A brainnetome atlas-based methamphetamine dependence identification using neighborhood component analysis and machine learning on functional MRI data

Addiction to methamphetamine (MA) is a major public health concern. Developing a predictive model that can classify and characterize the brain-based biomarkers predicting MA addicts may directly lead to improved treatment outcomes. In the current study, we applied the support vector machine (SVM)-ba...

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Detalhes bibliográficos

Principais autores:	Yanan Zhou, Jingsong Tang, Yunkai Sun, Winson Fu Zun Yang, Yuejiao Ma, Qiuxia Wu, Shubao Chen, Qianjin Wang, Yuzhu Hao, Yunfei Wang, Manyun Li, Tieqiao Liu, Yanhui Liao
Formato:	Artigo
Idioma:	Inglês
Publicado em:	Frontiers Media S.A. 2022-09-01
Colecção:	Frontiers in Cellular Neuroscience
Assuntos:	methamphetamine machine learning brainnetome atlas neighborhood component analysis resting-state functional magnetic resonance imaging classification
Acesso em linha:	https://www.frontiersin.org/articles/10.3389/fncel.2022.958437/full
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