Inferring functional relations from synthetic fMRI data using large-scale Nonlinear Granger Causality (lsNGC)
- DOI
- 10.1117/12.2613389
- Published
- 2022-04-04
- Container
- Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging
- Publisher
- SPIE
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1117/12.2613389,
title = {Inferring functional relations from synthetic fMRI data using large-scale Nonlinear Granger Causality (lsNGC)},
author = {Axel Wismüller and M. Ali Vosoughi and Adora DSouza and Anas Abidin},
year = {2022},
journal = {Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging},
doi = {10.1117/12.2613389},
url = {https://doi.org/10.1117/12.2613389}
}RIS
TY - JOUR TI - Inferring functional relations from synthetic fMRI data using large-scale Nonlinear Granger Causality (lsNGC) AU - Axel Wismüller AU - M. Ali Vosoughi AU - Adora DSouza AU - Anas Abidin PY - 2022 JO - Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging DO - 10.1117/12.2613389 UR - https://doi.org/10.1117/12.2613389 ER -
APA
Wismüller, A., Vosoughi, M. A., DSouza, A., & Abidin, A. (2022). Inferring functional relations from synthetic fMRI data using large-scale Nonlinear Granger Causality (lsNGC). Medical Imaging 2022: Biomedical Applications in Molecular, Structural, and Functional Imaging. https://doi.org/10.1117/12.2613389
Source records
- crossref · retrieved 2026-11-10T07:28:56.410Z