Analysis of functional connectivity using machine learning and deep learning in different data modalities from individuals with schizophrenia (2023)
- Authors:
- USP affiliated authors: RODRIGUES, FRANCISCO APARECIDO - ICMC ; ALVES, CAROLINE LOURENÇO - ICMC ; PORTO, JOEL AUGUSTO MOURA - IFSC ; PINEDA, ARUANE MELLO - ICMC
- Unidades: ICMC; IFSC
- DOI: 10.1088/1741-2552/acf734
- Subjects: TECNOLOGIAS DA SAÚDE; APRENDIZADO COMPUTACIONAL; ESQUIZOFRENIA; REDES COMPLEXAS; RECONHECIMENTO DE IMAGEM; DIAGNÓSTICO POR COMPUTADOR; CÉREBRO
- Keywords: schizophrenia; fMRI; EEG; Machine learning; Deep learning
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título do periódico: Journal of Neural Engineering
- ISSN: 1741-2560
- Volume/Número/Paginação/Ano: v. 20, n. 5, p. 056025-1-056025-28, Oct. 2023
- Este periódico é de assinatura
- Este artigo é de acesso aberto
- URL de acesso aberto
- Cor do Acesso Aberto: hybrid
- Licença: cc-by
-
ABNT
ALVES, Caroline Lourenço et al. Analysis of functional connectivity using machine learning and deep learning in different data modalities from individuals with schizophrenia. Journal of Neural Engineering, v. 20, n. 5, p. 056025-1-056025-28, 2023Tradução . . Disponível em: https://doi.org/10.1088/1741-2552/acf734. Acesso em: 29 maio 2024. -
APA
Alves, C. L., Oliveira, T. G. L. de, Porto, J. A. M., Aguiar, P. M. de C., Sena, E. P. de, Rodrigues, F. A., et al. (2023). Analysis of functional connectivity using machine learning and deep learning in different data modalities from individuals with schizophrenia. Journal of Neural Engineering, 20( 5), 056025-1-056025-28. doi:10.1088/1741-2552/acf734 -
NLM
Alves CL, Oliveira TGL de, Porto JAM, Aguiar PM de C, Sena EP de, Rodrigues FA, Pineda AM, Thielemann C. Analysis of functional connectivity using machine learning and deep learning in different data modalities from individuals with schizophrenia [Internet]. Journal of Neural Engineering. 2023 ; 20( 5): 056025-1-056025-28.[citado 2024 maio 29 ] Available from: https://doi.org/10.1088/1741-2552/acf734 -
Vancouver
Alves CL, Oliveira TGL de, Porto JAM, Aguiar PM de C, Sena EP de, Rodrigues FA, Pineda AM, Thielemann C. Analysis of functional connectivity using machine learning and deep learning in different data modalities from individuals with schizophrenia [Internet]. Journal of Neural Engineering. 2023 ; 20( 5): 056025-1-056025-28.[citado 2024 maio 29 ] Available from: https://doi.org/10.1088/1741-2552/acf734 - Analysis of quantile graphs in EGC data from elderly and young individuals using machine learning and deep learning
- Diagnosis of autism spectrum disorder based on functional brain networks and machine learning
- On the advances in machine learning and complex network measures to an EEG dataset from DMT experiments
- EEG functional connectivity and deep learning for automatic diagnosis of brain disorders: Alzheimer's disease and schizophrenia
- Revisiting the involvement of tau in complex neural network remodeling: analysis of the extracellular neuronal activity in organotypic brain slice co-cultures
- Diagnosis of neurological diseases based on data mining and complex network
- Complex networks to differentiate elderly and young people
- Machine learning-based prediction of Q-voter model in complex networks
- Application of machine learning and complex network measures to an EEG dataset from ayahuasca experiments
- Cultural heterogeneity constrains diffusion of innovations
Informações sobre o DOI: 10.1088/1741-2552/acf734 (Fonte: oaDOI API)
Download do texto completo
| Tipo | Nome | Link | |
|---|---|---|---|
 | 3156886.pdf |
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
