Sparse Estimation of Resting-State Effective Connectivity From fMRI Cross-Spectra
In functional magnetic resonance imaging (fMRI), functional connectivity is conventionally characterized by correlations between fMRI time series, which are intrinsically undirected measures of connectivity. Yet, some information about the directionality of network connections can nevertheless be ex...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2018-05-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | http://journal.frontiersin.org/article/10.3389/fnins.2018.00287/full |
| _version_ | 1818483623090716672 |
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| author | Carolin Lennartz Carolin Lennartz Jonathan Schiefer Jonathan Schiefer Stefan Rotter Stefan Rotter Jürgen Hennig Jürgen Hennig Pierre LeVan Pierre LeVan |
| author_facet | Carolin Lennartz Carolin Lennartz Jonathan Schiefer Jonathan Schiefer Stefan Rotter Stefan Rotter Jürgen Hennig Jürgen Hennig Pierre LeVan Pierre LeVan |
| author_sort | Carolin Lennartz |
| collection | DOAJ |
| description | In functional magnetic resonance imaging (fMRI), functional connectivity is conventionally characterized by correlations between fMRI time series, which are intrinsically undirected measures of connectivity. Yet, some information about the directionality of network connections can nevertheless be extracted from the matrix of pairwise temporal correlations between all considered time series, when expressed in the frequency-domain as a cross-spectral density matrix. Using a sparsity prior, it then becomes possible to determine a unique directed network topology that best explains the observed undirected correlations, without having to rely on temporal precedence relationships that may not be valid in fMRI. Applying this method on simulated data with 100 nodes yielded excellent retrieval of the underlying directed networks under a wide variety of conditions. Importantly, the method did not depend on temporal precedence to establish directionality, thus reducing susceptibility to hemodynamic variability. The computational efficiency of the algorithm was sufficient to enable whole-brain estimations, thus circumventing the problem of missing nodes that otherwise occurs in partial-brain analyses. Applying the method to real resting-state fMRI data acquired with a high temporal resolution, the inferred networks showed good consistency with structural connectivity obtained from diffusion tractography in the same subjects. Interestingly, this agreement could also be seen when considering high-frequency rather than low-frequency connectivity (average correlation: r = 0.26 for f < 0.3 Hz, r = 0.43 for 0.3 < f < 5 Hz). Moreover, this concordance was significantly better (p < 0.05) than for networks obtained with conventional functional connectivity based on correlations (average correlation r = 0.18). The presented methodology thus appears to be well-suited for fMRI, particularly given its lack of explicit dependence on temporal lag structure, and is readily applicable to whole-brain effective connectivity estimation. |
| first_indexed | 2024-12-10T15:44:20Z |
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| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-10T15:44:20Z |
| publishDate | 2018-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-b95d7a22cb954fa997082f0c1192630c2022-12-22T01:43:00ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2018-05-011210.3389/fnins.2018.00287307065Sparse Estimation of Resting-State Effective Connectivity From fMRI Cross-SpectraCarolin Lennartz0Carolin Lennartz1Jonathan Schiefer2Jonathan Schiefer3Stefan Rotter4Stefan Rotter5Jürgen Hennig6Jürgen Hennig7Pierre LeVan8Pierre LeVan9Department of Radiology, Medical Physics, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, GermanyBrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Freiburg, GermanyBrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Freiburg, GermanyBernstein Center Freiburg & Faculty of Biology, University of Freiburg, Freiburg, GermanyBrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Freiburg, GermanyBernstein Center Freiburg & Faculty of Biology, University of Freiburg, Freiburg, GermanyDepartment of Radiology, Medical Physics, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, GermanyBrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Freiburg, GermanyDepartment of Radiology, Medical Physics, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, GermanyBrainLinks-BrainTools Cluster of Excellence, University of Freiburg, Freiburg, GermanyIn functional magnetic resonance imaging (fMRI), functional connectivity is conventionally characterized by correlations between fMRI time series, which are intrinsically undirected measures of connectivity. Yet, some information about the directionality of network connections can nevertheless be extracted from the matrix of pairwise temporal correlations between all considered time series, when expressed in the frequency-domain as a cross-spectral density matrix. Using a sparsity prior, it then becomes possible to determine a unique directed network topology that best explains the observed undirected correlations, without having to rely on temporal precedence relationships that may not be valid in fMRI. Applying this method on simulated data with 100 nodes yielded excellent retrieval of the underlying directed networks under a wide variety of conditions. Importantly, the method did not depend on temporal precedence to establish directionality, thus reducing susceptibility to hemodynamic variability. The computational efficiency of the algorithm was sufficient to enable whole-brain estimations, thus circumventing the problem of missing nodes that otherwise occurs in partial-brain analyses. Applying the method to real resting-state fMRI data acquired with a high temporal resolution, the inferred networks showed good consistency with structural connectivity obtained from diffusion tractography in the same subjects. Interestingly, this agreement could also be seen when considering high-frequency rather than low-frequency connectivity (average correlation: r = 0.26 for f < 0.3 Hz, r = 0.43 for 0.3 < f < 5 Hz). Moreover, this concordance was significantly better (p < 0.05) than for networks obtained with conventional functional connectivity based on correlations (average correlation r = 0.18). The presented methodology thus appears to be well-suited for fMRI, particularly given its lack of explicit dependence on temporal lag structure, and is readily applicable to whole-brain effective connectivity estimation.http://journal.frontiersin.org/article/10.3389/fnins.2018.00287/fulleffective connectivityfunctional connectivitystructural connectivityfMRIresting statecorrelation |
| spellingShingle | Carolin Lennartz Carolin Lennartz Jonathan Schiefer Jonathan Schiefer Stefan Rotter Stefan Rotter Jürgen Hennig Jürgen Hennig Pierre LeVan Pierre LeVan Sparse Estimation of Resting-State Effective Connectivity From fMRI Cross-Spectra Frontiers in Neuroscience effective connectivity functional connectivity structural connectivity fMRI resting state correlation |
| title | Sparse Estimation of Resting-State Effective Connectivity From fMRI Cross-Spectra |
| title_full | Sparse Estimation of Resting-State Effective Connectivity From fMRI Cross-Spectra |
| title_fullStr | Sparse Estimation of Resting-State Effective Connectivity From fMRI Cross-Spectra |
| title_full_unstemmed | Sparse Estimation of Resting-State Effective Connectivity From fMRI Cross-Spectra |
| title_short | Sparse Estimation of Resting-State Effective Connectivity From fMRI Cross-Spectra |
| title_sort | sparse estimation of resting state effective connectivity from fmri cross spectra |
| topic | effective connectivity functional connectivity structural connectivity fMRI resting state correlation |
| url | http://journal.frontiersin.org/article/10.3389/fnins.2018.00287/full |
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