Cargando…
Decoding Task-Based fMRI Data with Graph Neural Networks, Considering Individual Differences
Task fMRI provides an opportunity to analyze the working mechanisms of the human brain during specific experimental paradigms. Deep learning models have increasingly been applied for decoding and encoding purposes study to representations in task fMRI data. More recently, graph neural networks, or n...
Autores principales: | Saeidi, Maham, Karwowski, Waldemar, Farahani, Farzad V., Fiok, Krzysztof, Hancock, P. A., Sawyer, Ben D., Christov-Moore, Leonardo, Douglas, Pamela K. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9405908/ https://www.ncbi.nlm.nih.gov/pubmed/36009157 http://dx.doi.org/10.3390/brainsci12081094 |
Ejemplares similares
-
Neural Decoding of EEG Signals with Machine Learning: A Systematic Review
por: Saeidi, Maham, et al.
Publicado: (2021) -
Explainable AI: A review of applications to neuroimaging data
por: Farahani, Farzad V., et al.
Publicado: (2022) -
The COVID-19 Infection Diffusion in the US and Japan: A Graph-Theoretical Approach
por: Davahli, Mohammad Reza, et al.
Publicado: (2022) -
Diurnal variations of resting-state fMRI data: A graph-based analysis
por: Farahani, Farzad V., et al.
Publicado: (2022) -
Graph-Based Analysis of Brain Connectivity in Multiple Sclerosis Using Functional MRI: A Systematic Review
por: Hejazi, Sara, et al.
Publicado: (2023)