Localization of individual area neuronal activity

A family of methods, collectively known as independent component analysis (ICA), has recently been added to the array of methods designed to decompose a multi-channel signal into components. ICA methods have been applied to raw magnetoencephalography (MEG) and electroencephalography (EEG) signals to remove artifacts, especially when sources such as power line or cardiac activity generate strong components that dominate the signal. More recently, successful ICA extraction of stimulus-evoked responses has been reported from single-trial raw MEG and EEG signals. The extraction of weak components has often been erratic, depending on which ICA method is employed and even on what parameters are used. In this work, we show that if the emphasis is placed on individual "independent components," as is usually the case with standard ICA applications, differences in the results obtained for different components are exaggerated. We propose instead the reconstruction of regional brain activations by combining tomographic estimates of individual independent components that have been selected by appropriate spatial and temporal criteria. Such localization of individual area neuronal activity (LIANA) allows reliable semi-automatic extraction of single-trial regional activations from raw MEG data. We demonstrate the new method with three different ICA algorithms applied to both computer-generated signals and real data. We show that LIANA provides almost identical results with each ICA method despite the fact that each method yields different individual components.     

Validation of SOBI components from high-density EEG

Second-order blind identification (SOBI) is a blind source separation (BSS) algorithm that can be used to decompose mixtures of signals into a set of components or putative recovered sources. Previously, SOBI, as well as other BSS algorithms, has been applied to magnetoencephalography (MEG) and electroencephalography (EEG) data. These BSS algorithms have been shown to recover components that appear to be physiologically and neuroanatomically interpretable. While some proponents of these algorithms suggest that fundamental discoveries about the human brain might be made through the application of these techniques, validation of BSS components has not yet received sufficient attention. Here we present two experiments for validating SOBI-recovered components. The first takes advantage of the fact that noise sources associated with individual sensors can be objectively validated independently from the SOBI process. The second utilizes the fact that the time course and location of primary somatosensory (SI) cortex activation by median nerve stimulation have been extensively characterized using converging imaging methods. In this paper, using both known noise sources and highly constrained and well-characterized neuronal sources, we provide validation for SOBI decomposition of high-density EEG data. We show that SOBI is able to (1) recover known noise sources that were either spontaneously occurring or artificially induced; (2) recover neuronal sources activated by median nerve stimulation that were spatially and temporally consistent with estimates obtained from previous EEG, MEG, and fMRI studies; (3) improve the signal-to-noise ratio (SNR) of somatosensory-evoked potentials (SEPs); and (4) reduce the level of subjectivity involved in the source localization process.     

基于独立元分析的脑磁图数据分析和处理(英文)

背景:诱发响应信号是由刺激的时间锁定的,对于一些特定的刺激呈现小的个人差距,脑磁图数据中诱发响应的提取对人脑功能的认识很重要。目的:将独立元分析应用于分离混迭的脑磁图多通道信号中的信号源,提出一个简单有效的基于独立元分析的脑磁图数据分析和处理方法。设计:单一样本分析。单位:复旦大学电子工程系和复旦大学脑科学研究中心。对象:实验于2002-09在日本通信综合研究所关西先端研究中心完成,选择日本东京药科大学的健康志愿者1例,男性;年龄23岁。受试者自愿参加。方法:①对脑磁图进行必要的预处理,如低通滤波和主成分分解。②采用独立元分析的方法对取自148个通道的脑磁图的数据进行分析和处理,尤其是诱发反应的提取。③对提取的各独立成分进行周期平均。主要观察指标:应用独立元分析方法对脑磁图数据分析。结果:①脑磁图信号有较高的冗余度,信号能量的绝大部分集中在前30个主成分中,从前30个主成分中抽取干扰源和诱发响应活动源。②眼动干扰源仍被清楚地检测和分离在第1个独立元中,心电干扰被分离在第20个独立元中。③α波呈现在第2,3,7和9等独立元中。波(13～30Hz)呈现在第11和第12独立元中。④诱发响应是响应于刺激的周期性波形,集中在第5独立元中。结论:利用独立元分析,可从混迭的脑磁图数据中分离这些干扰源,更进一步,消除这些干扰成分,可得到净化的脑磁图数据。借助独立元分析,有效的分离α波、β波以及眼动、眨眼等神经活动源,有可能为它们的脑神经活动研究提供新的方法和途径。利用独立元分析方法成功的进行了听觉诱发反应的分离和提取。     

Recovery of correlated neuronal sources from EEG: the good and bad ways of using SOBI

Second-order blind identification (SOBI) is a blind source separation (BSS) algorithm that has been applied to MEG and EEG data collected during a range of sensory, motor, and cognitive tasks. SOBI can decompose mixtures of electric or magnetic signals by utilizing detailed temporal structures present in the continuously recorded signals. Successful decomposition critically depends on the choice of temporal delay parameters used for computing multiple covariance matrices. Here, we present empirical findings from high-density EEG data (128 channels) to show that SOBI's ability to recover correlated neuronal sources critically depends on the appropriate use of these temporal delay parameters. Specifically, we applied SOBI to EEG data collected during correlated activation of the left and right primary somatosensory cortices (SI). We show that separation of signals originating from the left and right SI is better achieved by using a large number and a wide range of temporal delays between a few and several hundred milliseconds when compared to results using various subsets of these delays. The paper also offers non-mathematician/engineer users a gentle introduction to the inner workings of SOBI.     

Contrasting single-trial ERPs between experimental manipulations: improving differentiability by blind source separation

Contrasting event-related potentials (ERPs) generated under different experimental conditions and inferring differential brain responses is widely practiced in cognitive neuroscience research. Traditionally, these contrasts and subsequent inferences have proceeded directly from ERPs measured at the scalp. For certain tasks, it is not unusual that ERPs from a subset of channels are given particular emphasis in data analysis, such as the channels displaying the maximum peak amplitude in regions of interest ("best sensors") or channels showing the largest averaged ERP waveform differences. With the aid of a blind source separation (BSS) algorithm, second-order blind identification (SOBI), which has been recently validated for its ability to recover correlated neuronal sources, we show that single-trial ERPs from previously validated neuronal sources were more distinguishable among different experimental manipulations than the single-trial ERPs measured at the comparable "best sensors". This suggests that by using validated SOBI-recovered neuronal sources, ERP researchers can improve the ability to detect differences in neuronal responses induced by experimental manipulations. Critically, our observations were made at the level of single trials, as opposed to the averaged ERP. Therefore, our conclusions are particularly relevant to phenomena involving trial-to-trial changes in brain activation, for example, rapid induction of brain plasticity and perceptual learning, as well as to the development of brain-computer interfaces. Similar advantages would also apply to analogous situations with magnetoencephalography (MEG).     

Improving MEG source localizations: an automated method for complete artifact removal based on independent component analysis

The major limitation for the acquisition of high-quality magnetoencephalography (MEG) recordings is the presence of disturbances of physiological and technical origins: eye movements, cardiac signals, muscular contractions, and environmental noise are serious problems for MEG signal analysis. In the last years, multi-channel MEG systems have undergone rapid technological developments in terms of noise reduction, and many processing methods have been proposed for artifact rejection. Independent component analysis (ICA) has already shown to be an effective and generally applicable technique for concurrently removing artifacts and noise from the MEG recordings. However, no standardized automated system based on ICA has become available so far, because of the intrinsic difficulty in the reliable categorization of the source signals obtained with this technique. In this work, approximate entropy (ApEn), a measure of data regularity, is successfully used for the classification of the signals produced by ICA, allowing for an automated artifact rejection. The proposed method has been tested using MEG data sets collected during somatosensory, auditory and visual stimulation. It was demonstrated to be effective in attenuating both biological artifacts and environmental noise, in order to reconstruct clear signals that can be used for improving brain source localizations.     

ICA-based spatiotemporal approach for single-trial analysis of postmovement MEG beta synchronization

The extraction of event-related oscillatory neuromagnetic activities from single-trial measurement is challenging due to the non-phase-locked nature and variability from trial to trial. The present study presents a method based on independent component analysis (ICA) and the use of a template-based correlation approach to extract Rolandic beta rhythm from magnetoencephalographic (MEG) measurements of right finger lifting. A single trial recording was decomposed into a set of coupled temporal independent components and corresponding spatial maps using ICA and the reactive beta frequency band for each trial identified using a two-spectrum comparison between the postmovement interval and a reference period. Task-related components survived dual criteria of high correlation with both the temporal and the spatial templates with an acceptance rate of about 80%. Phase and amplitude information for noise-free MEG beta activities were preserved not only for optimal calculation of beta rebound (event-related synchronization) but also for profound penetration into subtle dynamics across trials. Given the high signal-to-noise ratio (SNR) of this method, various methods of source estimation were used on reconstructed single-trial data and the source loci coherently anchored in the vicinity of the primary motor area. This method promises the possibility of a window into the intricate brain dynamics of motor control mechanisms and the cortical pathophysiology of movement disorder on a trial-by-trial basis.     

Reliable detection of bilateral activation in human primary somatosensory cortex by unilateral median nerve stimulation

In non-human primates, a bilateral representation of unilaterally presented somatosensory information can be found at the lowest level of cortical processing as indicated by the presence of neurons with bilateral receptive fields in the hand region of primary somatosensory (SI) cortex. In humans, such bilateral activation of SI is considered controversial due to highly variable detection rates for the much weaker ipsilateral response across different studies (ranging from 3% to 100%). Second-order blind identification (SOBI) is a blind source separation algorithm that has been successfully used to isolate neuronal signals from functionally distinct brain regions, including the left- and right-SI. SOBI-aided extraction of left- and right-SI responses to median nerve stimulation from high-density EEG has been previously validated against the fMRI and MEG literature. Here, we applied SOBI to EEG data and examined whether relatively weaker ipsilateral activations could be reliably detected across subjects. In single subject analysis, statistically significant somatosensory evoked potentials (SEPs) in response to unilateral stimulation were detected from both SI contralateral to and SI ipsilateral to the side of stimulation. Furthermore, these ipsilateral responses were observed in both the left and right hemispheres of all 10 subjects studied. Together these results demonstrate that unilateral stimulation of the median nerve, whether applied to the left or right wrist, can activate both the left- and right-SI, raising the possibility that in humans, unilateral sensory input may be bilaterally represented at the lowest level of cortical processing.     

Let's face it, from trial to trial: Comparing procedures for N170 single-trial estimation

The estimation of event-related single trial EEG activity is notoriously difficult but is of growing interest in various areas of cognitive neuroscience, such as multimodal neuroimaging and EEG-based brain computer interfaces. However, an objective evaluation of different approaches is lacking. The present study therefore compared four frequently-used single-trial data filtering procedures: raw sensor amplitudes, regression-based estimation, bandpass filtering, and independent component analysis (ICA). High-density EEG data were recorded from 20 healthy participants in a face recognition task and were analyzed with a focus on the face-selective N170 single-trial event-related potential. Linear discriminant analysis revealed significantly better single-trial estimation for ICA compared to raw sensor amplitudes, whereas the other two approaches did not improve classification accuracy. Further analyses suggested that ICA enabled extraction of a face-sensitive independent component in each participant, which led to the superior performance in single trial estimation. Additionally, we show that the face-sensitive component does not directly represent activity from a neuronal population exclusively involved in face-processing, but rather the activity of a network involved in general visual processing. We conclude that ICA effectively facilitates the separation of physiological trial-by-trial fluctuations from measurement noise, in particular when the process of interest is reliably reflected in components representing the neural signature of interest.     

Independent component analysis of short-time Fourier transforms for spontaneous EEG/MEG analysis

Analysis of spontaneous EEG/MEG needs unsupervised learning methods. While independent component analysis (ICA) has been successfully applied on spontaneous fMRI, it seems to be too sensitive to technical artifacts in EEG/MEG. We propose to apply ICA on short-time Fourier transforms of EEG/MEG signals, in order to find more “interesting” sources than with time-domain ICA, and to more meaningfully sort the obtained components. The method is especially useful for finding sources of rhythmic activity. Furthermore, we propose to use a complex mixing matrix to model sources which are spatially extended and have different phases in different EEG/MEG channels. Simulations with artificial data and experiments on resting-state MEG demonstrate the utility of the method.     

Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis

Detecting artifacts produced in EEG data by muscle activity, eye blinks and electrical noise is a common and important problem in EEG research. It is now widely accepted that independent component analysis (ICA) may be a useful tool for isolating artifacts and/or cortical processes from electroencephalographic (EEG) data. We present results of simulations demonstrating that ICA decomposition, here tested using three popular ICA algorithms, Infomax, SOBI, and FastICA, can allow more sensitive automated detection of small non-brain artifacts than applying the same detection methods directly to the scalp channel data. We tested the upper bound performance of five methods for detecting various types of artifacts by separately optimizing and then applying them to artifact-free EEG data into which we had added simulated artifacts of several types, ranging in size from thirty times smaller (-50 dB) to the size of the EEG data themselves (0 dB). Of the methods tested, those involving spectral thresholding were most sensitive. Except for muscle artifact detection where we found no gain of using ICA, all methods proved more sensitive when applied to the ICA-decomposed data than applied to the raw scalp data: the mean performance for ICA was higher and situated at about two standard deviations away from the performance distribution obtained on raw data. We note that ICA decomposition also allows simple subtraction of artifacts accounted for by single independent components, and/or separate and direct examination of the decomposed non-artifact processes themselves.     

Single-trial magnetoencephalography signals encoded as an unfolding decision process

The model of a stochastic decision process unfolding in motor and premotor regions of the brain was encoded in single-trial magnetoencephalographic (MEG) recordings while ten healthy subjects performed a sensorimotor Reaction Time (RT) task. The duration of single-trial MEG signals preceding the motor response, recorded over the motor cortex contralateral to the responding hand, co-varied with RT across trials according to the model's prediction. Furthermore, these signals displayed the same properties of a “rising-to-a-fixed-threshold” decision process as posited by the model and observed in the activity of single neurons in the primate cortex. The present findings demonstrate that non-averaged, single-trial MEG recordings can be used to test models of cognitive processes, like decision-making, in humans.     

Investigations of dipole localization accuracy in MEG using the bootstrap

We describe the use of the nonparametric bootstrap to investigate the accuracy of current dipole localization from magnetoencephalography (MEG) studies of event-related neural activity. The bootstrap is well suited to the analysis of event-related MEG data since the experiments are repeated tens or even hundreds of times and averaged to achieve acceptable signal-to-noise ratios (SNRs). The set of repetitions or epochs can be viewed as a set of independent realizations of the brain's response to the experiment. Bootstrap resamples can be generated by sampling with replacement from these epochs and averaging. In this study, we applied the bootstrap resampling technique to MEG data from somatotopic experimental and simulated data. Four fingers of the right and left hand of a healthy subject were electrically stimulated, and about 400 trials per stimulation were recorded and averaged in order to measure the somatotopic mapping of the fingers in the S1 area of the brain. Based on single-trial recordings for each finger we performed 5000 bootstrap resamples. We reconstructed dipoles from these resampled averages using the Recursively Applied and Projected (RAP)-MUSIC source localization algorithm. We also performed a simulation for two dipolar sources with overlapping time courses embedded in realistic background brain activity generated using the prestimulus segments of the somatotopic data. To find correspondences between multiple sources in each bootstrap, sample dipoles with similar time series and forward fields were assumed to represent the same source. These dipoles were then clustered by a Gaussian Mixture Model (GMM) clustering algorithm using their combined normalized time series and topographies as feature vectors. The mean and standard deviation of the dipole position and the dipole time series in each cluster were computed to provide estimates of the accuracy of the reconstructed source locations and time series.     

A novel integrated MEG and EEG analysis method for dipolar sources

The ability of magnetoencephalography (MEG) to accurately localize neuronal currents and obtain tangential components of the source is largely due to MEG's insensitivity to the conductivity profile of the head tissues. However, MEG cannot reliably detect the radial component of the neuronal current. In contrast, the localization accuracy of electroencephalography (EEG) is not as good as MEG, but EEG can detect both the tangential and radial components of the source. In the present study, we investigated the conductivity dependence in a new approach that combines MEG and EEG to accurately obtain, not only the location and tangential components, but also the radial component of the source. In this approach, the source location and tangential components are obtained from MEG alone, and optimal conductivity values of the EEG model are estimated by best-fitting EEG signal, while precisely matching the tangential components of the source in EEG and MEG. Then, the radial components are obtained from EEG using the previously estimated optimal conductivity values. Computer simulations testing this integrated approach demonstrated two main findings. First, there are well-organized optimal combinations of the conductivity values that provide an accurate fit to the combined MEG and EEG data. Second, the radial component, in addition to the location and tangential components, can be obtained with high accuracy without needing to know the precise conductivity profile of the head. We then demonstrated that this new approach performed reliably in an analysis of the 20-ms component from human somatosensory responses elicited by electric median-nerve stimulation.     

Stimuli of varying spatial scale induce gamma activity with distinct temporal characteristics in human visual cortex

Gamma activity to stationary grating stimuli was studied non-invasively using MEG recordings in humans. Using a spatial filtering technique, we localized gamma activity to primary visual cortex. We tested the hypothesis that spatial frequency properties of visual stimuli may be related to the temporal frequency characteristics of the associated cortical responses. We devised a method to assess temporal frequency differences between stimulus-related responses that typically exhibit complex spectral shapes. We applied this methodology to either single-trial (induced) or time-averaged (evoked) responses in four frequency ranges (0-40, 20-60, 40-80 and 60-100 Hz) and two time windows (either the entire duration of stimulus presentation or the first second following stimulus onset). Our results suggest that stimuli of varying spatial frequency induce responses that exhibit significantly different temporal frequency characteristics. These effects were particularly accentuated for induced responses in the classical gamma frequency band (20-60 Hz) analyzed over the entire duration of stimulus presentation. Strikingly, examining the first second of the responses following stimulus onset resulted in significant loss in stimulus specificity, suggesting that late signal components contain functionally relevant information. These findings advocate a functional role of gamma activity in sensory representation. We suggest that stimulus specific frequency characteristics of MEG signals can be mapped to processes of neuronal synchronization within the framework of coupled dynamical systems.     

Validating the independent components of neuroimaging time series via clustering and visualization

Recently, independent component analysis (ICA) has been widely used in the analysis of brain imaging data. An important problem with most ICA algorithms is, however, that they are stochastic; that is, their results may be somewhat different in different runs of the algorithm. Thus, the outputs of a single run of an ICA algorithm should be interpreted with some reserve, and further analysis of the algorithmic reliability of the components is needed. Moreover, as with any statistical method, the results are affected by the random sampling of the data, and some analysis of the statistical significance or reliability should be done as well. Here we present a method for assessing both the algorithmic and statistical reliability of estimated independent components. The method is based on running the ICA algorithm many times with slightly different conditions and visualizing the clustering structure of the obtained components in the signal space. In experiments with magnetoencephalographic (MEG) and functional magnetic resonance imaging (fMRI) data, the method was able to show that expected components are reliable; furthermore, it pointed out components whose interpretation was not obvious but whose reliability should incite the experimenter to investigate the underlying technical or physical phenomena. The method is implemented in a software package called Icasso.     

Identifying reliable independent components via split-half comparisons

Independent component analysis (ICA) is a family of unsupervised learning algorithms that have proven useful for the analysis of the electroencephalogram (EEG) and magnetoencephalogram (MEG). ICA decomposes an EEG/MEG data set into a basis of maximally temporally independent components (ICs) that are learned from the data. As with any statistic, a concern with using ICA is the degree to which the estimated ICs are reliable. An IC may not be reliable if ICA was trained on insufficient data, if ICA training was stopped prematurely or at a local minimum (for some algorithms), or if multiple global minima were present. Consequently, evidence of ICA reliability is critical for the credibility of ICA results. In this paper, we present a new algorithm for assessing the reliability of ICs based on applying ICA separately to split-halves of a data set. This algorithm improves upon existing methods in that it considers both IC scalp topographies and activations, uses a probabilistically interpretable threshold for accepting ICs as reliable, and requires applying ICA only three times per data set. As evidence of the method's validity, we show that the method can perform comparably to more time intensive bootstrap resampling and depends in a reasonable manner on the amount of training data. Finally, using the method we illustrate the importance of checking the reliability of ICs by demonstrating that IC reliability is dramatically increased by removing the mean EEG at each channel for each epoch of data rather than the mean EEG in a prestimulus baseline.     

A K-means multivariate approach for clustering independent components from magnetoencephalographic data

Independent component analysis (ICA) is typically applied on functional magnetic resonance imaging, electroencephalographic and magnetoencephalographic (MEG) data due to its data-driven nature. In these applications, ICA needs to be extended from single to multi-session and multi-subject studies for interpreting and assigning a statistical significance at the group level. Here a novel strategy for analyzing MEG independent components (ICs) is presented, Multivariate Algorithm for Grouping MEG Independent Components K-means based (MAGMICK). The proposed approach is able to capture spatio-temporal dynamics of brain activity in MEG studies by running ICA at subject level and then clustering the ICs across sessions and subjects. Distinctive features of MAGMICK are: i) the implementation of an efficient set of "MEG fingerprints" designed to summarize properties of MEG ICs as they are built on spatial, temporal and spectral parameters; ii) the implementation of a modified version of the standard K-means procedure to improve its data-driven character. This algorithm groups the obtained ICs automatically estimating the number of clusters through an adaptive weighting of the parameters and a constraint on the ICs independence, i.e. components coming from the same session (at subject level) or subject (at group level) cannot be grouped together. The performances of MAGMICK are illustrated by analyzing two sets of MEG data obtained during a finger tapping task and median nerve stimulation. The results demonstrate that the method can extract consistent patterns of spatial topography and spectral properties across sessions and subjects that are in good agreement with the literature. In addition, these results are compared to those from a modified version of affinity propagation clustering method. The comparison, evaluated in terms of different clustering validity indices, shows that our methodology often outperforms the clustering algorithm. Eventually, these results are confirmed by a comparison with a MEG tailored version of the self-organizing group ICA, which is largely used for fMRI IC clustering.     

Identifying true cortical interactions in MEG using the nulling beamformer

Modeling functional brain interaction networks using non-invasive EEG and MEG data is more challenging than using intracranial recording data. This is because most interaction measures are not robust to the cross-talk (interference) between cortical regions, which may arise due to the limited spatial resolution of EEG/MEG inverse procedures. In this article, we describe a modified beamforming approach to accurately measure cortical interactions from EEG/MEG data, designed to suppress cross-talk between cortical regions. We estimate interaction measures from the output of the modified beamformer and test for statistical significance using permutation tests. Since the underlying neuronal sources and their interactions are unknown in real MEG data, we demonstrate the performance of the proposed beamforming method in a novel simulation scheme, where intracranial recordings from a macaque monkey are used as neural sources to simulate realistic MEG signals. The advantage of this approach is that local field potentials are more realistic representations of true neuronal sources than simulation models and therefore are more suitable to indicate the performance of our nulling beamforming method.     

Inferring task-related networks using independent component analysis in magnetoencephalography

A novel framework for analysing task-positive data in magnetoencephalography (MEG) is presented that can identify task-related networks. Techniques that combine beamforming, the Hilbert transform and temporal independent component analysis (ICA) have recently been applied to resting-state MEG data and have been shown to extract resting-state networks similar to those found in fMRI. Here we extend this approach in two ways. First, we systematically investigate optimisation of time-frequency windows for connectivity measurement. This is achieved by estimating the distribution of functional connectivity scores between nodes of known resting-state networks and contrasting it with a distribution of artefactual scores that are entirely due to spatial leakage caused by the inverse problem. We find that functional connectivity, both in the resting-state and during a cognitive task, is best estimated via correlations in the oscillatory envelope in the 8-20 Hz frequency range, temporally down-sampled with windows of 1-4s. Second, we combine ICA with the general linear model (GLM) to incorporate knowledge of task structure into our connectivity analysis. The combination of ICA with the GLM helps overcome problems of these techniques when used independently: namely, the interpretation and separation of interesting independent components from those that represent noise in ICA and the correction for multiple comparisons when applying the GLM. We demonstrate the approach on a 2-back working memory task and show that this novel analysis framework is able to elucidate the functional networks involved in the task beyond that which is achieved using the GLM alone. We find evidence of localised task-related activity in the area of the hippocampus, which is difficult to detect reliably using standard methods. Task-positive ICA, coupled with the GLM, has the potential to be a powerful tool in the analysis of MEG data.