Tractography of White Matter Fibers Using Diffusion MRI

2022 ◽  
pp. 98-112
Author(s):  
Strivathsav Ashwin Ramamoorthy
Keyword(s):  
White Matter ◽  
Human Brain ◽  
Diffusion Mri ◽  
Neural Circuits ◽  
Water Molecules ◽  
Neural Connectivity ◽  
Non Invasive ◽  
Diffusion Weighted Images ◽  
Different Types ◽  
The Brain

To understand more about the human brain and how it works, it is vital to understand how the neural circuits connect different regions of the brain. The human brain is filled predominantly with water and the majority of the water molecules undergo diffusion which can be captured with the help of diffusion MRI. Diffusion weighted images enable us to reconstruct the neural circuits in a non-invasive manner, and this procedure is referred to as tractography. Tractography aids neurosurgeons to understand the neural connectivity of the patient. This chapter attempts to explain the procedure of tractography and different types of algorithms.

scholarly journals The brain timewise: how timing shapes and supports brain function

2015 ◽  
Vol 370 (1668) ◽  
pp. 20140170 ◽  
Author(s):  
Riitta Hari ◽  
Lauri Parkkonen
Keyword(s):  
Human Brain ◽  
Brain Imaging ◽  
Brain Function ◽  
Temporal Dynamics ◽  
Generative Models ◽  
Network Activity ◽  
Brain Diseases ◽  
Specific Information ◽  
Non Invasive ◽  
The Brain

We discuss the importance of timing in brain function: how temporal dynamics of the world has left its traces in the brain during evolution and how we can monitor the dynamics of the human brain with non-invasive measurements. Accurate timing is important for the interplay of neurons, neuronal circuitries, brain areas and human individuals. In the human brain, multiple temporal integration windows are hierarchically organized, with temporal scales ranging from microseconds to tens and hundreds of milliseconds for perceptual, motor and cognitive functions, and up to minutes, hours and even months for hormonal and mood changes. Accurate timing is impaired in several brain diseases. From the current repertoire of non-invasive brain imaging methods, only magnetoencephalography (MEG) and scalp electroencephalography (EEG) provide millisecond time-resolution; our focus in this paper is on MEG. Since the introduction of high-density whole-scalp MEG/EEG coverage in the 1990s, the instrumentation has not changed drastically; yet, novel data analyses are advancing the field rapidly by shifting the focus from the mere pinpointing of activity hotspots to seeking stimulus- or task-specific information and to characterizing functional networks. During the next decades, we can expect increased spatial resolution and accuracy of the time-resolved brain imaging and better understanding of brain function, especially its temporal constraints, with the development of novel instrumentation and finer-grained, physiologically inspired generative models of local and network activity. Merging both spatial and temporal information with increasing accuracy and carrying out recordings in naturalistic conditions, including social interaction, will bring much new information about human brain function.

scholarly journals Non-local means based Rician noise filtering for diffusion tensor and kurtosis imaging in human brain and spinal cord

2020 ◽  
Author(s):  
Zhongping Zhang ◽  
Dhanashree Vernekar ◽  
Wenshu Qian ◽  
Mina Kim
Keyword(s):  
Spinal Cord ◽  
Corpus Callosum ◽  
Human Brain ◽  
Healthy Subjects ◽  
Diffusion Tensor ◽  
Mean Diffusivity ◽  
Chi Square ◽  
Lateral Column ◽  
Diffusion Weighted Images ◽  
The Brain

Abstract Background: To investigate the effect of using an Rician nonlocal means (NLM) filter on quantification of diffusion tensor (DT)- and diffusion kurtosis (DK)-derived metrics in various anatomical regions of the human brain and the spinal cord, when combined with a constrained linear least squares (CLLS) approach.Methods: Prospective brain data from 9 healthy subjects and retrospective spinal cord data from 5 healthy subjects from a 3T MRI scanner were included in the study. Prior to tensor estimation, registered diffusion weighted images were denoised by an optimized blockwise NLM filter with CLLS. Mean kurtosis (MK), radial kurtosis (RK), axial kurtosis (AK), mean diffusivity (MD), radial diffusivity (RD), axial diffusivity (AD) and fractional anisotropy (FA), were determined in anatomical structures of the brain and the spinal cord. DTI and DKI metrics, signal-to-noise ratio (SNR) and Chi-square values were quantified in distinct anatomical regions for all subjects, with and without Rician denoising. Results: The averaged SNR significantly increased with Rician denoising by a factor of 2 while the averaged Chi-square values significantly decreased up to 61 % in the brain and up to 43% in the spinal cord after Rician NLM filtering. In the brain, the mean MK varied from 0.70 (putamen) to 1.27 (internal capsule) while AK and RK varied from 0.58 (corpus callosum) to 0.92 (cingulum) and from 0.70 (putamen) to 1.98 (corpus callosum), respectively. In the spinal cord, FA varied from 0.78 in lateral column to 0.81 in dorsal column while MD varied from 0.91 × 10−3 mm2/s (lateral) to 0.93 × 10−3 mm2/s (dorsal). RD varied from 0.34 × 10−3 mm2/s (dorsal) to 0.38 × 10−3 mm2/s (lateral) and AD varied from 1.96 × 10−3 mm2/s (lateral) to 2.11 × 10−3 mm2/s (dorsal).Conclusions: Our results show Rician denoising NLM filter incorporated with CLLS significantly increases SNR and reduces estimation errors of DT- and KT-derived metrics, providing the reliable metrics estimation with adequate SNR levels.

scholarly journals Low Frequency Noise Remove from EEG Signal

2020 ◽  
Vol 9 (1) ◽  
pp. 1510-1513
Keyword(s):  
Human Brain ◽  
Random Noise ◽  
Low Frequency ◽  
Frequency Noise ◽  
Eeg Signal ◽  
Low Frequency Noise ◽  
Frequency Range ◽  
Average Value ◽  
Different Types ◽  
The Brain

The electrical activity of the brain recorded by EEG which used to detect different types of diseases and disorders of the human brain. There is contained a large amount of random noise present during EEG recording, such as artifacts and baseline changes. These noises affect the low -frequency range of the EEG signal. These artifacts hiding some valuable information during analyzing of the EEG signal. In this paper we used the FIR filter for removing low -frequency noise(<1Hz) from the EEG signal. The performance is measured by calculating the SNR and the RMSE. We obtained RMSE average value from the test is 0.08 and the SNR value at frequency(<1Hz) is 0.0190.

scholarly journals Early white matter development is abnormal in tuberous sclerosis complex patients who develop autism spectrum disorder

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Anna K. Prohl ◽  
◽  
Benoit Scherrer ◽  
Xavier Tomas-Fernandez ◽  
Peter E. Davis ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
White Matter ◽  
Tuberous Sclerosis ◽  
Tuberous Sclerosis Complex ◽  
Neural Circuits ◽  
Diffusion Tensor ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Fiber Bundles ◽  
The Brain

Abstract Background Autism spectrum disorder (ASD) is prevalent in tuberous sclerosis complex (TSC), occurring in approximately 50% of patients, and is hypothesized to be caused by disruption of neural circuits early in life. Tubers, or benign hamartomas distributed stochastically throughout the brain, are the most conspicuous of TSC neuropathology, but have not been consistently associated with ASD. Widespread neuropathology of the white matter, including deficits in myelination, neuronal migration, and axon formation, exist and may underlie ASD in TSC. We sought to identify the neural circuits associated with ASD in TSC by identifying white matter microstructural deficits in a prospectively recruited, longitudinally studied cohort of TSC infants. Methods TSC infants were recruited within their first year of life and longitudinally imaged at time of recruitment, 12 months of age, and at 24 months of age. Autism was diagnosed at 24 months of age with the ADOS-2. There were 108 subjects (62 TSC-ASD, 55% male; 46 TSC+ASD, 52% male) with at least one MRI and a 24-month ADOS, for a total of 187 MRI scans analyzed (109 TSC-ASD; 78 TSC+ASD). Diffusion tensor imaging properties of multiple white matter fiber bundles were sampled using a region of interest approach. Linear mixed effects modeling was performed to test the hypothesis that infants who develop ASD exhibit poor white matter microstructural integrity over the first 2 years of life compared to those who do not develop ASD. Results Subjects with TSC and ASD exhibited reduced fractional anisotropy in 9 of 17 white matter regions, sampled from the arcuate fasciculus, cingulum, corpus callosum, anterior limbs of the internal capsule, and the sagittal stratum, over the first 2 years of life compared to TSC subjects without ASD. Mean diffusivity trajectories did not differ between groups. Conclusions Underconnectivity across multiple white matter fiber bundles develops over the first 2 years of life in subjects with TSC and ASD. Future studies examining brain-behavior relationships are needed to determine how variation in the brain structure is associated with ASD symptoms.

scholarly journals A time-dependent diffusion MRI signature of axon caliber variations and beading

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Hong-Hsi Lee ◽  
Antonios Papaioannou ◽  
Sung-Lyoung Kim ◽  
Dmitry S. Novikov ◽  
Els Fieremans
Keyword(s):  
Time Dependence ◽  
Diffusion Mri ◽  
Cellular Level ◽  
3 Dimensional ◽  
Non Invasive ◽  
Brain White Matter ◽  
Cell Dimensions ◽  
Micrometer Scale ◽  
Exciting Possibility ◽  
The Brain

AbstractMRI provides a unique non-invasive window into the brain, yet is limited to millimeter resolution, orders of magnitude coarser than cell dimensions. Here, we show that diffusion MRI is sensitive to the micrometer-scale variations in axon caliber or pathological beading, by identifying a signature power-law diffusion time-dependence of the along-fiber diffusion coefficient. We observe this signature in human brain white matter and identify its origins by Monte Carlo simulations in realistic substrates from 3-dimensional electron microscopy of mouse corpus callosum. Simulations reveal that the time-dependence originates from axon caliber variation, rather than from mitochondria or axonal undulations. We report a decreased amplitude of time-dependence in multiple sclerosis lesions, illustrating the potential sensitivity of our method to axonal beading in a plethora of neurodegenerative disorders. This specificity to microstructure offers an exciting possibility of bridging across scales to image cellular-level pathology with a clinically feasible MRI technique.

Microstructural Hyperelastic Characterization of Brain White Matter in Tension

2019 ◽  
Author(s):  
Mohammadreza Ramzanpour ◽  
Mohammad Hosseini-Farid ◽  
Mariusz Ziejewski ◽  
Ghodrat Karami
Keyword(s):  
Finite Element ◽  
White Matter ◽  
Human Brain ◽  
Volume Fraction ◽  
Fibrous Composite ◽  
Diffuse Axonal Injury ◽  
Stiffness Ratio ◽  
Fibrous Composite Material ◽  
Brain White Matter ◽  
The Brain

Abstract Axons as microstructural constituent elements of brain white matter are highly oriented in extracellular matrix (ECM) in one direction. Therefore, it is possible to model the human brain white matter as a unidirectional fibrous composite material. A micromechanical finite element model of the brain white matter is developed to indirectly measure the brain white matter constituents’ properties including axon and ECM under tensile loading. Experimental tension test on corona radiata conducted by Budday et al. 2017 [1] is used in this study and one-term Ogden hyperelastic constitutive model is applied to characterize its behavior. By the application of genetic algorithm (GA) as a black box optimization method, the Ogden hyperelastic parameters of axon and ECM minimizing the error between numerical finite element simulation and experimental results are measured. Inverse analysis is conducted on the resultant optimized parameters shows high correlation of coefficient (&gt;99%) between the numerical and experimental data which verifies the accuracy of the optimization procedure. The volume fraction of axons in porcine brain white matter is taken to be 52.7% and the stiffness ratio of axon to ECM is perceived to be 3.0. As these values are not accurately known for human brain white matter, we study the material properties of axon and ECM for different stiffness ratio and axon volume fraction values. The results of this study helps to better understand the micromechanical structure of the brain and micro-level injuries such as diffuse axonal injury.

scholarly journals Magnetic resonance diffusion tensor imaging in psychiatry: a narrative review of its potential role in diagnosis

2020 ◽  
Author(s):  
Piotr Podwalski ◽  
Krzysztof Szczygieł ◽  
Ernest Tyburski ◽  
Leszek Sagan ◽  
Błażej Misiak ◽  
...  
Keyword(s):  
Diffusion Tensor Imaging ◽  
White Matter ◽  
Magnetic Resonance ◽  
Potential Role ◽  
Diffusion Tensor ◽  
Mean Diffusivity ◽  
Water Molecules ◽  
Mental Diseases ◽  
The Brain ◽  
Diffusion Tensor Imaging Dti

Abstract Diffusion tensor imaging (DTI) is an imaging technique that uses magnetic resonance. It measures the diffusion of water molecules in tissues, which can occur either without restriction (i.e., in an isotropic manner) or limited by some obstacles, such as cell membranes (i.e., in an anisotropic manner). Diffusion is most often measured in terms of, inter alia, fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD). DTI allows us to reconstruct, visualize, and evaluate certain qualities of white matter. To date, many studies have sought to associate various changes in the distribution of diffusion within the brain with mental diseases and disorders. A better understanding of white matter integrity disorders can help us recognize the causes of diseases, as well as help create objective methods of psychiatric diagnosis, identify biomarkers of mental illness, and improve pharmacotherapy. The aim of this work is to present the characteristics of DTI as well as current research on its use in schizophrenia, affective disorders, and other mental disorders.

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 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 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.

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