Network Diffusion Modeling Explains Longitudinal Tau PET Data
Alzheimer's disease is associated with the cerebral accumulation of neurofibrillary tangles of hyperphosphorylated tau protein. The progressive occurrence of tau aggregates in different brain regions is closely related to neurodegeneration and cognitive impairment. However, our current understa...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-12-01
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| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2020.566876/full |
| _version_ | 1818622611734659072 |
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| author | Amelie Schäfer Elizabeth C. Mormino Ellen Kuhl |
| author_facet | Amelie Schäfer Elizabeth C. Mormino Ellen Kuhl |
| author_sort | Amelie Schäfer |
| collection | DOAJ |
| description | Alzheimer's disease is associated with the cerebral accumulation of neurofibrillary tangles of hyperphosphorylated tau protein. The progressive occurrence of tau aggregates in different brain regions is closely related to neurodegeneration and cognitive impairment. However, our current understanding of tau propagation relies almost exclusively on postmortem histopathology, and the precise propagation dynamics of misfolded tau in the living brain remain poorly understood. Here we combine longitudinal positron emission tomography and dynamic network modeling to test the hypothesis that misfolded tau propagates preferably along neuronal connections. We follow 46 subjects for three or four annual positron emission tomography scans and compare their pathological tau profiles against brain network models of intracellular and extracellular spreading. For each subject, we identify a personalized set of model parameters that characterizes the individual progression of pathological tau. Across all subjects, the mean protein production rate was 0.21 ± 0.15 and the intracellular diffusion coefficient was 0.34 ± 0.43. Our network diffusion model can serve as a tool to detect non-clinical symptoms at an earlier stage and make informed predictions about the timeline of neurodegeneration on an individual personalized basis. |
| first_indexed | 2024-12-16T18:27:56Z |
| format | Article |
| id | doaj.art-e305cca4551e4ce8ba74c027a07eed65 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-16T18:27:56Z |
| publishDate | 2020-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-e305cca4551e4ce8ba74c027a07eed652022-12-21T22:21:22ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2020-12-011410.3389/fnins.2020.566876566876Network Diffusion Modeling Explains Longitudinal Tau PET DataAmelie Schäfer0Elizabeth C. Mormino1Ellen Kuhl2Department of Mechanical Engineering, Stanford University, Stanford, CA, United StatesDepartment of Neurology and Neurological Sciences, Stanford School of Medicine, Stanford, CA, United StatesDepartment of Mechanical Engineering, Stanford University, Stanford, CA, United StatesAlzheimer's disease is associated with the cerebral accumulation of neurofibrillary tangles of hyperphosphorylated tau protein. The progressive occurrence of tau aggregates in different brain regions is closely related to neurodegeneration and cognitive impairment. However, our current understanding of tau propagation relies almost exclusively on postmortem histopathology, and the precise propagation dynamics of misfolded tau in the living brain remain poorly understood. Here we combine longitudinal positron emission tomography and dynamic network modeling to test the hypothesis that misfolded tau propagates preferably along neuronal connections. We follow 46 subjects for three or four annual positron emission tomography scans and compare their pathological tau profiles against brain network models of intracellular and extracellular spreading. For each subject, we identify a personalized set of model parameters that characterizes the individual progression of pathological tau. Across all subjects, the mean protein production rate was 0.21 ± 0.15 and the intracellular diffusion coefficient was 0.34 ± 0.43. Our network diffusion model can serve as a tool to detect non-clinical symptoms at an earlier stage and make informed predictions about the timeline of neurodegeneration on an individual personalized basis.https://www.frontiersin.org/articles/10.3389/fnins.2020.566876/fulltau PETNeuroimagingmodel calibrationAlzheimer's diseasenetwork diffusion model |
| spellingShingle | Amelie Schäfer Elizabeth C. Mormino Ellen Kuhl Network Diffusion Modeling Explains Longitudinal Tau PET Data Frontiers in Neuroscience tau PET Neuroimaging model calibration Alzheimer's disease network diffusion model |
| title | Network Diffusion Modeling Explains Longitudinal Tau PET Data |
| title_full | Network Diffusion Modeling Explains Longitudinal Tau PET Data |
| title_fullStr | Network Diffusion Modeling Explains Longitudinal Tau PET Data |
| title_full_unstemmed | Network Diffusion Modeling Explains Longitudinal Tau PET Data |
| title_short | Network Diffusion Modeling Explains Longitudinal Tau PET Data |
| title_sort | network diffusion modeling explains longitudinal tau pet data |
| topic | tau PET Neuroimaging model calibration Alzheimer's disease network diffusion model |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2020.566876/full |
| work_keys_str_mv | AT amelieschafer networkdiffusionmodelingexplainslongitudinaltaupetdata AT elizabethcmormino networkdiffusionmodelingexplainslongitudinaltaupetdata AT ellenkuhl networkdiffusionmodelingexplainslongitudinaltaupetdata |