Park Taejun, Choi Yunjeong, Kwon Hyeok-Jae, Lee Mun Bae, Rhee Hak Young, Park Soonchan, Ryu Chang-Woo, Jahng Geon-Ho
Department of Radiology, Kyung Hee University Hospital at Gangdong, Seoul, Republic of Korea.
Department of Biomedical Engineering, Undergraduate School, College of Electronics and Information, Kyung Hee University, Yongin-si, Republic of Korea.
Quant Imaging Med Surg. 2025 May 1;15(5):4669-4688. doi: 10.21037/qims-24-2145. Epub 2025 Apr 28.
The aging brain undergoes various microstructural changes that influence its electrical properties. Conductivity, a measure of ion mobility, is particularly sensitive to these changes and can be assessed non-invasively using magnetic resonance electrical properties tomography (MREPT). Despite advancements in imaging techniques, the relationship between brain conductivity, diffusivity, and tissue volume in the context of aging and neurodegeneration remains incompletely understood. This study explores the relationships between electrical conductivity, diffusivity, and brain tissue volume in the aging brain, which is crucial for early diagnosis and monitoring of neurodegenerative diseases such as Alzheimer's, where these parameters could serve as potential biomarkers for disease progression.
In this cross-sectional, prospective study, 77 patients were assessed brain MREPT and diffusion tensor imaging with multiple shells and gradient directions (b=0, 800, and 2,000 s/mm). High-frequency conductivity (HFC) was calculated and separated into extra-neurite (EC) and intra-neurite conductivities (IC). We analyzed correlations between these conductivity indices and other magnetic resonance imaging (MRI) metrics, controlling for age, and explored the relationship between conductivity, diffusion, and Mini-Mental State Examination (MMSE) scores using multiple regression analysis.
EC within the insular region negatively correlated with MMSE scores (r=-0.3027, P=0.0079). HFC in the hippocampus was positively associated with mean diffusivity (MD; β=192.4, P=0.008) and radial diffusivity (RD; β=207.6, P=0.004). HFC in the insula was positively associated with axial diffusivity (AxD; β=356.9, P=0.0004), MD (β=314.4, P=0.004), RD (β=275.5, P=0.012). EC in the hippocampus was positively associated with AxD (β=309.3, P=0.0001), MD (β=333.7, P<0.001), RD (β=341.8, P<0.001). EC in the insular was positively associated with AxD (β=324.1, P=0.0009) and MD (β=270.4, P=0.01). IC was positively correlated with intra-neurite diffusivity (ID) in the amygdala, thalamus, and insula.
These findings suggest that increased conductivity is associated with altered diffusivity and reduced cognitive performance, suggesting the use of MREPT to differentiate between conductivity changes due to ion mobility versus proton density, and how this approach contributes to understanding the aging brain and neurodegeneration. MREPT-derived measurements primarily reflect ion mobility and caution that clinical interpretations should consider the direct relationships between conductivity and diffusion changes.
衰老的大脑会经历各种微观结构变化，这些变化会影响其电特性。电导率是衡量离子迁移率的指标，对这些变化特别敏感，可以使用磁共振电特性断层扫描（MREPT）进行无创评估。尽管成像技术取得了进展，但在衰老和神经退行性变的背景下，脑电导率、扩散率和组织体积之间的关系仍未完全明确。本研究探讨了衰老大脑中电导率、扩散率和脑组织体积之间的关系，这对于早期诊断和监测神经退行性疾病（如阿尔茨海默病）至关重要，因为这些参数可能作为疾病进展的潜在生物标志物。
在这项横断面前瞻性研究中，对77名患者进行了脑MREPT和具有多个壳层及梯度方向（b = 0、800和2000 s/mm²）的扩散张量成像。计算了高频电导率（HFC），并将其分为神经突外（EC）和神经突内电导率（IC）。我们分析了这些电导率指标与其他磁共振成像（MRI）指标之间的相关性，并控制了年龄，还使用多元回归分析探讨了电导率、扩散率与简易精神状态检查表（MMSE）评分之间的关系。
岛叶区域的EC与MMSE评分呈负相关（r = -0.3027，P = 0.0079）。海马体中的HFC与平均扩散率（MD；β = 192.4，P = 0.008）和径向扩散率（RD；β = 207.6，P = 0.004）呈正相关。岛叶中的HFC与轴向扩散率（AxD；β = 356.9，P = 0.0004）、MD（β = 314.4，P = 0.004）、RD（β = 275.5，P = 0.012）呈正相关。海马体中的EC与AxD（β = 309.3，P = 0.0001）、MD（β = 333.7，P < 0.001）、RD（β = 341.8，P < 0.001）呈正相关。岛叶中的EC与AxD（β = 324.1，P = 0.0009）和MD（β = 270.4，P = 0.01）呈正相关。IC与杏仁核、丘脑和岛叶中的神经突内扩散率（ID）呈正相关。
这些发现表明，电导率增加与扩散率改变和认知能力下降有关，这表明使用MREPT来区分由于离子迁移率与质子密度引起的电导率变化，以及这种方法如何有助于理解衰老大脑和神经退行性变。MREPT得出的测量结果主要反映离子迁移率，并提醒临床解释应考虑电导率和扩散变化之间的直接关系。
