scholarly journals The visual white matter: The application of diffusion MRI and fiber tractography to vision science

2016 ◽  
Author(s):  
Ariel Rokem ◽  
Hiromasa Takemura ◽  
Andrew Bock ◽  
K. Suzanne Scherf ◽  
Marlene Behrmann ◽  
...  
Keyword(s):  
White Matter ◽  
Visual Information ◽  
Diffusion Mri ◽  
Brain Connectivity ◽  
Open Data ◽  
Data Sets ◽  
Fiber Tractography ◽  
Vision Science ◽  
Visual Neuroscience

AbstractVisual neuroscience has traditionally focused much of its attention on understanding the response properties of neurons along the visual pathways. This review focuses instead on the properties of the white matter connections between these neurons. Specifically, we provide an introduction to methods to study the human visual white matter using diffusion MRI (dMRI). This method allows us to measure the white matter connections in individual visual systems in vivo, allows us to trace long-range connections between different parts of the visual system, and to measure the biophysical properties of these connections. We explain the principles underlying dMRI measurements and the basics of modeling these data. We review a range of findings from recent studies on connections between different visual field maps, on the effects of visual impairment on the white matter, and on the properties underlying networks that process visual information that supports visual face recognition. Finally, we discuss a few promising directions for future studies. These include new methods for analysis of MRI data, open data-sets that are becoming available to study brain connectivity and white matter properties, and open-source software for the analysis of these data.

scholarly journals Tractography Alterations in the Arcuate and Uncinate Fasciculi in Post-Stroke Aphasia

2021 ◽  
Vol 11 (1) ◽  
pp. 53
Author(s):  
Sara Kierońska ◽  
Milena Świtońska ◽  
Grzegorz Meder ◽  
Magdalena Piotrowska ◽  
Paweł Sokal
Keyword(s):  
White Matter ◽  
Brain Connectivity ◽  
Three Dimensional ◽  
Cerebral White Matter ◽  
Neural Pathways ◽  
Uncinate Fasciculus ◽  
Arcuate Fasciculus ◽  
Fiber Tractography ◽  
Post Stroke

Fiber tractography based on diffuse tensor imaging (DTI) can reveal three-dimensional white matter connectivity of the human brain. Tractography is a non-invasive method of visualizing cerebral white matter structures in vivo, including neural pathways surrounding the ischemic area. DTI may be useful for elucidating alterations in brain connectivity resulting from neuroplasticity after stroke. We present a case of a male patient who developed significant mixed aphasia following ischemic stroke. The patient had been treated by mechanical thrombectomy followed by an early rehabilitation, in conjunction with transcranial direct current stimulation (tDCS). DTI was used to examine the arcuate fasciculus and uncinate fasciculus upon admission and again at three months post-stroke. Results showed an improvement in the patient’s symptoms of aphasia, which was associated with changes in the volume and numbers of tracts in the uncinate fasciculus and the arcuate fasciculus.

scholarly journals Prevalence of white matter pathways coming into a single diffusion MRI voxel orientation: the bottleneck issue in tractography

2021 ◽  
Author(s):  
Kurt Schilling ◽  
Chantal M.W. Tax ◽  
Francois M.W. Rheault ◽  
Bennett A Landman ◽  
Adam W Anderson ◽  
...  
Keyword(s):  
White Matter ◽  
Human Brain ◽  
Diffusion Mri ◽  
Brain Connectivity ◽  
False Negative ◽  
Fiber Tractography ◽  
Single Voxel ◽  
Ill Posed ◽  
Pass Through

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.

scholarly journals Tractography dissection variability: what happens when 42 groups dissect 14 white matter bundles on the same dataset?

2020 ◽  
Author(s):  
Kurt G. Schilling ◽  
François Rheault ◽  
Laurent Petit ◽  
Colin B. Hansen ◽  
Vishwesh Nath ◽  
...  
Keyword(s):  
White Matter ◽  
Diffusion Mri ◽  
External Validation ◽  
Reproducible Research ◽  
Clinical Settings ◽  
Complex Data ◽  
Fiber Tractography ◽  
Considerable Variability ◽  
Human Brains

AbstractWhite matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human wholebrain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process.

scholarly journals The visual white matter: The application of diffusion MRI and fiber tractography to vision science

2017 ◽  
Vol 17 (2) ◽  
pp. 4 ◽  
Author(s):  
Ariel Rokem ◽  
Hiromasa Takemura ◽  
Andrew S. Bock ◽  
K. Suzanne Scherf ◽  
Marlene Behrmann ◽  
...  
Keyword(s):  
White Matter ◽  
Diffusion Mri ◽  
Fiber Tractography ◽  
Vision Science

scholarly journals Entrack: Probabilistic Spherical Regression with Entropy Regularization for Fiber Tractography

2020 ◽  
Author(s):  
Viktor Wegmayr ◽  
Joachim M. Buhmann
Keyword(s):  
Brain Connectivity ◽  
Magnetic Resonance Images ◽  
Probabilistic Methods ◽  
Fiber Tractography ◽  
Information Theoretic ◽  
Spherical Regression ◽  
Theoretic Technique ◽  
White Matter Tractography ◽  
Von Mises

Abstract White matter tractography, based on diffusion-weighted magnetic resonance images, is currently the only available in vivo method to gather information on the structural brain connectivity. The low resolution of diffusion MRI data suggests to employ probabilistic methods for streamline reconstruction, i.e., for fiber crossings. We propose a general probabilistic model for spherical regression based on the Fisher-von-Mises distribution, which efficiently estimates maximum entropy posteriors of local streamline directions with machine learning methods. The optimal precision of posteriors for streamlines is determined by an information-theoretic technique, the expected log-posterior agreement concept. It relies on the requirement that the posterior distributions of streamlines, inferred on retest measurements of the same subject, should yield stable results within the precision determined by the noise level of the data source.

scholarly journals Pandora: 4-D White Matter Bundle Population-Based Atlases Derived from Diffusion MRI Fiber Tractography

2020 ◽  
Author(s):  
Colin B Hansen ◽  
Qi Yang ◽  
Ilwoo Lyu ◽  
Francois Rheault ◽  
Cailey Kerley ◽  
...  
Keyword(s):  
White Matter ◽  
Diffusion Mri ◽  
Population Based ◽  
Fiber Tractography

scholarly journals Euclidean distance as a measure to distinguish ventral and dorsal white matter connectivity in the human brain

2016 ◽  
Author(s):  
Philipp Kellmeyer ◽  
Magnus-Sebastian Vry
Keyword(s):  
White Matter ◽  
Human Brain ◽  
Cognitive Processing ◽  
Euclidean Distance ◽  
Large Scale ◽  
Diffusion Tensor ◽  
Large Body ◽  
Coordinate Space ◽  
Fiber Tractography

AbstractFiber tractography based on diffusion tensor imaging (DTI) has become an important research tool for investigating the anatomical connectivity between brain regions in vivo. Combining DTI with functional magnetic resonance imaging (fMRI) allows for the mapping of structural and functional architecture of large-scale networks for cognitive processing. This line of research has shown that ventral and dorsal fiber pathways subserve different aspects of bottom-up- and top-down processing in the human brain.Here, we investigate the feasibility and applicability of Euclidean distance as a simple geometric measure to differentiate ventral and dorsal long-range white matter fiber pathways tween parietal and inferior frontal cortical regions, employing a body of studies that used probabilistic tractography.We show that ventral pathways between parietal and inferior frontal cortex have on average a significantly longer Euclidean distance in 3D-coordinate space than dorsal pathways. We argue that Euclidean distance could provide a simple measure and potentially a boundary value to assess patterns of connectivity in fMRI studies. This would allow for a much broader assessment of general patterns of ventral and dorsal large-scale fiber connectivity for different cognitive operations in the large body of existing fMRI studies lacking additional DTI data.

scholarly journals The R1-weighted connectome: complementing brain networks with a myelin-sensitive measure

2020 ◽  
pp. 1-15
Author(s):  
Tommy Boshkovski ◽  
Ljupco Kocarev ◽  
Julien Cohen-Adad ◽  
Bratislav Mišić ◽  
Stéphane Lehéricy ◽  
...  
Keyword(s):  
White Matter ◽  
Diffusion Mri ◽  
Brain Connectivity ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Added Value ◽  
Modular Organization ◽  
Sensitive Measure ◽  
Higher Order Functions ◽  
Insight Into

Myelin plays a crucial role in how well information travels between brain regions. Complementing the structural connectome, obtained with diffusion MRI tractography, with a myelin-sensitive measure could result in a more complete model of structural brain connectivity and give better insight into white-matter myeloarchitecture. In this work we weight the connectome by the longitudinal relaxation rate (R1), a measure sensitive to myelin, and then we assess its added value by comparing it with connectomes weighted by the number of streamlines (NOS). Our analysis reveals differences between the two connectomes both in the distribution of their weights and the modular organization. Additionally, the rank-based analysis shows that R1 can be used to separate transmodal regions (responsible for higher-order functions) from unimodal regions (responsible for low-order functions). Overall, the R1-weighted connectome provides a different perspective on structural connectivity taking into account white matter myeloarchitecture.

scholarly journals Pandora: 4-D white matter bundle population-based atlases derived from diffusion MRI fiber tractography

2020 ◽  
Author(s):  
Colin B Hansen ◽  
Qi Yang ◽  
Ilwoo Lyu ◽  
Francois Rheault ◽  
Cailey Kerley ◽  
...  
Keyword(s):  
White Matter ◽  
Diffusion Mri ◽  
State Of The Art ◽  
Population Based ◽  
Small Sample ◽  
Fiber Tractography ◽  
Statistical Inferences ◽  
Small Sample Sizes ◽  
The Brain ◽  
Brain Atlases

AbstractBrain atlases have proven to be valuable neuroscience tools for localizing regions of interest and performing statistical inferences on populations. Although many human brain atlases exist, most do not contain information about white matter structures, often neglecting them completely or labelling all white matter as a single homogenous substrate. While few white matter atlases do exist based on diffusion MRI fiber tractography, they are often limited to descriptions of white matter as spatially separate “regions” rather than as white matter “bundles” or fascicles, which are well-known to overlap throughout the brain. Additional limitations include small sample sizes, few white matter pathways, and the use of outdated diffusion models and techniques. Here, we present a new population-based collection of white matter atlases represented in both volumetric and surface coordinates in a standard space. These atlases are based on 2443 subjects, and include 216 white matter bundles derived from 6 different state-of-the-art tractography techniques. This atlas is freely available and will be a useful resource for parcellation and segmentation.

scholarly journals In vivo microstructural heterogeneity of white matter lesions in Alzheimer’s disease using tissue compositional analysis of diffusion MRI data

2019 ◽  
Author(s):  
Remika Mito ◽  
Thijs Dhollander ◽  
Ying Xia ◽  
David Raffelt ◽  
Olivier Salvado ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
White Matter ◽  
Diffusion Mri ◽  
Classification Scheme ◽  
Free Water ◽  
Imaging Biomarkers ◽  
White Matter Microstructure ◽  
Periventricular Lesions

AbstractWhite matter hyperintensities (WMH) are commonly observed in elderly individuals, and are typically more prevalent in Alzheimer’s disease subjects than in healthy subjects. These lesions can be identified on fluid attenuated inversion recovery (FLAIR) MRI, on which they are hyperintense compared to their surroundings. These MRI-visible lesions appear homogeneously hyperintense despite known heterogeneity in their pathological underpinnings, and are commonly regarded as surrogate markers of small vessel disease in in vivo studies. Consequently, the extent to which these lesions contribute to Alzheimer’s disease remains unclear, likely due to the somewhat limited way in which these lesions are assessed in vivo. Diffusion MRI is sensitive to white matter microstructure, and might thus be used to investigate microstructural changes within WMH. In this study, we applied a method called single-shell 3-tissue constrained spherical deconvolution, which models white matter microstructure while also accounting for other tissue compartments, to investigate WMH in vivo. Diffusion MRI data and FLAIR images were obtained from Alzheimer’s disease (n = 48) and healthy elderly control (n = 94) subjects from the Australian Imaging, Biomarkers and Lifestyle study of ageing. WMH were automatically segmented and classified as periventricular or deep lesions from FLAIR images based on their continuity with the lateral ventricles, and the 3-tissue profile of different classes of WMH was characterised by three metrics, which together characterised the relative tissue profile in terms of the white matter-, grey matter-, and fluid-like characteristics of the diffusion signal. Our findings revealed that periventricular and deep lesion classes could be distinguished from one another, and from normal-appearing white matter based on their 3-tissue profile, with substantially higher free water content in periventricular lesions than deep. Given the higher lesion load of periventricular lesions in Alzheimer’s disease patients, the 3-tissue profile of these WMH could be interpreted as reflecting the more deleterious pathological underpinnings that are associated with disease. However, when alternatively classifying lesion sub-regions in terms of distance contours from the ventricles to account for potential heterogeneity within confluent lesions, we found that the highest fluid content was present in lesion areas most proximal to the ventricles, which were common to both Alzheimer’s disease subjects and healthy controls. We argue that whatever classification scheme is used when investigating WMH, failure to account for heterogeneity within lesions may result in classification-scheme dependent conclusions. Future studies of WMH in Alzheimer’s Disease would benefit from inclusion of microstructural information when characterising lesions.

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