Source: Neuroimage-clinical. Unidade: FM
Subjects: IMAGEM POR RESSONÂNCIA MAGNÉTICA, DIAGNÓSTICO POR IMAGEM, DOENÇA DE ALZHEIMER, MORFOMETRIA
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: 17 ago. 2024.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.2017.10.026NLM
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 2024 ago. 17 ] Available from: https://doi.org/10.1016/j.nicl.2017.10.026Vancouver
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 2024 ago. 17 ] Available from: https://doi.org/10.1016/j.nicl.2017.10.026