Developing a Biomarker of Depression Using White-Box Machine Learning of Resting-State Functional Magnetic Resonance Imaging

Published in Neuroscience Applied, 2023

Recommended citation: Matthews, F., Dehsarvi, A., Dutta, A., Moriarty, A., Paton, L., & Smith, S. (2023). Developing a Biomarker of Depression Using White-Box Machine Learning of Resting-State Functional Magnetic Resonance Imaging. Neuroscience Applied, 2, 103668. https://doi.org/10.1016/j.neappl.2023.103668. https://doi.org/10.1016/j.neappl.2023.103668

This paper discusses the development of a biomarker of depression using white-box machine learning of resting-state functional magnetic resonance imaging. The study highlights the potential of machine learning approaches in identifying biomarkers for mental health disorders like depression.

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Recommended citation: Matthews, F., Dehsarvi, A., Dutta, A., Moriarty, A., Paton, L., & Smith, S. (2023). Developing a Biomarker of Depression Using White-Box Machine Learning of Resting-State Functional Magnetic Resonance Imaging. Neuroscience Applied, 2, 103668. https://doi.org/10.1016/j.neappl.2023.103668.