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000873849 1001_ $$0P:(DE-HGF)0$$aDe Bonis, Giulia$$b0
000873849 1112_ $$aHuman Brain Project Summit$$cAthens$$d2020-02-03 - 2020-02-06$$wGreece
000873849 245__ $$aMulti-scale, multi-species, multi-methodology experiments, analysis tools and simulation models of Brain States and Complexity in SP3-UseCase002
000873849 260__ $$c2020
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000873849 520__ $$aThe general goal of SP3-UseCase002 is to offer to external users, through EBRAINS Knowledge Graph, an integrated environment, dedicated to the topic of cortical slow wave activity (SWA) [1,2] in spontaneous and perturbed mode, and to sleep/awake transitions, measures of complexity (like PCI, perturbational complexity index) and the cognitive effects of sleep in thalamo-cortical systems. The offering includes multi-scale multi-species experimental data, simulation models, simulation results, and analysis tools. The analysis tools are designed to be applicable to both experimental data and simulation results since, for a fair comparison and accurate validation of the models, the outcome of data-driven biologically-plausible simulations [3] should be subjected to the same analysis tools used for the data. We note that the variety of the experimental techniques for data acquisition and the diversity of subjects and species involved (due to large biological variability, but also to brain states, physiological/pathological conditions, drug doses and data taking setups) make challenging the building of reliable and generalizable data analysis tools aimed at identifying common observables when comparing the outcome of different experiments acquired with different experimental modalities, and at obtaining reproducible results.SP3-UseCase002 integrates the results of WP3.2 (aka WaveScalES, focusing on sleep, anaesthesia and transition to wakefulness, KR3.2) and WP3.4 (aka ConsciousBrain, focusing on neural correlates and measure of consciousness in physiological and pathological brains, KR3.4).The analysis pipeline developed by WaveScalES [4,5], when applied to experimental data, enables the extraction of key spatio-temporal characteristics from slow waves acquired with multiple experimental methodologies (using micro-ECoG arrays and wide-field Calcium imaging techniques, to be extended to hd-EEG and stereo-EEG), at local and multi-areal spatial resolution. The platform also includes simulation models of SWA and AW-like cortical activity at biologically-plausible neural and synaptic densities [6] and simulation models demonstrating the effects of interactions among sleep and memories and the changes in cognitive performances of thalamo-cortical models passing through wakefulness-sleep-wakefulness cycles [7]. When applied to simulation results, similar features should be extracted, to enable a quantitative comparison between simulation and experimental data, fostering a better calibration of simulations.Concerning the ConsciousBrain research, the measure is based on a perturbational approach (i.e. perturbing the brain with an exogenous input and gauging the derived spatiotemporal dynamics). The proposed analysis pipeline calculates several complexity indices on multi-scale experimental data that includes TMS-EEG data in healthy humans and patients with disorder of consciousness, intracerebral recordings in epileptic patients undergoing presurgical evaluation as well as spikes and LFP signals in rats/mice. The Perturbational Complexity Index based on Lempel and Ziv algorithm (PCIlz)[8] correlates with the level of consciousness and has been validated using TMS-EEG data collected from a large cohort of healthy subjects and patients affected by disorder of consciousness [9]; the Perturbational Complexity Index based on State Transitions (PCIst)[10] is faster than PCIlz, it does not depend on source modelling algorithms and can be applied on data different from scalp EEG. In addition, a revisited version of PCIlz, calibrated on TMS/EEG and extracellular signals from cerebellar brain slices, will also be included.Here, we present the status of the implementation of the Use Case, with some preliminary results and conclusions.References    1. Steriade (1993), Journal of Neuroscience.    2. Sanchez-Vives, M.V. et al (2017), Neuron.    3. Capone C et al., Cerebral cortex (2019): 29, 1.     4. De Bonis, G. et al. (2019) Front. Syst. Neurosci, 13, 70.     5. Celotto M, et al (2018), arXiv:1811.11687    6. Pastorelli, E. et al. (2019) Front. Syst. Neurosci 13, 33.     7. Capone C., et al (2019), Sci. Rep. 9, 8990    8. Casali et al Science Tr. Med, 2013    9. Casarotto et al Ann. of Neurol, 2016    10. Comolatti et al. Brain Stim, 2019
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000873849 7001_ $$0P:(DE-HGF)0$$aPastorelli, Elena$$b1
000873849 7001_ $$0P:(DE-HGF)0$$aCapone, Cristiano$$b2
000873849 7001_ $$0P:(DE-Juel1)171572$$aGutzen, Robin$$b3$$eCorresponding author$$ufzj
000873849 7001_ $$0P:(DE-HGF)0$$aCamassa, Alessandra$$b4
000873849 7001_ $$0P:(DE-HGF)0$$aBerengué, Arnau Manasanch$$b5
000873849 7001_ $$0P:(DE-HGF)0$$aResta, Francesco$$b6
000873849 7001_ $$0P:(DE-HGF)0$$aMascaro, Anna Letizia Allegra$$b7
000873849 7001_ $$0P:(DE-HGF)0$$aPazienti, Antonio$$b8
000873849 7001_ $$0P:(DE-HGF)0$$aPigorini, Andrea$$b9
000873849 7001_ $$0P:(DE-HGF)0$$aNieus, Thierry$$b10
000873849 7001_ $$0P:(DE-HGF)0$$aArena, Alessandro$$b11
000873849 7001_ $$0P:(DE-HGF)0$$aStorm, Johan Frederik$$b12
000873849 7001_ $$0P:(DE-HGF)0$$aMassimini, Marcello$$b13
000873849 7001_ $$0P:(DE-HGF)0$$aPavone, Francesco Saverio$$b14
000873849 7001_ $$0P:(DE-HGF)0$$aSanchez-Vives, Maria V.$$b15
000873849 7001_ $$0P:(DE-HGF)0$$aMattia, Maurizio$$b16
000873849 7001_ $$0P:(DE-HGF)0$$aDavison, Andrew$$b17
000873849 7001_ $$0P:(DE-Juel1)144807$$aDenker, Michael$$b18$$ufzj
000873849 7001_ $$0P:(DE-HGF)0$$aPaolucci, Pier Stanislao$$b19
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