We care as much about how predictive neuroimaging is done as about any single biomarker. This page collects comments, primers, community standards work, and tooling that push for replicability, transparency, and responsible ML in the neurosciences.
Customisable anonymization/pseudonymization with LimeSurvey integration and two-factor authentication.
Related methodological packages for trustworthy predictive models (confound testing, adaptive external validation, cluster enhancement) are listed under Predictive models and Software. See also BWAS replicability.
Goltermann, O., Spisak, T., & Büchel, C. (2025). Concern About Predictive Performance of a Pain Sensitivity Biomarker. JAMA Neurology, 82(9), 968. 10.1001/jamaneurol.2025.2351
Poldrack, R. A., Markiewicz, C. J., Appelhoff, S., Ashar, Y. K., Auer, T., Baillet, S., Bansal, S., Beltrachini, L., Benar, C. G., Bertazzoli, G., Bhogawar, S., Blair, R. W., Bortoletto, M., Boudreau, M., Brooks, T. L., Calhoun, V. D., Castelli, F. M., Clement, P., Cohen, A. L., … Gorgolewski, K. J. (2024). The past, present, and future of the brain imaging data structure (BIDS). Imaging Neuroscience, 2. 10.1162/imag_a_00103