Alzheimer's Disease Projection From Normal to Mild Dementia Reflected in Functional Network Connectivity: A Longitudinal Study.
Alzheimer's Disease Projection From Normal to Mild Dementia Reflected in Functional Network Connectivity: A Longitudinal Study.
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阿尔茨海默氏病从正常痴呆到轻度痴呆的投射反映在功能网络连通性中：一项纵向研究。
DOI：
10.3389/fncir.2020.593263
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发表时间：
2020
影响因子：
3.5
通讯作者：
Calhoun VD
中科院分区：
文献类型：
--
作者：
Sendi MSE;Zendehrouh E;Miller RL;Fu Z;Du Y;Liu J;Mormino EC;Salat DH;Calhoun VD
阿尔茨海默病（AD）是最常见的与年龄相关的问题，并且在不同阶段进展，包括轻度认知障碍（早期）、轻度痴呆（中期）和重度痴呆（晚期）。最近的研究表明，在从健康老龄化到AD的过渡过程中，从静息态功能磁共振成像（rs-fMRI）获得的功能网络连接的变化。通过假设大脑交互在扫描时间期间是静态的，大多数先前的研究都集中在静态功能或功能网络连接（sFNC）上。动态功能网络连接（dFNC）探索功能连接的时间模式，并提供额外的信息，其静态对应。我们使用纵向rs-fMRI从1385扫描（910名受试者）在不同阶段的AD（从正常到非常轻度AD或vmAD）。我们使用组独立成分分析（组ICA），并提取了53个最大独立成分（IC）的整个大脑。接下来，我们使用滑动窗口方法从提取的53个IC中估计dFNC，然后使用聚类方法将它们分组为3种不同的大脑状态。然后，我们估计了一个隐马尔可夫模型（HMM）和占用率（OCR）为每个主题。最后，我们研究了每个受试者的临床率与特定状态的FNC，OCR和HMM之间的联系。所有状态在正常脑向vmAD 1的进展过程中均显示出显著中断。具体来说，我们发现，皮层下网络，听觉网络，视觉网络，感觉运动网络和小脑网络连接减少VMAD相比，健康的大脑。我们还发现了重组模式（即，增加和减少）的认知控制网络和默认模式网络连接的进展，从正常到轻度痴呆。类似地，我们发现当大脑从正常过渡到轻度痴呆时，网络间连接的重组模式。然而，与健康大脑相比，VMAD中视觉和感觉运动网络连接之间的连接性降低。最后，我们发现一个正常的大脑花更多的时间在视觉和感觉运动网络之间具有更高连通性的状态。我们的结果表明，全脑FNC的时间和空间模式将AD与健康对照区分开来，并表明多个动态状态存在实质性干扰。更详细地说，我们的结果表明感觉网络比其他大脑网络受到的影响更大，而默认模式网络是最后受到AD影响的大脑网络之一。此外，在AD的早期阶段发现了全脑dFNC的异常模式，并且一些异常与临床评分相关。
Alzheimer’s disease (AD) is the most common age-related problem and progresses in different stages, including mild cognitive impairment (early stage), mild dementia (middle-stage), and severe dementia (late-stage). Recent studies showed changes in functional network connectivity obtained from resting-state functional magnetic resonance imaging (rs-fMRI) during the transition from healthy aging to AD. By assuming that the brain interaction is static during the scanning time, most prior studies are focused on static functional or functional network connectivity (sFNC). Dynamic functional network connectivity (dFNC) explores temporal patterns of functional connectivity and provides additional information to its static counterpart. We used longitudinal rs-fMRI from 1385 scans (from 910 subjects) at different stages of AD (from normal to very mild AD or vmAD). We used group-independent component analysis (group-ICA) and extracted 53 maximally independent components (ICs) for the whole brain. Next, we used a sliding-window approach to estimate dFNC from the extracted 53 ICs, then group them into 3 different brain states using a clustering method. Then, we estimated a hidden Markov model (HMM) and the occupancy rate (OCR) for each subject. Finally, we investigated the link between the clinical rate of each subject with state-specific FNC, OCR, and HMM. All states showed significant disruption during progression normal brain to vmAD one. Specifically, we found that subcortical network, auditory network, visual network, sensorimotor network, and cerebellar network connectivity decrease in vmAD compared with those of a healthy brain. We also found reorganized patterns (i.e., both increases and decreases) in the cognitive control network and default mode network connectivity by progression from normal to mild dementia. Similarly, we found a reorganized pattern of between-network connectivity when the brain transits from normal to mild dementia. However, the connectivity between visual and sensorimotor network connectivity decreases in vmAD compared with that of a healthy brain. Finally, we found a normal brain spends more time in a state with higher connectivity between visual and sensorimotor networks. Our results showed the temporal and spatial pattern of whole-brain FNC differentiates AD form healthy control and suggested substantial disruptions across multiple dynamic states. In more detail, our results suggested that the sensory network is affected more than other brain network, and default mode network is one of the last brain networks get affected by AD In addition, abnormal patterns of whole-brain dFNC were identified in the early stage of AD, and some abnormalities were correlated with the clinical score.
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遗忘性轻度认知障碍受试者梭状回功能连接的改变：一项静息态功能磁共振成像研究
影响因子：
2.9
作者：
Cai S;Chong T;Zhang Y;Li J;von Deneen KM;Ren J;Dong M;Huang L;Alzheimer’s Disease Neuroimaging Initiative
通讯作者：
Alzheimer’s Disease Neuroimaging Initiative
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DOI：
10.1016/j.nicl.2020.102375
发表时间：
2020
期刊：
NeuroImage. Clinical
影响因子：
--
作者：
Du Y;Fu Z;Sui J;Gao S;Xing Y;Lin D;Salman M;Abrol A;Rahaman MA;Chen J;Hong LE;Kochunov P;Osuch EA;Calhoun VD;Alzheimer's Disease Neuroimaging Initiative
通讯作者：
Alzheimer's Disease Neuroimaging Initiative
DOI：
10.1162/netn_a_00155
发表时间：
2021
期刊：
Network neuroscience (Cambridge, Mass.)
影响因子：
--
作者：
Faghiri A;Iraji A;Damaraju E;Turner J;Calhoun VD
通讯作者：
Calhoun VD
影响因子：
14.5
作者：
Albert, Marilyn;Zhu, Yuxin;Wang, Mei-Cheng
通讯作者：
Wang, Mei-Cheng
影响因子：
3
作者：
Abrol, Anees;Bhattarai, Manish;Calhoun, Vince
通讯作者：
Calhoun, Vince