Results for complexity measures and a read-out of the state of cortical circuits after injury

The combination of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) allows to to measure non invasively with high temporal resolution the brain response to direct perturbation of different cortical areas. In healthy subjects, during wake, TMS induces spatio-temporally complex EEG responses, characterized by sequences of peaks and troughs that spread across the cortical regions following anatomical and functional networks. Conversely, during NREM sleep TMS-evoked EEG activity is much simpler and stereotyped, consisting in a positive-negative slow wave either confined to the stimulation site or spreading over the cortex without any regional specificity. In order to quantify the spatio-temporal complexity of the EEG responses to TMS, we developed and implemented the perturbational complexity index (PCI) (Casali et al., 2013). Interestingly, PCI distinguishes between conscious and unconscious states in healthy subjects during wake, NREM sleep and in experimentally manipulated conditions (i.e. with anesthetics). In an ensuing work (Casarotto et al. 2016) PCI was validated on a benchmark population of communicative subjects (including different anesthesia conditions and brain-injured conscious patients), thus deriving an optimal cutoff to distinguish between consciousness and unconsciousness; then, PCI was applied to infer the state of consciousness of severely brain-injured patients unable to communicate. PCI was demonstrated to be able to detect patients in a minimally conscious state with a sensitivity of 94.7%; moreover, PCI allowed the identification of a fraction of patients (9 of 43) in a vegetative state (VS, recently relabeled as unresponsive wakefulness syndrome UWS) condition with high-complexity EEG responses to TMS, who might be conscious albeit unable to show it behaviorally. The data delivered with this milestone consist of a representative subset of the TMS/EEG recordings that have been included in the studies published by our laboratory during the SGA1 and SGA2 phases of the Human Brain Project (Casarotto et al. 2016, Rosanova, Fecchio et al. 2018, Comolatti et al.2019). In particular, the dataset consists of neurophysiological recordings gathered from 3 minimum consciousness state (MCS) and 3 vegetative state (VS) patients including the T1 weighted MRI of the patients. References: Casarotto, S., Comanducci, A., Rosanova, M., Sarasso, S., Fecchio, M., Napolitani, M., et al. (2016). Stratification of unresponsive patients by an independently validated index of brain complexity. Annals of Neurology 80, 718–729. doi:10.1002/ana.24779 Comolatti, R., Pigorini, A., Casarotto, S., Fecchio, M., Faria, G., Sarasso, S., et al. (2019). A fast and general method to empirically estimate the complexity of brain responses to transcranial and intracranial stimulations. Brain Stimulation, 12(5),1280-1289. doi: 10.1016/j.brs.2019.05.013 Rosanova, M., Fecchio, M., Casarotto, S., Sarasso, S., Casali, A. G., Pigorini, A., et al. (2018). Sleep-like cortical OFF-periods disrupt causality and complexity in the brain of unresponsive wakefulness syndrome patients. Nature Communications 9, 4427. doi: 10.1038/s41467-018-06871-1 Acknowledgements: This project/research has received funding from the European Union’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 785907 (Human Brain Project SGA2).