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EEG signals are widely used to estimate brain circuits associated with specific tasks and cognitive processes. The testing of connectivity estimators is still an open issue because of the lack of a ground-truth in real data. Existing solutions such as the generation of simulated data based on a manually imposed connectivity pattern or mass oscillators can model only a few real cases with limited number of signals and spectral properties that do not reflect those of real brain activity. Furthermore, the generation of time series reproducing non-ideal and non-stationary ground-truth models is still missing. In this work, we present the SEED-G toolbox for the generation of pseudo-EEG data with imposed connectivity patterns, overcoming the existing limitations and enabling control of several parameters for data simulation according to the user’s needs. We first described the toolbox including guidelines for its correct use and then we tested its performances showing how, in a wide range of conditions, datasets composed by up to 60 time series were successfully generated in less than 5 s and with spectral features similar to real data. Then, SEED-G is employed for studying the effect of inter-trial variability Partial Directed Coherence (PDC) estimates, confirming its robustness.

Resting state connectivity has been increasingly studied to investigate the effects of aging on the brain. A reduced organization in the communication between brain areas was demonstrated by combining a variety of different imaging technologies (fMRI, EEG, and MEG) and graph theory. In this paper, we propose a methodology to get new insights into resting state connectivity and its variations with age, by combining advanced techniques of effective connectivity estimation, graph theoretical approach, and classification by SVM method. We analyzed high density EEG signals recorded at rest from 71 healthy subjects (age: 20–63 years). Weighted and directed connectivity was computed by means of Partial Directed Coherence based on a General Linear Kalman filter approach. To keep the information collected by the estimator, weighted and directed graph indices were extracted from the resulting networks. A relation between brain network properties and age of the subject was found, indicating a tendency of the network to randomly organize increasing with age. This result is also confirmed dividing the whole population into two subgroups according to the age (young and middle-aged adults): significant differences exist in terms of network organization measures. Classification of the subjects by means of such indices returns an accuracy greater than 80%.

In this study we illustrate a methodology able to follow and study concurrent and simultaneous brain processes during cooperation between individuals, with non invasive EEG methodologies. We collected data from fourteen pairs of subjects while they were playing a card game with EEG. Data collection was made simultaneously on all the subjects during the card game. An extension of the Granger-causality approach allows us to estimate the functional connection between signals estimated from different Regions of Interest (ROIs) in different brains during the analyzed task. Finally, with the use of graph theory, we contrast the functional connectivity patterns of the two players belonging to the same team. Statistically significant functional connectivities were obtained from signals estimated in the ROIs modeling the anterior cingulate cortex (ACC) and the prefrontal areas described by the Brodmann areas 8 with the signals estimated in all the other modelled cortical areas. Results presented suggested the existence of Granger-sense causal relations between the EEG activity estimated in the prefrontal areas 8 and 9/46 of one player with the EEG activity estimated in the ACC of their companion. We illustrated the feasibility of functional connectivity methodology on the EEG hyperscannings performed on a group of subjects. These functional connectivity estimated from the couple of brains could suggest, in statistical and mathematical terms, the modelled cortical areas that are correlated in Granger-sense during the solution of a particular task. EEG hyperscannings could be used to investigate experimental paradigms where the knowledge of the simultaneous interactions between the subjects have a value.

Objective The maintenance of a motor cortical program in the temporal domain is relevant to current neuroinformatic efforts to use non-invasive EEG signals to control neuroprosthetic devices designed to restore natural movements of paralyzed body parts. Here we use an advance neuroelectrical imaging approach to examine the motor cortical responsiveness in human tetraplegia. Methods High resolution-electroencephalographic (EEG) recordings were performed in five subjects with tetraplegia due to chronic, complete spinal cord injuries (SCIs) while they attempted self-generated movements of a plegic body part (foot), and in five healthy subjects executing simple foot movements. Results Self-generated movement attempts induced significant EEG sources of activity in a set of motor-related areas (including the primary motor area, MI) similar to what observed during the preparatory stages of movement execution (control subjects). Functional connectivity showed a preferential interaction between the “non-primary” motor areas and the putative MI foot site, as estimated for both motor execution and attempt. Under this latter condition however, it could be observed an “enlargement” of the functional network by including the left superior parietal cortex. Conclusions Our findings indicate the existence of a functional circuit subserving the attempted motion in SCI subjects that encompasses a set of areas known to play a role in motor execution, yet reveals differences in the functional interaction between these areas. Significance The understanding of changes in the motor circuitry is relevant to current neuroinformatic efforts to use non-invasive EEG signals to control neuroprosthetic devices designed to benefit paralyzed persons.

The present work intends to evaluate the dynamics of the cerebral networks during the preparation and the execution of the foot movement. In order to achieve this objective, we have used mathematical tools capable of estimating the cortical activity via high-resolution EEG techniques. Afterwards we estimated, the instantaneous relationships occurring among the time-series of sixteen regions of interest (ROIs) in the Alpha (7-12 Hz) and Beta (13-29 Hz) band through the adaptive multivariate autoregressive models. Eventually, we evaluated the weighted-topology of the cerebral networks by calculating some theoretical graph indexes. The results show that the main structural changes are encoded in the highest spectral contents (Beta band). In particular, during the execution of the foot movement the cingulate motor areas (CM) work as network "hubs" presenting a large amount of outgoing links to the other ROIs. Moreover, the connectivity pattern changes its structure according to the different temporal stages of the task. In particular, the communication between the ROIs reaches its highest level of efficiency during the preparation of the foot movement, as revealed by the "small-world" property of the network, which is characterized by the presence of abundant clustering connections combined with short average distances between the cortical areas.

Static hemodynamic or neuroelectric images of brain regions activated during particular tasks do not convey the information of how these regions communicate to each other. Cortical connectivity estimation aims at describing these interactions as connectivity patterns which hold the direction and strength of the information flow between cortical areas. In this study, we attempted to estimate the causality between distributed cortical systems during a movement volition task in preparation for execution of simple movements by a group of normal healthy subjects and by a group of Spinal Cord Injured (SCI) patients. To estimate the causality between the spatial distributed patterns of cortical activity in the frequency domain, we applied a series of processing steps on the recorded EEG data. From the high-resolution EEG recordings we estimated the cortical waveforms for the regions of interest (ROIs), each representing a selected sensor group population. The solutions of the linear inverse problem returned a series of cortical waveforms for each ROI considered and for each trial analyzed. For each subject, the cortical waveforms were then subjected to Independent Component Analysis (ICA) pre-processing. The independent components obtained by the application of the ThinICA algorithm were further processed by a Partial Directed Coherence algorithm, in order to extract the causality between spatial cortical patterns of the estimated data. The source-target cortical dependencies found in the group of normal subjects were relatively similar in all frequency bands analyzed. For the normal subjects we observed a common source pattern in an ensemble of cortical areas including the right parietal and right lip primary motor areas and bilaterally the primary foot and posterior SMA areas. The target of this cortical network, in the Granger-sense of causality, was shown to be a smaller network composed mostly by the primary foot motor areas and the posterior SMA bilaterally. In the case of the SCI population, both the source and the target cortical patterns had larger sizes than in the normal population. The source cortical areas included always the primary foot and lip motor areas, often bilaterally. In addition, the right parietal area and the bilateral premotor area 6 were also involved. Again, the patterns remained substantially stable across the different frequency bands analyzed. The target cortical patterns observed in the SCI population had larger extensions when compared to the normal ones, since in most cases they involved the bilateral activation of the primary foot movement areas as well as the SMA, the primary lip areas and the parietal cortical areas.

The problem of the definition and evaluation of brain connectivity has become a central one in neuroscience during recent years as a way to understand the organization and interaction of cortical areas during the execution of cognitive or motor tasks. In this article, we propose the use of the directed transfer function (DTF) method on cortical signals estimated from high-resolution electroencephalography (EEG) recordings. An application of the proposed technique to the estimation of the cortical connectivity pattern in normal subjects and in one spinal-cord-injured patient is also provided

We explored the cortical dynamics during movements of an unaffected body part in tetraplegic subjects with chronic spinal cord injury (SCI). The aims were to find out whether the intact movements were associated with a physiological time‐varying pattern of activity in the motor‐related cortical areas and whether the primary motor area (MI) activation followed a somatotopic distribution. Event‐related potentials to self‐initiated lip movements were analyzed by means of cortical source imaging of EEG recorded from seven tetraplegic subjects and seven control subjects. Regions of interest (ROIs) were selected on individual MRI and the time‐varying electrophysiologic activity (cortical current density, CCD) was estimated on these ROIs and subjected to across‐subject analysis. A significant, bilateral movement‐related pattern of MI activation was detected during motor task execution in SCI patients as well as in controls. The site of local maxima activation displayed a symmetrical discrete distribution within MI, consistently with a putative somatotopic lip representation, in all the subjects. The supplementary motor area proper (SMAp) was always coactivated with MI and coactivation was characterized by a time course with typical premotion and motion phases over both motor areas. A clear‐cut temporal delay between the SMAp and MI activation did not occur either in SCI patients or in controls. These findings obtained with noninvasive neuroelectrical source imaging document that in chronic SCI subjects “executive” motor areas are engaged with a preserved temporal and spatial pattern during preparation and execution of intact movements. Hum Brain Mapp, 2005. © 2005 Wiley‐Liss, Inc.

Today, the concept of brain connectivity plays a central role in the neuroscience. While functional connectivity is defined as the temporal coherence between the activities of different brain areas, the effective connectivity is defined as the simplest brain circuit that would produce the same temporal relationship as observed experimentally between cortical sites. The most used method to estimate effective connectivity in neuroscience is the structural equation modeling (SEM), typically used on data related to the brain hemodynamic behavior. However, the use of hemodynamic measures limits the temporal resolution on which the brain process can be followed. The present research proposes the use of the SEM approach on the cortical waveforms estimated from the high-resolution EEG data, which exhibits a good spatial resolution and a higher temporal resolution than hemodynamic measures. We performed a simulation study, in which different main factors were systematically manipulated in the generation of test signals, and the errors in the estimated connectivity were evaluated by the analysis of variance (ANOVA). Such factors were the signal-to-noise ratio and the duration of the simulated cortical activity. Since SEM technique is based on the use of a model formulated on the basis of anatomical and physiological constraints, different experimental conditions were analyzed, in order to evaluate the effect of errors made in the a priori model formulation on its performances. The feasibility of the proposed approach has been shown in a human study using high-resolution EEG recordings related to finger tapping movements.

Neuroimaging cognitive study of aging requires simple tasks ensuring a high rate of correct performances even in stressful neurophysiological settings. Here two simple delayed choice reaction time tasks were used to unveil event-related desynchronization (ERD) of theta (4-6 Hz) and alpha (6-12 Hz) electroencephalographic rhythms across normal aging. In the first condition, a cue stimulus (one bit) was memorized along a brief delay period (3.5-5.5 s). The explicit demand was visuo-spatial, but the retention could be also based on phonological and somatomotor coding. In the second condition, the cue stimulus remained available along the delay period. Correct performances were higher than 95% in both groups and tasks, although they were significantly better in young than elderly subjects (P < 0.03). During the delay period, theta and alpha ERD accompanying correct responses were recognized in the two groups, the alpha ERD being stronger and prolonged during the memory than non-memory task. On the other hand, the fronto-parietal theta and parietal alpha ERD were stronger in young than elderly subjects during both tasks. Notably, the frontal alpha ERD was negligible in elderly subjects. In conclusion, the present simple tasks unveiled in elderly compared to young subjects (i) a weaker involvement of (para)hippocampal-cortical circuits as revealed by theta ERD and (ii) a weaker involvement of "executive" thalamo-cortical circuits as revealed by frontal alpha ERD. These effects might worsen behavioral performances to the simple cognitive tasks with age. The present protocol is promising for the neuroimaging study of pathological aging.

One of the main limitations commonly encountered when dealing with the estimation of brain connectivity is the difficulty to perform a statistical assessment of significant changes in brain networks at a single-subject level. This is mainly due to the lack of information about the distribution of the connectivity estimators at different conditions. While group analysis is commonly adopted to perform a statistical comparison between conditions, it may impose major limitations when dealing with the heterogeneity expressed by a given clinical condition in patients. This holds true particularly for stroke when seeking for quantitative measurements of the efficacy of any rehabilitative intervention promoting recovery of function. The need is then evident of an assessment which may account for individual pathological network configuration associated with different level of patients' response to treatment; such network configuration is highly related to the effect that a given brain lesion has on neural networks. In this study we propose a resampling-based approach to the assessment of statistically significant changes in cortical connectivity networks at a single subject level. First, we provide the results of a simulation study testing the performances of the proposed approach under different conditions. Then, to show the sensitivity of the method, we describe its application to electroencephalographic (EEG) data recorded from two post-stroke patients who showed different clinical recovery after a rehabilitative intervention.

In recent years, network analyses have been used to evaluate brain reorganization following stroke. However, many studies have often focused on single topological scales, leading to an incomplete model of how focal brain lesions affect multiple network properties simultaneously and how changes on smaller scales influence those on larger scales. In an EEG-based experiment on the performance of hand motor imagery (MI) in 20 patients with unilateral stroke, we observed that the anatomic lesion affects the functional brain network on multiple levels. In the beta (13-30 Hz) frequency band, the MI of the affected hand (Ahand) elicited a significantly lower smallworldness and local efficiency (Eloc) versus the unaffected hand (Uhand). Notably, the abnormal reduction in Eloc significantly depended on the increase in interhemispheric connectivity, which was in turn determined primarily by the rise in regional connectivity in the parieto-occipital sites of the affected hemisphere. Further, in contrast to the Uhand MI, in which significantly high connectivity was observed for the contralateral sensorimotor regions of the unaffected hemisphere, the regions that increased in connection during the Ahand MI lay in the frontal and parietal regions of the contralaterally affected hemisphere. Finally, the overall sensorimotor function of our patients, as measured by Fugl-Meyer Assessment (FMA) index, was significantly predicted by the connectivity of their affected hemisphere. These results increase our understanding of stroke-induced alterations in functional brain networks., Neuroimage, accepted manuscript (unedited version) available online 19-June-2013

International audience; Graph theory is a powerful mathematical tool recently introduced in neuroscience field for quantitatively describing the main properties of investigated connectivity networks. Despite the technical advancements provided in the last few years, further investigations are needed for overcoming actual limitations in the field. In fact, the absence of a common procedure currently applied for the extraction of the adjacency matrix from a connectivity pattern has been leading to low consistency and reliability of ghaph indexes among the investigated population. In this paper we proposed a new approach for adjacency matrix extraction based on a statistical threshold as valid alternative to empirical approaches, extensively used in Neuroscience field (i.e. fixing the edge density). In particular we performed a simulation study for investigating the effects of the two different extraction approaches on the topological properties of the investigated networks. In particular, the comparison was performed on two different datasets, one composed by uncorrelated random signals (null-model) and the other one by signals acquired on a mannequin head used as a phantom (EEG null-model). The results highlighted the importance to use a statistical threshold for the adjacency matrix extraction in order to describe the real existing topological properties of the investigated networks. The use of an empirical threshold led to an erroneous definition of small-world properties for the considered connectivity patterns.

A brain-computer interface (BCI) provides a non-muscular communication channel to people with and without disabilities. BCI devices consist of hardware and software. BCI hardware records signals from the brain, either invasively or non-invasively, using a series of device components. BCI software then translates these signals into device output commands and provides feedback. One may categorize different types of BCI applications into the following four categories: basic research, clinical/translational research, consumer products, and emerging applications. These four categories use BCI hardware and software, but have different sets of requirements. For example, while basic research needs to explore a wide range of system configurations, and thus requires a wide range of hardware and software capabilities, applications in the other three categories may be designed for relatively narrow purposes and thus may only need a very limited subset of capabilities. This paper summarizes technical aspects for each of these four categories of BCI applications. The results indicate that BCI technology is in transition from isolated demonstrations to systematic research and commercial development. This process requires several multidisciplinary efforts, including the development of better integrated and more robust BCI hardware and software, the definition of standardized interfaces, and the development of certification, dissemination and reimbursement procedures.

Brain Hyperscanning, i.e. the simultaneous recording of the cerebral activity of different human subjects involved in interaction tasks, is a very recent field of Neuroscience aiming at understanding the cerebral processes generating and generated by social interactions. This approach allows the observation and modeling of the neural signature specifically dependent on the interaction between subjects, and, even more interestingly, of the functional links existing between the activities in the brains of the subjects interacting together. In this EEG hyperscanning study we explored the functional hyperconnectivity between the activity in different scalp sites of couples of Civil Aviation Pilots during different phases of a flight reproduced in a flight simulator. Results shown a dense network of connections between the two brains in the takeoff and landing phases, when the cooperation between them is maximal, in contrast with phases during which the activity of the two pilots was independent, when no or quite few links were shown. These results confirms that the study of the brain connectivity between the activity simultaneously acquired in human brains during interaction tasks can provide important information about the neural basis of the “spirit of the group”.

The main purpose of electroencephalography (EEG)-based brain-computer interface (BCI) technology is to provide an alternative channel to support communication and control when motor pathways are interrupted. Despite the considerable amount of research focused on the improvement of EEG signal detection and translation into output commands, little is known about how learning to operate a BCI device may affect brain plasticity. This study investigated if and how sensorimotor rhythm-based BCI training would induce persistent functional changes in motor cortex, as assessed with transcranial magnetic stimulation (TMS) and high-density EEG. Motor imagery (MI)-based BCI training in naïve participants led to a significant increase in motor cortical excitability, as revealed by post-training TMS mapping of the hand muscle's cortical representation; peak amplitude and volume of the motor evoked potentials recorded from the opponens pollicis muscle were significantly higher only in those subjects who develop a MI strategy based on imagination of hand grasping to successfully control a computer cursor. Furthermore, analysis of the functional brain networks constructed using a connectivity matrix between scalp electrodes revealed a significant decrease in the global efficiency index for the higher-beta frequency range (22-29 Hz), indicating that the brain network changes its topology with practice of hand grasping MI. Our findings build the neurophysiological basis for the use of non-invasive BCI technology for monitoring and guidance of motor imagery-dependent brain plasticity and thus may render BCI a viable tool for post-stroke rehabilitation.

Brain–computer interface (BCI) systems allow people with severe motor disabilities to communicate and interact with the external world. The P300 potential is one of the most used control signals for EEG-based BCIs. Classic P300-based BCIs work in a synchronous mode; the synchronous control assumes that the user is constantly attending to the stimulation, and the number of stimulation sequences is fixed a priori. This issue is an obstacle for the use of these systems in everyday life; users will be engaged in a continuous control state, their distractions will cause misclassification and the speed of selection will not take into account users' current psychophysical condition. An efficient BCI system should be able to understand the user's intentions from the ongoing EEG instead. Also, it has to refrain from making a selection when the user is engaged in a different activity and it should increase or decrease its speed of selection depending on the current user's state. We addressed these issues by introducing an asynchronous BCI and tested its capabilities for effective environmental monitoring, involving 11 volunteers in three recording sessions. Results show that this BCI system can increase the bit rate during control periods while the system is proved to be very efficient in avoiding false negatives when the users are engaged in other tasks.

Here we present an overview of some published papers of interest for the marketing research employing electroencephalogram (EEG) and magnetoencephalogram (MEG) methods. The interest for these methodologies relies in their high-temporal resolution as opposed to the investigation of such problem with the functional Magnetic Resonance Imaging (fMRI) methodology, also largely used in the marketing research. In addition, EEG and MEG technologies have greatly improved their spatial resolution in the last decades with the introduction of advanced signal processing methodologies. By presenting data gathered through MEG and high resolution EEG we will show which kind of information it is possible to gather with these methodologies while the persons are watching marketing relevant stimuli. Such information will be related to the memorization and pleasantness related to such stimuli. We noted that temporal and frequency patterns of brain signals are able to provide possible descriptors conveying information about the cognitive and emotional processes in subjects observing commercial advertisements. These information could be unobtainable through common tools used in standard marketing research. We also show an example of how an EEG methodology could be used to analyze cultural differences between fruition of video commercials of carbonated beverages in Western and Eastern countries.

In this study we measured simultaneously by EEG hyperscannings the neuroelectric activity in 6 couples of subjects during the performance of the "Chicken's game", derived from game theory. The simultaneous recording of the EEG in couples of interacting subjects allows to observe and model directly the neural signature of human interactions in order to understand the cerebral processes generating and generated by social cooperation or competition. Results suggested that the one of the most consistently activated structure in this particular social interaction paradigm is the left orbitofrontal cortex. Connectivity results also showed a significant involvement of the orbitofrontal regions of both hemispheres across the observed population. Taken together, results confirms that the study of the brain activities in humans during social interactions can take benefit from the simultaneous acquisition of brain activity during such interaction.

The aim of this research is to analyze the changes in the EEG frontal activity during the observation of commercial videoclips. In particular, we aimed to investigate the existence of EEG frontal asymmetries in the distribution of the signals’ power spectra related to experienced pleasantness of the video, as explicitly rated by the eleven experimental subjects investigated. In the analyzed population, maps of Power spectral density (PSD) showed an asymmetrical increase of theta and alpha activity related to the observation of pleasant (unpleasant) advertisements in the left (right) hemisphere. A correlation analysis revealed that the increase of PSD at left frontal sites is negatively correlated with the degree of pleasantness perceived. Conversely, the de-synchronization of left alpha frontal activity is positively correlated with judgments of high pleasantness. Moreover, our data presented an increase of PSD related to the observation of unpleasant commercials, which resulted higher with respect to the one elicited by pleasant advertisements.

In the present study, we propose a theoretical graph procedure to investigate the communication redundancy in brain networks. By taking into account all the possible paths between pairs of cortical regions, this method captures the network redundancy i.e. a critical resource of the brain enhancing the resilience to neural damages and dysfunctions. As an example for its potential, we apply this procedure to the cortical networks estimated from high-resolution EEG signals in a group of spinal cord injured patients during the attempt of the foot movement. Preliminary results suggest that in the high spectral contents the effects due to the spinal trauma affect the expected redundancy attitude by suppressing mainly the longer alternative pathways between the cortical regions.

In this paper different linear and non linear methodologies for the estimation of cortical connectivity from neuroelectric and hemodynamic measurements have been reviewed and applied on common data set in order to highlight similarities and differences in the obtained results. Different effective and functional connectivity methods have been applied to motor and cognitive data sets, including Structural Equation Modeling (SEM), Directed Transfer Function (DTF), Partial Directed Coherence (PDC) and direct Directed Transfer Function (dDTF). Comparisons will be made between these estimators in order to understand if, for a same dataset, effective and functional connectivity estimators (namely the SEM and the DTF) could return the same cortical connectivity patterns. An application of a non linear method (Phase Synchronization Index, PSI) to similar executed and imagined movements was also reviewed. Connectivity patterns estimated with the use of only the neuroelectric information or the information from the multimodal integration of neuroelectric and hemodynamic data were also compared. Results suggests that the estimation of the cortical connectivity patterns performed with the employed linear methods (SEM, DTF, PDC, dDTF) or with the non linear method (PSI) on movement related potentials returned essentially the same cortical network. Differences in cortical connectivity between the patterns estimated with the use of multimodal integration were noted when compared to those estimated by using only the neuroelectric data.

We investigated brain activity during the observation of TV commercials by tracking the cortical activity and the functional connectivity changes in normal subjects. The aim was to elucidate if the TV commercials that were remembered by the subjects several days after their first observation elicited particular brain activity and connectivity compared with those generated during the observation of TV commercials that were quickly forgotten. High-resolution electroencephalogram (EEG) recordings were performed in a group of healthy subjects and the cortical activity during the observation of TV commercials was evaluated in several regions of interest coincident with the Brodmann areas (BAs). The patterns of cortical connectivity were obtained in the four principal frequency bands, Theta (3-7 Hz), Alpha (8-12 Hz), Beta (13-30 Hz), Gamma (30-40 Hz) and the directed influences between any given pair of the estimated cortical signals were evaluated by use of a multivariate spectral technique known as partial directed coherence. The topology of the cortical networks has been identified with tools derived from graph theory. Results suggest that the cortical activity and connectivity elicited by the viewing of the TV commercials that were remembered by the experimental subjects are markedly different from the brain activity elicited during the observation of the TV commercials that were forgotten. In particular, during the observation of the TV commercials that were remembered, the amount of cortical spectral activity from the frontal areas (BA 8 and 9) and from the parietal areas (BA 5, 7, and 40) is higher compared with the activity elicited by the observation of TV commercials that were forgotten. In addition, network analysis suggests a clear role of the parietal areas as a target of the incoming flow of information from all the other parts of the cortex during the observation of TV commercials that have been remembered. The techniques presented here shed new light on all the cortical networks and their behavior during the memorization of TV commercials. Such techniques could also be relevant in neuroeconomics and neuromarketing for the investigation of the neural substrates subserving other decision-making and recognition tasks.

The aim of this study was to elucidate if the TV commercials that were remembered by the subjects after their observation within a documentary elicited particular brain activity when compared to the activity generated during the observation of TV commercials that were forgotten. High resolution EEG recordings were performed in a group of 10 healthy subjects with the steady state somatosensory evoked potentials (SSSEPs) technique, in which a series of light electrical stimulation at the left wrist were delivered at the frequency of 20 Hz. The brain activity was indexed by the phase delay of the EEG spectral responses at 20 Hz with respect to the stimulus delivering and evaluated at the scalp level as well as at the cortical surface using several regions of interest coincident with the Brodmann areas (BAs). Results suggest that the cerebral processes involved during the observation of TV commercials that were remembered by the population examined (RMB dataset) are generated by the posterior parietal cortices and the prefrontal areas, rather bilaterally. These results are compatible with previously results obtained in literature by using MEG and fMRI devices during similar experimental tasks. High resolution EEG is able to summarize, with the use of SSSEPs methodologies, the behavior of the estimated cortical networks subserving the proposed memory tasks. It is likely that such tool could play a role in the next future for the investigation of the neural substrates of the human behavior in decision-making and recognition tasks.

The extraction of the salient characteristics from brain connectivity patterns is an open challenging topic since often the estimated cerebral networks have a relative large size and complex structure. Since a graph is a mathematical representation of a network, which is essentially reduced to nodes and connections between them, the use of a theoretical graph approach would extract significant information from the functional brain networks estimated through different neuroimaging techniques. The present work intends to support the development of the ldquobrain network analysis:rdquo a mathematical tool consisting in a body of indexes based on the graph theory able to improve the comprehension of the complex interactions within the brain. In the present work, we applied for demonstrative purpose some graph indexes to the time-varying networks estimated from a set of high-resolution EEG data in a group of healthy subjects during the performance of a motor task. The comparison with a random benchmark allowed extracting the significant properties of the estimated networks in the representative Alpha (7-12 Hz) band. Altogether, our findings aim at proving how the brain network analysis could reveal important information about the time-frequency dynamics of the functional cortical networks.

The directed transfer function (DTF) and the partial directed coherence (PDC) are frequency-domain estimators that are able to describe interactions between cortical areas in terms of the concept of Granger causality. However, the classical estimation of these methods is based on the multivariate autoregressive modelling (MVAR) of time series, which requires the stationarity of the signals. In this way, transient pathways of information transfer remains hidden. The objective of this study is to test a time-varying multivariate method for the estimation of rapidly changing connectivity relationships between cortical areas of the human brain, based on DTF/PDC and on the use of adaptive MVAR modelling (AMVAR) and to apply it to a set of real high resolution EEG data. This approach will allow the observation of rapidly changing influences between the cortical areas during the execution of a task. The simulation results indicated that time-varying DTF and PDC are able to estimate correctly the imposed connectivity patterns under reasonable operative conditions of signal-to-noise ratio (SNR) ad number of trials. An SNR of five and a number of trials of at least 20 provide a good accuracy in the estimation. After testing the method by the simulation study, we provide an application to the cortical estimations obtained from high resolution EEG data recorded from a group of healthy subject during a combined foot-lips movement and present the time-varying connectivity patterns resulting from the application of both DTF and PDC. Two different cortical networks were detected with the proposed methods, one constant across the task and the other evolving during the preparation of the joint movement.

High-resolution electroencephalographic (HREEG) techniques allow estimation of cortical activity based on non-invasive scalp potential measurements, using appropriate models of volume conduction and of neuroelectrical sources. In this study we propose an application of this body of technologies, originally developed to obtain functional images of the brain's electrical activity, in the context of brain-computer interfaces (BCI). Our working hypothesis predicted that, since HREEG pre-processing removes spatial correlation introduced by current conduction in the head structures, by providing the BCI with waveforms that are mostly due to the unmixed activity of a small cortical region, a more reliable classification would be obtained, at least when the activity to detect has a limited generator, which is the case in motor related tasks. HREEG techniques employed in this study rely on (i) individual head models derived from anatomical magnetic resonance images, (ii) distributed source model, composed of a layer of current dipoles, geometrically constrained to the cortical mantle, (iii) depth-weighted minimum L(2)-norm constraint and Tikhonov regularization for linear inverse problem solution and (iv) estimation of electrical activity in cortical regions of interest corresponding to relevant Brodmann areas. Six subjects were trained to learn self modulation of sensorimotor EEG rhythms, related to the imagination of limb movements. Off-line EEG data was used to estimate waveforms of cortical activity (cortical current density, CCD) on selected regions of interest. CCD waveforms were fed into the BCI computational pipeline as an alternative to raw EEG signals; spectral features are evaluated through statistical tests (r(2) analysis), to quantify their reliability for BCI control. These results are compared, within subjects, to analogous results obtained without HREEG techniques. The processing procedure was designed in such a way that computations could be split into a setup phase (which includes most of the computational burden) and the actual EEG processing phase, which was limited to a single matrix multiplication. This separation allowed to make the procedure suitable for on-line utilization, and a pilot experiment was performed. Results show that lateralization of electrical activity, which is expected to be contralateral to the imagined movement, is more evident on the estimated CCDs than in the scalp potentials. CCDs produce a pattern of relevant spectral features that is more spatially focused, and has a higher statistical significance (EEG: 0.20+/-0.114 S.D.; CCD: 0.55+/-0.16 S.D.; p=10(-5)). A pilot experiment showed that a trained subject could utilize voluntary modulation of estimated CCDs for accurate (eight targets) on-line control of a cursor. This study showed that it is practically feasible to utilize HREEG techniques for on-line operation of a BCI system; off-line analysis suggests that accuracy of BCI control is enhanced by the proposed method.

In the present work, we estimated the functional networks in the frequency domain from a set of high-resolution EEG data in a group of healthy subjects during the showing of commercial spots within a neutral documentary. Then, we evaluated the differences in the cortical network associated with later remembered and not-remembered commercials by calculating the global- E(g) and local-efficiency E(l) indexes. During the visualization of the video-clips that will be forgotten (FRG), the cortical network exhibited high values of global- and local-efficiency, reflecting a small-world configuration. During the visualization of the video-clips that will be remembered (RMB), the same indexes appeared significantly lower. Such a difference seems not depending on the spectral content of the cortical activity. This result shows how the network communication efficiency would be affected by the presence of attentional and semantic processes that are behind a successful memory encoding in a lifelike situation.

Objective This work intends to evaluate the functional characteristics of the cerebral network during the successful memory encoding of TV commercials. Methods We estimated the functional networks in the frequency domain from a set of high-resolution EEG data in a group of healthy subjects during the showing of commercial spots within a neutral documentary. We evaluated the differences in the cortical network associated with later remembered and not-remembered commercials by calculating the global- E g and local-efficiency E l indexes. Results Successful encoding of TV spots significantly affects the functional communication among cortical areas irrespectively of the frequency band. During the visualization of the video-clips that will be forgotten (FRG), the cortical network exhibited high values of global- and local-efficiency, reflecting a small-world configuration. During the visualization of the video-clips that will be remembered (RMB), the same indexes appeared significantly lower. This difference was especially evident for E l in the alpha band and for E g in the beta and gamma bands. Conclusions The presence of attentional and semantic processes during the RMB condition leads to a significant reduction of the network communication efficiency. Significance Such a reduction could represent a predictive measure of accurate recalls of TV spots.

In the last decade, the possibility to noninvasively estimate cortical activity has been highlighted by the application of the techniques known as high resolution EEG. These techniques include a subject's multi-compartment head model (scalp, skull, dura mater, cortex) constructed from individual magnetic resonance images, multi-dipole source model, and regularized linear inverse source estimates of cortical current density. The aim of this paper is to demonstrate that the use of cortical activity estimated from noninvasive EEG recordings of motor imagery is useful in the context of a Brain Computer Interface as compared with others scalp spatial filters usually used on-line. Keywords— Linear inverse source estimate, High resolution EEG, Brain computer interface.

A major limitation of the approaches used in most of the studies performed so far for the characterization of the brain responses during social interaction is that only one of the participating brains is measured each time. The ldquointeractionrdquo between cooperating, competing or communicating brains is thus not measured directly, but inferred by independent observations aggregated by cognitive models and assumptions that link behavior and neural activation. In this paper, we use the simultaneous neuroelectric recording of several subjects engaged in cooperative games (EEG hyperscanning). This EEG hyperscanning allow us to observe and model directly the neural signature of human interactions in order to understand the cerebral processes generating and generated by social cooperation or competition. We used a paradigm called Prisoner's Dilemma derived from the game theory. Results collected in a population of 22 subjects suggested that the most consistently activated structure in social interaction paradigms is the medial prefrontal cortex, which is found to be active in all the conflict situations analyzed. The role of the anterior cingulated cortex (ACC) assumes a main character being a discriminant factor for the ldquodefectrdquo attitude of the entire population examined. This observation is compatible with the role that the Theory of Mind assigns to the ACC.

In this work, a novel approach is proposed in order to capture relevant features related to the structure and organization of the functional brain networks estimated in the time-frequency domain. To achieve this, we used a cascade of computational tools able to estimate first the electrical activity of the cortical surface by using high resolution EEG techniques. Then, on the cortical signals from different regions of interests we estimated the time-varying functional connectivity patterns by means of the adaptive partial directed coherence. Such time-varying connectivity estimation returns a series of causality patterns evolving during the examined task which can be summarized and interpreted with the aid of mathematical indexes based on the graph theory. The combination of all these methods is demonstrated on a set of high resolution EEG data recorded from a healthy subject performing a simple foot movement.

The aim of this work is to characterize quantitatively the performance of a body of techniques in the frequency domain for the estimation of cortical connectivity from high‐resolution EEG recordings in different operative conditions commonly encountered in practice. Connectivity pattern estimators investigated are the Directed Transfer Function (DTF), its modification known as direct DTF (dDTF) and the Partial Directed Coherence (PDC). Predefined patterns of cortical connectivity were simulated and then retrieved by the application of the DTF, dDTF, and PDC methods. Signal‐to‐noise ratio (SNR) and length (LENGTH) of EEG epochs were studied as factors affecting the reconstruction of the imposed connectivity patterns. Reconstruction quality and error rate in estimated connectivity patterns were evaluated by means of some indexes of quality for the reconstructed connectivity pattern. The error functions were statistically analyzed with analysis of variance (ANOVA). The whole methodology was then applied to high‐resolution EEG data recorded during the well‐known Stroop paradigm. Simulations indicated that all three methods correctly estimated the simulated connectivity patterns under reasonable conditions. However, performance of the methods differed somewhat as a function of SNR and LENGTH factors. The methods were generally equivalent when applied to the Stroop data. In general, the amount of available EEG affected the accuracy of connectivity pattern estimations. Analysis of 27 s of nonconsecutive recordings with an SNR of 3 or more ensured that the connectivity pattern could be accurately recovered with an error below 7% for the PDC and 5% for the DTF. In conclusion, functional connectivity patterns of cortical activity can be effectively estimated under general conditions met in most EEG recordings by combining high‐resolution EEG techniques, linear inverse estimation of the cortical activity, and frequency domain multivariate methods such as PDC, DTF, and dDTF. Hum. Brain Mapp, 2007. © 2006 Wiley‐Liss, Inc.

Over the last 20 years, a body of techniques known as high resolution EEG has allowed precise estimation of cortical activity from non-invasive EEG measurements. The availability of cortical waveforms from non-invasive EEG recordings allows to have not only the level of activation within a single region of interest (ROI) during a particular task, but also to estimate the causal relationships among activities of several cortical regions. However, interpreting resulting connectivity patterns is still an open issue, due to the difficulty to provide an objective measure of their properties across different subjects or groups. A novel approach addressed to solve this difficulty consists in manipulating these functional brain networks as graph objects for which a large body of indexes and tools are available in literature and already tested for complex networks at different levels of scale (Social, WorldWide-Web and Proteomics). In the present work, we would like to show the suitability of such approach, showing results obtained comparing separately two groups of subjects during the same motor task and two different motor tasks performed by the same group. In the first experiment two groups of subjects (healthy and spinal cord injured patients) were compared when they moved and attempted to move simultaneously their right foot and lips, respectively. The contrast between the foot-lips movement and the simple foot movement was addressed in the second experiment for the population of the healthy subjects. For both the experiments, the main question is whether the "architecture" of the functional connectivity networks obtained could show properties that are different in the two groups or in the two tasks. All the functional connectivity networks gathered in the two experiments showed ordered properties and significant differences from "random" networks having the same characteristic sizes. The proposed approach, based on the use of indexes derived from graph theory, can apply to cerebral connectivity patterns estimated not only from the EEG signals but also from different brain imaging methods.

The aim of this paper is to test a technique called partial directed coherence (PDC) and its modification (squared PDC; sPDC) for the estimation of human cortical connectivity by means of simulation study, in which both PDC and sPDC were studied by analysis of variance. The statistical analysis performed returned that both PDC and sPDC are able to estimate correctly the imposed connectivity patterns when data exhibit a signal-to-noise ratio of at least 3 and a length of at least 27 s of nonconsecutive recordings at 250 Hz of sampling rate, equivalent, more generally, to 6750 data samples.

The problem of the definition and evaluation of brain connectivity has become a central one in neuroscience during the latest years, as a way to understand the organization and interaction of cortical areas during the execution of cognitive or motor tasks. In this paper we propose the use of the DTF method on cortical signals estimated from high resolution EEG recordings. An application of the proposed technique to the estimation of cortical connectivity pattern in normal subjects and in one spinal cord injured patient is also provided.

The original article to which this Erratum refers was published in the March 2005 issue of Human Brain MappingHuman Brain Mapping (2005) 24(3) 184–192.

Magnetoencephalography (MEG) includes fast and comfortable recording procedures very suitable for the neurophysiological study of cognitive functions in aged people. In this exploratory MEG study in normal young adults, we tested whether very simple short‐term memory (STM) demands induce visible changes in amplitude and latency of surface α rhythms. Two delayed response tasks were used. In the STM condition, a simple cue stimulus (one bit) was memorized along a brief delay period (3.5–5.5 s). In the control (no short‐term memory; NSTM) condition, the cue stimulus remained available along the delay period. To make extremely simple the tasks, the explicit demand was visuospatial but the retention could be also based on phonological and somatomotor coding. Compared to the control condition, the amplitude of the α 1 (6–8 Hz) ERD decreased in the left hemisphere, whereas the amplitude of the α 2 (8–10 Hz) and α 3 (10–12 Hz) event‐related desynchronization (ERD) increased in right and left parietal areas, respectively. Furthermore, the latency of the α ERD peak was slightly but significantly (P < 0.05) later in STM compared to control condition. In conclusion, whole‐head MEG technology and very simple STM demands revealed significant changes of human neuromagnetic α rhythms in normal young adults. Hum. Brain Mapp 24:184–192, 2005. © 2004 Wiley‐Liss, Inc.

Previous simulation studies have stressed the importance of the multimodal integration of electroencephalography (EEG) and magnetoencephalography (MEG) data in the estimation of cortical current density. In such studies, no systematic variations of the signal‐to‐noise ratio (SNR) and of the number of sensors were explicitly taken into account in the estimation process. We investigated effects of variable SNR and number of sensors on the accuracy of current density estimate by using multimodal EEG and MEG data. This was done by using as the dependent variable both the correlation coefficient (CC) and the relative error (RE) between imposed and estimated waveforms at the level of cortical region of interests (ROI). A realistic head and cortical surface model was used. Factors used in the simulations were: (1) the SNR of the simulated scalp data (with seven levels: infinite, 30, 20, 10, 5, 3, 1); (2) the particular inverse operator used to estimate the cortical source activity from the simulated scalp data (INVERSE, with two levels, including minimum norm and weighted minimum norm); and (3) the number of EEG or MEG sensors employed in the analysis (SENSORS, with three levels: 128, 61, 29 for EEG and 153, 61, or 38 in MEG). Analysis of variance demonstrated that all the considered factors significantly affect the CC and the RE indexes. Combined EEG–MEG data produced statistically significant lower RE and higher CC in source current density reconstructions compared to that estimated by the EEG and MEG data considered separately. These observations hold for the range of SNR values presented by the analyzed data. The superiority of current density estimation by multimodal integration of EEG and MEG was not due to differences in number of sensors between unimodal (EEG, MEG) and combined (EEG–MEG) inverse estimates. In fact, the current density estimate relative to the EEG–MEG multimodal integration involved 61 EEG plus 63 MEG sensors, whereas estimations carried out with the single modalities alone involved 128 sensors for EEG and 153 sensors for MEG. The results of the simulations also suggest that the use of simultaneous 29 EEG sensors during the MEG measurements carried out with full sensor arrangements (153 sensors) returned an accuracy of the cortical source estimate statistically similar to that obtained by combining 64 EEG and 153 MEG sensors. Hum. Brain Mapp. 22:54–64, 2004. © 2004 Wiley‐Liss, Inc.

Nowadays, several types of brain imaging device are available to provide images of the functional activity of the cerebral cortex based on hemodynamic, metabolic, or electromagnetic measurements. However, static images of brain regions activated during particular tasks do not convey the information of how these regions communicate with each other. In this study, advanced methods for the estimation of cortical connectivity from combined high-resolution electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data are presented. These methods include a subject's multicompartment head model (scalp, skull, dura mater, cortex) constructed from individual magnetic resonance images, multidipole source model, and regularized linear inverse source estimates of cortical current density. Determination of the priors in the resolution of the linear inverse problem was performed with the use of information from the hemodynamic responses of the cortical areas as revealed by block-designed (strength of activated voxels) fMRI. We estimate functional cortical connectivity by computing the directed transfer function (DTF) on the estimated cortical current density waveforms in regions of interest (ROIs) on the modeled cortical mantle. The proposed method was able to unveil the direction of the information flow between the cortical regions of interest, as it is directional in nature. Furthermore, this method allows to detect changes in the time course of information flow between cortical regions in different frequency bands. The reliability of these techniques was further demonstrated by elaboration of high-resolution EEG and fMRI signals collected during visually triggered finger movements in four healthy subjects. Connectivity patterns estimated for this task reveal an involvement of right parietal and bilateral premotor and prefrontal cortical areas. This cortical region involvement resembles that revealed in previous studies where visually triggered finger movements were analyzed with the use of separate EEG or fMRI measurements.

Many recent neuroimaging studies of episodic memory have indicated an asymmetry in prefrontal involvement, with the left prefrontal cortex more involved than the right in encoding, the right more than the left in retrieval (hemispheric encoding and retrieval asymmetry, or HERA model). In this electroencephalographic (EEG) high-resolution study, we studied brain rhythmicity during a visual episodic memory (recognition) task. The theta (4-6 Hz), alpha (6-12 Hz) and gamma (28-48 Hz) oscillations were investigated during a visuospatial long-term episodic memory task including an encoding (ENC) and retrieval (RET) phases. During the ENC phase, 25 figures representing interiors of buildings ("indoor") were randomly intermingled with 25 figures representing landscapes ("landscapes"). Subject's response was given at left ("indoor") or right ("landscapes") mouse button. During the RET phase (1 h later), 25 figures representing previously presented "indoor" pictures ("tests") were randomly intermingled with 25 figures representing novel "indoor" ("distractors"). Again, a mouse response was required. Theta and alpha EEG results showed no change of frontal rhythmicity. In contrast, the HERA prediction of asymmetry was fitted only by EEG gamma responses, but only in the posterior parietal areas. The ENC phase was associated with gamma EEG oscillations over left parietal cortex. Afterward, the RET phase was associated with gamma EEG oscillations predominantly over right parietal cortex. The predicted HERA asymmetry was thus observed in an unexpected location. This discrepancy may be due to the differential sensitivity of neuroimaging methods to selected components of cognitive processing. The strict relation between gamma response and perception suggests that retrieval processes of long-term memory deeply impinged upon sensory representation of the stored material.

In the present high-resolution electroencephalographic (EEG) study, an omitted-stimulus paradigm induced a strong expectancy for a predictable painful stimulation (nonpainful in the control condition). During the expectancy of pain, concurrent cognitive demands were superimposed. The aim was to investigate the effects on primary sensorimotor and central midline areas of the competition among concurrent attentional processes related to cognition and pain expectancy, as indexed by behavioral performance and EEG data. A main issue was whether cognitive performance decreases, due to a re-allocation of attentional resources on primary sensorimotor and midline areas for the anticipation of pain. Behavioral results showed no differences in the cognitive (working memory) performance during the expectancy of nonpainful versus painful stimulations. In parallel, anticipatory event-related potentials (ERPs) were negligible in line with a low emotional reactivity/alertness as revealed by heart rate deceleration (HRD), skin conductance response (SCR), and low-band (6-10Hz) alpha EEG oscillations. In contrast, high-band alpha EEG oscillations (10-12Hz) over the contralateral primary sensorimotor cortex decreased more during the expectancy of painful compared to nonpainful stimuli, in line with an increased anticipatory preparation of the somatosensory channel. These findings provide further evidence on the fact that attentional processes at the basis of cognition can be defended by the anticipation of pain, at least when the incoming painful stimuli are repetitive and predictable. This happens even if the brain increases preparatory processes of the specific sensory channel to be targeted by the painful stimulus.

The authors delineated the time evolution of alpha event-related desynchronization over human frontal, parietal, and primary sensorimotor areas during the expectancy of a go/no-go task. The main issue under investigation was whether anticipatory processes impinged upon cortical areas in sequential or parallel mode. Compared with the control condition, in the experimental condition there was an Alpha 1 desynchronization over the central midline, an Alpha 2 desynchronization increasing over primary sensorimotor areas, and an Alpha 3 desynchronization increasing in parallel over bilateral primary sensorimotor areas. These processes had different temporal features. Results disclose an anticipatory activity of central midline areas and primary sensorimotor areas in both parallel and sequential modes. This reflects an adaptive, energy-consuming strategy rather than an economic waiting for the go stimulus.

In the present study, high-resolution electroencephalography techniques modelled the spatiotemporal pattern of human anticipatory cortical responses preceding expected galvanic painful stimuli (non-painful stimuli as a control). Do these responses reflect the activation of associative other than somatosensory systems? Anticipatory processes were probed by alpha oscillations (6-12 Hz) for the evaluation of thalamocortical channels and by negative event-related potentials for the evaluation of cortical excitability. Compared with the control condition, a progressive reduction of the alpha power was recognized over the primary somatosensory cortex from 2 s before the painful stimulation. In contrast, the anticipatory event-related potentials were negligible during the expectancy period. The results on the alpha power suggest that the expectancy of the painful stimulation specifically facilitated the somatosensory thalamocortical channel. Remarkably, the associative frontal-parietal areas were not involved, possibly due to the predictable and repetitive features of the painful stimulus. The present results also suggest that negative event-related potentials are modest preceding warned stimuli (even if painful) with a simple information content.

High‐resolution event‐related potentials (ERPs) were used to model the hemispherical representation of the transient cortical responses relating to the observation of movement during execution (right or left aimless finger extension). Subjects were seated in front of the observed person and looked at both their own and the observer's hand to receive similar visual feedback during the two conditions. In a visual control condition, a diode light moved at the observed person's hand. A first potential accompanying the movement execution peaked at about +110 msec over the contralateral somatomotor areas. It was followed by a potential (P300) peaking at about +350 msec over the central midline. In contrast, the potentials accompanying the movement observation peaked later over parietal‐occipital other than somatomotor areas (N200 peak, +200 msec; P300 peak, +400 msec). Notably, the N200 was maximum in left parietal area whereas the P300 was maximum in right parietal area regardless the side of the movement. They markedly differed by the potentials following the displacement of the diode light. These results suggest a rapid time evolution (∼200–400 msec) of the cortical responses characterizing the observation of aimless movements (as opposite to grasping or handling). The execution of these movements would mainly involve somatomotor cortical responses and would be scarcely founded on the visual feedback. In contrast, the observation of the same movements carried out by others would require dynamical responses of somatomotor and parietal‐occipital areas (especially of the right hemisphere), possibly for a stringent visuospatial analysis of the motor event. Hum. Brain Mapping 20:148–157, 2003. © 2003 Wiley‐Liss, Inc.

Previous simulation studies have stressed the importance of the use of fMRI priors in the estimation of cortical current density. However, no systematic variations of signal-to-noise ratio (SNR) and number of electrodes were explicitly taken into account in the estimation process. In this simulation study we considered the utility of including information as estimated from fMRI. This was done by using as the dependent variable both the correlation coefficient and the relative error between the imposed and the estimated waveforms at the level of cortical region of interests (ROI). A realistic head and cortical surface model was used. Factors used in the simulations were the different values of SNR of the scalp-generated data, the different inverse operators used to estimated the cortical source activity, the strengths of the fMRI priors in the fMRI-based inverse operators, and the number of scalp electrodes used in the analysis. Analysis of variance results suggested that all the considered factors significantly afflict the correlation and the relative error between the estimated and the simulated cortical activity. For the ROIs analyzed with simulated fMRI hot spots, it was observed that the best estimation of cortical source currents was performed with the inverse operators that used fMRI information. When the ROIs analyzed do not present fMRI hot spots, both standard (i.e., minimum norm) and fMRI-based inverse operators returned statistically equivalent correlation and relative error values.

In the masterpiece ‘The extraction of the stone of madness’ due to the famous Dutch painter Hieronymous Bosch (1450–1516) (see Fig. 1), a foolish man turns to a charlatan surgeon for his own release from illness. This theme is inspired by folk satire and by the hostility against the physicians’ guild at that time. In fact, in that age head surgery for the extraction of ‘stones’ from the brain in psychiatric disorders was relatively widespread. Five hundred years later, the invasive approach to the knowledge of brain diseases has providentially changed thanks to modern brain imaging techniques, such as—for instance— the computerized tomographic scan (CT), the electroencephalography (EEG), the magnetoencephalography (MEG) or the functional magnetic resonance imaging (fMRI). Only the last 3 techniques supply fruitful information on the brain function and can be integrated with CT or MRI methods able to offer a detailed topography of brain structures. In the following paragraphs, we will briefly recall some technical advances of these last 10 years in the improvement of our capability to extract ‘information’ about the brain activity in a non-invasive way. Then, we will comment on the article by Zhang et al. (2003) where a cortical imaging technique is used to estimate potential distributions over a simple model of dura mater in 5 epileptic pediatric patients before surgery. 2. The problems of conventional EEG recordings

Objective : To investigate the relationship between the electroencephalographic (EEG) power spectra features obtained by quantitative EEG (qEEG) and the hemodynamic parameters detected by dynamic susceptibility contrast-enhanced MR imaging (DSC MRI) in patients with Alzheimer's disease (AD). Methods : Fourteen patients with probable AD and 15 elderly healthy controls were included in the study. All subjects underwent both EEG recording in a rest condition and perfusion MRI. Three EEG scalp areas were defined (anterior, central and posterior) and power spectra values were obtained from each scalp area. Relative values of temporoparietal and sensorimotor regional cerebral blood volume (rCBV) were measured bilaterally and successively averaged to obtain a total perfusion index. The brain atrophy index was calculated and used as a covariate to rCBV. Correlation analysis was performed between EEG variables and hemodynamic-morphological parameters. Results : qEEG power spectra of AD patients were characterized by an increase in mean relative power of theta (4–7.75 Hz) associated with a decrease in alpha (8–12.75 Hz) frequency bands with a topographic distribution over the central and posterior EEG scalp regions, when compared with controls; beta (13–31 Hz) frequency band also displayed a significant decrease over the anterior and posterior EEG scalp regions of AD patients with respect to controls. The DSC MRI revealed a bilateral reduction in the temporoparietal and sensorimotor rCBV with respect to controls. Correlation analysis showed that the total level of hypoperfusion selectively correlates with the EEG power spectra in theta and alpha frequency bands distributed over anterior/central and central region, respectively. Within AD patients, the lower the level of hypoperfusion, the higher the content of EEG power spectra in theta frequency band, and the lower the level of hypoperfusion, the lower the content of EEG power spectra in alpha band. Conclusions : The combined qEEG and DSC MRI technology unveiled a selective correlation between neurophysiological and hemodynamical patterns in AD patients. Further investigations will ascertain the relevance of this multi-modal approach in the heterogeneous clinical context of AD.

In the present high-resolution electroencephalographic (EEG) study, we computed event-related desynchronization and synchronization (ERD/ERS) of alpha (about 10 Hz) and beta (about 20 Hz) rhythms in association with the execution (with visual feedback) and observation of brisk unilateral right and left aimless finger movements. A first scope was to test the topographical "functional equivalence" of cortical rhythmicity related to movement execution and observation, which would represent an ideal cortical observation/execution matching system. A second scope was to evaluate the hypothesis of a left or right hemisphere prevalence of the cortical rhythmicity related to the movement observation compared to the movement execution. EEG (128 electrodes) was recorded in 10 healthy right-handed volunteers. Surface Laplacian estimation spatially enhanced EEG data over a MRI-constraint head model. Under both conditions, ERD peaked during the movement execution or observation and was replaced by a ERS "rebound" or "recovery," which peaked during the postevent period. Topographical results are in favor of a "functional equivalence" (i.e., similar ERD/ERS values in magnitude and timing) of alpha and beta rhythmicity in central scalp regions overlying premotor/primary sensorimotor cortex. On the contrary, the functional equivalence of alpha rhythmicity was negligible (i.e., different ERD/ERS values in magnitude and timing) in parietal-occipital scalp regions overlying posterior parietal and parieto-occipital cortex, which could be the neural substrate to distinguish among the own motor intensions and others' aimless movements (i.e., visuomotor transformation integrated with sensorimotor, postural, and kinematics representations). Finally, the pattern of hemispherical cortical rhythmicity did not support a "simple concentration" of movement observation functions in the left or right hemisphere.