White matter tract density index is associated with disability in multiple sclerosis

被引:1
作者
Kim, Minhoe [1 ]
Seo, Ji Won [2 ]
Kim, Myung Sub [3 ]
Lee, Kyung Hoon [3 ]
Kim, Minchul [3 ]
机构
[1] Korea Univ, Dept Comp Convergence Software, Sejong, South Korea
[2] Natl Canc Ctr, Res Inst & Hosp, Dept Radiol, Goyang Si, South Korea
[3] Sungkyunkwan Univ, Sch Med, Kangbuk Samsung Hosp, Dept Radiol, 29 Saemunan Ro, Seoul 03181, South Korea
基金
新加坡国家研究基金会;
关键词
Multiple sclerosis; Tract density index; Disability; Clinico-radiological paradox; MRI; BRAIN; NETWORKS; MRI;
D O I
10.1016/j.nbd.2024.106548
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Background: The association between common neuroradiological markers of multiple sclerosis (MS) and clinical disability is weak. Given that the disability in patients with MS may depend on the underlying structural connectivity of the brain, our study aimed to examine the association between white matter tracts affected by MS and the patients' disability using a new tract density index (TDI). Method: This study included 53 patients diagnosed with MS, examined between 2019 and 2020. Manual lesion segmentation was performed on fluid -attenuated inversion recovery (FLAIR) images, and the density of white matter tracts encompassing the lesion (i.e., TDI) was calculated. Correlation analysis was employed to assess the association between TDI and disability. Additionally, the relationship between disability, TDI, and lesion -derived network metrics was examined by computing a partial correlation network. Results: The TDI significantly correlated with the expanded disability status scale (EDSS) ( r = 0.30, p = 0.03). Furthermore, the patient's disability is linked solely through TDI to lesion -derived network metrics -a key metric that 'bridges' the gap between the brain lesion and disability. Conclusions: In this study, MS lesions encompassing regions with high white matter tract density were associated and linked with severe physical disability. These findings indicate that TDI may be an outcome predictor that may connect radiologic findings to clinical practice.
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页数:6
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