阿尔茨海默病（Alzheimer's disease, AD）是一种表现为记忆、情感、认知和行为失常的神经退行性疾病。当前全世界AD患者量大、就诊率不足，且AD尚无有效的治疗措施，因此早期识别和早期干预对于延缓病情至关重要。大脑以网络的组织形式进行信息处理，支撑高级认知活动，既往研究揭示了AD是一种失连接综合征，其大脑发生了网络的而非局部的损伤，因此，刻画AD脑网络异常模式对寻找其客观定量的影像标记物以实现 AD 的早期识别和干预尤为关键。
1. AD 纤维束异常模式研究
白质病变是AD大脑改变的重要表现，纤维束异常是造成脑结构连接网络异常的重要因素，由于缺乏大样本多站点数据集，以往AD的白质纤维束异常模式结果并不一致。因此，为系统评估AD白质纤维束异常的稳定性和泛化性，本文在多站点数据集上，使用自动纤维束量化方法量化了大脑中 20 条白质纤维束的完整性，采用荟萃分析方法识别出AD纤维束的逐点异常模式，发现AD患者存在广泛的纤维束异常改变，纤维束完整性与个体认知能力显著相关，扣带束、胼胝体等是 AD 中存在重要改变的纤维束，这些纤维束的异常会造成大脑内默认网络、视觉网络等功能网络的连接异常。独立站点交叉验证的结果显示异常纤维束特征在AD个体化预测中能达到76.5%的站点平均精度。
2. AD 的结构连接网络异常模式及结构-功能耦合关系异常改变
Alzheimer's disease (AD) is a neurodegenerative disease characterized by memory, emotional, cognitive and behavioral disorders. Presently, it is an acknowledged fact that there are numerous AD patients with low consultation rate and no effective treatment, thus early recognition and intervention are crucial to slow down the disease progress. Human brain process information in the form of network to support high-level cognitive activities. Previous studies indicated that AD is a brain network disconnection syndrome with network-wide damage rather than regional damage. Therefore, characterizing the abnormal patterns of brain network is significant for finding quantitative neuroimaging biomarkers to realize the early recognition and intervention of AD.
Most of the previous studies were based on single-site small-sample single-mode datasets, which failed to robustly evaluate the abnormal pattern of brain networks, and ignored the changes in the relationship between structural and functional network in AD. Thus, the current paper aimed to use a large multi-site multimodal MRI dataset (865 subjects, 321 AD patients, 265 mild cognitive impairment patients, 279 controls) to systematically investigate the abnormal pattern of fiber tracts, structural connection network (SCN) and structure-function coupling in AD, and to analyze the relationship between brain network changes and individual cognitive ability, as well as to evaluate the feasibility of structural connection features in the AD identification using machine learning method. The main contributions of this paper were summarized as follows:
1. The abnormal pattern of white matter fiber tracts
White matter disruption is a critical brain change and also a main factor causing the disconnection of SCN in AD. Due to small sample size and various image processing pipelines, previous researches of white matter abnormality patterns in AD are inconsistent. Therefore, in order to evaluate the stability and generalization of the abnormal pattern of fiber tracts, based on multi-site dataset, automated fiber quantification was applied to quantify the integrity of 20 fiber tracts, meta-analyses were used to identify the point-wise changes along tracts. This study found that fiber tracts impaired widely, especially in the cingulum cingulate and callosum forceps, which possibly caused disconnections in the default-mode network and visual network in AD. The diffusion metrics of the altered regions were significantly correlated with cognitive ability. The prediction of individual diagnosis status using tract-based features achieved an site-averaged accuracy of 76.5% through independent site cross-validation.
2. The abnormal pattern of SCN and disrupted structure-function coupling in AD
AD is characterized as a disconnection syndrome, with numerous evidences of disrupted brain network. However, due to the small dataset and various network construction methods, the degenerated pattern of SCN in AD is still unclear, and the studies on the relationship between the structural and functional abnormal patterns were inadequate. Therefore, the current study firstly investigated the AD-associated SCN abnormalities from both connection strength and topological structure aspects, then evaluated the relationship between SCN and functional connection network (FCN). Results showed that in AD group, the integration of SCN was significantly reduced, connections within default mode network, frontoparietal network and visual network were weakened, while connections within subcortical nuclei (thalamus and basal ganglion) were enhanced. This abnormal pattern was consistent in SCN and FCN, and significantly associated with individual cognitive ability. The enhanced structure-function coupling suggested the reduction of brain functional flexibility in AD. Finally, The prediction of individual diagnosis status using SCN features achieved an site-averaged accuracy of 75.6% through independent site cross-validation.
The current study provided evidence for the disconnection of AD from the perspective of white matter fiber tract degeneration, clearly depicted the abnormal patterns of SCN, explored the coupling relationship between the SCN and FCN, as well as evaluated the effectiveness and generalization of structural connection features as the neuroimaging biomarker of AD, contributing to further understand the disconnection and cognitive degeneration of AD.
|Keyword||阿尔茨海默病 弥散张量成像 白质纤维束 结构连接网络 结构功能耦合|
|MOST Discipline Catalogue||工学::控制科学与工程|
|曲怡达. 基于多中心磁共振影像的阿尔茨海默病脑网络异常表征研究[D]. 中国科学院自动化研究所. 中国科学院自动化研究所,2022.|
|Files in This Item:|
|Recommend this item|
|Export to Endnote|
|Similar articles in Google Scholar|
|Similar articles in Baidu academic|
|Similar articles in Bing Scholar|
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.