TY - JOUR
T1 - Mapping effective connectivity by virtually perturbing a surrogate brain
AU - Luo, Zixiang
AU - Peng, Kaining
AU - Liang, Zhichao
AU - Cai, Shengyuan
AU - Xu, Chenyu
AU - Li, Dan
AU - Hu, Yu
AU - Zhou, Changsong
AU - Liu, Quanying
N1 - Publisher Copyright:
© The Author(s), under exclusive licence to Springer Nature America, Inc. 2025.
Funding Information:
Q.L. was supported by the National Natural Science Foundation of China (62472206), the National Key R&D Program of China (2021YFF1200804), Shenzhen Excellent Youth Project (RCYX20231211090405003), Shenzhen Science and Technology Innovation Committee (2022410129, KJZD20230923115221044, KCXFZ20201221173400001), Guangdong Provincial Key Laboratory of Advanced Biomaterials (2022B1212010003), and the Center for Computational Science and Engineering at Southern University of Science and Technology. C.Z. was supported by Hong Kong RGC Senior Research Fellowship Scheme (SRFS2324-2S05). Y.H. was partly supported by ECS-26303921 from the Research Grants Council of Hong Kong. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the paper. Z. Liang was supported by GuangDong Basic and Applied Basic Research Foundation (2025A1515011645). We thank the support from the Swarma-TCCI scholarship to Z. Luo and Z. Liang.
PY - 2025/4/22
Y1 - 2025/4/22
N2 - Effective connectivity (EC), which reflects the causal interactions between brain regions, is fundamental to understanding information processing in the brain; however, traditional methods for obtaining EC, which rely on neural responses to stimulation, are often invasive or limited in spatial coverage, making them unsuitable for whole-brain EC mapping in humans. Here, to address this gap, we introduce Neural Perturbational Inference (NPI), a data-driven framework for mapping whole-brain EC. NPI employs an artificial neural network trained to model large-scale neural dynamics, serving as a computational surrogate of the brain. By systematically perturbing all regions in the surrogate brain and analyzing the resulting responses in other regions, NPI maps the directionality, strength and excitatory/inhibitory properties of brain-wide EC. Validation of NPI on generative models with known ground-truth EC demonstrates its superiority over existing methods such as Granger causality and dynamic causal modeling. When applied to resting-state functional magnetic resonance imaging data across diverse datasets, NPI reveals consistent, structurally supported EC patterns. Furthermore, comparisons with cortico-cortical evoked potential data show a strong resemblance between NPI-inferred EC and real stimulation propagation patterns. By transitioning from correlational to causal understandings of brain functionality, NPI marks a stride in decoding the brain’s functional architecture and facilitating both neuroscience studies and clinical applications.
AB - Effective connectivity (EC), which reflects the causal interactions between brain regions, is fundamental to understanding information processing in the brain; however, traditional methods for obtaining EC, which rely on neural responses to stimulation, are often invasive or limited in spatial coverage, making them unsuitable for whole-brain EC mapping in humans. Here, to address this gap, we introduce Neural Perturbational Inference (NPI), a data-driven framework for mapping whole-brain EC. NPI employs an artificial neural network trained to model large-scale neural dynamics, serving as a computational surrogate of the brain. By systematically perturbing all regions in the surrogate brain and analyzing the resulting responses in other regions, NPI maps the directionality, strength and excitatory/inhibitory properties of brain-wide EC. Validation of NPI on generative models with known ground-truth EC demonstrates its superiority over existing methods such as Granger causality and dynamic causal modeling. When applied to resting-state functional magnetic resonance imaging data across diverse datasets, NPI reveals consistent, structurally supported EC patterns. Furthermore, comparisons with cortico-cortical evoked potential data show a strong resemblance between NPI-inferred EC and real stimulation propagation patterns. By transitioning from correlational to causal understandings of brain functionality, NPI marks a stride in decoding the brain’s functional architecture and facilitating both neuroscience studies and clinical applications.
UR - http://www.scopus.com/inward/record.url?scp=105003208682&partnerID=8YFLogxK
UR - https://www.nature.com/articles/s41592-025-02654-x
U2 - 10.1038/s41592-025-02654-x
DO - 10.1038/s41592-025-02654-x
M3 - Journal article
AN - SCOPUS:105003208682
SN - 1548-7091
SP - 1
EP - 20
JO - Nature Methods
JF - Nature Methods
ER -
