Folded concave penalized learning in identifying multimodal MRI marker for Parkinson’s disease

BACKGROUND: Brain MRI holds promise to gauge different aspects of Parkinson’s disease (PD)-related pathological changes. Its analysis, however, is hindered by the high-dimensional nature of the data. NEW METHOD: This study introduces folded concave penalized (FCP) sparse logistic regression to ident...

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Pubblicato in:J Neurosci Methods
Autori principali: Liu, Hongcheng, Du, Guangwei, Zhang, Lijun, Lewis, Mechelle M., Wang, Xue, Yao, Tao, Li, Runze, Huang, Xuemei
Natura: Artigo
Lingua:Inglês
Pubblicazione: 2016
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Article
Accesso online:https://ncbi.nlm.nih.gov/pmc/articles/PMC4913043/
https://ncbi.nlm.nih.gov/pubmed/27102045
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1016/j.jneumeth.2016.04.016
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