Virtual Brain and Digital Twins in Neurogenetics: From Multimodal Patient Data to Genomically Informed, Clinically Actionable Models

Molecular diagnosis has advanced rapidly in neurogenetic disorders, yet translating genotype into patient-specific predictions of brain network dysfunction and progression remains limited. Virtual brain models provide a structured solution by embedding individual anatomy and connectomics into biophysical whole-brain simulations. The critical step is to position genetics not as a diagnostic label, but as a constructive input to model design. This review outlines a genetics-centered framework for virtual brain modeling. First, atlas-derived transcriptomic and cell-type maps can define region-specific molecular priors, constraining vulnerability or excitability parameters and reducing model degeneracy. Second, when reproducible genotype-linked network phenotypes exist, mutation groups can inform stratified initialization and progression regimes. Third, at the patient level, exome and CNV data—summarized as pathway burdens and, where appropriate, calibrated polygenic modifiers—can be translated into individualized priors or regularizers, provided that mapping rules are explicit and externally validated. By integrating genetics at multiple levels of evidence, virtual brain models gain mechanistic plausibility, improved calibration, and explicit uncertainty quantification. The most realistic impact over the next few years is likely to be improved stratification, progression-aware forecasting, and scenario-based decision support in rare neurogenetic diseases, especially where longitudinal cohort infrastructure and validated biomarker inputs are already available, rather than deterministic individual prediction.