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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.

The discovery of a stable, whole-brain functional connectivity organization that is largely independent of external events has drastically extended our view of human brain function. However, this discovery has been primarily based on functional magnetic resonance imaging (fMRI). The role of this whole-brain organization in fast oscillation-based connectivity as measured, for example, by electroencephalography (EEG) and magnetoencephalography (MEG) is only beginning to emerge. Here, we review studies of intrinsic connectivity and its whole-brain organization in EEG, MEG, and intracranial electrophysiology with a particular focus on direct comparisons to connectome studies in fMRI. Synthesizing this literature, we conclude that irrespective of temporal scale over four orders of magnitude, intrinsic neurophysiological connectivity shows spatial similarity to the connectivity organization commonly observed in fMRI. A shared structural connectivity basis and cross-frequency coupling are possible mechanisms contributing to this similarity. Acknowledging that a stable whole-brain organization governs long-range coupling across all timescales of neural processing motivates researchers to take “baseline” intrinsic connectivity into account when investigating brain-behavior associations, and further encourages more widespread exploration of functional connectomics approaches beyond fMRI by using EEG and MEG modalities.

Dynamic Functional Connectivity (dFC) in the resting state (rs) is considered as a correlate of cognitive processing. Describing dFC as a flow across morphing connectivity configurations, our notion of dFC speed quantifies the rate at which FC networks evolve in time. Here we probe the hypothesis that variations of rs dFC speed and cognitive performance are selectively interrelated within specific functional subnetworks.In particular, we focus on Sleep Deprivation (SD) as a reversible model of cognitive dysfunction. We found that whole-brain level (global) dFC speed significantly slows down after 24h of SD. However, the reduction in global dFC speed does not correlate with variations of cognitive performance in individual tasks, which are subtle and highly heterogeneous. On the contrary, we found strong correlations between performance variations in individual tasks –including Rapid Visual Processing (RVP, assessing sustained visual attention)– and dFC speed quantified at the level of functional subnetworks of interest. Providing a compromise between classic static FC (no time) and global dFC (no space), modular dFC speed analyses allow quantifying a different speed of dFC reconfiguration independently for sub-networks overseeing different tasks. Importantly, we found that RVP performance robustly correlates with the modular dFC speed of a characteristic frontoparietal module.HighlightsSleep Deprivation (SD) slows down the random walk in FC space implemented by Dynamic Functional Connectivity (dFC) at rest.Whole-brain level slowing of dFC speed does not selectively correlate with fine and taskspecific changes in performanceWe quantify dFC speed separately for different link-based modules coordinated by distinct regional “meta-hubs”Modular dFC speed variations capture subtle and task-specific variations of cognitive performance induced by SD.Author summaryWe interpreted dynamic Functional Connectivity (dFC) as a random walk in the space of possible FC networks performed with a quantifiable “speed”.Here, we analyze a fMRI dataset in which subjects are scanned and cognitively tested both before and after Sleep Deprivation (SD), used as a reversible model of cognitive dysfunction. While global dFC speed slows down after a sleepless night, it is not a sufficiently sensitive metric to correlate with fine and specific cognitive performance changes. To boost the capacity of dFC speed analyses to account for fine and specific cognitive decline, we introduce the notion ofmodular dFC speed. Capitalizing on an edge-centric measure of functional connectivity, which we call Meta-Connectivity, we isolate subgraphs of FC describing relatively independent random walks (dFC modules) and controlled by distinct “puppet masters” (meta-hubs). We then find that variations of the random walk speed of distinct dFC modules now selectively correlate with SD-induced variations of performance in the different tasks. This is in agreement with the fact that different subsystems – distributed but functionally distinct– oversee different tasks.The high sensitivity of modular dFC analyses bear promise of future applications to the early detection and longitudinal characterization of pathologies such as Alzheimer’s disease.

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.

Long-range connectivity has become the most studied feature of human functional Magnetic Resonance Imaging (fMRI), yet the spatial and temporal relationship between its whole-brain dynamics and electrophysiological connectivity remains largely unknown. FMRI-derived functional connectivity exhibits spatial reconfigurations or time-varying dynamics at infraslow (

Concurrent electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) bridge brain connectivity across timescales. During concurrent EEG-fMRI resting-state recordings, whole-brain functional connectivity (FC) strength is spatially correlated across modalities. However, cross-modal investigations have commonly remained correlational, and joint analysis of EEG-fMRI connectivity is largely unexplored. Here we investigated if there exist (spatially) independent FC networks linked between modalities. We applied the recently proposed hybrid connectivity independent component analysis (connICA) framework to two concurrent EEG-fMRI resting-state datasets (total 40 subjects). Two robust components were found across both datasets. The first component has a uniformly distributed EEG frequency fingerprint linked mainly to intrinsic connectivity networks (ICNs) in both modalities. Conversely, the second component is sensitive to different EEG frequencies and is primarily linked to intra-ICN connectivity in fMRI but to inter-ICN connectivity in EEG. The first hybrid component suggests that connectivity dynamics within well-known ICNs span timescales, from millisecond range in all canonical frequencies of FCEEG to second range of FCfMRI. Conversely, the second component additionally exposes linked but spatially divergent neuronal processing at the two timescales. This work reveals the existence of joint spatially independent components, suggesting that parts of resting-state connectivity are co-expressed in a linked manner across EEG and fMRI over individuals., Author Summary Functional connectivity is governed by a whole-brain organization measurable over multiple timescales by functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). The relationship across the whole-brain organization captured at the different timescales of EEG and fMRI is largely unknown. Using concurrent EEG-fMRI, we identified spatially independent components consisting of brain connectivity patterns that co-occur in EEG and fMRI over subjects. We observed a component with similar connectivity organization across EEG and fMRI as well as a component with divergent connectivity. The former component governed all EEG frequencies while the latter was modulated by frequency. These findings show that part of functional connectivity organizes in a common spatial layout over several timescales, while a spatially independent part is modulated by frequency-specific information.

Objective To determine the impact of Zika virus (ZIKV) infection on brain structure and functional organization of severely affected adult patients with neurological complications that extend beyond Guillain-Barre Syndrome (GBS)-like manifestations and include symptoms of the central nervous system (CNS). Methods In this first case-control neuroimaging study, we obtained structural and functional magnetic resonance images in nine rare adult patients in the subacute phase, and healthy age- and sex-matched controls. ZIKV patients showed atypical descending and rapidly progressing peripheral nervous system (PNS) manifestations, and importantly, additional CNS presentations such as perceptual deficits. Voxel-based morphometry was utilized to evaluate gray matter volume, and resting state functional connectivity and Network Based Statistics were applied to assess the functional organization of the brain. Results Gray matter volume was decreased bilaterally in motor areas (supplementary motor cortex, specifically Frontal Eye Fields) and beyond (left inferior frontal sulcus). Additionally, gray matter volume increased in right middle frontal gyrus. Functional connectivity increased in a widespread network within and across temporal lobes. Interpretation We provide preliminary evidence for a link between ZIKV neurological complications and changes in adult human brain structure and functional organization, comprising both motor-related regions potentially secondary to prolonged PNS weakness, and nonsomatomotor regions indicative of PNS-independent alternations. The latter included the temporal lobes, particularly vulnerable in a range of neurological conditions. While future studies into the ZIKV-related neuroinflammatory mechanisms in adults are urgently needed, this study indicates that ZIKV infection can lead to an impact on the brain.

International audience; While averaged dynamics of brain function are known to estimate the underlying structure, the exact relationship between large-scale function and structure remains an unsolved issue in network neuroscience. These complex functional dynamics, measured by EEG and fMRI, are thought to arise from a shared underlying structural architecture, which can be measured by diffusion MRI (dMRI). While simulation and data transformation (e.g. graph theory measures) have been proposed to refine the understanding of the underlying function-structure relationship, the potential complementary and/or independent contribution of EEG and fMRI to this relationship is still poorly understood. As such, we explored this relationship by analyzing the function-structure correlation in fourteen healthy subjects with simultaneous resting-state EEG-fMRI and dMRI acquisitions. We show that the combination of EEG and fMRI connectivity better explains dMRI connectivity and that this represents a genuine model improvement over fMRI-only models for both group-averaged connectivity matrices and at the individual level. Furthermore, this model improves the prediction within each resting-state network. The best model fit to underlying structure is mediated by fMRI and EEG-δ connectivity in combination with Euclidean distance and interhemispheric connectivity with more local contributions of EEG-γ at the scale of resting state networks. This highlights that the factors mediating the relationship between functional and structural metrics of connectivity are context and scale dependent, influenced by topological, geometric and architectural features. It also suggests that fMRI studies employing simultaneous EEG measures may characterize additional and essential parts of the underlying neuronal activity of the resting-state, which might be of special interest for both clinical studies and the investigation of resting-state dynamics.

Background: Intermittent theta burst stimulation (iTBS) of the primary motor cortex improves transiently lower limbs spasticity in multiple sclerosis (MS). However, the cerebral mechanisms underlying this effect have never been investigated. Objective: To assess whether modulation of spasticity induced by iTBS is underlined by functional reorganization of the primary motor cortices. Methods: A total of 17 patients with MS suffering from lower limbs spasticity were randomized to receive real iTBS or sham iTBS during the first half of a 5-week indoor rehabilitation programme. Spasticity was assessed using the Modified Ashworth Scale and the Visual Analogue Scale at baseline, after the stimulation session and at the end of the rehabilitation programme. Resting-state functional magnetic resonance imaging (fMRI) was performed at the three time points, and brain functional networks topology was analysed using graph-theoretical approach. Results: At the end of stimulation, improvement of spasticity was greater in real iTBS group than in sham iTBS group ( p = 0.026). iTBS had a significant effect on the balance of the connectivity degree between the stimulated and the homologous primary motor cortex ( p = 0.005). Changes in inter-hemispheric balance were correlated with improvement of spasticity (rho = 0.56, p = 0.015). Conclusion: This longitudinal resting-state fMRI study evidences that functional reorganization of the primary motor cortices may underlie the effect of iTBS on spasticity in MS.

International audience; In light of technical advancements supporting exploration of MR signals other than (1)H, sodium ((23)Na) has received attention as a marker of ionic homeostasis and cell viability. Here, we evaluate for the first time the possibility that (23)Na-MRI is sensitive to pathological processes occurring in human epilepsy. A normative sample of 27 controls was used to normalize regions of interest (ROIs) from 1424 unique brain locales on quantitative (23)Na-MRI and high-resolution (1)H-MPRAGE images. ROIs were based on intracerebral electrodes in ten patients undergoing epileptic network mapping. The stereo-EEG gold standard was used to define regions as belonging to primarily epileptogenic, secondarily irritative and to non-involved regions. Estimates of total sodium concentration (TSC) on (23)Na-MRI and cerebrospinal fluid (CSF) on (1)H imaging were extracted for each patient ROI, and normalized against the same region in controls. ROIs with disproportionate CSF contributions (ZCSF≥1.96) were excluded. TSC levels were found to be elevated in patients relative to controls except in one patient, who suffered non-convulsive seizures during the scan, in whom we found reduced TSC levels. In the remaining patients, an ANOVA (F1100= 12.37, p\textless0.0001) revealed a highly significant effect of clinically-defined zones (F1100= 11.13, p\textless0.0001), with higher normalized TSC in the epileptogenic zone relative to both secondarily irritative (F1100= 11, p=0.0009) and non-involved regions (F1100= 17.8, p\textless0.0001). We provide the first non-invasive, in vivo evidence of a chronic TSC elevation alongside ZCSF levels within the normative range, associated with the epileptogenic region even during the interictal period in human epilepsy, and the possibility of reduced TSC levels due to seizure. In line with modified homeostatic mechanisms in epilepsy - including altered mechanisms underlying ionic gating, clearance and exchange - we provide the first indication of (23)Na-MRI as an assay of altered sodium concentrations occurring in epilepsy associated with the organization of clinically relevant divisions of pathological cortex.

For the first time in research in humans, we used simultaneous icEEG-fMRI to examine the link between connectivity in haemodynamic signals during the resting-state (rs) and connectivity derived from electrophysiological activity in terms of the inter-modal connectivity correlation (IMCC). We quantified IMCC in nine patients with drug-resistant epilepsy (i) within brain networks in 'healthy' non-involved cortical zones (NIZ) and (ii) within brain networks involved in generating seizures and interictal spikes (IZ1) or solely spikes (IZ2). Functional connectivity (h 2 ) estimates for 10 min of resting-state data were obtained between each pair of electrodes within each clinical zone for both icEEG and fMRI. A sliding window approach allowed us to quantify the variability over time of h 2 (vh 2) as an indicator of connectivity dynamics. We observe significant positive IMCC for h 2 and vh 2, for multiple bands in the NIZ only, with the strongest effect in the lower icEEG frequencies. Similarly, intra-modal h 2 and vh 2 were found to be differently modified as a function of different epileptic processes: compared to NIZ, [Formula: see text] was higher in IZ1, but lower in IZ2, while [Formula: see text] showed the inverse pattern. This corroborates previous observations of inter-modal connectivity discrepancies in pathological cortices, while providing the first direct invasive and simultaneous comparison in humans. We also studied time-resolved FC variability multimodally for the first time, finding that IZ1 shows both elevated internal [Formula: see text] and less rich dynamical variability, suggesting that its chronic role in epileptogenesis may be linked to greater homogeneity in self-sustaining pathological oscillatory states.

Simultaneous EEG-fMRI has opened up new avenues for improving the spatio-temporal resolution of functional brain studies. However, this method usually suffers from poor EEG quality, especially for evoked potentials (ERPs), due to specific artifacts. As such, the use of EEG-informed fMRI analysis in the context of cognitive studies has particularly focused on optimizing narrow ERP time windows of interest, which ignores the rich diverse temporal information of the EEG signal. Here, we propose to use simultaneous EEG-fMRI to investigate the neural cascade occurring during face recognition in 14 healthy volunteers by using the successive ERP peaks recorded during the cognitive part of this process. N170, N400 and P600 peaks, commonly associated with face recognition, were successfully and reproducibly identified for each trial and each subject by using a group independent component analysis (ICA). For the first time we use this group ICA to extract several independent components (IC) corresponding to the sequence of activation and used single-trial peaks as modulation parameters in a general linear model (GLM) of fMRI data. We obtained an occipital–temporal–frontal stream of BOLD signal modulation, in accordance with the three successive IC-ERPs providing an unprecedented spatio-temporal characterization of the whole cognitive process as defined by BOLD signal modulation. By using this approach, the pattern of EEG-informed BOLD modulation provided improved characterization of the network involved than the fMRI-only analysis or the source reconstruction of the three ERPs; the latter techniques showing only two regions in common localized in the occipital lobe.

Scientific Reports 5: Article number: 17650; published online: 03 December 2015; updated: 15 February 2016 The original version of this Article contained errors in the spelling of the authors Catherine Faget-Agius, Jonathan Wirsich, Jean-Philippe Ranjeva, Raphaelle Richieri, Elisabeth Soulier, Sylviane Confort-Gouny, Pascal Auquier, Maxime Guye and Christophe Lançon which were incorrectly given as Faget-Agius Catherine, Wirsich Jonathan, Ranjeva Jean-Philippe, Richieri Raphaelle, Soulier Elisabeth, Confort-Gouny Sylviane, Auquier Pascal, Guye Maxime & Lançon Christophe respectively.

International audience; Diagonistic dyspraxia (DD) is by far the most spectacular manifestation reported by sufferers of acute corpus callosum (CC) injury (so-called "split-brain"). In this form of alien hand syndrome, one hand acts at cross purposes with the other "against the patient's will". Although recent models view DD as a disorder of motor control, there is still little information regarding its neural underpinnings, due to widespread connectivity changes produced by CC insult, and the obstacle that non-volitional movements represent for task-based functional neuroimaging studies. Here, we studied patient AM, the first report of DD in patient with complete developmental CC agenesis. This unique case also offers the opportunity to study the resting-state connectomics of DD in the absence of diffuse changes subsequent to CC injury or surgery. AM developed DD following status epilepticus (SE) which resolved over a 2-year period. Whole brain functional connectivity (FC) was compared (Crawford-Howell [CH]) to 16 controls during the period of acute DD symptoms (Time 1) and after remission (Time 2). Whole brain graph theoretical models were also constructed and topological efficiency examined. At Time 1, disrupted FC was observed in inter-hemispheric and intra-hemispheric right edges, involving frontal superior and midline structures. Graph analysis indicated disruption of the efficiency of salience and right frontoparietal (FP) networks. At Time 2, after remission of diagnostic dyspraxia symptoms, FC and salience network changes had resolved. In sum, longitudinal analysis of connectivity in AM indicates that DD behaviors could result from disruption of systems that support the experience and control of volitional movements and the ability to generate appropriate behavioral responses to salient stimuli. This also raises the possibility that changes to large-scale functional architecture revealed by resting-state functional magnetic resonance imaging (fMRI) (rs-fMRI) may provide relevant information on the evolution of behavioral syndromes in addition to that provided by structural and task-based functional imaging.

The impact of the hemisphere affected by impairment in models of network disease is not fully understood. Among such models, focal epilepsies are characterised by recurrent seizures generated in epileptogenic areas also responsible for wider network dysfunction between seizures. Previous work focusing on functional connectivity within circumscribed networks suggests a divergence of network integrity and compensatory capacity between epilepsies as a function of the laterality of seizure onset. We evaluated the ability of complex network theory to reveal changes in focal epilepsy in global and nodal parameters using graph theoretical analysis of functional connectivity data obtained with resting-state fMRI. Graphs of functional connectivity networks were derived from 19 right and 13 left focal epilepsy patients and 15 controls. Topological metrics (degree, local efficiency, global efficiency and modularity) were computed for a whole-brain, atlas-defined network. We also calculated a hub disruption index for each graph metric, measuring the capacity of the brain network to demonstrate increased connectivity in some nodes for decreased connectivity in others. Our data demonstrate that the patient group as a whole is characterised by network-wide pattern of reorganization, even while global parameters fail to distinguish between groups. Furthermore, multiple metrics indicate that epilepsies with differently lateralized epileptic networks are asymmetric in their burden on functional brain networks; with left epilepsy patients being characterised by reduced efficiency and modularity, while in right epilepsy patients we provide the first evidence that functional brain networks are characterised by enhanced connectivity and efficiency at some nodes whereas reduced in others.

The aim of this study was to investigate the neural substrate underlying quality of life (QoL) and to demonstrate the microstructural abnormalities associated with impaired QoL in a large sample of patients with schizophrenia, using magnetisation transfer imaging. A total of 81 right-handed men with a diagnosis of schizophrenia and 25 age- and sex-similar healthy controls were included and underwent a 3T MRI with magnetization transfer ratio (MTR) to detect microstructural abnormalities. Compared with healthy controls, patients with schizophrenia had grey matter (GM) decreased MTR values in the temporal lobe (BA21, BA37 and BA38), the bilateral insula, the occipital lobe (BA17, BA18 and BA19) and the cerebellum. Patients with impaired QoL had lower GM MTR values relative to patients with preserved QoL in the bilateral temporal pole (BA38), the bilateral insula, the secondary visual cortex (BA18), the vermis and the cerebellum. Significant correlations between MTR values and QoL scores (p