Combining Multiple Resting-State fMRI Features during Classification: Optimized Frameworks and Their Application to Nicotine Addiction

Machine learning techniques have been applied to resting-state fMRI data to predict neurological or neuropsychiatric disease states. Existing studies have used either a single type of resting-state feature or a few feature types (<4) in the prediction model. However, resting-state data can be pro...

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Detalles Bibliográficos
Autores principales: Ding, Xiaoyu, Yang, Yihong, Stein, Elliot A., Ross, Thomas J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2017
Materias:
Neuroscience
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5506584/
https://www.ncbi.nlm.nih.gov/pubmed/28747877
http://dx.doi.org/10.3389/fnhum.2017.00362

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5506584/
https://www.ncbi.nlm.nih.gov/pubmed/28747877
http://dx.doi.org/10.3389/fnhum.2017.00362

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