Decoupling neural representation development from trial evaluation: an operational framework for target validation...

Randomized controlled trials (RCTs) targeting cognitive health in older adults increasingly incorporate resting-state functional MRI (rs-fMRI) to evaluate intervention-related neural change and its association with cognitive outcomes. However, these trials are typically small, and rs-fMRI data are high-dimensional and sensitive to measurement variability, making neural representations developed and evaluated within the same trial vulnerable to instability and overfitting. Neural representations learned from large observational datasets can reduce dependence on within-trial model development, but strong predictive performance after transfer does not establish prospective validity in an intervention setting. Here, we propose an operational framework for using externally developed neural representations in RCTs, separating representation development, optional adaptation, and trial use. The framework distinguishes target validation, in which the target and evaluation procedure are specified before outcomes are examined, from target discovery, in which outcomes may inform downstream fitting, selection, or interpretation, with the resulting patterns treated as candidate targets requiring independent confirmation. Using two RCTs as worked examples, we illustrate validation-oriented evaluation of an externally developed connectivity representation and discovery-oriented use of an adapted rs-fMRI foundation-model representation. This framework provides a practical basis for accumulating evidence across trials and distinguishing neural features that are consistently intervention-responsive from those that are context dependent.