Effective Connectivity Modeling for fMRI: Six Issues and Possible Solutions Using Linear Dynamic Systems
Analysis of directionally specific or causal interactions between regions in functional magnetic resonance imaging (fMRI) data has proliferated. Here we identify six issues with existing effective connectivity methods that need addressed. The issues are discussed within the framework of Linear Dynam...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2012-01-01
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| Series: | Frontiers in Systems Neuroscience |
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| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fnsys.2011.00104/full |
| _version_ | 1818117248441647104 |
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| author | Jason Fitzgerald Smith Ajay S Pillai Kewei eChen Kewei eChen Kewei eChen Barry eHorwitz |
| author_facet | Jason Fitzgerald Smith Ajay S Pillai Kewei eChen Kewei eChen Kewei eChen Barry eHorwitz |
| author_sort | Jason Fitzgerald Smith |
| collection | DOAJ |
| description | Analysis of directionally specific or causal interactions between regions in functional magnetic resonance imaging (fMRI) data has proliferated. Here we identify six issues with existing effective connectivity methods that need addressed. The issues are discussed within the framework of Linear Dynamic Systems for fMRI (LDSf). The first concerns the use of deterministic models to identify inter-regional effective connectivity. We show that deterministic dynamics are incapable of identifying the trial-to-trial variability typically investigated as the marker of connectivity while stochastic models can capture this variability. The second concerns the simplistic (constant) connectivity modeled by most methods. Connectivity parameters of the LDSf model can vary at the same timescale as the input data. Further, extending LDSf to mixtures of multiple models provides more robust connectivity variation. The third concerns the correct identification of the network itself including the number and anatomical origin of the network nodes. Augmentation of the LDSf state space can identify additional nodes of a network. The fourth concerns the locus of the signal used as a node in a network. A novel extension LDSf incorporating sparse canonical correlations can select most relevant voxels from an anatomically defined region based on connectivity. The fifth concerns connection interpretation. Individual parameter differences have received most attention. We present alternative network descriptors of connectivity changes which consider the whole network. The sixth concerns the temporal resolution of fMRI data relative to the timescale of the inter-regional interactions in the brain. LDSf includes an instantaneous connection term to capture connectivity occurring at timescales faster than the data resolution. The LDS framework can also be extended to statistically combine fMRI and EEG data. The LDSf framework is a promising foundation for effective connectivity analysis. |
| first_indexed | 2024-12-11T04:35:24Z |
| format | Article |
| id | doaj.art-6e91347d5cfe47b5a1e237bface7f893 |
| institution | Directory Open Access Journal |
| issn | 1662-5137 |
| language | English |
| last_indexed | 2024-12-11T04:35:24Z |
| publishDate | 2012-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Systems Neuroscience |
| spelling | doaj.art-6e91347d5cfe47b5a1e237bface7f8932022-12-22T01:20:45ZengFrontiers Media S.A.Frontiers in Systems Neuroscience1662-51372012-01-01510.3389/fnsys.2011.0010413036Effective Connectivity Modeling for fMRI: Six Issues and Possible Solutions Using Linear Dynamic SystemsJason Fitzgerald Smith0Ajay S Pillai1Kewei eChen2Kewei eChen3Kewei eChen4Barry eHorwitz5National Institutes of HealthNational Institutes of HealthArizona State UniversityBanner Good Samaritan Medical CenterArizona Alzheimer's ConsortiumNational Institutes of HealthAnalysis of directionally specific or causal interactions between regions in functional magnetic resonance imaging (fMRI) data has proliferated. Here we identify six issues with existing effective connectivity methods that need addressed. The issues are discussed within the framework of Linear Dynamic Systems for fMRI (LDSf). The first concerns the use of deterministic models to identify inter-regional effective connectivity. We show that deterministic dynamics are incapable of identifying the trial-to-trial variability typically investigated as the marker of connectivity while stochastic models can capture this variability. The second concerns the simplistic (constant) connectivity modeled by most methods. Connectivity parameters of the LDSf model can vary at the same timescale as the input data. Further, extending LDSf to mixtures of multiple models provides more robust connectivity variation. The third concerns the correct identification of the network itself including the number and anatomical origin of the network nodes. Augmentation of the LDSf state space can identify additional nodes of a network. The fourth concerns the locus of the signal used as a node in a network. A novel extension LDSf incorporating sparse canonical correlations can select most relevant voxels from an anatomically defined region based on connectivity. The fifth concerns connection interpretation. Individual parameter differences have received most attention. We present alternative network descriptors of connectivity changes which consider the whole network. The sixth concerns the temporal resolution of fMRI data relative to the timescale of the inter-regional interactions in the brain. LDSf includes an instantaneous connection term to capture connectivity occurring at timescales faster than the data resolution. The LDS framework can also be extended to statistically combine fMRI and EEG data. The LDSf framework is a promising foundation for effective connectivity analysis.http://journal.frontiersin.org/Journal/10.3389/fnsys.2011.00104/fulldynamic systemsfMRIeffective connectivity |
| spellingShingle | Jason Fitzgerald Smith Ajay S Pillai Kewei eChen Kewei eChen Kewei eChen Barry eHorwitz Effective Connectivity Modeling for fMRI: Six Issues and Possible Solutions Using Linear Dynamic Systems Frontiers in Systems Neuroscience dynamic systems fMRI effective connectivity |
| title | Effective Connectivity Modeling for fMRI: Six Issues and Possible Solutions Using Linear Dynamic Systems |
| title_full | Effective Connectivity Modeling for fMRI: Six Issues and Possible Solutions Using Linear Dynamic Systems |
| title_fullStr | Effective Connectivity Modeling for fMRI: Six Issues and Possible Solutions Using Linear Dynamic Systems |
| title_full_unstemmed | Effective Connectivity Modeling for fMRI: Six Issues and Possible Solutions Using Linear Dynamic Systems |
| title_short | Effective Connectivity Modeling for fMRI: Six Issues and Possible Solutions Using Linear Dynamic Systems |
| title_sort | effective connectivity modeling for fmri six issues and possible solutions using linear dynamic systems |
| topic | dynamic systems fMRI effective connectivity |
| url | http://journal.frontiersin.org/Journal/10.3389/fnsys.2011.00104/full |
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