Longitudinal neuroimaging biomarkers differ across Alzheimer's disease phenotypes.


Journal

Brain : a journal of neurology
ISSN: 1460-2156
Titre abrégé: Brain
Pays: England
ID NLM: 0372537

Informations de publication

Date de publication:
01 07 2020
Historique:
received: 26 09 2019
revised: 11 03 2020
accepted: 27 03 2020
pubmed: 24 6 2020
medline: 31 12 2020
entrez: 24 6 2020
Statut: ppublish

Résumé

Alzheimer's disease can present clinically with either the typical amnestic phenotype or with atypical phenotypes, such as logopenic progressive aphasia and posterior cortical atrophy. We have recently described longitudinal patterns of flortaucipir PET uptake and grey matter atrophy in the atypical phenotypes, demonstrating a longitudinal regional disconnect between flortaucipir accumulation and brain atrophy. However, it is unclear how these longitudinal patterns differ from typical Alzheimer's disease, to what degree flortaucipir and atrophy mirror clinical phenotype in Alzheimer's disease, and whether optimal longitudinal neuroimaging biomarkers would also differ across phenotypes. We aimed to address these unknowns using a cohort of 57 participants diagnosed with Alzheimer's disease (18 with typical amnestic Alzheimer's disease, 17 with posterior cortical atrophy and 22 with logopenic progressive aphasia) that had undergone baseline and 1-year follow-up MRI and flortaucipir PET. Typical Alzheimer's disease participants were selected to be over 65 years old at baseline scan, while no age criterion was used for atypical Alzheimer's disease participants. Region and voxel-level rates of tau accumulation and atrophy were assessed relative to 49 cognitively unimpaired individuals and among phenotypes. Principal component analysis was implemented to describe variability in baseline tau uptake and rates of accumulation and baseline grey matter volumes and rates of atrophy across phenotypes. The capability of the principal components to discriminate between phenotypes was assessed with logistic regression. The topography of longitudinal tau accumulation and atrophy differed across phenotypes, with key regions of tau accumulation in the frontal and temporal lobes for all phenotypes and key regions of atrophy in the occipitotemporal regions for posterior cortical atrophy, left temporal lobe for logopenic progressive aphasia and medial and lateral temporal lobe for typical Alzheimer's disease. Principal component analysis identified patterns of variation in baseline and longitudinal measures of tau uptake and volume that were significantly different across phenotypes. Baseline tau uptake mapped better onto clinical phenotype than longitudinal tau and MRI measures. Our study suggests that optimal longitudinal neuroimaging biomarkers for future clinical treatment trials in Alzheimer's disease are different for MRI and tau-PET and may differ across phenotypes, particularly for MRI. Baseline tau tracer retention showed the highest fidelity to clinical phenotype, supporting the important causal role of tau as a driver of clinical dysfunction in Alzheimer's disease.

Identifiants

pubmed: 32572464
pii: 5861030
doi: 10.1093/brain/awaa155
pmc: PMC7363492
doi:

Substances chimiques

MAPT protein, human 0
tau Proteins 0

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

2281-2294

Subventions

Organisme : NIA NIH HHS
ID : P50 AG016574
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG011378
Pays : United States
Organisme : NIA NIH HHS
ID : U01 AG006786
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG050603
Pays : United States
Organisme : NIA NIH HHS
ID : R37 AG011378
Pays : United States
Organisme : NINDS NIH HHS
ID : R21 NS094684
Pays : United States

Informations de copyright

© The Author(s) (2020). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For permissions, please email: journals.permissions@oup.com.

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Auteurs

Irene Sintini (I)

Department of Radiology, Mayo Clinic, Rochester, MN, USA.

Jonathan Graff-Radford (J)

Department of Neurology, Mayo Clinic, Rochester, MN, USA.

Matthew L Senjem (ML)

Department of Radiology, Mayo Clinic, Rochester, MN, USA.
Department of Information Technology, Mayo Clinic, Rochester, MN, USA.

Christopher G Schwarz (CG)

Department of Radiology, Mayo Clinic, Rochester, MN, USA.

Mary M Machulda (MM)

Department of Psychiatry and Psychology, Mayo Clinic, Rochester MN, USA.

Peter R Martin (PR)

Department of Health Science Research, Mayo Clinic, Rochester MN, USA.

David T Jones (DT)

Department of Neurology, Mayo Clinic, Rochester, MN, USA.

Bradley F Boeve (BF)

Department of Neurology, Mayo Clinic, Rochester, MN, USA.

David S Knopman (DS)

Department of Neurology, Mayo Clinic, Rochester, MN, USA.

Kejal Kantarci (K)

Department of Radiology, Mayo Clinic, Rochester, MN, USA.

Ronald C Petersen (RC)

Department of Neurology, Mayo Clinic, Rochester, MN, USA.

Clifford R Jack (CR)

Department of Radiology, Mayo Clinic, Rochester, MN, USA.

Val J Lowe (VJ)

Department of Radiology, Mayo Clinic, Rochester, MN, USA.

Keith A Josephs (KA)

Department of Neurology, Mayo Clinic, Rochester, MN, USA.

Jennifer L Whitwell (JL)

Department of Radiology, Mayo Clinic, Rochester, MN, USA.

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