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Contributed Talk Session: Thursday, August 6, 1:45 – 2:45 pm, Skirball Theater
Poster D in Poster Session D: Wednesday, August 5, 2:00 – 3:45 pm, Kimmel Center, Shorin & Rosenthal Rooms

Localizing excitation-inhibition imbalance in schizophrenia with virtual brains informed by white-matter microstructure

Kailin Zhu¹, Georg Reich², Xiaojun Zhou³, Trang-Anh Estelle Nghiem³; ¹Hertie Institute for AI in Brain Health, ²Technical University of Berlin, ³Eberhard-Karls-Universität Tübingen

Presenter: Kailin Zhu

Schizophrenia symptoms are believed to emerge from cellular-level excitation-inhibition imbalance. Yet, where imbalance may be localized in the brain to potentially cause brain dynamical differences underlying altered cognition in each patient remains unclear. To investigate cellular mechanisms underlying brain function and dysfunction at the individual level, personalized whole-brain models provide promising tools. However, their applicability to psychiatry is still limited as models fail to account for inter-individual differences in brain dynamics. We hypothesize that models can be substantially enhanced by incorporating any information about white matter microstructure. Here, we systematically compare metrics of white matter structure and microstructure to inform personalized simulations of brain activity in schizophrenia and controls. To do so, we infer regional parameters of whole-brain mean-field models with The Virtual Brain (TVB) to account for individual functional connectivity (FC) from resting-state functional magnetic resonance imaging (fMRI) data. Our results show that models informed by white-matter microstructure metrics drastically outperform models informed by white-matter fiber count and density as in the state of the art at reproducing empirical FC. The findings support that the regional specificity, but not individual specificity of white-matter microstructure metrics, influence data-model fit. Next, we reveal E/I imbalance localized in the posterior cingulate and paracentral areas in schizophrenia patients. Finally, we demonstrate that inferred E/I maps are meaningful in that they can enhance diagnostic classification. Our approach provides a white-matter-microstructure-informed platform to model brain activity at the individual level in health and pathology, allowing us to introduce and validate tools to map E/I balance, supporting machine-learning-based diagnostics and potentially treatment simulation for personalized intervention recommendations.

Topic Area: Development, Individual Differences & Clinical Populations