Individualized network analysis: A novel approach to investigate tau PET using graph theory in the Alzheimer’s disease continuum
IntroductionTau PET imaging has emerged as an important tool to detect and monitor tangle burden in vivo in the study of Alzheimer’s disease (AD). Previous studies demonstrated the association of tau burden with cognitive decline in probable AD cohorts. This study introduces a novel approach to anal...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2023.1089134/full |
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| author | Hillary Protas Hillary Protas Valentina Ghisays Valentina Ghisays Dhruman D. Goradia Dhruman D. Goradia Robert Bauer Robert Bauer Vivek Devadas Vivek Devadas Kewei Chen Kewei Chen Kewei Chen Kewei Chen Kewei Chen Eric M. Reiman Eric M. Reiman Eric M. Reiman Eric M. Reiman Eric M. Reiman Eric M. Reiman Yi Su Yi Su Yi Su |
| author_facet | Hillary Protas Hillary Protas Valentina Ghisays Valentina Ghisays Dhruman D. Goradia Dhruman D. Goradia Robert Bauer Robert Bauer Vivek Devadas Vivek Devadas Kewei Chen Kewei Chen Kewei Chen Kewei Chen Kewei Chen Eric M. Reiman Eric M. Reiman Eric M. Reiman Eric M. Reiman Eric M. Reiman Eric M. Reiman Yi Su Yi Su Yi Su |
| author_sort | Hillary Protas |
| collection | DOAJ |
| description | IntroductionTau PET imaging has emerged as an important tool to detect and monitor tangle burden in vivo in the study of Alzheimer’s disease (AD). Previous studies demonstrated the association of tau burden with cognitive decline in probable AD cohorts. This study introduces a novel approach to analyze tau PET data by constructing individualized tau network structure and deriving its graph theory-based measures. We hypothesize that the network- based measures are a measure of the total tau load and the stage through disease.MethodsUsing tau PET data from the AD Neuroimaging Initiative from 369 participants, we determine the network measures, global efficiency, global strength, and limbic strength, and compare with two regional measures entorhinal and tau composite SUVR, in the ability to differentiate, cognitively unimpaired (CU), MCI and AD. We also investigate the correlation of these network and regional measures and a measure of memory performance, auditory verbal learning test for long-term recall memory (AVLT-LTM). Finally, we determine the stages based on global efficiency and limbic strength using conditional inference trees and compare with Braak staging.ResultsWe demonstrate that the derived network measures are able to differentiate three clinical stages of AD, CU, MCI, and AD. We also demonstrate that these network measures are strongly correlated with memory performance overall. Unlike regional tau measurements, the tau network measures were significantly associated with AVLT-LTM even in cognitively unimpaired individuals. Stages determined from global efficiency and limbic strength, visually resembled Braak staging.DiscussionThe strong correlations with memory particularly in CU suggest the proposed technique may be used to characterize subtle early tau accumulation. Further investigation is ongoing to examine this technique in a longitudinal setting. |
| first_indexed | 2024-04-10T06:18:12Z |
| format | Article |
| id | doaj.art-29bdabdd8a3547f1bc0233a46c32ea4c |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-04-10T06:18:12Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-29bdabdd8a3547f1bc0233a46c32ea4c2023-03-02T05:03:04ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2023-03-011710.3389/fnins.2023.10891341089134Individualized network analysis: A novel approach to investigate tau PET using graph theory in the Alzheimer’s disease continuumHillary Protas0Hillary Protas1Valentina Ghisays2Valentina Ghisays3Dhruman D. Goradia4Dhruman D. Goradia5Robert Bauer6Robert Bauer7Vivek Devadas8Vivek Devadas9Kewei Chen10Kewei Chen11Kewei Chen12Kewei Chen13Kewei Chen14Eric M. Reiman15Eric M. Reiman16Eric M. Reiman17Eric M. Reiman18Eric M. Reiman19Eric M. Reiman20Yi Su21Yi Su22Yi Su23Banner Alzheimer’s Institute, Phoenix, AZ, United StatesArizona Alzheimer’s Consortium, Phoenix, AZ, United StatesBanner Alzheimer’s Institute, Phoenix, AZ, United StatesArizona Alzheimer’s Consortium, Phoenix, AZ, United StatesBanner Alzheimer’s Institute, Phoenix, AZ, United StatesArizona Alzheimer’s Consortium, Phoenix, AZ, United StatesBanner Alzheimer’s Institute, Phoenix, AZ, United StatesArizona Alzheimer’s Consortium, Phoenix, AZ, United StatesBanner Alzheimer’s Institute, Phoenix, AZ, United StatesArizona Alzheimer’s Consortium, Phoenix, AZ, United StatesBanner Alzheimer’s Institute, Phoenix, AZ, United StatesArizona Alzheimer’s Consortium, Phoenix, AZ, United StatesDepartment of Neurology, The University of Arizona, Tucson, AZ, United StatesDepartment of Psychiatry, The University of Arizona, Tucson, AZ, United StatesDepartment of Neuroscience, School of Computing and Augmented Intelligence, Biostatistical Core, School of Mathematics and Statistics, College of Health Solutions, Arizona State University, Tempe, AZ, United StatesBanner Alzheimer’s Institute, Phoenix, AZ, United StatesArizona Alzheimer’s Consortium, Phoenix, AZ, United StatesDepartment of Neurology, The University of Arizona, Tucson, AZ, United StatesDepartment of Psychiatry, The University of Arizona, Tucson, AZ, United StatesDepartment of Neuroscience, School of Computing and Augmented Intelligence, Biostatistical Core, School of Mathematics and Statistics, College of Health Solutions, Arizona State University, Tempe, AZ, United StatesTranslational Genomics Research Institute, Phoenix, AZ, United StatesBanner Alzheimer’s Institute, Phoenix, AZ, United StatesArizona Alzheimer’s Consortium, Phoenix, AZ, United StatesDepartment of Neuroscience, School of Computing and Augmented Intelligence, Biostatistical Core, School of Mathematics and Statistics, College of Health Solutions, Arizona State University, Tempe, AZ, United StatesIntroductionTau PET imaging has emerged as an important tool to detect and monitor tangle burden in vivo in the study of Alzheimer’s disease (AD). Previous studies demonstrated the association of tau burden with cognitive decline in probable AD cohorts. This study introduces a novel approach to analyze tau PET data by constructing individualized tau network structure and deriving its graph theory-based measures. We hypothesize that the network- based measures are a measure of the total tau load and the stage through disease.MethodsUsing tau PET data from the AD Neuroimaging Initiative from 369 participants, we determine the network measures, global efficiency, global strength, and limbic strength, and compare with two regional measures entorhinal and tau composite SUVR, in the ability to differentiate, cognitively unimpaired (CU), MCI and AD. We also investigate the correlation of these network and regional measures and a measure of memory performance, auditory verbal learning test for long-term recall memory (AVLT-LTM). Finally, we determine the stages based on global efficiency and limbic strength using conditional inference trees and compare with Braak staging.ResultsWe demonstrate that the derived network measures are able to differentiate three clinical stages of AD, CU, MCI, and AD. We also demonstrate that these network measures are strongly correlated with memory performance overall. Unlike regional tau measurements, the tau network measures were significantly associated with AVLT-LTM even in cognitively unimpaired individuals. Stages determined from global efficiency and limbic strength, visually resembled Braak staging.DiscussionThe strong correlations with memory particularly in CU suggest the proposed technique may be used to characterize subtle early tau accumulation. Further investigation is ongoing to examine this technique in a longitudinal setting.https://www.frontiersin.org/articles/10.3389/fnins.2023.1089134/fullflortaucipir PETgraph theoryAlzheimer’s diseasetangle burdenADNI |
| spellingShingle | Hillary Protas Hillary Protas Valentina Ghisays Valentina Ghisays Dhruman D. Goradia Dhruman D. Goradia Robert Bauer Robert Bauer Vivek Devadas Vivek Devadas Kewei Chen Kewei Chen Kewei Chen Kewei Chen Kewei Chen Eric M. Reiman Eric M. Reiman Eric M. Reiman Eric M. Reiman Eric M. Reiman Eric M. Reiman Yi Su Yi Su Yi Su Individualized network analysis: A novel approach to investigate tau PET using graph theory in the Alzheimer’s disease continuum Frontiers in Neuroscience flortaucipir PET graph theory Alzheimer’s disease tangle burden ADNI |
| title | Individualized network analysis: A novel approach to investigate tau PET using graph theory in the Alzheimer’s disease continuum |
| title_full | Individualized network analysis: A novel approach to investigate tau PET using graph theory in the Alzheimer’s disease continuum |
| title_fullStr | Individualized network analysis: A novel approach to investigate tau PET using graph theory in the Alzheimer’s disease continuum |
| title_full_unstemmed | Individualized network analysis: A novel approach to investigate tau PET using graph theory in the Alzheimer’s disease continuum |
| title_short | Individualized network analysis: A novel approach to investigate tau PET using graph theory in the Alzheimer’s disease continuum |
| title_sort | individualized network analysis a novel approach to investigate tau pet using graph theory in the alzheimer s disease continuum |
| topic | flortaucipir PET graph theory Alzheimer’s disease tangle burden ADNI |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2023.1089134/full |
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