The brain is a highly complex, multiphysics system in which electrical, chemical, and mechanical processes interact across a wide range of spatial and temporal scales. Accurate computational modeling of such phenomena requires not only sophisticated physical descriptions, but also advanced numerical methods capable of resolving multiscale features and strong couplings with high fidelity and efficiency. This minisymposium brings together researchers developing and applying advanced numerical methods for multiscale and multiphysics brain modeling. Topics of interest include high-order finite element and discontinuous Galerkin methods, polygonal/polyhedral and agglomerated discretizations, isogeometric analysis, robust and scalable solvers for coupled systems, adaptivity (p-, h-, and hp-refinement), error estimation and control, and numerical methods for complex geometries and heterogeneous biological tissues. Relevant application areas include the mechanics of brain tissue and cerebrospinal and interstitial fluids, transport and clearance processes (including glymphatic and perivascular pathways), electromechanical coupling, prion-like protein spreading, tissue atrophy, electrophysiology and network-scale dynamics. Contributions integrating physics-based modeling with data-driven approaches, including reduced-order modeling and data assimilation from imaging or clinical datasets, are especially encouraged.