Biology subjects

Nobili, L.

Publications and source records attributed to Nobili, L..

At least 19 recordsLinked to original sources

bioRxiv2026

Simultaneous recordings link intracranial and scalp correlates of somatosensory awareness in humans

The electrophysiological events underlying the conscious perception of touch in humans remain elusive. Scalp-EEG studies have identified a negative deflection peaking 120-200 ms after stimulus onset, known as the N140. This component, recently reinterpreted as the somatosensory awareness negativity (N140/SAN), has been proposed as a putative marker of tactile awareness, but its underlying neuronal dynamics are still unknown. Intracranial studies have shown that responses to median nerve stimulation include, after an initial phasic activation mainly localized to primary somatosensory cortices, a predominantly insulo-opercular tonic broadband high-frequency activity (BHA; 50-150 Hz) that has been linked to tactile awareness. Yet these scalp and intracranial signatures have never been directly linked. Using simultaneous high-density scalp EEG and intracranial EEG recordings, we show that the N140/SAN is the scalp manifestation of the insulo-opercular local field potential that follows the BHA response to stimulation. By bridging invasive and non-invasive recordings our results identify the neural origins of the N140/SAN and connect scalp and intracranial correlates of somatosensory awareness.

neuroscience↗

bioRxiv2026

Flattened brain hierarchy and increased EEG complexity during unusual bodily experiences and out-of-body experiences

Experiences in which the sense of the body becomes distorted offer a unique window into how the brain constructs the bodily self. Despite their clinical relevance in conditions such as depersonalization and body-image disorders, large-scale brain dynamics underlying unusual bodily experiences (UBEs) remain poorly understood, and no prior study has examined them systematically across wakefulness and sleep. Here, we analyzed a high-density EEG dataset including 36 UBE episodes, such as floating sensations, distorted body boundaries, and out-of-body experiences, recorded across rapid eye-movement (REM) sleep, light sleep, sleep arousals, and wakefulness during meditation (N=20). We quantified the temporal irreversibility of neural dynamics, a marker of cortical hierarchical organization, and complemented these analyses with whole-brain signal complexity measures following a within-subject design. UBEs were consistently associated with a global reduction in neural irreversibility and cortical hierarchical differentiation relative to non-UBEs and wakefulness, along with a concurrent increase in whole-brain complexity to levels comparable to wakefulness. Together, these findings suggest that UBEs reflect a neural state combining high informational richness with a transient flattening of cortical hierarchy, a profile that resembles that observed in psychedelic states. Key pointsO_LIUBEs are associated with a global flattening of brain hierarchy measured by EEG. C_LIO_LIUBEs are associated with increased global EEG complexity, resembling the psychedelic state. C_LI

neuroscience↗

bioRxiv2026

Frequency modulations of cortical synchronization in human cortex during wakefulness and sleep

Vigilance states are associated with reproducible reconfigurations of large-scale brain dynamics, reflected in changes in inter-areal phase synchronization and cross-frequency phase-amplitude coupling. Here, we characterized how these two forms of phase-based coordination jointly organize across sleep and wakefulness in the human brain at local and regional levels. We analysed phase-locking value (PLV) and phase-amplitude coupling (PAC) from intracranial stereo-electroencephalography (SEEG) recordings in 46 individuals with drug-resistant focal epilepsy, focusing on contacts outside the epileptogenic zone (non-epileptogenic zone, nEZ) to define physiological coupling profiles and comparing them with contacts within the EZ. For each subject, representative epochs of wakefulness, NREM sleep stages N2 and N3, and REM sleep were examined. Across vigilance states, large-scale phase synchronization exhibited distinct spectral fingerprints. Theta and sigma synchronization predominated during NREM sleep, beta synchronization increased during REM sleep, and theta interactions characterized wakefulness. PAC showed complementary state-dependent reorganizations: N3 was characterized by delta-driven modulation of broadband high-frequency activity; N2 additionally exhibited theta- and spindle-phase modulation of beta-gamma amplitudes; REM sleep showed reduced coupling; and wakefulness was marked by theta-to-beta interactions. Within vigilance states, epileptogenic regions displayed increased delta and gamma synchronization and enhanced delta-to-beta/gamma PAC, most prominently during N2 sleep and wakefulness, whereas differences between EZ and nEZ tissue were attenuated during REM sleep. Using partial least squares analysis, we further identified system-specific patterns of PLV-PAC covariation, with prominent involvement of temporal networks during NREM sleep and visual and limbic systems during REM sleep. Together, these findings delineate a frequency-specific, state-dependent architecture linking phase synchronization and phase-amplitude coupling in the human brain and describe how epileptogenic networks deviate from physiological coupling profiles across vigilance states.

neuroscience↗

bioRxiv2026

REM sleep reconfigures large-scale network dynamics: a link to its suppressive role in epilepsy

Converging evidence suggests that human brain activity operates near a critical-like regime in which balanced excitation and inhibition support efficient large-scale communication. The brains proximity to criticality may be dynamically reset across the sleep-wake cycle and altered by epilepsy, leading to aberrant oscillatory dynamics. Building on recent work demonstrating a tripartite interaction between networks synchronization, oscillatory amplitude bistability, and cross-frequency coupling in the human brain that seems to favour epileptic activity, we examined how this interaction, and its underlying large-scale dynamics are modulated across vigilance states. We analyzed overnight recordings from 20 patients with drug-resistant epilepsy undergoing presurgical evaluation and selected overall 20 minutes of continuous, artifact free stereo-electroencephalography (SEEG) spanning REM sleep, NREM stages N2 and N3, and eyes-closed resting wakefulness. Across states, we quantified phase synchronization, phase-amplitude coupling, bistability and their correlation. REM sleep was consistently associated with a reduction of these dynamics relative to NREM sleep and wakefulness. Importantly, the canonical correlation between these measures -- reflecting the strength of the tripartite interaction -- was significantly weaker during REM sleep. These findings indicate that vigilance states modulate this previously identified multiscale interaction in human brain networks and suggest that the reduced epileptogenicity of REM sleep can be associated with a disruption of coordinated synchronization, coupling, and bistable dynamics at the large-scale network level.

neuroscience↗

bioRxiv2025

Shared local brain dynamics in pediatric and adult NREM parasomnias

Disorders of arousal (DoA), a group of Non-Rapid Eye Movement parasomnias--including sleepwalking, night terrors, and confusional arousals--arise from incomplete awakenings during slow-wave sleep, yet their neural signatures remain poorly defined. Using high-density EEG and source-space spectral analysis in both children and adults, we mapped cortical dynamics in the seconds before and after DoA onset and compared them to physiological motor arousals. Across ages, DoA episodes emerged from a globally less activated cortical state, with pre-onset surges in delta and beta power peaking in premotor, orbitofrontal, and anterior cingulate cortices. After onset, episodes showed widespread beta enhancement and focal delta suppression in sensorimotor and parietal associative regions relative to stable slow-wave sleep, alongside sustained frontal delta and beta activity compared with physiological motor arousals. These age-invariant spatial patterns identify a stable neurophysiological signature of DoA, supporting the concept of local sleep-wake dissociation. Our findings provide a framework for mechanistic models and potential biomarkers to modulate, predict, and differentiate DoA from other nocturnal events.

neuroscience↗

bioRxiv2025

Quantifying cortical maturational aspects during different vigilance states in preterm infants by advanced EEG analysis

Preterm birth is associated with numerous neurodevelopmental adverse outcomes, even in the absence of acquired lesions, as it occurs during a critical period of brain development. Clear organization of vigilance states can be recognized from 30-32 weeks postmenstrual age (PMA). In this study, we investigated whether spatial and temporal properties of neuronal oscillatory dynamics (i.e., phase synchronization, bistability, and cross-frequency coupling) during different vigilance states provide insights into cortical maturation in preterm infants born very low birth weight (VLBW) at low neurological risk and devoid of detectable brain lesions. We analyzed artifact-free video-polysomnographic data from 11 VLBW preterm infants (PMA at recording: 33.0 {+/-} 1.6 weeks) who underwent brain MRI at term-equivalent age. For each vigilance state, we computed the weighted Phase Lag Index (wPLI), Bistability Index (BiS), and Phase-Amplitude Coupling (PAC), both globally and across anterior and posterior regions, and examined their correlation with PMA at recording. wPLI, BiS, and PAC showed specific trends across vigilance states. BiS and PAC exhibited posterior-to-anterior differences and correlated with PMA. Our study suggests that these electrophysiological markers, particularly BiS and PAC, may serve as indices to monitor aspects of cortical maturation in VLBW at low neurological risk.

neuroscience↗

bioRxiv2025

Toward Unified Biomarkers for Focal Epilepsy

Accurately localizing the epileptogenic network (EpiNet) remains a major barrier to effective epilepsy treatment, largely due to limited mechanistic understanding. The EpiNet is a patient-specific brain network shaped by complex, overlapping pathology. While combining biomarkers can improve localization, it also generates high-dimensional feature data that increases the risk of overfitting and reduces interpretability. We hypothesized that the core epileptogenic dynamics could be captured in a low-dimensional latent space derived from empirical data, without the need to record seizures. From interictal stereo-EEG (SEEG) recordings in 64 patients (29 females), we extracted 260 neuronal features and reduced them to 10 latent components using singular value decomposition. A classifier trained on these 10 components was then simplified into a probabilistic EpiNet model requiring only two components as input. Individual position in this two-dimensional latent space correlated with previously reported classification accuracy (r2=0.5), supporting its functional relevance. In three independent patients, the probabilistic model captured time-varying epileptogenic dynamics during sleep-SEEG recordings, corroborated clinical assessments, and achieved peak classification accuracies of 0.63, 0.85, and 0.94. These predictions were independently validated by tensor component analysis. Together, these results provide evidence for a robust low-dimensional representation of epileptogenicity across brain states and pathological substrates. This approach simplifies interpretation, facilitates integration of additional biomarkers, and enables large-scale cohort analyses, establishing a proof of concept for a unified framework for epilepsy biomarkers. Significance StatementTo advance mechanistic understanding of large-scale brain dynamics underlying epilepsy, we combined novel epilepsy biomarkers with interpretable machine learning. From interictal SEEG, we extracted 260 connectivity and criticality features. Dimensionality reduction of these raw features showed that only two components were needed to identify epileptogenic networks, reducing the feature space by >99%. These components were highlighted by abnormal power-law scaling, bistability, and strong inhibition or excitation in the 15-200 Hz range, consistent with prior findings. The model tracked neuropathological dynamics over hours and was validated through tensor component analysis, suggesting that epileptogenic activity is dynamic, subject-specific, and sparsely represented in both state space and cortical networks.

neuroscience↗

bioRxiv2025

Sleep-Modulated Cross-Frequency Coupling Between δ Phase and β-γ Bistability: A System-Level Modulation of Epileptic Activity

ObjectiveWhile slow waves in {delta} (0.5-4 Hz) characterize NREM sleep, in patients with sleep-related epilepsy, seizures most frequently emerge during NREM stage 2, known to be promoted by {delta}-band instability. Meanwhile, the epileptogenic zone (EZ) shows localized bistability in {beta}-{gamma} band (15-200 Hz) neuronal oscillations--indicating a catastrophic shift toward seizure. We aim to clarify the mechanistic link between {delta}-band synchrony and {beta}-{gamma} band bistability in epilepsy. MethodsWe studied a cohort of fourteen patients with Sleep Hypermotor Epilepsy (22.3 {+/-} 10.8 years old; 7 males). 7-9-hour stereo-EEG sleep recordings were segmented into 10-minute of uninterrupted, interictal N2 and N3 epochs, and phase synchrony, phase-amplitude coupling (PAC), and bistability were assessed. Canonical correlation was examined to answer whether PAC links {delta}-phase to {beta}-{gamma} bistability. ResultsCompared to non-EZ, the EZ exhibited larger 15-200 Hz bistability along with stronger 2-8 Hz and 15-100 Hz synchrony throughout N2 and N3. Compared to N3, N2 showed stronger PAC between 2-30 Hz phases in the non-EZ and 5-150 Hz amplitudes in the EZ. Canonical correlations between {delta}-phase modulated PAC and both bistability and synchrony were identified during N2 (r = 0.86 and 0.82) and N3 (r = 0.84 and 0.80), with the strongest contributors being 2-4 Hz synchrony and bistability in 2-4 Hz and 15-200 Hz bands. Correlations between interictal spikes and canonical covariates of bistability and PAC (r2 = 0.62 for N2 and 0.56 for N3) validated their relevance to epileptogenicity. Significance{delta}-band synchrony and {beta}-{gamma} band bistability are not isolated epileptogenic mechanisms but likely act synergistically, playing a pivotal role in seizure generation through the coupling of {delta} phases and {beta}-{gamma} amplitudes across large networks, with significant contributions from non-epileptogenic tissues. Key pointsO_LIStrong {beta}-{gamma} bistability in neuronal oscillations localizes the EZ throughout N2 and N3 sleep. C_LIO_LIElevated {delta}-band phase synchrony characterizes the EZ and its functional neighbors throughout N2 and N3 sleep. C_LIO_LIa-band synchrony modulates local {beta}-{gamma} bistability through PAC, with significant contributions from non-EZ tissues. C_LI

neuroscience↗

bioRxiv2023

Statistical diversity distinguishes global states of consciousness

Application of complexity measures to neurophysiological time series has seen increased use in recent years to identify neural correlates of global states of consciousness. Lempel-Ziv complexity is currently the de-facto complexity measure used in these investigations. However, by simply counting the number of patterns, this measure theoretically takes its maximum value for data that are completely random. Recently, a measure of statistical complexity - which calculates the diversity of statistical interactions - has been devised which aims to account for and remove randomness seen in data. It was recently found that this measure decreases during anaesthesia in fruit flies. This paper investigates this statistical complexity measure on human neurophysiology data from different stages of sleep, and from individuals under the effects of three psychedelic substances: ketamine, lysergic acid diethylamide (LSD), and psilocybin. Results indicate that statistical complexity: (i) differentiates the different stages of sleep analogously to Lempel-Ziv complexity; (ii) increases relative to placebo for all three psychedelic substances. Thus, statistical complexity is a useful alternative measure for investigating the complexity of neural activity associated with different states of consciousness.

neuroscience↗

bioRxiv2023

New vistas for epileptogenic zone (EZ) localization and interpretation

ObjectivePost-surgical seizure freedom in drug-resistant epilepsy (DRE) patients varies from 30 to 80%, implying that in many cases the current approaches fail to fully map the epileptogenic zone (EZ). This suggests that the EZ entails a broader epileptogenic brain network (EpiNet) beyond the seizure-zone (SZ) that show seizure activity. MethodsWe first used computational modeling to identify putative complex-systems- and systems-neuroscience-driven mechanistic biomarkers for epileptogenicity. We then extracted these epileptogenicity biomarkers from stereo-EEG (SEEG) resting-state data from DRE patients and trained supervised classifiers to localize the SZ with these biomarkers against gold-standard clinical localization. To further explore the prevalence of these pathological biomarkers in an extended network outside of the clinically-identified SZ, we also used unsupervised classification. ResultsSupervised SZ-classification trained on individual features achieved accuracies of 0.6-0.7 areaunder-the-receiver-operating-characteristics curve (AUC). However, combining all criticality and synchrony features improved the AUC up to 0.85. Unsupervised classification uncovered an EpiNet-like cluster of brain regions with 51% of regions outside of SZ. Brain regions in this cluster engaged in inter-areal hypersynchrony and locally exhibited high amplitude bistability and excessive inhibition, which was strikingly similar to the high seizure-risk regime revealed by computational modeling. SignificanceThe finding that combining biomarkers improves EZ localization shows that the different mechanistic biomarkers of epileptogenicity assessed here yield synergistic information. On the other hand, the discovery of SZ-like pathophysiological brain dynamics outside of the clinically-defined EZ provides experimental localization of an extended EpiNet. Key pointsO_LIWe advanced novel complex-systems- and systems-neuroscience-driven biomarkers for epileptogenicity C_LIO_LIIncreased bistability, inhibition, and power-low scaling exponents characterized our model operating in a high seizure-risk regime and SEEG oscillations in the seizure-zone (SZ) C_LIO_LICombining all biomarkers yielded more accurate supervised SZ-classification than using any individual biomarker alone C_LIO_LIUnsupervised classification revealed more extended pathological brain networks including the SZ and many non-seizure-zone areas that were previously considered healthy C_LI

physiology↗

bioRxiv2022

Brain criticality predicts individual synchronization levels in humans

Moderate levels of synchronization of neuronal oscillations are essential for healthy brain dynamics. Synchronization levels exhibit large inter-individual variability the origins of which are unknown. Neuronal systems have been postulated to operate near a critical transition point or in an extended regime between disorder (subcritical) and order (supercritical phase) characterized by moderate synchronization and emergent power-law long-range temporal correlations (LRTCs). We investigated whether inter-individual variability in synchronization levels is explained by the individual position along the critical regime by analyzing magnetoencephalography (MEG) and intra-cerebral stereo-electroencephalography (SEEG) human resting-state data. Here we show that variability in synchronization levels exhibits a positive linear and quadratic relationships with LRTCs in healthy participants and brain areas. In the epileptogenic zone this correlation was negative. These results show that variability in synchronization levels is regulated by the individual position along an extended critical-like regime, with healthy brain areas tending to operate in its subcritical and epileptogenic areas in its supercritical side.

neuroscience↗

bioRxiv2022

NLP-based tools for localization of the Epileptogenic Zone in patients with drug-resistant focal epilepsy

BackgroundDrug-resistant focal epilepsy, defined by failure of two antiepileptic drugs, affects about 30% of patients with epilepsy. Epilepsy surgery may represent an alternative options for this population. However, defining the epileptogenic zone to be surgically removed requires highly specialised medical expertise as well as advanced technologies. The aim of this work is building a cost-effective support system based on text, in particular based on the semiological descriptions of the seizures (temporal vs extratemporal lobe; right vs left hemisphere), in order to predict the localization of seizure origin. MethodsAmong a population of 121 surgically treated and seizure-free drug-resistant patients suffering with focal epilepsy, recruited at the Niguarda Hospital in Milan, we extracted a total number of 509 descriptions of seizures. After a data pre-processing phase, we used natural language processing tools to build numerical representations of the seizures descriptions, both using embedding and countbased methods. We then used machine learning models performing a binary classification into right/left and temporal/extra-temporal. ResultsAll predictive models show a better performance when using the representations relying on embedding models respect to count-based ones. Between all the combinations of representations and classifiers, the best performance obtained in terms of F1-score is 84.7% {+/-} 0.6. DiscussionThis preliminary work reached encouraging results considering both localization tasks. The main advantage is that no specific knowledge about epilepsy is used to build the models, rendering our pipeline applicable also in other scenarios. The major limitation lies in the fact that the text is highly specific to the writer.

bioengineering↗

bioRxiv2022

Critical-like bistable dynamics in the resting-state human brain

Brain activity exhibits scale-free avalanche dynamics and power-law long-range temporal correlations (LRTCs) across the nervous system. This has been thought to reflect "brain criticality", i.e., brains operating near a critical phase transition between disorder and excessive order. Neuronal activity is, however, metabolically costly and may be constrained by activity-limiting mechanisms and resource depletion, which could make the phase transition discontinuous and bistable. Observations of bistability in awake human brain activity have nonetheless remained scarce and its functional significance unclear. First, using computational modelling where bistable synchronization dynamics emerged through local positive feedback, we found bistability to occur exclusively in a regime of critical-like dynamics. We then assessed bistability in vivo with resting-state magnetoencephalography and stereo-encephalography. Bistability was a robust characteristic of cortical oscillations throughout frequency bands from {delta} (3-7 Hz) to high-{gamma} (100-225 Hz). As predicted by modelling, bistability and LRTCs were positively correlated. Importantly, while moderate levels of bistability were positively correlated with executive functioning, excessive bistability was associated with epileptic pathophysiology and predictive of local epileptogenicity. Critical bistability is thus a salient feature of spontaneous human brain dynamics in awake resting-state and is both functionally and clinically significant. These findings expand the framework of brain criticality and show that critical-like neuronal dynamics in vivo involves both continuous and discontinuous phase transitions in a frequency-, neuroanatomy-, and state-dependent manner.

neuroscience↗

bioRxiv2021

Simultaneous stereo-EEG and high-density scalp EEG recordings to study the effects of intracerebral stimulation parameters

BackgroundCortico-cortical evoked potentials (CCEPs) recorded by stereo-electroencephalography (SEEG) are a valuable clinical tool to investigate brain reactivity and effective connectivity. However, these invasive recordings are spatially sparse since they depend on clinical needs. This sparsity hampers systematic comparisons across-subjects, the detection of the whole-brain spatiotemporal properties of CCEPs, as well as their relationships with classic sensory evoked potentials. ObjectiveTo demonstrate that CCEPs recorded by high-density electroencephalography (hd-EEG) are sensitive to changes in stimulation parameters and compensate for the limitations typical of invasive recordings. MethodsSEEG and hd-EEG activities were simultaneously recorded during SPES in drug-resistant epileptic patients (N=36). Changes in stimulation parameters encompassed physical (pulse intensity and width), geometrical (angle and position with respect to white/grey matter) and topological (stimulated cortical area) properties. Differences were assessed by measuring the overall responses and the amplitude of N1 and N2 components of the CCEPs, and by their spectral profiles. ResultsWhile invasive and non-invasive CCEPs were generally correlated, differences in pulse duration, angle and stimulated cortical area were better captured by hd-EEG. Further, hd-EEG responses to SPES reproduced basic features of responses to transcranial magnetic stimulation and showed a much larger amplitude as compared to typical sensory evoked potentials. ConclusionsThe present results show that macroscale hd-EEG recordings are exquisitely sensitive to variations in SPES parameters, including local changes in physical and geometrical stimulus properties, while providing valuable information about whole-brain dynamics. Moreover, the common reference space across subjects represented by hd-EEG may facilitate the construction of a perturbational atlas of effective connectivity. HighlightsO_LICCEPs recorded with hd-EEG and SEEG are correlated. C_LIO_LIhd-EEG recording is highly sensitive to changes in stimulation parameters. C_LIO_LIhd-EEG responses show higher amplitude responses with respect to non-invasive ones. C_LIO_LISimultaneous recordings provide a fixed observation point across subjects. C_LI

neuroscience↗

bioRxiv2021

An in-vivo validation of ESI methods with focal sources

Electrical source imaging (ESI) aims at reconstructing the electrical brain activity from measurements of the electric field on the scalp. Even though the localization of single focal sources should be relatively straightforward, different methods provide diverse solutions due to the different underlying assumptions. Furthermore, their input parameter(s) further affects the solution provided by each method, making localization even more challenging. In addition, validations and comparisons are typically performed either on synthetic data or through post-operative outcomes, in both cases with considerable limitations. We use an in-vivo high-density EEG dataset recorded during intracranial single pulse electrical stimulation, in which the true sources are substantially dipolar and their locations are known. We compare ten different ESI methods under multiple choices of input parameters, to assess the accuracy of the best reconstruction, as well as the impact of the parameters on the localization performance. Best reconstructions often fall within 1 cm from the true source, with more accurate methods outperforming less accurate ones by 1 cm, on average. Expectedly, dipolar methods tend to outperform distributed methods. Sensitivity to input parameters varies widely between methods. Depth weighting played no role for three out of six methods implementing it. In terms of regularization parameters, for several distributed methods SNR=1 unexpectedly turned out to be the best choice among the tested ones. Our data show similar levels of accuracy of ESI techniques when applied to "conventional" (32 channels) and dense (64, 128, 256 channels) EEG recordings. Overall findings reinforce the importance that ESI may have in the clinical context, especially when applied to identify the surgical target in potential candidates for epilepsy surgery.

neuroscience↗

bioRxiv2021

Modules in connectomes of phase-synchronization comprise anatomically contiguous, spatially related regions

Modules in brain functional connectomes are essential to balancing segregation and integration of neuronal activity. Connectomes are the complete set of pairwise connections between brain regions. Non-invasive Electroencephalography (EEG) and Magnetoencephalography (MEG) have been used to identify modules in connectomes of phase-synchronization. However, their resolution is suboptimal because of spurious phase-synchronization due to EEG volume conduction or MEG field spread. Here, we used invasive, intracerebral recordings from stereo-electroencephalography (SEEG, N = 67), to identify modules in connectomes of phase-synchronization. To generate SEEG-based group-level connectomes affected only minimally by volume conduction, we used submillimeter accurate localization of SEEG contacts and referenced electrode contacts in cortical grey matter to their closest contacts in white matter. Combining community detection methods with consensus clustering, we found that the connectomes of phase-synchronization were characterized by distinct and stable modules at multiple spatial scales, across frequencies from 3 to 320 Hz. These modules were highly similar within canonical frequency bands. Unlike the distributed brain systems identified with functional Magnetic Resonance Imaging (fMRI), modules up to the high-gamma frequency band comprised only anatomically contiguous regions. Notably, the identified modules comprised cortical regions involved in shared repertoires of sensorimotor and cognitive functions including memory, language and attention. These results suggest that the identified modules represent functionally specialised brain systems, which only partially overlap with the brain systems reported with fMRI. Hence, these modules might regulate the balance between functional segregation and functional integration through phase-synchronization. HighlightsO_LILarge-cohort SEEG used for phase-synchronization connectomics C_LIO_LIConnectomes of phase-synchronization possess distinct and stable modules C_LIO_LIModules in connectomes are highly similar within canonical frequency bands C_LIO_LIModules in connectomes comprise anatomically contiguous regions C_LIO_LIModules in connectomes comprise functionally related regions C_LI

neuroscience↗

bioRxiv2020

Fast oscillations localize the epileptogenic zone: an electrical source imaging study using high-density electroencephalography

ObjectiveFast Oscillations (FO) >40 Hz are a promising biomarker of the epileptogenic zone (EZ). Evidence using scalp electroencephalography (EEG) remains scarce. We assessed if electrical source imaging of FO using 256-channel high-density EEG (HD-EEG) is useful for EZ identification. MethodsWe analyzed HD-EEG recordings of 10 focal drug-resistant epilepsy patients with seizure-free postsurgical outcome. We marked FO candidate events at the time of epileptic spikes and verified them by screening for an isolated peak in the time-frequency plot. We performed electrical source imaging of spikes and FO within the Maximum Entropy of the Mean framework. Source localization maps were validated against the surgical cavity. ResultsWe identified FO in five out of 10 patients who had a superficial or intermediate deep generator. The maximum of the FO maps was localized inside the cavity in all patients (100%). Analysis with a reduced electrode coverage using the 10-10 and 10-20 system showed a decreased localization accuracy of 60% and 40% respectively. ConclusionsFO recorded with HD-EEG localize the EZ. HD-EEG is better suited to detect and localize FO than conventional EEG approaches. SignificanceThis study acts as proof-of-concept that FO localization using 256-channel HD-EEG is a viable marker of the EZ. Highlights- Fast oscillations > 40Hz are able to correctly localize the epileptogenic zone. - HD-EEG is superior in detection and localization of fast oscillations compared to conventional EEG approaches. - Presence of fast oscillations on the scalp might point to a superficial epileptic generator.

neuroscience↗

bioRxiv2020

Simultaneous human intracerebral stimulation and HD-EEG: ground-truth for source localization methods

Precisely localizing the sources of brain activity as recorded by EEG is a fundamental procedure and a major challenge for both research and clinical practice. Even though many methods and algorithms have been proposed, their relative advantages and limitations are still not well established. Moreover, these methods involve tuning multiple parameters, for which no principled way of selection exists yet. These uncertainties are emphasized due to the lack of ground-truth for their validation and testing. Here we provide the first open dataset that comprises EEG recorded electrical activity originating from precisely known locations inside the brain of living humans. High-density EEG was recorded as single-pulse biphasic currents were delivered at intensities ranging from 0.1 to 5 mA through stereotactically implanted electrodes in diverse brain regions during pre-surgical evaluation of patients with drug-resistant epilepsy. The uses of this dataset range from the estimation of in vivo tissue conductivity to the development, validation and testing of forward and inverse solution methods.

neuroscience↗