Department of Radiology, Graduate School of Medicine, Hirosaki University, Hirosaki, Japan.
Department of Radiology, Kyoto Prefectural University of Medicine, 465 Kajiimachi, Jokyo-ku, Kyoto-shi, Kyoto-fu, Japan.
Sci Rep. 2023 Aug 16;13(1):13330. doi: 10.1038/s41598-023-39569-6.
Although altered networks inside the hippocampus (hippocampal intra-networks) have been observed in dementia, the evaluation of hippocampal intra-networks using magnetic resonance imaging (MRI) is challenging. We employed conventional structural imaging and incident component analysis (ICA) to investigate the structural covariance of the hippocampal intra-networks. We aimed to assess altered hippocampal intra-networks in patients with mild cognitive impairment (MCI). A cross-sectional study of 2122 participants with 3T MRI (median age 69 years, 60.9% female) were divided into 218 patients with MCI and 1904 cognitively normal older adults (CNOA). By employing 3D T1-weighted imaging, voxels within the hippocampus were entered into the ICA analysis to extract the structural covariance intra-networks within the hippocampus. The ICA extracted 16 intra-networks from the hippocampal structural images, which were divided into two bilateral networks and 14 ipsilateral networks. Of the 16 intra-networks, two (one bilateral network and one ipsilateral networks) were significant predictors of MCI from the CNOA after adjusting for age, sex, education, disease history, and hippocampal volume/total intracranial volume ratio. In conclusion, we found that the relationship between hippocampal intra-networks and MCI was independent from the hippocampal volume. Our results suggest that altered hippocampal intra-networks may reflect a different pathology in MCI from that of brain atrophy.
尽管在痴呆症患者中观察到海马内网络(海马内网络)发生了改变,但使用磁共振成像(MRI)评估海马内网络具有挑战性。我们采用常规结构成像和事件成分分析(ICA)来研究海马内网络的结构协方差。我们旨在评估轻度认知障碍(MCI)患者的海马内网络改变。对 2122 名接受 3T MRI 检查的参与者进行了一项横断面研究(中位年龄 69 岁,60.9%为女性),分为 218 名 MCI 患者和 1904 名认知正常的老年人(CNOA)。通过使用 3D T1 加权成像,将海马内的体素输入 ICA 分析中,以提取海马内的结构协方差内网络。ICA 从海马结构图像中提取了 16 个内网络,分为两个双侧网络和 14 个同侧网络。在调整年龄、性别、教育程度、病史和海马体积/总颅内体积比后,这 16 个内网络中有两个(一个双侧网络和一个同侧网络)是从 CNOA 中预测 MCI 的显著预测因子。总之,我们发现海马内网络与 MCI 之间的关系独立于海马体积。我们的结果表明,改变的海马内网络可能反映了 MCI 与脑萎缩不同的病理学。