Modeling functional connectivity with learning and memory in a mouse model of Alzheimer's disease.

Frontiers in neuroimaging Pub Date : 2025-04-25 eCollection Date: 2025-01-01 DOI:10.3389/fnimg.2025.1558759
Lindsay Fadel, Elizabeth Hipskind, Steen E Pedersen, Jonathan Romero, Caitlyn Ortiz, Eric Shin, Md Abul Hassan Samee, Robia G Pautler
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Abstract

Introduction: Functional connectivity (FC) is a metric of how different brain regions interact with each other. Although there have been some studies correlating learning and memory with FC, there have not yet been, to date, studies that use machine learning (ML) to explain how FC changes can be used to explain behavior not only in healthy mice, but also in mouse models of Alzheimer's Disease (AD). Here, we investigated changes in FC and their relationship to learning and memory in a mouse model of AD across disease progression.

Methods: We assessed the APP/PS1 mouse model of AD and wild-type controls at 3-, 6-, and 10-months of age. Using resting state functional magnetic resonance imaging (rs-fMRI) in awake, unanesthetized mice, we assessed FC between 30 brain regions. ML models were then used to define interactions between neuroimaging readouts with learning and memory performance.

Results: In the APP/PS1 mice, we identified a pattern of hyperconnectivity across all three time points, with 47 hyperconnected regions at 3 months, 46 at 6 months, and 84 at 10 months. Notably, FC changes were also observed in the Default Mode Network, exhibiting a loss of hyperconnectivity over time. Modeling revealed functional connections that support learning and memory performance differ between the 6- and 10-month groups.

Discussion: These ML models show potential for early disease detection by identifying connectivity patterns associated with cognitive decline. Additionally, ML may provide a means to begin to understand how FC translates into learning and memory performance.

阿尔茨海默病小鼠模型中学习和记忆功能连接的建模。
功能连接(FC)是衡量不同大脑区域如何相互作用的指标。虽然已经有一些研究将学习和记忆与FC联系起来,但迄今为止,还没有研究使用机器学习(ML)来解释FC变化如何不仅用于解释健康小鼠的行为,还用于解释阿尔茨海默病(AD)小鼠模型的行为。在这里,我们研究了阿尔茨海默病小鼠模型中FC的变化及其与学习和记忆的关系。方法:我们在3、6、10月龄时对AD的APP/PS1小鼠模型和野生型对照进行评估。我们使用静息状态功能磁共振成像(rs-fMRI)对清醒、未麻醉的小鼠进行了30个脑区之间的FC评估。然后使用ML模型来定义神经成像读数与学习和记忆表现之间的相互作用。结果:在APP/PS1小鼠中,我们在所有三个时间点发现了一种超连接模式,3个月时有47个超连接区域,6个月时有46个,10个月时有84个。值得注意的是,在默认模式网络中也观察到FC的变化,显示出随着时间的推移超连通性的丧失。模型显示,支持学习和记忆表现的功能连接在6个月和10个月大的组之间有所不同。讨论:这些ML模型通过识别与认知能力下降相关的连接模式显示出早期疾病检测的潜力。此外,机器学习可以提供一种开始理解FC如何转化为学习和记忆性能的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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