Many mental illnesses originate from an imbalance in brain function, and exacerbation of this imbalance would lead to further adverse health effects. With high temporal resolution, electroencephalograms (EEG) are widely used to study the variation in the neural mechanism of addicts and to evaluate the effectiveness of interventional treatments. In recent years, mental illnesses such as drug addiction, Internet addiction, and alcohol addiction have increased in adolescents and young people, making such research necessary and urgent. Until now, the majority of addiction diagnoses are still based on clinical symptoms rather than biological characterization. Some researchers have begun to use complex methods such as deep learning and graph theory to uncover variation in neural mechanisms across different mental addictions. Compared to using traditional methods alone, researchers could obtain better diagnostic results by analyzing EEG signals. Moreover, data from physiological measurements and EEG complement each other and bring new insights to studies. As a result, the advances of EEG and its application enabled early diagnosis and interventions based on physiological characteristics.
This Research Topic aims to investigate neural mechanisms for addictions by acquiring and analyzing signals using EEG or by using it with other measurements such as magnetic resonance imaging (MRI) and electrical physiological measurements. Studies on the neural mechanisms of resting-state or task-state EEG in drug addicts, especially on the cognitive aspect, are particularly welcome. We are open to a wide range of article types, including Original Research Article, General Commentary, Hypothesis and Theory, Methods, Mini Review, Opinion, Original Research, Perspective, Review, Study Protocol, Systematic Review and Technology and Code.
Topics of interests include but are not limited to:
• Specificity analysis of drug addiction
• Effectiveness assessment for intervention to addiction withdrawal
• Emotion, attention, cognitive and control, learning and memory of the addicted
• Psychophysiological computational model for the addicted
• Application of multimodal fusion technology in addiction diagnosis
Many mental illnesses originate from an imbalance in brain function, and exacerbation of this imbalance would lead to further adverse health effects. With high temporal resolution, electroencephalograms (EEG) are widely used to study the variation in the neural mechanism of addicts and to evaluate the effectiveness of interventional treatments. In recent years, mental illnesses such as drug addiction, Internet addiction, and alcohol addiction have increased in adolescents and young people, making such research necessary and urgent. Until now, the majority of addiction diagnoses are still based on clinical symptoms rather than biological characterization. Some researchers have begun to use complex methods such as deep learning and graph theory to uncover variation in neural mechanisms across different mental addictions. Compared to using traditional methods alone, researchers could obtain better diagnostic results by analyzing EEG signals. Moreover, data from physiological measurements and EEG complement each other and bring new insights to studies. As a result, the advances of EEG and its application enabled early diagnosis and interventions based on physiological characteristics.
This Research Topic aims to investigate neural mechanisms for addictions by acquiring and analyzing signals using EEG or by using it with other measurements such as magnetic resonance imaging (MRI) and electrical physiological measurements. Studies on the neural mechanisms of resting-state or task-state EEG in drug addicts, especially on the cognitive aspect, are particularly welcome. We are open to a wide range of article types, including Original Research Article, General Commentary, Hypothesis and Theory, Methods, Mini Review, Opinion, Original Research, Perspective, Review, Study Protocol, Systematic Review and Technology and Code.
Topics of interests include but are not limited to:
• Specificity analysis of drug addiction
• Effectiveness assessment for intervention to addiction withdrawal
• Emotion, attention, cognitive and control, learning and memory of the addicted
• Psychophysiological computational model for the addicted
• Application of multimodal fusion technology in addiction diagnosis