CNS*2026 Halifax

Introduction
Providing early diagnosis and personalized treatment for psychiatric disorders like schizophrenia remains challenging, due to important interpersonal differences and still elusive neuronal mechanisms. Whole-brain network models show promising results with clinical relevance for individualized treatment recommendations in neurological disorders. However, their applicability to psychiatry is still limited as models fail to account for inter-individual differences in the correlation structure of brain dynamics among psychiatric patients.


Methods
What physiological mechanisms should models incorporate to better account for individual profiles of brain dynamics in schizophrenia patients and healthy controls? Our study compares various metrics of white matter structure and microstructure to inform connection weights between regions. To do so, we inferred regional parameters of whole-brain mean-field models with The Virtual Brain simulator (Pille et al, 2025 bioRxiv) to account for empirical functional connectivity from resting-state functional magnetic resonance imaging of schizophrenia patients and healthy controls (2).


Results
We found that using global fractional anisotropy or apparent diffusion coefficient of white matter fibers to inform the weights in neural mass models can drastically improve model performance. The data-model correlations of simulated and empirical data were significantly improved (from 0.2 to 0.7) over state-of-the-art methods. This approach allows us to uncover personalized maps of excitation-inhibition imbalance, hypothesized to take place in schizophrenia. These maps prove meaningful in that they can predict diagnosis better than model-independent neuroimaging benchmarks.


Discussion
Our findings highlight the importance of white matter microstructure in whole-brain modeling. The findings provide a fundamentally novel bridge between cellular-scale E/I imbalance mechanisms hypothesized in schizophrenia and large-scale brain network dynamics associated with well-established biomarkers of the disorder. Personalized white-matter microstructure informed whole-brain models could therefore be relevant as platforms to simulate disorder progression for early diagnosis and to test and optimize intervention protocols toward individualized treatment recommendations.


References
Pille, M., Martin, L., Richter, E., Perdikis, D., Schirner, M., & Ritter, P. (2025). Fast and easy whole-brain network model parameter estimation with automatic differentiation. bioRxiv, 2025-11.
Vohryzek, J., Aleman-Gomez, Y., Griffa, A., Raoul, J., Cleusix, M., Baumann, P. S., ... & Hagmann, P. (2020). Structural and functional connectomes from 27 schizophrenic patients and 27 matched healthy adults [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3758534

Acknowledgement
This project was supported by the Hertie Foundation.