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Dynamic causal modelling of fluctuating connectivity in resting-state EEG

(2019) NEUROIMAGE. 189. p.476-484
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Abstract
Functional and effective connectivity are known to change systematically over time. These changes might be explained by several factors, including intrinsic fluctuations in activity-dependent neuronal coupling and contextual factors, like experimental condition and time. Furthermore, contextual effects may be subject-specific or conserved over subjects. To characterize fluctuations in effective connectivity, we used dynamic causal modelling (DCM) of cross spectral responses over 1- min of electroencephalogram (EEG) recordings during rest, divided into 1-sec windows. We focused on two intrinsic networks: the default mode and the saliency network. DCM was applied to estimate connectivity in each time-window for both networks. Fluctuations in DCM connectivity parameters were assessed using hierarchical parametric empirical Bayes (PEB). Within-subject, between-window effects were modelled with a second-level linear model with temporal basis functions as regressors. This procedure was conducted for every subject separately. Bayesian model reduction was then used to assess which (combination of) temporal basis functions best explain dynamic connectivity over windows. A third (betweensubject) level model was used to infer which dynamic connectivity parameters are conserved over subjects. Our results indicate that connectivity fluctuations in the default mode network and to a lesser extent the saliency network comprised both subject-specific components and a common component. For both networks, connections to higher order regions appear to monotonically increase during the 1- min period. These results not only establish the predictive validity of dynamic connectivity estimates - in virtue of detecting systematic changes over subjects - they also suggest a network-specific dissociation in the relative contribution of fluctuations in connectivity that depend upon experimental context. We envisage these procedures could be useful for characterizing brain state transitions that may be explained by their cognitive or neuropathological underpinnings.
Keywords
Dynamic causal modelling, Resting state, EEG, Fluctuating connectivity

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MLA
Van de Steen, Frederik, et al. “Dynamic Causal Modelling of Fluctuating Connectivity in Resting-State EEG.” NEUROIMAGE, vol. 189, 2019, pp. 476–84.
APA
Van de Steen, F., Almgren, H., Razi, A., Friston, K., & Marinazzo, D. (2019). Dynamic causal modelling of fluctuating connectivity in resting-state EEG. NEUROIMAGE, 189, 476–484.
Chicago author-date
Van de Steen, Frederik, Hannes Almgren, Adeel Razi, Karl Friston, and Daniele Marinazzo. 2019. “Dynamic Causal Modelling of Fluctuating Connectivity in Resting-State EEG.” NEUROIMAGE 189: 476–84.
Chicago author-date (all authors)
Van de Steen, Frederik, Hannes Almgren, Adeel Razi, Karl Friston, and Daniele Marinazzo. 2019. “Dynamic Causal Modelling of Fluctuating Connectivity in Resting-State EEG.” NEUROIMAGE 189: 476–484.
Vancouver
1.
Van de Steen F, Almgren H, Razi A, Friston K, Marinazzo D. Dynamic causal modelling of fluctuating connectivity in resting-state EEG. NEUROIMAGE. 2019;189:476–84.
IEEE
[1]
F. Van de Steen, H. Almgren, A. Razi, K. Friston, and D. Marinazzo, “Dynamic causal modelling of fluctuating connectivity in resting-state EEG,” NEUROIMAGE, vol. 189, pp. 476–484, 2019.
@article{8598674,
  abstract     = {Functional and effective connectivity are known to change systematically over time. These changes might be explained by several factors, including intrinsic fluctuations in activity-dependent neuronal coupling and contextual factors, like experimental condition and time. Furthermore, contextual effects may be subject-specific or conserved over subjects. To characterize fluctuations in effective connectivity, we used dynamic causal modelling (DCM) of cross spectral responses over 1- min of electroencephalogram (EEG) recordings during rest, divided into 1-sec windows. We focused on two intrinsic networks: the default mode and the saliency network. DCM was applied to estimate connectivity in each time-window for both networks. Fluctuations in DCM connectivity parameters were assessed using hierarchical parametric empirical Bayes (PEB). Within-subject, between-window effects were modelled with a second-level linear model with temporal basis functions as regressors. This procedure was conducted for every subject separately. Bayesian model reduction was then used to assess which (combination of) temporal basis functions best explain dynamic connectivity over windows. A third (betweensubject) level model was used to infer which dynamic connectivity parameters are conserved over subjects. Our results indicate that connectivity fluctuations in the default mode network and to a lesser extent the saliency network comprised both subject-specific components and a common component. For both networks, connections to higher order regions appear to monotonically increase during the 1- min period. These results not only establish the predictive validity of dynamic connectivity estimates - in virtue of detecting systematic changes over subjects - they also suggest a network-specific dissociation in the relative contribution of fluctuations in connectivity that depend upon experimental context. We envisage these procedures could be useful for characterizing brain state transitions that may be explained by their cognitive or neuropathological underpinnings.},
  author       = {Van de Steen, Frederik and Almgren, Hannes and Razi, Adeel and Friston, Karl and Marinazzo, Daniele},
  issn         = {1053-8119},
  journal      = {NEUROIMAGE},
  keywords     = {Dynamic causal modelling,Resting state,EEG,Fluctuating connectivity},
  language     = {eng},
  pages        = {476--484},
  title        = {Dynamic causal modelling of fluctuating connectivity in resting-state EEG},
  url          = {http://dx.doi.org/10.1016/j.neuroimage.2019.01.055},
  volume       = {189},
  year         = {2019},
}

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