scholarly journals Estimating intra-axonal axial diffusivity in the presence of fibre orientation dispersion

2020 ◽  
Author(s):  
Amy FD Howard ◽  
Frederik J Lange ◽  
Jeroen Mollink ◽  
Michiel Cottaar ◽  
Mark Drakesmith ◽  
...  
Keyword(s):  
White Matter ◽  
Fibre Orientation ◽  
Free Water ◽  
Orientation Distribution ◽  
B Value ◽  
Axial Diffusivity ◽  
High B Value ◽  
Axonal Compartment ◽  
Two Parameters

AbstractBy analysing the diffusion MRI signal, we can infer information about the microscopic structure of the brain. Two parameters of interest - the intra-axonal axial diffusivity and fibre orientation dispersion - are potential biomarkers for very different aspects of the white matter microstructure, yet they are difficult to disentangle. The parameters covary such that, if one is not accurately accounted for, the other will be biased. In this work we use high b-value data to isolate the signal from the intra-axonal compartment and resolve any degeneracies with the extra-axonal compartment. In the high b-value regime, we then use a model of dispersed sticks to estimate the intra-axonal axial diffusivity and fibre orientation distribution on a voxelwise basis. Our results in in vivo, human data show an intra-axonal axial diffusivity of ~ 2.3 – 3 μm2/ms, where 3 μm2/ms is the diffusivity of free water at 37°C. The intra-axonal axial diffusivity is seen to vary considerably across the white matter. For example, in the corpus callosum we find high values in the genu and splenium, and lower values in the midbody. Furthermore, the axial diffusivity and orientation dispersion appear negatively correlated, behaviour which we show is consistent with the presence of fibre undulations but not consistent with a degeneracy between fanning fibres and axial diffusivity. Finally, we demonstrate that the parameter maps output from Neurite Orientation Dispersion and Density Imaging (NODDI) change substantially when the assumed axial diffusivity was increased from 1.7 to 2.5 or 3 μm2/ms.

scholarly journals Increased extracellular fluid is associated with white matter fiber degeneration in CADASIL: in vivo evidence from diffusion magnetic resonance imaging

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Xinfeng Yu ◽  
Xinzhen Yin ◽  
Hui Hong ◽  
Shuyue Wang ◽  
Yeerfan Jiaerken ◽  
...  
Keyword(s):  
White Matter ◽  
Diffusion Tensor ◽  
Small Vessel Disease ◽  
Extracellular Fluid ◽  
Free Water ◽  
Inverse Correlation ◽  
Brain Regions ◽  
Fiber Density ◽  
Four Levels

Abstract Background White matter hyperintensities (WMHs) are one of the hallmarks of cerebral small vessel disease (CSVD), but the pathological mechanisms underlying WMHs remain unclear. Recent studies suggest that extracellular fluid (ECF) is increased in brain regions with WMHs. It has been hypothesized that ECF accumulation may have detrimental effects on white matter microstructure. To test this hypothesis, we used cerebral autosomal-dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) as a unique CSVD model to investigate the relationships between ECF and fiber microstructural changes in WMHs. Methods Thirty-eight CADASIL patients underwent 3.0 T MRI with multi-model sequences. Parameters of free water (FW) and apparent fiber density (AFD) obtained from diffusion-weighted imaging (b = 0 and 1000 s/mm2) were respectively used to quantify the ECF and fiber density. WMHs were split into four subregions with four levels of FW using quartiles (FWq1 to FWq4) for each participant. We analyzed the relationships between FW and AFD in each subregion of WMHs. Additionally, we tested whether FW of WMHs were associated with other accompanied CSVD imaging markers including lacunes and microbleeds. Results We found an inverse correlation between FW and AFD in WMHs. Subregions of WMHs with high-level of FW (FWq3 and FWq4) were accompanied with decreased AFD and with changes in FW-corrected diffusion tensor imaging parameters. Furthermore, FW was also independently associated with lacunes and microbleeds. Conclusions Our study demonstrated that increased ECF was associated with WM degeneration and the occurrence of lacunes and microbleeds, providing important new insights into the role of ECF in CADASIL pathology. Improving ECF drainage might become a therapeutic strategy in future.

High b-value diffusion-weighted imaging: A sensitive method to reveal white matter differences in schizophrenia

2012 ◽  
Vol 201 (2) ◽  
pp. 144-151 ◽  
Author(s):  
Philipp Sebastian Baumann ◽  
Leila Cammoun ◽  
Philippe Conus ◽  
Kim Quang Do ◽  
Pierre Marquet ◽  
...  
Keyword(s):  
White Matter ◽  
Diffusion Weighted Imaging ◽  
B Value ◽  
High B Value ◽  
Diffusion Weighted ◽  
Sensitive Method

Accelerated maturation of white matter in young children with autism: A high b value DWI study

NeuroImage ◽  
2007 ◽  
Vol 37 (1) ◽  
pp. 40-47 ◽  
Author(s):  
Dafna Ben Bashat ◽  
Vered Kronfeld-Duenias ◽  
Ditza A. Zachor ◽  
Perla M. Ekstein ◽  
Talma Hendler ◽  
...  
Keyword(s):  
White Matter ◽  
Young Children ◽  
Children With Autism ◽  
B Value ◽  
High B Value ◽  
Young Children With Autism

scholarly journals Large-Scale Evidence for an Association Between Peripheral Inflammation and White Matter Free Water in Schizophrenia and Healthy Individuals

2020 ◽  
Author(s):  
Maria A Di Biase ◽  
Andrew Zalesky ◽  
Suheyla Cetin-Karayumak ◽  
Yogesh Rathi ◽  
Jinglei Lv ◽  
...  
Keyword(s):  
White Matter ◽  
Large Scale ◽  
Interleukin 2 ◽  
Free Water ◽  
Markers Of Inflammation ◽  
Brain White Matter ◽  
Inflammatory Processes ◽  
Weighted Magnetic Resonance Imaging

Abstract Introduction Clarifying the role of neuroinflammation in schizophrenia is subject to its detection in the living brain. Free-water (FW) imaging is an in vivo diffusion-weighted magnetic resonance imaging (dMRI) technique that measures water molecules freely diffusing in the brain and is hypothesized to detect inflammatory processes. Here, we aimed to establish a link between peripheral markers of inflammation and FW in brain white matter. Methods All data were obtained from the Australian Schizophrenia Research Bank (ASRB) across 5 Australian states and territories. We first tested for the presence of peripheral cytokine deregulation in schizophrenia, using a large sample (N = 1143) comprising the ASRB. We next determined the extent to which individual variation in 8 circulating pro-/anti-inflammatory cytokines related to FW in brain white matter, imaged in a subset (n = 308) of patients and controls. Results Patients with schizophrenia showed reduced interleukin-2 (IL-2) (t = −3.56, P = .0004) and IL-12(p70) (t = −2.84, P = .005) and increased IL-6 (t = 3.56, P = .0004), IL-8 (t = 3.8, P = .0002), and TNFα (t = 4.30, P < .0001). Higher proinflammatory signaling of IL-6 (t = 3.4, P = .0007) and TNFα (t = 2.7, P = .0007) was associated with higher FW levels in white matter. The reciprocal increases in serum cytokines and FW were spatially widespread in patients encompassing most major fibers; conversely, in controls, the relationship was confined to the anterior corpus callosum and thalamic radiations. No relationships were observed with alternative dMRI measures, including the fractional anisotropy and tissue-related FA. Conclusions We report widespread deregulation of cytokines in schizophrenia and identify inflammation as a putative mechanism underlying increases in brain FW levels.

Diffusion restriction in the human spinal cord characterized in vivo with high b-value STEAM diffusion imaging

NeuroImage ◽  
2013 ◽  
Vol 82 ◽  
pp. 416-425 ◽  
Author(s):  
Novena A. Rangwala ◽  
David B. Hackney ◽  
Weiying Dai ◽  
David C. Alsop
Keyword(s):  
Spinal Cord ◽  
Diffusion Imaging ◽  
B Value ◽  
Diffusion Restriction ◽  
Human Spinal Cord ◽  
High B Value

The Diagnostic Value of Multi Ultra High-b-value DWI for Alzheimer’s Disease

2021 ◽  
Author(s):  
Tianxiu Zheng ◽  
Qiuyan Chen ◽  
Yanhua Qiu ◽  
Deyong Zhang ◽  
Liwei Shi ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
White Matter ◽  
B Value ◽  
Diagnostic Value ◽  
White Matter Hyperintensity ◽  
Periventricular White Matter ◽  
High B Value ◽  
Left Hippocampus ◽  
Hippocampus Volume

Abstract To evaluate the diagnostic value of multi-ultra high b-value diffusion-weighted imaging (UHBV-DWI) in Alzheimer’s disease (AD), and to build a regression prediction modelfor AD.90 participants including 30 AD, 30 mild cognitive impairments (MCI) and 30 volunteers without neurological diseases were enrolled to perform with hippocampal volume, white matter hyperintensities volume (WMH volume), periventricular white matter hyperintensity (PVWMH) score, deep white matter hyperintensity (DWMH) score and UHBV-DWI.We found UHBV-DWI outperformed in the diagnosis of AD (AUC = 0.858), and multiple linear regression model: y = 0.515 + 0.018 *(WMH volume) + 0.221 *(ADCuh value)-0.359 *(left hippocampus volume) were established.So we came to a conclusion: UHBV-DWI is helpful for diagnosing AD, and the combination of WMH volume and left hippocampus volume has a better diagnostic performance.

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.

scholarly journals ADC-Based Stratification of Molecular Glioma Subtypes Using High b-Value Diffusion-Weighted Imaging

2021 ◽  
Vol 10 (16) ◽  
pp. 3451
Author(s):  
Nils C. Nuessle ◽  
Felix Behling ◽  
Ghazaleh Tabatabai ◽  
Salvador Castaneda Vega ◽  
Jens Schittenhelm ◽  
...  
Keyword(s):  
Diffusion Weighted Imaging ◽  
Mean Diffusivity ◽  
Area Under The Curve ◽  
B Value ◽  
Diffusion Kurtosis Imaging ◽  
B Values ◽  
High B Value ◽  
Diffusion Weighted ◽  
Apparent Diffusion

Purpose: To investigate the diagnostic performance of in vivo ADC-based stratification of integrated molecular glioma grades. Materials and methods: Ninety-seven patients with histopathologically confirmed glioma were evaluated retrospectively. All patients underwent pre-interventional MRI-examination including diffusion-weighted imaging (DWI) with implemented b-values of 500, 1000, 1500, 2000, and 2500 s/mm2. Apparent Diffusion Coefficient (ADC), Mean Kurtosis (MK), and Mean Diffusivity (MD) maps were generated. The average values were compared among the molecular glioma subgroups of IDH-mutant and IDH-wildtype astrocytoma, and 1p/19q-codeleted oligodendroglioma. One-way ANOVA with post-hoc Games-Howell correction compared average ADC, MD, and MK values between molecular glioma groups. A Receiver Operating Characteristic (ROC) analysis determined the area under the curve (AUC). Results: Two b-value-dependent ADC-based evaluations presented statistically significant differences between the three molecular glioma sub-groups (p < 0.001, respectively). Conclusions: High-b-value ADC from preoperative DWI may be used to stratify integrated molecular glioma subgroups and save time compared to diffusion kurtosis imaging. Higher b-values of up to 2500 s/mm2 may present an important step towards increasing diagnostic accuracy compared to standard DWI protocol.

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