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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Bibliographic Details
Published in:Front Hum Neurosci
Main Authors: Ding, Xiaoyu, Yang, Yihong, Stein, Elliot A., Ross, Thomas J.
Format: Artigo
Language:Inglês
Published: Frontiers Media S.A. 2017
Subjects:
Neuroscience
Online Access:https://ncbi.nlm.nih.gov/pmc/articles/PMC5506584/
https://ncbi.nlm.nih.gov/pubmed/28747877
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3389/fnhum.2017.00362
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