植物状态神经可塑性的多模态证据: 基于脑结构网络和脑功能网络的纵向研究

Vegetative state, a special type of consciousness disorder, is an important model to study the biological basis of human consciousness. Anatomical and functional investigations on the patients in a vegetative state (VS) have been intensively discussed in cognitive neuroscience and clinical medicine in recent years..As human brain connectivity or networks is a basic feature of brain organization, its importance has been increasingly realized in studying human brain function and structure in health and illness, and unraveling the mystery of the mind or consciousness. .Recently, the resting state fMRI (R-fMRI) and DTI approaches have been proven useful in detecting higher order processing in VS patients without requiring active participation or language comprehension. However, less brain network studies of VS patients have been reported, and the factors affecting the reliability and stability of the brain network is to be discovered, resulting a lack of standard to construct brain network, which significantly impairs the sensitivity of brain network analysis and the comparability of different studies. .Filling this knowledge gap is in an urgent need and brain networks analysis may offer an effective way to detect and track recovery of consciousness in VS patients..The current project aims to study the parameters affecting the reliability of the brain networks, explore a universal method to construct brain network, and apply the method to a longitudinal study of VS patients who gradually recover consciousness. .The project will be achieved through the following specific efforts: 1) Optimize the existing R-fMRI and DTI pulse sequences for acquiring the networks data. 2) Optimize the definitions of node and edge for reconstructing brain networks. Set up links between the calculated connectivity patterns and existing architectonic data. 3) Acquire multimodal brain imaging (R-fMRI and DTI) data of VS patients longitudinally and transversely. Collect the sensory, motor, cognitive, and emotional information of each VS subject. 4) Construct VS patients' anatomical and functional networks, seek the aberrant connecting modes related to VS, and explore a network-based biomarker to scale consciousness recovery of VS. 5) Build up a multimodal brain imaging database of the VS patients. 6) Organize a national-wide workshop (mainly for postgraduates) to introduce the methods and applications of the brain networks..Overall, the current project in theory will provide a scientific standard of network construction, and in methodology will provide multimodal evidence on the brain plasticity of VS patients. Lying in the cutting edge research field, the project is of scientific and clinical and social significance.

植物状态（VS─Vegetative State）是一种特殊类型的意识障碍，对其进行研究是探究人类意识生物学基础的重要途径。近年来，VS患者的脑功能和脑结构的研究受到了脑科学和临床领域的关注。目前，脑网络分析方法已被广泛用于'脑功能-结构-行为'关系、以及多种脑疾病的研究中。然而，用大规模脑网络的方法对VS患者的研究还非常有限，且影响脑网络可重复性的因素尚缺乏系统的探讨，导致缺乏构建脑网络的统一标准，严重影响了脑网络分析的敏感性，以及不同研究之间的可比性。本项目拟研究影响脑网络稳定性的因素，探索构造脑网络的结点和边的普适方法。通过采集VS患者的纵向多模态神经影像数据集，构建VS患者的的脑结构网络、脑功能网络模型，寻找与该疾病相关的异常连接模式，探索检测VS患者的意识恢复和康复进展的脑网络影像学标记。本项目在理论上可以提供脑网络构建的科学标准，在方法上为VS患者的神经可塑性提供多模态的证据。

意识的生物学基础是2005年Science杂志提出的125个重点问题的第二个问题。本项目对意识障碍（disease of consciousness, DOC）患者进行有关脑网络属性和临床量表的研究，以期为探索意识及其生物学基础提供新的视角。. . 近年来，有关于DOC患者脑功能和脑结构的研究受到了脑科学和临床领域的关注。目前，脑网络分析方法已被广泛用于‘脑功能-结构-行为’关系、以及多种脑疾病的研究中。然而，采用脑网络模型对DOC患者进行相关研究还非常有限。本项目拟通过采集DOC患者的纵向多模态神经影像数据集，构建DOC患者的脑结构网络、脑功能网络，发现与该疾病相关的异常连接模式，探讨DOC患者脑损伤康复过程中神经网络的动态修复过程，探索检测DOC患者的意识恢复进展的脑网络影像学标记。.. 作为一年期的资助项目，目前我们用磁共振成像系统扫描了40余名DOC患者，经过严格的影像质量控制，获得DOC患者影像情况如下。关于静息态脑功能影像（resting-state fMRI, rsfMRI）：我们获得了16名DOC患者的大脑影像（颅脑完整）、其中11名VS（vegetative state, VS） 患者，5名MCS（minimal conscious state, MCS）患者。关于扩散加权图像（Diffusion tensor imaging, DTI）：现有可用DOC患者12名（年龄17-58岁，平均36岁，男/女 11/1） 。我们的目标是逐步建立国内DOC患者多模态磁共振影像数据库。.. 在数据分析方面，我们利用脑功能网络、脑结构网络分别对DOC患者的脑影像数据进行了分析，同时用局部一致性的方法（ALFF）分析了脑功能的局部特性。大脑网络模型突破了传统的仅研究孤立脑区的局限，可以从网络水平上探求DOC患者的意识状态诊断、预后判断及疗效评价，为探索DOC患者的病理生理机制和早期诊断及病情进展检测提供可能的影像学标记。这三部分计算研究初步结果已写成会议摘要，递交了2014年在德国汉堡举行的Human Brain Mapping年会。同时，课题组成员正在深化对数据的进一步分析，以期在2014年5月份左右完成SCI文章的撰写并投稿。

内容获取失败，请点击重试