Exploratory data analysis of evoked response single trials based on minimal spanning tree

First activity and interactions in thalamus and cortex using raw single-trial EEG and MEG elicited by somatosensory stimulation

Introduction

One of the primary motivations for studying the human brain is to comprehend how external sensory input is processed and ultimately perceived by the brain. A good understanding of these processes can promote the identification of biomarkers for the diagnosis of various neurological disorders; it can also provide ways of evaluating therapeutic techniques. In this work, we seek the minimal requirements for identifying key stages of activity in the brain elicited by median nerve stimulation.

Methods

We have used a priori knowledge and applied a simple, linear, spatial filter on the electroencephalography and magnetoencephalography signals to identify the early responses in the thalamus and cortex evoked by short electrical stimulation of the median nerve at the wrist. The spatial filter is defined first from the average EEG and MEG signals and then refined using consistency selection rules across ST. The refined spatial filter is then applied to extract the timecourses of each ST in each targeted generator. These ST timecourses are studied through clustering to quantify the ST variability. The nature of ST connectivity between thalamic and cortical generators is then studied within each identified cluster using linear and non-linear algorithms with time delays to extract linked and directional activities. A novel combination of linear and non-linear methods provides in addition discrimination of influences as excitatory or inhibitory.

Results

Our method identifies two key aspects of the evoked response. Firstly, the early onset of activity in the thalamus and the somatosensory cortex, known as the P14 and P20 in EEG and the second M20 for MEG. Secondly, good estimates are obtained for the early timecourse of activity from these two areas. The results confirm the existence of variability in ST brain activations and reveal distinct and novel patterns of connectivity in different clusters.

Discussion

It has been demonstrated that we can extract new insights into stimulus processing without the use of computationally costly source reconstruction techniques which require assumptions and detailed modeling of the brain. Our methodology, thanks to its simplicity and minimal computational requirements, has the potential for real-time applications such as in neurofeedback systems and brain-computer interfaces.

Dynamic Reconfiguration of Dominant Intrinsic Coupling Modes in Elderly at Prodromal Alzheimer's Disease Risk

Large-scale human brain networks interact across both spatial and temporal scales. Especially for electro- and magnetoencephalography (EEG/MEG), there are many evidences that there is a synergy of different subnetworks that oscillate on a dominant frequency within a quasi-stable brain temporal frame. Intrinsic cortical-level integration reflects the reorganization of functional brain networks that support a compensation mechanism for cognitive decline. Here, a computerized intervention integrating different functions of the medial temporal lobes, namely, object-level and scene-level representations, was conducted. One hundred fifty-eight patients with mild cognitive impairment underwent 90 min of training per day over 10 weeks. An active control (AC) group of 50 subjects was exposed to documentaries, and a passive control group of 55 subjects did not engage in any activity. Following a dynamic functional source connectivity analysis, the dynamic reconfiguration of intra- and cross-frequency coupling mechanisms before and after the intervention was revealed. After the neuropsychological and resting state electroencephalography evaluation, the ratio of inter versus intra-frequency coupling modes and also the contribution of β1 frequency was higher for the target group compared to its pre-intervention period. These frequency-dependent contributions were linked to neuropsychological estimates that were improved due to intervention. Additionally, the time-delays of the cortical interactions were improved in {δ, θ, α2, β1} compared to the pre-intervention period. Finally, dynamic networks of the target group further improved their efficiency over the total cost of the network. This is the first study that revealed a dynamic reconfiguration of intrinsic coupling modes and an improvement of time-delays due to a target intervention protocol.

Revisiting Riemannian geometry-based EEG decoding through approximate joint diagonalization

Objective.The wider adoption of Riemannian geometry in electroencephalography (EEG) processing is hindered by two factors: (a) it involves the manipulation of complex mathematical formulations and, (b) it leads to computationally demanding tasks. The main scope of this work is to simplify particular notions of Riemannian geometry and provide an efficient and comprehensible scheme for neuroscientific explorations.Approach.To overcome the aforementioned shortcomings, we exploit the concept of approximate joint diagonalization in order to reconstruct the spatial covariance matrices assuming the existence of (and identifying) a common eigenspace in which the application of Riemannian geometry is significantly simplified.Main results.The employed reconstruction process abides to physiologically plausible assumptions, reduces the computational complexity in Riemannian geometry schemes and bridges the gap between rigorous mathematical procedures and computational neuroscience. Our approach is both formally established and experimentally validated by employing real and synthetic EEG data.Significance.The implications of the introduced reconstruction process are highlighted by reformulating and re-introducing two signal processing methodologies, namely the 'Symmetric Positive Definite (SPD) Matrix Quantization' and the 'Coding over SPD Atoms'. The presented approach paves the way for robust and efficient neuroscientific explorations that exploit Riemannian geometry schemes.

RNeuMark: A Riemannian EEG Analysis Framework for Neuromarketing

Neuromarketing exploits neuroimaging techniques so as to reinforce the predictive power of conventional marketing tools, like questionnaires and focus groups. Electroencephalography (EEG) is the most commonly encountered neuroimaging technique due to its non-invasiveness, low-cost, and its very recent embedding in wearable devices. The transcription of brainwave patterns to consumer attitude is supported by various signal descriptors, while the quest for profitable novel ways is still an open research question. Here, we suggest the use of sample covariance matrices as alternative descriptors, that encapsulate the coordinated neural activity from distinct brain areas, and the adoption of Riemannian geometry for their handling. We first establish the suitability of Riemannian approach for neuromarketing-related problems and then suggest a relevant decoding scheme for predicting consumers' choices (e.g., willing to buy or not a specific product). Since the decision-making process involves the concurrent interaction of various cognitive processes and consequently of distinct brain rhythms, the proposed decoder takes the form of an ensemble classifier that builds upon a multi-view perspective, with each view dedicated to a specific frequency band. Adopting a standard machine learning procedure, and using a set of trials (training data) in conjunction with the associated behavior labels ("buy"/ "not buy"), we train a battery of classifiers accordingly. Each classifier is designed to operate in the space recovered from the inter-trial distances of SCMs and to cast a rhythm-depended decision that is eventually combined with the predictions of the rest ones. The demonstration and evaluation of the proposed approach are performed in 2 neuromarketing-related datasets of different nature. The first is employed to showcase the potential of the suggested descriptor, while the second to showcase the decoder's superiority against popular alternatives in the field.

GEVI cell-type specific labelling and a manifold learning approach provide evidence for lateral inhibition at the population level in the mouse hippocampal CA1 area

The CA1 area in the mammalian hippocampus is essential for spatial learning. Pyramidal cells are the hippocampus output neurons and their activities are regulated by inhibition exerted by a diversified population of interneurons. Lateral inhibition has been suggested as the mechanism enabling the reconfiguration of pyramidal cell assembly activity observed during spatial learning tasks in rodents. However, lateral inhibition in the CA1 lacks the overwhelming evidence reported in other hippocampal areas such as the CA3 and the dentate gyrus. The use of genetically encoded voltage indicators and fast optical recordings permits the construction of cell-type specific response maps of neuronal activity. Here, we labelled mouse CA1 pyramidal neurons with the genetically encoded voltage indicator ArcLight and optically recorded their response to Schaffer Collaterals stimulation in vitro. By undertaking a manifold learning approach, we report a hyperpolarization-dominated area focused in the perisomatic region of pyramidal cells receiving late excitatory synaptic input. Functional network organization metrics revealed that information transfer was higher in this area. The localized hyperpolarization disappeared when GABA_A receptors were pharmacologically blocked. This is the first report where the spatiotemporal pattern of lateral inhibition is visualized in the CA1 by expressing a genetically encoded voltage indicator selectively in principal neurons. Our analysis suggests a fundamental role of lateral inhibition in CA1 information processing.

A complex-valued functional brain connectivity descriptor amenable to Riemannian geometry

OBJECTIVE

We introduce a novel, phase-based, functional connectivity descriptor that encapsulates not only the synchronization strength between distinct brain regions, but also the time-lag between the involved neural oscillations. The new estimator employs complex-valued measurements and results in a brain network sketch that lives on the smooth manifold of Hermitian Positive Definite (HPD) matrices.

APPROACH

Leveraging the HPD property of the proposed descriptor, we adapt a recently introduced dimensionality reduction methodology that is based on Riemannian Geometry and discriminatively detects the recording sites which best reflect the differences in network organization between contrasting recording conditions in order to overcome the problem of high-dimensionality, usually encountered in the connectivity patterns derived from multisite encephalographic recordings.

MAIN RESULTS

The proposed framework is validated using an EEG dataset that refers to the challenging problem of differentiating between attentive and passive visual responses. We provide evidence that the reduced connectivity representation facilitates high classification performance and caters for neuroscientific explorations.

SIGNIFICANCE

Our paper is the very first that introduces an advanced connectivity descriptor that can take advantage of Riemannian geometry tools. The proposed descriptor, that inherently and simultaneously captures both the strength and the corresponding time-lag of the phase synchronization, is the first phase-based descriptor tailored to leverage the benefits of Riemannian geometry.

A Riemannian Geometry Approach to Reduced and Discriminative Covariance Estimation in Brain Computer Interfaces

OBJECTIVE

Spatial covariance matrices are extensively employed as brain activity descriptors in brain computer interface (BCI) research that, typically, involve the whole array of sensors. Here, we introduce a methodological framework for delineating the subset of sensors, the covariance structure of which offers a reduced, but more powerful, representation of brain's coordination patterns that ultimately leads to reliable mind reading.

METHODS

Adopting a Riemannian geometry approach, we turn the problem of sensor selection as a maximization of a functional that is computed over the manifold of symmetric positive definite (SPD) matrices and encapsulates class separability in a way that facilitates the search among subsets of different size. The introduced optimization task, namely discriminative covariance reduction (DCR), lacks an analytical solution and is tackled via the cross-entropy optimization technique.

RESULTS

Based on two different EEG datasets and three distinct classification schemes, we demonstrate that the DCR approach provides a noteworthy gain in terms of accuracy (in some cases exceeding 20%) and a remarkable reduction in classification time (on average 82%). Additionally, results include the intriguing empirical finding that the pattern of selected sensors in the case of disabled persons depends on the type of disability.

CONCLUSION

The proposed DCR framework can speed up the classification time in BCI-systems operating on the SPD manifolds by simultaneously enhancing their reliability. This is achieved without sacrificing the neuroscientific interpretability endowed in the topographical arrangement of the selected sensors.

SIGNIFICANCE

Riemannian geometry is exploited for DCR in BCI systems, in a dimensionality-agnostic manner, guaranteeing improved performance.

A Novel, Fast, Reliable, and Data-Driven Method for Simultaneous Single-Trial Mining and Amplitude-Latency Estimation Based on Proximity Graphs and Network Analysis

Both amplitude and latency of single-trial EEG/MEG recordings provide valuable information regarding functionality of the human brain. In this article, we provided a data-driven graph and network-based framework for mining information from multi-trial event-related brain recordings. In the first part, we provide the general outline of the proposed methodological approach. In the second part, we provide a more detailed illustration, and present the obtained results on every step of the algorithmic procedure. To justify the proposed framework instead of presenting the analytic data mining and graph-based steps, we address the problem of response variability, a prerequisite to reliable estimates for both the amplitude and latency on specific N/P components linked to the nature of the stimuli. The major question addressed in this study is the selection of representative single-trials with the aim of uncovering a less noisey averaged waveform elicited from the stimuli. This graph and network-based algorithmic procedure increases the signal-to-noise (SNR) of the brain response, a key pre-processing step to reveal significant and reliable amplitude and latency at a specific time after the onset of the stimulus and with the right polarity (N or P). We demonstrated the whole approach using electroencephalography (EEG) auditory mismatch negativity (MMN) recordings from 42 young healthy controls. The method is novel, fast and data-driven succeeding first to reveal the true waveform elicited by MMN on different conditions (frequency, intensity, duration, etc.). The proposed graph-oriented algorithmic pipeline increased the SNR of the characteristic waveforms and the reliability of amplitude and latency within the adopted cohort. We also demonstrated how different EEG reference schemes (REST vs. average) can influence amplitude-latency estimation. Simulation results revealed robust amplitude-latency estimations under different SNR and amplitude-latency variations with the proposed algorithm.

Functional Connectivity Analysis of Cerebellum Using Spatially Constrained Spectral Clustering

The human cerebellum contains almost 50% of the neurons in the brain, although its volume does not exceed 10% of the total brain volume. The goal of this study is to derive the functional network of the cerebellum during the resting-state and then compare the ensuing group networks between males and females. Toward this direction, a spatially constrained version of the classic spectral clustering algorithm is proposed and then compared against conventional spectral graph theory approaches, such as spectral clustering, and N-cut, on synthetic data as well as on resting-state fMRI data obtained from the Human Connectome Project (HCP). The extracted atlas was combined with the anatomical atlas of the cerebellum resulting in a functional atlas with 46 regions of interest. As a final step, a gender-based network analysis of the cerebellum was performed using the data-driven atlas along with the concept of the minimum spanning trees. The simulation analysis results confirm the dominance of the spatially constrained spectral clustering approach in discriminating activation patterns under noisy conditions. The network analysis results reveal statistically significant differences in the optimal tree organization between males and females. In addition, the dominance of the left VI lobule in both genders supports the results reported in a previous study of ours. To our knowledge, the extracted atlas comprises the first resting-state atlas of the cerebellum based on HCP data.

Mining Time-Resolved Functional Brain Graphs to an EEG-Based Chronnectomic Brain Aged Index (CBAI)

The brain at rest consists of spatially and temporal distributed but functionally connected regions that called intrinsic connectivity networks (ICNs). Resting state electroencephalography (rs-EEG) is a way to characterize brain networks without confounds associated with task EEG such as task difficulty and performance. A novel framework of how to study dynamic functional connectivity under the notion of functional connectivity microstates (FCμstates) and symbolic dynamics is further discussed. Furthermore, we introduced a way to construct a single integrated dynamic functional connectivity graph (IDFCG) that preserves both the strength of the connections between every pair of sensors but also the type of dominant intrinsic coupling modes (DICM). The whole methodology is demonstrated in a significant and unexplored task for EEG which is the definition of an objective Chronnectomic Brain Aged index (CBAI) extracted from resting-state data (N = 94 subjects) with both eyes-open and eyes-closed conditions. Novel features have been defined based on symbolic dynamics and the notion of DICM and FCμstates. The transition rate of FCμstates, the symbolic dynamics based on the evolution of FCμstates (the Markovian Entropy, the complexity index), the probability distribution of DICM, the novel Flexibility Index that captures the dynamic reconfiguration of DICM per pair of EEG sensors and the relative signal power constitute a valuable pool of features that can build the proposed CBAI. Here we applied a feature selection technique and Extreme Learning Machine (ELM) classifier to discriminate young adults from middle-aged and a Support Vector Regressor to build a linear model of the actual age based on EEG-based spatio-temporal features. The most significant type of features for both prediction of age and discrimination of young vs. adults age groups was the dynamic reconfiguration of dominant coupling modes derived from a subset of EEG sensor pairs. Specifically, our results revealed a very high prediction of age for eyes-open (R² = 0.60; y = 0.79x + 8.03) and lower for eyes-closed (R² = 0.48; y = 0.71x + 10.91) while we succeeded to correctly classify young vs. middle-age group with 97.8% accuracy in eyes-open and 87.2% for eyes-closed. Our results were reproduced also in a second dataset for further external validation of the whole analysis. The proposed methodology proved valuable for the characterization of the intrinsic properties of dynamic functional connectivity through the age untangling developmental differences using EEG resting-state recordings.

Topological Filtering of Dynamic Functional Brain Networks Unfolds Informative Chronnectomics: A Novel Data-Driven Thresholding Scheme Based on Orthogonal Minimal Spanning Trees (OMSTs)

The human brain is a large-scale system of functionally connected brain regions. This system can be modeled as a network, or graph, by dividing the brain into a set of regions, or “nodes,” and quantifying the strength of the connections between nodes, or “edges,” as the temporal correlation in their patterns of activity. Network analysis, a part of graph theory, provides a set of summary statistics that can be used to describe complex brain networks in a meaningful way. The large-scale organization of the brain has features of complex networks that can be quantified using network measures from graph theory. The adaptation of both bivariate (mutual information) and multivariate (Granger causality) connectivity estimators to quantify the synchronization between multichannel recordings yields a fully connected, weighted, (a)symmetric functional connectivity graph (FCG), representing the associations among all brain areas. The aforementioned procedure leads to an extremely dense network of tens up to a few hundreds of weights. Therefore, this FCG must be filtered out so that the “true” connectivity pattern can emerge. Here, we compared a large number of well-known topological thresholding techniques with the novel proposed data-driven scheme based on orthogonal minimal spanning trees (OMSTs). OMSTs filter brain connectivity networks based on the optimization between the global efficiency of the network and the cost preserving its wiring. We demonstrated the proposed method in a large EEG database (N = 101 subjects) with eyes-open (EO) and eyes-closed (EC) tasks by adopting a time-varying approach with the main goal to extract features that can totally distinguish each subject from the rest of the set. Additionally, the reliability of the proposed scheme was estimated in a second case study of fMRI resting-state activity with multiple scans. Our results demonstrated clearly that the proposed thresholding scheme outperformed a large list of thresholding schemes based on the recognition accuracy of each subject compared to the rest of the cohort (EEG). Additionally, the reliability of the network metrics based on the fMRI static networks was improved based on the proposed topological filtering scheme. Overall, the proposed algorithm could be used across neuroimaging and multimodal studies as a common computationally efficient standardized tool for a great number of neuroscientists and physicists working on numerous of projects.

A novel biomarker of amnestic MCI based on dynamic cross-frequency coupling patterns during cognitive brain responses

The detection of mild cognitive impairment (MCI), the transitional stage between normal cognitive changes of aging and the cognitive decline caused by AD, is of paramount clinical importance, since MCI patients are at increased risk of progressing into AD. Electroencephalographic (EEG) alterations in the spectral content of brainwaves and connectivity at resting state have been associated with early-stage AD. Recently, cognitive event-related potentials (ERPs) have entered into the picture as an easy to perform screening test. Motivated by the recent findings about the role of cross-frequency coupling (CFC) in cognition, we introduce a relevant methodological approach for detecting MCI based on cognitive responses from a standard auditory oddball paradigm. By using the single trial signals recorded at Pz sensor and comparing the responses to target and non-target stimuli, we first demonstrate that increased CFC is associated with the cognitive task. Then, considering the dynamic character of CFC, we identify instances during which the coupling between particular pairs of brainwave frequencies carries sufficient information for discriminating between normal subjects and patients with MCI. In this way, we form a multiparametric signature of impaired cognition. The new composite biomarker was tested using data from a cohort that consists of 25 amnestic MCI patients and 15 age-matched controls. Standard machine-learning algorithms were employed so as to implement the binary classification task. Based on leave-one-out cross-validation, the measured classification rate was found reaching very high levels (95%). Our approach compares favorably with the traditional alternative of using the morphology of averaged ERP response to make the diagnosis and the usage of features from spectro-temporal analysis of single-trial responses. This further indicates that task-related CFC measurements can provide invaluable analytics in AD diagnosis and prognosis.

MEG reveals a fast pathway from somatosensory cortex to occipital areas via posterior parietal cortex in a blind subject

Cross-modal activity in visual cortex of blind subjects has been reported during performance of variety of non-visual tasks. A key unanswered question is through which pathways non-visual inputs are funneled to the visual cortex. Here we used tomographic analysis of single trial magnetoencephalography (MEG) data recorded from one congenitally blind and two sighted subjects after stimulation of the left and right median nerves at three intensities: below sensory threshold, above sensory threshold and above motor threshold; the last sufficient to produce thumb twitching. We identified reproducible brain responses in the primary somatosensory (S1) and motor (M1) cortices at around 20 ms post-stimulus, which were very similar in sighted and blind subjects. Time-frequency analysis revealed strong 45-70 Hz activity at latencies of 20-50 ms in S1 and M1, and posterior parietal cortex Brodmann areas (BA) 7 and 40, which compared to lower frequencies, were substantially more pronounced in the blind than the sighted subjects. Critically, at frequencies from α-band up to 100 Hz we found clear, strong, and widespread responses in the visual cortex of the blind subject, which increased with the intensity of the somatosensory stimuli. Time-delayed mutual information (MI) revealed that in blind subject the stimulus information is funneled from the early somatosensory to visual cortex through posterior parietal BA 7 and 40, projecting first to visual areas V5 and V3, and eventually V1. The flow of information through this pathway occurred in stages characterized by convergence of activations into specific cortical regions. In sighted subjects, no linked activity was found that led from the somatosensory to the visual cortex through any of the studied brain regions. These results provide the first evidence from MEG that in blind subjects, tactile information is routed from primary somatosensory to occipital cortex via the posterior parietal cortex.

Spatiotemporal profiles of visual processing with and without primary visual cortex

The spatiotemporal profiles of visual processing are normally distributed in two temporal phases, each lasting about 100 ms. Within each phase, cortical processing begins in V1 and traverses the visual cortical hierarchy. However, the causal role of V1 in starting each of these two phases is unknown. Here we used magnetoencephalography to study the spatiotemporal profiles of visual processing and the causal contribution of V1 in three neurologically intact participants and in a rare patient (GY) with unilateral destruction of V1, in whom residual visual functions mediated by the extra-geniculostriate pathways have been reported. In healthy subjects, visual processing in the first 200 ms post-stimulus onset proceeded in the two usual phases. Normally perceived stimuli in the left hemifield of GY elicited a spatiotemporal profile in the intact right hemisphere that closely matched that of healthy subjects. However, stimuli presented in the cortically blind hemifield produced no detectable response during the first phase of processing, indicating that the responses in extrastriate visual areas during this phase are determined by the feedforward progression of activity initiated in V1. The first responses occurred during the second processing phase, in the ipsilesional high-level visual areas. The activity then spread forward toward higher-level areas and backward toward lower-level areas. However, in contrast to responses in the intact hemisphere, the back-propagated activity in the early visual cortex did not exhibit the classic retinotopic organization and did not have well-defined response peaks.

Consistency of the auditory evoked response: the presence of aberrant responses and their effect on N100 localization

The structure and distribution of the sources underlying the generation of evoked potentials (EPs) is often very complex. In an effort to improve localization accuracy of the auditory N100 (negative response occurring around 100ms poststimulus) component, we analyzed 13 datasets of single-trial EPs obtained from normal subjects using an iterative independent component analysis procedure which allowed us to detect a clear N100 component in each single trial and to study gross changes in component morphology across trials. We found that single-trial N100 amplitude was most often negative in polarity, as expected, but occasionally exhibited a marked reversal to become positive. The average N100, however, showed the typical negative polarity, in all subjects. Based on this observation, we separated the processed single trials in two groups of typical and aberrant responses, and from each group, we computed a partial EP that was used to localize the underlying intracranial sources. Additionally, we localized the classical ensemble average EP. Before processing, the N100 sources were identified correctly in the primary auditory cortex in only four datasets, while after processing, all 13 datasets yielded correct localizations, and the confidence volume of the sources improved by about 80%. Further analysis demonstrated that in nine datasets the improvement was mostly due to the typical responses, while the aberrant responses had an antagonistic effect. Our results suggest that aberrant responses should not be included in source localizations, especially when EEG-based brain mapping is intended as a clinical tool.

BA3b and BA1 activate in a serial fashion after median nerve stimulation: direct evidence from combining source analysis of evoked fields and cytoarchitectonic probabilistic maps

This study combines source analysis imaging data for early somatosensory processing and the probabilistic cytoarchitectonic maps (PCMs). Human somatosensory evoked fields (SEFs) were recorded by stimulating left and right median nerves. Filtering the recorded responses in different frequency ranges identified the most responsive frequency band. The short-latency averaged SEFs were analyzed using a single equivalent current dipole (ECD) model and magnetic field tomography (MFT). The identified foci of activity were superimposed with PCMs. Two major components of opposite polarity were prominent around 21 and 31 ms. A weak component around 25 ms was also identified. For the most responsive frequency band (50-150 Hz) ECD and MFT revealed one focal source at the contralateral Brodmann area 3b (BA3b) at the peak of N20. The component ~25 ms was localised in Brodmann area 1 (BA1) in 50-150 Hz. By using ECD, focal generators around 28-30 ms located initially in BA3b and 2 ms later to BA1. MFT also revealed two focal sources - one in BA3b and one in BA1 for these latencies. Our results provide direct evidence that the earliest cortical response after median nerve stimulation is generated within the contralateral BA3b. BA1 activation few milliseconds later indicates a serial mode of somatosensory processing within cytoarchitectonic SI subdivisions. Analysis of non-invasive magnetoencephalography (MEG) data and the use of PCMs allow unambiguous and quantitative (probabilistic) interpretation of cytoarchitectonic identity of activated areas following median nerve stimulation, even with the simple ECD model, but only when the model fits the data extremely well.

Cortical activity preceding vertical saccades: a MEG study

Previous studies have shown that upward saccade latencies are faster than downward saccade latencies in certain tasks. This asymmetry does not appear to represent a general main effect of the visual, or the vertical oculomotor system. In this study we examined the cortical activity underlying this latency asymmetry. We used MEG to assess cortical activity related to horizontal and vertical saccade preparation, and eye movement recordings to assess saccade latencies in a modified delay task. The reconstructed cortical activity was examined with respect to the onset of the target stimulus and the onset of the saccade. Upward saccades were faster than downward saccades, in agreement with previous studies. Although to a large extent, horizontal and vertical targets activated similar areas, there were also some differences. The earlier difference was found 100-150 ms after target onset over the right supramarginal gyrus when subjects attended to location-cues. Down cues activated this area faster than up cues. Moreover, cue-related activity was stronger over the left frontal cortex for up than down cues. In contrast, saccade-related activity over the same area was stronger when preceding downward than upward saccades. The results suggest that stimuli in the upper and lower visual field may have different impacts on accessing networks related to visual attention and motor preparation resulting in different behavioral asymmetries.

Morphology and dynamic repertoire of EEG short-term spectral patterns in rest: explorative study

In the present explorative experimental study, we examined the diversity of electroencephalographic (EEG) short-term spectral patterns (SPs) within a broad frequency band (1.5-30Hz) for healthy adult subjects during closed eyes and open eyes resting conditions. The types of EEG SPs were assessed by counting all identical SPs with peaks in the same frequency bins from the pools of SPs, which were built from all the SPs of the entire EEG signal (all locations) for all subjects separately for closed and open eyes conditions. This study demonstrated that independently of the resting functional state of the brain (closed eyes vs. open eyes) (a) the diversity of short-term EEG SP types was limited, (b) the percent distribution of SP types among different categories of SPs (based on morphology of SPs) was constant and (c) the most preferred frequencies were restricted to delta-theta and alpha bands. At the same time, closed eyes and open eyes conditions differed from each other by the percent distribution of different types of SPs. The probabilities for the occurrence of particular SP types were typical for each of the examined conditions with domination of alpha-rhythmical SPs during closed eyes condition and domination of delta-theta-rhythmical SPs during open eyes condition. The findings suggest that the diversity of SPs varies as a function of functional state of the brain during resting conditions. Understanding of the diversity of short-term EEG SP types is important theoretically and practically, and is significant for advancing the interpretation of the EEG signal.

Wired for her face? Male attentional bias for female faces

Under conditions of inattention or deficits in orienting attention, special classes of stimuli (e.g. faces, bodies) are more likely to be perceived than other stimuli. This suggests that biologically salient visual stimuli automatically recruit attention, even when they are task-irrelevant or ignored. Here we report results from a behavioral experiment with female and male subjects and two magnetoencephalography (MEG) experiments with male subjects only, in which we investigated attentional capture with face and hand stimuli. In both the behavioral and MEG experiments, subjects were required to count the number of gender-specific targets from either face or hand categories within a block of stimuli. In the behavioral experiment, we found that male subjects were significantly more accurate in response to female than male face target blocks. There was no corresponding effect found in response to hand target blocks. Female subjects did not show a gender-based difference in response to face or hand target blocks. MEG results indicated that the male subjects' responses to face stimuli in primary visual cortex (V1) and the face-selective part of the fusiform gyrus (FG) were reduced when male face stimuli were not relevant to the task, whereas female faces maintained a strong response in these areas in both task-relevant and task-irrelevant conditions. These results suggest that within the male brain, female face stimuli are more resilient to suppression than male faces, once attention is drawn to the part of the visual field where the face appears.

MEG's ability to localise accurately weak transient neural sources

OBJECTIVE

To investigate the accurate localisation of weak, transient, neural sources under conditions of varying difficulty.

METHODS

Multiple dipolar sources placed within a head-shaped phantom at superficial and deep locations were driven separately or simultaneously by a short-lasting current with varied amplitudes. Artificial MEG signals that were very similar to the human High Frequency Oscillations (HFO) were produced. MEG signals of HFO were also recorded from median nerve stimulation. Different inverse techniques were used to localise the phantom dipoles and the human HFO generators.

RESULTS

The human HFO were measured around 200 and 600Hz by using only 120 trials. The 200Hz HFO were localised to BA3b. The superficial phantom's source was localised with an accuracy of 2-3mm by all inverse techniques (120 trials). The 'subcortical' source was localised with an error of approximately 5mm. Localisation of deeper 'thalamic' sources required more trials.

CONCLUSION

MEG can detect and localise weak transient activations and the human HFO with an accuracy of a few mm at cortical and subcortical regions even when a small number of trials are used.

SIGNIFICANCE

Localizing HFO to specific anatomical structures has high clinical utility, for example in epilepsy, where discrete HFO appears to be generated just before focal epileptic activity.

Single-trial evoked brain responses modeled by multivariate matching pursuit

We present a new approach to the analysis of brain evoked electromagnetic potentials and fields. Multivariate version of the matching pursuit algorithm (MMP) performs an iterative, exhaustive search for waveforms, which optimally fit to signal structures, persistent in all the responses (trials) with the same time of occurrence, frequency, phase, and time width, but varying amplitude. The search is performed in a highly redundant time--frequency dictionary of Gabor functions, i.e., sines modulated by Gaussians. We present the feasibility of such a single-trial MMP analysis of the auditory M100 response, using an illustrative dataset acquired in a magnetoencephalographic (MEG) measurement with auditory stimulation with sinusoidal 1-kHz tones. We find that the morphology of the M100 estimate obtained from simple averaging of single trials can be very well explained by the average reconstruction with a few Gabor functions that parametrize those single trials. The M100 peak amplitude of single-trial reconstructions is observed to decrease with repetitions, which indicates habituation to the stimulus. This finding suggests that certain waveforms fitted by MMP could possibly be related to physiologically distinct components of evoked magnetic fields, which would allow tracing their dynamics on a single-trial level.

Evoked potential variability

An unsupervised correlation-based clustering method was developed to assess the trial-to-trial variability of auditory evoked potentials (AEPs). The method first decomposes single trials into three frequency bands, each containing activity primarily associated with one of the three major AEP components, i.e., P50, N100 and P200. Next, single-trial evoked potentials with similar post-stimulus characteristics are clustered and selectively averaged to determine the presence or absence of an AEP component. The method was evaluated on actual AEP and spontaneous EEG data collected from 25 healthy participants using a paradigm in which pairs of identical tones were presented, with the first stimulus (S1) presented 0.5s before the second stimulus (S2). Homogeneous, well-separated clusters were obtained and substantial AEP variability was found. Also, there was a trend for S2 to produce fewer 'complete' (and significantly smaller) responses than S1. Tests conducted on spontaneous EEG produced similar clusters as obtained from EP data, but significantly fewer stimuli produced responses containing all three EP components than seen in AEP data. These findings suggest that the clustering method presented here performs adequately to assess trial-to-trial EP variability. Also, the results suggest that the sensory gating observed in normal controls may be caused by the fact that the second stimulus generates fewer 'responsive' trials than the first stimulus, thus resulting in smaller ensemble averages.

Magnetoencephalography (MEG)

Magnetoencephalography (MEG) encompasses a family of non-contact, non-invasive techniques for detecting the magnetic field generated by the electrical activity of the brain, for analyzing this MEG signal and for using the results to study brain function. The overall purpose of MEG is to extract estimates of the spatiotemporal patterns of electrical activity in the brain from the measured magnetic field outside the head. The electrical activity in the brain is a manifestation of collective neuronal activity and, to a large extent, the currency of brain function. The estimates of brain activity derived from MEG can therefore be used to study mechanisms and processes that support normal brain function in humans and help us understand why, when and how they fail.

Brain-mind operational architectonics imaging: technical and methodological aspects

This review paper deals with methodological and technical foundations of the Operational Architectonics framework of brain and mind functioning. This theory provides a framework for mapping and understanding important aspects of the brain mechanisms that constitute perception, cognition, and eventually consciousness. The methods utilized within Operational Architectonics framework allow analyzing with an incredible detail the operational behavior of local neuronal assemblies and their joint activity in the form of unified and metastable operational modules, which constitute the whole hierarchy of brain operations, operations of cognition and phenomenal consciousness.

Mountains and valleys: binocular rivalry and the flow of experience

Binocular rivalry provides a useful situation for studying the relation between the temporal flow of conscious experience and the temporal dynamics of neural activity. After proposing a phenomenological framework for understanding temporal aspects of consciousness, we review experimental research on multistable perception and binocular rivalry, singling out various methodological, theoretical, and empirical aspects of this research relevant to studying the flow of experience. We then review an experimental study from our group explicitly concerned with relating the temporal dynamics of rivalrous experience to the temporal dynamics of cortical activity. Drawing attention to the importance of dealing with ongoing activity and its inherent changing nature at both phenomenological and neurodynamical levels, we argue that the notions of recurrence and variability are pertinent to understanding rivalry in particular and the flow of experience in general.

Estimation of individual evoked potential components using iterative independent component analysis

Independent component analysis (ICA) has been successfully employed in the study of single-trial evoked potentials (EPs). In this paper, we present an iterative temporal ICA methodology that processes multielectrode single-trial EPs, one channel at a time, in contrast to most existing methodologies which are spatial and analyze EPs from all recording channels simultaneously. The proposed algorithm aims at enhancing individual components in an EP waveform in each single trial, and relies on a dynamic template to guide EP estimation. To quantify the performance of this method, we carried out extensive analyses with artificial EPs, using different models for EP generation, including the phase-resetting and the classical additive-signal models, and several signal-to-noise ratios and EP component latency jitters. Furthermore, to validate the technique, we employed actual recordings of the auditory N100 component obtained from normal subjects. Our results with artificial data show that the proposed procedure can provide significantly better estimates of the embedded EP signals compared to plain averaging, while with actual EP recordings, the procedure can consistently enhance individual components in single trials, in all subjects, which in turn results in enhanced average EPs. This procedure is well suited for fast analysis of very large multielectrode recordings in parallel architectures, as individual channels can be processed simultaneously on different processors. We conclude that this method can be used to study the spatiotemporal evolution of specific EP components and may have a significant impact as a clinical tool in the analysis of single-trial EPs.

Composition of brain oscillations and their functions in the maintenance of auditory, visual and audio–visual speech percepts: an exploratory study

In the present exploratory study based on 7 subjects, we examined the composition of magnetoencephalographic (MEG) brain oscillations induced by the presentation of an auditory, visual, and audio-visual stimulus (a talking face) using an oddball paradigm. The composition of brain oscillations were assessed here by analyzing the probability-classification of short-term MEG spectral patterns. The probability index for particular brain oscillations being elicited was dependent on the type and the modality of the sensory percept. The maintenance of the integrated audio-visual percept was accompanied by the unique composition of distributed brain oscillations typical of auditory and visual modality, and the contribution of brain oscillations characteristic for visual modality was dominant. Oscillations around 20 Hz were characteristic for the maintenance of integrated audio-visual percept. Identifying the actual composition of brain oscillations allowed us (1) to distinguish two subjectively/consciously identical mental percepts, and (2) to characterize the types of brain functions involved in the maintenance of the multi-sensory percept.

Dynamic functional connectivity

Recent studies show that anatomical and functional brain networks exhibit similar small-world properties. However, the networks that are compared often differ in what the nodes represent (e.g. sensors or brain areas), what kind of connectivity is measured, and what temporal and spatial scales are probed. Here, I review studies of large-scale connectivity and recent results from a variety of real-time recording techniques, which together suggest that an adequate description of brain organization requires a hierarchy of networks rather than the single, binary networks that are currently in vogue. Pattern analysis methods now offer a principled way for constructing such network hierarchies. As shown at the end of this review, a correspondence principle can be formulated to guide the interpretation across network levels and to relate nodes to well defined anatomical entities.

Magnetoencephalography as a research tool in neuroscience: state of the art

Magnetoencephalography (MEG) is a noninvasive neuroimaging method for detecting, analyzing, and interpreting the magnetic field generated by the electrical activity in the brain. Modern hardware can capture the MEG signal at hundreds of points around the head in a snapshot lasting only a fraction of a millisecond. The sensitivity of modern hardware is high enough to permit the extraction of a clean signal generated by the brain well above the noise level of the MEG hardware. It is possible to identify signatures of superficial and often deep generators in the raw MEG signal, even in snapshots of data. In a more quantitative way, tomographic images of the electrical current density in the brain can be extracted from each snapshot of MEG signal, providing a direct correlate of coherent collective neuronal activity. A number of recent studies have scrutinized brain function in the new spatiotemporal window that real-time tomographic analysis of MEG signals has opened. The results have allowed the variability in a single area to be seen in the context of activity in other areas and background rhythmic activity. In this view, normal brain function is seen as a cascade of extremely fast events and the unfolding of specialized processes, segregated in space and time and organized into well-defined stages of processing.

Timing in cognition and EEG brain dynamics: discreteness versus continuity

This article provides an overview of recent developments in solving the timing problem (discreteness vs. continuity) in cognitive neuroscience. Both theoretical and empirical studies have been considered, with an emphasis on the framework of operational architectonics (OA) of brain functioning (Fingelkurts and Fingelkurts in Brain Mind 2:291-29, 2001; Neurosci Biobehav Rev 28:827-836, 2005). This framework explores the temporal structure of information flow and interarea interactions within the network of functional neuronal populations by examining topographic sharp transition processes in the scalp EEG, on the millisecond scale. We conclude, based on the OA framework, that brain functioning is best conceptualized in terms of continuity-discreteness unity which is also the characteristic property of cognition. At the end we emphasize where one might productively proceed for the future research.

Widely distributed magnetoencephalography spikes related to the planning and execution of human saccades

With sufficiently fast data sampling, ubiquitous sharp transients appear in magnetoencephalography (MEG) data. Initially, no known collective neuronal activity could explain MEG signal generation well above 100 Hz, so it was assumed that these transients were entirely composed of background electronic noise that could be eliminated by filtering and averaging. Recent studies at the cellular level provided evidence for synchronous synaptic input to dendrites and volleys of near-simultaneous action potentials. MEG studies have also identified high-frequency oscillations well above 200 Hz after averaging large number of somatosensory evoked responses. In this study, we searched for evidence of high-frequency neuronal activity in the raw MEG signal using the highest sampling rate available with our hardware. Two human subjects participated in three experiments using visual cues to define planning, preparation, and execution or inhibition of saccades. Tomographic analysis identified "MEG spikes" that were widely distributed across the cortex, cerebellum, and brainstem during cue presentations and saccades. Here we demonstrate how these MEG spikes can be recorded and localized in real time and show that task demands influence their properties. The MEG spikes were organized into feedforward and corollary discharge sequences that could, when combined with the slower activity-linked processing in discrete brain areas over long periods, lasting hundreds of milliseconds. Preparation for impending saccade began as soon as relevant information became available. Cues providing partial information initiated competing motor programs for as yet undecided future actions that were maintained until cues with new information resolved the uncertainty.

Evoked phase synchronization between adjacent high-density electrodes in human scalp EEG: Duration and time course related to behavior

OBJECTIVE

Data from a previous event-related potential (ERP) study in visual-perceptual grouping [Nikolaev AR, van Leeuwen C. Flexibility in spatial and non-spatial feature grouping: an event-related potentials study. Brain Res Cogn Brain Res 2004;22:13-25] were re-analyzed to identify event-related dynamics of phase-synchronization.

METHODS

In 20 Hz activity, uniform spreading of phase synchronization in closely spaced (approximately 2 cm) scalp electrodes appears and disappears spontaneously. The lengths of synchronized activity intervals and how they vary as a function of stimulus presentation were compared between task and control conditions.

RESULTS

Synchronization reached a maximum in the task condition about 180 ms post-stimulus onset, coinciding with the peak N180 ERP marking the deployment of task-specific attention. Synchronized intervals were longer in the task than in the control condition. Long (above 80 ms) intervals occurred at a stable rate before and just after stimulus onset, but steeply decreased 200-400 ms afterwards.

CONCLUSIONS

Perceptual tasks lead to longer synchronized intervals in early visual areas. Attention deployment resets the ongoing synchronization. Event-related activity, besides low-frequency ERP, consists of high-frequency short and long synchronized intervals corresponding to evoked bursts and ongoing oscillations, respectively.

SIGNIFICANCE

High-density scalp recorded EEG revealed synchronization dynamics in a local, early visual area of cortex that can be interpreted as modulation of spontaneous ongoing task-related processes by attention.

Clustering of early cortical responses to median nerve stimulation from average and single trial MEG and EEG signals

Median nerve electrical stimulation (MNES) produces early and strong averaged magnetoencephalography (MEG) or electroencephalography (EEG) signals, despite considerable single trial (ST) variability, demonstrated in separate MEG and EEG studies. Here, simultaneous MEG/EEG recordings are used to assess whether same or different aspects of ST variability are influencing EEG and MEG. Clustering techniques provided groupings for the ST timeseries for cortical responses to MNES derived from one modality. These groupings were applied to the corresponding ST timeseries derived from the other modality to quantify the similarity in variability captured by MEG and EEG signals. Estimates of early cortical activity elicited by MNES derived from MEG and EEG signals were very similar, provided ongoing mu rhythm was removed. Similarity between EEG and MEG estimates included both results based on average signals and measures of ST variability. Either MEG or EEG can provide a robust measure of the early cortical activity elicited by MNES as well as of its variability. Reliable indices of early cortical responses to MNES can be derived from either MEG or EEG data. These indices can be based on average signals, as is routinely done with clinical EEG, but it could also rely on hitherto little utilized measures of ST variability.

Imaging cerebellum activity in real time with magnetoencephalographic data

The cerebellum has traditionally been associated with motor movements but recent studies suggest its involvement with fine timing, sensory analysis and cognition. Much of the new data comes from neuroimaging techniques such as fMRI and PET, which have high spatial resolution and show that for even simple stimuli many cerebellar and cortical areas are involved. We use examples from recent studies to demonstrate that magnetic field tomography (MFT) offers a new and powerful tool for studying cerebellar function through real time localization of cortical, brainstem and cerebellar activations over timescales ranging from a fraction of a millisecond to seconds, minutes and hours. The examples include demonstration of cerebellar activations along well-established anatomical pathways during saccades and the visualization of the ascending medullar volley after median nerve stimulation. MFT analysis of single trial MEG signals elicited by the presentation of faces in emotion and object recognition tasks, show changes in cerebellar activation between schizophrenics and normal subjects in agreement with proposals for disturbed cerebellar function in schizophrenia. The ability of MFT to identify cerebellar, brainstem and cortical activations in real time can add new insights about dynamics of brain activity to the recent findings about cerebellar function from PET and fMRI.

Real-time neural activity and connectivity in healthy individuals and schizophrenia patients

Processing of facial information is distributed across several brain regions, as has been shown recently in many neuroimaging studies. Disturbances in accurate face processing have been repeatedly demonstrated in different stages of schizophrenia. Recently, electroencephalography (EEG) and tomographic analysis of average magnetoencephalographic (MEG) data were used to define the latencies of significant regional brain activations in healthy and schizophrenic subjects elicited during the recognition of facial expression of emotions. The current study re-examines these results using tomographic analysis of single trial MEG data. In addition to the areas identified by the analysis of the average MEG data, statistically significant activity is identified in several other areas, including a sustained increase in the right amygdala activity in response to emotional faces in schizophrenic subjects. The single trial analysis demonstrated that the reduced activations identified from the average MEG signal of schizophrenic subjects is due to high variability across single trials rather than reduced activity in each single trial. In control subjects, direct measures of linkage demonstrate distinct stages of processing of emotional faces within well-defined network of brain regions. Activity in each node of the network, confined to 30 to 40 ms latency windows, is linked to earlier and later activations of the other nodes of the network. In schizophrenic subjects, no such well-defined stages of processing were observed. Instead, the activations, although strong were poorly linked to each other, managing only isolated links between pairs of areas.

Enhancement of GABA-related signalling is associated with increase of functional connectivity in human cortex

Structural or operational synchrony analysis with EEG was conducted in order to detect functional interaction between cortical areas during an enhanced inhibition induced by the GABAergic agonist lorazepam in a double-blind, randomized, placebo-controlled, cross-over study in eight healthy human subjects. Specifically, we investigated whether a neuronal inhibitory system in the brain mediates functional decoupling of cortical areas. Single-dose lorazepam administration resulted in a widespread increase in the inter-area functional connectivity and an increase in the strength of functional long-range and interhemispheric connections. These results suggest that inhibition can be an efficient mechanism for synchronization of large neuronal populations.

Contribution of different EEG frequencies to auditory evoked potential abnormalities in schizophrenia

OBJECTIVE

We have shown previously [Clin Neurophysiol 2003;114:79] that phase reorganization of the ongoing electroencephalogram (EEG) plays an important role in the generation of auditory evoked potential (EP) components with a latency between 50 and 200 ms. In the present study, we investigate whether schizophrenia patients suffer from phase synchronization deficits as compared to normal subjects.

METHODS

The auditory EPs from 20 normal subjects and 19 schizophrenia patients were analyzed. EPs were obtained using a double stimulus paradigm, in which two identical tone bursts (S1 and S2) were delivered with an average inter-stimulus interval of 500 ms and an inter-pair interval of 8 s. The Piecewise Prony Method (PPM) was used to decompose single trial auditory evoked potentials into different frequency bands. Pre- and post-stimulus phase histograms were compared for each frequency band to determine the degree of phase synchronization produced by auditory stimulation in the two populations.

RESULTS

The S1 stimulus produced significantly less (P < 0.05) phase synchronization in schizophrenia patients than in normal subjects in the 2-12 Hz frequency range. Far fewer and smaller inter-population phase synchronization differences were seen for the S2 stimulus. Both populations showed more phase synchronization for S1 than S2. A significant correlation (P < 0.01) between N100 amplitude and phase synchronization 100 ms post S1 was observed for the normal population but not for the schizophrenia group. The correlation between P200 amplitude and phase synchronization 200 ms post S1 was significant for the normal group (P < 0.01) and the schizophrenia group (P < 0.03).

CONCLUSIONS

Schizophrenia patients have a phase synchronization deficiency, as compared to a normal control group, especially for the first stimulus, in the 2-12 Hz frequency range. This deficiency explains the lower EP amplitudes and may be a significant factor contributing to reduced sensory gating reported in schizophrenic subjects.

SIGNIFICANCE

The research presented here contributes to the understanding of the mechanism underlying sensory gating in health and gating deficiencies in schizophrenia.

The interplay of lorazepam-induced brain oscillations: microstructural electromagnetic study

OBJECTIVE

The effects on cortical rhythms of a single-dose (30 microg/kg) administration of the GABAA agonist lorazepam were examined in a randomized, double-blind, cross-over, placebo-controlled study with 8 healthy volunteers using simultaneous electroencephalography (EEG) and magnetoencephalography (MEG).

METHODS

The oscillations were assessed by means of adaptive classification of short-term spectral patterns.

RESULTS

Lorazepam (a) decreased the percentage of EEG/MEG segments with fast-theta, delta-alpha, fast-theta-alpha and alpha activity and increased percentage of EEG/MEG segments with delta, delta-slow-theta, delta-beta, slow-theta and polyrhythmic activity; (b) decreased diversity of EEG/MEG signals (in terms of spectral patterns) and increased the general instability of the signal; (c) increased stabilization periods of the spectral patterns (reduced brain information processing); (d) maintained larger maximum periods of temporal stabilization for delta, slow-theta, delta-slow-theta, delta-beta and polyrhythmic activity (in terms of spectral patterns); (e) did not increase power in the independent beta rhythm.

CONCLUSIONS

Lorazepam caused significant reorganization of the EEG/MEG microstructure. These results suggest also that adaptive classification analysis of single short-term spectral patterns may provide additional information to conventional spectral analyses.

The human primary somatosensory cortex response contains components related to stimulus frequency and perception in a frequency discrimination task

Somatosensory stimulation of primary somatosensory cortex (SI) using frequency discrimination offers a direct, well-defined and accessible way of studying cortical decisions at the locus of early input processing. Animal studies have identified and classified the neuronal responses in SI but they have not yet resolved whether during prolonged stimulation the collective SI response just passively reflects the input or actively participates in the comparison and decision processes. This question was investigated using tomographic analysis of single trial magnetoencephalographic data. Four right-handed males participated in a frequency discrimination task to detect changes in the frequency of an electrical stimulus applied to the right-hand digits 2+3+4. The subjects received approximately 600 pairs of stimuli with Stim1 always at 21 Hz, while Stim2 was either 21 Hz (50%) or varied from 22 to 29 Hz in steps of 1 Hz. Both stimuli were 1 s duration, separated by a 1 s interval of no stimulation. The left-SI was the most consistently activated area and showed the first activation peak at 35-48 ms after Stim1 onset and sustained activity during both stimulus periods. During the Stim2 period, we found that the left-SI activation started to differ significantly between two groups of trials (21 versus 26-29 Hz) within the first 100 ms and this difference was sustained and enhanced thereafter (approximately 600 ms). When only correct responses from the above two groups were used, the difference was even higher at later latencies (approximately 650 ms). For one subject who had enough trials of same perception to different input frequencies, e.g. responded 21 Hz to Stim2 at 21 Hz (correct) and 26-29 Hz (error), we found the sustained difference only before 650 ms. Our results suggest that SI is involved with the analysis of an input frequency and related to perception and decision at different latencies.

A multitrial analysis for revealing significant corticocortical networks in magnetoencephalography and electroencephalography

We present an MEG/EEG framework to reveal statistically significant brain areas engaged in the same cognitive process across trials without resort to averaging procedures. The variability of neuronal responses is assumed to take place only in the reconstructed time series of cortical sources and not in their positions. This hypothesis allows the use of the surrogate data method to detect recurrently active brain areas across trials adjusted with any cortically constrained focal MEEG inverse solution. Results obtained from synthetic data show that considering several trials enhances the accuracy of the source localisation. We apply this approach on MEG data recorded during a simple visual stimulation. The considered stimulus is frequency tagged in order to reveal the neural network correlated to its perception using phase synchronisation analysis. The results show consistent patterns of distributed synchronous networks centred on occipital areas.

Single-trial variability in early visual neuromagnetic responses: an explorative study based on the regional activation contributing to the N70m peak

Cortical activity evoked by repeated identical sensory stimulation is extremely variable. The source of this variability is often assigned to "random ongoing background activity" which is considered to be irrelevant to the processing of the stimuli and can therefore be eliminated by ensemble averaging. In this work, we studied the single-trial variability in neuromagnetic responses elicited by circular checkerboard pattern stimuli with radii of 1.8 degrees, 3.7 degrees, and 4.5 degrees. For most of the MEG sensors over the occipital areas, the averaged signal showed a clear early (N70m) response following the stimulus onset and this response was modulated by the checkerboard size. A data-driven spatial filter was used to extract one of the many possible composite time courses of single-trial activity corresponding to the complex of N70m generators. Pattern analysis principles were then employed to analyze, classify, and handle the extracted temporal patterns. We explored whether these patterns correspond to distinct response modes, which could characterize the evoked response better than the averaged signal and over an extended range of latencies around N70m. A novel scheme for detecting and organizing the structure in single-trial recordings was utilized. This served as a basis for comparisons between runs with different checkerboard sizes and provided a causal interpretation of variability in terms of regional dynamics, including the relatively weak activation in primary visual cortex. At the level of single trial activity, the polymorphic response to a simple stimulus is generated by a coupling of polymodal areas and cooperative activity in striate and extrastriate areas. Our results suggest a state-dependent response with a wide range of characteristic time scales and indicate the ongoing activity as a marker of the responsiveness state.

The regularities of the discrete nature of multi-variability of EEG spectral patterns

The short-term structure of electroencephalogram (EEG) spectral transformations during different brain functional states (closed/opened eyes and memory task) was studied. It was shown that approximately 50% of spectral pattern (SP) types occur not more than 2-3 times per 149 analysis epochs in a 1-min EEG. The remaining 50% of SP types were the same for the different EEG channels, in all subjects and various brain functional states. Additionally, a high incidence of the neighboring SP types in strongly overlapping (by 80%) 2-s analysis epochs of the EEG was shown. The SP identified in a given epoch has only a limited predictive value on the SPs identified in the subsequent epochs. The incidence effect was restricted by the limited SP set and by a 50% reduction in the functionally active SPs, which resulted in a temporary stabilization of SPs in sequential combinations. The parameters of temporary stabilization of SPs were significantly different from 'random' EEG which provides evidence of the non-occasional character of stabilization of the main dynamic parameters of neuronal activity. Thus, the findings suggest that the multi-variability of neuronal nets is discrete in time, and limited by the dynamics of the short quasi-stable brain states.

Phase synchronization of the ongoing EEG and auditory EP generation

OBJECTIVE

We investigated the role of phase synchronization of the spontaneous electroencephalogram (EEG) in auditory evoked potential (EP) generation in a sample of healthy individuals.

METHODS

Auditory responses were obtained from 20 healthy subjects following a double stimulus paradigm, using two identical tone bursts (S1 and S2) separated by 0.5s. Single-trial auditory evoked potentials were decomposed into sinusoidal, exponentially decaying/increasing components using the piecewise Prony method (PPM). Pre- and post-stimulus phase histograms were compared to determine the degree of phase synchronization produced by auditory stimulation.

RESULTS

Analysis of single responses revealed that the S1 stimuli produced phase synchronization in the 2-8Hz frequency range, with little or no concomitant amplitude increase. A significantly reduced phase effect was seen in response to S2 stimuli.

CONCLUSIONS

Stimulus-induced phase synchronization of the ongoing EEG is a major mechanism for the generation of auditory EP components with a latency in the 50-250ms range.

SIGNIFICANCE

The fact that the EP components accessed here are generated through phase synchronization implies that the ensemble-averaged EP will not resemble the single trial response, and it would certainly be misleading to consider the single trial response as an amplitude-scaled version of the ensemble average.

Semantic geodesic maps: a unifying geometrical approach for studying the structure and dynamics of single trial evoked responses

OBJECTIVES

A general framework for identifying and describing structure in a given sample of evoked response single-trial signals (STs) is introduced. The approach is based on conceptually simple geometrical ideas and enables the convergence of pattern analysis and non-linear time series analysis.

METHODS

Classical steps for analyzing the STs by waveform are first employed and the ST-analysis is transferred to a multidimensional space, the feature space, the geometry of which is systematically studied via multidimensional scaling (MDS) techniques giving rise to semantic maps. The structure in the feature space characterizes the trial-to-trial variability and this is utilized to probe functional connectivity between two brain areas. The underlying dynamic process responsible for the emerged structure can be described by a multidimensional trajectory in the feature space. This in turn enables the detection of dynamical interareal coupling as similarity between the corresponding trajectories.

RESULTS AND CONCLUSIONS

The utility of semantic maps was demonstrated using magnetoencephalographic data from a simple auditory paradigm. The coupling of ongoing activity and evoked response is vividly demonstrated and contrasted with the apparent deflection from zero baseline that survives averaging. Prototypes are easily identified as the end points of distinct paths in the semantic map representation, and their neighborhood is populated by STs with distinct properties not only in the latencies where the evoked response is expected to be strong, but also and very significantly in the prestimulus period. Finally our results provide evidence for interhemispheric binding in the (4-8 Hz) range and dynamical coupling at faster time scales.