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@INPROCEEDINGS{DeBonis:873849,
      author       = {De Bonis, Giulia and Pastorelli, Elena and Capone,
                      Cristiano and Gutzen, Robin and Camassa, Alessandra and
                      Berengué, Arnau Manasanch and Resta, Francesco and Mascaro,
                      Anna Letizia Allegra and Pazienti, Antonio and Pigorini,
                      Andrea and Nieus, Thierry and Arena, Alessandro and Storm,
                      Johan Frederik and Massimini, Marcello and Pavone, Francesco
                      Saverio and Sanchez-Vives, Maria V. and Mattia, Maurizio and
                      Davison, Andrew and Denker, Michael and Paolucci, Pier
                      Stanislao},
      title        = {{M}ulti-scale, multi-species, multi-methodology
                      experiments, analysis tools and simulation models of {B}rain
                      {S}tates and {C}omplexity in {SP}3-{U}se{C}ase002},
      reportid     = {FZJ-2020-01051},
      year         = {2020},
      abstract     = {The 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},
      month         = {Feb},
      date          = {2020-02-03},
      organization  = {Human Brain Project Summit, Athens
                       (Greece), 3 Feb 2020 - 6 Feb 2020},
      subtyp        = {Invited},
      cin          = {INM-6 / INM-10 / IAS-6},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)INM-10-20170113 /
                      I:(DE-Juel1)IAS-6-20130828},
      pnm          = {571 - Connectivity and Activity (POF3-571) / 574 - Theory,
                      modelling and simulation (POF3-574) / HBP SGA2 - Human Brain
                      Project Specific Grant Agreement 2 (785907)},
      pid          = {G:(DE-HGF)POF3-571 / G:(DE-HGF)POF3-574 /
                      G:(EU-Grant)785907},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/873849},
}