Risk assessment of mental illness remains challenging as it must take into consideration the complex interactions of behavioral, biological, and environmental factors underlying the disorders. Advances in the field of network sciences allow for a more holistic approach to assess and improve mental health through the study of networks of genes, proteins, cells, tissues, electronic health records, and epidemiological and clinical data. Novel concepts and approaches derived from recent progress in network theory, knowledge embedding, and computational biology, combined with the large-scale datasets produced from subcellular to social levels are promised to provide new insights into the complex processes involved in human psychology.
The goal of this Research Topic is to extend knowledge for traditional risk assessment tools for mental illness and dementia using network theories. The development of mental disorders is always attributable to multiple components. The components may also interact with each other in different aspects. Network science shows a remarkable advantage in highlighting the systematic biological and social networks. It has become a powerful tool to comprehensively assess the risk of mental illness. This Research Topic will focus on bringing together novel network approaches applied to various mental health-related datasets and will address the current challenges and bottlenecks towards future major advances by describing findings that can drive the implementation of network theory.
The topics of interest for this Research Topic include but are not limited to:
- Multilayer networks in mental health
- Precision network medicine for personalized risk assessment
- Network medicine for dementia therapy
- Biological networks and their applications in mental health
- Network pharmacology and drug repurposing for mental illness
- Network-based approaches for undiagnosed mental diseases
- Social network and electronic health record analysis for human well-being
- Connected objects for health
- Epidemiological data analysis and network dynamics for people with dementia
- Network physiology and brain network
Risk assessment of mental illness remains challenging as it must take into consideration the complex interactions of behavioral, biological, and environmental factors underlying the disorders. Advances in the field of network sciences allow for a more holistic approach to assess and improve mental health through the study of networks of genes, proteins, cells, tissues, electronic health records, and epidemiological and clinical data. Novel concepts and approaches derived from recent progress in network theory, knowledge embedding, and computational biology, combined with the large-scale datasets produced from subcellular to social levels are promised to provide new insights into the complex processes involved in human psychology.
The goal of this Research Topic is to extend knowledge for traditional risk assessment tools for mental illness and dementia using network theories. The development of mental disorders is always attributable to multiple components. The components may also interact with each other in different aspects. Network science shows a remarkable advantage in highlighting the systematic biological and social networks. It has become a powerful tool to comprehensively assess the risk of mental illness. This Research Topic will focus on bringing together novel network approaches applied to various mental health-related datasets and will address the current challenges and bottlenecks towards future major advances by describing findings that can drive the implementation of network theory.
The topics of interest for this Research Topic include but are not limited to:
- Multilayer networks in mental health
- Precision network medicine for personalized risk assessment
- Network medicine for dementia therapy
- Biological networks and their applications in mental health
- Network pharmacology and drug repurposing for mental illness
- Network-based approaches for undiagnosed mental diseases
- Social network and electronic health record analysis for human well-being
- Connected objects for health
- Epidemiological data analysis and network dynamics for people with dementia
- Network physiology and brain network
