Spatiotemporal Analysis of Multichannel EEG: CARTOOL

This paper describes methods to analyze the brain's electric fields recorded with multichannel Electroencephalogram (EEG) and demonstrates their implementation in the software CARTOOL. It focuses on the analysis of the spatial properties of these fields and on quantitative assessment of changes of field topographies across time, experimental conditions, or populations. Topographic analyses are advantageous because they are reference independents and thus render statistically unambiguous results. Neurophysiologically, differences in topography directly indicate changes in the configuration of the active neuronal sources in the brain. We describe global measures of field strength and field similarities, temporal segmentation based on topographic variations, topographic analysis in the frequency domain, topographic statistical analysis, and source imaging based on distributed inverse solutions. All analysis methods are implemented in a freely available academic software package called CARTOOL. Besides providing these analysis tools, CARTOOL is particularly designed to visualize the data and the analysis results using 3-dimensional display routines that allow rapid manipulation and animation of 3D images. CARTOOL therefore is a helpful tool for researchers as well as for clinicians to interpret multichannel EEG and evoked potentials in a global, comprehensive, and unambiguous way

A Novel Synergistic Model Fusing Electroencephalography and Functional Magnetic Resonance Imaging for Modeling Brain Activities

Study of the human brain is an important and very active area of research. Unraveling the way the human brain works would allow us to better understand, predict and prevent brain related diseases that affect a significant part of the population. Studying the brain response to certain input stimuli can help us determine the involved brain areas and understand the mechanisms that characterize behavioral and psychological traits. In this research work two methods used for the monitoring of brain activities, Electroencephalography (EEG) and functional Magnetic Resonance (fMRI) have been studied for their fusion, in an attempt to bridge together the advantages of each one. In particular, this work has focused in the analysis of a specific type of EEG and fMRI recordings that are related to certain events and capture the brain response under specific experimental conditions. Using spatial features of the EEG we can describe the temporal evolution of the electrical field recorded in the scalp of the head. This work introduces the use of Hidden Markov Models (HMM) for modeling the EEG dynamics. This novel approach is applied for the discrimination of normal and progressive Mild Cognitive Impairment patients with significant results. EEG alone is not able to provide the spatial localization needed to uncover and understand the neural mechanisms and processes of the human brain. Functional Magnetic Resonance imaging (fMRI) provides the means of localizing functional activity, without though, providing the timing details of these activations. Although, at first glance it is apparent that the strengths of these two modalities, EEG and fMRI, complement each other, the fusion of information provided from each one is a challenging task. A novel methodology for fusing EEG spatiotemporal features and fMRI features, based on Canonical Partial Least Squares (CPLS) is presented in this work. A HMM modeling approach is used in order to derive a novel feature-based representation of the EEG signal that characterizes the topographic information of the EEG. We use the HMM model in order to project the EEG data in the Fisher score space and use the Fisher score to describe the dynamics of the EEG topography sequence. The correspondence between this new feature and the fMRI is studied using CPLS. This methodology is applied for extracting features for the classification of a visual task. The results indicate that the proposed methodology is able to capture task related activations that can be used for the classification of mental tasks. Extensions on the proposed models are examined along with future research directions and applications

Identifying causal networks of neuronal sources from EEG/MEG data with the phase slope index: a simulation study

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The investigation of functional neuronal synchronization has recently become a growing field of research. With high temporal resolution, electroencephalography and magnetoencephalography are well-suited measurement techniques to identify networks of interacting sources underlying the recorded data. The analysis of the data in terms of effective connectivity, nevertheless, contains intrinsic issues such as the problem of volume conduction and the non-uniqueness of the inverse solution. Here, we briefly introduce a series of existing methods assessing these problems. To determine the locations of interacting brain sources robust to volume conduction, all computations are solely based on the imaginary part of the cross-spectrum as a trustworthy source of information. Furthermore, we demonstrate the feasibility of estimating causal relationships of systems of neuronal sources with the phase slope index in realistically simulated data. Finally, advantages and drawbacks of the applied methodology are highlighted and discussed

Is sensor space analysis good enough? Spatial patterns as a tool for assessing spatial mixing of EEG/MEG rhythms

Analyzing non-invasive recordings of electroencephalography (EEG) and magnetoencephalography (MEG) directly in sensor space, using the signal from individual sensors, is a convenient and standard way of working with this type of data. However, volume conduction introduces considerable challenges for sensor space analysis. While the general idea of signal mixing due to volume conduction in EEG/MEG is recognized, the implications have not yet been clearly exemplified. Here, we illustrate how different types of activity overlap on the level of individual sensors. We show spatial mixing in the context of alpha rhythms, which are known to have generators in different areas of the brain. Using simulations with a realistic 3D head model and lead field and data analysis of a large resting-state EEG dataset, we show that electrode signals can be differentially affected by spatial mixing by computing a sensor complexity measure. While prominent occipital alpha rhythms result in less heterogeneous spatial mixing on posterior electrodes, central electrodes show a diversity of rhythms present. This makes the individual contributions, such as the sensorimotor mu-rhythm and temporal alpha rhythms, hard to disentangle from the dominant occipital alpha. Additionally, we show how strong occipital rhythms can contribute the majority of activity to frontal channels, potentially compromising analyses that are solely conducted in sensor space. We also outline specific consequences of signal mixing for frequently used assessment of power, power ratios and connectivity profiles in basic research and for neurofeedback application. With this work, we hope to illustrate the effects of volume conduction in a concrete way, such that the provided practical illustrations may be of use to EEG researchers to in order to evaluate whether sensor space is an appropriate choice for their topic of investigation

Degradation of EEG microstates patterns in subjective cognitive decline and mild cognitive impairment: Early biomarkers along the Alzheimer's Disease continuum?

Alzheimer's disease (AD) pathological changes may begin up to decades earlier than the appearance of the first symptoms of cognitive decline. Subjective cognitive decline (SCD) could be the first pre-clinical sign of possible AD, which might be followed by mild cognitive impairment (MCI), the initial stage of clinical cognitive decline. However, the neural correlates of these prodromic stages are not completely clear yet. Recent studies suggest that EEG analysis tools characterizing the cortical activity as a whole, such as microstates and cortical regions connectivity, might support a characterization of SCD and MCI conditions. Here we test this approach by performing a broad set of analyses to identify the prominent EEG markers differentiating SCD (n = 57), MCI (n = 46) and healthy control subjects (HC, n = 19). We found that the salient differences were in the temporal structure of the microstates patterns, with MCI being associated with less complex sequences due to the altered transition probability, frequency and duration of canonic microstate C. Spectral content of EEG, network connectivity, and spatial arrangement of microstates were instead largely similar in the three groups. Interestingly, comparing properties of EEG microstates in different cerebrospinal fluid (CSF) biomarkers profiles, we found that canonic microstate C displayed significant differences in topography in AD-like profile. These results show that the progression of dementia might be associated with a degradation of the cortical organization captured by microstates analysis, and that this leads to altered transitions between cortical states. Overall, our approach paves the way for the use of non-invasive EEG recordings in the identification of possible biomarkers of progression to AD from its prodromal states

Qualitative differences in the spatiotemporal brain states supporting configural face processing emerge in adolescence in autism

BACKGROUND Studying the neural processing of faces can illuminate the mechanisms of compromised social expertise in autism. To resolve a longstanding debate, we examined whether differences in configural face processing in autism are underpinned by quantitative differences in the activation of typical face processing pathways, or the recruitment of non-typical neural systems. METHODS We investigated spatial and temporal characteristics of event-related EEG responses to upright and inverted faces in a large sample of children, adolescents, and adults with and without autism. We examined topographic analyses of variance and global field power to identify group differences in the spatial and temporal response to face inversion. We then examined how quasi-stable spatiotemporal profiles - microstates - are modulated by face orientation and diagnostic group. RESULTS Upright and inverted faces produced distinct profiles of topography and strength in the topographical analyses. These topographical profiles differed between diagnostic groups in adolescents, but not in children or adults. In the microstate analysis, the autistic group showed differences in the activation strength of normative microstates during early-stage processing at all ages, suggesting consistent quantitative differences in the operation of typical processing pathways; qualitative differences in microstate topographies during late-stage processing became prominent in adults, suggesting the increasing involvement of non-typical neural systems with processing time and over development. CONCLUSIONS These findings suggest that early difficulties with configural face processing may trigger later compensatory processes in autism that emerge in later development

Directed Cortical Information Flow during Human Object Recognition: Analyzing Induced EEG Gamma-Band Responses in Brain's Source Space

The increase of induced gamma-band responses (iGBRs; oscillations >30 Hz) elicited by familiar (meaningful) objects is well established in electroencephalogram (EEG) research. This frequency-specific change at distinct locations is thought to indicate the dynamic formation of local neuronal assemblies during the activation of cortical object representations. As analytically power increase is just a property of a single location, phase-synchrony was introduced to investigate the formation of large-scale networks between spatially distant brain sites. However, classical phase-synchrony reveals symmetric, pair-wise correlations and is not suited to uncover the directionality of interactions. Here, we investigated the neural mechanism of visual object processing by means of directional coupling analysis going beyond recording sites, but rather assessing the directionality of oscillatory interactions between brain areas directly. This study is the first to identify the directionality of oscillatory brain interactions in source space during human object recognition and suggests that familiar, but not unfamiliar, objects engage widespread reciprocal information flow. Directionality of cortical information-flow was calculated based upon an established Granger-Causality coupling-measure (partial-directed coherence; PDC) using autoregressive modeling. To enable comparison with previous coupling studies lacking directional information, phase-locking analysis was applied, using wavelet-based signal decompositions. Both, autoregressive modeling and wavelet analysis, revealed an augmentation of iGBRs during the presentation of familiar objects relative to unfamiliar controls, which was localized to inferior-temporal, superior-parietal and frontal brain areas by means of distributed source reconstruction. The multivariate analysis of PDC evaluated each possible direction of brain interaction and revealed widespread reciprocal information-transfer during familiar object processing. In contrast, unfamiliar objects entailed a sparse number of only unidirectional connections converging to parietal areas. Considering the directionality of brain interactions, the current results might indicate that successful activation of object representations is realized through reciprocal (feed-forward and feed-backward) information-transfer of oscillatory connections between distant, functionally specific brain areas

EEG Characterization of Sensorimotor Networks: Implications in Stroke

The purpose of this dissertation was to use electroencephalography (EEG) to characterize sensorimotor networks and examine the effects of stroke on sensorimotor networks. Sensorimotor networks play an essential role in completion of everyday tasks, and when damaged, as in stroke survivors, the successful completion of seemingly simple motor tasks becomes fantasy. When sensorimotor networks are impaired as a result of stroke, varying degrees of sensorimotor deficits emerge, most often including loss of sensation and difficulty generating upper extremity movements. Although sensory therapies, such as the application of tendon vibration, have been shown to reduce the sensorimotor deficits after stroke, the underlying sensorimotor mechanisms associated with such improvements are unknown. While sensorimotor networks have been studied extensively, unanswered questions still surround their role in basic control paradigms and how their role changes after stroke. EEG provides a way to probe the high-speed temporal dynamics of sensorimotor networks that other more common imaging modalities lack. Sensorimotor network function was examined in controls during a task designed to differentiate potential mechanisms of arm stabilization and determine to what degree the sensorimotor network is involved. After sensorimotor network function was characterized in controls, we examined the effect of stroke on the sensorimotor network during rest and described the reorganization that occurs. Lastly, we explored tendon vibration as a sensory therapy for stroke survivors and determined if sensorimotor network mechanisms underlie improvements in arm tracking performance due to wrist tendon vibration. We observed cortical activity and connectivity that suggests sensorimotor networks are involved in the control of arm stability, cortical networks reorganize to more asymmetric, local networks after stroke, and tendon vibration normalizes sensorimotor network activity and connectivity during motor control after stroke. This dissertation was among the first studies using EEG to characterize the high-speed temporal dynamics of sensorimotor networks following stroke. This new knowledge has led to a better understanding of how sensorimotor networks function under ordinary circumstances as well as extreme situations such as stroke and revealed previously unknown mechanisms by which tendon vibration improves motor control in stroke survivors, which will lead to better therapeutic approaches

Child and adolescent development of the brain oscillatory activity during a working memory task

The developmental trajectories of brain oscillations during the encoding and maintenance phases of a Working Memory (WM) task were calculated. The Delayed-Match-to-Sample Test (DMTS) was applied to 239 subjects of 6-29 years, while EEG was recorded. The Event-Related Spectral Perturbation (ERSP) was obtained in the range between 1 and 25 Hz during the encoding and maintenance phases. Behavioral parameters of reaction times (RTs) and response accuracy were simultaneously recorded. The results indicate a myriad of transient and sustained bursts of oscillatory activity from low frequencies (1 Hz) to the beta range (up to 19 Hz). Beta and Low-frequency ERSP increases were prominent in the encoding phase in all age groups, while low-frequency ERSP indexed the maintenance phase only in children and adolescents, but not in late adolescents and young adults, suggesting an age-dependent neural mechanism of stimulus trace maintenance. While the latter group showed Beta and Alpha indices of anticipatory attention for the retrieval phase. Mediation analysis showed an important role of early Delta-Theta and late Alpha oscillations for mediation between age and behavioral responses per-formance. In conclusion, the results show a complex pattern of oscillatory bursts during the encoding and maintenance phases with a consistent pattern of developmental changes.Agencia Estatal de Investigacion Gobierno de Espana (PID2019–105618RB-I00) and from the Agencia de Innovacion y Desarrollo de la Junta de Andalucía (P20_00537)info:eu-repo/semantics/publishedVersio