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ALICE is a general-purpose heavy-ion experiment designed to study the physics of strongly interacting matter and the quark–gluon plasma in nucleus–nucleus collisions at the LHC. It currently involves more than 900 physicists and senior engineers, from both the nuclear and high-energy physics sectors, from over 90 institutions in about 30 countries.The ALICE detector is designed to cope with the highest particle multiplicities above those anticipated for Pb–Pb collisions (dNch/dy up to 8000) and it will be operational at the start-up of the LHC. In addition to heavy systems, the ALICE Collaboration will study collisions of lower-mass ions, which are a means of varying the energy density, and protons (both pp and pA), which primarily provide reference data for the nucleus–nucleus collisions. In addition, the pp data will allow for a number of genuine pp physics studies.The detailed design of the different detector systems has been laid down in a number of Technical Design Reports issued between mid-1998 and the end of 2004. The experiment is currently under construction and will be ready for data taking with both proton and heavy-ion beams at the start-up of the LHC.Since the comprehensive information on detector and physics performance was last published in the ALICE Technical Proposal in 1996, the detector, as well as simulation, reconstruction and analysis software have undergone significant development. The Physics Performance Report (PPR) provides an updated and comprehensive summary of the performance of the various ALICE subsystems, including updates to the Technical Design Reports, as appropriate.The PPR is divided into two volumes. Volume I, published in 2004 (CERN/LHCC 2003-049, ALICE Collaboration 2004 J. Phys. G: Nucl. Part. Phys. 30 1517–1763), contains in four chapters a short theoretical overview and an extensive reference list concerning the physics topics of interest to ALICE, the experimental conditions at the LHC, a short summary and update of the subsystem designs, and a description of the offline framework and Monte Carlo event generators.The present volume, Volume II, contains the majority of the information relevant to the physics performance in proton–proton, proton–nucleus, and nucleus–nucleus collisions. Following an introductory overview, Chapter 5 describes the combined detector performance and the event reconstruction procedures, based on detailed simulations of the individual subsystems. Chapter 6 describes the analysis and physics reach for a representative sample of physics observables, from global event characteristics to hard processes.

In patients with pharmaco-resistant focal epilepsies investigated with intracranial electroencephalography (iEEG), direct electrical stimulations of a cortical region induce cortico-cortical evoked potentials (CCEP) in distant cerebral cortex, which properties can be used to infer large scale brain connectivity. In 2013, we proposed a new probabilistic functional tractography methodology to study human brain connectivity. We have now been revisiting this method in the F-TRACT project (f-tract.eu) by developing a large multicenter CCEP database of several thousand stimulation runs performed in several hundred patients, and associated processing tools to create a probabilistic atlas of human cortico-cortical connections. Here, we wish to present a snapshot of the methods and data of F-TRACT using a pool of 213 epilepsy patients, all studied by stereo-encephalography with intracerebral depth electrodes. The CCEPs were processed using an automated pipeline with the following consecutive steps: detection of each stimulation run from stimulation artifacts in raw intracranial EEG (iEEG) files, bad channels detection with a machine learning approach, model-based stimulation artifact correction, robust averaging over stimulation pulses. Effective connectivity between the stimulated and recording areas is then inferred from the properties of the first CCEP component, i.e. onset and peak latency, amplitude, duration and integral of the significant part. Finally, group statistics of CCEP features are implemented for each brain parcel explored by iEEG electrodes. The localization (coordinates, white/gray matter relative positioning) of electrode contacts were obtained from imaging data (anatomical MRI or CT scans before and after electrodes implantation). The iEEG contacts were repositioned in different brain parcellations from the segmentation of patients' anatomical MRI or from templates in the MNI coordinate system. The F-TRACT database using the first pool of 213 patients provided connectivity probability values for 95% of possible intrahemispheric and 56% of interhemispheric connections and CCEP features for 78% of intrahemisheric and 14% of interhemispheric connections. In this report, we show some examples of anatomo-functional connectivity matrices, and associated directional maps. We also indicate how CCEP features, especially latencies, are related to spatial distances, and allow estimating the velocity distribution of neuronal signals at a large scale. Finally, we describe the impact on the estimated connectivity of the stimulation charge and of the contact localization according to the white or gray matter. The most relevant maps for the scientific community are available for download on f-tract. eu (David et al., 2017) and will be regularly updated during the following months with the addition of more data in the F-TRACT database. This will provide an unprecedented knowledge on the dynamical properties of large fiber tracts in human.