A digital twin approach for simultaneous reconstruction of brain anatomy and dynamics from neural data.
Overview
- The BioRobotics Institute, Sant’Anna School of Advanced Studies, Pisa, Italy
- Department of Excellence in Robotics and AI, Sant’Anna School of Advanced Studies, Pisa, Italy
- Department of Physics, University of Pisa, Pisa, Italy
- Department of Developmental Neuroscience, IRCCS Fondazione Stella Maris, Pisa, Italy
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
- National Institute for Nuclear Physics, Pisa Division, Pisa, Italy
Abstract
Brain structure plays a pivotal role in shaping neural dynamics. Current models lack the anatomical and functional resolution needed to integrate whole-brain structure and dynamics within a unified computational framework. Here, we introduce the FEDE (high FidElity Digital brain modEl) pipeline, generating anatomically accurate brain digital twins from imaging data. Combining advanced techniques of finite-element analysis and biophysical modeling, FEDE reconstructs multi-scale brain structure with high spatial resolution, while also replicating whole-brain neural activity. We demonstrated FEDE’s application by creating the first brain digital twin of a toddler with autism spectrum disorder (ASD). Through parameter optimization, FEDE replicated experimental neural activity while reconstructing multi-scale structural features ranging from whole-brain connectivity to synaptic timescales. FEDE estimated possible patient-specific anomalies in synaptic transmission, consistent with ASD pathophysiology. Our pipeline represents a significant leap forward in brain modeling, paving the way for effective applications of digital twins in experimental and clinical settings.
Reproduced under the paper's license (CC BY), from the paper cited above.
Code
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Zenodo 5879293
Availability: 1 check, the latest on 27 September 2026: the link answers (HTTP 200)
- 27 September 2026: the link answers (HTTP 200)
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Data
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Data Availability
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Reproduced under the paper's license (CC BY), from the paper cited above.
Versions
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Version 1, 27 September 2026: the first record
Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 2 funders, 78 references.
Cite
This paper
Fabbrizzi, M., Amato, L. G., Martinelli, L., Carpaneto, J., Bartolini, E., Calderoni, S., Retico, A., Vergani, A. A., & Mazzoni, A. (2026). A digital twin approach for simultaneous reconstruction of brain anatomy and dynamics from neural data. PLOS digital health, 5(6), e0001445. https://
BibTeX
@article{fabbrizzi2026di
author = {Fabbrizzi, Michelangelo and Amato, Lorenzo Gaetano and Martinelli, Leonardo and Carpaneto, Jacopo and Bartolini, Emanuele and Calderoni, Sara and Retico, Alessandra and Vergani, Alberto Arturo and Mazzoni, Alberto},
title = {{A digital twin approach for simultaneous reconstruction of brain anatomy and dynamics from neural data}},
journal = {PLOS digital health},
year = {2026},
month = jun,
volume = {5},
number = {6},
pages = {e0001445},
publisher = {PLOS},
issn = {2767-3170},
doi = {10.1371/
url = {https://
pmid = {42275289},
pmcid = {PMC13258024}
}
RIS
TY - JOUR
AU - Fabbrizzi, Michelangelo
AU - Amato, Lorenzo Gaetano
AU - Martinelli, Leonardo
AU - Carpaneto, Jacopo
AU - Bartolini, Emanuele
AU - Calderoni, Sara
AU - Retico, Alessandra
AU - Vergani, Alberto Arturo
AU - Mazzoni, Alberto
TI - A digital twin approach for simultaneous reconstruction of brain anatomy and dynamics from neural data
T2 - PLOS digital health
J2 - PLOS Digit Health
PY - 2026
DA - 2026/
VL - 5
IS - 6
SP - e0001445
SN - 2767-3170
PB - PLOS
DO - 10.1371/
UR - https://
LA - en
ER -
CSL-JSON
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