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Current treatments, such as ventriculoperitoneal shunts, divert excess CSF from the brain but often face complications, mainly due to shunt obstructions caused by biological matter accumulation. While previous shunt designs aimed to improve fluid flow and reduce occlusion, they often lacked the precision needed for real-world applications due to simplified simulation models that did not fully capture the dynamics of the cerebral ventricular system. Here, we introduce BrainFlow, a computational model that integrates detailed anatomical and physiological features to simulate CSF dynamics in the presence of shunt implants. BrainFlow incorporates patient-specific medical imaging data, pulsatile flow to mimic cardiac cycles, adjustable parameters for various hydrocephalus conditions, and a biomolecule tracking feature to evaluate the long-term risk of shunt occlusion due to flow-mediated biomolecular transport. This model provides a more nuanced understanding of the factors contributing to shunt obstruction, offering insights into optimal shunt placement, design, and materials choice. Through validation against four-dimensional MRI flow data, BrainFlow demonstrates robust accuracy across multiple flow metrics. Our work lays the groundwork for the development of next-generation shunts tailored to individual patient anatomy and pathology, ultimately aiming to improve hydrocephalus treatment through informed, patient-specific design strategies.<\/jats:p>","DOI":"10.1073\/pnas.2426067122","type":"journal-article","created":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T11:38:07Z","timestamp":1750851487000},"update-policy":"https:\/\/doi.org\/10.1073\/pnas.cm10313","source":"Crossref","is-referenced-by-count":1,"title":["Fluid dynamics model of the cerebral ventricular system"],"prefix":"10.1073","volume":"122","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2892-1974","authenticated-orcid":false,"given":"Haritosh","family":"Patel","sequence":"first","affiliation":[{"name":"Harvard John A. 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