Distinct tau PET patterns in atrophy-defined subtypes of Alzheimer’s disease

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Abstract

Introduction:

Differential patterns of brain atrophy on structural magnetic resonance imaging (MRI) revealed four reproducible subtypes of Alzheimer’s disease (AD): (1) “typical”, (2) “limbic-predominant”, (3) “hippocampal-sparing”, and (4) “mild atrophy”. We examined the neurobiological characteristics and clinical progression of these atrophy-defined subtypes.

Methods:

The four subtypes were replicated using a clustering method on MRI data in 260 amyloid-β-positive patients with mild cognitive impairment or AD dementia, and we subsequently tested whether the subtypes differed on [ ¹⁸F]flortaucipir (tau) positron emission tomography, white matter hyperintensity burden, and rate of global cognitive decline.

Results:

Voxel-wise and region-of-interest analyses revealed the greatest neocortical tau load in hippocampal-sparing (frontoparietal-predominant) and typical (temporal-predominant) patients, while limbic-predominant patients showed particularly high entorhinal tau. Typical patients with AD had the most pronounced white matter hyperintensity load, and hippocampal-sparing patients showed the most rapid global cognitive decline.

Discussion:

Our data suggest that structural MRI can be used to identify biologically and clinically meaningful subtypes of AD.

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Most cited references 17

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Model-Based Gaussian and Non-Gaussian Clustering

Jeffrey D. Banfield, Adrian E. Raftery (1993)

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Correction for partial volume effects in PET: principle and validation.

Natalie C. Evans, O Rousset, Y. Ma (1998)

The accuracy of PET for measuring regional radiotracer concentrations in the human brain is limited by the finite resolution capability of the scanner and the resulting partial volume effects (PVEs). We designed a new algorithm to correct for PVEs by characterizing the geometric interaction between the PET system and the brain activity distribution. The partial volume correction (PVC) algorithm uses high-resolution volumetric MR images correlated with the PET volume. We used a PET simulator to calculate recovery and cross-contamination factors of identified tissue components in the brain model. These geometry-dependent transfer coefficients form a matrix representing the fraction of true activity from each distinct brain region observed in any given set of regions of interest. This matrix can be inverted to correct for PVEs, independent of the tracer concentrations in each tissue component. A sphere phantom was used to validate the simulated point-spread function of the PET scanner. Accuracy and precision of the PVC method were assessed using a human basal ganglia phantom. A constant contrast experiment was performed to explore the recovery capability and statistic error propagation of PVC in various noise conditions. In addition, a dual-isotope experiment was used to evaluate the ability of the PVC algorithm to recover activity concentrations in small structures surrounded by background activity with a different radioactive half-life. This models the time-variable contrast between regions that is often seen in neuroreceptor studies. Data from the three-dimensional brain phantom demonstrated a full recovery capability of PVC with less than 10% root mean-square error in terms of absolute values, which decreased to less than 2% when results from four PET slices were averaged. Inaccuracy in the estimation of 18F tracer half-life in the presence of 11C background activity was in the range of 25%-50% before PVC and 0%-6% after PVC, for resolution varying from 6 to 14 mm FWHM. In terms of noise propagation, the degradation of the coefficient of variation after PVC was found to be easily predictable and typically on the order of 25%. The PVC algorithm allows the correction for PVEs simultaneously in all identified brain regions, independent of tracer levels.

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Is Open Access

Longitudinal tau PET in ageing and Alzheimer’s disease

Clifford R. Jack, Heather Wiste, Christopher G Schwarz … (2018)

See Hansson and Mormino (doi:10.1093/brain/awy065) for a scientific commentary on this article. Where should measurements be taken to best capture tau accumulation in ageing and Alzheimer’s disease? Jack et al. report that in clinically symptomatic stages of Alzheimer’s disease, tau accumulation occurs throughout the brain, rather than only in specific areas. Rate measurements from simple meta-regions of interest may be sufficient to capture progressive within-person tau accumulation.

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Author and article information

Journal

Journal ID (nlm-journal-id): 101231978

Journal ID (pubmed-jr-id): 33173

Journal ID (nlm-ta): Alzheimers Dement

Journal ID (iso-abbrev): Alzheimers Dement

Title: Alzheimer's & dementia : the journal of the Alzheimer's Association

ISSN (Print): 1552-5260

ISSN (Electronic): 1552-5279

Publication date Nihms-submitted: 17 November 2019

Publication date (Electronic): 04 January 2020

Publication date (Print): February 2020

Publication date PMC-release: 11 February 2020

Volume: 16

Issue: 2

Pages: 335-344

Affiliations

[a ]Lund University, Clinical Memory Research Unit, Lund, Sweden

[b ]Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands

[c ]Department of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea

[d ]School of Biomedical Engineering and Imaging Sciences King’s College London, London, United Kingdom

[e ]Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, London, United Kingdom

[f ]Centre for Medical Image Computing, Department of Medical Physics, University College London, United Kingdom

[g ]Lund University, Diagnostic Radiology, Lund, Sweden

[h ]Department of Nuclear Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea

[i ]Division of RI-Convergence Research, Korea Institute Radiological and Medical Sciences, Seoul, South Korea

[j ]Division of Clinical Geriatrics, Department of Neurobiology, Karolinska Institute, Care Sciences and Society, Stockholm, Sweden

[k ]Department of Neurology, University of California San Francisco, Memory and Aging Center, San Francisco, USA

[l ]Memory Clinic, Skåne University Hospital, Malmö, Sweden

Author notes

[* ]Corresponding author. Tel.: +31 20 444 0816; Fax: +31 20 444 0816., r.ossenkoppele@ 123456amsterdamumc.nl (R.O.)

[** ]Corresponding author. Tel.: +46 46 171000; Fax: +46 46 171000., oskar.hansson@ 123456med.lu.se (O.H.)

Article

Manuscript ID: NIHMS1059705

DOI: 10.1016/j.jalz.2019.08.201

PMC ID: 7012375

PubMed ID: 31672482

SO-VID: 2bba1de1-df97-4024-894e-f6d972f51209

License:

This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

Distinct tau PET patterns in atrophy-defined subtypes of Alzheimer’s disease

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Abstract

Introduction:

Methods:

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Discussion:

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UCL: UN SDG 03 Good Health and Well-Being

Most cited references 17

Model-Based Gaussian and Non-Gaussian Clustering

Correction for partial volume effects in PET: principle and validation.

Longitudinal tau PET in ageing and Alzheimer’s disease

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