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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., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

Deep brain stimulation (DBS) of the subgenual cingulate gyrus (area CG25) is beneficial in treatment resistant depression. Though the mechanisms of action of Cg25 DBS remain largely unknown, it is commonly believed that Cg25 DBS modulates limbic activity of large networks to achieve thymic regulation of patients. To investigate how emotional attention is influenced by Cg25 DBS, we assessed behavioral and electroencephalographic (EEG) responses to an emotional Stroop task in 5 patients during ON and OFF stimulation conditions. Using EEG source localization, we found that the main effect of DBS was a reduction of neuronal responses in limbic regions (temporal pole, medial prefrontal and posterior cingulate cortices) and in ventral visual areas involved in face processing. In the dynamic causal modeling (DCM) approach, the changes of the evoked response amplitudes are assumed to be due to changes of long range connectivity induced by Cg25 DBS. Here, using a simplified neural mass model that did not take explicitly into account the cytoarchitecture of the considered brain regions, we showed that the remote action of Cg25 DBS could be explained by a reduced top-down effective connectivity of the amygdalo-temporo-polar complex. Overall, our results thus indicate that Cg25 DBS during the emotional Stroop task causes a decrease of top-down limbic influence on the ventral visual stream itself, rather than a modulation of prefrontal cognitive processes only. Tuning down limbic excitability in relation to sensory processing might be one of the biological mechanisms through which Cg25 DBS produces positive clinical outcome in the treatment of resistant depression., (Copyright © 2016 Elsevier Inc. All rights reserved.)

The relationship between structural connectivity (SC) and functional connectivity (FC) in the human brain can be studied using magnetic resonance imaging (MRI). However many of the underlying physiological mechanisms and parameters cannot be directly observed with MRI. This limitation has motivated the recent use of various computational models meant to bridge the gap. However their absolute and relative explanatory power and the properties that actually drive that power remain insufficiently characterized. We performed an extensive comparison of seven mainstream computational models predicting FC from SC. We investigated the extent to which simulated FC could predict empirical FC. We also applied graph theory to the entire set of simulated and empirical FCs in order to further characterize the relationships between the models and the MRI data. The comparison was performed at three different spatial scales. We found that (i) there were significant effects of scale and model on predictive power; (ii) among all models, the simplest model, the simultaneous autoregressive (SAR) model, was found to consistently perform better than the other models; (iii) the SAR also appeared more 'central' from a graph theory perspective; and (iv) empirical FC only appeared weakly correlated with simulated FCs, and was featured as 'peripheral' in the graph analysis. We conclude that the substantial differences existing between these computational models have little impact on their predictive power for FC and that their capacity to predict FC from SC appears to be both moderate and essentially underlined by a simple core linear process embodied by the SAR model., (Copyright © 2015 Elsevier Inc. All rights reserved.)

Lesion overlap difference maps have been used in studies designed to test anatomical hypotheses regarding brain systems critical for various cognitive and behavioral tasks, including naming and recognition of concrete entities [Damasio, H., Tranel, D., Grabowski, T., Adolphs, R., Damasio, A., 2004. Neural systems behind word and concept retrieval. Cognition 92, 179-229]. To date, the interpretation of these results has focused on areas of maximum lesion overlap differences. Here we explore formal methods for statistical thresholding and power analysis. We derive exact voxel-wise statistics describing the behavior of lesion overlap difference maps and lesion proportion difference maps under the null hypothesis of no association between lesion and deficit, and we apply the statistics to a large subset of the subjects previously reported in [Damasio, H., Tranel, D., Grabowski, T., Adolphs, R., Damasio, A., 2004. Neural systems behind word and concept retrieval. Cognition 92, 179-229], in order to reassess the lesion correlates of deficits in naming and recognition for five categories of concrete entities. The thresholded maps confirmed many of the results reported previously, but also revealed some differences. Differences in spatial distribution of the lesion correlates of impaired naming of unique versus nonunique entities were confirmed in the inferotemporal region (IT), although overlapping components across categories became apparent in left IT. Additionally, the left inferior frontal gyrus (IFG) was implicated in naming both categories of nonunique natural entities (animals and fruits/vegetables). In corresponding power analyses, we estimated where significant effects could be found under an assumption of maximal effect size given the observed spatial distribution of lesions. Such "effective coverage maps" are valuable for the interpretation of the results, notably because of heterogeneity in lesion coverage encountered in lesion studies. We strongly suggest that when inferential statistics are used in voxel-wise lesion-deficit statistical mapping, these or other power maps be included in the reports.

The quantification of phase synchrony between brain signals is of crucial importance for the study of large-scale interactions in the brain. Current methods are based on the estimation of the stability of the phase difference between pairs of signals over a time window, within successive frequency bands. This paper introduces a new approach to study the dynamics of brain synchronies, Frequency Flows Analysis (FFA). It allows direct tracking and characterization of the nonstationary time-frequency dynamics of phase synchrony among groups of signals. It is based on the use of the one-to-one relationship between frequency locking and phase synchrony, which applies when the concept of phase synchrony is not taken in an extended 'statistical' sense of a bias in the distribution of phase differences, but in the sense of a continuous phase difference conservation during a short period of time. In such a case, phase synchrony implies identical instantaneous frequencies among synchronized signals, with possible time varying frequencies of synchronization. In this framework, synchronous groups of signals or neural assemblies can be identified as belonging to common frequency flows, and the problem of studying synchronization becomes the problem of tracking frequency flows. We use the ridges of the analytic wavelet transforms of the signals of interest in order to estimate maps of instantaneous frequencies and reveal sustained periods of common instantaneous frequency among groups of signal. FFA is shown to track complex dynamics of synchrony in coupled oscillator models, reveal the time-frequency and spatial dynamics of synchrony convergence and divergence in epileptic seizures, and in MEG data the large-scale ongoing dynamics of synchrony correlated with conscious perception during binocular rivalry.