Buffalo Neuroimaging Analysis Center, Buffalo, NY, USA; Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, USA.
Buffalo Neuroimaging Analysis Center, Buffalo, NY, USA; Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, State University of New York at Buffalo, Buffalo, NY, USA; MR Research Laboratory, IRCCS, Don Gnocchi Foundation ONLUS, Milan, Italy.
Neuroimage. 2022 Nov 1;261:119503. doi: 10.1016/j.neuroimage.2022.119503. Epub 2022 Jul 22.
Brain iron homeostasis is necessary for healthy brain function. MRI and histological studies have shown altered brain iron levels in the brains of patients with multiple sclerosis (MS), particularly in the deep gray matter (DGM). Previous studies were able to only partially separate iron-modifying effects because of incomplete knowledge of iron-modifying processes and influencing factors. It is therefore unclear to what extent and at which stages of the disease different processes contribute to brain iron changes. We postulate that spatially covarying magnetic susceptibility networks determined with Independent Component Analysis (ICA) reflect, and allow for the study of, independent processes regulating iron levels. We applied ICA to quantitative susceptibility maps for 170 individuals aged 9-81 years without neurological disease ("Healthy Aging" (HA) cohort), and for a cohort of 120 patients with MS and 120 age- and sex-matched healthy controls (HC; together the "MS/HC" cohort). Two DGM-associated "susceptibility networks" identified in the HA cohort (the Dorsal Striatum and Globus Pallidus Interna Networks) were highly internally reproducible (i.e. "robust") across multiple ICA repetitions on cohort subsets. DGM areas overlapping both robust networks had higher susceptibility levels than DGM areas overlapping only a single robust network, suggesting that these networks were caused by independent processes of increasing iron concentration. Because MS is thought to accelerate brain aging, we hypothesized that associations between age and the two robust DGM-associated networks would be enhanced in patients with MS. However, only one of these networks was altered in patients with MS, and it had a null age association in patients with MS rather than a stronger association. Further analysis of the MS/HC cohort revealed three additional disease-related networks (the Pulvinar, Mesencephalon, and Caudate Networks) that were differentially altered between patients with MS and HCs and between MS subtypes. Exploratory regression analyses of the disease-related networks revealed differential associations with disease duration and T2 lesion volume. Finally, analysis of ROI-based disease effects in the MS/HC cohort revealed an effect of disease status only in the putamen ROI and exploratory regression analysis did not show associations between the caudate and pulvinar ROIs and disease duration or T2 lesion volume, showing the ICA-based approach was more sensitive to disease effects. These results suggest that the ICA network framework increases sensitivity for studying patterns of brain iron change, opening a new avenue for understanding brain iron physiology under normal and disease conditions.
脑内铁稳态对于大脑的正常功能至关重要。磁共振成像（MRI）和组织学研究显示，多发性硬化症（MS）患者的脑内铁水平发生改变，尤其在深部灰质（DGM）中。之前的研究由于对铁调节过程和影响因素的不完全了解，只能部分分离铁调节作用。因此，不同的过程在多大程度上以及在疾病的哪个阶段对脑内铁变化产生影响仍不清楚。我们假设，独立成分分析（ICA）确定的空间协变磁化率网络反映并允许研究调节铁水平的独立过程。我们对 170 名年龄在 9 至 81 岁之间、无神经疾病的个体（“健康衰老”（HA）队列）的定量磁化率图和 120 名 MS 患者和 120 名年龄和性别匹配的健康对照者（HC；统称为“MS/HC”队列）的定量磁化率图进行了 ICA 分析。在 HA 队列中确定的两个与 DGM 相关的“磁化率网络”（背侧纹状体和内苍白球网络）在多个 ICA 重复的队列子集中具有高度的内部可重复性（即“稳健”）。与两个稳健网络均重叠的 DGM 区域的磁化率水平高于仅与单个稳健网络重叠的 DGM 区域，表明这些网络是由增加铁浓度的独立过程引起的。由于 MS 被认为会加速脑老化，我们假设在 MS 患者中，年龄与两个稳健的 DGM 相关网络之间的关联会增强。然而，在 MS 患者中只有一个网络发生改变，并且与年龄没有关联，而是与 MS 患者的关联较弱。对 MS/HC 队列的进一步分析揭示了三个额外的与疾病相关的网络（丘脑下核、中脑和尾状核网络），这些网络在 MS 患者和 HC 之间以及 MS 亚型之间存在差异。对与疾病相关的网络的探索性回归分析显示，与疾病持续时间和 T2 病变体积存在差异关联。最后，对 MS/HC 队列的基于 ROI 的疾病效应分析显示，只有在纹状体 ROI 中存在疾病状态的影响，而探索性回归分析并未显示尾状核和丘脑下核 ROI 与疾病持续时间或 T2 病变体积之间的关联，表明基于 ICA 的方法对疾病效应更敏感。这些结果表明，ICA 网络框架提高了研究脑铁变化模式的敏感性，为理解正常和疾病状态下的脑铁生理学开辟了新途径。
