Modeling functional connectivity with learning and memory in a mouse model of Alzheimer's disease
IntroductionFunctional 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...
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Neuroimaging |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnimg.2025.1558759/full |
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| author | Lindsay Fadel Elizabeth Hipskind Steen E. Pedersen Jonathan Romero Caitlyn Ortiz Caitlyn Ortiz Eric Shin Md Abul Hassan Samee Robia G. Pautler Robia G. Pautler Robia G. Pautler |
| author_facet | Lindsay Fadel Elizabeth Hipskind Steen E. Pedersen Jonathan Romero Caitlyn Ortiz Caitlyn Ortiz Eric Shin Md Abul Hassan Samee Robia G. Pautler Robia G. Pautler Robia G. Pautler |
| author_sort | Lindsay Fadel |
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| description | IntroductionFunctional 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.MethodsWe 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.ResultsIn 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.DiscussionThese 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. |
| format | Article |
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| institution | OA Journals |
| issn | 2813-1193 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Neuroimaging |
| spelling | doaj-art-d2821bd7c17a4fae8b5e2bb32eb6f4af2025-08-20T02:28:26ZengFrontiers Media S.A.Frontiers in Neuroimaging2813-11932025-04-01410.3389/fnimg.2025.15587591558759Modeling functional connectivity with learning and memory in a mouse model of Alzheimer's diseaseLindsay Fadel0Elizabeth Hipskind1Steen E. Pedersen2Jonathan Romero3Caitlyn Ortiz4Caitlyn Ortiz5Eric Shin6Md Abul Hassan Samee7Robia G. Pautler8Robia G. Pautler9Robia G. Pautler10Department of Neuroscience, Baylor College of Medicine, Houston, TX, United StatesDepartment of Neuroscience, Baylor College of Medicine, Houston, TX, United StatesDepartment of Integrative Physiology, Baylor College of Medicine, Houston, TX, United StatesSmall Animal Imaging Facility, Texas Children's Hospital, Houston, TX, United StatesDepartment of Integrative Physiology, Baylor College of Medicine, Houston, TX, United StatesSmall Animal Imaging Facility, Texas Children's Hospital, Houston, TX, United StatesDepartment of Integrative Physiology, Baylor College of Medicine, Houston, TX, United StatesDepartment of Integrative Physiology, Baylor College of Medicine, Houston, TX, United StatesDepartment of Neuroscience, Baylor College of Medicine, Houston, TX, United StatesDepartment of Integrative Physiology, Baylor College of Medicine, Houston, TX, United StatesSmall Animal Imaging Facility, Texas Children's Hospital, Houston, TX, United StatesIntroductionFunctional 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.MethodsWe 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.ResultsIn 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.DiscussionThese 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.https://www.frontiersin.org/articles/10.3389/fnimg.2025.1558759/fullAlzheimer's diseasers-fMRIfunctional connectivitymodelingbehaviormouse model |
| spellingShingle | Lindsay Fadel Elizabeth Hipskind Steen E. Pedersen Jonathan Romero Caitlyn Ortiz Caitlyn Ortiz Eric Shin Md Abul Hassan Samee Robia G. Pautler Robia G. Pautler Robia G. Pautler Modeling functional connectivity with learning and memory in a mouse model of Alzheimer's disease Frontiers in Neuroimaging Alzheimer's disease rs-fMRI functional connectivity modeling behavior mouse model |
| title | Modeling functional connectivity with learning and memory in a mouse model of Alzheimer's disease |
| title_full | Modeling functional connectivity with learning and memory in a mouse model of Alzheimer's disease |
| title_fullStr | Modeling functional connectivity with learning and memory in a mouse model of Alzheimer's disease |
| title_full_unstemmed | Modeling functional connectivity with learning and memory in a mouse model of Alzheimer's disease |
| title_short | Modeling functional connectivity with learning and memory in a mouse model of Alzheimer's disease |
| title_sort | modeling functional connectivity with learning and memory in a mouse model of alzheimer s disease |
| topic | Alzheimer's disease rs-fMRI functional connectivity modeling behavior mouse model |
| url | https://www.frontiersin.org/articles/10.3389/fnimg.2025.1558759/full |
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