U-fiber diffusion kurtosis and susceptibility characteristics in relapsing–remitting multiple sclerosis may be related to cognitive deficits and neurodegeneration

Luo, Dan; Peng, Yuling; Zhu, Qiyuan; Zheng, Qiao; Luo, Qi; Han, Yongliang; Chen, Xiaoya; Li, Yongmei

doi:10.1007/s00330-023-10114-3

Cited by 4 publications

(2 citation statements)

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“…Due to the late development of U-fibers, they are susceptible to damage in demyelinating diseases, such as multiple sclerosis ( 47 ). Moreover, the quantified diffusion parameters can reflect the damage of U-fiber earlier than WMHs ( 67 ).…”

Section: Discussionmentioning

confidence: 99%

“…In this paper, the multi-shell, multi-tissue constrained spherical deconvolution technique was adopted to model the local fiber orientation distribution ( 24 ). First, the MRtrix3 command ‘dwi2response’ was used to generate the response kernel function of GM, WM, and CSF ( 47 ). Then, the MRtrix3 command ‘dwi2fod’ was used to estimate the local fiber orientation distributions ( 21 , 22 ).…”

Section: Methodsmentioning

confidence: 99%

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U-fiber analysis: a toolbox for automated quantification of U-fibers and white matter hyperintensities

Zheng,

Fei,

et al. 2024

Quant Imaging Med Surg

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Background Whether white matter hyperintensities (WMHs) involve U-fibers is of great value in understanding the different etiologies of cerebral white matter (WM) lesions. However, clinical practice currently relies only on the naked eye to determine whether WMHs are in the vicinity of U-fibers, and there is a lack of good neuroimaging tools to quantify WMHs and U-fibers. Methods Here, we developed a multimodal neuroimaging toolbox named U-fiber analysis (UFA) that can automatically extract WMHs and quantitatively characterize the volume and number of WMHs in different brain regions. In addition, we proposed an anatomically constrained U-fiber tracking scheme and quantitatively characterized the microstructure diffusion properties, fiber length, and number of U-fibers in different brain regions to help clinicians to quantitatively determine whether WMHs in the proximal cortex disrupt the microstructure of U-fibers. To validate the utility of the UFA toolbox, we analyzed the neuroimaging data from 246 patients with cerebral small vessel disease (cSVD) enrolled at Zhongshan Hospital between March 2018 and November 2019 in a cross-sectional study. Results According to the manual judgment of the clinician, the patients with cSVD were divided into a WMHs involved U-fiber group (U-fiber-involved group, 51 cases) and WMHs not involved U-fiber group (U-fiber-spared group, 163 cases). There were no significant differences between the U-fiber-spared group and the U-fiber-involved group in terms of age (P=0.143), gender (P=0.462), education (P=0.151), Mini-Mental State Examination (MMSE) scores (P=0.151), and Montreal Cognitive Assessment (MoCA) scores (P=0.411). However, patients in the U-fiber-involved group had higher Fazekas scores (P<0.001) and significantly higher whole brain WMHs (P=0.046) and deep WMH volumes (P<0.001) compared to patients in the U-fiber-spared group. Moreover, the U-fiber-involved group had higher WMH volumes in the bilateral frontal [P(left) <0.001, P(right) <0.001] and parietal lobes [P(left) <0.001, P(right) <0.001]. On the other hand, patients in the U-fiber-involved group had higher mean diffusivity (MD) and axial diffusivity (AD) in the bilateral parietal [P(left, MD) =0.048, P(right, MD) =0.045, P(left, AD) =0.015, P(right, AD) =0.015] and right frontal-parietal regions [P(MD) =0.048, P(AD) =0.027], and had significantly reduced mean fiber length and number in the right parietal [P(length) =0.013, P(number) =0.028] and right frontal-parietal regions [P(length) =0.048] compared to patients in the U-fiber-spared group. Conclusions Our results suggest that WMHs in the proximal cortex may disrupt the microstructure of U-fibers. Our tool may provide new insights into the understanding of WM lesions of different etiologies in the brain.

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Section: Discussionmentioning

confidence: 99%