Researchers have developed the FEDE (high FidElity Digital brain modEl) system to create patient-specific virtual brain models that replicate brain structure and neural activity. The method was demonstrated in a 2.4-year-old child with autism spectrum disorder (ASD).
Method
- Construction: The pipeline uses T1-weighted, T2-weighted, and diffusion-weighted MRI scans to construct an anatomical model.
- Integration: The finite-element method (FEM) integrates brain connectivity from diffusion imaging with biophysical recordings.
- Simulation: Brain activity was simulated through virtual electrodes and compared with EEG recordings.
- Optimization: Parameter optimization adjusted noise levels and excitatory-to-inhibitory (EI) ratio to match EEG data.
Key Findings
- High Correlation: The model reproduced selected EEG features with high correlation.
- Faster Transmission: Estimated neural transmission delays were shorter than conventional models, attributed to inclusion of myelination.
- Elevated Noise: The optimal noise level was ~100 times higher than standard models, suggesting greater neural fluctuations in ASD.
- Imbalance: The EI ratio was ~3 times higher than expected in a healthy brain, indicating an imbalance.
- Important Caveat: The findings are based on a single patient; thus, interpretations regarding ASD are considered hypotheses.
Conclusions
The FEDE approach provides a high-resolution framework combining brain structure and function. Validation in larger, diverse populations is needed before it can be used for diagnosis or treatment guidance.