{"ID":5346761,"CreatedAt":"2026-06-30T04:09:55.830587294Z","UpdatedAt":"2026-07-02T13:56:16.32655622Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.30329","arxiv_id":"2606.30329","title":"Cohort-amortized personalization: navigating the privacy-utility frontier for virtual brain twins","abstract":"Personalized generative brain models require individual neuroimaging data that privacy constraints and re-identification risk make difficult to share, while per-subject fitting procedures cost hours of compute -- limiting clinical translation and multi-site collaboration. We introduce cohort-amortized personalization (CAP), which replaces data sharing with model sharing: a neural density estimator is trained on simulations from a mechanistic whole-brain model under a low-rank cohort prior, and only the compact estimator is distributed, so new subjects are personalized in seconds on their own data alone. To make this prior both compact and atlas-independent, a cross-atlas autoencoder (CrossCoder) maps connectomes from 20 anatomical atlases into a shared latent space, enabling deployment across sites with heterogeneous atlases. We validate CAP on two cohorts: 21 patients with drug-resistant epilepsy (epileptogenic-zone localization F1=0.56) and 832 subjects from the 1000BRAINS aging cohort (predicted age r=0.44); in both, CAP matches or exceeds per-subject inference with hours-to-seconds speed-up. Because the shared artifact couples a cohort prior to a mechanistic simulator, it can serve as a mechanistic surrogate supporting in-silico experimentation and synthetic-cohort generation without raw-data access -- a governance-audited alternative we term synthetic access, allowing for wider adoption of personalized modeling in more diverse settings.","short_abstract":"Personalized generative brain models require individual neuroimaging data that privacy constraints and re-identification risk make difficult to share, while per-subject fitting procedures cost hours of compute -- limiting clinical translation and multi-site collaboration. We introduce cohort-amortized personalization (...","url_abs":"https://arxiv.org/abs/2606.30329","url_pdf":"https://arxiv.org/pdf/2606.30329v1","authors":"[\"Amirhossein Esmaeili\",\"Marmaduke Woodman\",\"Nina Baldy\",\"Abolfazl Ziaeemehr\",\"Julia Makhalova\",\"Huifang Wang\",\"Daniele Marinazzo\",\"Svenja Caspers\",\"Fabrice Bartolomei\",\"Meysam Hashemi\",\"Viktor Jirsa\"]","published":"2026-06-29T14:10:07Z","proceeding":"q-bio.NC","tasks":"[\"q-bio.NC\"]","methods":"[]","has_code":false}