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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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Main Authors: 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
Format: Artigo
Language:Inglês
Published: Frontiers Media S.A. 2022-09-01
Series:Frontiers in Cellular Neuroscience
Subjects:
methamphetamine
machine learning
brainnetome atlas
neighborhood component analysis
resting-state functional magnetic resonance imaging
classification
Online Access:https://www.frontiersin.org/articles/10.3389/fncel.2022.958437/full
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