Mapping single-trial EEG records on the cortical surface through a spatiotemporal modality

Event-related potentials (ERPs) induced by visual perception and cognitive tasks have been extensively studied in neuropsychological experiments. ERP activities time-locked to stimulus presentation and task performance are often observed separately at individual scalp channels based on averaged time series across epochs and experimental subjects. An analysis using averaged EEG dynamics could discount information regarding interdependency between ongoing EEG and salient ERP features. Advanced tools such as independent component analysis (ICA) have been developed for decomposing collections of single-trial EEG records into separate features. Those features (or independent components) can then be mapped onto the cortical surface using source localization algorithms to visualize brain activation maps and to study between-subject consistency. In this study, we propose a statistical framework for estimating the time course of spatiotemporally independent EEG components simultaneously with their cortical distributions. Within this framework, we implemented Bayesian spatiotemporal analysis for imaging the sources of EEG features on the cortical surface. The framework allows researchers to include prior knowledge regarding spatial locations as well as spatiotemporal independence of different EEG sources. The use of the Electromagnetic Spatiotemporal ICA (EMSICA) method is illustrated by mapping event-related EEG dynamics induced by events in a visual two-back continuous performance task. The proposed method successfully identified several interesting components with plausible corresponding cortical activation topographies, including processes contributing to the late positive complex (LPC) located in central parietal, frontal midline, and anterior cingulate cortex, to atypical mu rhythms associated with the precentral gyrus, and to the central posterior alpha activity in the precuneus.     

Reliability of ERP and single-trial analyses

A reliable measure is one we can trust in the long run. Thus, the reliability of measurements is as important as their validity. Here we investigated the reliability of brain electrical visual evoked responses to faces and noise textures. For the first time, we provide reliability measures for the full time course of event-related potentials (ERPs). Our analyses were also performed on a R(2)(t) metric that reflects results from single-trial analyses, therefore providing the first reliability analysis of ERP single-trial analyses. Results show that ERPs and R(2)(t) are highly reliable (cross-correlation ~0.9, lag ~4/6ms, intra-class correlation ~0.9) but also idiosyncratic: ERPs and R(2)(t) are highly reproducible within subjects, who differ reliably from each other and the grand average across subjects. Consequently, grand averages, although highly reliable, can be misleading because they might not reflect the actual brain dynamic of any subjects.     

Independent components of magnetoencephalography: single-trial response onset times

Werecently demonstrated that second-order blind identification (SOBI), an independent component analysis (ICA) method, can separate the mixture of neuronal and noise signals in magnetoencephalographic (MEG) data into neuroanatomically and neurophysiologically meaningful components. When the neuronal signals had relatively higher trial-to-trial variability, SOBI offered a particular advantage in identifying and localizing neuronal source activations with increased source detectability (A. C. Tang et al., 2002, Neural Comput. 14, 1827-1858). Here, we explore the utility of SOBI in the analysis of temporal aspects of neuromagnetic signals from MEG data. From SOBI components, we were able to measure single-trial response onset times of neuronal populations in visual, auditory, and somatosensory modalities during cognitive and sensory activation tasks, with a detection rate as high as 96% under optimal conditions. Comparing the SOBI-aided detection results with those obtained directly from the sensors, we found that with SOBI preprocessing, we were able to measure, among a greater proportion of trials, single-trial response onset times that are above background neuronal activity. We suggest that SOBI ICA can improve our current capability in measuring single-trial responses from human subjects using the noninvasive brain imaging method MEG.     

Single-trial EEG dynamics of object and face visual processing

There has been extensive work using early event-related potentials (ERPs) to study visual object processing. ERP analyses focus traditionally on mean amplitude differences, with the implicit assumption that all of the neuronal activity of interest is evoked by the stimulus in a time-locked manner from trial to trial. However, several recent studies have suggested that visual ERP components might be explained to a large extent by the partial phase resetting of ongoing activity in restricted frequency bands. Here we apply that approach to the neural processing of visual objects. We examine the single-trial dynamics of the EEG signal elicited by the presentation of noise textures, houses and faces. We show that the brain response to those stimuli is best explained by amplitude increase that is maximal in the 5- to 15-Hz frequency band. The results indicate also the presence of a substantial increase in phase coherence in the same frequency band. However, analyses of residual activity, after subtracting the mean from single trials, show that this increase in phase coherence is not due to phase resetting per se, but rather to the presence of the ERP+noise in each trial. In keeping with this idea, a simulation demonstrates that a purely evoked model of the ERP produces quantitatively very similar results. Finally, the stronger response to faces compared to other objects (the 'N170 face effect') can be explained by a pure modulation of amplitude centered in the 5- to 15-Hz band.     

Combined ICA-LORETA analysis of mismatch negativity

A major challenge for neuroscience is to map accurately the spatiotemporal patterns of activity of the large neuronal populations that are believed to underlie computing in the human brain. To study a specific example, we selected the mismatch negativity (MMN) brain wave (an event-related potential, ERP) because it gives an electrophysiological index of a "primitive intelligence" capable of detecting changes, even abstract ones, in a regular auditory pattern. ERPs have a temporal resolution of milliseconds but appear to result from mixed neuronal contributions whose spatial location is not fully understood. Thus, it is important to separate these sources in space and time. To tackle this problem, a two-step approach was designed combining the independent component analysis (ICA) and low-resolution tomography (LORETA) algorithms. Here we implement this approach to analyze the subsecond spatiotemporal dynamics of MMN cerebral sources using trial-by-trial experimental data. We show evidence that a cerebral computation mechanism underlies MMN. This mechanism is mediated by the orchestrated activity of several spatially distributed brain sources located in the temporal, frontal, and parietal areas, which activate at distinct time intervals and are grouped in six main statistically independent components.     

Selection of independent components representing event-related brain potentials: a data-driven approach for greater objectivity

Following the development of increasingly precise measurement instruments and fine-grain analysis tools for electroencephalographic (EEG) data, analysis of single-trial event-related EEG has considerably widened the utility of this non-invasive method to investigate brain activity. Recently, independent component analysis (ICA) has become one of the most prominent techniques for increasing the feasibility of single-trial EEG. This blind source separation technique extracts statistically independent components (ICs) from the EEG raw signal. By restricting the signal analysis to those ICs representing the processes of interest, single-trial analysis becomes more flexible. Still, the selection-criteria for in- or exclusion of certain ICs are largely subjective and unstandardized, as is the actual selection process itself. We present a rationale for a bottom-up, data-driven IC selection approach, using clear-cut inferential statistics on both temporal and spatial information to identify components that significantly contribute to a certain event-related brain potential (ERP). With time-range being the only necessary input, this approach considerably reduces the pre-assumptions for IC selection and promotes greater objectivity of the selection process itself. To test the validity of the approach presented here, we present results from a simulation and re-analyze data from a previously published ERP experiment on error processing. We compare the ERP-based IC selections made by our approach to the selection made based on mere signal power. The comparison of ERP integrity, signal-to-noise ratio, and single-trial properties of the back-projected ICs outlines the validity of the approach presented here. In addition, functional validity of the extracted error-related EEG signal is tested by investigating whether it is predictive for subsequent behavioural adjustments.     

The time-course of single-word reading: evidence from fast behavioral and brain responses

We usually feel that we understand a familiar word "immediately". However, even basic aspects of the time-line of word recognition are still controversial. Different domains of research have still not converged on a coherent account. An integration of multiple sources of information would lead to more strongly constrained theoretical models, and help finding optimal measures when monitoring specific aspects of word recognition impairments in patient groups. In our multimodal approach--combining fast behavioral measures, ERPs and EEG/MEG source estimation--we provide converging evidence for the latencies of earliest lexical and semantic information retrieval in visual word recognition. Participants performed lexical and semantic decisions (LD, SD) in a Go/NoGo paradigm. We introduced eye-blink latencies as a dependent variable, in order to measure behavioral responses that are faster and less variable than traditional button presses. We found that the earliest behavioral responses distinguishing stimulus categories can occur around 310 ms. Ex-Gaussian analysis of behavioral responses did not reveal reliable differences between LD and SD. The earliest ERP differences between Go and NoGo conditions occurred around 160 ms for both LD and SD. Distributed source analysis of combined EEG/MEG data estimated neuronal generators for the lexicality effect around 200 ms in the left anterior middle temporal lobe. Thus, behavior and brain responses provide coherent evidence that the brain starts retrieving lexical and semantic information near-simultaneously within 200 ms of word onset. Our results support models of word recognition that assume a continuous accumulation of task-related information from the stimulus, which might be described by Bayesian principles.     

Time course of ERP generators to syllables in infants: a source localization study using age-appropriate brain templates

Event-related potentials (ERPs) have become an important tool in the quest to understand how infants process perceptual information. Identification of the activation loci of the ERP generators is a technique that provides an opportunity to explore the neural substrates that underlie auditory processing. Nevertheless, as infant brain templates from healthy, non-clinical samples have not been available, the majority of source localization studies in infants have used non-realistic head models, or brain templates derived from older children or adults. Given the dramatic structural changes seen across infancy, all of which profoundly affect the electrical fields measured with EEG, it is important to use individual MRIs or age-appropriate brain templates and parameters to explore the localization and time course of auditory ERP sources. In this study 6-month-old infants were presented with a passive oddball paradigm using consonant-vowel (CV) syllables that differed in voice onset time. Dense-array EEG/ERPs were collected while the infants were awake and alert. In addition, MRIs were acquired during natural non-sedated sleep for a subset of the sample. Discrete dipole and distributed source models were mapped onto individual and averaged infant MRIs. The CV syllables elicited a positive deflection at about 200 ms followed by a negative deflection that peaked around 400 ms. The source models generated placed the dipoles at temporal areas close to auditory cortex for both positive and negative responses. Notably, an additional dipole for the positive peak was localized at the frontal area, at the anterior cingulate cortex (ACC) level. ACC activation has been reported in adults, but has not, to date, been reported in infants during processing of speech-related signals. The frontal ACC activation was earlier but smaller in amplitude than the left and right auditory temporal activations. These results demonstrate that in infancy the ERP generators to CV syllables are localized in cortical areas similar to that reported in adults, but exhibit a notably different temporal course. Specifically, ACC activation in infants significantly precedes auditory temporal activation, whereas in adults ACC activation follows that of temporal cortex. We suggest that these timing differences could be related to current maturational changes, to the ongoing construction of language-specific phonetic maps, and/or to more sensitive attentional switching as a response to speech signals in infancy.     

Dynamic causal modelling of evoked potentials: a reproducibility study

Dynamic causal modelling (DCM) has been applied recently to event-related responses (ERPs) measured with EEG/MEG. DCM attempts to explain ERPs using a network of interacting cortical sources and waveform differences in terms of coupling changes among sources. The aim of this work was to establish the validity of DCM by assessing its reproducibility across subjects. We used an oddball paradigm to elicit mismatch responses. Sources of cortical activity were modelled as equivalent current dipoles, using a biophysical informed spatiotemporal forward model that included connections among neuronal subpopulations in each source. Bayesian inversion provided estimates of changes in coupling among sources and the marginal likelihood of each model. By specifying different connectivity models we were able to evaluate three different hypotheses: differences in the ERPs to rare and frequent events are mediated by changes in forward connections (F-model), backward connections (B-model) or both (FB-model). The results were remarkably consistent over subjects. In all but one subject, the forward model was better than the backward model. This is an important result because these models have the same number of parameters (i.e., the complexity). Furthermore, the FB-model was significantly better than both, in 7 out of 11 subjects. This is another important result because it shows that a more complex model (that can fit the data more accurately) is not necessarily the most likely model. At the group level the FB-model supervened. We discuss these findings in terms of the validity and usefulness of DCM in characterising EEG/MEG data and its ability to model ERPs in a mechanistic fashion.     

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.     

Dynamic causal modeling of evoked responses in EEG and MEG

Neuronally plausible, generative or forward models are essential for understanding how event-related fields (ERFs) and potentials (ERPs) are generated. In this paper, we present a new approach to modeling event-related responses measured with EEG or MEG. This approach uses a biologically informed model to make inferences about the underlying neuronal networks generating responses. The approach can be regarded as a neurobiologically constrained source reconstruction scheme, in which the parameters of the reconstruction have an explicit neuronal interpretation. Specifically, these parameters encode, among other things, the coupling among sources and how that coupling depends upon stimulus attributes or experimental context. The basic idea is to supplement conventional electromagnetic forward models, of how sources are expressed in measurement space, with a model of how source activity is generated by neuronal dynamics. A single inversion of this extended forward model enables inference about both the spatial deployment of sources and the underlying neuronal architecture generating them. Critically, this inference covers long-range connections among well-defined neuronal subpopulations. In a previous paper, we simulated ERPs using a hierarchical neural-mass model that embodied bottom-up, top-down and lateral connections among remote regions. In this paper, we describe a Bayesian procedure to estimate the parameters of this model using empirical data. We demonstrate this procedure by characterizing the role of changes in cortico-cortical coupling, in the genesis of ERPs. In the first experiment, ERPs recorded during the perception of faces and houses were modeled as distinct cortical sources in the ventral visual pathway. Category-selectivity, as indexed by the face-selective N170, could be explained by category-specific differences in forward connections from sensory to higher areas in the ventral stream. We were able to quantify and make inferences about these effects using conditional estimates of connectivity. This allowed us to identify where, in the processing stream, category-selectivity emerged. In the second experiment, we used an auditory oddball paradigm to show that the mismatch negativity can be explained by changes in connectivity. Specifically, using Bayesian model selection, we assessed changes in backward connections, above and beyond changes in forward connections. In accord with theoretical predictions, there was strong evidence for learning-related changes in both forward and backward coupling. These examples show that category- or context-specific coupling among cortical regions can be assessed explicitly, within a mechanistic, biologically motivated inference framework.     

Linear spatial integration for single-trial detection in encephalography

Conventional analysis of electroencephalography (EEG) and magnetoencephalography (MEG) often relies on averaging over multiple trials to extract statistically relevant differences between two or more experimental conditions. In this article we demonstrate single-trial detection by linearly integrating information over multiple spatially distributed sensors within a predefined time window. We report an average, single-trial discrimination performance of Az approximately 0.80 and faction correct between 0.70 and 0.80, across three distinct encephalographic data sets. We restrict our approach to linear integration, as it allows the computation of a spatial distribution of the discriminating component activity. In the present set of experiments the resulting component activity distributions are shown to correspond to the functional neuroanatomy consistent with the task (e.g., contralateral sensorymotor cortex and anterior cingulate). Our work demonstrates how a purely data-driven method for learning an optimal spatial weighting of encephalographic activity can be validated against the functional neuroanatomy.     

Olfactory and trigeminal event-related potentials in migraine

BACKGROUND: Trigeminal/neuronal hyperexcitability and spreading depression activating the trigemino-vascular system are discussed in migraine-pathophysiology. This study investigated trigeminal and olfactory event-related potentials in migraineurs. METHODS: Nasal chemosensitivity was assessed in 19 female migraineurs with or without aura > 72 h before or after an attack and in 19 healthy females employing event-related cortical potentials (ERPs) after specific trigeminal stimulation of nasal nociceptors with short pulses of CO2, and specific olfactory stimulation with H2S. Odour thresholds and odour identification performance were also tested. RESULTS: Migraineurs exhibited greater responses to trigeminal stimulation, indicated by significantly larger ERP amplitudes N1. In contrast, olfactory ERP amplitudes P1N1 were significantly smaller in migraineurs. A leave-one-out classification procedure on the basis of these two parameters assigned 76.3% cases correctly. The olfactory ERP amplitude discriminated better between groups than trigeminal ERPs (71.1 vs. 68.4% correct classification). CONCLUSIONS: Our data suggest trigeminal hyperexcitability in migraineurs. A general increase of nasal chemosensitivity is not supported because of smaller olfactory ERP amplitudes in migraineurs. Olfactory ERPs discriminate better than trigeminal ERPs between migraineurs and controls, emphasizing the significance of the olfactory system in migraine.     

Cognitive neurophysiology: beyond averaging

Averaging of repeated responses to sensory stimuli is the standard approach in cognitive electrophysiology. This procedure can give rise to inappropriate interpretations in some situations, because two factors contribute to the average ERP responses: the amplitude of the responses during the individual experimental trials, and the concentration of the phases (phase-locking) across responses. Larger poststimulus single-trial amplitudes compared to prestimulus baseline are thought to correspond to a stimulus-related increase of postsynaptic potentials or/and activation of an increased amount of neural assemblies. But the functional interpretation of an enhanced inter-trial phase-locking is unclear. BOLD responses are probably related to single-trial EEG amplitudes, but not to the phase concentration across trials. Therefore, separation of amplitude and phase contributions is indispensable to avoid misinterpretations and to gain a deeper understanding of the relation between event-related EEG and fMRI.     

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.     

Dissociable medial frontal negativities from a common monitoring system for self- and externally caused failure of goal achievement

Goal-directed behavior requires the ability to adapt performance strategies based on the attribution of unintended outcomes to internal or external causes. Using event-related brain potentials, the present research compared neural activity following self-generated errors induced by a flanker task and following externally generated errors induced by supposed “technical malfunctions”. Errors and malfunctions were associated with temporally dissociable ERP components, the short-latency error-related negativity (ERN) and the longer-latency feedback-related negativity (FRN), respectively. Independent component analysis (ICA) was applied to compare the underlying neural components of ERN and FRN. ICA results revealed that the FRN is attributable to the neural sources of the ERN, suggesting that the two components share a source network. Furthermore, single-trial amplitudes of ERN and FRN were specifically related to the implementation of error correction and malfunction compensation: the stronger the failure signal, the more efficient was remedial behavior. Together the results demonstrate that internally and externally generated unintended action outcomes engage a common monitoring mechanism that manifests in two temporally distinct ERP components and induces similar compensatory processes. The temporal dissociation of the ERP components might provide the basis for further processes of outcome attribution underlying action selection and changes in performance strategy. In line with recent neuroimaging findings, ERN and FRN appear to reflect the use of different sources of information about action outcome to update action value.     

Errors can be related to pre-stimulus differences in ERP topography and their concomitant sources

Much of the variation in both neuronal and behavioral responses to stimuli can be explained by pre-stimulus fluctuations in brain activity. We hypothesized that also errors are the result of stochastic fluctuations in pre-stimulus activity and investigated the temporal dynamics of the scalp topography and their concomitant intracranial generators of stimulus- and response-locked high-density event-related potentials (ERPs) to errors and correct trials in a Stroop task. We found significant differences in ERP map topography and intracranial sources before the onset of the stimulus and after the initiation of the response but not as a function of stimulus-induced conflict. Before the stimulus, topographic differences were accompanied by differential activity in lateral frontal, parietal and temporal areas known to be involved in voluntary reorientation of attention and cognitive control. Differential post-response activity propagated both medially and laterally on a rostral–caudal axis of a network typically involved in performance monitoring. Analysis of the statistical properties of error occurrences revealed their stochasticity.     

Nonlinear dynamic causal models for fMRI

Models of effective connectivity characterize the influence that neuronal populations exert over each other. Additionally, some approaches, for example Dynamic Causal Modelling (DCM) and variants of Structural Equation Modelling, describe how effective connectivity is modulated by experimental manipulations. Mathematically, both are based on bilinear equations, where the bilinear term models the effect of experimental manipulations on neuronal interactions. The bilinear framework, however, precludes an important aspect of neuronal interactions that has been established with invasive electrophysiological recording studies; i.e., how the connection between two neuronal units is enabled or gated by activity in other units. These gating processes are critical for controlling the gain of neuronal populations and are mediated through interactions between synaptic inputs (e.g. by means of voltage-sensitive ion channels). They represent a key mechanism for various neurobiological processes, including top-down (e.g. attentional) modulation, learning and neuromodulation. This paper presents a nonlinear extension of DCM that models such processes (to second order) at the neuronal population level. In this way, the modulation of network interactions can be assigned to an explicit neuronal population. We present simulations and empirical results that demonstrate the validity and usefulness of this model. Analyses of synthetic data showed that nonlinear and bilinear mechanisms can be distinguished by our extended DCM. When applying the model to empirical fMRI data from a blocked attention to motion paradigm, we found that attention-induced increases in V5 responses could be best explained as a gating of the V1-->V5 connection by activity in posterior parietal cortex. Furthermore, we analysed fMRI data from an event-related binocular rivalry paradigm and found that interactions amongst percept-selective visual areas were modulated by activity in the middle frontal gyrus. In both practical examples, Bayesian model selection favoured the nonlinear models over corresponding bilinear ones.