Survey reveals varied definitions and evaluation criteria for generative models in neuroimaging, suggesting a need for clearer standards.
The term generative model is widely used in human neuroimaging; however, its meaning is often left implicit. Prompted by observations and discussions at the 2025 Organization for Human Brain Mapping (OHBM) Annual Meeting, we surveyed members of the neuroimaging community to examine how generative models are defined, used, and evaluated in practice. Responses revealed some agreement on functional criteria — such as a model’s ability to simulate data — alongside marked disagreement about whether specific, widely used methods should be considered generative models. Evaluative priorities also varied across respondents, though out-of-sample generalization and interpretability were consistently emphasized. In light of these findings, we propose a pragmatic working definition of generative modeling, and a short set of reporting commitments intended to make generative claims easier to interpret and evaluate.
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Greaves et al. (2026) studied this question.
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