Disclosing large-scale directed functional connections in MEG with the multivariate phase slope index.

Abstract The phase slope index (PSI) is a method to disclose the direction of frequency-specific neural interactions from magnetoencephalographic (MEG) time series. A fundamental property of PSI is that of vanishing for linear mixing of independent neural sources. This property allows PSI to cope with the artificial instantaneous connectivity among MEG sensors or brain sources induced by the field spread. Nevertheless, PSI is limited by being a bivariate estimator of directionality as opposite to the multidimensional nature of brain activity as revealed by MEG. The purpose of this work is to provide a multivariate generalization of PSI. We termed this measure as the multivariate phase slope index (MPSI). In order to test the ability of MPSI in estimating the directionality, and to compare the MPSI results to those obtained by bivariate PSI approaches based on maximizing imaginary part of coherency and on canonical correlation analysis, we used extensive simulations. We proved that MPSI achieves the highest performance and that in a large number of simulated cases, the bivariate methods, as opposed to MPSI, do not detect a statistically significant directionality. Finally, we applied MPSI to assess seed-based directed functional connectivity in the alpha band from resting state MEG data of 61 subjects from the Human Connectome Project. The obtained results highlight a directed functional coupling in the alpha band between the primary visual cortex and several key regions of well-known resting state networks, e.g. dorsal attention network and fronto-parietal network

Multi-dimensional connectivity: a conceptual and mathematical review.

The estimation of functional connectivity between regions of the brain, for example based on statistical dependencies between the time series of activity in each region, has become increasingly important in neuroimaging. Typically, multiple time series (e.g. from each voxel in fMRI data) are first reduced to a single time series that summarises the activity in a region of interest, e.g. by averaging across voxels or by taking the first principal component; an approach we call one-dimensional connectivity. However, this summary approach ignores potential multi-dimensional connectivity between two regions, and a number of recent methods have been proposed to capture such complex dependencies. Here we review the most common multi-dimensional connectivity methods, from an intuitive perspective, from a formal (mathematical) point of view, and through a number of simulated and real (fMRI and MEG) data examples that illustrate the strengths and weaknesses of each method. The paper is accompanied with both functions and scripts, which implement each method and reproduce all the examples

Time-Lagged Multidimensional Pattern Connectivity (TL-MDPC): An EEG/MEG Pattern Transformation Based Functional Connectivity Metric

Functional and effective connectivity methods are essential to study the complex information flow in brain networks underlying human cognition. Only recently have connectivity methods begun to emerge that make use of the full multidimensional information contained in patterns of brain activation, rather than unidimensional summary measures of these patterns. To date, these methods have mostly been applied to fMRI data, and no method allows vertex-to-vertex transformations with the temporal specificity of EEG/MEG data. Here, we introduce time-lagged multidimensional pattern connectivity (TL-MDPC) as a novel bivariate functional connectivity metric for EEG/MEG research. TL-MDPC estimates the vertex-to-vertex transformations among multiple brain regions and across different latency ranges. It determines how well patterns in ROI at time point can linearly predict patterns of ROI at time point . In the present study, we use simulations to demonstrate TL-MDPC's increased sensitivity to multidimensional effects compared to a unidimensional approach across realistic choices of number of trials and signal-to-noise ratios. We applied TL-MDPC, as well as its unidimensional counterpart, to an existing dataset varying the depth of semantic processing of visually presented words by contrasting a semantic decision and a lexical decision task. TL-MDPC detected significant effects beginning very early on, and showed stronger task modulations than the unidimensional approach, suggesting that it is capable of capturing more information. With TL-MDPC only, we observed rich connectivity between core semantic representation (left and right anterior temporal lobes) and semantic control (inferior frontal gyrus and posterior temporal cortex) areas with greater semantic demands. TL-MDPC is a promising approach to identify multidimensional connectivity patterns, typically missed by unidimensional approaches

Neuronal phase- and amplitude-coupling in the healthy and diseased human brain

Oscillatory neuronal activity has been proposed to facilitate and multiplex the communication between distant brain regions. In particular, two key coupling modes have been discussed in this regard; amplitude- and phase-coupling between distant signals. Both can describe large-scale neuronal interactions independently of one another and have theoretically been linked to distinct neuronal mechanisms. However, to date the direct relations between functional networks derived from these two coupling modes remain unclear. In this thesis we conducted two magnetoencephalography (MEG) studies to empirically assess the relationship between amplitude- and phase-coupling networks in the healthy and diseased human brain. In the first study we analyzed the publicly available human connectome project (HCP S900) dataset of 95 healthy subjects. We applied source-reconstruction to systematically compare cortical amplitude-coupling and phase-coupling patterns in the healthy human brain. We found significant similarities between amplitude- and phase-coupling patterns for almost the entire spectrum and cortex. We further showed that these patterns are similar but non-redundant, indicating a complex spatial and spectral distribution. By combining empirical measurements with simulations and attenuation correction, we sought to ensure that these results were not due to methodological biases but instead reflected relations between genuine amplitude- and phase-coupling, which may indicate at least partially distinct neuronal mechanisms. Additionally, we highlight and clarify the compound nature of amplitude-coupling of orthogonalized signals. In our second study, we measured MEG in 17 relapsing-remitting Multiple Sclerosis patients at an early disease stage (median EDSS = 1.5, range 0 to 3.5) and 17 healthy controls to investigate brain-wide phase- and amplitude-coupling of frequency specific neuronal activity. We developed a new analysis approach that combines dimensionality reduction, bootstrap aggregating and multivariate classification to identify changes of brain-wide coupling in Multiple Sclerosis. We identified systematic and non-redundant changes of both phase- and amplitude-coupling. Changes included both, increased and decreased neuronal coupling in wide-spread, bilateral neuronal networks across a broad range of frequencies. These changes allowed to successfully classify patients and controls with an accuracy of 84%. Furthermore, classification confidence predicted behavioral scores of disease severity. Our results unravel systematic changes of large-scale neuronal coupling in Multiple Sclerosis and suggest non-invasive electrophysiological coupling measures as powerful biomarkers of Multiple Sclerosis. Overall, our two studies provide, to our knowledge, the first systematic analyses describing the relationship between amplitude- and phase-coupling networks. In both, the healthy as well as in the diseased brain, these two coupling modes are related but show distinguishable features. Our findings highlight that amplitude- and phase-coupling might at least partially originate from distinct neuronal mechanisms

Modulations of local synchrony over time lead to resting-state functional connectivity in a parsimonious large-scale brain model

Biophysical models of large-scale brain activity are a fundamental tool for understanding the mechanisms underlying the patterns observed with neuroimaging. These models combine a macroscopic description of the within- and between-ensemble dynamics of neurons within a single architecture. A challenge for these models is accounting for modulations of within-ensemble synchrony over time. Such modulations in local synchrony are fundamental for modeling behavioral tasks and resting-state activity. Another challenge comes from the difficulty in parametrizing large scale brain models which hinders researching principles related with between-ensembles differences. Here we derive a parsimonious large scale brain model that can describe fluctuations of local synchrony. Crucially, we do not reduce within-ensemble dynamics to macroscopic variables first, instead we consider within and between-ensemble interactions similarly while preserving their physiological differences. The dynamics of within-ensemble synchrony can be tuned with a parameter which manipulates local connectivity strength. We simulated resting-state static and time-resolved functional connectivity of alpha band envelopes in models with identical and dissimilar local connectivities. We show that functional connectivity emerges when there are high fluctuations of local and global synchrony simultaneously (i.e. metastable dynamics). We also show that for most ensembles, leaning towards local asynchrony or synchrony correlates with the functional connectivity with other ensembles, with the exception of some regions belonging to the default-mode network

An Empirical Comparative Study on the Two Methods of Eliciting Singers’ Emotions in Singing: Self-Imagination and VR Training

Emotional singing can affect vocal performance and the audience’s engagement. Chinese universities use traditional training techniques for teaching theoretical and applied knowledge. Self-imagination is the predominant training method for emotional singing. Recently, virtual reality (VR) technologies have been applied in several fields for training purposes. In this empirical comparative study, a VR training task was implemented to elicit emotions from singers and further assist them with improving their emotional singing performance. The VR training method was compared against the traditional self-imagination method. By conducting a two-stage experiment, the two methods were compared in terms of emotions’ elicitation and emotional singing performance. In the first stage, electroencephalographic (EEG) data were collected from the subjects. In the second stage, self-rating reports and third-party teachers’ evaluations were collected. The EEG data were analyzed by adopting the max-relevance and min-redundancy algorithm for feature selection and the support vector machine (SVM) for emotion recognition. Based on the results of EEG emotion classification and subjective scale, VR can better elicit the positive, neutral, and negative emotional states from the singers than not using this technology (i.e., self-imagination). Furthermore, due to the improvement of emotional activation, VR brings the improvement of singing performance. The VR hence appears to be an effective approach that may improve and complement the available vocal music teaching methods

Modern Developments in Transcranial Magnetic Stimulation (TMS) – Applications and Perspectives in Clinical Neuroscience

Transcranial magnetic stimulation (TMS) is being increasingly used in neuroscience and clinics. Modern advances include but are not limited to the combination of TMS with precise neuronavigation as well as the integration of TMS into a multimodal environment, e.g., by guiding the TMS application using complementary techniques such as functional magnetic resonance imaging (fMRI), electroencephalography (EEG), diffusion tensor imaging (DTI), or magnetoencephalography (MEG). Furthermore, the impact of stimulation can be identified and characterized by such multimodal approaches, helping to shed light on the basic neurophysiology and TMS effects in the human brain. Against this background, the aim of this Special Issue was to explore advancements in the field of TMS considering both investigations in healthy subjects as well as patients

Oscillations cérébrales et performances cognitives : études à l'état de repos en MEG chez des sujets contrôles et des survivants de cancer pédiatrique

Cette étude s’intéresse au lien entre les dynamiques cérébrales et les capacités cognitives, cette problématique a déjà été explorée auparavant en imagerie cérébrale, notamment à l’aide de tâches effectuées pendant l’imagerie. Cependant la caractérisation de l’activité spontanée a principalement été faite soit avec une faible précision spatiale (capteur EEG/MEG), soit en IRMf qui a une faible résolution temporelle. L’objectif de cette thèse est de caractériser l’activité spontanée au repos au niveau cortical associée à différents processus cognitifs et leur performance. Le second chapitre cherche à établir les corrélats neuronaux de la performance de la mémoire au repos à l’aide des puissances spectrales localisées au niveau des sources corticales. Le troisième chapitre cherche à répliquer les méthodes utilisées dans l’article 1 avec les mêmes participants, mais dans un autre domaine cognitif afin d’établir les corrélats neuronaux de la fluence verbale ainsi que de discriminer une composante verbale et exécutive. Ces deux composantes ont été mises en évidence en utilisant une factorisation avec un test purement exécutif (Trail making test- condition 4) et un autre purement verbal (richesse du vocabulaire). Dans le quatrième chapitre, nous répliquons encore la méthode de l’article 1 avec les mêmes sujets, mais sur un test d’apprentissage verbal. Lors de l’apprentissage verbal, deux stratégies d’apprentissage (sériel et sémantique) possibles sont utilisées de manière concurrente, nous avons cherché à établir si des différences comportementales se traduisaient par des patrons d’activation différents. Dans le cinquième chapitre, nous avons cherché à établir des différences fonctionnelles entre les survivants de la leucémie et des sujets contrôles, puis à établir un lien entre la neurotoxicité et le déficit cognitif rencontré chez cette population, finalement nous avons établi un modèle intégrant neurotoxicité, performance cognitive et marqueur neurophysiologique fonctionnel cérébral. Cette recherche aura approfondi les connaissances sur l’état de repos et principalement fourni les premiers travaux qui mettent en lien l’activité cérébrale spontanée au repos au niveau des sources corticales avec plusieurs tests neuropsychologiques comportementaux. Les résultats ont amené des patrons d’activation spatio-fréquentielle différents, démontrant des spécificités reliées à certains tests comportementaux ou des traitements de l’information (sériel ou sémantique). Finalement les travaux sur les survivants de la leucémie ont montré que l’état de repos pouvait caractériser le fonctionnement des déficits cognitifs à long terme et être un marqueur de remédiation pour de futurs traitements.This study is interested in the link between brain dynamics and cognitive abilities. This problem has already been explored before in brain imaging, notably with the help of task performed during imaging. However, the characterization of spontaneous activity has mainly been done either with weak spatial resolution (EEG/MEG sensor) or in fMRI which has a low temporal resolution. The objective of this thesis is to characterize the spontaneous activity at rest at the cortical level associated with different cognitive processes and their performance. The second chapter seeks to establish the neural correlates of resting memory performance using spectral powers localized at cortical sources. The third chapter seeks to replicate the methods used in article 1 with the same participants but in another cognitive domain in order to establish the neural correlates of verbal fluency as well as to discriminate a verbal and an executive component. These two components were highlighted using a factorization with a purely executive test (Trail making test-condition 4) and another purely verbal one (vocabulary richness). In the fourth chapter, we replicate the method of article 1 with the same subjects, but on a verbal learning test. During verbal learning, two possible learning strategies (serial and semantic) are used concurrently, we sought to establish whether behavioural differences translate into different activation patterns. In the fifth chapter, we sought to establish functional differences between leukemia survivors and control subjects, then to search for a link between neurotoxicity and the cognitive deficit encountered in this population; finally we established a model integrating neurotoxicity, cognitive performance and functional neurophysiological brain markers. This research will have deepened the knowledge on the resting state and mainly provided the first works that link the spontaneous brain activity at rest at the level of cortical sources with several behavioural neuropsychological tests. The results led to different spatio-frequential activation patterns, showing specificities related to certain behavioural tests or information processing (serial or semantic). Finally, work on leukemia survivors has shown that resting states could characterize the functioning of long-term cognitive deficits and be a remediation marker for future treatments

29th Annual Computational Neuroscience Meeting: CNS*2020

Meeting abstracts This publication was funded by OCNS. The Supplement Editors declare that they have no competing interests. Virtual | 18-22 July 202