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We recently described a virtual brain network simulation method for optimizing tDCS interventions and now propose a method for further personalizing this approach. We now personalized the model for six female and four male biomarker-confirmed AD patients based on their brain structure and functional connectivity by using individual structural magnetic resonance imaging data and amplitude envelope correlation-based connectivity matrices extracted from magnetoencephalography (MEG) scans, respectively. We then assessed a set of previously established stimulation strategies based on their ability to improve relevant neurophysiological outcome parameters in each personalized model while undergoing AD damage. Personalized tDCS strategies were able to delay neurophysiological deterioration, but while the general model favored posterior anodal stimulation targeting the precuneus region, the personalized models favored frontal anodal stimulation targeting the dorsolateral prefrontal cortex region in 90% of the cases. This may be explained by higher connectivity levels of frontal regions in the personalized connectivity matrices, as anodal stimulation of highly connected regions produced more beneficial effects. In this methodological study, we propose several ways to improve personalized computational tDCS stimulation prediction modeling. We conclude that connectome-guided personalization of tDCS effects lead to different strategies with potentially better intervention outcomes. For external validation of this model-guided tDCS approach, model predictions are being tested in an ongoing clinical tDCS\u2013MEG trial in AD patients.<\/jats:p>","DOI":"10.1523\/eneuro.0407-25.2026","type":"journal-article","created":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T17:50:26Z","timestamp":1785347426000},"page":"ENEURO.0407-25.2026","update-policy":"https:\/\/doi.org\/10.1523\/eneuro.crossmarkpolicy","source":"Crossref","is-referenced-by-count":0,"title":["Connectome-Guided Personalization of Optimal TDCS Intervention Selection in Alzheimer's Disease: A Modeling Study"],"prefix":"10.1523","volume":"13","author":[{"given":"Janne J.","family":"Luppi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Annel P.","family":"Koomen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cornelis 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