A. Unless you think that brain science is immoral science, then I do not see any problem with this kind of research. As a neuroscientist, I strongly object to the insinuation as unfounded.

B. "Digital twins" e.g. [1] are a growing class of brain simulations that can successfully approximate brain activity patterns at large scale. I think these can be very useful, but we shouldn't think that they are at the level of actually simulating a brain. They are usually made of model neuronal approximative simulations (e.g. integrate and fire, balancing excitatory and inhibitatory neural populations within units), then using diffusion imaging to estimate white matter axonal wiring between those populations from the subject to increase the accuracy of the simulation. These are increasingly being used to, for example, model how a surgical intervention would effect seizure propagation prior to actual surgery. Here is a nice episode of Theoretical Neuroscience podcast [2] on the Virtual Brain [3], one of the available models for this kind of work.

C. In terms of validation. Only partly. From my quick read, this NEVO model optimized neural response only in the digital twin encoding model. While the digital twin model reportedly has solid predictive validity [4], which by the way was not the Virtual Brain model I mentioned in point B. Moreover, the outputs looked neurobiologically plausible, but at this point, there is no independent model or new fMRI showing the optimized stimuli actually drive the target regions. This was performed using previously collected fMRI data, and full validation of this model *IS* the obvious next step, but the money to collect such data does not come from nowhere: funding will be needed, and such a paper as this can help them get it.

D. A final point I'd make. We have long been able to create static stimuli that we can be fairly certain will activate above baseline certain brain regions, on average. Certain stimuli-region pairs ar emore homogenous between people, others e.g. the fusiform face area (FFA), are small enough that individual differences prevent a simple ROI approach, and identification depends on using face stimuli to identify at the individual level, but for the most part, it is reliably locatable. Brain activations are very coarse things. In fMRI, you are talking about ~3x3x3mm voxels (27mm^3) where the hemodynamic responses have a ton of spatial autocorrelation, or in EEG, where the surface spatial area of the reeptive fields are very large(~400 mm^2). These virtual twin models already do a decent job of modeling dynamics of the brain there parameters are tuned to *at this scale*..but this scale does not have a ton of information content. Automating this with video content is not that much a reach.

[1] https://spj.science.org/doi/10.34133/icomputing.0055 [2] https://theoreticalneuroscience.no/thn23/ [3] https://www.thevirtualbrain.org/tvb/zwei/ [4] https://www.biorxiv.org/content/10.1101/2025.07.22.664908v2....

AI-generated videos to maximally drive a target brain region