Mapping the whole-brain effective connectome with excitatory-inhibitory causal relationship

Understanding the large-scale causal relationship among brain regions is crucial for elucidating the information flow that the brain integrates external stimuli and generates behaviors. Despite the availability of neurostimulation and computational methods to infer causal relationships among a limited number of regions, these approaches are not capable of mapping the causal network of the entire brain, also known as the effective brain connectome (EBC). To address this gap, we propose a data-driven framework called Neural Perturbational Inference (NPI) and map the human EBC for the first time. NPI uses an artificial neural network trained to learn large-scale neural dynamics as a surrogate brain. By perturbing each region of the surrogate brain and observing the resulting responses in all other regions, the human EBC is obtained. This connectome captures the directionality, strength, and excitatory-inhibitory distinction of brain-wide causal relationships, offering mechanistic insights into cognitive processes. EBC provides a complete picture of information flow both within and across brain functional networks as well as reveals the large-scale hierarchy of the organization of excitatory and inhibitory ECs. As EBC captures the neurostimulation transmission pathways in the brain, it has great potential to guide the target selection in personalized neurostimulation of neurological disorders.