Identifying effective connectivity parameters in simulated fMRI: a direct comparison of switching linear dynamic system, stochastic dynamic causal, and multivariate autoregressive models
The number and variety of connectivity estimation methods is likely to continue to grow over the coming decade. Comparisons between methods are necessary to prune this growth to only the most accurate and robust methods. However, the nature of connectivity is elusive with different methods potential...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2013-05-01
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| Series: | Frontiers in Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fnins.2013.00070/full |
| _version_ | 1818930500618682368 |
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| author | Jason Fitzgerald Smith Kewei eChen Kewei eChen Kewei eChen Ajay S. Pillai Barry eHorwitz |
| author_facet | Jason Fitzgerald Smith Kewei eChen Kewei eChen Kewei eChen Ajay S. Pillai Barry eHorwitz |
| author_sort | Jason Fitzgerald Smith |
| collection | DOAJ |
| description | The number and variety of connectivity estimation methods is likely to continue to grow over the coming decade. Comparisons between methods are necessary to prune this growth to only the most accurate and robust methods. However, the nature of connectivity is elusive with different methods potentially attempting to identify different aspects of connectivity. Commonalities of connectivity definitions across methods upon which base direct comparisons can be difficult to derive. Here we explicitly define effective connectivity using a common set of observation and state equations that are appropriate for three connectivity methods: Dynamic Causal Modeling (DCM), Multivariate Autoregressive Modeling (MAR), and Switching Linear Dynamic Systems for fMRI (sLDSf). In addition while deriving this set, we show how many other popular functional and effective connectivity methods are actually simplifications of these equations. We discuss implications of these connections for the practice of using one method to simulate data for another method. After mathematically connecting the three effective connectivity methods, simulated fMRI data with varying numbers of regions and task conditions is generated from the common equation. This simulated data explicitly contains the type of the connectivity that the three models were intended to identify. Each method is applied to the simulated data sets and the accuracy of parameter identification is analyzed. All methods perform above chance levels at identifying correct connectivity parameters. The sLDSf method was superior in parameter estimation accuracy to both DCM and MAR for all types of comparisons. |
| first_indexed | 2024-12-20T04:01:41Z |
| format | Article |
| id | doaj.art-f2ead375244d497684b132352b9d0694 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-20T04:01:41Z |
| publishDate | 2013-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-f2ead375244d497684b132352b9d06942022-12-21T19:54:09ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2013-05-01710.3389/fnins.2013.0007047087Identifying effective connectivity parameters in simulated fMRI: a direct comparison of switching linear dynamic system, stochastic dynamic causal, and multivariate autoregressive modelsJason Fitzgerald Smith0Kewei eChen1Kewei eChen2Kewei eChen3Ajay S. Pillai4Barry eHorwitz5National Institutes of HealthBanner Good Samaritan Medial CenterArizona State UniversityArizona Alzheimer's Disease ConsortiumNational Institutes of HealthNational Institutes of HealthThe number and variety of connectivity estimation methods is likely to continue to grow over the coming decade. Comparisons between methods are necessary to prune this growth to only the most accurate and robust methods. However, the nature of connectivity is elusive with different methods potentially attempting to identify different aspects of connectivity. Commonalities of connectivity definitions across methods upon which base direct comparisons can be difficult to derive. Here we explicitly define effective connectivity using a common set of observation and state equations that are appropriate for three connectivity methods: Dynamic Causal Modeling (DCM), Multivariate Autoregressive Modeling (MAR), and Switching Linear Dynamic Systems for fMRI (sLDSf). In addition while deriving this set, we show how many other popular functional and effective connectivity methods are actually simplifications of these equations. We discuss implications of these connections for the practice of using one method to simulate data for another method. After mathematically connecting the three effective connectivity methods, simulated fMRI data with varying numbers of regions and task conditions is generated from the common equation. This simulated data explicitly contains the type of the connectivity that the three models were intended to identify. Each method is applied to the simulated data sets and the accuracy of parameter identification is analyzed. All methods perform above chance levels at identifying correct connectivity parameters. The sLDSf method was superior in parameter estimation accuracy to both DCM and MAR for all types of comparisons.http://journal.frontiersin.org/Journal/10.3389/fnins.2013.00070/fulleffective connectivityModeling and simulationsparameter estimationfMRI BOLDlinear dynamical models |
| spellingShingle | Jason Fitzgerald Smith Kewei eChen Kewei eChen Kewei eChen Ajay S. Pillai Barry eHorwitz Identifying effective connectivity parameters in simulated fMRI: a direct comparison of switching linear dynamic system, stochastic dynamic causal, and multivariate autoregressive models Frontiers in Neuroscience effective connectivity Modeling and simulations parameter estimation fMRI BOLD linear dynamical models |
| title | Identifying effective connectivity parameters in simulated fMRI: a direct comparison of switching linear dynamic system, stochastic dynamic causal, and multivariate autoregressive models |
| title_full | Identifying effective connectivity parameters in simulated fMRI: a direct comparison of switching linear dynamic system, stochastic dynamic causal, and multivariate autoregressive models |
| title_fullStr | Identifying effective connectivity parameters in simulated fMRI: a direct comparison of switching linear dynamic system, stochastic dynamic causal, and multivariate autoregressive models |
| title_full_unstemmed | Identifying effective connectivity parameters in simulated fMRI: a direct comparison of switching linear dynamic system, stochastic dynamic causal, and multivariate autoregressive models |
| title_short | Identifying effective connectivity parameters in simulated fMRI: a direct comparison of switching linear dynamic system, stochastic dynamic causal, and multivariate autoregressive models |
| title_sort | identifying effective connectivity parameters in simulated fmri a direct comparison of switching linear dynamic system stochastic dynamic causal and multivariate autoregressive models |
| topic | effective connectivity Modeling and simulations parameter estimation fMRI BOLD linear dynamical models |
| url | http://journal.frontiersin.org/Journal/10.3389/fnins.2013.00070/full |
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