Topics in magnetic resonance imaging : TMRI · 2026
Mapping the Evolution of Machine Learning in Functional Magnetic Resonance Imaging Research: A Comprehensive Scientometric Analysis.
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Sadeghi-Bazargani Homayoun, Ghojazadeh Morteza, Ahmadi Bonabi Melika, Narimani Mahshad, Abdolrahmanzadeh Razieh, Asadi Shally Adeleh, Taghizadeh Sepita
Аннотация
OBJECTIVES: Coupled with functional magnetic resonance imaging (fMRI), machine learning (ML), enables advanced pattern recognition and predictive modeling, driving progress in cognitive neuroscience and clinical diagnostics. Scientometrics offers a framework for examining the evolving intersection of fMRI and ML, uncovering knowledge gaps, trends, and collaborations.
MATERIALS AND METHODS: A scientometric analysis was conducted using data from the Scopus database, focusing on studies combining fMRI and ML. After filtering for peer-reviewed articles, reviews, and editorials, eligible studies were preprocessed using KNIME tools to standardize text. Data were analyzed with the Bibliometrix package in R-Studio and VOS-viewer to generate thematic maps, citation networks, and collaboration visualizations. These analyses identified key trends, influential contributors, and research gaps, offering a structured view of this interdisciplinary field.
RESULTS: The Scopus search yielded 4657 documents, with 2639 included after filtering. Scientific production in this field has grown steadily since 2004, with an upward trend resuming after 2021. Thematic mapping identified 4 types of research topics: motor (hot and well-developed), basic (foundational and cross-cutting), niche (specialized), and emerging or declining (less developed). Key active themes include "machine learning" and "functional connectivity," while advanced techniques like generative adversarial networks are gaining attention. Collaboration among 139 researchers and 47 countries highlighted the United States and China as leaders in publications and citations. Among 582 journals, Neuroimage had the highest number of articles and citations.
CONCLUSIONS: The evaluated studies depict steady progress, with notable contributions to clinical diagnostics, cognitive neuroscience, and therapy development. Thematic analysis highlights foundational methods, such as fMRI and support vector machines, alongside emerging innovations like generative adversarial networks and dynamic functional connectomics.
MATERIALS AND METHODS: A scientometric analysis was conducted using data from the Scopus database, focusing on studies combining fMRI and ML. After filtering for peer-reviewed articles, reviews, and editorials, eligible studies were preprocessed using KNIME tools to standardize text. Data were analyzed with the Bibliometrix package in R-Studio and VOS-viewer to generate thematic maps, citation networks, and collaboration visualizations. These analyses identified key trends, influential contributors, and research gaps, offering a structured view of this interdisciplinary field.
RESULTS: The Scopus search yielded 4657 documents, with 2639 included after filtering. Scientific production in this field has grown steadily since 2004, with an upward trend resuming after 2021. Thematic mapping identified 4 types of research topics: motor (hot and well-developed), basic (foundational and cross-cutting), niche (specialized), and emerging or declining (less developed). Key active themes include "machine learning" and "functional connectivity," while advanced techniques like generative adversarial networks are gaining attention. Collaboration among 139 researchers and 47 countries highlighted the United States and China as leaders in publications and citations. Among 582 journals, Neuroimage had the highest number of articles and citations.
CONCLUSIONS: The evaluated studies depict steady progress, with notable contributions to clinical diagnostics, cognitive neuroscience, and therapy development. Thematic analysis highlights foundational methods, such as fMRI and support vector machines, alongside emerging innovations like generative adversarial networks and dynamic functional connectomics.