Initial Validation for the Estimation of Resting-State fMRI Effective Connectivity by a Generalization of the Correlation Approach
Resting-state functional MRI (rs-fMRI) is widely used to noninvasively study human brain networks. Network functional connectivity is often estimated by calculating the timeseries correlation between blood-oxygen-level dependent (BOLD) signal from different regions of interest (ROIs). However, stand...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2017-05-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | http://journal.frontiersin.org/article/10.3389/fnins.2017.00271/full |
| _version_ | 1819330645998960640 |
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| author | Nan Xu R. Nathan Spreng Peter C. Doerschuk Peter C. Doerschuk |
| author_facet | Nan Xu R. Nathan Spreng Peter C. Doerschuk Peter C. Doerschuk |
| author_sort | Nan Xu |
| collection | DOAJ |
| description | Resting-state functional MRI (rs-fMRI) is widely used to noninvasively study human brain networks. Network functional connectivity is often estimated by calculating the timeseries correlation between blood-oxygen-level dependent (BOLD) signal from different regions of interest (ROIs). However, standard correlation cannot characterize the direction of information flow between regions. In this paper, we introduce and test a new concept, prediction correlation, to estimate effective connectivity in functional brain networks from rs-fMRI. In this approach, the correlation between two BOLD signals is replaced by a correlation between one BOLD signal and a prediction of this signal via a causal system driven by another BOLD signal. Three validations are described: (1) Prediction correlation performed well on simulated data where the ground truth was known, and outperformed four other methods. (2) On simulated data designed to display the “common driver” problem, prediction correlation did not introduce false connections between non-interacting driven ROIs. (3) On experimental data, prediction correlation recovered the previously identified network organization of human brain. Prediction correlation scales well to work with hundreds of ROIs, enabling it to assess whole brain interregional connectivity at the single subject level. These results provide an initial validation that prediction correlation can capture the direction of information flow and estimate the duration of extended temporal delays in information flow between regions of interest ROIs based on BOLD signal. This approach not only maintains the high sensitivity to network connectivity provided by the correlation analysis, but also performs well in the estimation of causal information flow in the brain. |
| first_indexed | 2024-12-24T14:01:45Z |
| format | Article |
| id | doaj.art-2e2d185c21824df0a52e53666a6a8d16 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-24T14:01:45Z |
| publishDate | 2017-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-2e2d185c21824df0a52e53666a6a8d162022-12-21T16:52:27ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2017-05-011110.3389/fnins.2017.00271219881Initial Validation for the Estimation of Resting-State fMRI Effective Connectivity by a Generalization of the Correlation ApproachNan Xu0R. Nathan Spreng1Peter C. Doerschuk2Peter C. Doerschuk3School of Electrical and Computer Engineering, Cornell UniversityIthaca, NY, United StatesLaboratory of Brain and Cognition, Human Neuroscience Institute, Department of Human Development, Cornell UniversityIthaca, NY, United StatesSchool of Electrical and Computer Engineering, Cornell UniversityIthaca, NY, United StatesNancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell UniversityIthaca, NY, United StatesResting-state functional MRI (rs-fMRI) is widely used to noninvasively study human brain networks. Network functional connectivity is often estimated by calculating the timeseries correlation between blood-oxygen-level dependent (BOLD) signal from different regions of interest (ROIs). However, standard correlation cannot characterize the direction of information flow between regions. In this paper, we introduce and test a new concept, prediction correlation, to estimate effective connectivity in functional brain networks from rs-fMRI. In this approach, the correlation between two BOLD signals is replaced by a correlation between one BOLD signal and a prediction of this signal via a causal system driven by another BOLD signal. Three validations are described: (1) Prediction correlation performed well on simulated data where the ground truth was known, and outperformed four other methods. (2) On simulated data designed to display the “common driver” problem, prediction correlation did not introduce false connections between non-interacting driven ROIs. (3) On experimental data, prediction correlation recovered the previously identified network organization of human brain. Prediction correlation scales well to work with hundreds of ROIs, enabling it to assess whole brain interregional connectivity at the single subject level. These results provide an initial validation that prediction correlation can capture the direction of information flow and estimate the duration of extended temporal delays in information flow between regions of interest ROIs based on BOLD signal. This approach not only maintains the high sensitivity to network connectivity provided by the correlation analysis, but also performs well in the estimation of causal information flow in the brain.http://journal.frontiersin.org/article/10.3389/fnins.2017.00271/fullresting-state fMRIeffective connectivityfunctional connectivityfunctional networkscorrelation analysis |
| spellingShingle | Nan Xu R. Nathan Spreng Peter C. Doerschuk Peter C. Doerschuk Initial Validation for the Estimation of Resting-State fMRI Effective Connectivity by a Generalization of the Correlation Approach Frontiers in Neuroscience resting-state fMRI effective connectivity functional connectivity functional networks correlation analysis |
| title | Initial Validation for the Estimation of Resting-State fMRI Effective Connectivity by a Generalization of the Correlation Approach |
| title_full | Initial Validation for the Estimation of Resting-State fMRI Effective Connectivity by a Generalization of the Correlation Approach |
| title_fullStr | Initial Validation for the Estimation of Resting-State fMRI Effective Connectivity by a Generalization of the Correlation Approach |
| title_full_unstemmed | Initial Validation for the Estimation of Resting-State fMRI Effective Connectivity by a Generalization of the Correlation Approach |
| title_short | Initial Validation for the Estimation of Resting-State fMRI Effective Connectivity by a Generalization of the Correlation Approach |
| title_sort | initial validation for the estimation of resting state fmri effective connectivity by a generalization of the correlation approach |
| topic | resting-state fMRI effective connectivity functional connectivity functional networks correlation analysis |
| url | http://journal.frontiersin.org/article/10.3389/fnins.2017.00271/full |
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