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 (2018)
- Authors:
- USP affiliated authors: LEITE, CLAUDIA DA COSTA - FM ; NITRINI, RICARDO - FM ; BUCHPIGUEL, CARLOS ALBERTO - FM ; BUSATTO FILHO, GERALDO - FM
- Unidade: FM
- DOI: 10.1016/j.nicl.2017.10.026
- Subjects: IMAGEM POR RESSONÂNCIA MAGNÉTICA; DIAGNÓSTICO POR IMAGEM; DOENÇA DE ALZHEIMER; MORFOMETRIA
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: Neuroimage-clinical
- ISSN: 2213-1582
- Volume/Número/Paginação/Ano: v. 17, p. 628-641, 2018
- Status:
- Artigo publicado em periódico de acesso aberto (Gold Open Access)
- Versão do Documento:
- Versão publicada (Published version)
- Acessar versão aberta:
-
ABNT
RONDINA, Jane Maryam et al. 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. Neuroimage-clinical, v. 17, p. 628-641, 2018Tradução . . Disponível em: https://doi.org/10.1016/j.nicl.2017.10.026. Acesso em: 02 abr. 2026. -
APA
Rondina, J. M., Leite, C. C., Nitrini, R., Buchpiguel, C. A., & Busatto Filho, G. (2018). 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. Neuroimage-clinical, 17, 628-641. doi:10.1016/j.nicl.2017.10.026 -
NLM
Rondina JM, Leite CC, Nitrini R, Buchpiguel CA, Busatto Filho G. 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 [Internet]. Neuroimage-clinical. 2018 ; 17 628-641.[citado 2026 abr. 02 ] Available from: https://doi.org/10.1016/j.nicl.2017.10.026 -
Vancouver
Rondina JM, Leite CC, Nitrini R, Buchpiguel CA, Busatto Filho G. 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 [Internet]. Neuroimage-clinical. 2018 ; 17 628-641.[citado 2026 abr. 02 ] Available from: https://doi.org/10.1016/j.nicl.2017.10.026 - Support vector machine-based classification of neuroimages in Alzheimer's disease: direct comparison of FDG-PET, rCBF-SPECT and MRI data acquired from the same individuals
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