EEG changes induced by meditative practices: State and trait effects in healthy subjects and in patients with epilepsy.

The effect of meditation on brain activity has been the topic of many studies in healthy subjects and in patients suffering from chronic diseases. These effects are either explored during meditation practice (state effects) or as a longer-term result of meditation training during the resting-state (trait). The topic of this article is to first review these findings by focusing on electroencephalography (EEG) changes in healthy subjects with or without experience in meditation. Modifications in EEG baseline rhythms, functional connectivity and advanced nonlinear parameters are discussed in regard to feasibility in clinical applications. Secondly, we provide a state-of-the-art of studies that proposed meditative practices as a complementary therapy in patients with epilepsy, in whom anxiety and depressive symptoms are prevalent. In these studies, the effects of standardized meditation programs including elements of traditional meditation practices such as mindfulness, loving-kindness and compassion are explored both at the level of psychological functioning and on the occurrence of seizures. Lastly, preliminary results are given regarding our ongoing study, the aim of which is to quantify the effects of a mindfulness self-compassion (MSC) practice on interictal and ictal epileptic activity. Feasibility, difficulties, and prospects of this study are discussed.

SISSY: An efficient and automatic algorithm for the analysis of EEG sources based on structured sparsity.

Over the past decades, a multitude of different brain source imaging algorithms have been developed to identify the neural generators underlying the surface electroencephalography measurements. While most of these techniques focus on determining the source positions, only a small number of recently developed algorithms provides an indication of the spatial extent of the distributed sources. In a recent comparison of brain source imaging approaches, the VB-SCCD algorithm has been shown to be one of the most promising algorithms among these methods. However, this technique suffers from several problems: it leads to amplitude-biased source estimates, it has difficulties in separating close sources, and it has a high computational complexity due to its implementation using second order cone programming. To overcome these problems, we propose to include an additional regularization term that imposes sparsity in the original source domain and to solve the resulting optimization problem using the alternating direction method of multipliers. Furthermore, we show that the algorithm yields more robust solutions by taking into account the temporal structure of the data. We also propose a new method to automatically threshold the estimated source distribution, which permits to delineate the active brain regions. The new algorithm, called Source Imaging based on Structured Sparsity (SISSY), is analyzed by means of realistic computer simulations and is validated on the clinical data of four patients.

Complex patterns of spatially extended generators of epileptic activity: Comparison of source localization methods cMEM and 4-ExSo-MUSIC on high resolution EEG and MEG data.

Electric Source Imaging (ESI) and Magnetic Source Imaging (MSI) of EEG and MEG signals are widely used to determine the origin of interictal epileptic discharges during the pre-surgical evaluation of patients with epilepsy. Epileptic discharges are detectable on EEG/MEG scalp recordings only when associated with a spatially extended cortical generator of several square centimeters, therefore it is essential to assess the ability of source localization methods to recover such spatial extent. In this study we evaluated two source localization methods that have been developed for localizing spatially extended sources using EEG/MEG data: coherent Maximum Entropy on the Mean (cMEM) and 4th order Extended Source Multiple Signal Classification (4-ExSo-MUSIC). In order to propose a fair comparison of the performances of the two methods in MEG versus EEG, this study considered realistic simulations of simultaneous EEG/MEG acquisitions taking into account an equivalent number of channels in EEG (257 electrodes) and MEG (275 sensors), involving a biophysical computational neural mass model of neuronal discharges and realistically shaped head models. cMEM and 4-ExSo-MUSIC were evaluated for their sensitivity to localize complex patterns of epileptic discharges which includes (a) different locations and spatial extents of multiple synchronous sources, and (b) propagation patterns exhibited by epileptic discharges. Performance of the source localization methods was assessed using a detection accuracy index (Area Under receiver operating characteristic Curve, AUC) and a Spatial Dispersion (SD) metric. Finally, we also presented two examples illustrating the performance of cMEM and 4-ExSo-MUSIC on clinical data recorded using high resolution EEG and MEG. When simulating single sources at different locations, both 4-ExSo-MUSIC and cMEM exhibited excellent performance (median AUC significantly larger than 0.8 for EEG and MEG), whereas, only for EEG, 4-ExSo-MUSIC showed significantly larger AUC values than cMEM. On the other hand, cMEM showed significantly lower SD values than 4-ExSo-MUSIC for both EEG and MEG. When assessing the impact of the source spatial extent, both methods provided consistent and reliable detection accuracy for a wide range of source spatial extents (source sizes ranging from 3 to 20cm2 for MEG and 3 to 30cm2 for EEG). For both EEG and MEG, 4-ExSo-MUSIC localized single source of large signal-to-noise ratio better than cMEM. In the presence of two synchronous sources, cMEM was able to distinguish well the two sources (their location and spatial extent), while 4-ExSo-MUSIC only retrieved one of them. cMEM was able to detect the spatio-temporal propagation patterns of two synchronous activities while 4-ExSo-MUSIC favored the strongest source activity. Overall, in the context of localizing sources of epileptic discharges from EEG and MEG data, 4-ExSo-MUSIC and cMEM were found accurately sensitive to the location and spatial extent of the sources, with some complementarities. Therefore, they are both eligible for application on clinical data.

EEG extended source localization: tensor-based vs. conventional methods.

The localization of brain sources based on EEG measurements is a topic that has attracted a lot of attention in the last decades and many different source localization algorithms have been proposed. However, their performance is limited in the case of several simultaneously active brain regions and low signal-to-noise ratios. To overcome these problems, tensor-based preprocessing can be applied, which consists in constructing a space-time-frequency (STF) or space-time-wave-vector (STWV) tensor and decomposing it using the Canonical Polyadic (CP) decomposition. In this paper, we present a new algorithm for the accurate localization of extended sources based on the results of the tensor decomposition. Furthermore, we conduct a detailed study of the tensor-based preprocessing methods, including an analysis of their theoretical foundation, their computational complexity, and their performance for realistic simulated data in comparison to conventional source localization algorithms such as sLORETA, cortical LORETA (cLORETA), and 4-ExSo-MUSIC. Our objective consists, on the one hand, in demonstrating the gain in performance that can be achieved by tensor-based preprocessing, and, on the other hand, in pointing out the limits and drawbacks of this method. Finally, we validate the STF and STWV techniques on real measurements to demonstrate their usefulness for practical applications.

Recording of fast activity at the onset of partial seizures: depth EEG vs. scalp EEG.

Rapid discharges (25-80 Hz), a characteristic EEG pattern often recorded at seizure onset in partial epilepsies, are often considered as electrophysiological signatures of the epileptogenic zone. While the recording of rapid discharges from intracranial electrodes has long been established, their observation from the scalp is challenging. The prevailing view is that rapid discharges cannot be seen clearly (or at all) in scalp EEG because they have low signal-to-noise ratio. To date, however, no studies have investigated the 'observability' of rapid discharges, i.e. under what conditions and to what extent they can be visible in recorded EEG signals. Here, we used a model-based approach to examine the impact of several factors (distance to sources, skull conductivity, source area, source synchrony, and background activity) on the observability of rapid discharges in simultaneously simulated depth EEG and scalp EEG signals. In our simulations, the rapid discharge was clearly present in depth EEG signals but mostly almost not visible in scalp EEG signals. We identified some of the factors that may limit the observability of the rapid discharge on the scalp. Notably, surrounding background activity was found to be the most critical factor. The findings are discussed in relation to the presurgical evaluation of epilepsy.

The neuronal sources of EEG: modeling of simultaneous scalp and intracerebral recordings in epilepsy.

In many applications which make use of EEG to investigate brain functions, a central question is often to relate the recorded signals to the spatio-temporal organization of the underlying neuronal sources of activity. A modeling attempt to quantitatively investigate this imperfectly known relationship is reported. The proposed plausible model of EEG generation relies on an accurate representation of the neuronal sources of activity. It combines both an anatomically realistic description of the spatial features of the sources (convoluted dipole layer) and a physiologically relevant description of their temporal activities (coupled neuronal populations). The model was used in the particular context of epileptiform activity (interictal spikes) to interpret simultaneously generated scalp and intracerebral EEG. Its integrative properties allowed for the bridging between source-related parameters (spatial extent, location, synchronization) and the properties of resulting EEG signals (amplitude of spikes, amplitude gradient along intracerebral electrodes, topography over scalp electrodes). The sensitivity of both recording modalities to source-related parameters was also studied. The model confirmed that the cortical area involved in interictal spikes is rather large. Its relative location with respect to recording electrodes was found to strongly influence the properties of EEG signals as the source geometry is a critical parameter. The influence, on simulated signals, of the synchronization degree between neuronal populations within the epileptic source was also investigated. The model revealed that intracerebral EEG can reflect epileptic activities corresponding to weak synchronization between neuronal populations of the epileptic patch. These results, as well as the limitations of the model, are discussed.

Interictal brain 5-HT1A receptors binding in migraine without aura: a 18F-MPPF-PET study.

In this study we aimed to assess the brain distribution of 5-HT(1A) receptors in migraine patients without aura. Ten female migraine patients and 24 female healthy volunteers underwent magnetic resonance imaging and positron emission tomography using a radioligand antagonist of 5-HT(1A) receptors [4-(2'-methoxyphenyl)-1-[2'-(N-2-pirydynyl)-p-fluorobenzamido]-ethylpiperazine ((18)F-MPPF)]. A simplified reference tissue model was used to generate parametric images of 5-HT(1A) receptor binding potential (BP) values. Statistical Parametrical Mapping (SPM) analysis showed increased MPPF BP in posterior cortical areas and hippocampi bilaterally in patients compared with controls. Region of interest (ROI) analysis showed a non-significant trend in favour of a BP increase patients in cortical regions identified by the SPM analysis except in hippocampi, left parietal areas and raphe nuclei. During the interictal period of migraine patients without aura, the increase of MPPF BP in posterior cortical and limbic areas could reflect an increase in receptor density or a decrease of endogenous serotonin, which could explain their altered cortical excitability.

Computational modeling of high frequency oscillations recorded with clinical intracranial macroelectrodes.

High Frequency Oscillations (HFOs) are a potential biomarker of epileptogenic regions. They have been extensively investigated in terms of automatic detection, classification and feature extraction. However, the mechanisms governing the generation of HFOs as well as the observability conditions on clinical intracranial macroelectrodes remain elusive. In this paper, we propose a novel physiologically-relevant macroscopic model for accurate simulation of HFOs as invasively recorded in epileptic patients. This model accounts for both the temporal and spatial properties of the cortical patch at the origin of epileptiform activity. Indeed, neuronal populations are combined with a 3D geometrical representation to simulate an extended epileptic source. Then, by solving the forward problem, the contributions of neuronal population signals are projected onto intracerebral electrode contacts. The obtained signals are qualitatively and quantitatively compared to real HFOs, and a relationship is drawn between macroscopic model parameters such as synchronization and spatial extent on the one hand, and HFO features such as the wave and fast ripple (200-600 Hz) components, on the other hand.

Identification of epileptogenic networks from dense EEG: A model-based study.

Epilepsy is a network disease. Identifying the epileptogenic networks from noninvasive recordings is a challenging issue. In this context, M/EEG source connectivity is a promising tool to identify brain networks with high temporal and spatial resolution. In this paper, we analyze the impact of the two main factors that intervene in EEG source connectivity processing: i) the algorithm used to solve the EEG inverse problem and ii) the method used to measure the functional connectivity. We evaluate three inverse solutions algorithms (dSPM, wMNE and cMEM) and three connectivity measures (r(2), h(2) and MI) on data simulated from a combined biophysical/physiological model able to generate realistic interictal epileptic spikes reflected in scalp EEG. The performance criterion used here is the similarity between the network identified by each of the inverse/connectivity combination and the original network used in the source model. Results show that the choice of the combination has a high impact on the identified network. Results suggest also that nonlinear methods (nonlinear correlation coefficient and mutual information) for measuring the connectivity are more efficient than the linear one (the cross correlation coefficient). The dSPM as inverse solution shows the lowest performance compared to cMEM and wMNE.

Feasibility of blind source separation methods for the denoising of dense-array EEG.

High-density electroencephalographic recordings have recently been proved to bring useful information during the pre-surgical evaluation of patients suffering from drug-resistant epilepsy. However, these recordings can be particularly obscured by noise and artifacts. This paper focuses on the denoising of dense-array EEG data (e.g. 257 channels) contaminated with muscle artifacts. In this context, we compared the efficiency of several Independent Component Analysis (ICA) methods, namely SOBI, SOBIrob, PICA, InfoMax, two different implementations of FastICA, COM2, ERICA, and SIMBEC, as well as that of Canonical Correlation Analysis (CCA). We evaluated the performance using the Normalized Mean Square Error (NMSE) criterion and calculated the numerical complexity. Quantitative results obtained on realistic simulated data show that some of the ICA methods as well as CCA can properly remove muscular artifacts from dense-array EEG.

Localization of spatially distributed brain sources after a tensor-based preprocessing of interictal epileptic EEG data.

This paper addresses the localization of spatially distributed sources from interictal epileptic electroencephalographic data after a tensor-based preprocessing. Justifying the Canonical Polyadic (CP) model of the space-time-frequency and space-time-wave-vector tensors is not an easy task when two or more extended sources have to be localized. On the other hand, the occurrence of several amplitude modulated spikes originating from the same epileptic region can be used to build a space-time-spike tensor from the EEG data. While the CP model of this tensor appears more justified, the exact computation of its loading matrices can be limited by the presence of highly correlated sources or/and a strong background noise. An efficient extended source localization scheme after the tensor-based preprocessing has then to be set up. Different strategies are thus investigated and compared on realistic simulated data: the "disk algorithm" using a precomputed dictionary of circular patches, a standardized Tikhonov regularization and a fused LASSO scheme.

Apparent asynchrony between interictal electric and magnetic spikes.

We recorded simultaneous multichannel electroencephalogram (EEG) and magnetoencephalogram (MEG) in four children with partial epilepsy. Sources of averaged spikes were modelled with current dipoles. Of 10 spike averages obtained, three peaked simultaneously in MEG and EEG, and in seven averages, the MEG peak preceded the main EEG peak by 9-40 ms. A small positive early EEG signal coincided with the MEG peak in six asynchronous spikes. The simultaneous MEG and EEG spikes originated within 5-23 mm, while sources of asynchronous peaks were 12-67 mm apart. We conclude that non-identical neurone currents underlie the MEG and EEG signals, and emphasize the importance of modelling early phases of EEG spikes when localizing interictal epileptic zones.

Activation of human mesial cortex during somatosensory target detection task.

We recorded somatosensory evoked fields (SEFs) from 10 healthy subjects to ulnar and median nerve stimuli presented at random intervals of 2.4-21.6 s. The subjects either counted the stimuli or ignored them by reading a book. The stimuli activated in both conditions the contralateral SI cortex, the ipsi- and contralateral SII cortices, and the posterior parietal cortex (PPC), in line with earlier observations. In addition, a novel response was observed in nine subjects at 120-160 ms. It was clearly enhanced by attention and was generated in the mesial cortex of the paracentral lobule, close to the end of the central sulcus.

Dipole modeling of scalp electroencephalogram epileptic discharges: correlation with intracerebral fields.

OBJECTIVE: In order to evaluate the feasibility of modeling seizures and the reliability of dipole models, we compared source localizations of scalp seizures with the distribution of simultaneous intracerebral electroencephalogram (SEEG). METHODS: In a first session, only scalp electroencephalogram (EEG) was recorded from 15 patients. We averaged the first detectable ictal activity in two consecutive segments of stable topography and morphology. Spatio-temporal dipole sources were estimated for each segment and projected on 3D-magnetic resonance images. In a second session, SEEG was recorded simultaneously with control scalp electrodes, allowing the identification of ictal patterns similar to those submitted to dipole modeling. RESULTS: Ictal discharges could be analyzed in only 6 of 15 patients. In the remaining 9, scalp discharges were undetectable or non-reproducible in 6, and solutions were unstable despite an apparently stable discharge in 3. In the 6 patients successfully modeled, dipoles were found in regions where SEEG discharges were present. However, when intracerebral discharges were very focal, there was no corresponding scalp activity. When intracerebral signals were maximal in the mesial temporal regions at the seizure onset, only lateral neocortical dipoles were found. When discharges reached the frontal lobes, we could identify lateral and mesial frontal sources. CONCLUSIONS: In most seizures, it was not possible to obtain satisfactory dipole models, probably a reflection of the high noise level or widespread generators. When modeling was possible, our results suggested that mesial temporal seizure discharges did not contribute to scalp EEG activity. This activity appears to reflect signals synchronized and distributed over the lateral temporal or frontal neocortex, as well as signals generated in mesial frontal areas.

Reliability of dipole models of epileptic spikes.

OBJECTIVE: In order to validate dipole-modeling results, we compared dipole localizations with the distribution of intracerebral potentials occurring simultaneously with scalp EEG paroxysms. METHODS: Firstly, scalp EEGs were recorded from 11 patients. Dipole sources were estimated on averaged spikes and projected on 3D-MRIs. Secondly, stereoelectroencephalography (SEEG) was recorded from implanted electrodes with direct identification onto MRI. Simultaneously with SEEG, control scalp electrodes were pasted where spikes peaked during the first session. SEEG was averaged, triggered by the main peak of scalp spikes. RESULTS: SEEG activity during scalp spikes always involved several contacts. In 13 of 14 spikes, maximal fields occurred in neocortical regions. In 4 of 5 cases where intracerebral activity was simple, spikes could be modeled by one source. In all cases where intracerebral activity was complex, spikes had to be modeled by several sources. The main dipole source was 11 +/- 4.2 mm from the SEEG contact showing the maximal intracerebral potential. Early and late dipole localization and SEEG fields were concordant in two thirds of cases. CONCLUSION: Results indicate that in our group of patients scalp spikes reflect activity in large neocortical areas and never activity limited to mesial structures. Dipole locations and time activation were confirmed most often and were more reliable for sources representing the main negative component than for early or late sources.

Separation of spikes from background by independent component analysis with dipole modeling and comparison to intracranial recording.

OBJECTIVE: Epileptiform discharges can be objectively separated from the EEG background by independent component analysis (ICA) into the discharge's waveform and its spatial distribution. The correspondence between ICA components, including epileptiform transients extracted from the scalp EEG and intracranial epileptic fields, was investigated. METHODS: In 11 spike patterns from 8 patients, the scalp EEG data were decomposed by ICA. The corresponding averaged intracranial data were compared with the extracted epileptic components regarding the number of source patterns and source locations estimated from ICA maps. RESULTS: Clear epileptic components could be separated in 10/11 spike patterns. The number of epileptic components was identical to the number of intracranial field peaks in 7 spike patterns with simple intracranial fields, and was less in the remaining 3 patterns with complex intracranial peaks. The distance between the contact of the maximal intracranial field and the dipole location estimated by the single dipole model for the clearest epileptic component ranged from 4.7 to 31.9 mm. CONCLUSIONS: The number of epileptic ICA components largely matched the number of intracranial field patterns, and the dipole location estimated for the map of the clearest epileptic component was generally correct. This establishes the validity of epileptic components extracted by ICA from the scalp background.

Relationships between the epileptic focus and hand area in central epilepsy: combining dipole models and anatomical landmarks.

OBJECT: When considering resection of epileptic generators near the central sulcus, it is essential to define the spatial relationship between the epileptic generator and the primary sensorimotor hand area. In this study, the authors assessed the accuracy of dipole modeling of electroencephalographic spikes and median nerve somatosensory evoked potentials (SSEPs) in defining this relationship preoperatively and noninvasively. METHODS: Epileptic spikes and SSEPs in patients with focal central area epilepsy were represented by dipole models coregistered onto global magnetic resonance images. In patients who underwent surgery, spike dipoles were also compared with findings of electrocorticography (ECoG) and with the resection area. To improve the accuracy of the dipole models, anatomical landmarks of the hand area were used to assess the error in SSEP dipole location, and this error measure was used to correct the location of spike dipoles. Five patients with central epilepsy were studied, three of whom underwent ECoG-guided surgical resections. The location of SSEP dipoles correlated well with anatomical landmarks of the primary sensory hand area. The relative position of the spike and SSEP dipoles correlated well with the patients' ictal symptoms, ECoG findings, and the location of the epileptic focus (as defined by the resection cavity in patients who became seizure free postoperatively). Corrected spike dipoles were located even closer to the resection cavity. CONCLUSIONS: The calculation of the relative location of spike and SSEP dipoles is a simple noninvasive method of determining the relationship between the primary hand area and an epileptic focus in the central area. The spatial resolution of this technique can be further improved using easily identifiable anatomical landmarks.

Simplified projection of EEG dipole sources onto human brain anatomy.

This study was aimed at determining an easy way to project dipole modelling results onto brain anatomy. This simplified projection is based on the estimation of the mean location of the centre of the dipole sphere according to internal brain landmarks. The mean values for the centre location were calculated from ten epileptic patients. To define the axes of the dipole model frame on the patient's magnetic resonance image (MRI), markers were pasted at some electrode positions during the acquisition. An estimation was then made of the mean position of the model centre from the bicommissural line (anterior commissure-posterior commissure [AC-PC]), and a simple transformation to pass from the model cartesian coordinates to the anatomical correlates either in the subject MRI or in the Talairach atlas. These data were then tested in four additional subjects in whom no markers had been placed during the MRI acquisition. On average, the horizontal plane of the sphere model was pitched up 1.9 degrees +/- 1.8 only with respect to the AC-PC horizontal plane, which allowed the projection of dipoles directly onto the Talairach atlas, without pitch. The mean sphere centre was located 7.4 +/- 4.2 mm above the bicommissural line, and 8.2 +/- 1 mm in front of the posterior commissure. In the four additional subjects, projections on MRI and atlas indicated the same anatomical regions and showed high congruence with the physiology or the pathology. This simplified way we report herein has proved to give reliable results. We believe that this method will be useful as a first approximation to project dipole coordinated onto MRI data; moreover, when MRI is unavailable, our results show that dipole modelling results can be superimposed onto atlas slices provided that they are represented according to the AC-PC plane.

Dipole modeling of interictal and ictal EEG and MEG paroxysms.

Dipole modeling procedures can be used to statistically estimate the location and the orientation of intracerebral sources of electroencephalographic (EEG) and magnetoencephalographic (MEG) signals. These methods have been applied to interictal spikes for more than 20 years and suggest that interictal paroxysms might be generated by a network of cortical structures rather than by a focal area. In this review we address the questions of (1) the spatial extend of this network in different types of epilepsies, (2) the spatial relationship between this network and other structural of functional abnormalities as assessed by Magnetic Resonance Imaging (MRI) or Positron Emission Tomography (PET), and (3) the reliability of dipole sources of interictal and ictal paroxysms. Dipole modeling results suggest that, in temporal lobe epilepsies, both neocortical and mesio-temporal structures are involved during interictal spikes; frontal lobe epilepsies are often characterized by more complex source distributions, that, in general, involve a large area and bilateral frontal structures. In addition, dipole modeling results are also found in close agreement with MRI data in cases where focal dysplasia or heterotopia are diagnosed. Nevertheless, in most other cases, sources of interictal spikes and MRI lesions, though overlapping in space, are not totally congruent. The best concordance between sources of interictal spikes and glucose hypometabolism on PET data is usually found for temporal lobe epilepsies. Most often, intracranial and intracerebral recordings validate both the localization and the time activation of interictal spike dipoles. However, results obtained for ictal discharges are less reliable, which therefore addresses the usefulness of dipole modeling procedures in assessing sources of ictal discharges. In conclusion, dipole modeling results can rarely be used in planning a selective surgery without invasive recordings. However, the studies reviewed in this paper strongly suggest that their analysis, in combination with other non-invasive data, might be useful to better delineate the epileptogenic zone, and help the implantation of intracerebral electrodes.

Activation of a distributed somatosensory cortical network in the human brain: a dipole modelling study of magnetic fields evoked by median nerve stimulation. Part II: Effects of stimulus rate, attention and stimulus detection.

In this study we used a repeated measures design and univariate analysis of variance to study the respective effects of ISI, spatial attention and stimulus detection on the strengths of the sources previously identified by modelling SEFs during the 200 ms following mentally counted left median nerve stimuli delivered at long and random ISIs (Part I). We compared the SEF source strengths in response to frequent and rare stimuli, both in detection and ignoring conditions. This permitted us to establish a hierarchy in the effects of ISI, attention and stimulus detection on the activation of the cortical network of SEF sources distributed in SI and posterior parietal cortex contralateral to stimulation, and in the parietal operculum (SII) and premotor frontal cortex of both hemispheres. In all experimental conditions the SI and parietal opercular sources were the most active. All sources were more active in response to stimuli delivered at long and random ISIs and the frontal sources were activated only in this condition of stimulation. Driving the subject's attention toward the side stimulated had no detectable effect on the activity of SEF sources at short ISI. At long ISIs mental counting of the stimuli increased the responses of all sources except SI. These results suggest that activation of frontal sources during mental counting could reflect a working memory process, and that of posterior parietal sources a spatial attention effect detectable only at long ISIs.