186 research outputs found

    Brain network analyses in clinical neuroscience

    Get PDF
    Network analyses are now considered fundamental for understanding brain function. Nonetheless neuroimaging characterisations of connectivity are just emerging in clinical neuroscience. Here, we briefly outline the concepts underlying structural, functional and effective connectivity, and discuss some cutting-edge approaches to the quantitative assessment of brain architecture and dynamics. As illustrated by recent evidence, comprehensive and integrative network analyses offer the potential for refining pathophysiological concepts and therapeutic strategies in neurological and psychiatric conditions across the lifespan

    Genetic and Neuroanatomical Support for Functional Brain Network Dynamics in Epilepsy

    Full text link
    Focal epilepsy is a devastating neurological disorder that affects an overwhelming number of patients worldwide, many of whom prove resistant to medication. The efficacy of current innovative technologies for the treatment of these patients has been stalled by the lack of accurate and effective methods to fuse multimodal neuroimaging data to map anatomical targets driving seizure dynamics. Here we propose a parsimonious model that explains how large-scale anatomical networks and shared genetic constraints shape inter-regional communication in focal epilepsy. In extensive ECoG recordings acquired from a group of patients with medically refractory focal-onset epilepsy, we find that ictal and preictal functional brain network dynamics can be accurately predicted from features of brain anatomy and geometry, patterns of white matter connectivity, and constraints complicit in patterns of gene coexpression, all of which are conserved across healthy adult populations. Moreover, we uncover evidence that markers of non-conserved architecture, potentially driven by idiosyncratic pathology of single subjects, are most prevalent in high frequency ictal dynamics and low frequency preictal dynamics. Finally, we find that ictal dynamics are better predicted by white matter features and more poorly predicted by geometry and genetic constraints than preictal dynamics, suggesting that the functional brain network dynamics manifest in seizures rely on - and may directly propagate along - underlying white matter structure that is largely conserved across humans. Broadly, our work offers insights into the generic architectural principles of the human brain that impact seizure dynamics, and could be extended to further our understanding, models, and predictions of subject-level pathology and response to intervention

    Clinical applications of magnetic resonance imaging based functional and structural connectivity

    Get PDF
    Advances in computational neuroimaging techniques have expanded the armamentarium of imaging tools available for clinical applications in clinical neuroscience. Non-invasive, in vivo brain MRI structural and functional network mapping has been used to identify therapeutic targets, define eloquent brain regions to preserve, and gain insight into pathological processes and treatments as well as prognostic biomarkers. These tools have the real potential to inform patient-specific treatment strategies. Nevertheless, a realistic appraisal of clinical utility is needed that balances the growing excitement and interest in the field with important limitations associated with these techniques. Quality of the raw data, minutiae of the processing methodology, and the statistical models applied can all impact on the results and their interpretation. A lack of standardization in data acquisition and processing has also resulted in issues with reproducibility. This limitation has had a direct impact on the reliability of these tools and ultimately, confidence in their clinical use. Advances in MRI technology and computational power as well as automation and standardization of processing methods, including machine learning approaches, may help address some of these issues and make these tools more reliable in clinical use. In this review, we will highlight the current clinical uses of MRI connectomics in the diagnosis and treatment of neurological disorders; balancing emerging applications and technologies with limitations of connectivity analytic approaches to present an encompassing and appropriate perspective

    Methods and models for brain connectivity assessment across levels of consciousness

    Get PDF
    The human brain is one of the most complex and fascinating systems in nature. In the last decades, two events have boosted the investigation of its functional and structural properties. Firstly, the emergence of novel noninvasive neuroimaging modalities, which helped improving the spatial and temporal resolution of the data collected from in vivo human brains. Secondly, the development of advanced mathematical tools in network science and graph theory, which has recently translated into modeling the human brain as a network, giving rise to the area of research so called Brain Connectivity or Connectomics. In brain network models, nodes correspond to gray-matter regions (based on functional or structural, atlas-based parcellations that constitute a partition), while links or edges correspond either to structural connections as modeled based on white matter fiber-tracts or to the functional coupling between brain regions by computing statistical dependencies between measured brain activity from different nodes. Indeed, the network approach for studying the brain has several advantages: 1) it eases the study of collective behaviors and interactions between regions; 2) allows to map and study quantitative properties of its anatomical pathways; 3) gives measures to quantify integration and segregation of information processes in the brain, and the flow (i.e. the interacting dynamics) between different cortical and sub-cortical regions. The main contribution of my PhD work was indeed to develop and implement new models and methods for brain connectivity assessment in the human brain, having as primary application the analysis of neuroimaging data coming from subjects at different levels of consciousness. I have here applied these methods to investigate changes in levels of consciousness, from normal wakefulness (healthy human brains) or drug-induced unconsciousness (i.e. anesthesia) to pathological (i.e. patients with disorders of consciousness)

    Distinct connectivity patterns in human medial parietal cortices: Evidence from standardized connectivity map using cortico-cortical evoked potential

    Get PDF
    The medial parietal cortices are components of the default mode network (DMN), which are active in the resting state. The medial parietal cortices include the precuneus and the dorsal posterior cingulate cortex (dPCC). Few studies have mentioned differences in the connectivity in the medial parietal cortices, and these differences have not yet been precisely elucidated. Electrophysiological connectivity is essential for understanding cortical function or functional differences. Since little is known about electrophysiological connections from the medial parietal cortices in humans, we evaluated distinct connectivity patterns in the medial parietal cortices by constructing a standardized connectivity map using cortico-cortical evoked potential (CCEP). This study included nine patients with partial epilepsy or a brain tumor who underwent chronic intracranial electrode placement covering the medial parietal cortices. Single-pulse electrical stimuli were delivered to the medial parietal cortices (38 pairs of electrodes). Responses were standardized using the z-score of the baseline activity, and a response density map was constructed in the Montreal Neurological Institutes (MNI) space. The precuneus tended to connect with the inferior parietal lobule (IPL), the occipital cortex, superior parietal lobule (SPL), and the dorsal premotor area (PMd) (the four most active regions, in descending order), while the dPCC tended to connect to the middle cingulate cortex, SPL, precuneus, and IPL. The connectivity pattern differs significantly between the precuneus and dPCC stimulation (p<0.05). Regarding each part of the medial parietal cortices, the distributions of parts of CCEP responses resembled those of the functional connectivity database. Based on how the dPCC was connected to the medial frontal area, SPL, and IPL, its connectivity pattern could not be explained by DMN alone, but suggested a mixture of DMN and the frontoparietal cognitive network. These findings improve our understanding of the connectivity profile within the medial parietal cortices. The electrophysiological connectivity is the basis of propagation of electrical activities in patients with epilepsy. In addition, it helps us to better understand the epileptic network arising from the medial parietal cortices

    Mapping dynamical properties of cortical microcircuits using robotized TMS and EEG: Towards functional cytoarchitectonics

    Get PDF
    International audienceBrain dynamics at rest depend on the large-scale interactions between oscillating cortical microcircuits arranged into macrocolumns. Cytoarchitectonic studies have shown that the structure of those microcircuits differs between cortical regions, but very little is known about interregional differences of their intrinsic dynamics at a macro-scale in human. We developed here a new method aiming at mapping the dynamical properties of cortical microcircuits non-invasively using the coupling between robotized transcranial magnetic stimulation and elec-troencephalography. We recorded the responses evoked by the stimulation of 18 cortical targets largely covering the accessible neocortex in 22 healthy volunteers. Specific data processing methods were developed to map the local source activity of each cortical target, which showed interregional differences with very good interhemi-spheric reproducibility. Functional signatures of cortical microcircuits were further studied using spatio-temporal decomposition of local source activities in order to highlight principal brain modes. The identified brain modes revealed that cortical areas with similar intrinsic dynamical properties could be distributed either locally or not, with a spatial signature that was somewhat reminiscent of resting state networks. Our results provide the proof of concept of " functional cytoarchitectonics " , that would guide the parcellation of the human cortex using not only its cytoarchitecture but also its intrinsic responses to local perturbations. This opens new avenues for brain modelling and physiopathology readouts

    Causal characterization of functional connectivity through the spread of electrically induced oscillations in the epileptic human brain

    Get PDF
    Little is known about the rules governing the spread of local entrainment within synchronized networks distributed across the brain. The assessment of the causal influences impacting information flow between two brain regions have mainly relied on confirmatory model-driven approaches (such as dynamic causal modeling and structural equation modeling) and exploratory data driven approaches (such as Granger Causality analysis). However, stimulation-driven approaches offer a unique opportunity to impact ongoing brain activity and describe the causal consequences of such manipulations, performed on a specific node of a complex cerebral network. In this project, we characterize causal functional interactions between brain regions by assessing how frequency-tuned electrical currents delivered intracranially in awaken epileptic patients enhance inter-regional synchrony between pairs of areas. To achieve this goal, we worked with an existing iEEG database from 18 medication-resistant epilepsy patients undergoing Intracortical Stimulation Mapping Procedures (ISMP) performed to causally identify and localize the epileptogenic foci, prior to neurosurgical removal. Patients are implanted with series of multi-electrodes in well-known brain regions under MRI guidance. Intracranial EEG contacts allow continuous recordings and the delivery through pairs of adjacent contacts of biphasic pulses of rhythmic Direct Electric Stimulations (DES) at a 50Hz frequency coupled to electrophysiological recordings. Measuring significant increases in gamma power ( 50Hz) observed during the stimulation period (vs. prior the stimulation), and significant increases of Phase-Locking Value (PLV) between signals recorded in the electrically stimulated regions and activity evoked in the rest of implanted regions during stimulation (vs. prior simulation), we characterize the spread of oscillatory entrainment from the stimulated region to the remaining regions, thus establishing a network of functional connectivity in the brain. By comparing this network with the one shown during resting-state, we assess how entrainment to frequency-tuned electrical currents delivered intracranially is predicted by the resting-state functional connectivity network

    Probabilistic functional tractography of the human cortex revisited

    Get PDF
    In patients with pharmaco-resistant focal epilepsies investigated with intracranial electroencephalography (iEEG), direct electrical stimulations of a cortical region induce cortico-cortical evoked potentials (CCEP) in distant cerebral cortex, which properties can be used to infer large scale brain connectivity. In 2013, we proposed a new probabilistic functional tractography methodology to study human brain connectivity. We have now been revisiting this method in the F-TRACT project (f-tract.eu) by developing a large multicenter CCEP database of several thousand stimulation runs performed in several hundred patients, and associated processing tools to create a probabilistic atlas of human cortico-cortical connections. Here, we wish to present a snapshot of the methods and data of F-TRACT using a pool of 213 epilepsy patients, all studied by stereo-encephalography with intracerebral depth electrodes. The CCEPs were processed using an automated pipeline with the following consecutive steps: detection of each stimulation run from stimulation artifacts in raw intracranial EEG (iEEG) files, bad channels detection with a machine learning approach, model-based stimulation artifact correction, robust averaging over stimulation pulses. Effective connectivity between the stimulated and recording areas is then inferred from the properties of the first CCEP component, i.e. onset and peak latency, amplitude, duration and integral of the significant part. Finally, group statistics of CCEP features are implemented for each brain parcel explored by iEEG electrodes. The localization (coordinates, white/gray matter relative positioning) of electrode contacts were obtained from imaging data (anatomical MRI or CT scans before and after electrodes implantation). The iEEG contacts were repositioned in different brain parcellations from the segmentation of patients' anatomical MRI or from templates in the MNI coordinate system. The F-TRACT database using the first pool of 213 patients provided connectivity probability values for 95% of possible intrahemispheric and 56% of interhemispheric connections and CCEP features for 78% of intrahemisheric and 14% of interhemispheric connections. In this report, we show some examples of anatomo-functional connectivity matrices, and associated directional maps. We also indicate how CCEP features, especially latencies, are related to spatial distances, and allow estimating the velocity distribution of neuronal signals at a large scale. Finally, we describe the impact on the estimated connectivity of the stimulation charge and of the contact localization according to the white or gray matter. The most relevant maps for the scientific community are available for download on f-tract. eu (David et al., 2017) and will be regularly updated during the following months with the addition of more data in the F-TRACT database. This will provide an unprecedented knowledge on the dynamical properties of large fiber tracts in human.Peer reviewe

    Human brain networks: consensus, reproducibility, inter-modal comparison and epilepsy pathology

    Get PDF
    Classical and contemporary research in neuroscience postulates that connectivity is a fundamental component of human brain function. Recently, advances in computational neuroimaging have enabled reconstruction of macroscopic human brain structural connectivity in vivo using diffusion MRI. Studies show that the structural network topology may discriminate between neurological phenotypes or relate to individual brain function. To investigate disease effectively, it is necessary to determine the network methodological and biological variability. Reproducibility was calculated for two state-of-the-art reconstruction pipelines in healthy subjects. High reproducibility of connection weights was observed, which increased with connection strength. A high agreement between pipelines was found across network density thresholds. In addition, a robust core network was identified coinciding with a peak in similarity across thresholds, and replicated with alternative atlases. This study demonstrates the utility of applying multiple structural network pipelines to diffusion data in order to identify the most important connections. Focal epilepsy is characterised by seizures that can spread to contiguous and non-contiguous sites. Diffusion MRI and cortico-cortical evoked potentials were acquired in focal epilepsy patients to reconstruct and correlate their structural and effective brain networks and examine connectivity of the ictal-onset zone and propagative regions. Automated methods are described to reconstruct comparable largescale structural and effective networks. A high overlap and low correlation was observed between network modalities. Low correlation may be due to imperfections in methodology, such as difficulty tracing U-fibers using tractography. Effective connectivity amplitude, baseline fluctuation, and outward connectivity tended to be higher at ictal-onset regions, while higher structural connectivity between ictal-onset regions was observed. Furthermore, a high prevalence of structural and effective connections to sites of non-contiguous seizure spread was found. These results support the concept of highly excitable cortex underlying ictal-onset regions which promotes non-contiguous seizure spread via high outward connectivity
    corecore