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Dysphagia Oct 2022Aspiration after stroke is associated with pneumonia and mortality. In this study, we investigated brain structural connectivity associated with aspiration after...
Aspiration after stroke is associated with pneumonia and mortality. In this study, we investigated brain structural connectivity associated with aspiration after unilateral supratentorial stroke. Patients on oral feeding after stroke were divided into liquid aspiration (22 patients) and normal (18 patients) groups based on videofluoroscopic swallowing studies. Voxel-based lesion-symptom mapping and voxel-wise group comparison of fractional anisotropy, mode of anisotropy, and mean diffusivity maps were conducted. Voxel-based lesion-symptom mapping revealed no significant lesion differences between groups. The aspiration group showed significantly increased fractional anisotropy and mode of anisotropy in the anterior limb and the genu of the internal capsule in the right hemisphere. In contrast, the normal group showed significantly increased mean diffusivity, mainly in the superior longitudinal fasciculus in the right hemisphere (P < 0.05). Degeneration of the internal capsule in the right hemisphere was found to affect aspiration after stroke.
Topics: Anisotropy; Brain; Diffusion Magnetic Resonance Imaging; Humans; Nerve Net; Stroke
PubMed: 34762204
DOI: 10.1007/s00455-021-10388-z -
Journal of Magnetic Resonance Imaging :... Apr 2023Dynamic diffusion magnetic resonance imaging (ddMRI) metrics can assess transient microstructural alterations in tissue diffusivity but requires additional scan time...
BACKGROUND
Dynamic diffusion magnetic resonance imaging (ddMRI) metrics can assess transient microstructural alterations in tissue diffusivity but requires additional scan time hindering its clinical application.
PURPOSE
To determine whether a diffusion gradient table can simultaneously acquire data to estimate dynamic and diffusion tensor imaging (DTI) metrics.
STUDY TYPE
Prospective.
SUBJECTS
Seven healthy subjects, 39 epilepsy patients (15 female, 31 male, age ± 15).
FIELD STRENGTH/SEQUENCE
Two-dimensional diffusion MRI (b = 1000 s/mm ) at a field strength of 3 T. Sessions in healthy subjects-standard ddMRI (30 directions), standard DTI (15 and 30 directions), and nested cubes scans (15 and 30 directions). Sessions in epilepsy patients-two 30 direction (standard ddMRI, 10 nested cubes) or two 15 direction scans (standard DTI, 5 nested cubes).
ASSESSMENT
Fifteen direction DTI was repeated twice for within-session test-retest measurements in healthy subjects. Bland-Altman analysis computed bias and limits of agreement for DTI metrics using test-retest scans and standard 15 direction vs. 5 nested cubes scans. Intraclass correlation (ICC) analysis compared tensor metrics between 15 direction DTI scans (standard vs. 5 nested cubes) and the coefficients of variation (CoV) of trace and apparent diffusion coefficient (ADC) between 30 direction ddMRI scans (standard vs. 10 nested cubes).
STATISTICAL TESTS
Bland-Altman and ICC analysis using a P-value of 0.05 for statistical significance.
RESULTS
Correlations of mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were strong and significant in gray (ICC > 0.95) and white matter (ICC > 0.95) between standard vs. nested cubes DTI acquisitions. Correlation of white matter fractional anisotropy was also strong (ICC > 0.95) and significant. ICCs of the CoV of dynamic ADC measured using repeated cubes and nested cubes acquisitions were modest (ICC >0.60), but significant in gray matter.
CONCLUSION
A nested cubes diffusion gradient table produces tensor-based and dynamic diffusion measurements in a single acquisition.
LEVEL OF EVIDENCE
2 TECHNICAL EFFICACY: Stage 1.
Topics: Humans; Male; Female; Adolescent; Diffusion Tensor Imaging; Prospective Studies; Diffusion Magnetic Resonance Imaging; White Matter; Epilepsy; Anisotropy
PubMed: 36056625
DOI: 10.1002/jmri.28407 -
NeuroImage. Clinical 2021Misophonia is a condition in which specific ordinary sounds provoke disproportionately strong negative affect and physiological arousal. Evidence for neurobiological...
Misophonia is a condition in which specific ordinary sounds provoke disproportionately strong negative affect and physiological arousal. Evidence for neurobiological abnormalities underlying misophonia is scarce. Since many psychiatric disorders show white matter (WM) abnormalities, we tested for both macro and micro-structural WM differences between misophonia patients and healthy controls. We collected T1-weighted and diffusion-weighted magnetic resonance images from 24 patients and 25 matched controls. We tested for group differences in WM volume using whole-brain voxel-based morphometry and used the significant voxels from this analysis as seeds for probabilistic tractography. After calculation of diffusion tensors, we compared group means for fractional anisotropy, mean diffusivity, and directional diffusivities, and applied tract-based spatial statistics for voxel-wise comparison. Compared to controls, patients had greater left-hemispheric WM volumes in the inferior fronto-occipital fasciculus, anterior thalamic radiation, and body of the corpus callosum connecting bilateral superior frontal gyri. Patients also had lower averaged radial and mean diffusivities and voxel-wise comparison indicated large and widespread clusters of lower mean diffusivity. We found both macro and microstructural WM abnormalities in our misophonia sample, suggesting misophonia symptomatology is associated with WM alterations. These biological alterations may be related to differences in social-emotional processing, particularly recognition of facial affect, and to attention for affective information.
Topics: Anisotropy; Brain; Diffusion Tensor Imaging; Humans; Phobic Disorders; White Matter
PubMed: 34461433
DOI: 10.1016/j.nicl.2021.102787 -
Psychological Medicine May 2023Aberrant microstructure of the uncinate fasciculus (UNC), a white matter (WM) tract implicated in emotion regulation, has been hypothesized as a neurobiological... (Meta-Analysis)
Meta-Analysis Review
Aberrant microstructure of the uncinate fasciculus (UNC), a white matter (WM) tract implicated in emotion regulation, has been hypothesized as a neurobiological mechanism of depression. However, studies testing this hypothesis have yielded inconsistent results. The present meta-analysis consolidates evidence from 44 studies comparing fractional anisotropy (FA) and radial diffusivity (RD), two metrics characterizing WM microstructure, of the UNC in individuals with depression ( = 5016) to healthy individuals ( = 18 425). We conduct meta-regressions to identify demographic and clinical characteristics that contribute to cross-study heterogeneity in UNC findings. UNC FA was reduced in individuals with depression compared to healthy individuals. UNC RD was comparable between individuals with depression and healthy individuals. Comorbid anxiety explained inter-study heterogeneity in UNC findings. Depression is associated with perturbations in UNC microstructure, specifically with respect to UNC FA and not UNC RD. The association between depression and UNC microstructure appears to be moderated by anxiety. Future work should unravel the cellular mechanisms contributing to aberrant UNC microstructure in depression; clarify the relationship between UNC microstructure, depression, and anxiety; and link UNC microstructure to psychological processes, such as emotion regulation.
Topics: Humans; White Matter; Depression; Diffusion Tensor Imaging; Uncinate Fasciculus; Diffusion Magnetic Resonance Imaging; Anisotropy; Brain
PubMed: 37051913
DOI: 10.1017/S0033291723000107 -
Journal of Neuroscience Methods Jan 2021Diffusion Magnetic Resonance Imaging (dMRI) is one of the most important contemporary non-invasive modalities for probing tissue structure at the microscopic scale. The... (Review)
Review
Diffusion Magnetic Resonance Imaging (dMRI) is one of the most important contemporary non-invasive modalities for probing tissue structure at the microscopic scale. The majority of dMRI techniques employ standard single diffusion encoding (SDE) measurements, covering different sequence parameter ranges depending on the complexity of the method. Although many signal representations and biophysical models have been proposed for SDE data, they are intrinsically limited by a lack of specificity. Advanced dMRI methods have been proposed to provide additional microstructural information beyond what can be inferred from SDE. These enhanced contrasts can play important roles in characterizing biological tissues, for instance upon diseases (e.g. neurodegenerative, cancer, stroke), aging, learning, and development. In this review we focus on double diffusion encoding (DDE), which stands out among other advanced acquisitions for its versatility, ability to probe more specific diffusion correlations, and feasibility for preclinical and clinical applications. Various DDE methodologies have been employed to probe compartment sizes (Section 3), decouple the effects of microscopic diffusion anisotropy from orientation dispersion (Section 4), probe displacement correlations, study exchange, or suppress fast diffusing compartments (Section 6). DDE measurements can also be used to improve the robustness of biophysical models (Section 5) and study intra-cellular diffusion via magnetic resonance spectroscopy of metabolites (Section 7). This review discusses all these topics as well as important practical aspects related to the implementation and contrast in preclinical and clinical settings (Section 9) and aims to provide the readers a guide for deciding on the right DDE acquisition for their specific application.
Topics: Anisotropy; Brain; Diffusion; Diffusion Magnetic Resonance Imaging; Magnetic Resonance Imaging; Magnetic Resonance Spectroscopy
PubMed: 33144100
DOI: 10.1016/j.jneumeth.2020.108989 -
Obesity Reviews : An Official Journal... Mar 2022Obesity is a major global health problem leading to serious complications. It has been consistently associated with alterations in brain structure. Diffusion tensor... (Review)
Review
Obesity is a major global health problem leading to serious complications. It has been consistently associated with alterations in brain structure. Diffusion tensor imaging is used to examine brain white matter microstructure by assessing the dynamics of water diffusion in white matter tracts. Fractional anisotropy and mean diffusivity are two parameters measuring the directionality and rate of diffusion, respectively. Changes in these indices associated with obesity have been previously reported in numerous fiber tracts. This systematic review investigates microstructural white matter alterations in obesity using diffusion tensor imaging. A computerized search was performed in PubMed, Web of Science, and Livivo databases. Based on the inclusion/exclusion criteria, 31 cross-sectional studies comparing individuals with obesity and lean controls were identified. The studies included mixed-gender samples of children, young, middle-aged, and older adults. The majority of included studies reported decreased fractional anisotropy and increased mean diffusivity associated with elevated body mass index, suggesting white matter abnormalities. Nevertheless, a pattern of alterations is inconsistent across studies. This could be explained by several potential biases assessed by the National Institute of Health quality assessment tool. Furthermore, a direct assessment of body fat is recommended for a more accurate characterization of the brain-body relationship.
Topics: Aged; Anisotropy; Brain; Child; Cross-Sectional Studies; Diffusion Tensor Imaging; Humans; Middle Aged; Obesity; White Matter
PubMed: 34908217
DOI: 10.1111/obr.13388 -
Magnetic Resonance in Medicine Aug 2017To introduce a novel diffusion pulse sequence, namely double oscillating diffusion encoding (DODE), and to investigate whether it adds sensitivity to microscopic...
PURPOSE
To introduce a novel diffusion pulse sequence, namely double oscillating diffusion encoding (DODE), and to investigate whether it adds sensitivity to microscopic diffusion anisotropy (µA) compared to the well-established double diffusion encoding (DDE) methodology.
METHODS
We simulate measurements from DODE and DDE sequences for different types of microstructures exhibiting restricted diffusion. First, we compare the effect of varying pulse sequence parameters on the DODE and DDE signal. Then, we analyse the sensitivity of the two sequences to the microstructural parameters (pore diameter and length) which determine µA. Finally, we investigate specificity of measurements to particular substrate configurations.
RESULTS
Simulations show that DODE sequences exhibit similar signal dependence on the relative angle between the two gradients as DDE sequences, however, the effect of varying the mixing time is less pronounced. The sensitivity analysis shows that in substrates with elongated pores and various orientations, DODE sequences increase the sensitivity to pore diameter, while DDE sequences are more sensitive to pore length. Moreover, DDE and DODE sequence parameters can be tailored to enhance/suppress the signal from a particular range of substrates.
CONCLUSIONS
A combination of DODE and DDE sequences maximize sensitivity to µA, compared to using just the DDE method. Magn Reson Med 78:550-564, 2017. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Topics: Anisotropy; Cytological Techniques; Diffusion Magnetic Resonance Imaging; Image Processing, Computer-Assisted; Microscopy; Models, Theoretical; Signal Processing, Computer-Assisted
PubMed: 27580027
DOI: 10.1002/mrm.26393 -
Journal of Neuroscience Methods Jan 2021Diffusion MRI is a non-invasive technique to study brain microstructure. Differences in the microstructural properties of tissue, including size and anisotropy, can be... (Review)
Review
Diffusion MRI is a non-invasive technique to study brain microstructure. Differences in the microstructural properties of tissue, including size and anisotropy, can be represented in the signal if the appropriate method of acquisition is used. However, to depict the underlying properties, special care must be taken when designing the acquisition protocol as any changes in the procedure might impact on quantitative measurements. This work reviews state-of-the-art methods for studying brain microstructure using diffusion MRI and their sensitivity to microstructural differences and various experimental factors. Microstructural properties of the tissue at a micrometer scale can be linked to the diffusion signal at a millimeter-scale using modeling. In this paper, we first give an introduction to diffusion MRI and different encoding schemes. Then, signal representation-based methods and multi-compartment models are explained briefly. The sensitivity of the diffusion MRI signal to the microstructural components and the effects of curvedness of axonal trajectories on the diffusion signal are reviewed. Factors that impact on the quality (accuracy and precision) of derived metrics are then reviewed, including the impact of random noise, and variations in the acquisition parameters (i.e., number of sampled signals, b-value and number of acquisition shells). Finally, yet importantly, typical approaches to deal with experimental factors are depicted, including unbiased measures and harmonization. We conclude the review with some future directions and recommendations on this topic.
Topics: Anisotropy; Axons; Brain; Diffusion; Diffusion Magnetic Resonance Imaging
PubMed: 33017644
DOI: 10.1016/j.jneumeth.2020.108951 -
Handbook of Clinical Neurology 2018Diffusion tensor imaging (DTI) is a noninvasive neuroimaging tool assessing the organization of white-matter tracts and brain microstructure in vivo. The technique takes... (Review)
Review
Diffusion tensor imaging (DTI) is a noninvasive neuroimaging tool assessing the organization of white-matter tracts and brain microstructure in vivo. The technique takes into account the three-dimensional (3D) direction of diffusion of water in space, the brownian movements of water being constrained by the brain microstructure. The main direction of diffusion in the brain is extracted to obtain the principal direction of axonal projection within a given voxel. Overall, the diffusion tensor is a mathematic analysis of the magnitude/directionality (anisotropy) of the movement of water molecules in 3D space. Tracts running in the white matter are subsequently reconstructed graphically with fiber tractography. Tractography can be applied to myelinated and unmyelinated fibers or axonopathy. Decreased fractional anisotropy in white-matter tracts occurs in cases of injury with disorganized or disrupted myelin sheaths. Furthermore, high angular resolution methods enable detection of fiber crossings or convergence. DTI is a modern tool which complements conventional magnetic resonance techniques and is particularly relevant to assess the organization of cerebellar tracts. Indeed, both the afferent and efferent pathways of the cerebellar circuitry passing through the inferior, middle, and superior cerebellar peduncles can be visualized in vivo, including in children. The microanatomy of the cerebellar cortex and cerebellar nuclei is also emerging as a future assessment. Applications in the field of cerebellar disorders are multiple, ranging from developmental disorders to adult-onset cerebellar ataxias.
Topics: Anisotropy; Cerebellum; Diffusion Tensor Imaging; Humans; Image Processing, Computer-Assisted; Neural Pathways
PubMed: 29903442
DOI: 10.1016/B978-0-444-63956-1.00014-X -
Magnetic Resonance in Medicine Feb 2021To develop and evaluate machine-learning methods that reconstruct fractional anisotropy (FA) values and mean diffusivities (MD) from 3-direction diffusion MRI (dMRI)...
PURPOSE
To develop and evaluate machine-learning methods that reconstruct fractional anisotropy (FA) values and mean diffusivities (MD) from 3-direction diffusion MRI (dMRI) acquisitions.
METHODS
Two machine-learning models were implemented to map undersampled dMRI signals with high-quality FA and MD maps that were reconstructed from fully sampled DTI scans. The first model was a previously described multilayer perceptron (MLP), which maps signals and FA/MD values from a single voxel. The second was a convolutional neural network U-Net model, which maps dMRI slices to full FA/MD maps. Each method was trained on dMRI brain scans (N = 46), and reconstruction accuracies were compared with conventional linear-least-squares (LLS) reconstructions.
RESULTS
In an independent testing cohort (N = 20), 3-direction U-Net reconstructions had significantly lower absolute FA error than both 3-direction MLP (U-Net : 0.06 ± 0.01 vs. MLP : 0.08 ± 0.01, P < 1 × 10 ) and 6-direction LLS (LLS : 0.09 ± 0.03, P = 1 × 10 ). The MD errors were not significantly different among 3-direction MLP (0.06 ± 0.01 × 10 mm /s), 3-direction U-Net (0.06 ± 0.01 × 10 mm /s), and 6-direction LLS (0.07 ± 0.02 × 10 mm /s, P > .1).
CONCLUSION
The proposed U-Net model reconstructed FA from 3-direction dMRI scans with improved accuracy compared with both a previously described MLP approach and LLS fitting from 6-direction scans. The MD reconstruction accuracies did not differ significantly between reconstructions.
Topics: Anisotropy; Deep Learning; Diffusion Magnetic Resonance Imaging; Diffusion Tensor Imaging; Humans; Magnetic Resonance Imaging
PubMed: 32810351
DOI: 10.1002/mrm.28470