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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Detalles Bibliográficos
Autores principales: Zhou, Yanan, Tang, Jingsong, Sun, Yunkai, Yang, Winson Fu Zun, Ma, Yuejiao, Wu, Qiuxia, Chen, Shubao, Wang, Qianjin, Hao, Yuzhu, Wang, Yunfei, Li, Manyun, Liu, Tieqiao, Liao, Yanhui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Cellular Neuroscience
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9550874/
https://www.ncbi.nlm.nih.gov/pubmed/36238830
http://dx.doi.org/10.3389/fncel.2022.958437

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9550874/
https://www.ncbi.nlm.nih.gov/pubmed/36238830
http://dx.doi.org/10.3389/fncel.2022.958437

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