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.
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.