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Characterizing and understanding the limitations of diffusion MRI fiber tractography is a prerequisite for methodological advances and innovations which will allow these techniques to accurately map the connections of the human brain. The so‐called “crossing fiber problem” has received tremendous attention and has continuously triggered the community to develop novel approaches for disentangling distinctly oriented fiber populations. Perhaps an even greater challenge occurs when multiple white matter bundles converge within a single voxel, or throughout a single brain region, and share the same parallel orientation, before diverging and continuing towards their final cortical or sub‐cortical terminations. These so‐called “bottleneck” regions contribute to the ill‐posed nature of the tractography process, and lead to both false positive and false negative estimated connections. Yet, as opposed to the extent of crossing fibers, a thorough characterization of bottleneck regions has not been performed. The aim of this study is to quantify the prevalence of bottleneck regions. To do this, we use diffusion tractography to segment known white matter bundles of the brain, and assign each bundle to voxels they pass through and to specific orientations within those voxels (i.e. fixels). We demonstrate that bottlenecks occur in greater than 50‐70% of fixels in the white matter of the human brain. We find that all projection, association, and commissural fibers contribute to, and are affected by, this phenomenon, and show that even regions traditionally considered “single fiber voxels” often contain multiple fiber populations. Together, this study shows that a majority of white matter presents bottlenecks for tractography which may lead to incorrect or erroneous estimates of brain connectivity or quantitative tractography (i.e., tractometry), and underscores the need for a paradigm shift in the process of tractography and bundle segmentation for studying the fiber pathways of the human brain. \"Bottleneck\" pose a challenge for diffusion tractography, and hinder our ability to accurately map the structural connections of the brain. We demonstrate that bottlenecks occur in greater than 50–70% of fixels in the human brain white matter, and find that all projection, association, and commissural fibers contribute to, and are affected by, this phenomenon. These results emphasize the use of caution when interpreting quantitative diffusion magnetic resonance imaging connectomics results, and underscore the need for a paradigm shift in the process of tractography for studying the fiber pathways of the human brain.

Fractional anisotropy (FA), axial diffusivity (AD) and radial diffusivity (RD) are commonly used as MRI biomarkers of white matter microstructure in diffusion MRI studies of neurodevelopment, brain aging, and neurologic injury/disease. Some of the more frequent practices include performing voxel-wise or region-based analyses of these measures to cross-sectionally compare individuals or groups, longitudinally assess individuals or groups, and/or correlate with demographic, behavioral or clinical variables. However, it is now widely recognized that the majority of cerebral white matter voxels contain multiple fiber populations with different trajectories, which renders these metrics highly sensitive to the relative volume fractions of the various fiber populations, the microstructural integrity of each constituent fiber population, and the interaction between these factors. Many diffusion imaging experts are aware of these limitations and now generally avoid using FA, AD or RD (at least in isolation) to draw strong reverse inferences about white matter microstructure, but based on the continued application and interpretation of these metrics in the broader biomedical/neuroscience literature, it appears that this has perhaps not yet become common knowledge among diffusion imaging end-users. Therefore, this paper will briefly discuss the complex biophysical underpinnings of these measures in the context of crossing fibers, provide some intuitive “thought experiments” to highlight how conventional interpretations can lead to incorrect conclusions, and suggest that future studies refrain from using (over-interpreting) FA, AD and RD values as standalone biomarkers of cerebral white matter microstructure.

The ability of diffusion MRI (dMRI) fiber tractography to non-invasively map three-dimensional (3D) anatomical networks in the human brain has made it a valuable tool in both clinical and research settings. However, there are many assumptions inherent to any tractography algorithm that can limit the accuracy of the reconstructed fiber tracts. Among them is the assumption that the diffusion-weighted images accurately reflect the underlying fiber orientation distribution (FOD) in the MRI voxel. Consequently, validating dMRI's ability to assess the underlying fiber orientation in each voxel is critical for its use as a biomedical tool. Here, using post-mortem histology and confocal microscopy, we present a method to perform histological validation of orientation functions in 3D, which has previously been limited to two-dimensional analysis of tissue sections. We demonstrate the ability to extract the 3D FOD from confocal z-stacks, and quantify the agreement between the MRI estimates of orientation information obtained using constrained spherical deconvolution (CSD) and the true geometry of the fibers. We find an orientation error of approximately 6° in voxels containing nearly parallel fibers, and 10–11° in crossing fiber regions, and note that CSD was unable to resolve fibers crossing at angles below 60° in our dataset. This is the first time that the 3D white matter orientation distribution is calculated from histology and compared to dMRI. Thus, this technique serves as a gold standard for dMRI validation studies — providing the ability to determine the extent to which the dMRI signal is consistent with the histological FOD, and to establish how well different dMRI models can predict the ground truth FOD. •We present a method to perform histological validation of diffusion MRI orientation distribution functions in 3D•Previous validation studies have been limited to 2D analysis of tissue sections•Structure Tensor analysis is performed on confocal z-stacks to extract the ground truth fiber orientation distribution•Comparisons are made between the histological fiber orientation distribution and the corresponding diffusion MRI estimates

In diffusion tensor magnetic resonance imaging (DT-MRI), limitations concerning complex fiber architecture (when an image voxel contains fiber populations with more than one dominant orientation) are well-known. Fractional anisotropy (FA) values are lower in such areas because of a lower directionality of diffusion on the voxel-scale, which makes the interpretation of FA less straightforward. Moreover, the interpretation of the axial and radial diffusivities is far from trivial when there is more than one dominant fiber orientation within a voxel. In this work, using (i) theoretical considerations, (ii) simulations, and (iii) experimental data, it is demonstrated that the mean diffusivity (or the trace of the diffusion tensor) is lower in complex white matter configurations, compared with tissue where there is a single dominant fiber orientation within the voxel. We show that the magnitude of this reduction depends on various factors, including configurational and microstructural properties (e.g., the relative contributions of different fiber populations) and acquisition settings (e.g., the b-value). These results increase our understanding of the quantitative metrics obtained from DT-MRI and, in particular, the effect of the microstructural architecture on the mean diffusivity. More importantly, they reinforce the growing awareness that differences in DT-MRI metrics need to be interpreted cautiously. ► The mean diffusivity (MD) in diffusion tensor MRI is affected by crossing fibers. ► MD values are lower in complex fiber architecture than in single fiber voxels. ► This is shown using theoretical considerations, simulations and in vivo experiments. ► In vivo, mean diffusivity values decrease when fibers cross at larger angles.

The perivascular space (PVS) is integral to glymphatic function, facilitating fluid exchange and waste clearance in the brain. Diffusion tensor imaging along the perivascular space (DTI‐ALPS) has been proposed as a noninvasive marker of perivascular diffusion, yet its specificity remains unclear. ALPS measures assume radial symmetry in white matter (characterized by equal transverse diffusion eigenvalues, λ2 = λ3) and interpret deviations (i.e., radial asymmetry, where λ2 > λ3) as reflecting PVS contributions. However, anatomical and microstructural confounds may influence these metrics. We systematically evaluated potential biases in ALPS‐derived measures using high‐resolution, multishell diffusion MRI from the Human Connectome Project (HCP) and high‐field imaging. Specifically, we examined (1) the prevalence of radial asymmetry across white matter, (2) the influence of crossing fibers on ALPS indices, (3) the impact of axonal undulations and dispersion, and (4) the spatial alignment of vasculature with white matter in ALPS‐associated regions. Radial asymmetry is widespread across white matter and persists even at high b‐values, suggesting a dominant contribution from axonal geometry rather than faster PVS‐specific diffusion. Crossing fibers significantly inflate ALPS indices, with greater radial asymmetry observed in regions with a greater prevalence of crossing fibers. Furthermore, anisotropic axonal dispersion and undulations introduce systematic asymmetry independent of perivascular diffusion. Finally, high‐resolution vascular imaging reveals substantial heterogeneity in medullary vein orientation, challenging the assumption that PVS consistently aligns with the left–right axis in ALPS regions. ALPS indices are significantly influenced by white matter microstructure, including fiber crossings, undulations, and dispersion. These findings suggest that ALPS‐derived metrics may not provide a direct measure of glymphatic function but rather reflect underlying axonal geometry. Interpretations of ALPS‐derived metrics as biomarkers of glymphatic function must consider these anatomical complexities, and future studies should integrate advanced modeling approaches to disentangle perivascular contributions from white matter structure. Our findings reveal that the DTI‐ALPS index, a proposed marker of glymphatic function, is strongly influenced by white matter geometry—including fiber crossings, dispersion, and undulations. These findings challenge the specificity of ALPS and highlight the need for careful interpretation in studies linking ALPS metrics to glymphatic function.

The mammalian central nervous system has a tremendous structural complexity, and diffusion tensor imaging (DTI) is unique in its ability to extract microstructural tissue properties at a macroscopic scale. However, despite its widespread use and applications in clinical and research settings, accurate validation of DTI has notoriously lagged the advances in image acquisition and analysis. In this report, we demonstrate an approach to visualize and quantify the microscopic features of histological sections on multiple length scales using techniques derived from image texture analysis. Structure tensor (ST) analysis was applied to fluorescence microscopy images of rat brain sections to visualize and quantify tissue microstructure. Images were digitally color-coded based on the local orientation in the pixelwise ST implementation, which allowed direct visualization of white matter complexity at the microscopic level. A piecewise ST algorithm was also employed to quantify anisotropy and orientation at a resolution comparable to that typically acquired with DTI. Anisotropy measured with ST analysis of stained histological sections was highly correlated with anisotropy measured by ex vivo DTI of the same brains (R2=0.92). Furthermore, angular histograms, or Fiber Orientation Distributions (FODs), were computed to mimic similar measures derived from high angular resolution diffusion imaging methods. The FODs for each pixel were fit to a mixture of von Mises distributions to identify putative regions of multiple fiber populations (i.e. crossing fibers). Despite its current application to two-dimensional microscopy, the ST analysis is a novel approach to visualize and quantify microstructure in the central nervous system in both health and disease, and advances the available set of tools for validating DTI and other diffusion MRI techniques. [Display omitted] ► Structure tensor analysis was applied to stained rat brain histological sections. ► Anisotropy derived from histology and DTI was highly correlated. ► Crossing fibers were visualized and quantified from microscopic images.

MRI tractography is the mapping of neural fiber pathways based on diffusion MRI of tissue diffusion anisotropy. Tractography based on diffusion tensor imaging (DTI) cannot directly image multiple fiber orientations within a single voxel. To address this limitation, diffusion spectrum MRI (DSI) and related methods were developed to image complex distributions of intravoxel fiber orientation. Here we demonstrate that tractography based on DSI has the capacity to image crossing fibers in neural tissue. DSI was performed in formalin-fixed brains of adult macaque and in the brains of healthy human subjects. Fiber tract solutions were constructed by a streamline procedure, following directions of maximum diffusion at every point, and analyzed in an interactive visualization environment (TrackVis). We report that DSI tractography accurately shows the known anatomic fiber crossings in optic chiasm, centrum semiovale, and brainstem; fiber intersections in gray matter, including cerebellar folia and the caudate nucleus; and radial fiber architecture in cerebral cortex. In contrast, none of these examples of fiber crossing and complex structure was identified by DTI analysis of the same data sets. These findings indicate that DSI tractography is able to image crossing fibers in neural tissue, an essential step toward non-invasive imaging of connectional neuroanatomy.

Micro-architectural characteristics of white matter can be inferred through analysis of diffusion-weighted magnetic resonance imaging (dMRI). The diffusion-dependent signal can be analyzed through several methods, with the tensor model being the most frequently used due to its straightforward interpretation and low requirements for acquisition parameters. While valuable information can be gained from the tensor-derived metrics in regions of homogeneous tissue organization, this model does not provide reliable microstructural information at crossing fiber regions, which are pervasive throughout human white matter. Several multiple fiber models have been proposed that seem to overcome the limitations of the tensor, with few providing per-bundle dMRI-derived metrics. However, biological interpretations of such metrics are limited by the lack of histological confirmation. To this end, we developed a straightforward biological validation framework. Unilateral retinal ischemia was induced in ten rats, which resulted in axonal (Wallerian) degeneration of the corresponding optic nerve, while the contralateral was left intact; the intact and injured axonal populations meet at the optic chiasm as they cross the midline, generating a fiber crossing region in which each population has different diffusion properties. Five rats served as controls. High-resolution ex vivo dMRI was acquired five weeks after experimental procedures. We correlated and compared histology to per-bundle descriptors derived from three methodologies for dMRI analysis (constrained spherical deconvolution and two multi-tensor representations). We found a tight correlation between axonal density (as evaluated through automatic segmentation of histological sections) with per-bundle apparent fiber density and fractional anisotropy (derived from dMRI). The multi–fiber methods explored were able to correctly identify the damaged fiber populations in a region of fiber crossings (chiasm). Our results provide validation of metrics that bring substantial and clinically useful information about white-matter tissue at crossing fiber regions. Our proposed framework is useful to validate other current and future dMRI methods. •Axonal degeneration can be detected using diffusion-weighted MRI.•Can disentangle intact and injured axonal populations in crossing regions.•Reliable histological correspondence to metrics of diffusion of water.

Diffusion tensor imaging (DTI) is widely used to characterize tissue micro-architecture and brain connectivity. In regions of crossing fibers, however, the tensor model fails because it cannot represent multiple, independent intra-voxel orientations. Most of the methods that have been proposed to resolve this problem require diffusion magnetic resonance imaging (MRI) data that comprise large numbers of angles and high b-values, making them problematic for routine clinical imaging and many scientific studies. We present a technique based on compressed sensing that can resolve crossing fibers using diffusion MRI data that can be rapidly and routinely acquired in the clinic (30 directions, b-value equal to 700s/mm2). The method assumes that the observed data can be well fit using a sparse linear combination of tensors taken from a fixed collection of possible tensors each having a different orientation. A fast algorithm for computing the best orientations based on a hierarchical compressed sensing algorithm and a novel metric for comparing estimated orientations are also proposed. The performance of this approach is demonstrated using both simulations and in vivo images. The method is observed to resolve crossing fibers using conventional data as well as a standard q-ball approach using much richer data that requires considerably more image acquisition time. ► Develop a compressed sensing framework for estimating intra-voxel structure. ► Resolve crossing fibers with diffusion tensor imaging. ► Compare compressed sensing directions to q-ball imaging. ► Demonstrate reliable multi-fiber resolution with low b-value DTI data.

Abstract Schizophrenia (SZ) and bipolar disorder (BD) share some similarities in terms of genetic-risk genes and abnormalities of gray-matter structure in the brain, but white matter (WM) abnormalities have not been studied in depth. We undertook a comparative multimodal meta-analysis to identify common and disorder-specific abnormalities in WM structure between SZ and BD. Anisotropic effect size-signed differential mapping software was used to conduct a comparative meta-analysis of 68 diffusion tensor imaging (DTI) and 34 voxel-based morphometry (VBM) studies comparing fractional anisotropy (FA) and white matter volume (WMV), respectively, between patients with SZ (DTI: N = 1543; VBM: N = 1068) and BD (DTI: N = 983; VBM: N = 518) and healthy controls (HCs). The bilateral corpus callosum (extending to the anterior and superior corona radiata) showed shared decreased WMV and FA in SZ and BD. Compared with BD patients, SZ patients showed remarkable disorder-specific WM abnormalities: decreased FA and increased WMV in the left cingulum, and increased FA plus decreased WMV in the right anterior limb of the internal capsule. SZ patients showed more extensive alterations in WM than BD cases, which may be the pathophysiological basis for the clinical continuity of both disorders. The disorder-specific regions in the left cingulum and right anterior limb of the internal capsule provided novel insights into both disorders. Our study adds value to further understanding of the pathophysiology, classification, and differential diagnosis of SZ and BD.