See the published version of this preprint at Brain Informatics.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
Research Article
Fei Li
Zhejiang University of Technology
Corresponding Author
ORCiD: https://orcid.org/0000-0001-7556-6226
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Unified Granger causality analysis (uGCA) alters conventional two-stage Granger causality analysis into a unified code-length guided framework. We have presented several forms of uGCA methods to investigate causal connectivities, and different forms of uGCA have their own characteristics, which capable of approaching the ground truth networks well in their suitable contexts. In this paper, we considered comparing these several forms of uGCA in detail, then recommend a relatively more robust uGCA method among them, uGCA-NML, to reply to more general scenarios. Then, we clarified the distinguished advantages of uGCA-NML in a synthetic 6-node network. Moreover, uGCA-NML presented its good robustness in mental arithmetic experiments, which identified a stable similarity among causal networks under visual/auditory stimulus. Whereas, due to its commendable stability and accuracy, uGCA-NML will be a prior choice in this unified causal investigation paradigm.
Keywords
Unified Granger causality analysis, Nor- malized maximum likelihood, Inherent redundancy, Granger causality analysis, fMRI
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Loading...
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at Brain Informatics.
Version 1
Posted 17 May, 2021
No community comments so far
Review #2 received
Received 17 Jun, 2021
Editorial decision: Minor revision
On 17 Jun, 2021
Review #1 received
Received 24 May, 2021
Reviews received
Received 12 May, 2021
Reviewers invited
Invitations sent on 12 May, 2021
Editor assigned
On 12 May, 2021
Reviewer #2 agreed
On 11 May, 2021
Reviewer #1 agreed
On 11 May, 2021
Submission checks complete
On 11 May, 2021
Editor invited
On 11 May, 2021
First submitted
On 10 May, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Head & Neck Surgery
Learn more about our company.
A new preprint design is coming!
We have improved readability, clarity, accessibility, and opportunities for engagement.
See the published version of this preprint at Brain Informatics.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Fei Li
Zhejiang University of Technology
Corresponding Author
ORCiD: https://orcid.org/0000-0001-7556-6226
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at Brain Informatics.
Version 1
Posted 17 May, 2021
No community comments so far
Review #2 received
Received 17 Jun, 2021
Editorial decision: Minor revision
On 17 Jun, 2021
Review #1 received
Received 24 May, 2021
Reviews received
Received 12 May, 2021
Reviewers invited
Invitations sent on 12 May, 2021
Editor assigned
On 12 May, 2021
Reviewer #2 agreed
On 11 May, 2021
Reviewer #1 agreed
On 11 May, 2021
Submission checks complete
On 11 May, 2021
Editor invited
On 11 May, 2021
First submitted
On 10 May, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Head & Neck Surgery
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at Brain Informatics.
Unified Granger causality analysis (uGCA) alters conventional two-stage Granger causality analysis into a unified code-length guided framework. We have presented several forms of uGCA methods to investigate causal connectivities, and different forms of uGCA have their own characteristics, which capable of approaching the ground truth networks well in their suitable contexts. In this paper, we considered comparing these several forms of uGCA in detail, then recommend a relatively more robust uGCA method among them, uGCA-NML, to reply to more general scenarios. Then, we clarified the distinguished advantages of uGCA-NML in a synthetic 6-node network. Moreover, uGCA-NML presented its good robustness in mental arithmetic experiments, which identified a stable similarity among causal networks under visual/auditory stimulus. Whereas, due to its commendable stability and accuracy, uGCA-NML will be a prior choice in this unified causal investigation paradigm.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Loading...