Visibility graph analysis for brain: scoping review

Author:

Sulaimany Sadegh,Safahi Zhino

Abstract

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.

Publisher

Frontiers Media SA

Subject

General Neuroscience

Reference72 articles.

1. Application of horizontal visibility graph as a robust measure of neurophysiological signals synchrony;Ahmadi;Proceedings of the 2016 IEEE 29th International Symposium on Computer-Based Medical Systems (CBMS),2016

2. New diagnostic EEG markers of the Alzheimer’s disease using visibility graph.;Ahmadlou;J. Neural Transm.,2010

3. Improved visibility graph fractality with application for the diagnosis of autism spectrum disorder.;Ahmadlou;Phys. A Stat. Mech. Appl.,2012

4. Visibility graph similarity: A new measure of generalized synchronization in coupled dynamic systems.;Ahmadlou;Phys. D Nonlinear Phenomena,2012

5. Disrupted small-world brain network in children with Down syndrome.;Ahmadlou;Clin. Neurophysiol.,2013

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