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Predictive modelling using neuroimaging data in the presence of confounds
When training predictive models from neuroimaging data, we typically have available non-imaging variables such as age and gender that affect the imaging data but which we may be uninterested in from a clinical perspective. Such variables are commonly referred to as ‘confounds’. In this work, we firs...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Academic Press
2017
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5391990/ https://www.ncbi.nlm.nih.gov/pubmed/28143776 http://dx.doi.org/10.1016/j.neuroimage.2017.01.066 |