Combining tau-PET and fMRI meta-analyses for patient-centered prediction of cognitive decline in Alzheimer’s disease

Tau-PET is a prognostic marker for cognitive decline in Alzheimer's disease, and the heterogeneity of tau-PET patterns matches cognitive symptom heterogeneity. Thus, tau-PET may allow precision-medicine prediction of individual tau-related cognitive trajectories, which can be important for dete...

Full description

Saved in:
Bibliographic Details
Published inAlzheimer's research & therapy Vol. 14; no. 1; pp. 166 - 12
Main Authors Biel, Davina, Luan, Ying, Brendel, Matthias, Hager, Paul, Dewenter, Anna, Moscoso, Alexis, Otero Svaldi, Diana, Higgins, Ixavier A., Pontecorvo, Michael, Römer, Sebastian, Steward, Anna, Rubinski, Anna, Zheng, Lukai, Schöll, Michael, Shcherbinin, Sergey, Ewers, Michael, Franzmeier, Nicolai
Format Journal Article
LanguageEnglish
Published England BioMed Central 07.11.2022
BMC
Subjects
Accuracy
Age
Aged
Aged, 80 and over
Alzheimer Disease - diagnosis
Alzheimer's disease
Amyloid - metabolism
Amyloid beta-Peptides - metabolism
association workgroups
Brain
Brain - diagnostic imaging
Brain - metabolism
Clinical trials
Cognitive ability
Cognitive decline
Cognitive Dysfunction - diagnostic imaging
Cognitive Dysfunction - psychology
criteria
Dementia
diagnostic guidelines
fMRI
functional connectivity
Humans
impairment
Magnetic Resonance Imaging - methods
Medical imaging
medicine
Memory
national institute
Neurodegeneration
Neuroimaging
Neurosciences
Neurosciences & Neurology
Neurovetenskaper
Pathology
Patient-Centered Care
Patients
Positron-Emission Tomography - methods
Precision
Precision medicine
recommendations
tau Proteins - metabolism
Tau-PET
trials
Working groups
Cognitive decline
fMRI
Alzheimer’s disease
Precision medicine
Tau-PET
Online AccessGet full text

Cover

Abstract Tau-PET is a prognostic marker for cognitive decline in Alzheimer's disease, and the heterogeneity of tau-PET patterns matches cognitive symptom heterogeneity. Thus, tau-PET may allow precision-medicine prediction of individual tau-related cognitive trajectories, which can be important for determining patient-specific cognitive endpoints in clinical trials. Here, we aimed to examine whether tau-PET in cognitive-domain-specific brain regions, identified via fMRI meta-analyses, allows the prediction of domain-specific cognitive decline. Further, we aimed to determine whether tau-PET-informed personalized cognitive composites capture patient-specific cognitive trajectories more sensitively than conventional cognitive measures. We included Alzheimer's Disease Neuroimaging Initiative (ADNI) participants classified as controls (i.e., amyloid-negative, cognitively normal, n = 121) or Alzheimer's disease-spectrum (i.e., amyloid-positive, cognitively normal to dementia, n = 140), plus 111 AVID-1451-A05 participants for independent validation (controls/Alzheimer's disease-spectrum=46/65). All participants underwent baseline F-flortaucipir tau-PET, amyloid-PET, and longitudinal cognitive testing to assess annual cognitive changes (i.e., episodic memory, language, executive functioning, visuospatial). Cognitive changes were calculated using linear mixed models. Independent meta-analytical task-fMRI activation maps for each included cognitive domain were obtained from the Neurosynth database and applied to tau-PET to determine tau-PET signal in cognitive-domain-specific brain regions. In bootstrapped linear regression, we assessed the strength of the relationship (i.e., partial R ) between cognitive-domain-specific tau-PET vs. global or temporal-lobe tau-PET and cognitive changes. Further, we used tau-PET-based prediction of domain-specific decline to compose personalized cognitive composites that were tailored to capture patient-specific cognitive decline. In both amyloid-positive cohorts (ADNI [age = 75.99±7.69] and A05 [age = 74.03±9.03]), cognitive-domain-specific tau-PET outperformed global and temporal-lobe tau-PET for predicting future cognitive decline in episodic memory, language, executive functioning, and visuospatial abilities. Further, a tau-PET-informed personalized cognitive composite across cognitive domains enhanced the sensitivity to assess cognitive decline in amyloid-positive subjects, yielding lower sample sizes required for detecting simulated intervention effects compared to conventional cognitive endpoints (i.e., memory composite, global cognitive composite). However, the latter effect was less strong in A05 compared to the ADNI cohort. Combining tau-PET with task-fMRI-derived maps of major cognitive domains facilitates the prediction of domain-specific cognitive decline. This approach may help to increase the sensitivity to detect Alzheimer's disease-related cognitive decline and to determine personalized cognitive endpoints in clinical trials.
AbstractList Background Tau-PET is a prognostic marker for cognitive decline in Alzheimer's disease, and the heterogeneity of tau-PET patterns matches cognitive symptom heterogeneity. Thus, tau-PET may allow precision-medicine prediction of individual tau-related cognitive trajectories, which can be important for determining patient-specific cognitive endpoints in clinical trials. Here, we aimed to examine whether tau-PET in cognitive-domain-specific brain regions, identified via fMRI meta-analyses, allows the prediction of domain-specific cognitive decline. Further, we aimed to determine whether tau-PET-informed personalized cognitive composites capture patient-specific cognitive trajectories more sensitively than conventional cognitive measures. Methods We included Alzheimer's Disease Neuroimaging Initiative (ADNI) participants classified as controls (i.e., amyloid-negative, cognitively normal, n = 121) or Alzheimer's disease-spectrum (i.e., amyloid-positive, cognitively normal to dementia, n = 140), plus 111 AVID-1451-A05 participants for independent validation (controls/Alzheimer's disease-spectrum=46/65). All participants underwent baseline F-18-flortaucipir tau-PET, amyloid-PET, and longitudinal cognitive testing to assess annual cognitive changes (i.e., episodic memory, language, executive functioning, visuospatial). Cognitive changes were calculated using linear mixed models. Independent meta-analytical task-fMRI activation maps for each included cognitive domain were obtained from the Neurosynth database and applied to tau-PET to determine tau-PET signal in cognitive-domain-specific brain regions. In bootstrapped linear regression, we assessed the strength of the relationship (i.e., partial R-2) between cognitive-domain-specific tau-PET vs. global or temporal-lobe tau-PET and cognitive changes. Further, we used tau-PET-based prediction of domain-specific decline to compose personalized cognitive composites that were tailored to capture patient-specific cognitive decline. Results In both amyloid-positive cohorts (ADNI [age = 75.99 +/- 7.69] and A05 [age = 74.03 +/- 9.03]), cognitive-domain-specific tau-PET outperformed global and temporal-lobe tau-PET for predicting future cognitive decline in episodic memory, language, executive functioning, and visuospatial abilities. Further, a tau-PET-informed personalized cognitive composite across cognitive domains enhanced the sensitivity to assess cognitive decline in amyloid-positive subjects, yielding lower sample sizes required for detecting simulated intervention effects compared to conventional cognitive endpoints (i.e., memory composite, global cognitive composite). However, the latter effect was less strong in A05 compared to the ADNI cohort. Conclusion Combining tau-PET with task-fMRI-derived maps of major cognitive domains facilitates the prediction of domain-specific cognitive decline. This approach may help to increase the sensitivity to detect Alzheimer's disease-related cognitive decline and to determine personalized cognitive endpoints in clinical trials.
Abstract Background Tau-PET is a prognostic marker for cognitive decline in Alzheimer’s disease, and the heterogeneity of tau-PET patterns matches cognitive symptom heterogeneity. Thus, tau-PET may allow precision-medicine prediction of individual tau-related cognitive trajectories, which can be important for determining patient-specific cognitive endpoints in clinical trials. Here, we aimed to examine whether tau-PET in cognitive-domain-specific brain regions, identified via fMRI meta-analyses, allows the prediction of domain-specific cognitive decline. Further, we aimed to determine whether tau-PET-informed personalized cognitive composites capture patient-specific cognitive trajectories more sensitively than conventional cognitive measures. Methods We included Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants classified as controls (i.e., amyloid-negative, cognitively normal, n = 121) or Alzheimer’s disease-spectrum (i.e., amyloid-positive, cognitively normal to dementia, n = 140), plus 111 AVID-1451-A05 participants for independent validation (controls/Alzheimer’s disease-spectrum=46/65). All participants underwent baseline 18F-flortaucipir tau-PET, amyloid-PET, and longitudinal cognitive testing to assess annual cognitive changes (i.e., episodic memory, language, executive functioning, visuospatial). Cognitive changes were calculated using linear mixed models. Independent meta-analytical task-fMRI activation maps for each included cognitive domain were obtained from the Neurosynth database and applied to tau-PET to determine tau-PET signal in cognitive-domain-specific brain regions. In bootstrapped linear regression, we assessed the strength of the relationship (i.e., partial R 2) between cognitive-domain-specific tau-PET vs. global or temporal-lobe tau-PET and cognitive changes. Further, we used tau-PET-based prediction of domain-specific decline to compose personalized cognitive composites that were tailored to capture patient-specific cognitive decline. Results In both amyloid-positive cohorts (ADNI [age = 75.99±7.69] and A05 [age = 74.03±9.03]), cognitive-domain-specific tau-PET outperformed global and temporal-lobe tau-PET for predicting future cognitive decline in episodic memory, language, executive functioning, and visuospatial abilities. Further, a tau-PET-informed personalized cognitive composite across cognitive domains enhanced the sensitivity to assess cognitive decline in amyloid-positive subjects, yielding lower sample sizes required for detecting simulated intervention effects compared to conventional cognitive endpoints (i.e., memory composite, global cognitive composite). However, the latter effect was less strong in A05 compared to the ADNI cohort. Conclusion Combining tau-PET with task-fMRI-derived maps of major cognitive domains facilitates the prediction of domain-specific cognitive decline. This approach may help to increase the sensitivity to detect Alzheimer’s disease-related cognitive decline and to determine personalized cognitive endpoints in clinical trials.
Tau-PET is a prognostic marker for cognitive decline in Alzheimer's disease, and the heterogeneity of tau-PET patterns matches cognitive symptom heterogeneity. Thus, tau-PET may allow precision-medicine prediction of individual tau-related cognitive trajectories, which can be important for determining patient-specific cognitive endpoints in clinical trials. Here, we aimed to examine whether tau-PET in cognitive-domain-specific brain regions, identified via fMRI meta-analyses, allows the prediction of domain-specific cognitive decline. Further, we aimed to determine whether tau-PET-informed personalized cognitive composites capture patient-specific cognitive trajectories more sensitively than conventional cognitive measures.BACKGROUNDTau-PET is a prognostic marker for cognitive decline in Alzheimer's disease, and the heterogeneity of tau-PET patterns matches cognitive symptom heterogeneity. Thus, tau-PET may allow precision-medicine prediction of individual tau-related cognitive trajectories, which can be important for determining patient-specific cognitive endpoints in clinical trials. Here, we aimed to examine whether tau-PET in cognitive-domain-specific brain regions, identified via fMRI meta-analyses, allows the prediction of domain-specific cognitive decline. Further, we aimed to determine whether tau-PET-informed personalized cognitive composites capture patient-specific cognitive trajectories more sensitively than conventional cognitive measures.We included Alzheimer's Disease Neuroimaging Initiative (ADNI) participants classified as controls (i.e., amyloid-negative, cognitively normal, n = 121) or Alzheimer's disease-spectrum (i.e., amyloid-positive, cognitively normal to dementia, n = 140), plus 111 AVID-1451-A05 participants for independent validation (controls/Alzheimer's disease-spectrum=46/65). All participants underwent baseline 18F-flortaucipir tau-PET, amyloid-PET, and longitudinal cognitive testing to assess annual cognitive changes (i.e., episodic memory, language, executive functioning, visuospatial). Cognitive changes were calculated using linear mixed models. Independent meta-analytical task-fMRI activation maps for each included cognitive domain were obtained from the Neurosynth database and applied to tau-PET to determine tau-PET signal in cognitive-domain-specific brain regions. In bootstrapped linear regression, we assessed the strength of the relationship (i.e., partial R2) between cognitive-domain-specific tau-PET vs. global or temporal-lobe tau-PET and cognitive changes. Further, we used tau-PET-based prediction of domain-specific decline to compose personalized cognitive composites that were tailored to capture patient-specific cognitive decline.METHODSWe included Alzheimer's Disease Neuroimaging Initiative (ADNI) participants classified as controls (i.e., amyloid-negative, cognitively normal, n = 121) or Alzheimer's disease-spectrum (i.e., amyloid-positive, cognitively normal to dementia, n = 140), plus 111 AVID-1451-A05 participants for independent validation (controls/Alzheimer's disease-spectrum=46/65). All participants underwent baseline 18F-flortaucipir tau-PET, amyloid-PET, and longitudinal cognitive testing to assess annual cognitive changes (i.e., episodic memory, language, executive functioning, visuospatial). Cognitive changes were calculated using linear mixed models. Independent meta-analytical task-fMRI activation maps for each included cognitive domain were obtained from the Neurosynth database and applied to tau-PET to determine tau-PET signal in cognitive-domain-specific brain regions. In bootstrapped linear regression, we assessed the strength of the relationship (i.e., partial R2) between cognitive-domain-specific tau-PET vs. global or temporal-lobe tau-PET and cognitive changes. Further, we used tau-PET-based prediction of domain-specific decline to compose personalized cognitive composites that were tailored to capture patient-specific cognitive decline.In both amyloid-positive cohorts (ADNI [age = 75.99±7.69] and A05 [age = 74.03±9.03]), cognitive-domain-specific tau-PET outperformed global and temporal-lobe tau-PET for predicting future cognitive decline in episodic memory, language, executive functioning, and visuospatial abilities. Further, a tau-PET-informed personalized cognitive composite across cognitive domains enhanced the sensitivity to assess cognitive decline in amyloid-positive subjects, yielding lower sample sizes required for detecting simulated intervention effects compared to conventional cognitive endpoints (i.e., memory composite, global cognitive composite). However, the latter effect was less strong in A05 compared to the ADNI cohort.RESULTSIn both amyloid-positive cohorts (ADNI [age = 75.99±7.69] and A05 [age = 74.03±9.03]), cognitive-domain-specific tau-PET outperformed global and temporal-lobe tau-PET for predicting future cognitive decline in episodic memory, language, executive functioning, and visuospatial abilities. Further, a tau-PET-informed personalized cognitive composite across cognitive domains enhanced the sensitivity to assess cognitive decline in amyloid-positive subjects, yielding lower sample sizes required for detecting simulated intervention effects compared to conventional cognitive endpoints (i.e., memory composite, global cognitive composite). However, the latter effect was less strong in A05 compared to the ADNI cohort.Combining tau-PET with task-fMRI-derived maps of major cognitive domains facilitates the prediction of domain-specific cognitive decline. This approach may help to increase the sensitivity to detect Alzheimer's disease-related cognitive decline and to determine personalized cognitive endpoints in clinical trials.CONCLUSIONCombining tau-PET with task-fMRI-derived maps of major cognitive domains facilitates the prediction of domain-specific cognitive decline. This approach may help to increase the sensitivity to detect Alzheimer's disease-related cognitive decline and to determine personalized cognitive endpoints in clinical trials.
Background Tau-PET is a prognostic marker for cognitive decline in Alzheimer’s disease, and the heterogeneity of tau-PET patterns matches cognitive symptom heterogeneity. Thus, tau-PET may allow precision-medicine prediction of individual tau-related cognitive trajectories, which can be important for determining patient-specific cognitive endpoints in clinical trials. Here, we aimed to examine whether tau-PET in cognitive-domain-specific brain regions, identified via fMRI meta-analyses, allows the prediction of domain-specific cognitive decline. Further, we aimed to determine whether tau-PET-informed personalized cognitive composites capture patient-specific cognitive trajectories more sensitively than conventional cognitive measures. Methods We included Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants classified as controls (i.e., amyloid-negative, cognitively normal, n = 121) or Alzheimer’s disease-spectrum (i.e., amyloid-positive, cognitively normal to dementia, n = 140), plus 111 AVID-1451-A05 participants for independent validation (controls/Alzheimer’s disease-spectrum=46/65). All participants underwent baseline 18F-flortaucipir tau-PET, amyloid-PET, and longitudinal cognitive testing to assess annual cognitive changes (i.e., episodic memory, language, executive functioning, visuospatial). Cognitive changes were calculated using linear mixed models. Independent meta-analytical task-fMRI activation maps for each included cognitive domain were obtained from the Neurosynth database and applied to tau-PET to determine tau-PET signal in cognitive-domain-specific brain regions. In bootstrapped linear regression, we assessed the strength of the relationship (i.e., partial R2) between cognitive-domain-specific tau-PET vs. global or temporal-lobe tau-PET and cognitive changes. Further, we used tau-PET-based prediction of domain-specific decline to compose personalized cognitive composites that were tailored to capture patient-specific cognitive decline. Results In both amyloid-positive cohorts (ADNI [age = 75.99±7.69] and A05 [age = 74.03±9.03]), cognitive-domain-specific tau-PET outperformed global and temporal-lobe tau-PET for predicting future cognitive decline in episodic memory, language, executive functioning, and visuospatial abilities. Further, a tau-PET-informed personalized cognitive composite across cognitive domains enhanced the sensitivity to assess cognitive decline in amyloid-positive subjects, yielding lower sample sizes required for detecting simulated intervention effects compared to conventional cognitive endpoints (i.e., memory composite, global cognitive composite). However, the latter effect was less strong in A05 compared to the ADNI cohort. Conclusion Combining tau-PET with task-fMRI-derived maps of major cognitive domains facilitates the prediction of domain-specific cognitive decline. This approach may help to increase the sensitivity to detect Alzheimer’s disease-related cognitive decline and to determine personalized cognitive endpoints in clinical trials.
Tau-PET is a prognostic marker for cognitive decline in Alzheimer's disease, and the heterogeneity of tau-PET patterns matches cognitive symptom heterogeneity. Thus, tau-PET may allow precision-medicine prediction of individual tau-related cognitive trajectories, which can be important for determining patient-specific cognitive endpoints in clinical trials. Here, we aimed to examine whether tau-PET in cognitive-domain-specific brain regions, identified via fMRI meta-analyses, allows the prediction of domain-specific cognitive decline. Further, we aimed to determine whether tau-PET-informed personalized cognitive composites capture patient-specific cognitive trajectories more sensitively than conventional cognitive measures. We included Alzheimer's Disease Neuroimaging Initiative (ADNI) participants classified as controls (i.e., amyloid-negative, cognitively normal, n = 121) or Alzheimer's disease-spectrum (i.e., amyloid-positive, cognitively normal to dementia, n = 140), plus 111 AVID-1451-A05 participants for independent validation (controls/Alzheimer's disease-spectrum=46/65). All participants underwent baseline F-flortaucipir tau-PET, amyloid-PET, and longitudinal cognitive testing to assess annual cognitive changes (i.e., episodic memory, language, executive functioning, visuospatial). Cognitive changes were calculated using linear mixed models. Independent meta-analytical task-fMRI activation maps for each included cognitive domain were obtained from the Neurosynth database and applied to tau-PET to determine tau-PET signal in cognitive-domain-specific brain regions. In bootstrapped linear regression, we assessed the strength of the relationship (i.e., partial R ) between cognitive-domain-specific tau-PET vs. global or temporal-lobe tau-PET and cognitive changes. Further, we used tau-PET-based prediction of domain-specific decline to compose personalized cognitive composites that were tailored to capture patient-specific cognitive decline. In both amyloid-positive cohorts (ADNI [age = 75.99±7.69] and A05 [age = 74.03±9.03]), cognitive-domain-specific tau-PET outperformed global and temporal-lobe tau-PET for predicting future cognitive decline in episodic memory, language, executive functioning, and visuospatial abilities. Further, a tau-PET-informed personalized cognitive composite across cognitive domains enhanced the sensitivity to assess cognitive decline in amyloid-positive subjects, yielding lower sample sizes required for detecting simulated intervention effects compared to conventional cognitive endpoints (i.e., memory composite, global cognitive composite). However, the latter effect was less strong in A05 compared to the ADNI cohort. Combining tau-PET with task-fMRI-derived maps of major cognitive domains facilitates the prediction of domain-specific cognitive decline. This approach may help to increase the sensitivity to detect Alzheimer's disease-related cognitive decline and to determine personalized cognitive endpoints in clinical trials.
ArticleNumber 166
Author Biel, Davina
Luan, Ying
Moscoso, Alexis
Pontecorvo, Michael
Steward, Anna
Shcherbinin, Sergey
Brendel, Matthias
Higgins, Ixavier A.
Rubinski, Anna
Ewers, Michael
Franzmeier, Nicolai
Zheng, Lukai
Dewenter, Anna
Otero Svaldi, Diana
Schöll, Michael
Hager, Paul
Römer, Sebastian
Author_xml – sequence: 1
  givenname: Davina
  orcidid: 0000-0002-2597-1992
  surname: Biel
  fullname: Biel, Davina
– sequence: 2
  givenname: Ying
  surname: Luan
  fullname: Luan, Ying
– sequence: 3
  givenname: Matthias
  surname: Brendel
  fullname: Brendel, Matthias
– sequence: 4
  givenname: Paul
  surname: Hager
  fullname: Hager, Paul
– sequence: 5
  givenname: Anna
  surname: Dewenter
  fullname: Dewenter, Anna
– sequence: 6
  givenname: Alexis
  surname: Moscoso
  fullname: Moscoso, Alexis
– sequence: 7
  givenname: Diana
  surname: Otero Svaldi
  fullname: Otero Svaldi, Diana
– sequence: 8
  givenname: Ixavier A.
  surname: Higgins
  fullname: Higgins, Ixavier A.
– sequence: 9
  givenname: Michael
  surname: Pontecorvo
  fullname: Pontecorvo, Michael
– sequence: 10
  givenname: Sebastian
  surname: Römer
  fullname: Römer, Sebastian
– sequence: 11
  givenname: Anna
  surname: Steward
  fullname: Steward, Anna
– sequence: 12
  givenname: Anna
  surname: Rubinski
  fullname: Rubinski, Anna
– sequence: 13
  givenname: Lukai
  surname: Zheng
  fullname: Zheng, Lukai
– sequence: 14
  givenname: Michael
  surname: Schöll
  fullname: Schöll, Michael
– sequence: 15
  givenname: Sergey
  surname: Shcherbinin
  fullname: Shcherbinin, Sergey
– sequence: 16
  givenname: Michael
  surname: Ewers
  fullname: Ewers, Michael
– sequence: 17
  givenname: Nicolai
  orcidid: 0000-0001-9736-2283
  surname: Franzmeier
  fullname: Franzmeier, Nicolai
BackLink https://www.ncbi.nlm.nih.gov/pubmed/36345046$$D View this record in MEDLINE/PubMed
https://gup.ub.gu.se/publication/320401$$DView record from Swedish Publication Index
BookMark eNp9ks1u1DAUhSNURNuBF2CBLLFhE_B_7A1SNSowUhEIlbXlODepq8Qe4mSqsuI1eD2eBM9MqTpdsLm2rr9zdG2f0-IoxABF8ZLgt4Qo-S4RRrQoMaUlJgSLUjwpTkglVKmJZkcP9sfFaUrXGEtJFX9WHDPJuMBcnhSbZRxqH3zo0GTn8uv5JbKhQe3nbys0wGRLG2x_myChNo5obScPYSpdLjBCg9a5eDf5GFBskYtd8JPfAGrA9T4A8gGd9T-vwA8w_vn1O6HGJ7AJnhdPW9sneHG3LorvH84vl5_Kiy8fV8uzi9IJxqdSVODaWnHd8rbFUqtWNNqBs0oq4jjBtVMMC0sll5bIWhGBW6g0sLrWllG2KFZ73ybaa7Me_WDHWxOtN7tGHDtjx8m7HoxzrVUWuAXmuBBSOUoZJozV3DHG6-xV7r3SDazn-sCtm9cmt7rZJDCMYp6Fi-L9ns_wAM32zUbbH8gOT4K_Ml3cGC2Zpkpmgzd3BmP8MUOazOCTg763AeKcDK0YJ1JUeou-foRex3nMX7ejqkpTjXWmXj2c6H6Uf3HIAN0DbowpjdDeIwSbbebMPnMmZ87sMmdEFqlHIucnu81EvpXv_yf9CwQQ3ZA
CitedBy_id crossref_primary_10_1016_j_neurobiolaging_2024_01_014
crossref_primary_10_1038_s41583_023_00731_8
crossref_primary_10_31083_j_jin2206172
crossref_primary_10_1001_jamaneurol_2023_4038
crossref_primary_10_3389_fnagi_2023_1168840
crossref_primary_10_1038_s41380_023_02230_9
crossref_primary_10_1097_WCO_0000000000001198
crossref_primary_10_1126_scitranslmed_adp2564
crossref_primary_10_1038_s41467_023_44374_w
crossref_primary_10_3390_brainsci14060575
crossref_primary_10_1093_brain_awae327
crossref_primary_10_1186_s13195_023_01302_w
crossref_primary_10_1186_s13244_024_01848_9
Cites_doi 10.1016/j.conb.2011.10.021
10.1016/j.jalz.2014.02.002
10.15252/emmm.202012308
10.1371/journal.pcbi.1005649
10.1038/s41380-018-0342-8
10.1093/brain/awaa058
10.1093/brain/awy059
10.1001/jamanetworkopen.2020.0413
10.1093/brain/awab114
10.1186/s13195-018-0401-z
10.1016/j.jalz.2011.03.008
10.1016/j.neurobiolaging.2020.03.009
10.1371/journal.pcbi.1002251
10.1093/brain/awz090
10.1016/j.neuron.2016.01.028
10.1016/S1474-4422(21)00066-1
10.1001/jamaneurol.2021.1858
10.1038/s41591-021-01369-8
10.1093/cercor/bhs410
10.1001/archneur.64.9.1323
10.1016/S1474-4422(07)70178-3
10.3233/JAD-200808
10.1016/j.trci.2019.04.001
10.1016/j.jalz.2018.02.018
10.1038/s41591-021-01309-6
10.1126/sciadv.abd1327
10.1002/trc2.12072
10.1038/s41467-020-15701-2
10.1101/cshperspect.a006171
10.1523/JNEUROSCI.3263-16.2017
10.1007/s11682-012-9186-z
10.1001/jamaneurol.2020.5505
10.1001/jamaneurol.2014.3314
10.1186/s13054-016-1388-0
10.1186/s13195-021-00813-8
10.1212/WNL.0000000000012022
10.1523/JNEUROSCI.3539-11.2011
10.1016/j.dadm.2018.01.007
10.1038/nmeth.1635
10.1016/j.jalz.2011.03.005
10.1007/BF00308809
10.1093/brain/awx243
10.1186/s13195-018-0455-y
10.1016/0022-3956(75)90026-6
10.1093/brain/awy053
10.1016/S1474-4422(14)70090-0
10.1186/s13195-021-00880-x
10.1038/s41380-021-01263-2
10.1093/brain/aww027
10.1038/s41593-018-0289-8
ContentType Journal Article
Copyright 2022. The Author(s).
2022. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
The Author(s) 2022
Copyright_xml – notice: 2022. The Author(s).
– notice: 2022. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
– notice: The Author(s) 2022
CorporateAuthor the Alzheimer’s Disease Neuroimaging Initiative
Alzheimer’s Disease Neuroimaging Initiative
CorporateAuthor_xml – name: the Alzheimer’s Disease Neuroimaging Initiative
– name: Alzheimer’s Disease Neuroimaging Initiative
DBID AAYXX
CITATION
CGR
CUY
CVF
ECM
EIF
NPM
3V.
7X7
7XB
88E
8FI
8FJ
8FK
ABUWG
AFKRA
AZQEC
BENPR
CCPQU
DWQXO
FYUFA
GHDGH
K9.
M0S
M1P
PHGZM
PHGZT
PIMPY
PJZUB
PKEHL
PPXIY
PQEST
PQQKQ
PQUKI
PRINS
7X8
5PM
ADTPV
AOWAS
F1U
DOA
DOI 10.1186/s13195-022-01105-5
DatabaseName CrossRef
Medline
MEDLINE
MEDLINE (Ovid)
MEDLINE
MEDLINE
PubMed
ProQuest Central (Corporate)
Health & Medical Collection
ProQuest Central (purchase pre-March 2016)
Medical Database (Alumni Edition)
Hospital Premium Collection
Hospital Premium Collection (Alumni Edition)
ProQuest Central (Alumni) (purchase pre-March 2016)
ProQuest Central (Alumni Edition)
ProQuest Central UK/Ireland
ProQuest Central Essentials
ProQuest Central
ProQuest One Community College
ProQuest Central Korea
Health Research Premium Collection
Health Research Premium Collection (Alumni)
ProQuest Health & Medical Complete (Alumni)
Health & Medical Collection (Alumni Edition)
Medical Database
ProQuest Central Premium
ProQuest One Academic (New)
Publicly Available Content Database
ProQuest Health & Medical Research Collection
ProQuest One Academic Middle East (New)
ProQuest One Health & Nursing
ProQuest One Academic Eastern Edition (DO NOT USE)
ProQuest One Academic
ProQuest One Academic UKI Edition
ProQuest Central China
MEDLINE - Academic
PubMed Central (Full Participant titles)
SwePub
SwePub Articles
SWEPUB Göteborgs universitet
DOAJ Directory of Open Access Journals
DatabaseTitle CrossRef
MEDLINE
Medline Complete
MEDLINE with Full Text
PubMed
MEDLINE (Ovid)
Publicly Available Content Database
ProQuest One Academic Middle East (New)
ProQuest Central Essentials
ProQuest Health & Medical Complete (Alumni)
ProQuest Central (Alumni Edition)
ProQuest One Community College
ProQuest One Health & Nursing
ProQuest Central China
ProQuest Central
Health Research Premium Collection
Health and Medicine Complete (Alumni Edition)
ProQuest Central Korea
Health & Medical Research Collection
ProQuest Central (New)
ProQuest Medical Library (Alumni)
ProQuest One Academic Eastern Edition
ProQuest Hospital Collection
Health Research Premium Collection (Alumni)
ProQuest Hospital Collection (Alumni)
ProQuest Health & Medical Complete
ProQuest Medical Library
ProQuest One Academic UKI Edition
ProQuest One Academic
ProQuest One Academic (New)
ProQuest Central (Alumni)
MEDLINE - Academic
DatabaseTitleList

MEDLINE - Academic
Publicly Available Content Database
MEDLINE
Database_xml – sequence: 1
  dbid: DOA
  name: DOAJ Directory of Open Access Journals
  url: https://www.doaj.org/
  sourceTypes: Open Website
– sequence: 2
  dbid: NPM
  name: PubMed
  url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed
  sourceTypes: Index Database
– sequence: 3
  dbid: EIF
  name: MEDLINE
  url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search
  sourceTypes: Index Database
– sequence: 4
  dbid: BENPR
  name: ProQuest Central
  url: https://www.proquest.com/central
  sourceTypes: Aggregation Database
DeliveryMethod fulltext_linktorsrc
Discipline Medicine
EISSN 1758-9193
EndPage 12
ExternalDocumentID oai_doaj_org_article_ccfa8ae4ae3c45568c2230133b4c334b
oai_gup_ub_gu_se_320401
PMC9639286
36345046
10_1186_s13195_022_01105_5
Genre Research Support, Non-U.S. Gov't
Journal Article
GrantInformation_xml – fundername: ;
GroupedDBID ---
0R~
23M
2WC
53G
5VS
6J9
7X7
88E
8FI
8FJ
AAFWJ
AAJSJ
AASML
AAYXX
ABDBF
ABUWG
ACGFS
ACIHN
ACJQM
ACUHS
ADBBV
ADUKV
AEAQA
AFKRA
AFPKN
AHBYD
AHMBA
AHYZX
ALIPV
ALMA_UNASSIGNED_HOLDINGS
AMKLP
AMTXH
AOIAM
AOIJS
BAPOH
BAWUL
BCNDV
BENPR
BFQNJ
BMC
BPHCQ
BVXVI
C6C
CCPQU
CITATION
DIK
E3Z
EBD
EBLON
EBS
ESX
F5P
FYUFA
GROUPED_DOAJ
GX1
HMCUK
HZ~
IAO
IEA
IHR
IHW
INH
INR
ITC
KQ8
M1P
M~E
O5R
O5S
O9-
OK1
P2P
P6G
PGMZT
PHGZM
PHGZT
PIMPY
PQQKQ
PROAC
PSQYO
RBZ
ROL
RPM
RSV
SBL
SOJ
TR2
TUS
UKHRP
-56
-5G
-BR
3V.
ACRMQ
ADINQ
C24
CGR
CUY
CVF
ECM
EIF
NPM
7XB
8FK
AZQEC
DWQXO
K9.
PJZUB
PKEHL
PPXIY
PQEST
PQUKI
PRINS
7X8
5PM
4.4
ADTPV
AHSBF
AOWAS
EJD
F1U
H13
HYE
PUEGO
ID FETCH-LOGICAL-c534t-57ecfb849f4ff0698f5d9ceca8681c410bc8305a2646a16b8150fe79e3bb9a323
IEDL.DBID 7X7
ISSN 1758-9193
IngestDate Wed Aug 27 01:29:24 EDT 2025
Thu Aug 21 06:44:16 EDT 2025
Thu Aug 21 18:39:39 EDT 2025
Fri Jul 11 08:23:34 EDT 2025
Fri Jul 25 05:26:03 EDT 2025
Thu Jan 02 22:53:41 EST 2025
Tue Jul 01 02:38:52 EDT 2025
Thu Apr 24 23:09:53 EDT 2025
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 1
Keywords Cognitive decline
fMRI
Alzheimer’s disease
Precision medicine
Tau-PET
Language English
License 2022. The Author(s).
Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c534t-57ecfb849f4ff0698f5d9ceca8681c410bc8305a2646a16b8150fe79e3bb9a323
Notes ObjectType-Article-2
SourceType-Scholarly Journals-1
content type line 14
ObjectType-Feature-3
ObjectType-Evidence Based Healthcare-1
ObjectType-Article-1
ObjectType-Feature-2
content type line 23
ORCID 0000-0002-2597-1992
0000-0001-9736-2283
OpenAccessLink https://www.proquest.com/docview/2737792909?pq-origsite=%requestingapplication%
PMID 36345046
PQID 2737792909
PQPubID 2040174
PageCount 12
ParticipantIDs doaj_primary_oai_doaj_org_article_ccfa8ae4ae3c45568c2230133b4c334b
swepub_primary_oai_gup_ub_gu_se_320401
pubmedcentral_primary_oai_pubmedcentral_nih_gov_9639286
proquest_miscellaneous_2734165796
proquest_journals_2737792909
pubmed_primary_36345046
crossref_primary_10_1186_s13195_022_01105_5
crossref_citationtrail_10_1186_s13195_022_01105_5
ProviderPackageCode CITATION
AAYXX
PublicationCentury 2000
PublicationDate 2022-11-07
PublicationDateYYYYMMDD 2022-11-07
PublicationDate_xml – month: 11
  year: 2022
  text: 2022-11-07
  day: 07
PublicationDecade 2020
PublicationPlace England
PublicationPlace_xml – name: England
– name: London
PublicationTitle Alzheimer's research & therapy
PublicationTitleAlternate Alzheimers Res Ther
PublicationYear 2022
Publisher BioMed Central
BMC
Publisher_xml – name: BioMed Central
– name: BMC
References M Ewers (1105_CR33) 2020; 12
R Ossenkoppele (1105_CR14) 2016; 139
B Dubois (1105_CR36) 2007; 6
M Gallagher (1105_CR35) 2011; 21
NL Komarova (1105_CR4) 2011; 7
PK Crane (1105_CR29) 2012; 6
CR Jack Jr (1105_CR2) 2018; 14
MA Busche (1105_CR41) 2019; 22
MD Devous Sr (1105_CR16) 2021; 80
M Scholl (1105_CR27) 2016; 89
L Lemoine (1105_CR22) 2018; 10
GM McKhann (1105_CR21) 2011; 7
PK Crane (1105_CR38) 2012; 6
TN Rubin (1105_CR18) 2017; 13
G Devi (1105_CR7) 2018; 10
TJ Iwashyna (1105_CR19) 2016; 20
MS Albert (1105_CR20) 2011; 7
D Berron (1105_CR51) 2021; 144
MJ Pontecorvo (1105_CR12) 2019; 142
M Lu (1105_CR13) 2021; 78
MF Folstein (1105_CR34) 1975; 12
J Harrison (1105_CR39) 2007; 64
E Kocagoncu (1105_CR44) 2020; 92
JW Vogel (1105_CR25) 2020; 11
CR Jack Jr (1105_CR28) 2018; 141
A Leuzy (1105_CR23) 2019; 24
M Malek-Ahmadi (1105_CR31) 2018; 10
JP Chhatwal (1105_CR43) 2018; 141
N Franzmeier (1105_CR24) 2020; 6
CJ Swanson (1105_CR8) 2021; 13
AE Blanken (1105_CR49) 2020; 3
AS Fleisher (1105_CR1) 2015; 72
SE Choi (1105_CR30) 2020; 6
T Yarkoni (1105_CR17) 2011; 8
Y Liu (1105_CR42) 2014; 24
R Ossenkoppele (1105_CR9) 2021; 78
B Dubois (1105_CR37) 2014; 13
C Ballard (1105_CR6) 2019; 5
AM Tetreault (1105_CR40) 2020; 143
B Dubois (1105_CR3) 2021; 20
D Biel (1105_CR10) 2021; 13
MP van den Heuvel (1105_CR46) 2011; 31
H Braak (1105_CR26) 1991; 82
S Weintraub (1105_CR47) 2012; 2
A Bejanin (1105_CR15) 2017; 140
M Bucci (1105_CR11) 2021; 26
RJ Jutten (1105_CR48) 2021; 96
JB Langbaum (1105_CR32) 2014; 10
AP Schultz (1105_CR45) 2017; 37
S Salloway (1105_CR50) 2021; 27
JW Vogel (1105_CR5) 2021; 27
References_xml – volume: 21
  start-page: 929
  issue: 6
  year: 2011
  ident: 1105_CR35
  publication-title: Curr Opin Neurobiol
  doi: 10.1016/j.conb.2011.10.021
– volume: 10
  start-page: 666
  issue: 6
  year: 2014
  ident: 1105_CR32
  publication-title: Alzheimers Dement
  doi: 10.1016/j.jalz.2014.02.002
– volume: 12
  start-page: e12308
  issue: 9
  year: 2020
  ident: 1105_CR33
  publication-title: EMBO Mol Med
  doi: 10.15252/emmm.202012308
– volume: 13
  start-page: e1005649
  issue: 10
  year: 2017
  ident: 1105_CR18
  publication-title: PLoS Comput Biol
  doi: 10.1371/journal.pcbi.1005649
– volume: 24
  start-page: 1112
  issue: 8
  year: 2019
  ident: 1105_CR23
  publication-title: Mol Psychiatry
  doi: 10.1038/s41380-018-0342-8
– volume: 143
  start-page: 1249
  issue: 4
  year: 2020
  ident: 1105_CR40
  publication-title: Brain.
  doi: 10.1093/brain/awaa058
– volume: 141
  start-page: 1517
  issue: 5
  year: 2018
  ident: 1105_CR28
  publication-title: Brain.
  doi: 10.1093/brain/awy059
– volume: 3
  start-page: e200413
  issue: 3
  year: 2020
  ident: 1105_CR49
  publication-title: JAMA Netw Open
  doi: 10.1001/jamanetworkopen.2020.0413
– volume: 144
  start-page: 2771
  issue: 9
  year: 2021
  ident: 1105_CR51
  publication-title: Brain.
  doi: 10.1093/brain/awab114
– volume: 10
  start-page: 90
  issue: 1
  year: 2018
  ident: 1105_CR31
  publication-title: Alzheimers Res Ther
  doi: 10.1186/s13195-018-0401-z
– volume: 7
  start-page: 270
  issue: 3
  year: 2011
  ident: 1105_CR20
  publication-title: Alzheimers Dement
  doi: 10.1016/j.jalz.2011.03.008
– volume: 92
  start-page: 141
  year: 2020
  ident: 1105_CR44
  publication-title: Neurobiol Aging
  doi: 10.1016/j.neurobiolaging.2020.03.009
– volume: 7
  start-page: e1002251
  issue: 11
  year: 2011
  ident: 1105_CR4
  publication-title: PLoS Comput Biol
  doi: 10.1371/journal.pcbi.1002251
– volume: 142
  start-page: 1723
  issue: 6
  year: 2019
  ident: 1105_CR12
  publication-title: Brain.
  doi: 10.1093/brain/awz090
– volume: 89
  start-page: 971
  issue: 5
  year: 2016
  ident: 1105_CR27
  publication-title: Neuron.
  doi: 10.1016/j.neuron.2016.01.028
– volume: 20
  start-page: 484
  issue: 6
  year: 2021
  ident: 1105_CR3
  publication-title: Lancet Neurol
  doi: 10.1016/S1474-4422(21)00066-1
– volume: 78
  start-page: 961
  issue: 8
  year: 2021
  ident: 1105_CR9
  publication-title: JAMA Neurol
  doi: 10.1001/jamaneurol.2021.1858
– volume: 27
  start-page: 1187
  issue: 7
  year: 2021
  ident: 1105_CR50
  publication-title: Nat Med
  doi: 10.1038/s41591-021-01369-8
– volume: 24
  start-page: 1422
  issue: 6
  year: 2014
  ident: 1105_CR42
  publication-title: Cereb Cortex
  doi: 10.1093/cercor/bhs410
– volume: 64
  start-page: 1323
  issue: 9
  year: 2007
  ident: 1105_CR39
  publication-title: Arch Neurol
  doi: 10.1001/archneur.64.9.1323
– volume: 6
  start-page: 734
  issue: 8
  year: 2007
  ident: 1105_CR36
  publication-title: Lancet Neurol
  doi: 10.1016/S1474-4422(07)70178-3
– volume: 80
  start-page: 1091
  issue: 3
  year: 2021
  ident: 1105_CR16
  publication-title: J Alzheimers Dis
  doi: 10.3233/JAD-200808
– volume: 5
  start-page: 164
  year: 2019
  ident: 1105_CR6
  publication-title: Alzheimers Dement (N Y)
  doi: 10.1016/j.trci.2019.04.001
– volume: 14
  start-page: 535
  issue: 4
  year: 2018
  ident: 1105_CR2
  publication-title: Alzheimers Dement
  doi: 10.1016/j.jalz.2018.02.018
– volume: 27
  start-page: 871
  issue: 5
  year: 2021
  ident: 1105_CR5
  publication-title: Nat Med
  doi: 10.1038/s41591-021-01309-6
– volume: 6
  start-page: eabd1327
  issue: 48
  year: 2020
  ident: 1105_CR24
  publication-title: Sci Adv
  doi: 10.1126/sciadv.abd1327
– volume: 6
  start-page: e12072
  issue: 1
  year: 2020
  ident: 1105_CR30
  publication-title: Alzheimers Dement (N Y)
  doi: 10.1002/trc2.12072
– volume: 11
  start-page: 2612
  issue: 1
  year: 2020
  ident: 1105_CR25
  publication-title: Nat Commun
  doi: 10.1038/s41467-020-15701-2
– volume: 2
  start-page: a006171
  issue: 4
  year: 2012
  ident: 1105_CR47
  publication-title: Cold Spring Harb Perspect Med
  doi: 10.1101/cshperspect.a006171
– volume: 37
  start-page: 4323
  issue: 16
  year: 2017
  ident: 1105_CR45
  publication-title: J Neurosci
  doi: 10.1523/JNEUROSCI.3263-16.2017
– volume: 6
  start-page: 502
  issue: 4
  year: 2012
  ident: 1105_CR29
  publication-title: Brain Imaging Behavior.
  doi: 10.1007/s11682-012-9186-z
– volume: 78
  start-page: 445
  issue: 4
  year: 2021
  ident: 1105_CR13
  publication-title: JAMA Neurol
  doi: 10.1001/jamaneurol.2020.5505
– volume: 72
  start-page: 316
  issue: 3
  year: 2015
  ident: 1105_CR1
  publication-title: JAMA Neurol
  doi: 10.1001/jamaneurol.2014.3314
– volume: 20
  start-page: 218
  issue: 1
  year: 2016
  ident: 1105_CR19
  publication-title: Crit Care
  doi: 10.1186/s13054-016-1388-0
– volume: 13
  start-page: 80
  issue: 1
  year: 2021
  ident: 1105_CR8
  publication-title: Alzheimers Res Ther
  doi: 10.1186/s13195-021-00813-8
– volume: 96
  start-page: e2673
  issue: 22
  year: 2021
  ident: 1105_CR48
  publication-title: Neurology.
  doi: 10.1212/WNL.0000000000012022
– volume: 31
  start-page: 15775
  issue: 44
  year: 2011
  ident: 1105_CR46
  publication-title: J Neurosci
  doi: 10.1523/JNEUROSCI.3539-11.2011
– volume: 10
  start-page: 232
  year: 2018
  ident: 1105_CR22
  publication-title: Alzheimers Dement (Amst)
  doi: 10.1016/j.dadm.2018.01.007
– volume: 6
  start-page: 502
  issue: 4
  year: 2012
  ident: 1105_CR38
  publication-title: Brain Imaging Behav
  doi: 10.1007/s11682-012-9186-z
– volume: 8
  start-page: 665
  issue: 8
  year: 2011
  ident: 1105_CR17
  publication-title: Nat Methods
  doi: 10.1038/nmeth.1635
– volume: 7
  start-page: 263
  issue: 3
  year: 2011
  ident: 1105_CR21
  publication-title: Alzheimers Dement
  doi: 10.1016/j.jalz.2011.03.005
– volume: 82
  start-page: 239
  issue: 4
  year: 1991
  ident: 1105_CR26
  publication-title: Acta Neuropathol
  doi: 10.1007/BF00308809
– volume: 140
  start-page: 3286
  issue: 12
  year: 2017
  ident: 1105_CR15
  publication-title: Brain.
  doi: 10.1093/brain/awx243
– volume: 10
  start-page: 122
  issue: 1
  year: 2018
  ident: 1105_CR7
  publication-title: Alzheimers Res Ther
  doi: 10.1186/s13195-018-0455-y
– volume: 12
  start-page: 189
  issue: 3
  year: 1975
  ident: 1105_CR34
  publication-title: J Psychiatr Res
  doi: 10.1016/0022-3956(75)90026-6
– volume: 141
  start-page: 1486
  issue: 5
  year: 2018
  ident: 1105_CR43
  publication-title: Brain.
  doi: 10.1093/brain/awy053
– volume: 13
  start-page: 614
  issue: 6
  year: 2014
  ident: 1105_CR37
  publication-title: Lancet Neurol
  doi: 10.1016/S1474-4422(14)70090-0
– volume: 13
  start-page: 137
  issue: 1
  year: 2021
  ident: 1105_CR10
  publication-title: Alzheimers Res Ther
  doi: 10.1186/s13195-021-00880-x
– volume: 26
  start-page: 5888
  issue: 10
  year: 2021
  ident: 1105_CR11
  publication-title: Mol Psychiatry
  doi: 10.1038/s41380-021-01263-2
– volume: 139
  start-page: 1551
  issue: Pt 5
  year: 2016
  ident: 1105_CR14
  publication-title: Brain.
  doi: 10.1093/brain/aww027
– volume: 22
  start-page: 57
  issue: 1
  year: 2019
  ident: 1105_CR41
  publication-title: Nat Neurosci
  doi: 10.1038/s41593-018-0289-8
SSID ssj0066284
Score 2.3711326
Snippet Tau-PET is a prognostic marker for cognitive decline in Alzheimer's disease, and the heterogeneity of tau-PET patterns matches cognitive symptom heterogeneity....
Background Tau-PET is a prognostic marker for cognitive decline in Alzheimer’s disease, and the heterogeneity of tau-PET patterns matches cognitive symptom...
Background Tau-PET is a prognostic marker for cognitive decline in Alzheimer's disease, and the heterogeneity of tau-PET patterns matches cognitive symptom...
Abstract Background Tau-PET is a prognostic marker for cognitive decline in Alzheimer’s disease, and the heterogeneity of tau-PET patterns matches cognitive...
SourceID doaj
swepub
pubmedcentral
proquest
pubmed
crossref
SourceType Open Website
Open Access Repository
Aggregation Database
Index Database
Enrichment Source
StartPage 166
SubjectTerms Accuracy
Age
Aged
Aged, 80 and over
Alzheimer Disease - diagnosis
Alzheimer's disease
Amyloid - metabolism
Amyloid beta-Peptides - metabolism
association workgroups
Brain
Brain - diagnostic imaging
Brain - metabolism
Clinical trials
Cognitive ability
Cognitive decline
Cognitive Dysfunction - diagnostic imaging
Cognitive Dysfunction - psychology
criteria
Dementia
diagnostic guidelines
fMRI
functional connectivity
Humans
impairment
Magnetic Resonance Imaging - methods
Medical imaging
medicine
Memory
national institute
Neurodegeneration
Neuroimaging
Neurosciences
Neurosciences & Neurology
Neurovetenskaper
Pathology
Patient-Centered Care
Patients
Positron-Emission Tomography - methods
Precision
Precision medicine
recommendations
tau Proteins - metabolism
Tau-PET
trials
Working groups
SummonAdditionalLinks – databaseName: DOAJ Directory of Open Access Journals
  dbid: DOA
  link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LaxUxFA7SlRtRfF2tEkHcSOhkkskkyyotVaiItNBdSM5N2gvt3MudmS5c-Tf8e_4STzJzBwdFN27zYELO68sk5zuEvC6E47UGzaKXJZPSANMKFKurJSDa9VHmYhOnn9TJufx4UV38UuorvQkb6IGHjTsAiE67IF0QIBNdFmBAQ9wivAQhpE_eF2Pe7jA1-GCl0OvuUmS0Omi54DkTOT1DQETBqlkYymz9f4KYv7-UnPGJ5hh0fJ_cG8EjPRwW_YDcCc1Dcosm7XOZB9q5nn0-OqOuWdJ4-uUDvQmdYy7zjoSWIj6lI48qS59KZTrpZpuuapJ46DrS6TURXYaUNBnoqqGH11-vwuombH98-97S8UrnETk_Pjp7f8LGagoMKiE7VtUBotfSRBljoYyO1dJAAKeV5iB54UGj8TtESMpx5TVCxRhqE4T3xolSPCZ7zboJTwlVUhfgweiQ4Av3pqwNIqPSlypyUGJB-G5zLYxU46nixbXNRw6t7CAQiwKxWSC2WpC305zNQLTx19HvksymkYkkOzeg6thRdey_VGdB9ncSt6PlthbhXF0jZizMgryautHm0kWKa8K6z2MQx6Ys3gV5MijItBKhhKwKiT31THVmS533NKurzOuNvtCUGme-GZRsNuWy31hsuuxtG6wo0ffyZ_9jE56Tu2Wyi_SnvN4ne922Dy8QZ3X-ZTapn0c3Jrc
  priority: 102
  providerName: Directory of Open Access Journals
Title Combining tau-PET and fMRI meta-analyses for patient-centered prediction of cognitive decline in Alzheimer’s disease
URI https://www.ncbi.nlm.nih.gov/pubmed/36345046
https://www.proquest.com/docview/2737792909
https://www.proquest.com/docview/2734165796
https://pubmed.ncbi.nlm.nih.gov/PMC9639286
https://gup.ub.gu.se/publication/320401
https://doaj.org/article/ccfa8ae4ae3c45568c2230133b4c334b
Volume 14
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV3LatwwFBVtsummtPQ1aTqoULopIrYly9KqJMOEtJAQQgKzE5IsTQYSezoed9FVf6O_1y_plexxMS1ZWg8sfB86vro6F6EPCdVpIawg3rCMMCYtEdxyUuSlBbRrPIvFJs4v-NkN-7rIF33ArenTKnc-MTrqsrYhRn4E22xRwF6eyM_rbyRUjQqnq30JjcdoP1CXhZSuYjH8cHEOvnd3UUbwoyalabyPHJIRAFeQfLQZRc7-_wHNf_MlR6yicSc6fYae9hASH3cyf44eueoF-g6GbWKxB7zVLbmcX2NdldifX33B926riY7sI67BgFJxz6ZKwqtCsU683oQDmyAkXHs85BTh0oWrkw6vKnx89-PWre7d5vfPXw3uD3ZeopvT-fXsjPQ1FYjNKduSvHDWG8GkZ94nXAqfl9I6qwUXqWVpYqwAF6ABJ3GdciMAMHpXSEeNkZpm9BXaq-rKvUGYM5FYY6VwAcSkRmaFBHyUmYz71HI6Qenu4yrbE46Huhd3Kv54CK46gSgQiIoCUfkEfRrmrDu6jQdHnwSZDSMDVXZsqDdL1VuestZroR3TjloW-NYsICIAvtQwSykzE3S4k7jq7bdRf7Vtgt4P3WB54ThFV65u4xhAs-Eu7wS97hRkWAnllOUJg55ipDqjpY57qtVtZPcGjygzATM_dko2mrJs1wqalq1qnKIZeOD04OH1v0VPsqDxIRJeHKK97aZ17wBHbc00GssU7Z_MLy6v4GnGZ9MYk_gDWJ8hcg
linkProvider ProQuest
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3dbtMwFLam7gJuEIi_jgFGAm6QtSR2HOcCoQ06tWytpqmTdmds1-4qbWlpWhBc8Rq8BA_Fk3DsJEURaHe7jW3Fyjnn8-fY5zsIvYyoijNhBHGaJYSx3BDBDSdZOjHAdrVjodjEcMT7Z-zjeXq-hX41uTD-WmWDiQGoJ3Pj_5HvwTKbZbCWR_m7xWfiq0b509WmhEblFkf221fYspVvBx_Avq-S5LA3ft8ndVUBYlLKViTNrHFasNwx5yKeC5dOcmONElzEhsWRNgKCQAFT4CrmWgBlcjbLLdU6V9QLHQDkbzMKW5kO2j7ojU5OG-znHNC-Sc0RfK-MaRwyoP31B2AyJG0tf6FKwP-o7b83NFs6pmHtO7yL7tSkFe9XXnYPbdniPvoCUKJDeQm8Umty0htjVUywG54O8JVdKaKC3oktMfBiXOu3Ev8qXx4UL5b-iMi7BZ47vLnFhCfWJ2taPCvw_uX3Czu7ssvfP36WuD5KeoDObuR7P0SdYl7YxwhzJiKjTS6sp02xzpMsB0aW6IS72HDaRXHzcaWpJc59pY1LGbY6gsvKIBIMIoNBZNpFbzZjFpXAx7W9D7zNNj29OHd4MF9OZR3r0hinhLJMWWqYV3gzwMGAalPNDKVMd9FuY3FZI0Yp__p3F73YNEOs-wMcVdj5OvQB_uyzh7voUeUgm5lQTlkaMWjJWq7Tmmq7pZhdBD1xwOA8ETDydeVkrSHT9ULCo-lallbSBDA_3rl-_s_Rrf54eCyPB6OjJ-h24r3f_4fPdlFntVzbp8DiVvpZHToYfbrpaP0Dtzpcjw
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Combining+tau-PET+and+fMRI+meta-analyses+for+patient-centered+prediction+of+cognitive+decline+in+Alzheimer%E2%80%99s+disease&rft.jtitle=Alzheimer%27s+research+%26+therapy&rft.au=Biel%2C+Davina&rft.au=Luan%2C+Ying&rft.au=Brendel%2C+Matthias&rft.au=Hager%2C+Paul&rft.date=2022-11-07&rft.pub=BioMed+Central&rft.eissn=1758-9193&rft.volume=14&rft.spage=1&rft_id=info:doi/10.1186%2Fs13195-022-01105-5
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1758-9193&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1758-9193&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1758-9193&client=summon