Probabilistic functional tractography of the human cortex revisited

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.Peer reviewe

Développement d'outils de traitement du signal et statistiques pour l'analyse de groupe des réponses induites par des stimulations électriques corticales directes chez l'humain

Introduction: Low-frequency direct electrical stimulation is performed in drug-resistant epileptic patients, implanted with depth electrodes. It induces cortico-cortical evoked potentials (CCEP) that allow in vivo connectivity mapping of local networks. The multicentric project F-TRACT aims at gathering data of several hundred patients in a database to build a propabilistic functional tractography atlas that estimates connectivity at the cortex level.Methods: Semi-automatic processing pipelines have been developed to handle the amount of stereo-electroencephalography (SEEG) and imaging data and store them in a database. New signal processing and machine-learning methods have been developed and included in the pipelines, in order to automatically identify bad channels and correct the stimulation artifact. Group analyses have been performed using CCEP features and time-frequency maps of the stimulation responses.Results: The new methods performance has been assessed on heterogeneous data, coming from different hospital center recording and stimulating using variable parameters. The atlas was generated from a sample of 173 patients, providing a connectivity probability value for 79% of the possible connections and estimating biophysical properties of fibers for 46% of them. The methodology was applied on patients who experienced auditory symptoms that allowed the identification of different networks involved in hallucination or illusion generation. Oscillatory group analysis showed that anatomy was driving the stimulation response pattern.Discussion: A methodology for CCEP study at the cerebral cortex scale is presented in this thesis. Heterogeneous data in terms of acquisition and stimulation parameters and spatially were used and handled. An increasing number of patients’ data will allow the maximization of the statistical power of the atlas in order to study causal cortico-cortical interactions.Introduction : La stimulation électrique directe basse fréquence est pratiquée sur des patients épileptiques pharmaco-résistants implantés avec des électrodes profondes. Elle induit de potentiels évoqués cortico-corticaux (PECC) qui permettent d’estimer la connectivité in vivo et ont permis de caractériser des réseaux locaux. Pour estimer la connectivité à l’échelle du cortex, le projet multicentrique F-TRACT vise à rassembler plusieurs centaines de patients dans une base de données pour proposer un atlas probabiliste de tractographie fonctionnelle.Méthodes : La construction de la base de données à nécessité la mise en place technique de pipelines de traitement semi-automatiques pour faciliter la gestion du nombre important de données de stéréo-électroencéphalographie (SEEG) et d’imagerie. Ces pipelines incluent des nouvelles méthodes de traitement du signal et d’apprentissage automatique, qui ont été développées pour identifier automatiquement les mauvais contacts et corriger l’artefact induit par la stimulation. Les analyses de groupe se sont basées sur des métriques des PECC et des cartes temps-fréquences des réponses à la stimulation.Résultats : La performance des méthodes développées pour le projet a été validée sur des données hétérogènes, en termes de paramètres d’acquisition et de stimulation, provenant de différents centres hospitaliers. L’atlas a été généré à partir d’un échantillon de 173 patients, fournissant une mesure de probabilité de connectivité pour 79% des connexions et d’estimer des propriétés biophysiques des fibres pour 46% d’entre elles. Son application à une sous-population de patients a permis d’étudier les réseaux impliqués dans la génération de symptômes auditifs. L’analyse de groupe oscillatoire a mis en avant l’influence de l’anatomie sur la réponse à la stimulation.Discussion : Cette thèse présente une méthodologie d’étude des PECC à l’échelle du cortex cérébral, utilisant des données hétérogènes en termes d’acquisition, de paramètres de stimulation et spatialement. L’incrémentation du nombre de patients dans l’atlas généré permettra d’étudier les interactions cortico-corticales de manière causale

Development of signal processing and statistical tools for group analysis of responses to direct electrical stimulations induced in humans

Introduction : La stimulation électrique directe basse fréquence est pratiquée sur des patients épileptiques pharmaco-résistants implantés avec des électrodes profondes. Elle induit de potentiels évoqués cortico-corticaux (PECC) qui permettent d’estimer la connectivité in vivo et ont permis de caractériser des réseaux locaux. Pour estimer la connectivité à l’échelle du cortex, le projet multicentrique F-TRACT vise à rassembler plusieurs centaines de patients dans une base de données pour proposer un atlas probabiliste de tractographie fonctionnelle.Méthodes : La construction de la base de données à nécessité la mise en place technique de pipelines de traitement semi-automatiques pour faciliter la gestion du nombre important de données de stéréo-électroencéphalographie (SEEG) et d’imagerie. Ces pipelines incluent des nouvelles méthodes de traitement du signal et d’apprentissage automatique, qui ont été développées pour identifier automatiquement les mauvais contacts et corriger l’artefact induit par la stimulation. Les analyses de groupe se sont basées sur des métriques des PECC et des cartes temps-fréquences des réponses à la stimulation.Résultats : La performance des méthodes développées pour le projet a été validée sur des données hétérogènes, en termes de paramètres d’acquisition et de stimulation, provenant de différents centres hospitaliers. L’atlas a été généré à partir d’un échantillon de 173 patients, fournissant une mesure de probabilité de connectivité pour 79% des connexions et d’estimer des propriétés biophysiques des fibres pour 46% d’entre elles. Son application à une sous-population de patients a permis d’étudier les réseaux impliqués dans la génération de symptômes auditifs. L’analyse de groupe oscillatoire a mis en avant l’influence de l’anatomie sur la réponse à la stimulation.Discussion : Cette thèse présente une méthodologie d’étude des PECC à l’échelle du cortex cérébral, utilisant des données hétérogènes en termes d’acquisition, de paramètres de stimulation et spatialement. L’incrémentation du nombre de patients dans l’atlas généré permettra d’étudier les interactions cortico-corticales de manière causale.Introduction: Low-frequency direct electrical stimulation is performed in drug-resistant epileptic patients, implanted with depth electrodes. It induces cortico-cortical evoked potentials (CCEP) that allow in vivo connectivity mapping of local networks. The multicentric project F-TRACT aims at gathering data of several hundred patients in a database to build a propabilistic functional tractography atlas that estimates connectivity at the cortex level.Methods: Semi-automatic processing pipelines have been developed to handle the amount of stereo-electroencephalography (SEEG) and imaging data and store them in a database. New signal processing and machine-learning methods have been developed and included in the pipelines, in order to automatically identify bad channels and correct the stimulation artifact. Group analyses have been performed using CCEP features and time-frequency maps of the stimulation responses.Results: The new methods performance has been assessed on heterogeneous data, coming from different hospital center recording and stimulating using variable parameters. The atlas was generated from a sample of 173 patients, providing a connectivity probability value for 79% of the possible connections and estimating biophysical properties of fibers for 46% of them. The methodology was applied on patients who experienced auditory symptoms that allowed the identification of different networks involved in hallucination or illusion generation. Oscillatory group analysis showed that anatomy was driving the stimulation response pattern.Discussion: A methodology for CCEP study at the cerebral cortex scale is presented in this thesis. Heterogeneous data in terms of acquisition and stimulation parameters and spatially were used and handled. An increasing number of patients’ data will allow the maximization of the statistical power of the atlas in order to study causal cortico-cortical interactions

Automatic bad channel detection in intracranial electroencephalographic recordings using ensemble machine learning

International audienceObjective : Intracranial electroencephalographic (iEEG) recordings contain “bad channels”, which show non-neuronal signals. Here, we developed a new method that automatically detects iEEG bad channels using machine learning of seven signal features.Methods : The features quantified signals’ variance, spatial–temporal correlation and nonlinear properties. Because the number of bad channels is usually much lower than the number of good channels, we implemented an ensemble bagging classifier known to be optimal in terms of stability and predictive accuracy for datasets with imbalanced class distributions. This method was applied on stereo-electroencephalographic (SEEG) signals recording during low frequency stimulations performed in 206 patients from 5 clinical centers.Results : We found that the classification accuracy was extremely good: It increased with the number of subjects used to train the classifier and reached a plateau at 99.77% for 110 subjects. The classification performance was thus not impacted by the multicentric nature of data.Conclusions : The proposed method to automatically detect bad channels demonstrated convincing results and can be envisaged to be used on larger datasets for automatic quality control of iEEG data.Significance : This is the first method proposed to classify bad channels in iEEG and should allow to improve the data selection when reviewing iEEG signals

A brain atlas of axonal and synaptic delays based on modelling of cortico-cortical evoked potentials.

Epilepsy presurgical investigation may include focal intracortical single-pulse electrical stimulations with depth electrodes, which induce cortico-cortical evoked potentials at distant sites because of white matter connectivity. Cortico-cortical evoked potentials provide a unique window on functional brain networks because they contain sufficient information to infer dynamical properties of large-scale brain connectivity, such as preferred directionality and propagation latencies. Here, we developed a biologically informed modelling approach to estimate the neural physiological parameters of brain functional networks from the cortico-cortical evoked potentials recorded in a large multicentric database. Specifically, we considered each cortico-cortical evoked potential as the output of a transient stimulus entering the stimulated region, which directly propagated to the recording region. Both regions were modelled as coupled neural mass models, the parameters of which were estimated from the first cortico-cortical evoked potential component, occurring before 80 ms, using dynamic causal modelling and Bayesian model inversion. This methodology was applied to the data of 780 patients with epilepsy from the F-TRACT database, providing a total of 34 354 bipolar stimulations and 774 445 cortico-cortical evoked potentials. The cortical mapping of the local excitatory and inhibitory synaptic time constants and of the axonal conduction delays between cortical regions was obtained at the population level using anatomy-based averaging procedures, based on the Lausanne2008 and the HCP-MMP1 parcellation schemes, containing 130 and 360 parcels, respectively. To rule out brain maturation effects, a separate analysis was performed for older (>15 years) and younger patients (<15 years). In the group of older subjects, we found that the cortico-cortical axonal conduction delays between parcels were globally short (median = 10.2 ms) and only 16% were larger than 20 ms. This was associated to a median velocity of 3.9 m/s. Although a general lengthening of these delays with the distance between the stimulating and recording contacts was observed across the cortex, some regions were less affected by this rule, such as the insula for which almost all efferent and afferent connections were faster than 10 ms. Synaptic time constants were found to be shorter in the sensorimotor, medial occipital and latero-temporal regions, than in other cortical areas. Finally, we found that axonal conduction delays were significantly larger in the group of subjects younger than 15 years, which corroborates that brain maturation increases the speed of brain dynamics. To our knowledge, this study is the first to provide a local estimation of axonal conduction delays and synaptic time constants across the whole human cortex in vivo, based on intracerebral electrophysiological recordings