potentials in the nervous system. Imaging them non-invasively with MRI-based methods is, thus, valuable for studying brain microstructure and function. Electron microscopy studies suggest that axon diameter and myelin thickness are closely related to each other. However, the relationship between MRI-based estimates of these microstructural measures, known to be relative indices, has not been investigated across the brain mainly due to methodological limitations. In recent years, studies using ultra-high-gradient strength diffusion MRI (dMRI) have demonstrated improved estimation of axon diameter index across white-matter (WM) tracts in the human brain, making such investigations feasible. In this study, we aim to investigate relationships between tissue microstructure properties across white-matter tracts, as estimated with MRI-based methods. We collected dMRI with ultra-high-gradient strength and multi-echo spin-echo MRI on ex vivo macaque and human brain samples on a preclinical scanner. From these data, we estimated axon diameter index, intra-axonal signal fraction, myelin water fraction (MWF), and aggregate g-ratio and investigated their correlations. We found that the correlations between axon diameter index and other microstructural imaging parameters were weak but consistent across WM tracts in samples estimated with sufficient signal-to-noise ratio. In well-myelinated regions, tissue voxels with larger axon diameter indices were associated with lower packing density, lower MWF, and a tendency of higher g-ratio. We also found that intra-axonal signal fractions and MWF were not consistently correlated when assessed in different samples. Overall, the findings suggest that MRI-based axon geometry and myelination measures can provide complementary information about fiber morphology, and the relationships between these measures agree with prior electron microscopy studies in smaller field of views. Combining these advanced measures to characterize tissue morphology may help differentiate tissue changes during disease processes such as demyelination versus axonal damage. The regional variations and relationships of microstructural measures in control samples as reported in this study may serve as a point of reference for investigating such tissue changes in disease.

Anatomic tracing is the gold standard tool for delineating brain connections and for validating more recently developed imaging approaches such as diffusion MRI tractography. A key step in the analysis of data from tracer experiments is the careful, manual charting of fiber trajectories on histological sections. This is a very time-consuming process, which limits the amount of annotated tracer data that are available for validation studies. Thus, there is a need to accelerate this process by developing a method for computer-assisted segmentation. Such a method must be robust to the common artifacts in tracer data, including variations in the intensity of stained axons and background, as well as spatial distortions introduced by sectioning and mounting the tissue. The method should also achieve satisfactory performance using limited manually charted data for training. Here, we propose the first deep-learning method, with a self-supervised loss function, for segmentation of fiber bundles on histological sections from macaque brains that have received tracer injections. We address the limited availability of manual labels with a semi-supervised training technique that takes advantage of unlabeled data to improve performance. We also introduce anatomic and across-section continuity constraints to improve accuracy. We show that our method can be trained on manually charted sections from a single case and segment unseen sections from different cases, with a true positive rate of ∼ 0.80. We further demonstrate the utility of our method by quantifying the density of fiber bundles as they travel through different white-matter pathways. We show that fiber bundles originating in the same injection site have different levels of density when they travel through different pathways, a finding that can have implications for microstructure-informed tractography methods. The code for our method is available at https://github.com/v-sundaresan/fiberbundle_seg_tracing .

Diffusion-weighted magnetic resonance imaging (dMRI) permits a detailed in-vivo analysis of neuroanatomical microstructure, invaluable for clinical and population studies. However, many measurements with different diffusion-encoding directions and possibly b -values are necessary to infer the underlying tissue microstructure within different imaging voxels accurately. Two challenges particularly limit the utility of dMRI: long acquisition times limit feasible scans to only a few directional measurements, and the heterogeneity of acquisition schemes across studies makes it difficult to combine datasets. Left unaddressed by previous learning-based methods that only accept dMRI data adhering to the specific acquisition scheme used for training, there is a need for methods that accept and predict signals for arbitrary diffusion encodings. Addressing these challenges, we describe the first geometric deep learning method for continuous dMRI signal reconstruction for arbitrary diffusion sampling schemes for both the input and output. Our method combines the reconstruction accuracy and robustness of previous learning-based methods with the flexibility of model-based methods, for example, spherical harmonics or SHORE. We demonstrate that our method outperforms model-based methods and performs on par with discrete learning-based methods on single-, multi-shell, and grid-based diffusion MRI datasets. Relevant for dMRI-derived analyses, we show that our reconstruction translates to higher-quality estimates of frequently used microstructure models compared to other reconstruction methods, enabling high-quality analyses even from very short dMRI acquisitions.

In this editorial we introduce a new non-profit open access journal, Imaging Neuroscience . In April 2023, editors of the journals NeuroImage and NeuroImage:Reports resigned, and a month later launched Imaging Neuroscience . NeuroImage had long been the leading journal in the field of neuroimaging. While the move to fully open access in 2020 represented a positive step toward modern academic practices, the publication fee was set to a level that the editors found unethical and unsustainable. The publisher of NeuroImage , Elsevier, was unwilling to reduce the fee after much discussion. This led us to launch Imaging Neuroscience with MIT Press, intended to replace NeuroImage as our field’s leading journal, but with greater control by the neuroimaging academic community over publication fees and adoption of modern and ethical publishing practices.