fMRI volume classification using a 3D convolutional neural network robust to shifted and scaled neuronal activations.

Vu, Hanh; Kim, Hyun-Chul; Jung, Minyoung; Lee, Jong-Hwan

Vu, Hanh; Kim, Hyun-Chul; Jung, Minyoung; Lee, Jong-Hwan.

Vu H; Department of Brain and Cognitive Engineering, Korea University, Anam-ro 145, Seongbuk-gu, Seoul 02841, Republic of Korea.
Kim HC; Department of Brain and Cognitive Engineering, Korea University, Anam-ro 145, Seongbuk-gu, Seoul 02841, Republic of Korea.
Jung M; Department of Brain and Cognitive Engineering, Korea University, Anam-ro 145, Seongbuk-gu, Seoul 02841, Republic of Korea.
Lee JH; Department of Brain and Cognitive Engineering, Korea University, Anam-ro 145, Seongbuk-gu, Seoul 02841, Republic of Korea. Electronic address: jonghwan_lee@korea.ac.kr.

Neuroimage ; 223: 117328, 2020 12.

Article en En | MEDLINE | ID: mdl-32896633

Asunto(s)

Mapeo Encefálico/métodos; Encéfalo/fisiología; Imagen por Resonancia Magnética/métodos; Redes Neurales de la Computación; Desempeño Psicomotor; Adulto; Atención/fisiología; Percepción Auditiva/fisiología; Humanos; Masculino; Actividad Motora; Máquina de Vectores de Soporte; Percepción Visual/fisiología; Adulto Joven

Palabras clave

Classification; Convolutional neural networks; Deep neural networks; Functional MRI; Human Connectome Project; Machine learning; Real-time fMRI; Sensorimotor tasks

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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Desempeño Psicomotor / Encéfalo / Mapeo Encefálico / Imagen por Resonancia Magnética / Redes Neurales de la Computación Límite: Adult / Humans / Male Idioma: En Año: 2020 Tipo del documento: Article

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