Prospective validation and hardware alignment of surrogate-predicted neural patterns

Determine whether the distributed neural patterns predicted by the fMRI-derived AI surrogate can be approximated by non-invasive stimulation hardware, whether compensatory network dynamics would oppose those patterns, and whether the patterns generalize beyond visual cortex.

Background

The study generates candidate representational perturbations and surrogate-predicted cortical patterns from fMRI-to-latent mappings, but it does not deliver neural stimulation or measure neural responses to the proposed perturbations. The inferred patterns are high-dimensional and spatially distributed, whereas existing non-invasive neuromodulation systems have limited spatial specificity.

Consequently, the translational relevance of the predicted patterns remains unresolved along several dimensions: whether available hardware can approximate them, whether biological network dynamics would counteract or compensate for induced changes, and whether patterns derived primarily from visually responsive cortex would extend to other neural systems. Resolving these issues requires direct prospective experiments involving controlled perturbation or neurostimulation and neural measurement.