During the past few decades, scientific discoveries have enabled people to shape the basic understanding of neurobiology related to psychiatric disorders. However, there is still no effective and objective standard for the diagnosis of psychiatric disorders. Currently, the diagnostic method is mainly based on individual subjective experience rather than in-depth knowledge of pathophysiology, which may cause the misdiagnosis. Psychiatric disease is a type of complex brain disease, and its occurrence and development may be related to biological factors such as neural circuit and neural function abnormality. Therefore, there is an urgent need to study the neurobiomarkers of the onset and symptoms of psychiatric disorders. EEG / MEG is a valuable tool to detect brain function and investigate how the human brain processes information. The application of EEG/MEG in studying brain function and psychiatric disorders has always been the focus of research on psychiatric illness. Many studies have revealed the significance of EEG for the diagnosis of psychiatric illness. Research shows that the diagnosis of psychiatric disorders can be carried out by examining EEG signals. Moreover, compared with PET and fMRI, EEG has the strengths of higher temporal resolution and lower usage cost. Therefore, more research is needed to explore the cutting-edge trends and future directions using EEG / MEG for diagnosing psychiatric disorders.
The purpose of this research topic is to advance the studies on the neurobiomarkers and EEG/MEG based diagnostic methods of psychiatric disorders. We hope to use EEG / MEG to do in-depth research on the pathogenesis and diagnostic measures of psychiatric disorders, such as schizophrenia, depression disorder, anxiety disorder, bipolar disorder, post-traumatic stress disorder, attention deficit hyperactivity disorder, addiction, autism spectrum disorder, etc. Research on statistical analysis and AI based methods in psychiatric disorders are also welcome as to improve the performance of AI for psychiatric disorders and to develop the diagnosis system.
The Research Topic can include, but is not limited to:
• EEG/MEG based novel machine learning/deep learning algorithms for diagnosing psychiatric disorders.
• Novel experimental paradigms to find biological biomarkers for psychiatric disorders.
• Application of visualization and statistical techniques for biological biomarkers.
• Interpretation techniques for biological biomarkers.
• EEG/MEG datasets for psychiatric disorders available for the public.
During the past few decades, scientific discoveries have enabled people to shape the basic understanding of neurobiology related to psychiatric disorders. However, there is still no effective and objective standard for the diagnosis of psychiatric disorders. Currently, the diagnostic method is mainly based on individual subjective experience rather than in-depth knowledge of pathophysiology, which may cause the misdiagnosis. Psychiatric disease is a type of complex brain disease, and its occurrence and development may be related to biological factors such as neural circuit and neural function abnormality. Therefore, there is an urgent need to study the neurobiomarkers of the onset and symptoms of psychiatric disorders. EEG / MEG is a valuable tool to detect brain function and investigate how the human brain processes information. The application of EEG/MEG in studying brain function and psychiatric disorders has always been the focus of research on psychiatric illness. Many studies have revealed the significance of EEG for the diagnosis of psychiatric illness. Research shows that the diagnosis of psychiatric disorders can be carried out by examining EEG signals. Moreover, compared with PET and fMRI, EEG has the strengths of higher temporal resolution and lower usage cost. Therefore, more research is needed to explore the cutting-edge trends and future directions using EEG / MEG for diagnosing psychiatric disorders.
The purpose of this research topic is to advance the studies on the neurobiomarkers and EEG/MEG based diagnostic methods of psychiatric disorders. We hope to use EEG / MEG to do in-depth research on the pathogenesis and diagnostic measures of psychiatric disorders, such as schizophrenia, depression disorder, anxiety disorder, bipolar disorder, post-traumatic stress disorder, attention deficit hyperactivity disorder, addiction, autism spectrum disorder, etc. Research on statistical analysis and AI based methods in psychiatric disorders are also welcome as to improve the performance of AI for psychiatric disorders and to develop the diagnosis system.
The Research Topic can include, but is not limited to:
• EEG/MEG based novel machine learning/deep learning algorithms for diagnosing psychiatric disorders.
• Novel experimental paradigms to find biological biomarkers for psychiatric disorders.
• Application of visualization and statistical techniques for biological biomarkers.
• Interpretation techniques for biological biomarkers.
• EEG/MEG datasets for psychiatric disorders available for the public.
