Neural perturbational inference: a data-driven framework for mapping the whole-brain causal connectome

The human brain is composed of numerous interconnected brain regions, both structurally and functionally. The complex flow of information between these regions underpins human cognition and behavior. Therefore, characterizing the causal relationship maps between brain regions is crucial for understanding the integration and propagation of information in the brain, as well as the emergence of behavior. The causal connectivity between all brain regions is also referred to as the Effective Brain Connectome (EBC). It describes the strength and direction of causal relationships between brain regions and can distinguish between excitatory and inhibitory relationships. Although effective connectivity inference methods based on neural stimulation and computational modeling have been proposed, these methods can only infer causal relationships between a limited number of brain regions and cannot depict the entire brain's causal network. Consequently, we introduce a novel data-driven framework called Neural Perturbational Inference (NPI) to infer the whole-brain EBC for the first time.

The NPI framework integrates traditional neural stimulation-based effective connectivity inference methods into a data-driven framework. Traditional methods typically involve electrical or magnetic stimulation of a brain region, and the strength of effective connectivity between the stimulated region and other regions is measured based on their response levels after stimulation. Specifically, the NPI first trains an artificial neural network model to learn the brain's dynamical properties. Once trained, the NPI artificial neural network serves as a surrogate for the brain. By perturbing each region of the surrogate brain and observing the responses of other regions, the NPI can infer the direction, strength, and excitatory-inhibitory differences of whole-brain causal connections. We verified the generalization ability of ANN and the ability of NPI to infer real connections from time series data by using a recurrent neural network with known connection structures for generating data. At the same time, on the real data, we verify that ANN has good predictive ability and the ability to learn functional connectivity.

Next, we use the NPI framework to get the EBC of human brain for the following analysis. 1. We applied NPI to the resting state functional MRI data of human brain and obtained the resting state EBC for the first time. The inferred EBC explains how information flows within and between functional networks in the brain are organized, and reveals differences in excitatory and inhibitory connection strength between functional networks. It helps to understand many higher cognitive functions and the neural mechanisms of human behavior. 2. In order to explore the evolutionary dynamics behind the organizational structure of brain networks, we constructed a regularized autoregressive model to explore the optimal brain connection structure under the constraint of total connection strength. The results show that the combined constraints on the optimization of total connection length and the efficient completion of calculations during brain evolution lead to the formation of lognormal distribution and modular structure of brain connections. 3. We applied NPI to functional MRI data of infants at different levels of development and revealed key changes in effective connectivity across the brain during human brain development. The results showed that the developed brain showed a more stable network structure.

In conclusion, we introduced the NPI framework and characterized the human EBC for the first time. Scientifically, it deepens our understanding of the relationship between the brain's structural and functional networks. Technologically, the EBC offers new approaches for developing personalized stimulation treatments for neurological disorders.

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