Alien Hand, Restless Brain: Salience Network and Interhemispheric Connectivity Disruption Parallel Emergence and Extinction of Diagonistic Dyspraxia

International audienceDiagonistic 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

Fusion de données EEG-IRMf et IRMd chez des sujets sains et des patients atteints d'épilepsie du lobe temporal : vers une caractérisation trimodale du réseau structure-fonction du cerveau humain

La caractérisation de la structure du cerveau humain et les motifs fonctionnelles qu’il fait apparaitre est un défi central pour une meilleure compréhension des pathologies du réseau cérébral telle que l’épilepsie du lobe temporal. Cette caractérisation pourrait aider à améliorer la prédictibilité clinique des résultats d’une chirurgie visant à traiter l’épilepsie.Le fonctionnement du cerveau peut être étudié par l’électroencéphalographie (EEG) ou par l’imagerie de résonance magnétique fonctionnelle (IRMf), alors que la structure peut être caractérisé par l’IRM de diffusion (IRMd). Nous avons utilisé ces modalités pour mesurer le fonctionnement du cerveau pendant une tache de reconnaissance de visages et pendant le repos dans le but de faire le lien entre les modalités d’une façon optimale en termes de résolution temporale et spatiale. Avec cette approche on a mis en évidence une perturbation des relations structure-fonction chez les patients épileptiques.Ce travail a contribué à améliorer la compréhension de l’épilepsie comme une maladie de réseau qui affecte le cerveau à large échelle et non pas au niveau d’un foyer épileptique local. Dans le futur, ces résultats pourraient être utilisés pour guider des interventions chirurgicales mais ils fournissent également des approches nouvelles pour évaluer des traitements pharmacologiques selon ses implications fonctionnelles à l’échelle du cerveau entier.The understanding human brain structure and the function patterns arising from it is a central challenge to better characterize brain network pathologies such as temporal lobe epilepsies, which could help to improve the clinical predictability of epileptic surgery outcome.Brain functioning can be accessed by both electroencephalography (EEG) or functional magnetic resonance imaging (fMRI), while brain structure can be measured with diffusion MRI (dMRI). We use these modalities to measure brain functioning during a face recognition task and in rest in order to link the different modalities in an optimal temporal and spatial manner. We discovered disruption of the network processing famous faces as well a disruption of the structure-function relation during rest in epileptic patients.This work broadened the understanding of epilepsy as a network disease that changes the brain on a large scale not limited to a local epileptic focus. In the future these results could be used to guide clinical intervention during epilepsy surgery but also they provide new approaches to evaluate pharmacological treatment on its functional implications on a whole brain scale

Intrinsic connectome organization across temporal scales: New insights from cross-modal approaches

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.</p

Concurrent EEG- and fMRI-derived functional connectomes exhibit linked dynamics

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 (&lt;0.1Hz) speeds. Conversely, electrophysiological connectivity is based on cross-region coupling of fast oscillations (~1-100Hz). It is unclear whether such fast oscillation-based coupling varies at infraslow speeds, temporally coinciding with infraslow dynamics across the fMRI-based connectome. If so, does the association of fMRI-derived and electrophysiological dynamics spatially vary over the connectome across the functionally distinct electrophysiological oscillation bands? In two concurrent electroencephalography (EEG)-fMRI resting-state datasets, oscillation-based coherence in all canonical bands (delta through gamma) indeed reconfigured at infraslow speeds in tandem with fMRI-derived connectivity changes in corresponding region-pairs. Interestingly, irrespective of EEG frequency-band the cross-modal tie of connectivity dynamics comprised a large proportion of connections distributed across the entire connectome. However, there were frequency-specific differences in the relative strength of the cross-modal association. This association was strongest in visual to somatomotor connections for slower EEG-bands, and in connections involving the Default Mode Network for faster EEG-bands. Methodologically, the findings imply that neural connectivity dynamics can be reliably measured by fMRI despite heavy susceptibility to noise, and by EEG despite shortcomings of source reconstruction. Biologically, the findings provide evidence that contrast with known territories of oscillation power, oscillation coupling in all bands slowly reconfigures in a highly distributed manner across the whole-brain connectome

Experimental Design and Data Analysis Strategies

As described in earlier chapters, EEG and fMRI are two powerful, noninvasive tools for studying human brain activity. Since they have complementary spatiotemporal properties, with EEG providing millisecond temporal resolution and fMRI millimetre spatial resolution, there has been a drive over the last two decades to record them simultaneously, a technique referred to as simultaneous EEG–fMRI or simply EEG–fMRI. However, acquiring EEG in the MRI scanner brings additional constraints in terms of setup and signal analysis. In particular, EEG signals are degraded by the variations of the magnetic fields that are central to image acquisition, as well as to cardiac-related pulsations (see the Chaps. 8 and 9). More positively, concurrent acquisition provides new opportunities compared to serial recordings (see Chap. 1), since it allows brain activity to be recorded with the two modalities in the exact same brain state. This is particularly important for spontaneous activities such as epileptic discharges, sleep stages or alpha waves, but also in protocols involving cognitive processes such as memory or attention. It also permits the covariation in single trial fluctuations to be used as a powerful way to link the two modalities. In this chapter, we review the different strategies for acquiring and processing simultaneous EEG-fMRI data in epilepsy, cognition and for spontaneous activity

Safety and data quality of EEG recorded simultaneously with multi-band fMRI

Purpose: Simultaneously recorded electroencephalography and functional magnetic resonance imaging (EEG-fMRI) is highly informative yet technically challenging. Until recently, there has been little information about EEG data quality and safety when used with newer multi-band (MB) fMRI sequences. Here, we measure the relative heating of a MB protocol compared with a standard single-band (SB) protocol considered to be safe. We also evaluated EEG quality recorded concurrently with the MB protocol on humans

Complementary contributions of concurrent EEG and fMRI connectivity for predicting structural connectivity

International audienceWhile 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

Brain’s Dynamic Functional Organization with Simultaneous EEG-fMRI Networks

The brain’s functional networks can be assessed using imaging techniques like functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). Recent studies have suggested a link between the dynamic functional connectivity (dFC) captured by these two modalities, but the exact relationship between their spatiotemporal organization is still unclear. Since these networks are spatially embedded, a question arises whether the topological features captured can be explained exclusively by the spatial constraints. We investigated the global structure of resting-state EEG and fMRI data, including a spatially informed null model and found that fMRI networks are more modular over time, in comparison to EEG, which captured a less clustered topology. This resulted in overall low similarity values. However, when investigating the community structure beyond spatial constraints, this similarity decreased. We show that even though EEG and fMRI functional connectomes are slightly linked, the two modalities essentially capture different information over time, with most but not all topology being explained by the underlying spatial embedding