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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. ; he research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme (FP/2007-2013) / ERC Grant Agreement n. 616268 "F-TRACT". All the computations presented in this paper were performed using the CIMENT infrastructure (https://ciment.ujf-grenoble.fr), which is supported by the Rhône-Alpes region (GRANT CPER07_13 CIRA: http://www.ci-ra.org). We thank Bruno Bzenik and Romain Cavagna for their help on the CIMENT facility, and Jean-François Mangin and Cyril Poupon from Neurospin, CEA Saclay, for providing us with the ARCHI DTI database.

OBJECTIVE: We introduce a method that quantifies the consistent involvement of intracranially monitored regions in recurrent focal seizures. METHODS: We evaluated the consistency of two ictal spectral activation patterns (mean power change and power change onset time) in intracranial recordings across focal seizures from seven patients with clinically marked seizure onset zone (SOZ). We examined SOZ discrimination using both patterns in different frequency bands and periods of interest. RESULTS: Activation patterns were proved to be consistent across more than 80% of recurrent ictal epochs. In all patients, whole-seizure mean activations were significantly higher for SOZ than non-SOZ regions (P<0.05) while activation onset times were significantly lower for SOZ than for non-SOZ regions (P<0.001) in six patients. Alpha-beta bands (8-20Hz) achieved the highest patient-average effect size on the whole-seizure period while gamma band (20-70Hz) achieved the highest discrimination values between SOZ and non-SOZ sites near seizure onset (0-5s). CONCLUSIONS: Consistent spectral activation patterns in focal epilepsies discriminate the SOZ with high effect sizes upon appropriate selection of frequency bands and activation periods. SIGNIFICANCE: The present method may be used to improve epileptogenic identification as well as pinpoint additional regions that are functionally altered during ictal events. ; The research was supported by the European Union's Horizon2020 research and innovation program under Grant AgreementNo. 720270. M.V. was supported by the Catalan Research projectAL08814 - AGAUR - 2014SGR856. G.D. was supported by the Euro-pean Research Council Advanced Grant DYSTRUCTURE (Grant295129) and by the Spanish Research Project AEI/FEDER-PSI2016–75688-P. A.T.C. was supported by the European ResearchCouncil Advanced Grant DYSTRUCTURE (Grant 295129) and by theSpanish Research Project AEI/FEDER-PSI2016–75688-P.

The spatial mapping of localized events in brain activity critically depends on the correct identification of the pattern signatures associated with those events. For instance, in the context of epilepsy research, a number of different electrophysiological patterns have been associated with epileptogenic activity. Motivated by the need to define automated seizure focus detectors, we propose a novel data-driven algorithm for the spatial identification of localized events that is based on the following rationale: the distribution of emerging oscillations during confined events across all recording sites is highly non-uniform and can be mapped using a spatial entropy function. By applying this principle to EEG recording obtained from 67 distinct seizure epochs, our method successfully identified the seizure focus on a group of ten drug-resistant temporal lobe epilepsy patients (average sensitivity: 0.94, average specificity: 0.90) together with its characteristic electrophysiological pattern signature. Cross-validation of the method outputs with postresective information revealed the consistency of our findings in long follow-up seizure-free patients. Overall, our methodology provides a reliable computational procedure that might be used as in both experimental and clinical domains to identify the neural populations undergoing an emerging functional or pathological transition. ; M.V was supported by a fellowship from "la Caixa" Foundation, Spain (ID 100010434, fellowship code LCF/BQ/DE17/11600022). G.D. was supported by the Spanish Ministry of Economy and Competitiveness, Spain (grant agreement number PSI2016-75688-P, MINECO/AEI/FEDER-EU); European Union's Horizon 2020 FET Flagship Human Brain Project (grant agreement number 785907, HBP SGA2); the Catalan Agency for Management of University and Research Grants, Spain (grant agreement number 2017 SGR 1545), and the Swiss National Science Foundation, Switzerland (grant agreement number CRSII5_170873).

Cognitive processing requires the ability to flexibly integrate and process information across large brain networks. How do brain networks dynamically reorganize to allow broad communication between many different brain regions in order to integrate information? We record neural activity from 12 epileptic patients using intracranial EEG while performing three cognitive tasks. We assess how the functional connectivity between different brain areas changes to facilitate communication across them. At the topological level, this facilitation is characterized by measures of integration and segregation. Across all patients, we found significant increases in integration and decreases in segregation during cognitive processing, especially in the gamma band (50–90 Hz). We also found higher levels of global synchronization and functional connectivity during task execution, again particularly in the gamma band. More importantly, functional connectivity modulations were not caused by changes in the level of the underlying oscillations. Instead, these modulations were caused by a rearrangement of the mutual synchronization between the different nodes as proposed by the "Communication Through Coherence" Theory. ; The authors would like to thank all patients for their participation and the IMIM-Hospital del Mar Epilepsy Unit staff for their technical assistance in collecting the data. This work was supported by Gustavo Deco's ERC Advanced Grant: DYSTRUCTURE (n. 295129), from the European Union's Horizon 2020 research and innovation programme under grant agreement No. (HBP SGA1) and by the Spanish Research Project PSI2013-42091-P. M.L.K. is supported by the ERC Consolidator Grant CAREGIVING (n. 615539) and the Center for Music in the Brain, funded by the Danish National Research Foundation (DNRF117).