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Mapping Effective Connectivity by Virtually Perturbing a Surrogate Brain by Luo, Zixiang; Peng, Kaining; Liang, Zhichao; Cai, Shengyuan; Xu, Chenyu; Li, Dan; Hu, Yu; Zhou, Changsong; Liu, Quanying is a scholarly article available to read on EtoBox.

What is Mapping Effective Connectivity by Virtually Perturbing a Surrogate Brain about?

Effective connectivity (EC), indicative of the causal interactions between brain regions, is fundamental to understanding information processing in the brain. Traditional approaches, which infer EC from neural responses to stimulations, are not suited for mapping whole-brain EC in humans due to being invasive and having limited spatial coverage of stimulations. To address this gap, we present Neural Perturbational Inference (NPI), a data-driven framework designed to map EC across the entire brain. NPI employs an artificial neural network trained to learn large-scale neural dynamics as a computational surrogate of the brain. NPI maps EC by perturbing each region of the surrogate brain and observing the resulting responses in all other regions. NPI captures the directionality, strength, and excitatory/inhibitory properties of brain-wide EC. Our validation of NPI, using models having ground-truth EC, shows its superiority over Granger causality and dynamic causal modeling. Applying NPI to resting-state fMRI data from diverse datasets reveals consistent and structurally supported EC. Further validation using a cortico-cortical evoked potentials dataset reveals a significant correlation

Author

Luo, Zixiang; Peng, Kaining; Liang, Zhichao; Cai, Shengyuan; Xu, Chenyu; Li, Dan; Hu, Yu; Zhou, Changsong; Liu, Quanying

Published

2022

Language

EN