scholarly journals Limits to anatomical accuracy of diffusion tractography using modern approaches

2018 ◽  
Author(s):  
Kurt G Schilling ◽  
Vishwesh Nath ◽  
Colin Hansen ◽  
Prasanna Parvathaneni ◽  
Justin Blaber ◽  
...  
Keyword(s):  
White Matter ◽  
Validation Studies ◽  
Diffusion Mri ◽  
Ex Vivo ◽  
Structural Connectivity ◽  
Fiber Tractography ◽  
Research Groups ◽  
Wide Range ◽  
Modern Methods ◽  
The Brain

AbstractDiffusion MRI fiber tractography is widely used to probe the structural connectivity of thebrain, with a range of applications in both clinical and basic neuroscience. Despite widespread use, tractography has well-known pitfalls that limits the anatomical accuracy of this technique. Numerous modern methods have been developed to address these shortcomings through advances in acquisition, modeling, and computation. To test whether these advances improve tractography accuracy, we organized the ISBI 2018 3D Validation of Tractography with Experimental MRI (3D-VoTEM) challenge. We made available three unique independent tractography validation datasets – a physical phantom and two ex vivo brain specimens - resulting in 176 distinct submissions from 9 research groups. By comparing results over a wide range of fiber complexities and algorithmic strategies, this challenge provides a more comprehensive assessment of tractography’s inherent limitations than has been reported previously. The central results were consistent across all sub-challenges in that, despite advances in tractography methods, the anatomical accuracy of tractography has not dramatically improved in recent years. Taken together, our results independently confirm findings from decades of tractography validation studies, demonstrate inherent limitations in reconstructing white matter pathways using diffusion MRI data alone, and highlight the need for alternative or combinatorial strategies to accurately map the fiber pathways of the brain.

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 Does powder averaging remove dispersion bias in diffusion MRI diameter estimates within real 3D axonal architectures?

2021 ◽  
Author(s):  
Mariam Andersson ◽  
Marco Pizzolato ◽  
Hans Martin Kjer ◽  
Katrine Forum Skodborg ◽  
Henrik Lundell ◽  
...  
Keyword(s):  
White Matter ◽  
Diffusion Mri ◽  
Ex Vivo ◽  
Dynamic Properties ◽  
High Signal ◽  
Axon Diameter ◽  
Signal To Noise ◽  
B Values ◽  
Wide Range

Noninvasive estimation of axon diameter with diffusion MRI holds potential to investigate the dynamic properties of the brain network and pathology of neurodegenerative diseases. Recent methods use powder averaging to account for complex white matter architectures, such as fibre crossing regions, but these have not been validated for real axonal geometries. Here, we present 120-313 μm long segmented axons from X-ray nano-holotomography volumes of a splenium and crossing fibre region of a vervet monkey brain. We show that the axons in the complex crossing fibre region, which contains callosal, association, and corticospinal connections, are larger and exhibit a wider distribution than those of the splenium region. To accurately estimate the axon diameter in these regions, therefore, sensitivity to a wide range of diameters is required. We demonstrate how the q-value, b-value, signal-to-noise ratio and the assumed intra-axonal parallel diffusivity influence the range of measurable diameters with powder average approaches. Furthermore, we show how Gaussian distributed noise results in a wider range of measurable diameter at high b-values than with Rician distributed noise, even at high signal-to-noise ratios of 100. The number of gradient directions is also shown to impose a lower bound on measurable diameter. Our results indicate that axon diameter estimation can be performed with only few b-shells, and that additional shells do not improve the accuracy of the estimate. Through Monte Carlo simulations of diffusion, we show that powder averaging techniques succeed in providing accurate estimates of axon diameter across a range of sequence parameters and diffusion times, even in complex white matter architectures. At sufficiently low b-values, the acquisition becomes sensitive to axonal microdispersion and the intra-axonal parallel diffusivity shows time dependency at both in vivo and ex vivo intrinsic diffusivities.

scholarly journals Harmonic Brain Modes: A Unifying Framework for Linking Space and Time in Brain Dynamics

2017 ◽  
Vol 24 (3) ◽  
pp. 277-293 ◽  
Author(s):  
Selen Atasoy ◽  
Gustavo Deco ◽  
Morten L. Kringelbach ◽  
Joel Pearson
Keyword(s):  
Neural Activity ◽  
Brain Activity ◽  
Activity Patterns ◽  
Structural Connectivity ◽  
Building Blocks ◽  
Mental States ◽  
Brain Dynamics ◽  
Space And Time ◽  
Wide Range ◽  
The Brain

A fundamental characteristic of spontaneous brain activity is coherent oscillations covering a wide range of frequencies. Interestingly, these temporal oscillations are highly correlated among spatially distributed cortical areas forming structured correlation patterns known as the resting state networks, although the brain is never truly at “rest.” Here, we introduce the concept of harmonic brain modes—fundamental building blocks of complex spatiotemporal patterns of neural activity. We define these elementary harmonic brain modes as harmonic modes of structural connectivity; that is, connectome harmonics, yielding fully synchronous neural activity patterns with different frequency oscillations emerging on and constrained by the particular structure of the brain. Hence, this particular definition implicitly links the hitherto poorly understood dimensions of space and time in brain dynamics and its underlying anatomy. Further we show how harmonic brain modes can explain the relationship between neurophysiological, temporal, and network-level changes in the brain across different mental states ( wakefulness, sleep, anesthesia, psychedelic). Notably, when decoded as activation of connectome harmonics, spatial and temporal characteristics of neural activity naturally emerge from the interplay between excitation and inhibition and this critical relation fits the spatial, temporal, and neurophysiological changes associated with different mental states. Thus, the introduced framework of harmonic brain modes not only establishes a relation between the spatial structure of correlation patterns and temporal oscillations (linking space and time in brain dynamics), but also enables a new dimension of tools for understanding fundamental principles underlying brain dynamics in different states of consciousness.

scholarly journals Fiber tractography bundle segmentation depends on scanner effects, acquisition, diffusion sensitization, and bundle segmentation workflow

2021 ◽  
Author(s):  
Kurt Schilling ◽  
Chantal M.W. Tax ◽  
Francois Rheault ◽  
Colin B Hansen ◽  
Qi Yang ◽  
...  
Keyword(s):  
White Matter ◽  
Mean Diffusivity ◽  
B Value ◽  
Fiber Tractography ◽  
Acquisition Protocol ◽  
Segmentation Methods ◽  
Combining Data ◽  
Potential Sources ◽  
Sources Of Variation ◽  
The Brain

When investigating connectivity and microstructure of white matter pathways of the brain using diffusion tractography bundle segmentation, it is important to understand potential confounds and sources of variation in the process. While cross-scanner and cross-protocol effects on diffusion microstructure measures are well described (in particular fractional anisotropy and mean diffusivity), it is unknown how potential sources of variation effect bundle segmentation results, which features of the bundle are most affected, where variability occurs, nor how these sources of variation depend upon the method used to reconstruct and segment bundles. In this study, we investigate four potential sources of variation, or confounds, for bundle segmentation: variation (1) across scan repeats, (2) across scanners, (3) across acquisition protocol, and (4) across diffusion sensitization. We employ four different bundle segmentation workflows on two benchmark multi-subject cross-scanner and cross-protocol databases, and investigate reproducibility and biases in volume overlap, shape geometry features of fiber pathways, and microstructure features within the pathways. We find that the effects of acquisition protocol, in particular acquisition resolution, result in the lowest reproducibility of tractography and largest variation of features, followed by scanner-effects, and finally b-value effects which had similar reproducibility as scan-rescan variation. However, confounds varied both across pathways and across segmentation workflows, with some bundle segmentation workflows more (or less) robust to sources of variation. Despite variability, bundle dissection is consistently able to recover the same location of pathways in the deep white matter, with variation at the gray matter/ white matter interface. Next, we show that differences due to the choice of bundle segmentation workflows are larger than any other studied confound, with low-to-moderate overlap of the same intended pathway when segmented using different methods. Finally, quantifying microstructure features within a pathway, we show that tractography adds variability over-and-above that which exists due to noise, scanner effects, and acquisition effects. Overall, these confounds need to be considered when harmonizing diffusion datasets, interpreting or combining data across sites, and when attempting to understand the successes and limitations of different methodologies in the design and development of new tractography or bundle segmentation methods.

MR Brain Image Segmentation to Detect White Matter, Gray Matter, and Cerebro Spinal Fluid Using TLBO Algorithm

2021 ◽  
pp. 2150034
Author(s):  
Sandhya Gudise ◽  
Giri Babu Kande ◽  
T. Satya Savithri
Keyword(s):  
White Matter ◽  
Gray Matter ◽  
Population Based ◽  
Spinal Fluid ◽  
Brain Image ◽  
Tlbo Algorithm ◽  
Wide Range ◽  
Skull Stripping ◽  
The Brain ◽  
Mr Brain

This paper proposes an advanced and precise technique for the segmentation of Magnetic Resonance Image (MRI) of the brain. Brain MRI segmentation is to be familiar with the anatomical structure, to recognize the deformities, and to distinguish different tissues which help in treatment planning and diagnosis. Nature’s inspired population-based evolutionary algorithms are extremely popular for a wide range of applications due to their best solutions. Teaching Learning Based Optimization (TLBO) is an advanced population-based evolutionary algorithm designed based on Teaching and Learning process of a classroom. TLBO uses common controlling parameters and it won’t require algorithm-specific parameters. TLBO is more appropriate to optimize the real variables which are fuzzy valued, computationally efficient, and does not require parameter tuning. In this work, the pixels of the brain image are automatically grouped into three distinct homogeneous tissues such as White Matter (WM), Gray Matter (GM), and Cerebro Spinal Fluid (CSF) using the TLBO algorithm. The methodology includes skull stripping and filtering in the pre-processing stage. The outcomes for 10 MR brain images acquired by utilizing the proposed strategy proved that the three brain tissues are segmented accurately. The segmentation outputs are compared with the ground truth images and high values are obtained for the measure’s sensitivity, specificity, and segmentation accuracy. Four different approaches, namely Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Bacterial Foraging Algorithm (BFA), and Electromagnetic Optimization (EMO) are likewise implemented to compare with the results of the proposed methodology. From the results, it can be proved that the proposed method performed effectively than the other.

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 Local connectome phenotypes predict social, health, and cognitive factors

2018 ◽  
Vol 2 (1) ◽  
pp. 86-105 ◽  
Author(s):  
Michael A. Powell ◽  
Javier O. Garcia ◽  
Fang-Cheng Yeh ◽  
Jean M. Vettel ◽  
Timothy Verstynen
Keyword(s):  
White Matter ◽  
Diffusion Mri ◽  
Social Health ◽  
Cognitive Factors ◽  
High Dimensional ◽  
Cognitive Measures ◽  
Human Connectome Project ◽  
Low Dimensional ◽  
The Brain ◽  
Over Time

The unique architecture of the human connectome is defined initially by genetics and subsequently sculpted over time with experience. Thus, similarities in predisposition and experience that lead to similarities in social, biological, and cognitive attributes should also be reflected in the local architecture of white matter fascicles. Here we employ a method known as local connectome fingerprinting that uses diffusion MRI to measure the fiber-wise characteristics of macroscopic white matter pathways throughout the brain. This fingerprinting approach was applied to a large sample ( N = 841) of subjects from the Human Connectome Project, revealing a reliable degree of between-subject correlation in the local connectome fingerprints, with a relatively complex, low-dimensional substructure. Using a cross-validated, high-dimensional regression analysis approach, we derived local connectome phenotype (LCP) maps that could reliably predict a subset of subject attributes measured, including demographic, health, and cognitive measures. These LCP maps were highly specific to the attribute being predicted but also sensitive to correlations between attributes. Collectively, these results indicate that the local architecture of white matter fascicles reflects a meaningful portion of the variability shared between subjects along several dimensions.

scholarly journals Diffusion tensor imaging analysis of the brain in the canine model of Krabbe disease

2016 ◽  
Vol 29 (6) ◽  
pp. 417-424 ◽  
Author(s):  
Allison Bradbury ◽  
David Peterson ◽  
Charles Vite ◽  
Steven Chen ◽  
N Matthew Ellinwood ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
White Matter ◽  
Mr Imaging ◽  
Gray Matter ◽  
Diffusion Tensor ◽  
Ex Vivo ◽  
Small Animal ◽  
Krabbe Disease ◽  
Adc Values ◽  
The Brain

Purpose The goal of this study was to compare the diffusion tensor imaging (DTI) metrics from an end-stage canine Krabbe brain evaluated by MR imaging ex vivo to those of a normal dog brain. We hypothesized that the white matter of the canine Krabbe brain would show decreased fractional anisotropy (FA) values and increased apparent diffusion coefficient (ADC) and radial diffusivity (RD) values. Methods An 11-week-old Krabbe dog was euthanized after disease progression. The brain was removed and was placed in a solution of 10% formalin. MR imaging was performed and compared to the brain images of a normal dog that was similarly fixed post-mortem. Both brains were scanned using similar protocols on a 7 T small-animal MRI system. For each brain, maps of ADC, FA, and RD were calculated for 11 white-matter regions and five control gray-matter regions. Results Large decreases in FA values, increases in ADC values, and increases in RD (consistent with demyelination) values, were seen in white matter of the Krabbe brain but not gray matter. ADC values in gray matter of the Krabbe brain were decreased by approximately 29% but increased by approximately 3.6% in white matter of the Krabbe brain. FA values in gray matter were decreased by approximately 3.3% but decreased by approximately 29% in white matter. RD values were decreased by approximately 27.2% in gray matter but increased by approximately 20% in white matter. Conclusion We found substantial abnormalities of FA, ADC, and RD values in an ex vivo canine Krabbe brain.

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 Development of white matter microstructure and executive functions during childhood and adolescence: a review of diffusion MRI studies

2020 ◽  
Author(s):  
Anne-Lise Goddings ◽  
David Roalf ◽  
Catherine Lebel ◽  
Christian K. Tamnes
Keyword(s):  
White Matter ◽  
Executive Functions ◽  
Structural Connectivity ◽  
Child And Adolescent ◽  
Childhood And Adolescence ◽  
Cross Sectional ◽  
Indirect Measures ◽  
White Matter Microstructure ◽  
P Diffusion ◽  
The Brain

Diffusion magnetic resonance imaging (dMRI) provides indirect measures of white matter microstructure that can be used to make inferences about structural connectivity within the brain. Over the last decade, a growing literature of cross-sectional and longitudinal studies have documented relationships between dMRI indices and cognitive development. In this review, we provide a brief overview of dMRI methods and how they can be used to study white matter and connectivity, briefly discuss challenges with using dMRI in child and adolescent populations, and review the extant literature examining the links between dMRI indices and executive functions during development. We explore the links between white matter microstructure and specific executive functions: inhibition, working memory and cognitive shifting, as well as performance on complex executive function tasks. Where there is concordance in findings across studies, this is highlighted, and potential explanations for discrepancies between results are discussed. Finally, we explore future directions that are necessary to better understand the links between child and adolescent development of executive functions and structural connectivity of the brain.

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