Cortical and subcortical correlates of electroencephalographic alpha rhythm modulation

Neural correlates of electroencephalographic (EEG) alpha rhythm are poorly understood. Here, we related EEG alpha rhythm in awake humans to blood-oxygen-level-dependent (BOLD) signal change determined by functional magnetic resonance imaging (fMRI). Topographical EEG was recorded simultaneously with fMRI during an open versus closed eyes and an auditory stimulation versus silence condition. EEG was separated into spatial components of maximal temporal independence using independent component analysis. Alpha component amplitudes and stimulus conditions served as general linear model regressors of the fMRI signal time course. In both paradigms, EEG alpha component amplitudes were associated with BOLD signal decreases in occipital areas, but not in thalamus, when a standard BOLD response curve (maximum effect at approximately 6 s) was assumed. The part of the alpha regressor independent of the protocol condition, however, revealed significant positive thalamic and mesencephalic correlations with a mean time delay of approximately 2.5 s between EEG and BOLD signals. The inverse relationship between EEG alpha amplitude and BOLD signals in primary and secondary visual areas suggests that widespread thalamocortical synchronization is associated with decreased brain metabolism. While the temporal relationship of this association is consistent with metabolic changes occurring simultaneously with changes in the alpha rhythm, sites in the medial thalamus and in the anterior midbrain were found to correlate with short time lag. Assuming a canonical hemodynamic response function, this finding is indicative of activity preceding the actual EEG change by some seconds.     

Optimisation of a post-processing method to remove the pulse artifact from EEG data recorded during fMRI: an application to P300 recordings during e-fMRI

In functional cerebral studies, it has been established that co-registered electroencephalography (EEG) measurements and functional magnetic resonance imaging (fMRI) were complementary. However, EEG data recorded inside an MRI scanner are heavily distorted, mainly by the most prominent artifact, the cardiac pulse artifact (PA). We describe an original algorithm which yields a high-quality PA filter and demonstrates how this tool can be used to improve the quality of P300 ERP measurements during event-related fMRI (e-fMRI) experiments. EEG data were acquired in interleaved mode during e-fMRI while six healthy volunteers performed a visual odd-ball task, involving Distractors, Target and Novel stimuli, to elicit P300 components. The PA was corrected with the original algorithm. The temporal variations in the PA were evidenced using a principal component analysis (PCA), on each EEG channel. The procedure yielded several PA templates, which were regressed from the EEG data. The PA removal procedure was optimised, and then implemented to improve the measured P300 components. Regressing the most adequate PA template resulted in a high-quality reduction in spectral power at frequencies associated with the cardiac PA. More reliable P300 component measurements were obtained, evidencing higher amplitudes for Novels (9.76-11.20 microV) than for to Targets (6.3-9.09 microV) in centro-parietal and prefrontal areas. The improvement of the processing of EEG data acquired simultaneously with fMRI data provides a new tool and casts perspectives to study the functional organisation of the brain.     

Low-Dimensional Dynamics of Resting-State Cortical Activity

Endogenous brain activity supports spontaneous human thought and shapes perception and behavior. Connectivity-based analyses of endogenous, or resting-state, functional magnetic resonance imaging (fMRI) data have revealed the existence of a small number of robust networks which have a rich spatial structure. Yet the temporal information within fMRI data is limited, motivating the complementary analysis of electrophysiological recordings such as electroencephalography (EEG). Here we provide a novel method based on multivariate time–frequency interdependence to reconstruct the principal resting-state network dynamics in human EEG data. The stability of network expression across subjects is assessed using resampling techniques. We report the presence of seven robust networks, with distinct topographic organizations and high frequency (～5–45 Hz) fingerprints, nested within slow temporal sequences that build up and decay over several orders of magnitude. Interestingly, all seven networks are expressed concurrently during these slow dynamics, although there is a temporal asymmetry in the pattern of their formation and dissolution. These analyses uncover the complex temporal character of endogenous cortical fluctuations and, in particular, offer an opportunity to reconstruct the low dimensional linear subspace in which they unfold.     

Incorporating fMRI Functional Networks in EEG Source Imaging: A Bayesian Model Comparison Approach

Brain functional networks extracted from fMRI can improve the accuracy of EEG source localization. However, the coupling between EEG and fMRI remains poorly understood, i.e., whether fMRI networks provide information about the magnitude of neural activity, and whether neural sources demonstrate temporal correlations within each network. In this paper, we present an improved version of the NEtwork-based SOurce Imaging method (iNESOI) through Bayesian model comparison. Different models correspond to various matching between EEG and fMRI, and the appropriate one is selected by data with the model evidence. Synthetic and real data tests show that iNESOI has potential to select the appropriate fMRI priors to reach a better source reconstruction than some other typical approaches.     

Effective Connectivity of Cortical Sensorimotor Networks During Finger Movement Tasks: A Simultaneous fNIRS,fMRI, EEG Study

Recently, interest has been growing to understand the underlying dynamic directional relationship between simultaneously activated regions of the brain during motor task performance. Such directionality analysis (or effective connectivity analysis), based on non-invasive electrophysiological (electroencephalography—EEG) and hemodynamic (functional near infrared spectroscopy—fNIRS; and functional magnetic resonance imaging—fMRI) neuroimaging modalities can provide an estimate of the motor task-related information flow from one brain region to another. Since EEG, fNIRS and fMRI modalities achieve different spatial and temporal resolutions of motor-task related activation in the brain, the aim of this study was to determine the effective connectivity of cortico-cortical sensorimotor networks during finger movement tasks measured by each neuroimaging modality. Nine healthy subjects performed right hand finger movement tasks of different complexity (simple finger tapping-FT, simple finger sequence-SFS, and complex finger sequence-CFS). We focused our observations on three cortical regions of interest (ROIs), namely the contralateral sensorimotor cortex (SMC), the contralateral premotor cortex (PMC) and the contralateral dorsolateral prefrontal cortex (DLPFC). We estimated the effective connectivity between these ROIs using conditional Granger causality (GC) analysis determined from the time series signals measured by fMRI (blood oxygenation level-dependent-BOLD), fNIRS (oxygenated-O₂Hb and deoxygenated-HHb hemoglobin), and EEG (scalp and source level analysis) neuroimaging modalities. The effective connectivity analysis showed significant bi-directional information flow between the SMC, PMC, and DLPFC as determined by the EEG (scalp and source), fMRI (BOLD) and fNIRS (O₂Hb and HHb) modalities for all three motor tasks. However the source level EEG GC values were significantly greater than the other modalities. In addition, only the source level EEG showed a significantly greater forward than backward information flow between the ROIs. This simultaneous fMRI, fNIRS and EEG study has shown through independent GC analysis of the respective time series that a bi-directional effective connectivity occurs within a cortico-cortical sensorimotor network (SMC, PMC and DLPFC) during finger movement tasks.     

Integrated Analysis of EEG and fMRI Using Sparsity of Spatial Maps

Integration of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) is an open problem, which has motivated many researches. The most important challenge in EEG-fMRI integration is the unknown relationship between these two modalities. In this paper, we extract the same features (spatial map of neural activity) from both modality. Therefore, the proposed integration method does not need any assumption about the relationship of EEG and fMRI. We present a source localization method from scalp EEG signal using jointly fMRI analysis results as prior spatial information and source separation for providing temporal courses of sources of interest. The performance of the proposed method is evaluated quantitatively along with multiple sparse priors method and sparse Bayesian learning with the fMRI results as prior information. Localization bias and source distribution index are used to measure the performance of different localization approaches with or without a variety of fMRI-EEG mismatches on simulated realistic data. The method is also applied to experimental data of face perception of 16 subjects. Simulation results show that the proposed method is significantly stable against the noise with low localization bias. Although the existence of an extra region in the fMRI data enlarges localization bias, the proposed method outperforms the other methods. Conversely, a missed region in the fMRI data does not affect the localization bias of the common sources in the EEG-fMRI data. Results on experimental data are congruent with previous studies and produce clusters in the fusiform and occipital face areas (FFA and OFA, respectively). Moreover, it shows high stability in source localization against variations in different subjects.     

Brain electrical activity signatures during performance of the Multisource Interference Task

The Multisource Interference Task (MSIT) was developed to test cognitive control in normal and pathological conditions and has become a reliable tool for exploring the integrity of cingulo‐frontal‐parietal cognitive/attentional networks in fMRI studies. Analysis of EEG recordings made during performance of the MSIT may provide additional information about the temporal dynamics of cognitive control. However, this has not yet been investigated in depth. In this study, we analyzed the ERPs and carried out time‐frequency decomposition of EEG recorded during control and interference conditions of the MSIT. The N2 ERP component and midfrontal theta power (both considered neural signatures of conflict processing) were significantly larger in interference than in control trials. Theta also showed higher phase synchronization between midfrontal and right frontolateral scalp locations in the interference condition, supporting the view that this frequency band entrains additional brain resources when a need for greater control arises. In interference trials, we also observed longer P3 latency, larger P3 amplitude, and greater reduction of posterior alpha (modulations related to allocation of attentional resources), in addition to a greater reduction of central beta power (related to motor preparation). In conclusion, the MSIT reliably modulated brain electrical activity related to cognitive control and attention. The EEG indices obtained during the performance of this task may be useful for exploring the functioning of cognitive/attentional networks in healthy and clinical populations.     

Dealing with mismatched fMRI activations in fMRI constrained EEG cortical source imaging: a simulation study assuming various mismatch types

Although fMRI constrained EEG source imaging could be a promising approach to enhancing both spatial and temporal resolutions of independent fMRI and EEG analyses, it has been frequently reported that a hard fMRI constraint may cause severe distortion or elimination of significant EEG sources when there are distinct mismatches between fMRI activations and EEG sources. If estimating actual EEG source locations is important and fMRI prior information is used as an auxiliary tool to enhance the concentration of widespread EEG source distributions, it is reasonable to weaken the fMRI constraint when significantly mismatched sources exist. The present study demonstrates that the mismatch problem may be partially solved by extending the prior fMRI activation regions based on the conventional source imaging results. A hard fMRI constraint is then applied when there is no distinct mismatch, while a weakened fMRI constraint is applied when there are significant mismatches. A preliminary simulation study assuming different types of mismatches such as fMRI invisible, extra, and discrepancy sources demonstrated that this approach can be a promising option to treat mismatched fMRI activations in fMRI constrained EEG source imaging.     

Single trial ERP reading based on parallel factor analysis

The extraction of task‐related single trial ERP features has recently gained much interest, in particular in simultaneous EEG‐fMRI applications. In this study, a specific decomposition known as parallel factor analysis (PARAFAC) was used, in order to retrieve the task‐related activity from the raw signals. Using visual detection task data, acquired in normal circumstances and simultaneously with fMRI, differences between distinct task‐related conditions can be captured in the trial signatures of specific PARAFAC components when applied to ERP data arranged in Channels × Time × Trials arrays, but the signatures did not correlate with the fMRI data. Despite the need for parameter tuning and careful preprocessing, the approach is shown to be successful, especially when prior knowledge about the expected ERPs is incorporated.     

Evaluating the efficacy of fully automated approaches for the selection of eyeblink ICA components

Independent component analysis (ICA) offers a powerful approach for the isolation and removal of eyeblink artifacts from EEG signals. Manual identification of the eyeblink ICA component by inspection of scalp map projections, however, is prone to error, particularly when nonartifactual components exhibit topographic distributions similar to the blink. The aim of the present investigation was to determine the extent to which automated approaches for selecting eyeblink‐related ICA components could be utilized to replace manual selection. We evaluated popular blink selection methods relying on spatial features (EyeCatch), combined stereotypical spatial and temporal features (ADJUST), and a novel method relying on time series features alone (icablinkmetrics) using both simulated and real EEG data. The results of this investigation suggest that all three methods of automatic component selection are able to accurately identify eyeblink‐related ICA components at or above the level of trained human observers. However, icablinkmetrics, in particular, appears to provide an effective means of automating ICA artifact rejection while at the same time eliminating human errors inevitable during manual component selection and false positive component identifications common in other automated approaches. Based upon these findings, best practices for (a) identifying artifactual components via automated means, and (b) reducing the accidental removal of signal‐related ICA components are discussed.     

Modalities of Thinking: State and Trait Effects on Cross-Frequency Functional Independent Brain Networks

Functional states of the brain are constituted by the temporally attuned activity of spatially distributed neural networks. Such networks can be identified by independent component analysis (ICA) applied to frequency-dependent source-localized EEG data. This methodology allows the identification of networks at high temporal resolution in frequency bands of established location-specific physiological functions. EEG measurements are sensitive to neural activity changes in cortical areas of modality-specific processing. We tested effects of modality-specific processing on functional brain networks. Phasic modality-specific processing was induced via tasks (state effects) and tonic processing was assessed via modality-specific person parameters (trait effects). Modality-specific person parameters and 64-channel EEG were obtained from 70 male, right-handed students. Person parameters were obtained using cognitive style questionnaires, cognitive tests, and thinking modality self-reports. EEG was recorded during four conditions: spatial visualization, object visualization, verbalization, and resting. Twelve cross-frequency networks were extracted from source-localized EEG across six frequency bands using ICA. RMANOVAs, Pearson correlations, and path modelling examined effects of tasks and person parameters on networks. Results identified distinct state- and trait-dependent functional networks. State-dependent networks were characterized by decreased, trait-dependent networks by increased alpha activity in sub-regions of modality-specific pathways. Pathways of competing modalities showed opposing alpha changes. State- and trait-dependent alpha were associated with inhibitory and automated processing, respectively. Antagonistic alpha modulations in areas of competing modalities likely prevent intruding effects of modality-irrelevant processing. Considerable research suggested alpha modulations related to modality-specific states and traits. This study identified the distinct electrophysiological cortical frequency-dependent networks within which they operate.     

Variability of ICA decomposition may impact EEG signals when used to remove eyeblink artifacts

Despite the growing use of independent component analysis (ICA) algorithms for isolating and removing eyeblink‐related activity from EEG data, we have limited understanding of how variability associated with ICA uncertainty may be influencing the reconstructed EEG signal after removing the eyeblink artifact components. To characterize the magnitude of this ICA uncertainty and to understand the extent to which it may influence findings within ERP and EEG investigations, ICA decompositions of EEG data from 32 college‐aged young adults were repeated 30 times for three popular ICA algorithms. Following each decomposition, eyeblink components were identified and removed. The remaining components were back‐projected, and the resulting clean EEG data were further used to analyze ERPs. Findings revealed that ICA uncertainty results in variation in P3 amplitude as well as variation across all EEG sampling points, but differs across ICA algorithms as a function of the spatial location of the EEG channel. This investigation highlights the potential of ICA uncertainty to introduce additional sources of variance when the data are back‐projected without artifact components. Careful selection of ICA algorithms and parameters can reduce the extent to which ICA uncertainty may introduce an additional source of variance within ERP/EEG studies.     

A componential analysis of the ERP elicited by novel events using a dense electrode array.

In this study, we examined the relationship between the novelty P3 and the P300 components of the brain event-related potential (ERP). Fifteen subjects responded manually to the rare stimuli embedded either in a classical auditory oddball series or in a series in which "novel" stimuli were inserted. The electroencephalogram (EEG) was recorded with a dense array of 129 electrodes. The data were analyzed by using spatial Principal Components Analysis (PCA) to identify a set of orthogonal scalp distributions, "virtual electrodes" that account for the spatial variance. The data were then expressed as ERPs measured at each of the virtual electrodes. These ERPs were analyzed using temporal PCA, yielding a set of "virtual epochs." Most of the temporal variance of the rare events was associated with a virtual electrode with a posterior topography, that is, with a classical P300, which was active during the virtual epoch associated with the P300. The novel stimuli were found to elicit both a classical P300 and a component focused on a virtual electrode with a frontal topography. We propose that the term Novelty P3 should be restricted to this frontal component.     

Investigating human audio-visual object perception with a combination of hypothesis-generating and hypothesis-testing fMRI analysis tools

Primate multisensory object perception involves distributed brain regions. To investigate the network character of these regions of the human brain, we applied data-driven group spatial independent component analysis (ICA) to a functional magnetic resonance imaging (fMRI) data set acquired during a passive audio-visual (AV) experiment with common object stimuli. We labeled three group-level independent component (IC) maps as auditory (A), visual (V), and AV, based on their spatial layouts and activation time courses. The overlap between these IC maps served as definition of a distributed network of multisensory candidate regions including superior temporal, ventral occipito-temporal, posterior parietal and prefrontal regions. During an independent second fMRI experiment, we explicitly tested their involvement in AV integration. Activations in nine out of these twelve regions met the max-criterion (A??V) for multisensory integration. Comparison of this approach with a general linear model-based region-of-interest definition revealed its complementary value for multisensory neuroimaging. In conclusion, we estimated functional networks of uni- and multisensory functional connectivity from one dataset and validated their functional roles in an independent dataset. These findings demonstrate the particular value of ICA for multisensory neuroimaging research and using independent datasets to test hypotheses generated from a data-driven analysis.     

Functional topography of the thalamocortical system in human

Various studies have indicated that the thalamus is involved in controlling both cortico-cortical information flow and cortical communication with the rest of the brain. Detailed anatomy and functional connectivity patterns of the thalamocortical system are essential to understanding the cortical organization and pathophysiology of a wide range of thalamus-related neurological and neuropsychiatric diseases. The current study used resting-state fMRI to investigate the topography of the human thalamocortical system from a functional perspective. The thalamus-related cortical networks were identified by performing independent component analysis on voxel-based thalamic functional connectivity maps across a large group of subjects. The resulting functional brain networks were very similar to well-established resting-state network maps. Using these brain network components in a spatial regression model with each thalamic voxel’s functional connectivity map, we localized the thalamic subdivisions related to each brain network. For instance, the medial dorsal nucleus was shown to be associated with the default mode, the bilateral executive, the medial visual networks; and the pulvinar nucleus was involved in both the dorsal attention and the visual networks. These results revealed that a single nucleus may have functional connections with multiple cortical regions or even multiple functional networks, and may be potentially related to the function of mediation or modulation of multiple cortical networks. This observed organization of thalamocortical system provided a reference for studying the functions of thalamic sub-regions. The importance of intrinsic connectivity-based mapping of the thalamocortical relationship is discussed, as well as the applicability of the approach for future studies.     

Data-based functional template for sorting independent components of fMRI activity

In human brain imaging with naturalistic stimuli, hemodynamic responses are difficult to predict and thus data-driven approaches, such as independent component analysis (ICA), may be beneficial. Here we propose inter-subject correlation (ISC) maps as stimulus-sensitive functional templates for sorting the independent components (ICs) to identify the most stimulus-related networks without stimulus-dependent temporal covariates. We collected 3-T functional magnetic resonance imaging (fMRI) data during perception of continuous audiovisual speech. Ten adults viewed a video, in which speech intelligibility was varied by altering the sound level. Five ICs with strongest overlap with the ISC map comprised auditory and visual cortices, and the sixth was a left-hemisphere-dominant network (left posterior superior temporal sulcus, inferior frontal gyrus, anterior superior temporal pole, supplementary motor cortex, and right angular gyrus) that was activated stronger during soft than loud speech. Corresponding temporal-model-based analysis revealed only temporal- and parietal-lobe activations without involvement of the anterior areas. The performance of the ISC-based IC selection was confirmed with fMRI data collected during free viewing of movie. Since ISC-ICA requires no predetermined temporal models on stimulus timing, it seems feasible for fMRI studies where hemodynamic variations are difficult to model because of the complex temporal structure of the naturalistic stimulation.     

Simultaneous recording of EEG and BOLD responses: a historical perspective.

Electromagnetic fields as measured with electroencephalogram (EEG) are a direct consequence of neuronal activity and feature the same timescale as the underlying cognitive processes, while hemodynamic signals as measured with functional magnetic resonance imaging (fMRI) are related to the energy consumption of neuronal populations. It is obvious that a combination of both techniques is a very attractive aim in neuroscience, in order to achieve both high temporal and spatial resolution for the non-invasive study of cognitive brain function. During the last decade a number of research groups have taken up this challenge. Here, we review the development of the combined EEG-fMRI approach. We summarize the main data integration approaches developed to achieve such a combination, discuss the current state-of-the-art in this field and outline challenges for the future success of this promising approach.     

Data-based functional template for sorting independent components of fMRI activity

In human brain imaging with naturalistic stimuli, hemodynamic responses are difficult to predict and thus data-driven approaches, such as independent component analysis (ICA), may be beneficial. Here we propose inter-subject correlation (ISC) maps as stimulus-sensitive functional templates for sorting the independent components (ICs) to identify the most stimulus-related networks without stimulus-dependent temporal covariates. We collected 3-T functional magnetic resonance imaging (fMRI) data during perception of continuous audiovisual speech. Ten adults viewed a video, in which speech intelligibility was varied by altering the sound level. Five ICs with strongest overlap with the ISC map comprised auditory and visual cortices, and the sixth was a left-hemisphere-dominant network (left posterior superior temporal sulcus, inferior frontal gyrus, anterior superior temporal pole, supplementary motor cortex, and right angular gyrus) that was activated stronger during soft than loud speech. Corresponding temporal-model-based analysis revealed only temporal- and parietal-lobe activations without involvement of the anterior areas. The performance of the ISC-based IC selection was confirmed with fMRI data collected during free viewing of movie. Since ISC–ICA requires no predetermined temporal models on stimulus timing, it seems feasible for fMRI studies where hemodynamic variations are difficult to model because of the complex temporal structure of the naturalistic stimulation.     

动态因果模型在脑网络研究中的应用进展

大脑各功能区之间的有效连接是脑科学研究领域的一个重要内容.研究在不同情形下相关脑区之间有效连接所构成的大脑网络,对于全面理解大脑的功能机制,治疗各种与大脑相关疾病,开发脑功能具有重要意义.动态因果模型是一种分析大脑有效连接的优势方法.结合功能性磁共振成像、脑电、近红外脑功能成像等3种检测技术,综述动态因果模型的相关研究...