Early brain connectivity alterations and cognitive impairment in a rat model of Alzheimer's disease.

Early brain connectivity alterations and cognitive impairment in a rat model of Alzheimer's disease.
复制标题

DOI:
10.1186/s13195-018-0346-2
复制
发表时间:
2018-02-07
期刊:
Alzheimer's research & therapy
影响因子:
--
通讯作者:
Soria G
Soria G
中科院分区:
其他
文献类型:
--
作者:
Muñoz-Moreno E;Tudela R;López-Gil X;Soria G

文献摘要

参考文献

被引文献

相似文献

阿尔茨海默病(AD)的动物模型对于了解疾病的进展和开发早期生物标志物是必不可少的。由于AD被描述为一种断开综合征,基于磁共振成像(MRI)的连接学提供了一种高度翻译的方法来表征与疾病相关的连接中断。在这项研究中,分析了一个阿尔茨海默病转基因大鼠模型(TgF344-AD),以描述在β-淀粉样斑块显著集中之前的早期阶段(5个月龄)的认知表现和脑连接。认知能力通过延迟的不匹配到样本(DNMS)任务进行评估,然后在训练阶段中动物学习该任务。记录和评估达到学习标准所需的培训课程数量。DNMS后进行MRI采集,包括弥散加权MRI和静息状态功能MRI,分别进行处理以获得结构连接和功能连接。计算全局和区域图度量来评估转基因大鼠和对照大鼠的网络组织。结果表明,AD大鼠在学习与工作记忆相关的任务方面存在延迟,在DNMS任务中完成的试验次数也较少。在连接特性方面,与对照组相比,转基因大鼠的结构性脑网络结构的组织效率较低。在结构网络和功能网络中都发现了连通性方面的具体区域差异。此外,观察到认知表现与大脑网络之间存在很强的相关性,包括全脑结构连接以及与记忆和奖励过程相关的区域的功能和结构网络指标。在这项研究中,TgF344-AD大鼠在疾病的非常早期阶段就发现了连接和神经认知障碍,而大多数病理特征还没有被检测到。与奖赏、记忆和感觉表现相关的区域的结构和功能网络指标与认知结果密切相关。动物模型的使用对于早期识别这些改变是必不可少的,并有助于基于MRI连接学的疾病早期生物标志物的开发。本文的在线版本(10.1186/s13195-0180346-2)包含补充材料,可供授权用户使用。
Animal models of Alzheimer’s disease (AD) are essential to understanding the disease progression and to development of early biomarkers. Because AD has been described as a disconnection syndrome, magnetic resonance imaging (MRI)-based connectomics provides a highly translational approach to characterizing the disruption in connectivity associated with the disease. In this study, a transgenic rat model of AD (TgF344-AD) was analyzed to describe both cognitive performance and brain connectivity at an early stage (5 months of age) before a significant concentration of β-amyloid plaques is present. Cognitive abilities were assessed by a delayed nonmatch-to-sample (DNMS) task preceded by a training phase where the animals learned the task. The number of training sessions required to achieve a learning criterion was recorded and evaluated. After DNMS, MRI acquisition was performed, including diffusion-weighted MRI and resting-state functional MRI, which were processed to obtain the structural and functional connectomes, respectively. Global and regional graph metrics were computed to evaluate network organization in both transgenic and control rats. The results pointed to a delay in learning the working memory-related task in the AD rats, which also completed a lower number of trials in the DNMS task. Regarding connectivity properties, less efficient organization of the structural brain networks of the transgenic rats with respect to controls was observed. Specific regional differences in connectivity were identified in both structural and functional networks. In addition, a strong correlation was observed between cognitive performance and brain networks, including whole-brain structural connectivity as well as functional and structural network metrics of regions related to memory and reward processes. In this study, connectivity and neurocognitive impairments were identified in TgF344-AD rats at a very early stage of the disease when most of the pathological hallmarks have not yet been detected. Structural and functional network metrics of regions related to reward, memory, and sensory performance were strongly correlated with the cognitive outcome. The use of animal models is essential for the early identification of these alterations and can contribute to the development of early biomarkers of the disease based on MRI connectomics. The online version of this article (10.1186/s13195-018-0346-2) contains supplementary material, which is available to authorized users.
DOI: 10.1016/j.neuropharm.2012.06.034
发表时间: 2012-10-01
期刊: NEUROPHARMACOLOGY
影响因子: 4.7
作者:
Callaghan, Charlotte K.;Hok, Vincent;O'Mara, Shane M.
通讯作者: O'Mara, Shane M.
DOI: 10.3389/fninf.2014.00008
发表时间: 2014
影响因子: 3.5
作者:
Garyfallidis E;Brett M;Amirbekian B;Rokem A;van der Walt S;Descoteaux M;Nimmo-Smith I;Dipy Contributors
通讯作者: Dipy Contributors
DOI: 10.1186/1750-1326-8-37
发表时间: 2013-10-25
影响因子: 15.1
作者:
Do Carmo S;Cuello AC
通讯作者: Cuello AC
DOI: 10.3389/fnagi.2014.00012
发表时间: 2014
影响因子: 4.8
作者:
Gomez-Ramirez J;Wu J
通讯作者: Wu J
DOI: 10.1016/j.neuroimage.2011.10.003
发表时间: 2012-02-01
期刊: NEUROIMAGE
影响因子: 5.7
作者:
Dai, Zhengjia;Yan, Chaogan;He, Yong
通讯作者: He, Yong