Using QSM to investigate the iron deposition patterns in deep gray matter nuclei during the aging process in healthy populations
Year: 2026
Type: article
Study design: Observational Study
Abstract: Objective We utilized 3.0T quantitative susceptibility mapping (QSM) technology to simultaneously investigate (a) the relationship between susceptibility values of deep gray matter (DGM) nuclei and age in healthy populations throughout the aging process, and (b) the correlation of iron deposition among DGM nuclei and its potential clinical significance. Methods QSM images of DGM nuclei were obtained from 100 healthy participants using a 3.0T MRI scanner. Susceptibility values of corresponding DGM nuclei were acquired by manually delineating regions of interest (ROIs). Pearson correlation analysis and univariate regression analysis were performed between age and the susceptibility values of each DGM nucleus. Partial correlation analysis was employed to investigate the relationships between mean susceptibility values across multiple nuclei. Results Except for the CN, the susceptibility values of the other five DGM nuclei showed a significant positive correlation with age. Among them, the PUT had the strongest correlation with age ( r = 0.71, p < 0.001), and the iron deposition rate in the PUT increased the most with age (linear regression slope = 0.93 ppb/year). Additionally, the average susceptibility values between multiple pairs of DGM nuclei, including SN and GP ( r = 0.59, p < 0.001), SN and DN ( r = 0.46, p < 0.001), etc., still shows a significant positive correlation after controlling for age. Conclusion Iron deposition in the PUT exhibits the most significant age dependence; the correlation of iron deposition among nuclei may indicate that specific nuclei share the same iron deposition patterns or are involved in the same pathological mechanisms.
Source: Frontiers in Aging Neuroscience
Authors: Xingyu Wang, Yiming Wang, Lin Liu, Ziqi Wang, Qingfeng Zhu, Duo Gao, Xuefang Han, Huandi Lv, Song Zhao, Hong Song, Xueshan Cao, Zuojun Geng
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,801
Funder: Hebei Medical University
OpenAlex ID: W7203723717
