Amortizing personalization in virtual brain twins
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866908423498498048 |
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| author | Baldy, Nina Woodman, Marmaduke M Jirsa, Viktor K |
| author_facet | Baldy, Nina Woodman, Marmaduke M Jirsa, Viktor K |
| contents | Virtual brain twins are personalized digital models of individual human subject or patient's brains, allowing for mechanistic interpretation of neuroimaging data features. Training and inference with these models however presents a pair of challenges: large shared infrastructure do not allow for use of personal data and inference in clinical applications should not require significant resources. We introduce "anonymized personalization" to address both by expanding model priors to include personalization which under amortized inference allows training to be performed anonymously, while inference is both personalized and lightweight. We illustrate the basic approach, demonstrate reliability in an example, and discuss the impact on both experimental and computational neuroscience. Code is available at https://github.com/ins-amu/apvbt. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_21155 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Amortizing personalization in virtual brain twins Baldy, Nina Woodman, Marmaduke M Jirsa, Viktor K Neurons and Cognition Virtual brain twins are personalized digital models of individual human subject or patient's brains, allowing for mechanistic interpretation of neuroimaging data features. Training and inference with these models however presents a pair of challenges: large shared infrastructure do not allow for use of personal data and inference in clinical applications should not require significant resources. We introduce "anonymized personalization" to address both by expanding model priors to include personalization which under amortized inference allows training to be performed anonymously, while inference is both personalized and lightweight. We illustrate the basic approach, demonstrate reliability in an example, and discuss the impact on both experimental and computational neuroscience. Code is available at https://github.com/ins-amu/apvbt. |
| title | Amortizing personalization in virtual brain twins |
| topic | Neurons and Cognition |
| url | https://arxiv.org/abs/2506.21155 |