What question did this study set out to answer?

Develop an open-source computational framework to create and validate Digital Twins of the human brain for neurological disease.

April 7, 2026Open Access

Digital Twin of the Human Brain A Clinical-Grade Computational Framework Integrating Neuroimaging Physics, Biophysical Simulation, and Geometric Deep Learning for Neurological Disease Modeling

Key Points

Develop an open-source computational framework to create and validate Digital Twins of the human brain for neurological disease.
Implemented a nine-phase framework covering neuroimaging, data acquisition, and simulation techniques.
Utilized various deep learning architectures including Graph Neural Networks and Physics-Informed Neural Networks.
Incorporated Bayesian calibration for improved model accuracy and clinical application deployment.
Framework validation demonstrated improvement in functional connectivity and structural modeling over versions.
Achieved a Pearson correlation coefficient of r=0.051 in v2, aiming for r ≥ 0.70 for production use.
Successful clinical applications included drug testing, seizure prediction, and virtual lesion analysis.

Abstract

We present a clinical-grade, open-source computational framework for constructing and validating subject-specific Digital Twins of the human brain (BDT). The framework integrates the full neuroimaging pipeline — from MRI physics and BOLD signal acquisition through structural connectome construction and biophysical simulation — with state-of-the-art geometric deep learning architectures. The system is organized into nine sequential phases: (1) neuroscience foundations, (2) multi-modal data acquisition, (3) fMRI preprocessing and ComBat harmonization, (4) biophysical modeling using the Wilson-Cowan neural mass framework, (5) Graph Neural Network (GNN) simulation engines including GCN, GAT, and Physics-Informed Neural Networks (PINNs), (6) Bayesian calibration using Sequential Neural Posterior Estimation (SNPE), (7) 3D visualization and clinical dashboards, (8) clinical application deployment including drug simulation and seizure prediction, and (9) federated learning with privacy-preserving inference. Validation on the HCP-demo cohort (n=8, AAL-116 atlas, 116 regions of interest) demonstrates progressive improvement across framework versions: v1 achieves FC-SC Pearson r=−0.006 (saturating fixed-point regime), v2 achieves r=0.051 (optimal G*=0.82), with the r ≥ 0.70 benchmark targeted for production validation on HCP (n=1,200). The framework is fully open-source, compatible with free-tier T4 GPU (Google Colab), and reproducible against public datasets including HCP, OpenNeuro, ADNI, PPMI, and the Allen Brain Atlas. Clinical applications demonstrated include in-silico drug testing for 13 compounds, seizure prediction via bifurcation point detection, Alzheimer tau diffusion modeling, virtual lesion analysis, and BCI motor decoding

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