Source: Neuroimage-clinical. Unidade: FM
Subjects: IMAGEM POR RESSONÂNCIA MAGNÉTICA, DIAGNÓSTICO POR IMAGEM, DOENÇA DE ALZHEIMER, MORFOMETRIA
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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
