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Over a Unified Connectivity Estimator for Intra and Inter-Frequency Couplings through Symbolic Transfer Entropy: A MEG Resting-State Analysis

Stavros I. Dimitriadis1*
  • 1 Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff, UK, School of Medicine, Greece

Last years, a significant number of studies appeared in the literature where they presented brain connectivity at resting-state and during cognitive tasks through the development and in both health and disease states. Additionally, there is an increasing amount of studies that focus on cross-frequency coupling which has been proposed to be the key mechanism that coordinate brain dynamics in spatio-temporal scales. Despite the increased number of studies over different experimental neuroimaging datasets, there are many difficulties on comparing results from the same imaging method and the same cognitive task and target group. One of these reasons deal with the adopted connectivity estimator where one can select over a large repertoire of connectivity estimators for quantifying the strength and also the direction of causal directions in both intra and inter-frequency coupling. The most frequent connectivity estimators for intra-frequency coupling are the : coherence (coh), imaginary part of coherence (icoh), mutual information (MI), phase lag index (PLI), weighted phase lag index (wPLI), phase entropy (PE), transfer entropy (TE), granger causality (GC) etc while for cross-frequency couplings are the following: phase-to-amplitude coupling (PAC) based on modulation index (MI) and (imaginary) phase-locking value ((i)PLV ; Dimitriadis et al., 2015,2016b), the amplitude-to-amplitude coupling (AAC) based on the correlation of the envelopes of two time series and recently the first cross-frequency estimator for causal interactions between different frequencies, the delay symbolic transfer entropy (dSTE ; Dimitriadis et al., 2016a). There are significant evidence by studying critical brain dynamics for the distinct role of amplitude and phase dynamics at human resting-state (Ton et al., 2015). A recent study based on MEG beamformed signals revealed the dominant information flow within each frequency bands using phase transfer entropy as an appropriate tool for defining the dominant direction of causal interactions between functionally segregated brain networks (Hillebrand et al., 2016). It is significant to discover the dominant direction of causal interactions within and between brain rhythms in both amplitude and phase under a common framework and connectivity estimator. In the present preliminary study, i employed dSTE as an appropriate tool to explore both intra and inter-frequency coupling independently for amplitude and phase but using a common framework through symbolic dynamics (Dimitriadis et al.,2016). Here, i demonstrated a method to estimate transfer entropy (TE) through a symbolization scheme, which is based on neural-gas algorithm (NG) and transforms multivariate time series in the form of multichannel symbolic sequences. Given the symbolic sequences, the delay symbolic transfer entropy (dSTENG) is defined that can uncover the direction, the strength and the delay of causal interactions between and within different frequencies. Our approach is a standard symbolic transfer entropy (STE) that incorporates the notion of ordinal pattern (OP) symbolization technique but using a more efficient neuroinformatic tool for the common symbolization of multichannel recordings, the NG algorithm. We presented the whole analysis using MEG resting-state recordings from an open database (OMEGA). Our results revealed posterior-to-anterior patterns of information flow for high-frequency bands (α,β,γ) and an opposite pattern for low frequencies (δ,θ) in both amplitude and phase domain but with different coupling strength and delay of information flux. Additionally, a developmental trend was observed between the two age-groups. To conclude, i presented a novel unified method based on Symbolic Transfer Entropy (STE) for detecting causal interactions between and within frequencies and independently for amplitude and phase domain. Uncovering the different causal profile of amplitude and phase intra and inter-frequency coupling between the spatial distinct brain networks is essential for a better understanding of the neural dynamics in both healthy and disease brain states.

Acknowledgements

I would like to acknowledgement the CUBRIC Research Center in Cardiff, the School of Medicine (Cardiff), the Psychology Department (Cardiff) and the MEG Group within CUBRIC.

References

References:
Dimitriadis SI, Laskaris NA, Bitzidou MP, Tarnanas I and Tsolaki MN (2015) A novel biomarker of amnestic MCI based on dynamic cross-frequency coupling patterns during cognitive brain responses. Front. Neurosci. 9:350. doi: 10.3389/fnins.2015.00350
Dimitriadis SI, Yu Sun, Laskaris NA, Thakor N, Bezerianos A (2016a).Revealing cross-frequency causal interactions during a mental arithmetic task through symbolic transfer entropy: a novel vector-quantization approach. IEEE TNSR [Epub ahead of print]
Dimitriadis SI, Laskaris NA, Simos PG, Fletcher JM and Papanicolaou AC (2016b) Greater Repertoire and Temporal Variability of Cross-Frequency Coupling (CFC) Modes in Resting-State Neuromagnetic Recordings among Children with Reading Difficulties. Front. Hum. Neurosci. 10:163. doi: 10.3389/fnhum.2016.00163
Hillebrand A, Tewarie P, van Dellen E, Yu M, Carbo EW, Douw L, Gouw AA, van Straaten EC, Stam CJ. Direction of information flow in large-scale resting-state networks is frequency-dependent. Proc Natl Acad Sci U S A. 2016 Mar 21. pii: 201515657. [Epub ahead of print]
Niso, G., et al., 2015. Omega: The Open MEG Archive. Volume 124, Part B, 1 January 2016, Pages 1182–1187 :https://938omega.bic.mni.mcgill.
Ton, R., Deco, G., Kringelbach, M.L., Woolrich, M., Daffertshofer, A., 2015.Distinct Criticality of Phase and Amplitude Dynamics in the Resting Brain (Submitted for publication) (arXiv:1512.02574 [q-bio.NC])

Keywords: information flow, Magnetoencephalography (MEG), resting-state networks, causal inference, Amplitude, phase, Symbolic dynamics

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

Presentation Type: Investigator presentations

Topic: Neuroimaging

Citation: Dimitriadis SI (2016). Over a Unified Connectivity Estimator for Intra and Inter-Frequency Couplings through Symbolic Transfer Entropy: A MEG Resting-State Analysis. Front. Neuroinform. Conference Abstract: Neuroinformatics 2016. doi: 10.3389/conf.fninf.2016.20.00006

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

* Correspondence: Dr. Stavros I Dimitriadis, Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff, UK, School of Medicine, Cardiff, UK, Greece, stidimitriadis@gmail.com