White-matter-microstructure-informed whole-brain models reveal localized excitation-inhibition imbalance in schizophrenia

Abstract

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. 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 to account for empirical functional connectivity from resting-state functional magnetic resonance imaging of schizophrenia patients and healthy controls. 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 using the number or density of fibers as in many state-of-the-art methods. This approach allows us to uncover personalized maps of excitation-inhibition imbalance, hypothesized to underlie symptoms in schizophrenia. These maps prove meaningful in that they can predict diagnosis better than model-independent neuroimaging benchmarks. Our findings highlight the importance of white matter microstructure in whole-brain modeling. The novel white-matter-informed models reveal mechanisms that can cause altered brain dynamics in schizophrenia and could inform treatment in personalized psychiatry.