Visibility graph analysis for brain: scoping review

In the past two decades, network-based analysis has garnered considerable attention for analyzing time series data across various fields. Time series data can be transformed into graphs or networks using different methods, with the visibility graph (VG) being a widely utilized approach. The VG holds extensive applications in comprehending, identifying, and predicting specific characteristics of time series data. Its practicality extends to domains such as medicine, economics, meteorology, tourism, and others. This research presents a scoping review of scholarly articles published in reputable English-language journals and conferences, focusing on VG-based analysis methods related to brain disorders. The aim is to provide a foundation for further and future research endeavors, beginning with an introduction to the VG and its various types. To achieve this, a systematic search and refinement of relevant articles were conducted in two prominent scientific databases: Google Scholar and Scopus. A total of 51 eligible articles were selected for a comprehensive analysis of the topic. These articles categorized based on publication year, type of VG used, rationale for utilization, machine learning algorithms employed, frequently occurring keywords, top authors and universities, evaluation metrics, applied network properties, and brain disorders examined, such as Epilepsy, Alzheimer's disease, Autism, Alcoholism, Sleep disorders, Fatigue, Depression, and other related conditions. Moreover, there are recommendations for future advancements in research, which involve utilizing cutting-edge techniques like graph machine learning and deep learning. Additionally, the exploration of understudied medical conditions such as attention deficit hyperactivity disorder and Parkinson's disease is also suggested.

Automated summary

In the past two decades, network-based analysis has gained significant attention for analyzing time series data in various fields. The visibility graph (VG) approach, widely used in transforming time series data into graphs or networks, has extensive applications in comprehending, identifying, and predicting specific characteristics of time series data. This research presents a scoping review of scholarly articles focusing on VG-based analysis methods related to brain disorders, aiming to provide a foundation for future research and exploration. The study conducted a systematic search and analysis of 51 selected articles, covering publication years, types of VG used, rationale for utilization, machine learning algorithms employed, frequently occurring keywords, top authors and universities, evaluation metrics, applied network properties, and brain disorders examined. Recommendations for future advancements were also provided, including the utilization of cutting-edge techniques like graph machine learning and deep learning, as well as exploring understudied medical conditions such as attention deficit hyperactivity disorder and Parkinson's disease.