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Effective Connectivity Analysis in Fronto-Centro-Parietal Network during Altered Levels of Consciousness

Dheeraj Rathee1*, Hubert Cecotti1 and Girijesh Prasad1
  • 1 Ulster University, Intelligent Systems Research Centre, United Kingdom

Introduction Administration of sedation is a common practice in most of the clinical and/or surgical interventions, especially in critical care environment. Although it is necessary to relieve critically ill patients from unpleasant physical and psychological experiences, continuous assessment of sedation is highly crucial as improper sedation practice may lead to serious complications such as delirium. To deal with these concerns, bio-markers for sedative-induced unconsciousness must be manifested through objective brain signal analyses. A balanced amalgamation of cortical integration and differentiation has been proposed by several theoretical developments in the field of consciousness, which involve substantial variations in information transfer networks across multiple scales (Tononi, 2004). These findings motivate further research to correlate these variations with altered states of consciousness and quantify them. In this work, Partial Granger Causality (PGC) (Guo et al., 2008) has been utilised to assess the effect of sedation on the Fronto-Centro-Parietal cortical network. Method An effective connectivity analysis has been performed on a 128-channel EEG dataset involving Seven healthy subjects under three distinct levels of sedation (Awake, Mild, and Moderate) (Chennu et al., 2016). The study has been restricted to artefact cleaned and bandpassed EEG data (8-12 Hz) of eight scalp electrodes (FP1, FP2, F3, F4, C3, C4, P3, and P4) covering the related brain areas. Levinson-Robinson-Wiggins (LWR) algorithm has been implemented for estimating Multivariate Autoregressive (MVAR) model coefficients over multiple 500 ms data segments. Akaike Information Criterion (AIC) and Durbin-Watson whiteness test have been used to evaluate the optimal model order and validate the model consistency, respectively. Furthermore, to remove the statistical biasness, permutation resampling test was used with a window size of 75 sample points and 20 permutations. The inference was done through statistical comparison (p<0.05, FDR corrected for multiple comparisons) of each causal interaction and PGC node strength among the three conditions. In our previous work, this methodology has been implemented for estimation of effective connectivity during distinct motor-imagery tasks (Rathee et al., 2016). Results The results analysis presented a statistically significant decrease in strength of the effective connectivity features and node strengths at various electrodes as the level of sedation increases from awake to moderate level. These findings are consistent with previous study (Boly et al., 2013), which involved functional connectivity analysis at source level networks. Fig.1 depicts the mean PGC (across subjects) matrices while Fig.2 displays head plots of mean node strength (across subjects) for all the three conditions. Pairwise comparisons shown in Fig. 3 revealed maximum statistically significant directional connectivity indices and node strengths changes in Awake-Moderate pair. Mild-moderate comparison provided four significant node strengths and six connectivity indices changes while least significant features (two node strengths and four connectivities) changes were observed in Awake-Mild comparison. Conclusion This study showed that scalp-level directed connectivity analysis can be implemented to distinguish between altered states of consciousness using a small number of electrodes and therefore reducing the computational cost and analysis time. Moreover, the Fronto-Centro-Parietal Network exhibits strong variations in the node strengths and it could be exploited for real-time monitoring of consciousness in a clinical setting. Figure 1. The results corresponding to the partial G-causality analysis for resting state EEG data for three levels of sedation. Each matrix consists of 56 non-zero mean effective connectivity indices (normalized PGC values) across 7 subjects. Figure 2. Head plots showing the mean PGC node strength values across 7 subjects for three levels of sedation. Colorbar represents normalized PGC values. Figure 3. Significant directional connectivities and node strengths (p<0.05, FDR corrected) for three pair-wise comparisons.

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Acknowledgements

D.R. is supported by Ulster University Vice Chancellor’s Research scholarship (VCRS). G.P. and H.C. are supported by the Northern Ireland Functional Brain Mapping Facility project (1303/101154803), funded by InvestNI and the University of Ulster. G.P. is also supported by the UKIERI DST Thematic Partnership project ”A BCI operated hand exoskeleton based neuro-rehabilitation system” (UKIERIDST-2013-14/126).

References

Boly, M., Moran, R., Murphy, M., Boveroux, P., Bruno, M. A., Noirhomme, Q., et al. (2012). Connectivity changes underlying spectral EEG changes during propofol-induced loss of consciousness. The Journal of Neuroscience, 32(20), 7082-7090.

Chennu, S., O'Connor, S., Adapa, R., Menon, D. K., & Bekinschtein, T. A. (2016). Brain Connectivity Dissociates Responsiveness from Drug Exposure during Propofol-Induced Transitions of Consciousness. PLoS Computational Biology, 12(1), e1004669-e1004669.

Guo, S., Seth, A. K., Kendrick, K. M., Zhou, C., & Feng, J. (2008). Partial Granger causality—eliminating exogenous inputs and latent variables. Journal of neuroscience methods, 172(1), 79-93.

Rathee, D., Ceccoti, H., Prasad, G. (2016). Estimation of effective Fronto-Parietal connectivity during Motor Imagery using Partial Granger Causality analysis. In Neural Networks (IJCNN), 2016 International Joint Conference on. IEEE

Tononi, G. (2004). An information integration theory of consciousness. BMC neuroscience, 5(1), 42.

Keywords: effective connectivity, Partial Granger Causality, Consciousness, sedation, cortical networks

Conference: Neuroinformatics 2016, Reading, United Kingdom, 3 Sep - 4 Sep, 2016.

Presentation Type: Poster

Topic: Neuroimaging

Citation: Rathee D, Cecotti H and Prasad G (2016). Effective Connectivity Analysis in Fronto-Centro-Parietal Network during Altered Levels of Consciousness. Front. Neuroinform. Conference Abstract: Neuroinformatics 2016. doi: 10.3389/conf.fninf.2016.20.00058

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Received: 30 Apr 2016; Published Online: 18 Jul 2016.

* Correspondence: Mr. Dheeraj Rathee, Ulster University, Intelligent Systems Research Centre, Derry/Londonderry, BT48 7JL, United Kingdom, rathee-d@email.ulster.ac.uk