...
首页> 外文期刊>BioMed research international >Intrinsic Functional Connectivity Networks in Healthy Elderly Subjects: A Multiparametric Approach with Structural Connectivity Analysis
【24h】

Intrinsic Functional Connectivity Networks in Healthy Elderly Subjects: A Multiparametric Approach with Structural Connectivity Analysis

机译:健康老年人中的内在功能连接网络:具有结构连接分析的多参数方法

获取原文
获取原文并翻译 | 示例

摘要

Intrinsic functional connectivity magnetic resonance imaging (iFCMRI) provides an encouraging approach for mapping large-scale intrinsic connectivity networks (ICNs) in the "resting" brain. Structural connections as measured by diffusion tensor imaging (DTI) are a major constraint on the identified ICNs. This study aimed at the combined investigation often well-defined ICNs in healthy elderly subjects at single subject level as well as at the group level, together with the underlying structural connectivity. IFCMRI and DTI data were acquired in twelve subjects (68 ± 7 years) at a 3T scanner and were studied using the tensor imaging and fiber tracking software package. The seed-based iFCMRI analysis approach was comprehensively performed with DTI analysis, following standardized procedures including an 8-step processing of iFCMRI data. Our findings demonstrated robust ICNs at the single subject level and conclusive brain maps at the group level in the healthy elderly sample, supported by the complementary fiber tractography. The findings demonstrated here provide a methodological framework for future comparisons of pathological (e.g., neurodegenerative) conditions with healthy controls on the basis of multiparametric functional connectivity mapping.
机译:本质功能连接磁共振成像(iFCMRI)提供了一种令人鼓舞的方法,可在“静止”大脑中绘制大规模内部连接网络(ICN)。通过扩散张量成像(DTI)测量的结构连接是对已识别ICN的主要限制。这项研究的目的是对健康的老年受试者(无论是在单个受试者水平还是在小组水平)经常进行定义明确的ICN以及潜在的结构连通性进行联合研究。 IFCMRI和DTI数据是通过3T扫描仪在十二个受试者(68±7年)中获取的,并使用张量成像和纤维跟踪软件包进行了研究。基于种子的iFCMRI分析方法通过DTI分析全面执行,遵循包括8个步骤的iFCMRI数据处理在内的标准化程序。我们的研究结果表明,在老年受试者的健康样本中,单纤维水平的健壮的ICNs和组水平的确凿性脑图得到了补充纤维束成像的支持。此处展示的发现为基于多参数功能连接映射的病理(例如神经退行性)疾病状况与健康对照的未来比较提供了一种方法框架。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号