Fully bayesian longitudinal unsupervised learning for the assessment and visualization of AD heterogeneity and progression-Reference-Cited by-同舟云学术

Fully bayesian longitudinal unsupervised learning for the assessment and visualization of AD heterogeneity and progression

Published:2020-07-15 Issue:13 Volume:12 Page:12622-12647
ISSN:1945-4589
Container-title:Aging
language:en
Short-container-title:Aging

Author:

Poulakis Konstantinos¹,Ferreira Daniel¹,Pereira Joana B.¹,Smedby Örjan²,Vemuri Prashanthi³,Westman Eric¹⁴

Affiliation:

1. Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden

2. Department of Biomedical Engineering and Health Systems (MTH), KTH Royal Institute of Technology, Stockholm, Sweden

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

4. Department of Neuroimaging, Centre for Neuroimaging Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK

Publisher

Impact Journals, LLC

Subject

Cell Biology,Ageing

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