Cingulum bundle white matter in MAG-knockout mice

Segal, Devorah; Carpenter, David M.; Höistad, Malin; Haroutunian, Vahram; Tang, Cheuk Y.; Hof, Patrick R.

doi:10.2478/v10134-010-0019-6

Cited by 3 publications

(3 citation statements)

References 61 publications

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“…In Figure 5 , we observe a significant decrease in FA values in an age-related sequence, with the highest values in young individuals, decreasing through middle-aged individuals, and reaching the lowest values in old individuals. This aligns with expectations and shows results consistent with previous research on the cingulum (Catheline et al, 2010; Jang et al, 2016; Segal et al, 2010; Sibilia et al, 2017). Figure 6 displays a similar trend in RTOP values, showing high significance.…”

Section: Discussionsupporting

confidence: 93%

“…Also, cognitive control in older adults is sensitive to variations in cingulum microstructure for subjects with MCI (Metzler-Baddeley et al, 2012). In mice, there was a decrease in myelinated fiber length density and FA in the cingulum bundle with increasing age, regardless of genotype, hemisphere or sex (Segal et al, 2010). In our investigation, we have expanded the study of white matter integrity using MAP properties including RTOP, RTAP and RTPP.…”

Section: Discussionmentioning

confidence: 99%

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Feature attention graph neural network for estimating brain age and identifying important neural connections in mouse models of genetic risk for Alzheimer’s disease

Moon,

Mahzarnia,

Stout

et al. 2023

Preprint

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Alzheimer’s disease (AD) remains one of the most extensively researched neurodegenerative disorders due to its widespread prevalence and complex risk factors. Age is a crucial risk factor for AD, which can be estimated by the disparity between physiological age and estimated brain age. To model AD risk more effectively, integrating biological, genetic, and cognitive markers is essential. Here, we utilized mouse models expressing the major APOE human alleles and human nitric oxide synthase 2 to replicate genetic risk for AD and a humanized innate immune response. We estimated brain age employing a multivariate dataset that includes brain connectomes, APOE genotype, subject traits such as age and sex, and behavioral data. Our methodology used Feature Attention Graph Neural Networks (FAGNN) for integrating different data types. Behavioral data were processed with a 2D Convolutional Neural Network (CNN), subject traits with a 1D CNN, brain connectomes through a Graph Neural Network using quadrant attention module. The model yielded a mean absolute error for age prediction of 31.85 days, with a root mean squared error of 41.84 days, outperforming other, reduced models. In addition, FAGNN identified key brain connections involved in the aging process. The highest weights were assigned to the connections between cingulum and corpus callosum, striatum, hippocampus, thalamus, hypothalamus, cerebellum, and piriform cortex. Our study demonstrates the feasibility of predicting brain age in models of aging and genetic risk for AD. To verify the validity of our findings, we compared Fractional Anisotropy (FA) along the tracts of regions with the highest connectivity, the Return-to-Origin Probability (RTOP), Return-to-Plane Probability (RTPP), and Return-to-Axis Probability (RTAP), which showed significant differences between young, middle-aged, and old age groups. Younger mice exhibited higher FA, RTOP, RTAP, and RTPP compared to older groups in the selected connections, suggesting that degradation of white matter tracts plays a critical role in aging and for FAGNN’s selections. Our analysis suggests a potential neuroprotective role of APOE2, relative to APOE3 and APOE4, where APOE2 appears to mitigate age-related changes. Our findings highlighted a complex interplay of genetics and brain aging in the context of AD risk modeling.

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Section: Discussionsupporting

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Feature attention graph neural network for estimating brain age and identifying important neural connections in mouse models of genetic risk for Alzheimer’s disease

Moon,

Mahzarnia,

Stout

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The comparative analysis of these groups, including young vs. middle-aged, middle-aged vs. old, and young vs. old, across all top 6 connections, revealed statistically significant differences with p-value of < 0.001. This not only validates the importance of these connectivity but also underscores the age-related changes in white matter integrity and is in line with previous literature [30][31][32][33].…”

Section: Discussionsupporting

confidence: 91%

Predicting brain age and associated structural networks in mouse models with humanized APOE alleles using integrative and interpretable graph neural networks

Moon,

Mahzarnia,

Stout

et al. 2024

Medical Imaging 2024: Computer-Aided Diagnosis

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Alzheimer's disease (AD), a prevalent neurodegenerative disorder, is influenced by an intricate mix of risk factors including age, genetics, and environmental variables. In our study, we employed mouse models with human APOE alleles and nitric oxide synthase 2, and adjusted environmental factors like diet, to replicate controlled genetic risk and innate immune response associated with AD in human subjects. We utilized a Feature Attention Graph Neural Network (FAGNN), integrating brain structural connectomes, genetic traits, environmental factors, and behavioral data, to estimate brain age. Our method demonstrated improved accuracy in age prediction over other methods and highlighted ageassociated brain connections. The most impactful connections included the cingulum, striatum, corpus callosum, and hippocampus. We further investigated these findings through fractional anisotropy in different age groups of mice, and our results underlined the significance of white matter degradation in aging. Our results underscore the effectiveness of integrative graph neural networks in predicting brain age and delineating important neural connections associated with brain aging.

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Feature attention graph neural network for estimating brain age and identifying important neural connections in mouse models of genetic risk for Alzheimer’s disease

Moon,

Mahzarnia,

Stout

et al. 2024

Imaging Neuroscience

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Alzheimer's disease (AD), a widely studied neurodegenerative disorder, poses significant research challenges due to its high prevalence and complex etiology. Age, a critical risk factor for AD, is typically assessed by comparing physiological and estimated brain ages. This study utilizes mouse models expressing human alleles of APOE and human nitric oxide synthase 2 (hNOS2), replicating genetic risks for AD alongside a human-like immune response. We developed a multivariate model that incorporates brain structural connectomes, APOE genotypes, demographic traits (age and sex), environmental factors such as diet, and behavioral data to estimate brain age. Our methodology employs a Feature Attention Graph Neural Network (FAGNN) to integrate these diverse datasets. Behavioral data are processed using a 2D convolutional neural network (CNN), demographic traits via a 1D CNN, and brain connectomes through a graph neural network equipped with a quadrant attention module that accentuates critical neural connections. The FAGNN model demonstrated a mean absolute error in age prediction of 31.85 days and a root mean squared error of 41.84 days, significantly outperforming simpler models. Our analysis further focused on the brain age delta, which assesses accelerated or delayed aging by comparing brain age, predicted by FAGNN, to the chronological age. A high-fat diet and the presence of the human NOS2 gene were identified as significant accelerators of brain aging in the old age group. Key neural connections identified by FAGNN, such as those between the cingulum, corpus callosum, striatum, hippocampus, thalamus, hypothalamus, cerebellum, and piriform cortex, were found to be significant in the aging process. Validation using diffusion MRI-based metrics, including fractional anisotropy and return-to-origin probability measures across these connections, revealed significant age-related differences. These findings suggest that white matter degradation in the connections highlighted by FAGNN plays a key role in aging. Our findings suggest that the complex interplay of APOE genotype with sex, immunity, and environmental factors modulates brain aging and enhances our understanding of AD risk in mouse models of aging.

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Cingulum bundle white matter in MAG-knockout mice

Cited by 3 publications

References 61 publications

Feature attention graph neural network for estimating brain age and identifying important neural connections in mouse models of genetic risk for Alzheimer’s disease

Feature attention graph neural network for estimating brain age and identifying important neural connections in mouse models of genetic risk for Alzheimer’s disease

Predicting brain age and associated structural networks in mouse models with humanized APOE alleles using integrative and interpretable graph neural networks

Feature attention graph neural network for estimating brain age and identifying important neural connections in mouse models of genetic risk for Alzheimer’s disease

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