Structural connectivity centrality changes mark the path toward Alzheimer's disease
Abstract Introduction The pathophysiological process of Alzheimer's disease is thought to begin years before clinical decline, with evidence suggesting prion‐like spreading processes of neurofibrillary tangles and amyloid plaques. Methods Using diffusion magnetic resonance imaging data from the...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2019-12-01
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| Series: | Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring |
| Subjects: | |
| Online Access: | https://doi.org/10.1016/j.dadm.2018.12.004 |
| _version_ | 1818060062582636544 |
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| author | Luis R. Peraza Antonio Díaz‐Parra Oliver Kennion David Moratal John‐Paul Taylor Marcus Kaiser Roman Bauer Alzheimer's Disease Neuroimaging Initiative |
| author_facet | Luis R. Peraza Antonio Díaz‐Parra Oliver Kennion David Moratal John‐Paul Taylor Marcus Kaiser Roman Bauer Alzheimer's Disease Neuroimaging Initiative |
| author_sort | Luis R. Peraza |
| collection | DOAJ |
| description | Abstract Introduction The pathophysiological process of Alzheimer's disease is thought to begin years before clinical decline, with evidence suggesting prion‐like spreading processes of neurofibrillary tangles and amyloid plaques. Methods Using diffusion magnetic resonance imaging data from the Alzheimer's Disease Neuroimaging Initiative database, we first identified relevant features for dementia diagnosis. We then created dynamic models with the Nathan Kline Institute‐Rockland Sample database to estimate the earliest detectable stage associated with dementia in the simulated disease progression. Results A classifier based on centrality measures provides informative predictions. Strength and closeness centralities are the most discriminative features, which are associated with the medial temporal lobe and subcortical regions, together with posterior and occipital brain regions. Our model simulations suggest that changes associated with dementia begin to manifest structurally at early stages. Discussion Our analyses suggest that diffusion magnetic resonance imaging–based centrality measures can offer a tool for early disease detection before clinical dementia onset. |
| first_indexed | 2024-12-10T13:26:27Z |
| format | Article |
| id | doaj.art-15c451297d55494382e2db8c4628b6cc |
| institution | Directory Open Access Journal |
| issn | 2352-8729 |
| language | English |
| last_indexed | 2024-12-10T13:26:27Z |
| publishDate | 2019-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring |
| spelling | doaj.art-15c451297d55494382e2db8c4628b6cc2022-12-22T01:47:07ZengWileyAlzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring2352-87292019-12-011119810710.1016/j.dadm.2018.12.004Structural connectivity centrality changes mark the path toward Alzheimer's diseaseLuis R. Peraza0Antonio Díaz‐Parra1Oliver Kennion2David Moratal3John‐Paul Taylor4Marcus Kaiser5Roman Bauer6Alzheimer's Disease Neuroimaging Initiative7Institute of Neuroscience, Newcastle UniversityNewcastle upon TyneUnited KingdomCenter for Biomaterials and Tissue Engineering, Universitat Politècnica de ValènciaValenciaSpainInterdisciplinary Computing and Complex Biosystems Research Group, School of Computing, Newcastle UniversityNewcastle upon TyneUnited KingdomCenter for Biomaterials and Tissue Engineering, Universitat Politècnica de ValènciaValenciaSpainInstitute of Neuroscience, Newcastle UniversityNewcastle upon TyneUnited KingdomInstitute of Neuroscience, Newcastle UniversityNewcastle upon TyneUnited KingdomInterdisciplinary Computing and Complex Biosystems Research Group, School of Computing, Newcastle UniversityNewcastle upon TyneUnited KingdomInstitute of Neuroscience, Newcastle UniversityNewcastle upon TyneUnited KingdomAbstract Introduction The pathophysiological process of Alzheimer's disease is thought to begin years before clinical decline, with evidence suggesting prion‐like spreading processes of neurofibrillary tangles and amyloid plaques. Methods Using diffusion magnetic resonance imaging data from the Alzheimer's Disease Neuroimaging Initiative database, we first identified relevant features for dementia diagnosis. We then created dynamic models with the Nathan Kline Institute‐Rockland Sample database to estimate the earliest detectable stage associated with dementia in the simulated disease progression. Results A classifier based on centrality measures provides informative predictions. Strength and closeness centralities are the most discriminative features, which are associated with the medial temporal lobe and subcortical regions, together with posterior and occipital brain regions. Our model simulations suggest that changes associated with dementia begin to manifest structurally at early stages. Discussion Our analyses suggest that diffusion magnetic resonance imaging–based centrality measures can offer a tool for early disease detection before clinical dementia onset.https://doi.org/10.1016/j.dadm.2018.12.004Alzheimer's diseaseDiffusion MRIStructural brain connectivityNetwork centralityComputational modelingMachine learning |
| spellingShingle | Luis R. Peraza Antonio Díaz‐Parra Oliver Kennion David Moratal John‐Paul Taylor Marcus Kaiser Roman Bauer Alzheimer's Disease Neuroimaging Initiative Structural connectivity centrality changes mark the path toward Alzheimer's disease Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring Alzheimer's disease Diffusion MRI Structural brain connectivity Network centrality Computational modeling Machine learning |
| title | Structural connectivity centrality changes mark the path toward Alzheimer's disease |
| title_full | Structural connectivity centrality changes mark the path toward Alzheimer's disease |
| title_fullStr | Structural connectivity centrality changes mark the path toward Alzheimer's disease |
| title_full_unstemmed | Structural connectivity centrality changes mark the path toward Alzheimer's disease |
| title_short | Structural connectivity centrality changes mark the path toward Alzheimer's disease |
| title_sort | structural connectivity centrality changes mark the path toward alzheimer s disease |
| topic | Alzheimer's disease Diffusion MRI Structural brain connectivity Network centrality Computational modeling Machine learning |
| url | https://doi.org/10.1016/j.dadm.2018.12.004 |
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