Literature-Guided Minimax Optimization of Virtual Epilepsy Neurostimulation
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Computer Science > Machine Learning
Title:Literature-Guided Minimax Optimization of Virtual Epilepsy Neurostimulation
Abstract:Computational models of epilepsy promise patient-specific treatment design, but most optimization workflows still search for parameters that perform well on average. In neuromodulation, this is a weak target: a protocol that improves the mean response can still fail in the patient whose network is least tolerant to stimulation. We present a literature-guided minimax pipeline that couples PubMed-scale hypothesis extraction, The Virtual Brain (TVB) Epileptor simulations, and large-language-model-guided black-box optimization. The optimizer proposes either intrinsic model-control parameters or clinically interpretable external-stimulation protocols; TVB evaluates each proposal across sampled virtual patients; and the objective maximizes worst-case reward, defined as the negative variance of simulated seizure activity. In the intrinsic model-control experiment, the best archived parameter set improved worst-case reward from -0.5285 to -0.3182, a 39.8% gain over baseline. The clinical-style external-stimulation search produced a much smaller worst-case improvement (1.7%), and a 20-patient virtual cohort showed no aggregate benefit (p=0.9019), despite a 55% responder rate and a positive temporal-lobe subgroup signal. The study should be read as an in silico proof of concept for robust, literature-aware neurostimulation design, not as clinical evidence.
| Comments: | 9 pages, 4 figures. Code and interactive essay at this https URL |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2606.04339 [cs.LG] |
| (or arXiv:2606.04339v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2606.04339
arXiv-issued DOI via DataCite (pending registration)
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