The strip-shaped deep white matter hyperintensities may be related to neurodegeneration: A study based on diffusion tensor imaging
Abstract
Purpose This study investigated the properties of strip-shaped white matter hyperintensities (WMHs) by examining the relationship between their principal axes and maximum diffusion direction, and comparing their diffusion tensor metrics with punctate and early confluent WMHs to explore their pathological features and potential link to chronic axonal injury. Methods Eighty-seven participants with isolated WMHs were included and received diffusion tensor imaging (DTI) using a 3.0-T MR scanner. Lesions were classified by elongation (strip-shaped: ≥ 2; punctate: < 2). We recorded the maximum diffusion direction of punctate, strip-shaped, and early confluent WMHs and evaluated their consistency with the principal axial direction. Additionally, the diffusion tensor metrics of strip-shaped WMHs were measured and compared with those of punctate and early confluent WMHs. Results Among 996 WMHs, 57.5% of strip-shaped lesions showed principal axis alignment with the maximum diffusion direction, significantly higher than punctate (44.7%) and early confluent (42.1%) lesions (P < 0.05). Mixed-effects models showed significant lesion type effects on FA, MD, AD, and RD (all P < 0.05). Post-hoc comparisons revealed that stripe lesions had higher FA but lower MD and RD than confluence lesions (all P < 0.05), with no significant differences between stripe and punctate groups for MD or RD. No AD differences were found among groups. Fiber tracking confirmed alignment in most strip-shaped lesions. Conclusion Strip-shaped WMHs exhibit unique diffusion metrics and fiber alignment, suggesting chronic axonal injury due to neurodegeneration. These findings underscore the importance of morphological and microstructural analysis in understanding WMH heterogeneity and clinical significance.
Article Details
Authors (9)
Xuexia Sheng
Long Xu
Donghua Xu
Daihai Yuan
Wenchao Xie
School of Materials Science and Engineering
Jiyuan Ge
Yizhi Zhao
Chaoyue Yang
Zhigang Min