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Selecting the most relevant brain regions to discriminate Alzheimer's disease patients from healthy controls using multiple kernel learning: A comparison across functional and structural imaging modalities and atlases

Crossref DOI link: https://doi.org/10.1016/j.nicl.2017.10.026

Published Print: 2018

Update policy: https://doi.org/10.1016/elsevier_cm_policy

Authors

Rondina, Jane Maryam https://orcid.org/0000-0003-3548-014X
Ferreira, Luiz Kobuti

de Souza Duran, Fabio Luis

Kubo, Rodrigo

Ono, Carla Rachel

Leite, Claudia Costa

Smid, Jerusa

Nitrini, Ricardo

Buchpiguel, Carlos Alberto

Busatto, Geraldo F.
Funding

Funding for this research was provided by:

Fundação de Amparo à Pesquisa do Estado de São Paulo

Conselho Nacional de Desenvolvimento Científico e Tecnológico (305237/2013-6)

Fundação de Amparo à Pesquisa do Estado de São Paulo (2012/50239-6)

Fundação de Amparo à Pesquisa do Estado de São Paulo (2009/17398-1)
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Text and Data Mining valid from 2018-01-01

Text and Data Mining valid from 2018-01-01

Version of Record valid from 2017-10-24
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This article is maintained by: Elsevier

Article Title: Selecting the most relevant brain regions to discriminate Alzheimer's disease patients from healthy controls using multiple kernel learning: A comparison across functional and structural imaging modalities and atlases

Journal Title: NeuroImage: Clinical

CrossRef DOI link to publisher maintained version: https://doi.org/10.1016/j.nicl.2017.10.026

Content Type: article

Copyright: © 2017 The Authors. Published by Elsevier Inc.

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