Mapping effective connectivity by virtually perturbing a surrogate brain
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2022
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866916412139765760 |
|---|---|
| author | Luo, Zixiang Peng, Kaining Liang, Zhichao Cai, Shengyuan Xu, Chenyu Li, Dan Hu, Yu Zhou, Changsong Liu, Quanying |
| author_facet | Luo, Zixiang Peng, Kaining Liang, Zhichao Cai, Shengyuan Xu, Chenyu Li, Dan Hu, Yu Zhou, Changsong Liu, Quanying |
| contents | 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 between NPI-inferred EC and real stimulation propagation pathways. 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 research and clinical applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2301_00148 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Mapping effective connectivity by virtually perturbing a surrogate brain Luo, Zixiang Peng, Kaining Liang, Zhichao Cai, Shengyuan Xu, Chenyu Li, Dan Hu, Yu Zhou, Changsong Liu, Quanying Neurons and Cognition 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 between NPI-inferred EC and real stimulation propagation pathways. 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 research and clinical applications. |
| title | Mapping effective connectivity by virtually perturbing a surrogate brain |
| topic | Neurons and Cognition |
| url | https://arxiv.org/abs/2301.00148 |