Machine-learning support to individual diagnosis of mild cognitive impairment using multimodal MRI and cognitive assessments

De Marco, M., Beltrachini, L., Biancardi, A. et al. (2 more authors) (2017) Machine-learning support to individual diagnosis of mild cognitive impairment using multimodal MRI and cognitive assessments. Alzheimer Disease & Associated Disorders, 31 (4). pp. 278-286. ISSN 0893-0341

Abstract

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Authors/Creators:
Copyright, Publisher and Additional Information: © 2017 The Author(s). Published by Wolters Kluwer Health, Inc. This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. https://creativecommons.org/licenses/by/4.0/
Keywords: machine learning; magnetic resonance imaging; semantics; hippocampus; resting-state
Dates:
  • Accepted: 1 August 2017
  • Published (online): 1 October 2017
  • Published: October 2017
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield)
The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > Department of Infection, Immunity and Cardiovascular Disease
The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > Department of Neuroscience (Sheffield)
The University of Sheffield > Sheffield Teaching Hospitals
Funding Information:
FunderGrant number
EUROPEAN COMMISSION - FP6/FP7VPH DARE - 601055
ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL (EPSRC)EP/M006328/1
Depositing User: Symplectic Sheffield
Date Deposited: 13 Dec 2017 11:19
Last Modified: 13 Dec 2023 15:57
Status: Published
Publisher: Lippincott, Williams & Wilkins
Refereed: Yes
Identification Number: https://doi.org/10.1097/WAD.0000000000000208

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