Construction of functional brain connectivity networks from fMRI data with driving and modulatory inputs: an extended conditional Granger causality approach

Evangelos Almpanis; Constantinos Siettos; Evangelos Almpanis; Constantinos Siettos

doi:10.3934/Neuroscience.2020005

AIMS Neuroscience

2020, Volume 7, Issue 2: 66-88. doi: 10.3934/Neuroscience.2020005

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Research article Topical Sections

Construction of functional brain connectivity networks from fMRI data with driving and modulatory inputs: an extended conditional Granger causality approach

Evangelos Almpanis ^1,2,
Constantinos Siettos ^{3
,
,}

1.
Section of Condensed Matter Physics, National and Kapodistrian University of Athens, Greece
2.
Institute of Nanoscience and Nanotechnology, NCSR “Demokritos,” Athens, Greece
3.
Dipartimento di Matematica e Applicazioni “Renato Caccioppoli”, Università degli Studi di Napoli Federico II, Italy

Received: 06 January 2020 Accepted: 25 March 2020 Published: 10 April 2020

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. The particular 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.
- functional connectivity networks,
- data-based analysis,
- Granger Causality,
- task fMRI,
- stimuli and modulatory inputs,
- dynamical causal modelling
Citation: Evangelos Almpanis, Constantinos Siettos. Construction of functional brain connectivity networks from fMRI data with driving and modulatory inputs: an extended conditional Granger causality approach[J]. AIMS Neuroscience, 2020, 7(2): 66-88. doi: 10.3934/Neuroscience.2020005

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Abstract

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. The particular 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.

Acknowledgments

E.A. acknowledges fruitful discussions with Dr. Panagiotis Athanasopoulos.

Conflict of interest

All authors declare no conflicts of interest in this paper.

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