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The cingulate cortex is part of the limbic system. Its function and connectivity are organized in a rostro-caudal and ventral-dorsal manner which was addressed by various other studies using rather coarse cortical parcellations. In this study, we aim at describing its function and connectivity using invasive recordings from patients explored for focal drug-resistant epilepsy. We included patients that underwent stereo-electroencephalographic recordings using intracranial electrodes in the University Emergency Hospital Bucharest between 2012 and 2019. We reviewed all high frequency stimulations (50 ​Hz) performed for functional mapping of the cingulate cortex. We used two methods to characterize brain connectivity. Effective connectivity was inferred based on the analysis of cortico-cortical potentials (CCEPs) evoked by single pulse electrical stimulation (SPES) (15 ​s inter-pulse interval). Functional connectivity was estimated using the non-linear regression method applied to 60 ​s spontaneous electrical brain signal intervals. The effective (stimulation-evoked) and functional (non-evoked) connectivity analyses highlight brain networks in a different way. While non-evoked connectivity evidences areas having related activity, often in close proximity to each other, evoked connectivity highlights spatially extended networks. To highlight in a comprehensive way the cingulate cortex’s network, we have performed a bi-modal connectivity analysis that combines the resting-state broadband h2 non-linear correlation with cortico-cortical evoked potentials. We co-registered the patient’s anatomy with the fsaverage FreeSurfer template to perform the automatic labeling based on HCP-MMP parcellation. At a group level, connectivity was estimated by averaging responses over stimulated/recorded or recorded sites in each pair of parcels. Finally, for multiple regions that evoked a clinical response during high frequency stimulation, we combined the connectivity of individual pairs using maximum intensity projection. Connectivity was assessed by applying SPES on 2094 contact pairs and recording CCEPs on 3580 contacts out of 8582 contacts of 660 electrodes implanted in 47 patients. Clinical responses elicited by high frequency stimulations in 107 sites (pairs of contacts) located in the cingulate cortex were divided in 10 groups: affective, motor behavior, motor elementary, versive, speech, vestibular, autonomic, somatosensory, visual and changes in body perception. Anterior cingulate cortex was shown to be connected to the mesial temporal, orbitofrontal and prefrontal cortex. In the middle cingulate cortex, we located affective, motor behavior in the anterior region, and elementary motor and somatosensory in the posterior part. This region is connected to the prefrontal, premotor and primary motor network. Finally, the posterior cingulate was shown to be connected with the visual areas, mesial and lateral parietal and temporal cortex.

Body awareness is the result of sensory integration in the posterior parietal cortex; however, other brain structures are part of this process. Our goal is to determine how the cingulate cortex is involved in the representation of our body. We retrospectively selected patients with drug‐resistant epilepsy, explored by stereo‐electroencephalography, that had the cingulate cortex sampled outside the epileptogenic zone. The clinical effects of high‐frequency electrical stimulation were reviewed and only those sites that elicited changes related to body perception were included. Connectivity of the cingulate cortex and other cortical structures was assessed using the h (2) coefficient, following a nonlinear regression analysis of the broadband EEG signal. Poststimulation changes in connectivity were compared between two sets of stimulations eliciting or not eliciting symptoms related to body awareness (interest and control groups). We included 17 stimulations from 12 patients that reported different types of body perception changes such as sensation of being pushed toward right/left/up, one limb becoming heavier/lighter, illusory sensation of movement, sensation of pressure, sensation of floating or detachment of one hemi‐body. High‐frequency stimulation in the cingulate cortex (1 anterior, 15 middle, 1 posterior part) elicits body perception changes, associated with a decreased connectivity of the dominant posterior insula and increased coupling between other structures, located particularly in the nondominant hemisphere.

In the context of the human brain, the term "connectivity" can refer to structural, functional or effective connectivity. Intracranial electrical stimulation is perhaps the most direct way of investigating the effective connectivity. We propose a method of mapping the effective connectivity, revealed by the electrical stimulation of brain structures, over the structural connectome (SC), obtained through diffusion spectrum imaging (DSI), to form a structural-effective connectome (SEC). A number of 24 patients with refractory epilepsy were implanted with depth electrodes for pre-surgical evaluation. Effective connectivity was assessed by analyzing the responses to single pulse electrical stimulation (SPES). Stimulation pulses having variable amplitude were applied to each pair of adjacent contacts and responses evoked by stimulation were recorded from other contacts located in other brain areas. Early responses (10-110 ms) on the stimulation-activated contacts located outside the epileptogenic zone were averaged for each patient, resulting in a patient-level physiological effective connectome (EC). The population level EC is computed by averaging the connections of the individual ECs, on a structure by structure basis. A fiber activation factor is used to weight the number of fibers connecting a pair of structures in the SC by its corresponding normalized EC value. The resulting number of effectively activated fibers describes the directional connection strength between two structures in the SEC. A physiological SEC comprising directional connections between 70 segmented brain areas in both hemispheres, was obtained by inclusion of structures outside the epileptogenic zone only. Over the entire structure set, the Spearman's correlation coefficient ρ between the number of fibers extracted from the DSI Atlas and the normalized RMS responses to SPES was ρ=0.21 (p0.001), while Kendall's tau coefficients ranged -0.52-0.44 (p0.05). The physiological structural-effective connectomics approach we have introduced can be applied for the creation of a whole-brain connectivity atlas that can be used as a reference tool for differential analysis of altered versus normal brain connectivity in epileptic patients.

Highlights • New method for stimulation artifact removal from cortico-cortical evoked potentials. • The method is based on electrical modeling of tissue-electrode interface. • It allows disambiguation of time-locked physiological responses and artifacts. • Method validation is based on synthetic and experimental data., Background Effective connectivity can be explored using direct electrical stimulations in patients suffering from drug-resistant focal epilepsies and investigated with intracranial electrodes. Responses to brief electrical pulses mimic the physiological propagation of signals and manifest as cortico-cortical evoked potentials (CCEP). The first CCEP component is believed to reflect direct connectivity with the stimulated region but the stimulation artifact, a sharp deflection occurring during a few milliseconds, frequently contaminates it. New method In order to recover the characteristics of early CCEP responses, we developed an artifact correction method based on electrical modeling of the electrode–tissue interface. The biophysically motivated artifact templates are then regressed out of the recorded data as in any classical template-matching removal artifact methods. Results Our approach is able to make the distinction between the physiological responses time-locked to the stimulation pulses and the non-physiological component. We tested the correction on simulated CCEP data in order to quantify its efficiency for different stimulation and recording parameters. We demonstrated the efficiency of the new correction method on simulations of single trial recordings for early responses contaminated with the stimulation artifact. The results highlight the importance of sampling frequency for an accurate analysis of CCEP. We then applied the approach to experimental data. Comparison with existing method The model-based template removal was compared to a correction based on the subtraction of the averaged artifact. Conclusions This new correction method of stimulation artifact will enable investigators to better analyze early CCEP components and infer direct effective connectivity in future CCEP studies.

Objective Epilepsy is characterized by transient alterations in brain synchronization resulting in seizures with a wide spectrum of manifestations. Seizure severity and risks for patients depend on the evolution and spread of the hypersynchronous discharges. With standard visual inspection and pattern classification, this evolution could not be predicted early on. It is still unclear to what degree the seizure onset zone determines seizure severity. Such information would improve our understanding of ictal epileptic activity and the existing electroencephalogram (EEG)-based warning and intervention systems, providing specific reactions to upcoming seizure types. We investigate the possibility of predicting the future development of an epileptic seizure during the first seconds of recordings after their electrographic onset. Methods Based on intracranial EEG recordings of 493 ictal events from 26 patients with focal epilepsy, a set of 25 time and frequency domain features was computed using nonoverlapping 1-second time windows, from the first 3, 5, and 10 seconds of ictal EEG. Three random forest classifiers were trained to predict the future evolution of the seizure, distinguishing between subclinical events, focal onset aware and impaired awareness, and focal to bilateral tonic-clonic seizures. Results Results show that early seizure type prediction is possible based on a single EEG channel located in the seizure onset zone with correct prediction rates of 76.2 ± 14.5% for distinguishing subclinical electrographic events from clinically manifest seizures, 75 ± 16.8% for distinguishing focal onset seizures that are or are not bilateral tonic-clonic, and 71.4 ± 17.2% for distinguishing between focal onset seizures with or without impaired awareness. All predictions are above the chance level (P Significance These findings provide the basis for developing systems for specific early warning of patients and health care providers, and for targeting EEG-based closed-loop intervention approaches to electrographic patterns with a high inherent risk to become clinically manifest.