Construction of functional brain connectivity networks from fMRI data with driving and modulatory inputs: an extended conditional Granger causality approach
We propose a numerical-based approach extending the conditional MVAR Granger causality (MVGC) analysis for the construction of directed connectivity networks in the presence of both exogenous/stimuli and modulatory inputs. The performance of the proposed scheme is validated using both synthetic stoc...
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| Format: | Article |
| Language: | English |
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AIMS Press
2020-06-01
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| Series: | AIMS Neuroscience |
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| Online Access: | https://www.aimspress.com/article/10.3934/Neuroscience.2020005/fulltext.html |
| _version_ | 1828861779001737216 |
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| author | Evangelos Almpanis Constantinos Siettos |
| author_facet | Evangelos Almpanis Constantinos Siettos |
| author_sort | Evangelos Almpanis |
| collection | DOAJ |
| description | We propose a numerical-based approach extending the conditional MVAR Granger causality (MVGC) analysis for the construction of directed connectivity networks in the presence of both exogenous/stimuli and modulatory inputs. The performance of the proposed scheme is validated using both synthetic stochastic data considering also the influence of haemodynamics latencies and a benchmark fMRI dataset related to the role of attention in the perception of visual motion. Theparticular fMRI dataset has been used in many studies to evaluate alternative model hypotheses using the Dynamic Causal Modelling (DCM) approach. Based on the use of the Bayes factor, we show that the obtained GC connectivity network compares well to a reference model that has been selected through DCM analysis among other candidate models. Thus, our findings suggest that the proposed scheme can be successfully used as a stand-alone or complementary to DCM approach to find directed causal connectivity patterns in task-related fMRI studies. |
| first_indexed | 2024-12-13T03:10:24Z |
| format | Article |
| id | doaj.art-2530302dae1d48c5a713076d9cc67fe1 |
| institution | Directory Open Access Journal |
| issn | 2373-7972 |
| language | English |
| last_indexed | 2024-12-13T03:10:24Z |
| publishDate | 2020-06-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | AIMS Neuroscience |
| spelling | doaj.art-2530302dae1d48c5a713076d9cc67fe12022-12-22T00:01:37ZengAIMS PressAIMS Neuroscience2373-79722020-06-0172668810.3934/Neuroscience.2020005Construction of functional brain connectivity networks from fMRI data with driving and modulatory inputs: an extended conditional Granger causality approachEvangelos Almpanis0Constantinos Siettos11 Section of Condensed Matter Physics, National and Kapodistrian University of Athens, Greece 2 Institute of Nanoscience and Nanotechnology, NCSR “Demokritos,” Athens, Greece3 Dipartimento di Matematica e Applicazioni “Renato Caccioppoli”, Università degli Studi di Napoli Federico II, ItalyWe propose a numerical-based approach extending the conditional MVAR Granger causality (MVGC) analysis for the construction of directed connectivity networks in the presence of both exogenous/stimuli and modulatory inputs. The performance of the proposed scheme is validated using both synthetic stochastic data considering also the influence of haemodynamics latencies and a benchmark fMRI dataset related to the role of attention in the perception of visual motion. Theparticular fMRI dataset has been used in many studies to evaluate alternative model hypotheses using the Dynamic Causal Modelling (DCM) approach. Based on the use of the Bayes factor, we show that the obtained GC connectivity network compares well to a reference model that has been selected through DCM analysis among other candidate models. Thus, our findings suggest that the proposed scheme can be successfully used as a stand-alone or complementary to DCM approach to find directed causal connectivity patterns in task-related fMRI studies.https://www.aimspress.com/article/10.3934/Neuroscience.2020005/fulltext.htmlfunctional connectivity networksdata-based analysisgranger causalitytask fmristimuli and modulatory inputsdynamical causal modelling |
| spellingShingle | Evangelos Almpanis Constantinos Siettos Construction of functional brain connectivity networks from fMRI data with driving and modulatory inputs: an extended conditional Granger causality approach AIMS Neuroscience functional connectivity networks data-based analysis granger causality task fmri stimuli and modulatory inputs dynamical causal modelling |
| title | Construction of functional brain connectivity networks from fMRI data with driving and modulatory inputs: an extended conditional Granger causality approach |
| title_full | Construction of functional brain connectivity networks from fMRI data with driving and modulatory inputs: an extended conditional Granger causality approach |
| title_fullStr | Construction of functional brain connectivity networks from fMRI data with driving and modulatory inputs: an extended conditional Granger causality approach |
| title_full_unstemmed | Construction of functional brain connectivity networks from fMRI data with driving and modulatory inputs: an extended conditional Granger causality approach |
| title_short | Construction of functional brain connectivity networks from fMRI data with driving and modulatory inputs: an extended conditional Granger causality approach |
| title_sort | construction of functional brain connectivity networks from fmri data with driving and modulatory inputs an extended conditional granger causality approach |
| topic | functional connectivity networks data-based analysis granger causality task fmri stimuli and modulatory inputs dynamical causal modelling |
| url | https://www.aimspress.com/article/10.3934/Neuroscience.2020005/fulltext.html |
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