Network biomarkers of Alzheimer's disease risk derived from joint volume and texture covariance patterns in mouse models.
Alzheimer's disease (AD) lacks effective cures and is typically detected after substantial pathological changes have occurred, making intervention challenging. Alzheimer's disease (AD) intervention requires early detection of risk factors and understanding their complex interactions before...
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0327118 |
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| author | Eric W Bridgeford Jaewon Chung Robert J Anderson Ali Mahzarnia Jacques A Stout Hae Sol Moon Zay Yar Han Joshua T Vogelstein Alexandra Badea |
| author_facet | Eric W Bridgeford Jaewon Chung Robert J Anderson Ali Mahzarnia Jacques A Stout Hae Sol Moon Zay Yar Han Joshua T Vogelstein Alexandra Badea |
| author_sort | Eric W Bridgeford |
| collection | DOAJ |
| description | Alzheimer's disease (AD) lacks effective cures and is typically detected after substantial pathological changes have occurred, making intervention challenging. Alzheimer's disease (AD) intervention requires early detection of risk factors and understanding their complex interactions before substantial pathological changes manifest. Current research often examines individual risk factors in isolation, limiting our understanding of their combined effects. We present a novel multivariate analytical framework to simultaneously assess multiple AD risk factors using mouse models expressing human ApoE alleles. Our methodological innovation lies in combining high-resolution magnetic resonance diffusion imaging with a comprehensive multifactorial analysis that integrates genotype, age, sex, diet, and immunity as interacting variables. This approach enables the simultaneous examination of regional brain volume and fractional anisotropy changes across multiple risk factors, providing a more holistic view than traditional univariate analyses. Our proposed method effectively identified how these factors converge on specific brain regions - with genotype influencing the caudate putamen, pons, cingulate cortex, and cerebellum; sex affecting the amygdala and piriform cortex; and immune status impacting association cortices and cerebellar nuclei. Importantly, our integrated approach revealed factor interactions that would remain undetected in single-variable studies, particularly in the amygdala, thalamus, and pons. While many findings align with previous research, our multidimensional framework offers a methodological advancement for studying AD risk factors by modeling their combined effects rather than isolated impacts. This approach creates a template for future studies to investigate mechanisms underlying coordinated changes in brain structure through network analyses of gene expression, metabolism, and structural pathways involved in neurodegeneration. |
| format | Article |
| id | doaj-art-ae6dece43ebd45b2a624cd30eb710399 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-ae6dece43ebd45b2a624cd30eb7103992025-08-23T05:32:39ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01208e032711810.1371/journal.pone.0327118Network biomarkers of Alzheimer's disease risk derived from joint volume and texture covariance patterns in mouse models.Eric W BridgefordJaewon ChungRobert J AndersonAli MahzarniaJacques A StoutHae Sol MoonZay Yar HanJoshua T VogelsteinAlexandra BadeaAlzheimer's disease (AD) lacks effective cures and is typically detected after substantial pathological changes have occurred, making intervention challenging. Alzheimer's disease (AD) intervention requires early detection of risk factors and understanding their complex interactions before substantial pathological changes manifest. Current research often examines individual risk factors in isolation, limiting our understanding of their combined effects. We present a novel multivariate analytical framework to simultaneously assess multiple AD risk factors using mouse models expressing human ApoE alleles. Our methodological innovation lies in combining high-resolution magnetic resonance diffusion imaging with a comprehensive multifactorial analysis that integrates genotype, age, sex, diet, and immunity as interacting variables. This approach enables the simultaneous examination of regional brain volume and fractional anisotropy changes across multiple risk factors, providing a more holistic view than traditional univariate analyses. Our proposed method effectively identified how these factors converge on specific brain regions - with genotype influencing the caudate putamen, pons, cingulate cortex, and cerebellum; sex affecting the amygdala and piriform cortex; and immune status impacting association cortices and cerebellar nuclei. Importantly, our integrated approach revealed factor interactions that would remain undetected in single-variable studies, particularly in the amygdala, thalamus, and pons. While many findings align with previous research, our multidimensional framework offers a methodological advancement for studying AD risk factors by modeling their combined effects rather than isolated impacts. This approach creates a template for future studies to investigate mechanisms underlying coordinated changes in brain structure through network analyses of gene expression, metabolism, and structural pathways involved in neurodegeneration.https://doi.org/10.1371/journal.pone.0327118 |
| spellingShingle | Eric W Bridgeford Jaewon Chung Robert J Anderson Ali Mahzarnia Jacques A Stout Hae Sol Moon Zay Yar Han Joshua T Vogelstein Alexandra Badea Network biomarkers of Alzheimer's disease risk derived from joint volume and texture covariance patterns in mouse models. PLoS ONE |
| title | Network biomarkers of Alzheimer's disease risk derived from joint volume and texture covariance patterns in mouse models. |
| title_full | Network biomarkers of Alzheimer's disease risk derived from joint volume and texture covariance patterns in mouse models. |
| title_fullStr | Network biomarkers of Alzheimer's disease risk derived from joint volume and texture covariance patterns in mouse models. |
| title_full_unstemmed | Network biomarkers of Alzheimer's disease risk derived from joint volume and texture covariance patterns in mouse models. |
| title_short | Network biomarkers of Alzheimer's disease risk derived from joint volume and texture covariance patterns in mouse models. |
| title_sort | network biomarkers of alzheimer s disease risk derived from joint volume and texture covariance patterns in mouse models |
| url | https://doi.org/10.1371/journal.pone.0327118 |
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