Probing axons using multi‐compartmental diffusion in multiple sclerosis

Bagnato, Francesca; Franco, Giulia; Li, Hua; Kaden, Enrico; Ye, Fei; Fan, Ruitai; Chen, Amalie; Alexander, Daniel C.; Smith, Seth A.; Dortch, Richard D.; Xu, Junzhong

doi:10.1002/acn3.50836

Cited by 18 publications

(22 citation statements)

References 29 publications

(42 reference statements)

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“…Accordingly, despite the MC-SMT model does not allow the quantification of non-monoexponential behavior to describe the deviation of diffusion displacement from the Gaussian profile specifically ( Jensen et al, 2005 ), a significant decrease of μFA and ƒ in have been demonstrated for different degrees of brain and SC tissue damage in MS compared with normal WM tissue ( By et al, 2018 , Lakhani et al, 2020 ). Furthermore, such microscopic features seem to be able to distinguish MS lesions with more axonal damage from the lesions that are hyperintense in T2-weighted sequences ( Bagnato et al, 2019 , Bonet-Carne et al, 2019 ), identified as black-holes in T1-spin echo sequences ( van Walderveen et al, 1998 ). When compared with the observation of black holes, the use of quantitative diffusion metrics increases the accuracy and reproducibility of the results.…”

Section: Discussionmentioning

confidence: 99%

“…The MC-SMT model computes a multi-compartment domain, encompassing extra-axonal and intra-axonal water diffusion spaces, and microscopic diffusion tensor maps to estimate distinct local tissue properties ( Kaden et al, 2016 ). In MS, MC-SMT seems to be able to distinguish chronic black-holes and thus, lesions with greater tissue damage from hyperintense T2 lesions ( Bagnato et al, 2019 , Bonet-Carne et al, 2019 ), and this approach can detect reductions in the apparent axon volume fraction in the spinal cord (SC) ( By et al, 2018 ). Therefore, SMT-derived tissue features could be used as biomarkers to quantify the heterogeneous mechanisms involved in MS lesion pathogenesis in vivo .…”

Section: Introductionmentioning

confidence: 99%

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Characterization of multiple sclerosis lesions with distinct clinical correlates through quantitative diffusion MRI

Martínez‐Heras

Solana

Prados

et al. 2020

NeuroImage: Clinical

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Characterization of multiple sclerosis lesions with distinct clinical correlates through quantitative diffusion MRI

Martínez‐Heras

Solana

Prados

et al. 2020

NeuroImage: Clinical

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“…In brains 22 and spinal cords 23 of patients with MS, V ax and D ax (brain only 22 ) differed between lesions and normal-appearing WM. Decreased values of V ax were also observed in normalappearing WM compared with normal WM of healthy controls (spinal cord only 23 ).…”

Section: Histopathologic Validationmentioning

confidence: 92%

“…Clinical Application. Only 2 studies investigating the feasibility and applicability of SMT in the brains 22 and spinal cords 23 of patients with MS have been performed thus far. The authors reported acquisition times between $18 minutes 23 (single-section C-spine, 1.25 Â 1.25 Â 10 mm resolution, 2 b-shells, with 32 and 64 directions) and $22 minutes (full brain coverage, 0.4 Â 0.4 Â 2 mm resolution, 90 directions).…”

Section: Histopathologic Validationmentioning

confidence: 99%

“…The authors reported acquisition times between $18 minutes 23 (single-section C-spine, 1.25 Â 1.25 Â 10 mm resolution, 2 b-shells, with 32 and 64 directions) and $22 minutes (full brain coverage, 0.4 Â 0.4 Â 2 mm resolution, 90 directions). 22 The implementation of a recently developed multiband technique can remarkably reduce the scan time while keeping similar image quality. For example, a multiband factor of 3 can reduce the total scan time of SMT from $22 minutes to $10 minutes.…”

Section: Histopathologic Validationmentioning

confidence: 99%

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Advanced Multicompartment Diffusion MRI Models and Their Application in Multiple Sclerosis

Lakhani

Schilling

et al. 2020

AJNR Am J Neuroradiol

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Conventional MR imaging techniques are sensitive to pathologic changes of the brain and spinal cord seen in MS, but they lack specificity for underlying axonal and myelin integrity. By isolating the signal contribution from different tissue compartments, newly developed advanced multicompartment diffusion MR imaging models have the potential to detect specific tissue subtypes and associated injuries with increased pathologic specificity. These models include neurite orientation dispersion and density imaging, diffusion basis spectrum imaging, multicompartment microscopic diffusion MR imaging with the spherical mean technique, and models enabled through high-gradient diffusion MR imaging. In this review, we provide an appraisal of the current literature on the physics principles, histopathologic validation, and clinical applications of each of these techniques in both brains and spinal cords of patients with MS. We discuss limitations of each of the methods and directions that future research could take to provide additional validation of their roles as biomarkers of axonal and myelin injury in MS. ABBREVIATIONS: AD ¼ axial diffusivity; D ax ¼ intra-axonal diffusivity; DBSI ¼ diffusion basis spectrum imaging; FF ¼ fiber fraction; IVF ¼ isotropic volume fraction; NDI ¼ neurite density index (also V ic , V in , or f icvf ); NODDI ¼ neurite orientation dispersion and density imaging; ODI ¼ orientation dispersion index; RD ¼ radial diffusivity; SMT ¼ spherical mean technique; V ax ¼ intra-axonal volume fraction

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Microscopic fractional anisotropy outperforms multiple sclerosis lesion assessment and clinical outcome associations over standard fractional anisotropy tensor

Vivó,

Solana,

Calvi

et al. 2024

Human Brain Mapping

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We aimed to compare the ability of diffusion tensor imaging and multi‐compartment spherical mean technique to detect focal tissue damage and in distinguishing between different connectivity patterns associated with varying clinical outcomes in multiple sclerosis (MS). Seventy‐six people diagnosed with MS were scanned using a SIEMENS Prisma Fit 3T magnetic resonance imaging (MRI), employing both conventional (T1w and fluid‐attenuated inversion recovery) and advanced diffusion MRI sequences from which fractional anisotropy (FA) and microscopic FA (μFA) maps were generated. Using automated fiber quantification (AFQ), we assessed diffusion profiles across multiple white matter (WM) pathways to measure the sensitivity of anisotropy diffusion metrics in detecting localized tissue damage. In parallel, we analyzed structural brain connectivity in a specific patient cohort to fully grasp its relationships with cognitive and physical clinical outcomes. This evaluation comprehensively considered different patient categories, including cognitively preserved (CP), mild cognitive deficits (MCD), and cognitively impaired (CI) for cognitive assessment, as well as groups distinguished by physical impact: those with mild disability (Expanded Disability Status Scale [EDSS] <=3) and those with moderate–severe disability (EDSS >3). In our initial objective, we employed Ridge regression to forecast the presence of focal MS lesions, comparing the performance of μFA and FA. μFA exhibited a stronger association with tissue damage and a higher predictive precision for focal MS lesions across the tracts, achieving an R‐squared value of .57, significantly outperforming the R‐squared value of .24 for FA (p‐value <.001). In structural connectivity, μFA exhibited more pronounced differences than FA in response to alteration in both cognitive and physical clinical scores in terms of effect size and number of connections. Regarding cognitive groups, FA differences between CP and MCD groups were limited to 0.5% of connections, mainly around the thalamus, while μFA revealed changes in 2.5% of connections. In the CP and CI group comparison, which have noticeable cognitive differences, the disparity was 5.6% for FA values and 32.5% for μFA. Similarly, μFA outperformed FA in detecting WM changes between the MCD and CI groups, with 5% versus 0.3% of connections, respectively. When analyzing structural connectivity between physical disability groups, μFA still demonstrated superior performance over FA, disclosing a 2.1% difference in connectivity between regions closely associated with physical disability in MS. In contrast, FA spotted a few regions, comprising only 0.6% of total connections. In summary, μFA emerged as a more effective tool than FA in predicting MS lesions and identifying structural changes across patients with different degrees of cognitive and global disability, offering deeper insights into the complexities of MS‐related impairments.

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Probing axons using multi‐compartmental diffusion in multiple sclerosis

Cited by 18 publications

References 29 publications

Characterization of multiple sclerosis lesions with distinct clinical correlates through quantitative diffusion MRI

Characterization of multiple sclerosis lesions with distinct clinical correlates through quantitative diffusion MRI

Advanced Multicompartment Diffusion MRI Models and Their Application in Multiple Sclerosis

Microscopic fractional anisotropy outperforms multiple sclerosis lesion assessment and clinical outcome associations over standard fractional anisotropy tensor

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