Dimensionality and Ramping: Signatures of Sentence Integration in the Dynamics of Brains and Deep Language Models.

A sentence is more than the sum of its words: its meaning depends on how they combine with one another. The brain mechanisms underlying such semantic composition remain poorly understood. To shed light on the neural vector code underlying semantic composition, we introduce two hypotheses: (1) the intrinsic dimensionality of the space of neural representations should increase as a sentence unfolds, paralleling the growing complexity of its semantic representation; and (2) this progressive integration should be reflected in ramping and sentence-final signals. To test these predictions, we designed a dataset of closely matched normal and jabberwocky sentences (composed of meaningless pseudo words) and displayed them to deep language models and to 11 human participants (5 men and 6 women) monitored with simultaneous MEG and intracranial EEG. In both deep language models and electrophysiological data, we found that representational dimensionality was higher for meaningful sentences than jabberwocky. Furthermore, multivariate decoding of normal versus jabberwocky confirmed three dynamic patterns: (1) a phasic pattern following each word, peaking in temporal and parietal areas; (2) a ramping pattern, characteristic of bilateral inferior and middle frontal gyri; and (3) a sentence-final pattern in left superior frontal gyrus and right orbitofrontal cortex. These results provide a first glimpse into the neural geometry of semantic integration and constrain the search for a neural code of linguistic composition.SIGNIFICANCE STATEMENT Starting from general linguistic concepts, we make two sets of predictions in neural signals evoked by reading multiword sentences. First, the intrinsic dimensionality of the representation should grow with additional meaningful words. Second, the neural dynamics should exhibit signatures of encoding, maintaining, and resolving semantic composition. We successfully validated these hypotheses in deep neural language models, artificial neural networks trained on text and performing very well on many natural language processing tasks. Then, using a unique combination of MEG and intracranial electrodes, we recorded high-resolution brain data from human participants while they read a controlled set of sentences. Time-resolved dimensionality analysis showed increasing dimensionality with meaning, and multivariate decoding allowed us to isolate the three dynamical patterns we had hypothesized.

Stereo-EEG-based ictal functional connectivity in patients with periventricular nodular heterotopia-related epilepsy.

Nodular heterotopia (NH)-related drug-resistant epilepsy is challenging due to the deep location of the NH and the complexity of the underlying epileptogenic network. Using ictal stereo-electroencephalography (SEEG) and functional connectivity (FC) analyses in 14 patients with NH-related drug-resistant epilepsy, we aimed to determine the leading structure during seizures. For this purpose, we compared node IN and OUT strength between bipolar channels inside the heterotopia and inside gray matter, at the group level and at the individual level. At seizure onset, the channels within NH belonging to the epileptogenic and/or propagation network showed higher node OUT-strength than the channels within the gray matter (p = .03), with higher node OUT-strength than node IN-strength (p = .03). These results are in favor of a "leading" role of NH during seizure onset when involved in the epileptogenic- or propagation-zone network (50% of patients). However, when looking at the individual level, no significant difference between NH and gray matter was found, except for one patient (in two of three seizures). This result confirms the heterogeneity and the complexity of the epileptogenic network organization in NH and the need for SEEG exploration to characterize more precisely patient-specific epileptogenic network organization.

Comparison of beamformer and ICA for dynamic connectivity analysis: A simultaneous MEG-SEEG study.

Magnetoencephalography (MEG) is a powerful tool for estimating brain connectivity with both good spatial and temporal resolution. It is particularly helpful in epilepsy to characterize non-invasively the epileptic networks. However, using MEG to map brain networks requires solving a difficult inverse problem that introduces uncertainty in the activity localization and connectivity measures. Our goal here was to compare independent component analysis (ICA) followed by dipole source localization and the linearly constrained minimum-variance beamformer (LCMV-BF) for characterizing regions with interictal epileptic activity and their dynamic connectivity. After a simulation study, we compared ICA and LCMV-BF results with intracerebral EEG (stereotaxic EEG, SEEG) recorded simultaneously in 8 epileptic patients, which provide a unique 'ground truth' to which non-invasive results can be confronted. We compared the signal time courses extracted applying ICA and LCMV-BF on MEG data to that of SEEG, both for the actual signals and the dynamic connectivity computed using cross-correlation (evolution of links in time). With our simulations, we illustrated the different effect of the temporal and spatial correlation among sources on the two methods. While ICA was more affected by the temporal correlation but robust against spatial configurations, LCMV-BF showed opposite behavior. Moreover, ICA seems more suited to retrieve the simulated networks. In case of real patient data, good MEG/SEEG correlation and good localization were obtained in 6 out of 8 patients. In 4 of them ICA had the best performance (higher correlation, lower localization distance). In terms of dynamic connectivity, the evolution in time of the cross-correlation links could be retrieved in 5 patients out of 6, however, with more variable results in terms of correlation and distance. In two patients LCMV-BF had better results than ICA. In one patient the two methods showed equally good outcomes, and in the remaining two patients ICA performed best. In conclusion, our results obtained by exploiting simultaneous MEG/SEEG recordings suggest that ICA and LCMV-BF have complementary qualities for retrieving the dynamics of interictal sources and their network interactions.

Reconstruction and localization of auditory sources from intracerebral SEEG using independent component analysis.

Stereo-electroencephalography (SEEG) is the surgical implantation of electrodes in the brain to better localize the epileptic network in pharmaco-resistant epileptic patients. This technique has exquisite spatial and temporal resolution. Still, the number and the position of the electrodes in the brain is limited and determined by the semiology and/or preliminary non-invasive examinations, leading to a large number of unexplored brain structures in each patient. Here, we propose a new approach to reconstruct the activity of non-sampled structures in SEEG, based on independent component analysis (ICA) and dipole source localization. We have tested this approach with an auditory stimulation dataset in ten patients. The activity directly recorded from the auditory cortex served as ground truth and was compared to the ICA applied on all non-auditory electrodes. Our results show that the activity from the auditory cortex can be reconstructed at the single trial level from contacts as far as ∼40 mm from the source. Importantly, this reconstructed activity is localized via dipole fitting in the proximity of the original source. In addition, we show that the size of the confidence interval of the dipole fitting is a good indicator of the reliability of the result, which depends on the geometry of the SEEG implantation. Overall, our approach allows reconstructing the activity of structures far from the electrode locations, partially overcoming the spatial sampling limitation of intracerebral recordings.

Virtual MEG sensors based on beamformer and independent component analysis can reconstruct epileptic activity as measured on simultaneous intracerebral recordings.

The prevailing gold standard for presurgical determination of epileptogenic brain networks is intracerebral EEG, a potent yet invasive approach. Magnetoencephalography (MEG) is a state-of-the art non-invasive method for investigating epileptiform discharges. However, it is not clear at what level the precision offered by MEG can reach that of SEEG. Here, we present a strategy for non-invasively retrieving the constituents of the interictal network, with high spatial and temporal precision. Our method is based on MEG and a combination of spatial filtering and independent component analysis (ICA). We validated this approach in twelve patients with drug-resistant focal epilepsy, thanks to the unprecedented ground truth provided by simultaneous recordings of MEG and SEEG. A minimum variance adaptive beamformer estimated the source time series and ICA was used to further decompose these time series into network constituents (MEG-ICs), each having a time series (virtual electrode) and a topography (spatial distribution of amplitudes in the brain). We show that MEG has a considerable sensitivity of 0.80 and 0.84 and a specificity of 0.93 and 0.91 for reconstructing deep and superficial sources, respectively, when compared to the ground truth (SEEG). For each epileptic MEG-IC (n = 131), we found at least one significantly correlating SEEG contact close to zero lag after correcting for multiple comparisons. All the patients except one had at least one epileptic component that was highly correlated (Spearman rho>0.3) with that of SEEG traces. MEG-ICs correlated well with SEEG traces. The strength of correlation coefficients did not depend on the depth of the SEEG contacts or the clinical outcome of the patient. A significant proportion of the MEG-ICs (n = 83/131) were localized in proximity with their maximally correlating SEEG, within a mean distance of 20±12.18mm. Our research is the first to validate the MEG-retrieved beamformer IC sources against SEEG-derived ground truth in a simultaneous MEG-SEEG framework. Observations from the present study suggest that non-invasive MEG source components may potentially provide additional information, comparable to SEEG in a number of instances.

Magnetoencephalography can reveal deep brain network activities linked to memory processes.

Recording from deep neural structures such as hippocampus noninvasively and yet with high temporal resolution remains a major challenge for human neuroscience. Although it has been proposed that deep neuronal activity might be recordable during cognitive tasks using magnetoencephalography (MEG), this remains to be demonstrated as the contribution of deep structures to MEG recordings may be too small to be detected or might be eclipsed by the activity of large-scale neocortical networks. In the present study, we disentangled mesial activity and large-scale networks from the MEG signals thanks to blind source separation (BSS). We then validated the MEG BSS components using intracerebral EEG signals recorded simultaneously in patients during their presurgical evaluation of epilepsy. In the MEG signals obtained during a memory task involving the recognition of old and new images, we identified with BSS a putative mesial component, which was present in all patients and all control subjects. The time course of the component selectively correlated with stereo-electroencephalography signals recorded from hippocampus and rhinal cortex, thus confirming its mesial origin. This finding complements previous studies with epileptic activity and opens new possibilities for using MEG to study deep brain structures in cognition and in brain disorders.

Connectivity Alterations in Emotional and Cognitive Networks During a Manic State Induced by Direct Electrical Stimulation.

Mania is characterized by affective and cognitive alterations, with heightened external and self-awareness that are opposite to the alteration of awareness during epileptic seizures. Electrical stimulations carried out routinely during stereotactic intracerebral EEG (SEEG) recordings for presurgical evaluation of epilepsy may represent a unique opportunity to study the pathophysiology of such complex emotional-behavioral phenomenon, particularly difficult to reproduce in experimental setting. We investigated SEEG signals-based functional connectivity between different brain regions involved in emotions and in consciousness processing during a manic state induced by electrical stimulation in a patient with drug-resistant focal epilepsy. The stimulation inducing manic state and an asymptomatic stimulation of the same site, as well as a seizure with alteration of awareness (AOA) were analyzed. Functional connectivity analysis was performed by measuring interdependencies (nonlinear regression analysis based on the h2 coefficient) between broadband SEEG signals and within typical sub-bands, before and after stimulation, or before and during the seizure with AOA, respectively. Stimulation of the right lateral prefrontal cortex induced a manic state lasting several hours. Its onset was associated with significant increase of broadband-signal functional coupling between the right hemispheric limbic nodes, the temporal pole and the claustrum, whereas significant decorrelation between the right lateral prefrontal and the anterior cingulate cortex was observed in theta-band. In contrast, ictal alteration of awareness was associated with increased broadband and sub-bands synchronization within and between the internal and external awareness networks, including the anterior and middle cingulate, the mesial and lateral prefrontal, the inferior parietal and the temporopolar cortex. Our data suggest the existence of network- and frequency-specific functional connectivity patterns during manic state. A transient desynchronization of theta activity between the external and internal awareness network hubs is likely to increase awareness, with potential therapeutic effect.

Slow oscillations open susceptible time windows for epileptic discharges.

OBJECTIVE: In patients with epilepsy, interictal epileptic discharges are a diagnostic hallmark of epilepsy and represent abnormal, so-called "irritative" activity that disrupts normal cognitive functions. Despite their clinical relevance, their mechanisms of generation remain poorly understood. It is assumed that brain activity switches abruptly, unpredictably, and supposedly randomly to these epileptic transients. We aim to study the period preceding these epileptic discharges, to extract potential proepileptogenic mechanisms supporting their expression. METHODS: We used multisite intracortical recordings from patients who underwent intracranial monitoring for refractory epilepsy, the majority of whom had a mesial temporal lobe seizure onset zone. Our objective was to evaluate the existence of proepileptogenic windows before interictal epileptic discharges. We tested whether the amplitude and phase synchronization of slow oscillations (.5-4 Hz and 4-7 Hz) increase before epileptic discharges and whether the latter are phase-locked to slow oscillations. Then, we tested whether the phase-locking of neuronal activity (assessed by high-gamma activity, 60-160 Hz) to slow oscillations increases before epileptic discharges to provide a potential mechanism linking slow oscillations to interictal activities. RESULTS: Changes in widespread slow oscillations anticipate upcoming epileptic discharges. The network extends beyond the irritative zone, but the increase in amplitude and phase synchronization is rather specific to the irritative zone. In contrast, epileptic discharges are phase-locked to widespread slow oscillations and the degree of phase-locking tends to be higher outside the irritative zone. Then, within the irritative zone only, we observe an increased coupling between slow oscillations and neuronal discharges before epileptic discharges. SIGNIFICANCE: Our results show that epileptic discharges occur during vulnerable time windows set up by a specific phase of slow oscillations. The specificity of these permissive windows is further reinforced by the increased coupling of neuronal activity to slow oscillations. These findings contribute to our understanding of epilepsy as a distributed oscillopathy and open avenues for future neuromodulation strategies aiming at disrupting proepileptic mechanisms.

Interictal stereotactic-EEG functional connectivity in refractory focal epilepsies.

Drug-refractory focal epilepsies are network diseases associated with functional connectivity alterations both during ictal and interictal periods. A large majority of studies on the interictal/resting state have focused on functional MRI-based functional connectivity. Few studies have used electrophysiology, despite its high temporal capacities. In particular, stereotactic-EEG is highly suitable to study functional connectivity because it permits direct intracranial electrophysiological recordings with relative large-scale sampling. Most previous studies in stereotactic-EEG have been directed towards temporal lobe epilepsy, which does not represent the whole spectrum of drug-refractory epilepsies. The present study aims at filling this gap, investigating interictal functional connectivity alterations behind cortical epileptic organization and its association with post-surgical prognosis. To this purpose, we studied a large cohort of 59 patients with malformation of cortical development explored by stereotactic-EEG with a wide spatial sampling (76 distinct brain areas were recorded, median of 13.2 per patient). We computed functional connectivity using non-linear correlation. We focused on three zones defined by stereotactic-EEG ictal activity: the epileptogenic zone, the propagation zone and the non-involved zone. First, we compared within-zone and between-zones functional connectivity. Second, we analysed the directionality of functional connectivity between these zones. Third, we measured the associations between functional connectivity measures and clinical variables, especially post-surgical prognosis. Our study confirms that functional connectivity differs according to the zone under investigation. We found: (i) a gradual decrease of the within-zone functional connectivity with higher values for epileptogenic zone and propagation zone, and lower for non-involved zones; (ii) preferential coupling between structures of the epileptogenic zone; (iii) preferential coupling between epileptogenic zone and propagation zone; and (iv) poorer post-surgical outcome in patients with higher functional connectivity of non-involved zone (within- non-involved zone, between non-involved zone and propagation zone functional connectivity). Our work suggests that, even during the interictal state, functional connectivity is reinforced within epileptic cortices (epileptogenic zone and propagation zone) with a gradual organization. Moreover, larger functional connectivity alterations, suggesting more diffuse disease, are associated with poorer post-surgical prognosis. This is consistent with computational studies suggesting that connectivity is crucial in order to model the spatiotemporal dynamics of seizures.10.1093/brain/awy214_video1awy214media15833456182001.

MULAN: Evaluation and ensemble statistical inference for functional connectivity.

Many analysis methods exist to extract graphs of functional connectivity from neuronal networks. Confidence in the results is limited because, (i) different methods give different results, (ii) parameter setting directly influences the final result, and (iii) systematic evaluation of the results is not always performed. Here, we introduce MULAN (MULtiple method ANalysis), which assumes an ensemble based approach combining multiple analysis methods and fuzzy logic to extract graphs with the most probable structure. In order to reduce the dependency on parameter settings, we determine the best set of parameters using a genetic algorithm on simulated datasets, whose temporal structure is similar to the experimental one. After a validation step, the selected set of parameters is used to analyze experimental data. The final step cross-validates experimental subsets of data and provides a direct estimate of the most likely graph and our confidence in the proposed connectivity. A systematic evaluation validates our strategy against empirical stereotactic electroencephalography (SEEG) and functional magnetic resonance imaging (fMRI) data.

Discrimination of a medial functional module within the temporal lobe using an effective connectivity model: A CCEP study.

The temporal lobe is classically divided in two functional systems: the ventral visual pathway and the medial temporal memory system. However, their functional separation has been challenged by studies suggesting that the medial temporal lobe could be best understood as an extension of the hierarchically organized ventral visual pathway. Our purpose was to investigate (i) whether cerebral regions within the temporal lobe could be grouped into distinct functional assemblies, and (ii) which regions were central within these functional assemblies. We studied low intensity and low frequency electrical stimulations (0.5 mA, 1 Hz, 4 ms) performed during sixteen pre-surgical intracerebral EEG investigations in patients with medically intractable temporal or temporo-occipital lobe epilepsies. Eleven regions of interest were delineated per anatomical landmarks such as gyri and sulci. Effective connectivity based on electrophysiological feature (amplitude) of cortico-cortical evoked potentials (CCEPs) was evaluated and subjected to graph metrics. The amplitudes discriminated one medial module where the hippocampus could act as a signal amplifier. Mean amplitudes of CCEPs in regions of the temporal lobe showed a generalized Pareto distribution of probability suggesting neural synchronies to be self-organized critically. Our description of effective interactions within the temporal lobe provides a regional electrophysiological model of effective connectivity which is discussed in the context of the current hypothesis of pattern completion.

Estimating Parallel Processing in a Language Task Using Single-Trial Intracerebral Electroencephalography.

We provide a quantitative assessment of the parallel-processing hypothesis included in various language-processing models. First, we highlight the importance of reasoning about cognitive processing at the level of single trials rather than using averages. Then, we report the results of an experiment in which the hypothesis was tested at an unprecedented level of granularity with intracerebral data recorded during a picture-naming task. We extracted patterns of significant high-gamma activity from multiple patients and combined them into a single analysis framework that identified consistent patterns. Average signals from different brain regions, presumably indexing distinct cognitive processes, revealed a large degree of concurrent activity. In comparison, at the level of single trials, the temporal overlap of detected significant activity was unexpectedly low, with the exception of activity in sensory cortices. Our novel methodology reveals some limits on the degree to which word production involves parallel processing.

Interneurons contribute to the hemodynamic/metabolic response to epileptiform discharges.

Interpretation of hemodynamic responses in epilepsy is hampered by an incomplete understanding of the underlying neurovascular coupling, especially the contributions of excitation and inhibition. We made simultaneous multimodal recordings of local field potentials (LFPs), firing of individual neurons, blood flow, and oxygen level in the somatosensory cortex of anesthetized rats. Epileptiform discharges induced by bicuculline injections were used to trigger large local events. LFP and blood flow were robustly coupled, as were LFP and tissue oxygen. In a parametric linear model, LFP and the baseline activities of cerebral blood flow and tissue partial oxygen tension contributed significantly to blood flow and oxygen responses. In an analysis of recordings from 402 neurons, blood flow/tissue oxygen correlated with the discharge of putative interneurons but not of principal cells. Our results show that interneuron activity is important in the vascular and metabolic responses during epileptiform discharges.

Comparison of Brain Networks During Interictal Oscillations and Spikes on Magnetoencephalography and Intracerebral EEG.

Electromagnetic source localization in electroencephalography (EEG) and magnetoencephalography (MEG) allows finding the generators of transient interictal epileptiform discharges ('interictal spikes'). In intracerebral EEG (iEEG), oscillatory activity (above 30 Hz) has also been shown to be a marker of neuronal dysfunction. Still, the difference between networks involved in transient and oscillatory activities remains largely unknown. Our goal was thus to extract and compare the networks involved in interictal oscillations and spikes, and to compare the non-invasive results to those obtained directly within the brain. In five patients with both MEG and iEEG recordings, we computed correlation graphs across regions, for (1) interictal spikes and (2) epileptic oscillations around 30 Hz. We show that the corresponding networks can involve a widespread set of regions (average of 10 per patient), with only partial overlap (38 % of the total number of regions in MEG, 50 % in iEEG). The non-invasive results were concordant with intracerebral recordings (79 % for the spikes and 50 % for the oscillations). We compared our interictal results to iEEG ictal data. The regions labeled as seizure onset zone (SOZ) belonged to interictal networks in a large proportion of cases: 75 % (resp. 58 %) for spikes and 58 % (resp. 33 %) for oscillations in iEEG (resp. MEG). A subset of SOZ regions were detected by one type of discharges but not the other (25 % for spikes and 8 % for oscillations). Our study suggests that spike and oscillatory activities involve overlapping but distinct networks, and are complementary for presurgical mapping.

Simultaneous recording of MEG, EEG and intracerebral EEG during visual stimulation: from feasibility to single-trial analysis.

Electroencephalography (EEG), magnetoencephalography (MEG), and intracerebral stereotaxic EEG (SEEG) are the three neurophysiological recording techniques, which are thought to capture the same type of brain activity. Still, the relationships between non-invasive (EEG, MEG) and invasive (SEEG) signals remain to be further investigated. In early attempts at comparing SEEG with either EEG or MEG, the recordings were performed separately for each modality. However such an approach presents substantial limitations in terms of signal analysis. The goal of this technical note is to investigate the feasibility of simultaneously recording these three signal modalities (EEG, MEG and SEEG), and to provide strategies for analyzing this new kind of data. Intracerebral electrodes were implanted in a patient with intractable epilepsy for presurgical evaluation purposes. This patient was presented with a visual stimulation paradigm while the three types of signals were simultaneously recorded. The analysis started with a characterization of the MEG artifact caused by the SEEG equipment. Next, the average evoked activities were computed at the sensor level, and cortical source activations were estimated for both the EEG and MEG recordings; these were shown to be compatible with the spatiotemporal dynamics of the SEEG signals. In the average time-frequency domain, concordant patterns between the MEG/EEG and SEEG recordings were found below the 40 Hz level. Finally, a fine-grained coupling between the amplitudes of the three recording modalities was detected in the time domain, at the level of single evoked responses. Importantly, these correlations have shown a high level of spatial and temporal specificity. These findings provide a case for the ability of trimodal recordings (EEG, MEG, and SEEG) to reach a greater level of specificity in the investigation of brain signals and functions.

How fast does the brain recover after an epileptic seizure?

The postictal state, an abnormal cerebral condition following a seizure until the return to the interictal baseline, is frequently overlooked, despite often exceeding ictal duration and significantly impacting patients' lives. This study analyzes stereo-EEG (SEEG) signal dynamics using permutation entropy to quantify recovery time (postictal alteration time - PAT) in focal epilepsy and its clinical correlations. The average PAT was 4.5 min, extending up to an hour and was highest in temporal epilepsy and hippocampal sclerosis. Correlating with age at seizure onset and at SEEG, PAT provides a solution for operationally defining the postictal state and guiding interventions.

Combining independent component analysis and source localization for improving spatial sampling of stereoelectroencephalography in epilepsy.

Stereoelectroencephalography is a powerful intracerebral EEG recording method for the presurgical evaluation of epilepsy. It consists in implanting depth electrodes in the patient's brain to record electrical activity and map the epileptogenic zone, which should be resected to render the patient seizure-free. Stereoelectroencephalography has high spatial accuracy and signal-to-noise ratio but remains limited in the coverage of the explored brain regions. Thus, the implantation might provide a suboptimal sampling of epileptogenic regions. We investigate the potential of improving a suboptimal stereoelectroencephalography recording by performing source localization on stereoelectroencephalography signals. We propose combining independent component analysis, connectivity measures to identify components of interest, and distributed source modelling. This approach was tested on two patients with two implantations each, the first failing to characterize the epileptogenic zone and the second giving a better diagnosis. We demonstrate that ictal and interictal source localization performed on the first stereoelectroencephalography recordings matches the findings of the second stereo-EEG exploration. Our findings suggest that independent component analysis followed by source localization on the topographies of interest is a promising method for retrieving the epileptogenic zone in case of suboptimal implantation.

Identification of Early Hippocampal Dynamics during Recognition Memory with Independent Component Analysis.

The hippocampus is generally considered to have relatively late involvement in recognition memory, its main electrophysiological signature being between 400 and 800 ms after stimulus onset. However, most electrophysiological studies have analyzed the hippocampus as a single responsive area, selecting only a single-site signal exhibiting the strongest effect in terms of amplitude. These classical approaches may not capture all the dynamics of this structure, hindering the contribution of other hippocampal sources that are not located in the vicinity of the selected site. We combined intracerebral electroencephalogram recordings from epileptic patients with independent component analysis during a recognition memory task involving the recognition of old and new images. We identified two sources with different responses emerging from the hippocampus: a fast one (maximal amplitude at ∼250 ms) that could not be directly identified from raw recordings and a latter one, peaking at ∼400 ms. The former component presented different amplitudes between old and new items in 6 out of 10 patients. The latter component had different delays for each condition, with a faster activation (∼290 ms after stimulus onset) for recognized items. We hypothesize that both sources represent two steps of hippocampal recognition memory, the faster reflecting the input from other structures and the latter the hippocampal internal processing. Recognized images evoking early activations would facilitate neural computation in the hippocampus, accelerating memory retrieval of complementary information. Overall, our results suggest that the hippocampal activity is composed of several sources with an early activation related to recognition memory.

Simultaneous invasive and non-invasive recordings in humans: A novel Rosetta stone for deciphering brain activity.

Simultaneous noninvasive and invasive electrophysiological recordings provide a unique opportunity to achieve a comprehensive understanding of human brain activity, much like a Rosetta stone for human neuroscience. In this review we focus on the increasingly-used powerful combination of intracranial electroencephalography (iEEG) with scalp electroencephalography (EEG) or magnetoencephalography (MEG). We first provide practical insight on how to achieve these technically challenging recordings. We then provide examples from clinical research on how simultaneous recordings are advancing our understanding of epilepsy. This is followed by the illustration of how human neuroscience and methodological advances could benefit from these simultaneous recordings. We conclude with a call for open data sharing and collaboration, while ensuring neuroethical approaches and argue that only with a true collaborative approach the promises of simultaneous recordings will be fulfilled.