To understand multifactorial conditions such as Alzheimer's disease (AD) we need brain signatures that predict the impact of multiple pathologies and their interactions. To help uncover the relationships between pathology affected brain circuits and cognitive markers we have used mouse models that represent, at least in part, the complex interactions altered in AD, while being raised in uniform environments and with known genotype alterations. In particular, we aimed to understand the relationship between vulnerable brain circuits and memory deficits measured in the Morris water maze, and we tested several predictive modeling approaches. We used in vivo manganese enhanced MRI traditional voxel based analyses to reveal regional differences in volume (morphometry), signal intensity (activity), and magnetic susceptibility (iron deposition, demyelination). These regions included hippocampus, olfactory areas, entorhinal cortex and cerebellum, as well as the frontal association area. The properties of these regions, extracted from each of the imaging markers, were used to predict spatial memory. We next used eigenanatomy, which reduces dimensionality to produce sets of regions that explain the variance in the data. For each imaging marker, eigenanatomy revealed networks underpinning a range of cognitive functions including memory, motor function, and associative learning, allowing the detection of associations between context, location, and responses. Finally, the integration of multivariate markers in a supervised sparse canonical correlation approach outperformed single predictor models and had significant correlates to spatial memory. Among a priori selected regions, expected to play a role in memory dysfunction, the fornix also provided good predictors, raising the possibility of investigating how disease propagation within brain networks leads to cognitive deterioration. Our cross-sectional results support that modeling approaches integrating multivariate imaging markers provide sensitive predictors of AD-like behaviors. Such strategies for mapping brain circuits responsible for behaviors may help in the future predict disease progression, or response to interventions.

中文翻译：

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多变量 MR 生物标志物可以更好地预测阿尔茨海默病小鼠模型的认知功能障碍。

为了了解阿尔茨海默病 (AD) 等多因素疾病，我们需要大脑特征来预测多种病理及其相互作用的影响。为了帮助揭示受病理影响的大脑回路和认知标记之间的关系，我们使用了小鼠模型，这些模型至少部分代表了 AD 中复杂的相互作用，同时在统一的环境中和已知的基因型改变中长大。特别是，我们旨在了解在 Morris 水迷宫中测量的易受攻击的大脑回路与记忆缺陷之间的关系，并且我们测试了几种预测建模方法。我们使用体内锰增强 MRI 基于传统体素的分析来揭示体积（形态测量学）、信号强度（活性）和磁化率（铁沉积、脱髓鞘）。这些区域包括海马、嗅觉区、内嗅皮层和小脑，以及额叶联合区。从每个成像标记中提取的这些区域的特性用于预测空间记忆。我们接下来使用特征解剖学，它降低了维度以产生解释数据方差的区域集。对于每个成像标记，特征解剖揭示了支持一系列认知功能的网络，包括记忆、运动功能和联想学习，允许检测上下文、位置和响应之间的关联。最后，多变量标记在监督稀疏规范相关方法中的整合优于单一预测模型，并且与空间记忆具有显着相关性。在先验选择的区域中，穹窿预计在记忆功能障碍中发挥作用，也提供了良好的预测因子，提高了研究大脑网络内疾病传播如何导致认知恶化的可能性。我们的横截面结果支持集成多变量成像标记的建模方法提供了 AD 样行为的敏感预测因子。这种绘制负责行为的大脑回路的策略可能有助于在未来预测疾病进展或对干预的反应。我们的横截面结果支持集成多变量成像标记的建模方法提供了 AD 样行为的敏感预测因子。这种绘制负责行为的大脑回路的策略可能有助于在未来预测疾病进展或对干预的反应。我们的横截面结果支持集成多变量成像标记的建模方法提供了 AD 样行为的敏感预测因子。这种绘制负责行为的大脑回路的策略可能有助于在未来预测疾病进展或对干预的反应。