White Matter Lesions in Migraine Eikermann-Haerter, Katharina; Huang, Susie Y.
The American journal of pathology,
11/2021, Letnik:
191, Številka:
11
Journal Article
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Migraine, the third most common disease worldwide, is a well-known independent risk factor for subclinical focal deep white matter lesions (WMLs), even in young and otherwise healthy individuals with ...no cardiovascular risk factors. These WMLs are more commonly seen in migraine patients with transient neurologic symptoms preceding their headaches, the so-called aura, and those with a high attack frequency. The pathophysiology of migraine-related deep white matter hyperintensities remains poorly understood despite their prevalence. Characteristic differences in their distribution compared with those of common periventricular WMLs in the elderly suggest a different underlying mechanism. Both ischemic and inflammatory mechanisms have been proposed, as there is increased cerebral vulnerability to ischemia in migraineurs, whereas there is also evidence of blood–brain barrier disruption with associated release of proinflammatory substances during migraine attacks. An enhanced susceptibility to spreading depolarization, the electrophysiological event underlying migraine, may be the mechanism that causes repetitive episodes of cerebral hypoperfusion and neuroinflammation during migraine attacks. WMLs can negatively affect both physical and cognitive function, underscoring the public health importance of migraine, and suggesting that migraine is an important contributor to neurologic deficits in the general population.
Diffusion tensor magnetic resonance imaging (DTI) is unsurpassed in its ability to map tissue microstructure and structural connectivity in the living human brain. Nonetheless, the angular sampling ...requirement for DTI leads to long scan times and poses a critical barrier to performing high-quality DTI in routine clinical practice and large-scale research studies. In this work we present a new processing framework for DTI entitled DeepDTI that minimizes the data requirement of DTI to six diffusion-weighted images (DWIs) required by conventional voxel-wise fitting methods for deriving the six unique unknowns in a diffusion tensor using data-driven supervised deep learning. DeepDTI maps the input non-diffusion-weighted (b = 0) image and six DWI volumes sampled along optimized diffusion-encoding directions, along with T1-weighted and T2-weighted image volumes, to the residuals between the input and high-quality output b = 0 image and DWI volumes using a 10-layer three-dimensional convolutional neural network (CNN). The inputs and outputs of DeepDTI are uniquely formulated, which not only enables residual learning to boost CNN performance but also enables tensor fitting of resultant high-quality DWIs to generate orientational DTI metrics for tractography. The very deep CNN used by DeepDTI leverages the redundancy in local and non-local spatial information and across diffusion-encoding directions and image contrasts in the data. The performance of DeepDTI was systematically quantified in terms of the quality of the output images, DTI metrics, DTI-based tractography and tract-specific analysis results. We demonstrate rotationally-invariant and robust estimation of DTI metrics from DeepDTI that are comparable to those obtained with two b = 0 images and 21 DWIs for the primary eigenvector derived from DTI and two b = 0 images and 26–30 DWIs for various scalar metrics derived from DTI, achieving 3.3–4.6 × acceleration, and twice as good as those of a state-of-the-art denoising algorithm at the group level. The twenty major white-matter tracts can be accurately identified from the tractography of DeepDTI results. The mean distance between the core of the major white-matter tracts identified from DeepDTI results and those from the ground-truth results using 18 b = 0 images and 90 DWIs measures around 1–1.5 mm. DeepDTI leverages domain knowledge of diffusion MRI physics and power of deep learning to render DTI, DTI-based tractography, major white-matter tracts identification and tract-specific analysis more feasible for a wider range of neuroscientific and clinical studies.
•A new processing framework for DTI using data-driven supervised deep learning.•DeepDTI minimizes the data requirement of DTI to one b=0 and six DWI volumes.•The DeepDTI framework maps both scalar and orientational DTI metrics.•Enables DTI-based tractography and tract-specific analysis using a 30-60 second scan.•Comparable to fully-sampled DTI scan and better than benchmark denoising algorithm.
Diffusion tensor magnetic resonance imaging (DTI) is a widely adopted neuroimaging method for the in vivo mapping of brain tissue microstructure and white matter tracts. Nonetheless, the noise in the ...diffusion-weighted images (DWIs) decreases the accuracy and precision of DTI derived microstructural parameters and leads to prolonged acquisition time for achieving improved signal-to-noise ratio (SNR). Deep learning-based image denoising using convolutional neural networks (CNNs) has superior performance but often requires additional high-SNR data for supervising the training of CNNs, which reduces the feasibility of supervised learning-based denoising in practice. In this work, we develop a self-supervised deep learning-based method entitled “SDnDTI” for denoising DTI data, which does not require additional high-SNR data for training. Specifically, SDnDTI divides multi-directional DTI data into many subsets of six DWI volumes and transforms DWIs from each subset to along the same diffusion-encoding directions through the diffusion tensor model, generating multiple repetitions of DWIs with identical image contrasts but different noise observations. SDnDTI removes noise by first denoising each repetition of DWIs using a deep 3-dimensional CNN with the average of all repetitions with higher SNR as the training target, following the same approach as normal supervised learning based denoising methods, and then averaging CNN-denoised images for achieving higher SNR. The denoising efficacy of SDnDTI is demonstrated in terms of the similarity of output images and resultant DTI metrics compared to the ground truth generated using substantially more DWI volumes on two datasets with different spatial resolutions, b-values and numbers of input DWI volumes provided by the Human Connectome Project (HCP) and the Lifespan HCP in Aging. The SDnDTI results preserve image sharpness and textural details and substantially improve upon those from the raw data. The results of SDnDTI are comparable to those from supervised learning-based denoising and outperform those from state-of-the-art conventional denoising algorithms including BM4D, AONLM and MPPCA. By leveraging domain knowledge of diffusion MRI physics, SDnDTI makes it easier to use CNN-based denoising methods in practice and has the potential to benefit a wider range of research and clinical applications that require accelerated DTI acquisition and high-quality DTI data for mapping of tissue microstructure, fiber tracts and structural connectivity in the living human brain.
Research in ultrahigh magnetic field strength combined with ultrahigh and ultrafast gradient technology has provided enormous gains in sensitivity, resolution, and contrast for neuroimaging. This ...article provides an overview of the technical advantages and challenges of performing clinical neuroimaging studies at ultrahigh magnetic field strength combined with ultrahigh and ultrafast gradient technology. Emerging clinical applications of 7-T MRI and state-of-the-art gradient systems equipped with up to 300 mT/m gradient strength are reviewed, and the impact and benefits of such advances to anatomical, structural and functional MRI are discussed in a variety of neurological conditions. Finally, an outlook and future directions for ultrahigh field MRI combined with ultrahigh and ultrafast gradient technology in neuroimaging are examined.
The engineering of a 3T human MRI scanner equipped with 300mT/m gradients – the strongest gradients ever built for an in vivo human MRI scanner – was a major component of the NIH Blueprint Human ...Connectome Project (HCP). This effort was motivated by the HCP's goal of mapping, as completely as possible, the macroscopic structural connections of the in vivo healthy, adult human brain using diffusion tractography. Yet, the 300mT/m gradient system is well suited to many additional types of diffusion measurements. Here, we present three initial applications of the 300mT/m gradients that fall outside the immediate scope of the HCP. These include: 1) diffusion tractography to study the anatomy of consciousness and the mechanisms of brain recovery following traumatic coma; 2) q-space measurements of axon diameter distributions in the in vivo human brain and 3) postmortem diffusion tractography as an adjunct to standard histopathological analysis. We show that the improved sensitivity and diffusion-resolution provided by the gradients are rapidly enabling human applications of techniques that were previously possible only for in vitro and animal models on small-bore scanners, thereby creating novel opportunities to map the microstructure of the human brain in health and disease.
•Diffusion spectrum imaging to study traumatic coma recovery•In vivo human axon diameter measurements using 300mT/m gradients•High-resolution (0.6mm isotropic) diffusion imaging in whole, fixed human brain
Axon diameter mapping using high-gradient diffusion MRI has generated great interest as a noninvasive tool for studying trends in axonal size in the human brain. One of the main barriers to mapping ...axon diameter across the whole brain is accounting for complex white matter fiber configurations (e.g., crossings and fanning), which are prevalent throughout the brain. Here, we present a framework for generalizing axon diameter index estimation to the whole brain independent of the underlying fiber orientation distribution using the spherical mean technique (SMT). This approach is shown to significantly benefit from the use of real-valued diffusion data with Gaussian noise, which reduces the systematic bias in the estimated parameters resulting from the elevation of the noise floor when using magnitude data with Rician noise. We demonstrate the feasibility of obtaining whole-brain orientationally invariant estimates of axon diameter index and relative volume fractions in six healthy human volunteers using real-valued diffusion data acquired on a dedicated high-gradient 3-Tesla human MRI scanner with 300 mT/m maximum gradient strength. The trends in axon diameter index are consistent with known variations in axon diameter from histology and demonstrate the potential of this generalized framework for revealing coherent patterns in axonal structure throughout the living human brain. The use of real-valued diffusion data provides a viable solution for eliminating the Rician noise floor and should be considered for all spherical mean approaches to microstructural parameter estimation.
Background
There is a clinical association between migraine and multiple sclerosis.
Main body
Migraine and MS patients share similar demographics, with the highest incidence among young, female and ...otherwise healthy patients. The same hormonal constellations/changes trigger disease exacerbation in both entities. Migraine prevalence is increased in MS patients, which is further enhanced by disease-modifying treatment. Clinical data show that onset of migraine typically starts years before the clinical diagnosis of MS, suggesting that there is either a unidirectional relationship with migraine predisposing to MS, and/or a “shared factor” underlying both conditions. Brain imaging studies show white matter lesions in both MS and migraine patients. Neuroinflammatory mechanisms likely play a key role, at least as a shared downstream pathway. In this review article, we provide an overview of the literature about 1) the clinical association between migraine and MS as well as 2) brain MRI studies that help us better understand the mechanistic relationship between both diseases with implications on their underlying pathophysiology.
Conclusion
Studies suggest a migraine history predisposes patients to develop MS. Advanced brain MR imaging may shed light on shared and distinct features, while helping us better understand mechanisms underlying both disease entities.
•Denoised sub-millimeter isotropic MPRAGE images using DnCNN, BM4D and AONLM.•Systematically quantified image and cortical surface quality improvement by denoising.•Results equivalent to averaging ...~2.5 repetitions of the data in terms of image similarity.•Results equivalent to averaging 1.6–2.2 repetitions in terms of the cortical surface accuracy.•Results are more accurate than those from 1-mm isotropic resolution data.
Automatic cerebral cortical surface reconstruction is a useful tool for cortical anatomy quantification, analysis and visualization. Recently, the Human Connectome Project and several studies have shown the advantages of using T1-weighted magnetic resonance (MR) images with sub-millimeter isotropic spatial resolution instead of the standard 1-mm isotropic resolution for improved accuracy of cortical surface positioning and thickness estimation. Nonetheless, sub-millimeter resolution images are noisy by nature and require averaging multiple repetitions to increase the signal-to-noise ratio for precisely delineating the cortical boundary. The prolonged acquisition time and potential motion artifacts pose significant barriers to the wide adoption of cortical surface reconstruction at sub-millimeter resolution for a broad range of neuroscientific and clinical applications. We address this challenge by evaluating the cortical surface reconstruction resulting from denoised single-repetition sub-millimeter T1-weighted images. We systematically characterized the effects of image denoising on empirical data acquired at 0.6 mm isotropic resolution using three classical denoising methods, including denoising convolutional neural network (DnCNN), block-matching and 4-dimensional filtering (BM4D) and adaptive optimized non-local means (AONLM). The denoised single-repetition images were found to be highly similar to 6-repetition averaged images, with a low whole-brain averaged mean absolute difference of ~0.016, high whole-brain averaged peak signal-to-noise ratio of ~33.5 dB and structural similarity index of ~0.92, and minimal gray matter–white matter contrast loss (2% to 9%). The whole-brain mean absolute discrepancies in gray matter–white matter surface placement, gray matter–cerebrospinal fluid surface placement and cortical thickness estimation were lower than 165 μm, 155 μm and 145 μm—sufficiently accurate for most applications. These discrepancies were approximately one third to half of those from 1-mm isotropic resolution data. The denoising performance was equivalent to averaging ~2.5 repetitions of the data in terms of image similarity, and 1.6–2.2 repetitions in terms of the cortical surface placement accuracy. The scan-rescan variability of the cortical surface positioning and thickness estimation was lower than 170 μm. Our unique dataset and systematic characterization support the use of denoising methods for improved cortical surface reconstruction at sub-millimeter resolution.
Sensitive and quantitative protocols for characterizing low-dose effects are needed to meet the demands of 21st century chemical hazard assessment. To test the hypothesis that xenobiotic exposure at ...environmentally relevant concentrations produces specific biochemical fingerprints in organisms, metabolomic perturbations in zebrafish (Danio rerio) embryo/larvae were measured following 24 h exposures to 13 individual chemicals covering a wide range of contaminant classes. Measured metabolites (208 in total) included amino acids, biogenic amines, fatty acids, bile acids, sugars, and lipids. The 96–120 h post-fertilization developmental stage was the most appropriate model for detecting xenobiotic-induced metabolomic perturbations. Metabolomic fingerprints were largely chemical- and dose-specific and were reproducible in multiple exposures over a 16-month period. Furthermore, chemical-specific responses were detected in the presence of an effluent matrix; importantly, in the absence of morphological response. In addition to improving sensitivity for detecting biological responses to low-level xenobiotic exposures, these data can aid the classification of novel contaminants based on the similarity of metabolomic responses to well-characterized “model” compounds. This approach is clearly of use for rapid, sensitive, and specific analyses of chemical effect on organisms, and can supplement existing methods, such as the Zebrafish Embryo Toxicity assay (OECD TG236), with molecular-level information.
The first phase of the Human Connectome Project pioneered advances in MRI technology for mapping the macroscopic structural connections of the living human brain through the engineering of a ...whole-body human MRI scanner equipped with maximum gradient strength of 300 mT/m, the highest ever achieved for human imaging. While this instrument has made important contributions to the understanding of macroscale connectional topology, it has also demonstrated the potential of dedicated high-gradient performance scanners to provide unparalleled in vivo assessment of neural tissue microstructure. Building on the initial groundwork laid by the original Connectome scanner, we have now embarked on an international, multi-site effort to build the next-generation human 3T Connectome scanner (Connectome 2.0) optimized for the study of neural tissue microstructure and connectional anatomy across multiple length scales. In order to maximize the resolution of this in vivo microscope for studies of the living human brain, we will push the diffusion resolution limit to unprecedented levels by (1) nearly doubling the current maximum gradient strength from 300 mT/m to 500 mT/m and tripling the maximum slew rate from 200 T/m/s to 600 T/m/s through the design of a one-of-a-kind head gradient coil optimized to minimize peripheral nerve stimulation; (2) developing high-sensitivity multi-channel radiofrequency receive coils for in vivo and ex vivo human brain imaging; (3) incorporating dynamic field monitoring to minimize image distortions and artifacts; (4) developing new pulse sequences to integrate the strongest diffusion encoding and highest spatial resolution ever achieved in the living human brain; and (5) calibrating the measurements obtained from this next-generation instrument through systematic validation of diffusion microstructural metrics in high-fidelity phantoms and ex vivo brain tissue at progressively finer scales with accompanying diffusion simulations in histology-based micro-geometries. We envision creating the ultimate diffusion MRI instrument capable of capturing the complex multi-scale organization of the living human brain – from the microscopic scale needed to probe cellular geometry, heterogeneity and plasticity, to the mesoscopic scale for quantifying the distinctions in cortical structure and connectivity that define cyto- and myeloarchitectonic boundaries, to improvements in estimates of macroscopic connectivity.
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