Resting-state functional magnetic resonance imaging (rs-fMRI), initially proposed by Biswal et al. in 1995, has emerged as a pivotal facet of neuroimaging research. Its ability to examine brain activity during the resting state without the need for explicit tasks or stimuli has made it an integral component of brain imaging studies. In recent years, rs-fMRI has witnessed substantial growth and found widespread application in the investigation of functional connectivity within the brain. To delineate the developmental trajectory of rs-fMRI over the past two decades, we conducted a comprehensive analysis using bibliometric tool Citespace. Our analysis encompassed publication trends, authorship networks, institutional affiliations, international collaborations, as well as emergent themes in references and keywords. Our study reveals a remarkable increase in the volume of rs-fMRI publications over the past two decades, underscoring the burgeoning interest and potential within this field. Harvard University stands out as the institution with the highest number of research papers published in the realm of RS-fMRI, while the United States holds the highest overall influence in this domain. The recent emergence of keywords such as "machine learning" and "default mode," coupled with citation surges in reference to rs-fMRI, have paved new avenues for research within this field. Our study underscores the critical importance of integrating machine learning techniques into rs-fMRI investigations, offering valuable insights into brain function and disease diagnosis. These findings hold profound significance for the field of neuroscience and may furnish insights for future research employing rs-fMRI as a diagnostic tool for a wide array of neurological disorders, thus emphasizing its pivotal role and potential as a tool for investigating brain functionality.
Keywords: Bibliometric; Citespace; Web of Science; rs-fMRI.
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