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We report the case of a patient with nonlesional, pharmacoresistant epilepsy. Bilateral synchronous discharges as well as focal discharges were identified on electroencephalogram (EEG). After non-invasive and invasive presurgical evaluation, a probable epileptogenic zone was identified with concordant results of focal origin involvement of the left temporal pole. Resective epilepsy surgery provided seizure freedom (last follow-up at 12 months). The case nicely illustrates that bilateral synchronous discharges or/and a lack of lesions on neuroimaging are not contraindications to presurgical evaluation of epilepsy, which on the contrary should be offered to all patients with pharmacoresistant epilepsy. The case also illustrates the importance of combining electrophysiological, imaging, and neuropsychological information to guide intracranial EEG placement and improve postsurgical outcome.

Dementia is a global public health problem with increasing prevalence and incidence worldwide. The African continent is expected to bear the biggest brunt of the burden of dementia by 2050 because of the rapid demographic changes, including rapid population growth, an increase in life expectancy, and ageing. However, French-speaking Sub-Saharan African (FS-SSA) countries are underrepresented in research on dementia in Africa. While the reasons are diverse and complex, linguistic and cultural barriers to research, disproportionately affect these countries and may be significant factors. Any efforts, therefore, to redress the burden of dementia in Africa must consider the specific demographic, cultural, and linguistic characteristics of FS-SSA countries. This scoping review explores the current state of knowledge in dementia and cognitive impairment in Sub-Saharan Africa, highlighting research gaps and specific patterns unique to FS-SSA Africa. We identify pathways for research to bridge the knowledge gaps on dementia in FS-SSA as part of the global endeavor to tackle dementia worldwide.

BACKGROUND AND PURPOSE The purpose was to evaluate whether intracranial interictal epileptiform discharges (IEDs) that are not visible on the scalp are associated with changes in the frequency spectrum on scalp electroencephalograms (EEGs). METHODS Simultaneous scalp high-density EEG and intracranial EEG recordings were recorded in nine patients undergoing pre-surgical invasive recordings for pharmaco-resistant temporal lobe epilepsy. Epochs with hippocampal IED visible on intracranial EEG (ic-IED) but not on scalp EEG were selected, as well as control epochs without ic-IED. Welch's power spectral density was computed for each scalp electrode and for each subject; the power spectral density was further averaged across the canonical frequency bands and compared between the two conditions with and without ic-IED. For each patient the peak frequency in the delta band (the significantly strongest frequency band in all patients) was determined during periods of ic-IED. The five electrodes showing strongest power at the peak frequency were also determined. RESULTS It was found that intracranial IEDs are associated with an increase in delta power on scalp EEGs, in particular at a frequency ≥1.4 Hz. Electrodes showing slow frequency power changes associated with IEDs were consistent with the hemispheric lateralization of IEDs. Electrodes with maximum power of slow activity were not limited to temporal regions but also involved frontal (bilateral or unilateral) regions. CONCLUSIONS In patients with a clinical picture suggestive of temporal lobe epilepsy, the presence of delta slowing ≥1.4 Hz in anterior temporal regions can represent a scalp marker of hippocampal IEDs. To our best knowledge this is the first study that demonstrates the co-occurrence of ic-IED and increased delta power.

Studies at 3T have shown that TTThe proposed method for morphometry delivered segmentation masks without statistically significant differences from those derived with the original pipeline at 3T and achieved accurate segmentation at 7T. The established normative atlas allowed characterizing tissue alterations in single-subject comparisons at 7T, and showed greater anatomical details compared with 3T results.A high-resolution quantitative atlas with an adapted pipeline was introduced and validated. Several case studies on different clinical conditions showed the feasibility, potential and limitations of high-resolution single-subject comparisons based on quantitative MRI atlases. This method in conjunction with 7T higher resolution broadens the range of potential applications of quantitative MRI in clinical practice.

Whole brain, large-scale functional connectivity networks or connectomes have been characterized on different temporal and spatial scales in humans using EEG and fMRI. Whole brain epileptic networks have been investigated with both EEG and fMRI, but due to the different acquisition approaches it is unclear to what extent those results can be related. In consequence clinical research in epilepsy would profit from a unified multimodal functional connectome description as a linking framework to better map underlying brain function and pathological functional networks. In this study we aim to characterize the spatial correlation between EEG and fMRI connectivity in temporal lobe epilepsy.From two independent centers, we acquired resting-state concurrent EEG-fMRI from a total of 35 healthy controls and 34 TLE patients (18 right TLE and 16 left TLE). Data was projected into the Desikan brain atlas (mean BOLD activity for fMRI and source reconstruction for EEG). Whole brain functional connectivity from fMRI (Pearson correlation) and EEG (corrected imaginary part of the coherency) were correlated for all subjects.In healthy controls, average EEG and fMRI whole-brain connectivity was moderately correlated (r∼0.3). For both imaging centers, correlation between EEG and fMRI whole brain connectivity was increased in rTLE when compared to controls for lower frequency bands (EEG-delta, theta and alpha). Conversely correlation between EEG and fMRI connectivity of lTLE patients was decreased in respect to healthy subjects (EEG-beta vs. fMRI connectivity only). While the alteration of the EEG-fMRI correlation in rTLE patients could not be related to a local effect, in lTLE patients it was locally linked to the Default Mode Network.We demonstrated, using two independent datasets, that EEG and fMRI connectivity is correlated for both healthy subjects and patients. The increased correlation of EEG and fMRI connectivity in rTLE patients vs. controls and decreased correlation in lTLE patients vs. controls suggests a differential organization of mono-lateral focal epilepsy of the same type, which needs to be considered when comparing fMRI to EEG connectivity. It also demonstrates that each modality provides distinct information, highlighting the benefit of multimodal assessment in epilepsy. The observed property of distinct topological patterns depending on the lateralization of the epilepsy could be taken into account when clinically defining the epileptic focus of patients.

The large-scale organization of functional connectivity (FC) — the functional connectome — traverses distinct spatial patterns in a dynamic trajectory as demonstrated independently in fMRI and electrophysiological studies. These patterns are thought to satisfy ever-changing processing demands. FMRI and electrophysiology capture partly non-overlapping neural populations at different timescales, and it remains unknown to what degree the dynamic connectome trajectories across the two modalities are associated. We sought to clarify this relationship by studying resting wakefulness in a rare concurrent intracranial EEG and functional MRI dataset (iEEG-fMRI; 9 human neurosurgical patients) and in whole-brain connectomes obtained from source-localized EEG-fMRI (26 healthy humans). We measured “spatial convergence” as cross-modal spatial similarity of connectome configurations at a given time, and “temporal convergence” as synchronous occurrence of spatial convergence. We investigated three possible scenarios characterizing the cross-modal association of connectome trajectories: I) spatially and temporally convergent, II) spatially convergent but temporally divergent, and III) spatially and temporally divergent. We found that the behavior of fMRI and iEEG/EEG is consistent with scenario II: connectome trajectories spatially converge at intermittent times. Importantly, such asynchronous spatial convergence of connectome configurations was driven by cross-modally matched recurrent connectome states, independently across electrophysiological timescales. This connectome-level multi-frequency spatial convergence and temporal divergence suggests that hemodynamic and electrophysiological signals capture distinct aspects of FC, rather than serving as intermodal measurements of the same phenomenon. The multitude of flexible trajectories across timescales may concurrently enable FC across multiple independent sets of distributed brain regions.

For the first time, an ecstatic aura has been evoked through the electrical stimulation of the dorsal anterior insula during presurgical invasive intracerebral monitoring in a patient who did not suffer from an ecstatic form of epilepsy. This case provides more evidence that the anterior insula is the major generator of such a mystical-type experience even in individuals with no underlying brain network changes related to a preexisting ecstatic epilepsy. ANN NEUROL 2022;91:289-292.

Acute awareness impairment, confusion, or other neurologic symptoms occurring in supine position should raise the possibility of transient intracranial hypertension, notably through the compression of a possible pseudomeningocele.

Objective: In patients with epilepsy, interictal epileptic discharges are a diag-nostic 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 epi-leptic 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 under-went intracranial monitoring for refractory epilepsy, the majority of whom had a mesial temporal lobe seizure onset zone. Our objective was to evaluate the ex-istence 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 oscil-lations 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 neu-ronal activity to slow oscillations. These findings contribute to our understanding of epilepsy as a distributed oscillopathy and open avenues for future neuromodu-lation strategies aiming at disrupting proepileptic mechanisms.

Objective: Epilepsy is a network disease with epileptic activity and cognitive impairment involving large-scale brain networks. A complex network is involved in the seizure and in the interictal epileptiform discharges (IEDs). Directed connectivity analysis, describing the information transfer between brain regions, and graph analysis are applied to high-density EEG to characterise networks. Methods: We analysed 19 patients with focal epilepsy who had high-density EEG containing IED and underwent surgery. We estimated cortical activity during IED using electric source analysis in 72 atlas-based cortical regions of the individual brain MRI. We applied directed connectivity analysis (information Partial Directed Coherence) and graph analysis on these sources and compared patients with good vs poor post-operative outcome at global, hemispheric and lobar level. Results: We found lower network integration reflected by global, hemispheric, lobar efficiency during the IED (p

Objective: To compare the spatial accuracy of 6 linear distributed inverse solutions for EEG source localisation of interictal epileptic discharges: Minimum Norm, Weighted Minimum Norm, Low-Resolution Electromagnetic Tomography (LORETA), Local Autoregressive Average (LAURA), Standardised LORETA, and Exact LORETA.Methods: Spatial accuracy was assessed clinically by retrospectively comparing the maximum source of averaged interictal discharges to the resected brain area in 30 patients with successful epilepsy surgery, based on 204-channel EEG. Additionally, localisation errors of the inverse solutions were assessed in computer simulations, with different levels of noise added to the signal in both sensor space and source space.Results: In the clinical evaluations, the source maximum was located inside the resected brain area in 50-57% of patients when using LORETA or LAURA, while all other inverse solutions performed significantly worse (17-30%; corrected p < 0.01). In the simulation studies, when noise levels exceeded 10%, LORETA and LAURA had substantially smaller localisation errors than the other inverse solutions.Conclusions: LORETA and LAURA provided the highest spatial accuracy both in clinical and simulated data, alongside with a comparably high robustness towards noise.Significance: Among the different linear inverse solution algorithms tested, LORETA and LAURA might be preferred for interictal EEG source localisation.

SUMMARY High-density EEG (HD-EEG) recordings use a higher spatial sampling of scalp electrodes than a standard 10-20 low-density EEG montage. Although several studies have demonstrated improved localization of the epileptogenic cortex using HD-EEG, widespread implementation is impeded by cost, setup and interpretation time, and lack of specific or sufficient procedural billing codes. Despite these barriers, HD-EEG has been in use at several institutions for years. These centers have noted utility in a variety of clinical scenarios where increased spatial resolution from HD-EEG has been required, justifying the extra time and cost. We share select scenarios from several centers, using different recording techniques and software, where HD-EEG provided information above and beyond the standard low-density EEG. We include seven cases where HD-EEG contributed directly to current clinical care of epilepsy patients and highlight two novel techniques which suggest potential opportunities to improve future clinical care. Cases illustrate how HD-EEG allows clinicians to: case 1-lateralize falsely generalized interictal epileptiform discharges; case 2-improve localization of falsely generalized epileptic spasms; cases 3 and 4-improve localization of interictal epileptiform discharges in anatomic regions below the circumferential limit of standard low-density EEG coverage; case 5-improve noninvasive localization of the seizure onset zone in lesional epilepsy; cases 6 and 7-improve localization of the seizure onset zone to guide invasive investigation near eloquent cortex; case 8-identify epileptic fast oscillations; and case 9-map language cortex. Together, these nine cases illustrate that using both visual analysis and advanced techniques, HD-EEG can play an important role in clinical management.

The functional organization of neural processes is constrained by the brain’ s intrinsic structural connectivity. Here, we explore the potential of exploiting this structure in order to improve the signal representation properties of brain activity and its dynamics. Using a multi-modal imaging dataset (electroencephalography, structural MRI and diffusion MRI), we represent electrical brain activity at the cortical surface as a time-varying composition of harmonic modes of structural connectivity. The harmonic modes are termed connectome harmonics, and their representation is known as the connectome spectrum of the signal. We found that: first, the brain activity signal is more compactly represented by the connectome spectrum than by the traditional area-based representation; second, the connectome spectrum characterizes fast brain dynamics in terms of signal broadcasting profile, revealing different temporal regimes of integration and segregation that are consistent across participants. And last, the connectome spectrum characterises fast brain dynamics with fewer degrees of freedom than area-based signal representations. Specifically, we show that with the connectome spectrum representation, fewer dimensions are needed to capture the differences between low-level and high-level visual processing, and the topological properties of the signal. In summary, this work provides statistical, functional and topological evidence supporting that by accounting for the brain’ s structural connectivity fosters a more comprehensive understanding of large-scale dynamic neural functioning.

International audience; Identifying a structural brain lesion on MRI has important implications in epilepsy and is the most important factor that correlates with seizure freedom after surgery in patients with drug-resistant focal onset epilepsy. However, at conventional magnetic field strengths (1.5 and 3T), only approximately 60%–85% of MRI examinations reveal such lesions. Over the last decade, studies have demonstrated the added value of 7T MRI in patients with and without known epileptogenic lesions from 1.5 and/or 3T. However, translation of 7T MRI to clinical practice is still challenging, particularly in centers new to 7T, and there is a need for practical recommendations on targeted use of 7T MRI in the clinical management of patients with epilepsy. The 7T Epilepsy Task Force—an international group representing 21 7T MRI centers with experience from scanning over 2,000 patients with epilepsy—would hereby like to share its experience with the neurology community regarding the appropriate clinical indications, patient selection and preparation, acquisition protocols and setup, technical challenges, and radiologic guidelines for 7T MRI in patients with epilepsy. This article mainly addresses structural imaging; in addition, it presents multiple nonstructural MRI techniques that benefit from 7T and hold promise as future directions in epilepsy. Answering to the increased availability of 7T MRI as an approved tool for diagnostic purposes, this article aims to provide guidance on clinical 7T MRI epilepsy management by giving recommendations on referral, suitable 7T MRI protocols, and image interpretation.

The detection of causal effects among simultaneous observations provides knowledge about the underlying network, and is a topic of interests in many scientific areas. Over the years different causality measures have been developed, each with their own advantages and disadvantages. However, an extensive evaluation study is missing. In this work we consider some of the best-known causality measures i.e., cross-correlation, (conditional) Granger causality index (CGCI), partial directed coherence (PDC), directed transfer function (DTF), and partial mutual information on mixed embedding (PMIME). To correct for noise-related spurious connections, each measure (except PMIME) is tested for statistical significance based on surrogate data. The performance of the causality metrics is evaluated on a set of simulation models with distinct characteristics, to assess how well they work in- as well as outside of their “comfort zone.” PDC and DTF perform best on systems with frequency-specific connections, while PMIME is the only one able to detect non-linear interactions. The varying performance depending on the system characteristics warrants the use of multiple measures and comparing their results to avoid errors. Furthermore, lags between coupled variables are inherent to real-world systems and could hold essential information on the network dynamics. They are however often not taken into account and we lack proper tools to estimate them. We propose three new methods for lag estimation in multivariate time series, based on autoregressive modelling and information theory. One of the autoregressive methods and the one based on information theory were able to reliably identify the correct lag value in different simulated systems. However, only the latter was able to maintain its performance in the case of non-linear interactions. As a clinical application, the same methods are also applied on an intracranial recording of an epileptic seizure. The combined knowledge from the causality measures and insights from the simulations, on how these measures perform under different circumstances and when to use which one, allow us to recreate a plausible network of the seizure propagation that supports previous observations of desynchronisation and synchronisation during seizure progression. The lag estimation results show absence of a relationship between connectivity strength and estimated lag values, which contradicts the line of thinking in connectivity shaped by the neuron doctrine.

Interictal high-frequency oscillations are discussed as biomarkers for epileptogenic brain tissue that should be resected in epilepsy surgery to achieve seizure freedom. The prospective classification of tissue sampled by individual electrode contacts remains a challenge. We have developed an automated, prospective definition of clinically relevant high-frequency oscillations in intracranial EEG from Montreal and tested it in recordings from Zurich. We here validated the algorithm on intracranial EEG that was recorded in an independent epilepsy centre so that the analysis was blinded to seizure outcome. We selected consecutive patients who underwent resective epilepsy surgery in Geneva with post-surgical follow-up > 12 months. We analysed long-term recordings during sleep that we segmented into intervals of 5 min. High-frequency oscillations were defined in the ripple (80–250 Hz) and the fast ripple (250–500 Hz) frequency bands. Contacts with the highest rate of ripples co-occurring with fast ripples designated the relevant area. As a validity criterion, we calculated the test–retest reliability of the high-frequency oscillations area between the 5 min intervals (dwell time ≥50%). If the area was not fully resected and the patient suffered from recurrent seizures, this was classified as a true positive prediction. We included recordings from 16 patients (median age 32 years, range 18–53 years) with stereotactic depth electrodes and/or with subdural electrode grids (median follow-up 27 months, range 12–55 months). For each patient, we included several 5 min intervals (median 17 intervals). The relevant area had high test–retest reliability across intervals (median dwell time 95%). In two patients, the test–retest reliability was too low (dwell time < 50%) so that outcome prediction was not possible. The area was fully included in the resected volume in 2/4 patients who achieved post-operative seizure freedom (specificity 50%) and was not fully included in 9/10 patients with recurrent seizures (sensitivity 90%), leading to an accuracy of 79%. An additional exploratory analysis suggested that high-frequency oscillations were associated with interictal epileptic discharges only in channels within the relevant area and not associated in channels outside the area. We thereby validated the automated procedure to delineate the clinically relevant area in each individual patient of an independently recorded dataset and achieved the same good accuracy as in our previous studies. The reproducibility of our results across datasets is promising for a multicentre study to test the clinical application of high-frequency oscillations to guide epilepsy surgery., Dimakopoulos et al. applied a fully automated high-frequency oscillations detection algorithm on pre-surgical intracranial EEG recordings from patients that underwent resective epilepsy surgery. The analysis was blind to clinical outcome and achieved good ‘prediction’ of seizure outcome. The high-frequency oscillations reproducibility indicates the value of a future multicenter study to test clinical application., Graphical Abstract Graphical Abstract

The functional organization of neural processes is constrained by the brain's intrinsic structural connectivity, i.e., the connectome. Here, we explore how structural connectivity can improve the representation of brain activity signals and their dynamics. Using a multi-modal imaging dataset (electroencephalography, structural MRI, and diffusion MRI), we represent electrical brain activity at the cortical surface as a time-varying composition of harmonic modes of structural connectivity. These harmonic modes are known as connectome harmonics. Here we describe brain activity signal as a time-varying combination of connectome harmonics. We term this description as the connectome spectrum of the signal. We found that: first, the brain activity signal is represented more compactly by the connectome spectrum than by the traditional area-based representation; second, the connectome spectrum characterizes fast brain dynamics in terms of signal broadcasting profile, revealing different temporal regimes of integration and segregation that are consistent across participants. And last, the connectome spectrum characterizes fast brain dynamics with fewer degrees of freedom than area-based signal representations. Specifically, we show that a smaller number of dimensions capture the differences between low-level and high-level visual processing in the connectome spectrum. Also, we demonstrate that connectome harmonics capture more sensitively the topological properties of brain activity. In summary, this work provides statistical, functional, and topological evidence indicating that the description of brain activity in terms of structural connectivity fosters a more comprehensive understanding of large-scale dynamic neural functioning.

It has been suggested that slow oscillations in the subthalamic nucleus (STN) reflect top-down inputs from the medial prefrontal cortex, thus implementing behavior control. It is unclear, however, whether the STN oscillations are related to cortical activity in a bottom-up manner. To assess resting-state subcortico-cortical interactions, we recorded simultaneous scalp electroencephalographic activity and local field potentials in the STN (LFP-STN) in 11 patients with Parkinson’s disease implanted with deep brain stimulation electrodes in the on-medication state during rest. We assessed the cross-structural phase-amplitude coupling (PAC) between the STN and cortical activity within a wide frequency range of 1 to 100 Hz. The PAC was dominant between the δ/θ STN phase and β/γ cortical amplitude in most investigated scalp regions and between the δ cortical phase and θ/α STN amplitude in the frontal and temporal regions. The cross-frequency linkage between the slow oscillations of the LFP-STN activity and the amplitude of the scalp-recorded cortical activity at rest was demonstrated, and similar involvement of the left and right STNs in the coupling was observed. Our results suggest that the STN plays a role in both bottom-up and top-down processes within the subcortico-cortical circuitries of the human brain during the resting state. A relative left–right symmetry in the STN-cortex functional linkage was suggested. Practical treatment studies would be necessary to assess whether unilateral stimulation of the STN might be sufficient for treatment of Parkinson’s disease.

We present an approach for tracking fast spatiotemporal cortical dynamics in which we combine white matter connectivity data with source-projected electroencephalographic (EEG) data. We employ the mathematical framework of graph signal processing in order to derive the Fourier modes of the brain structural connectivity graph, or “network harmonics”. These network harmonics are naturally ordered by smoothness. Smoothness in this context can be understood as the amount of variation along the cortex, leading to a multi-scale representation of brain connectivity. We demonstrate that network harmonics provide a sparse representation of the EEG signal, where, at certain times, the smoothest 15 network harmonics capture 90% of the signal power. This suggests that network harmonics are functionally meaningful, which we demonstrate by using them as a basis for the functional EEG data recorded from a face detection task. There, only 13 network harmonics are sufficient to track the large-scale cortical activity during the processing of the stimuli with a 50 ms resolution, reproducing well-known activity in the fusiform face area as well as revealing co-activation patterns in somatosensory/motor and frontal cortices that an unconstrained ROI-by-ROI analysis fails to capture. The proposed approach is simple and fast, provides a means of integration of multimodal datasets, and is tied to a theoretical framework in mathematics and physics. Thus, network harmonics point towards promising research directions both theoretically - for example in exploring the relationship between structure and function in the brain - and practically - for example for network tracking in different tasks and groups of individuals, such as patients.

Both electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) are non-invasive methods that show complementary aspects of human brain activity. Despite measuring different proxies of brain activity, both the measured blood-oxygenation (fMRI) and neurophysiological recordings (EEG) are indirectly coupled. The electrophysiological and BOLD signal can map the underlying functional connectivity structure at the whole brain scale at different timescales. Previous work demonstrated a moderate but significant correlation between resting-state functional connectivity of both modalities, however there is a wide range of technical setups to measure simultaneous EEG-fMRI and the reliability of those measures between different setups remains unknown. This is true notably with respect to different magnetic field strengths (low and high field) and different spatial sampling of EEG (medium to high-density electrode coverage).Here, we investigated the reproducibility of the bimodal EEG-fMRI functional connectome in the most comprehensive resting-state simultaneous EEG-fMRI dataset compiled to date including a total of 72 subjects from four different imaging centers. Data was acquired from 1.5T, 3T and 7T scanners with simultaneously recorded EEG using 64 or 256 electrodes. We demonstrate that the whole-brain monomodal connectivity reproducibly correlates across different datasets and that a moderate crossmodal correlation between EEG and fMRI connectivity of r≈0.3 can be reproducibly extracted in low- and high-field scanners. The crossmodal correlation was strongest in the EEG-β frequency band but exists across all frequency bands. Both homotopic and within intrinsic connectivity network (ICN) connections contributed the most to the crossmodal relationship.This study confirms, using a considerably diverse range of recording setups, that simultaneous EEG-fMRI offers a consistent estimate of multimodal functional connectomes in healthy subjects that are dominantly linked through a functional core of ICNs across spanning across the different timescales measured by EEG and fMRI. This opens new avenues for estimating the dynamics of brain function and provides a better understanding of interactions between EEG and fMRI measures. This observed level of reproducibility also defines a baseline for the study of alterations of this coupling in pathological conditions and their role as potential clinical markers.

Background Olfactory hallucinations can be part of epileptic seizures of orbitofrontal origin. Olfactory hallucinations, however, are rare and therefore the semiology, localization and lateralization characteristics are underdetermined. In addition, many discrepancies are found in the literature regarding olfactory processing and orbitofrontal (OF) functions and olfactory function. Particularly, the questions of laterality and affective component in coding of odors in the OF cortex remain controversial. Aims This study explored whether cortical electrical stimulation of the OF and mesiotemporal brain can trigger olfactory hallucinations with special focus on olfactory percepts in terms of laterality and hedonics. Materials and methods Eight patients with temporal lobe epilepsy participated in the study, at the time of invasive exploration of their epilepsy. The most distal contact of the OF and anterior hippocampus depth electrodes were stimulated (50 Hz, 0.2 ms biphasic pulse; maximal stimulation 4 mA). Patients were instructed to report any kind of sensation they might experience. Intracranial depth electrodes were localized (iElectrodes): subject-specific brain mask, subcortical segmentation and cortical parcellation based on the Destrieux atlas (FreeSurfer) were superposed to the coregistered T1-weighted MRI and CT images (SPM). The center of mass of each electrode-artifact cluster determined the electrode localization. The electrode labeling was done in patient space. To obtain the electrode coordinates in Montreal Neurological Institute (MNI) space, the images obtained previously in the patient space were first segmented and normalized (SPM). Then, the localization procedure (iElectrodes) was run again with these new normalized images in MNI space. Results No hallucination was evoked by stimulation, neither of the right nor the left hippocampus (8/8 patients). Pleasant olfactory hallucinations were evoked by OF stimulation in 5/8 patients in either hemisphere. Patients named the percept as the smell of lemon or coffee for example. Among those 5 patients, electrodes were localized in the cortex of the olfactory sulcus, medial orbital sulcus or medial OF gyrus. Increasing stimulation amplitude changed the olfactory percept identification in 3 out of those 5 patients. No affective judgement or change in perceived odor intensity was reported by the patients. No hallucination was evoked by the stimulation of the white matter of the medial OF brain in 3/8 patients independently of the hemisphere stimulated. Conclusions This study demonstrated that stimulation of the cortex of the medial OF brain and not of its white matter elicits specific pleasant olfactory hallucinations independently of the hemisphere stimulated, supporting one symmetrical olfactory processing in human.

Electroencephalographic (EEG) source imaging localizes the generators of neural activity in the brain. During presurgical epilepsy evaluation, EEG source imaging of interictal epileptiform discharges is an established tool to estimate the irritative zone. However, the origin of interictal activity can be partly or fully discordant with the origin of seizures. Therefore, source imaging based on ictal EEG data to determine the seizure onset zone can provide precious clinical information. In this descriptive review, we address the importance of localizing the seizure onset zone based on noninvasive EEG recordings as a complementary analysis that might reduce the burden of the presurgical evaluation. We identify three major challenges (low signal-to-noise ratio of the ictal EEG data, spread of ictal activity in the brain, and validation of the developed methods) and discuss practical solutions. We provide an extensive overview of the existing clinical studies to illustrate the potential clinical utility of EEG-based localization of the seizure onset zone. Finally, we conclude with future perspectives and the needs for translating ictal EEG source imaging into clinical practice.

Aim Generalized periodic discharge (GPD) is an EEG pattern of poor neurological outcome, frequently observed in comatose patients after cardiac arrest. The aim of our study was to identify the neuronal network generating ≤2.5 Hz GPD using EEG source localization and connectivity analysis. Methods We analyzed 40 comatose adult patients with anoxic-ischemic encephalopathy, who had 19 channel-EEG recording. We computed electric source analysis based on distributed inverse solution (LAURA) and we estimated cortical activity in 82 atlas-based cortical brain regions. We applied directed connectivity analysis (Partial Directed Coherence) on these sources to estimate the main drivers. Results Source analysis suggested that the GPD are generated in the cortex of the limbic system in the majority of patients (87.5%). Connectivity analysis revealed main drivers located in thalamus and hippocampus for the large majority of patients (80%), together with important activation also in amygdala (70%). Conclusions We hypothesize that the anoxic-ischemic dysfunction, leading to hyperactivity of the thalamo-cortical (limbic presumably) circuit, can result in an oscillatory thalamic activity capable of inducing periodic cortical (limbic, mostly medial-temporal and orbitofrontal) discharges, similarly to the case of generalized rhythmic spike-wave discharge in convulsive or non-convulsive status epilepticus.

Neuroimaging studies have shown that spontaneous brain activity is characterized as changing networks of coherent activity across multiple brain areas. However, the directionality of functional interactions between the most active regions in our brain at rest remains poorly understood. Here, we examined, at the whole-brain scale, the main drivers and directionality of interactions that underlie spontaneous human brain activity by applying directed functional connectivity analysis to electroencephalography (EEG) source signals. We found that the main drivers of electrophysiological activity were the posterior cingulate cortex (PCC), the medial temporal lobes (MTL), and the anterior cingulate cortex (ACC). Among those regions, the PCC was the strongest driver and had both the highest integration and segregation importance, followed by the MTL regions. The driving role of the PCC and MTL resulted in an effective directed interaction directed from posterior toward anterior brain regions. Our results strongly suggest that the PCC and MTL structures are the main drivers of electrophysiological spontaneous activity throughout the brain and suggest that EEG-based directed functional connectivity analysis is a promising tool to better understand the dynamics of spontaneous brain activity in healthy subjects and in various brain disorders.

Epileptic spasms represent a subcategory of motor seizures that have been extensively documented and recently re-classified by the International League Against Epilepsy as either generalized, focal or of unknown onset. Atypical characteristics continue to be reported in case studies, emphasizing the divergent morphological traits and putting into question the underlying aetiopathophysiology. Here, we report the findings of an infant with a triad of clinical manifestations during a single ictal event, comprising a cluster of epileptic spasms, vertical binocular nystagmus, and a focal tonic seizure. A video recording is presented that enabled clinical data to be correlated with EEG modifications. To date, a focal lesion has not been identified on brain imaging. The co-occurrence of these ictal paroxysms provides insight into the anatomical localization of seizure onset and complex epileptic networks involved, and challenges the pathophysiological hypothesis for epileptic spasms, implicating cortical-subcortical dysfunction and the implication of structures deep within the sulcus. Furthermore, the focal components both clinically and electrographically implicate involvement of the frontal eye field in the generation of vertical ictal nystagmus. [Published with video sequence on www.epilepticdisorders.com].

Background and purpose Surgery is the first choice therapeutic approach in case of drug-resistant epilepsy. Unfortunately, up to 43% of patients referred for presurgical assessment do not have a lesion detectable by routine 3T magnetic resonance imaging (MRI) (MRI-negative), although most of them likely have an underlying epileptogenic lesion. Thus, new MRI modalities with increased sensibility for epileptogenic lesions are required. This paper describes the magnetization-prepared two rapid acquisition gradient echoes (MP2RAGE) and susceptibility-weighted imaging (SWI) findings at 7T in a series of patients with drug-resistant epilepsy of different etiologies. Methods Prospective pilot study of 7 patients with drug-resistant lesional epilepsy and absence of contraindications for MRI underwent a research 7T head-only scanner. Qualitative analysis of the high-resolution MP2RAGE and SWI sequences is given for each case. This study was approved by the local ethics committee. Written informed consent was obtained from each participant. Results This study shows that such sequences at ultra-high field are new and valuable approaches to unravel and characterize epileptogenic lesions. Particularly, MP2RAGE shows a better delineation of lesions due to high gray-white matter contrast and structural resolution, and SWI reveals new imaging signs related to improved magnitude and phase contrast imaging. Conclusion MRI at ultra-high field is very promising for the detection of inconspicuous epileptogenic lesions and may facilitate epilepsy surgery of a great number of to-date MRI-negative patients.