Adaptation of the novel intrinsic ignition framework to EEG for use in patients with Disorders of Consciousness

Introduction
Recently, a framework has been developed to investigate intrinsic ignitions, spontaneous neural activity crossing a threshold (Deco & Kringelbach, 2017). Original research in functional Magnetic Resonance Imaging (fMRI) showed that integration and the variability of integration following an intrinsic ignition could distinguish the states conscious wakefulness and sleep (Deco et al., 2017). With regard to other altered states of consciousness, such as those experienced by patients with disorders of consciousness (DOC), albeit in unresponsive wakefulness syndrome (UWS) where arousal is apparent without awareness, or minimally conscious state (MCS), where there are signs of residual awareness, the framework might be clinically interesting. The underlying idea of studying integration has already been shown to be is predictive of the level of consciousness in this patient group (López-González et al., 2020; Rizkallah et al., 2019). However, clinical translation may benefit from adaptation to the more accessible electroencephalography (EEG). Therefore, we aim to adopt the intrinsic ignition framework to EEG and apply it to DOC patients to assess its clinical diagnostic use.
Methods
Here we will attempt to translate the intrinsic ignition framework to the clinically easily employable EEG, by adapting methods (e.g., the measure of functional connectivity to one that deals with volume conduction) and tuning parameters to suit EEG data. In our database (healthy controls (n=38), UWS (n=36), MCS (n=86)) of 256-electrode EEG recordings we show the detection of ignitions. We plan to compare their amount and both temporal and spatial spread for different levels of consciousness. In an ERP-like investigation, with random samples as a control, we will show the temporal extend of an ignition. Elaborating on this, we will calculate functional connectivity with the whole brain following an ignition from which we derive a metric of integration (i.e., largest subcomponent in the thresholded connectivity matrix).
Results
Based on the fMRI results we expect balanced integration in healthy controls which is disturbed and has higher variability with decreasing levels of consciousness in DOC patients.
Conclusion
Consequently, our exploratory study may show an adaptation of the intrinsic ignition framework to EEG is both feasible and useful in the discrimination of pathological states of consciousness. Therefore, the framework could be one step closer to clinical implementation. Future efforts will include increased characterization of the intrinsic ignition events (e.g., are they slow-waves? Bursts of high-frequency activity?) and elaboration of the integration following an ignition (e.g., attempting source reconstruction to provide more spatial information).
References
Deco, G., & Kringelbach, M. L. (2017). Perspective Hierarchy of Information Processing in the Brain : A Novel ‘ Intrinsic Ignition ’ Framework. Neuron, 94(5), 961–968. https://doi.org/10.1016/j.neuron.2017.03.028
Deco, G., Tagliazucchi, E., Laufs, H., Sanjuán, A., & Kringelbach, M. L. (2017). Novel Intrinsic Ignition Method Measuring Local- Global Integration Characterizes Wakefulness and Deep Sleep. ENeuro, 4(5), 1–12.
López-González, A., Panda, R., Ponce-Alvarez, A., Zamora-López, G., Escrichs, A., Martial, C., Thibaut, A., Gosseries, O., Kringelbach, M. L., Annen, J., Laureys, S., & Deco, G. (2020). Loss of consciousness reduces the stability of brain hubs and the heterogeneity of brain dynamics. BioRxiv. https://doi.org/10.1101/2020.11.20.391482
Rizkallah, J., Annen, J., Modolo, J., Gosseries, O., Benquet, P., Mortaheb, S., Amoud, H., Cassol, H., Mheich, A., Thibaut, A., Chatelle, C., Hassan, M., Panda, R., Wendling, F., & Laureys, S. (2019). Clinical Decreased integration of EEG source-space networks in disorders of consciousness. NeuroImage: Clinical, 23, 1–9. https://doi.org/10.1016/j.nicl.2019.101841