The neural mechanisms of audiotactile binding depend on asynchrony.

Asynchrony is a critical cue informing the brain whether sensory signals are caused by a common source and should be integrated or segregated. This psychophysics-electroencephalography (EEG) study investigated the influence of asynchrony on how the brain binds audiotactile (AT) signals to enable faster responses in a redundant target paradigm. Human participants actively responded (psychophysics) or passively attended (EEG) to noise bursts, "taps-to-the-face" and their AT combinations at seven AT asynchronies: 0, ±20, ±70 and ±500 ms. Behaviourally, observers were faster at detecting AT than unisensory stimuli within a temporal integration window: the redundant target effect was maximal for synchronous stimuli and declined within a ≤70 ms AT asynchrony. EEG revealed a cascade of AT interactions that relied on different neural mechanisms depending on AT asynchrony. At small (≤20 ms) asynchronies, AT interactions arose for evoked response potentials (ERPs) at 110 ms and ~400 ms post-stimulus. Selectively at ±70 ms asynchronies, AT interactions were observed for the P200 ERP, theta-band inter-trial coherence (ITC) and power at ~200 ms post-stimulus. In conclusion, AT binding was mediated by distinct neural mechanisms depending on the asynchrony of the AT signals. Early AT interactions in ERPs and theta-band ITC and power were critical for the behavioural response facilitation within a ≤±70 ms temporal integration window.

Hemispheric Asymmetry of Globus Pallidus Relates to Alpha Modulation in Reward-Related Attentional Tasks.

Whereas subcortical structures such as the basal ganglia have been widely explored in relation to motor control, recent evidence suggests that their mechanisms extend to the domain of attentional switching. We here investigated the subcortical involvement in reward related top-down control of visual alpha-band oscillations (8-13 Hz), which have been consistently linked to mechanisms supporting the allocation of visuospatial attention. Given that items associated with contextual saliency (e.g., monetary reward or loss) attract attention, it is not surprising that the acquired salience of visual items further modulates. The executive networks controlling such reward-dependent modulations of oscillatory brain activity have yet to be fully elucidated. Although such networks have been explored in terms of corticocortical interactions, subcortical regions are likely to be involved. To uncover this, we combined MRI and MEG data from 17 male and 11 female participants, investigating whether derived measures of subcortical structural asymmetries predict interhemispheric modulation of alpha power during a spatial attention task. We show that volumetric hemispheric lateralization of globus pallidus (GP) and thalamus (Th) explains individual hemispheric biases in the ability to modulate posterior alpha power. Importantly, for the GP, this effect became stronger when the value saliency parings in the task increased. Our findings suggest that the GP and Th in humans are part of a subcortical executive control network, differentially involved in modulating posterior alpha activity in the presence of saliency. Further investigation aimed at uncovering the interaction between subcortical and neocortical attentional networks would provide useful insight in future studies.SIGNIFICANCE STATEMENT Whereas the involvement of subcortical regions into higher level cognitive processing, such as attention and reward attribution, has been already indicated in previous studies, little is known about its relationship with the functional oscillatory underpinnings of said processes. In particular, interhemispheric modulation of alpha band (8-13 Hz) oscillations, as recorded with magnetoencephalography, has been previously shown to vary as a function of salience (i.e., monetary reward/loss) in a spatial attention task. We here provide novel insights into the link between subcortical and cortical control of visual attention. Using the same reward-related spatial attention paradigm, we show that the volumetric lateralization of subcortical structures (specifically globus pallidus and thalamus) explains individual biases in the modulation of visual alpha activity.

Occipital Alpha and Gamma Oscillations Support Complementary Mechanisms for Processing Stimulus Value Associations.

Selective attention is reflected neurally in changes in the power of posterior neural oscillations in the alpha (8-12 Hz) and gamma (40-100 Hz) bands. Although a neural mechanism that allows relevant information to be selectively processed has its advantages, it may lead to lucrative or dangerous information going unnoticed. Neural systems are also in place for processing rewarding and punishing information. Here, we examine the interaction between selective attention (left vs. right) and stimulus's learned value associations (neutral, punished, or rewarded) and how they compete for control of posterior neural oscillations. We found that both attention and stimulus-value associations influenced neural oscillations. Whereas selective attention had comparable effects on alpha and gamma oscillations, value associations had dissociable effects on these neural markers of attention. Salient targets (associated with positive and negative outcomes) hijacked changes in alpha power-increasing hemispheric alpha lateralization when salient targets were attended, decreasing it when they were being ignored. In contrast, hemispheric gamma-band lateralization was specifically abolished by negative distractors. Source analysis indicated occipital generators of both attentional and value effects. Thus, posterior cortical oscillations support both the ability to selectively attend while at the same time retaining the ability to remain sensitive to valuable features in the environment. Moreover, the versatility of our attentional system to respond separately to salient from merely positively valued stimuli appears to be carried out by separate neural processes reflected in different frequency bands.

Posterior alpha oscillations reflect attentional problems in boys with Attention Deficit Hyperactivity Disorder.

OBJECTIVE: This study aimed to characterize alpha modulations in children with ADHD in relation to their attentional performance. METHODS: The posterior alpha activity (8-12Hz) was measured in 30 typically developing children and 30 children with ADHD aged 7-10years, using EEG while they performed a visuospatial covert attention task. We focused the analyses on typically developing boys (N=9) and boys with ADHD (N=17). RESULTS: Alpha activity in typically developing boys was similar to previous results of healthy adults: it decreased in the hemisphere contralateral to the attended hemifield, whereas it relatively increased in the other hemisphere. However, in boys with ADHD this hemispheric lateralization in the alpha band was not obvious (group contrast, p=.018). A robust relation with behavioral performance was lacking in both groups. CONCLUSIONS: The ability to modulate alpha oscillations in visual regions with the allocation of spatial attention was clearly present in typically developing boys, but not in boys with ADHD. SIGNIFICANCE: These results open up the possibility to further study the underlying mechanisms of ADHD by examining how differences in the fronto-striatal network might explain different abilities in modulating the alpha band activity.

Lateralized modulation of posterior alpha oscillations in children.

The evidence for a functionally inhibitory role of alpha oscillations is growing stronger, mostly derived from studies in healthy adults investigating spatial attention. It remains unexplored if the modulation of alpha band oscillations plays a similar functional role in typically developing children. The aim of this study was to characterize alpha modulations in children in relation to attentional performance. To this end, the posterior alpha activity (8-12Hz) in children between 7 and 10years old was measured using EEG while they performed a visuospatial covert attention task. We found that the alpha activity decreased in the hemisphere contralateral to the attended hemifield, whereas it relatively increased in the other hemisphere. In addition, we found that the degree of lateralized alpha modulation predicted performance on the attention task by negatively predicting the response time on invalid trials. Of note, children who were behaviorally less influenced by spatial cueing also were children with a clear lateralized alpha modulation pattern, with a significantly stronger alpha lateralization in the left hemisphere than children who were influenced more by spatial cueing. In addition, a bias to the right visual field such as that commonly observed in children, was significantly smaller or absent in the children influenced least by spatial cueing. Among all children, the magnitude of this visual field bias was positively related to the ability to modulate alpha activity. In conclusion, we have shown that the pattern of alpha oscillations modulated by attention is already present in 7-10year old typically developing children. Although a similar pattern is observed in adults, the consequences for behavior are different. The fact that alpha modulation is already present at this age opens up the possibility of using hemispheric alpha lateralization as a tool to study the physiological basis of attention deficits in clinical disorders such as ADHD.

Occipital alpha activity during stimulus processing gates the information flow to object-selective cortex.

Given the limited processing capabilities of the sensory system, it is essential that attended information is gated to downstream areas, whereas unattended information is blocked. While it has been proposed that alpha band (8-13 Hz) activity serves to route information to downstream regions by inhibiting neuronal processing in task-irrelevant regions, this hypothesis remains untested. Here we investigate how neuronal oscillations detected by electroencephalography in visual areas during working memory encoding serve to gate information reflected in the simultaneously recorded blood-oxygenation-level-dependent (BOLD) signals recorded by functional magnetic resonance imaging in downstream ventral regions. We used a paradigm in which 16 participants were presented with faces and landscapes in the right and left hemifields; one hemifield was attended and the other unattended. We observed that decreased alpha power contralateral to the attended object predicted the BOLD signal representing the attended object in ventral object-selective regions. Furthermore, increased alpha power ipsilateral to the attended object predicted a decrease in the BOLD signal representing the unattended object. We also found that the BOLD signal in the dorsal attention network inversely correlated with visual alpha power. This is the first demonstration, to our knowledge, that oscillations in the alpha band are implicated in the gating of information from the visual cortex to the ventral stream, as reflected in the representationally specific BOLD signal. This link of sensory alpha to downstream activity provides a neurophysiological substrate for the mechanism of selective attention during stimulus processing, which not only boosts the attended information but also suppresses distraction. Although previous studies have shown a relation between the BOLD signal from the dorsal attention network and the alpha band at rest, we demonstrate such a relation during a visuospatial task, indicating that the dorsal attention network exercises top-down control of visual alpha activity.

Hypothesis-driven methods to augment human cognition by optimizing cortical oscillations.

Cortical oscillations have been shown to represent fundamental functions of a working brain, e.g., communication, stimulus binding, error monitoring, and inhibition, and are directly linked to behavior. Recent studies intervening with these oscillations have demonstrated effective modulation of both the oscillations and behavior. In this review, we collect evidence in favor of how hypothesis-driven methods can be used to augment cognition by optimizing cortical oscillations. We elaborate their potential usefulness for three target groups: healthy elderly, patients with attention deficit/hyperactivity disorder, and healthy young adults. We discuss the relevance of neuronal oscillations in each group and show how each of them can benefit from the manipulation of functionally-related oscillations. Further, we describe methods for manipulation of neuronal oscillations including direct brain stimulation as well as indirect task alterations. We also discuss practical considerations about the proposed techniques. In conclusion, we propose that insights from neuroscience should guide techniques to augment human cognition, which in turn can provide a better understanding of how the human brain works.

Localization of cortico-peripheral coherence with electroencephalography.

BACKGROUND: The analysis of coherent networks from continuous recordings of neural activity with functional MRI or magnetoencephalography has provided important new insights into brain physiology and pathology. Here we assess whether valid localizations of coherent cortical networks can also be obtained from high-resolution electroencephalography (EEG) recordings. METHODS: EEG was recorded from healthy subjects and from patients with ischemic brain lesions during a tonic hand muscle contraction task and during continuous visual stimulation with an alternating checkerboard. These tasks induce oscillations in the primary hand motor area or in the primary visual cortex, respectively, which are coherent with extracerebral signals (hand muscle electromyogram or visual stimulation frequency). Cortical oscillations were reconstructed with different inverse solutions and the coherence between oscillations at each cortical voxel and the extracerebral signals was calculated. Moreover, simulations of coherent point sources were performed. RESULTS: Cortico-muscular coherence was correctly localized to the primary hand motor area and the steady-state visual evoked potentials to the primary visual cortex in all subjects and patients. Sophisticated head models tended to yield better localization accuracy than a single sphere model. A Minimum Variance Beamformer (MVBF) provided more accurate and focal localizations of simulated point sources than an L2 Minimum Norm (MN) inverse solution. In the real datasets, the MN maps had less localization error but were less focal than MVBF maps. CONCLUSIONS: EEG can localize coherent cortical networks with sufficient accuracy.

Measuring functional connectivity using MEG: methodology and comparison with fcMRI.

Functional connectivity (FC) between brain regions is thought to be central to the way in which the brain processes information. Abnormal connectivity is thought to be implicated in a number of diseases. The ability to study FC is therefore a key goal for neuroimaging. Functional connectivity (fc) MRI has become a popular tool to make connectivity measurements but the technique is limited by its indirect nature. A multimodal approach is therefore an attractive means to investigate the electrodynamic mechanisms underlying hemodynamic connectivity. In this paper, we investigate resting state FC using fcMRI and magnetoencephalography (MEG). In fcMRI, we exploit the advantages afforded by ultra high magnetic field. In MEG we apply envelope correlation and coherence techniques to source space projected MEG signals. We show that beamforming provides an excellent means to measure FC in source space using MEG data. However, care must be taken when interpreting these measurements since cross talk between voxels in source space can potentially lead to spurious connectivity and this must be taken into account in all studies of this type. We show good spatial agreement between FC measured independently using MEG and fcMRI; FC between sensorimotor cortices was observed using both modalities, with the best spatial agreement when MEG data are filtered into the ß band. This finding helps to reduce the potential confounds associated with each modality alone: while it helps reduce the uncertainties in spatial patterns generated by MEG (brought about by the ill posed inverse problem), addition of electrodynamic metric confirms the neural basis of fcMRI measurements. Finally, we show that multiple MEG based FC metrics allow the potential to move beyond what is possible using fcMRI, and investigate the nature of electrodynamic connectivity. Our results extend those from previous studies and add weight to the argument that neural oscillations are intimately related to functional connectivity and the BOLD response.

MEG/EEG source reconstruction, statistical evaluation, and visualization with NUTMEG.

NUTMEG is a source analysis toolbox geared towards cognitive neuroscience researchers using MEG and EEG, including intracranial recordings. Evoked and unaveraged data can be imported to the toolbox for source analysis in either the time or time-frequency domains. NUTMEG offers several variants of adaptive beamformers, probabilistic reconstruction algorithms, as well as minimum-norm techniques to generate functional maps of spatiotemporal neural source activity. Lead fields can be calculated from single and overlapping sphere head models or imported from other software. Group averages and statistics can be calculated as well. In addition to data analysis tools, NUTMEG provides a unique and intuitive graphical interface for visualization of results. Source analyses can be superimposed onto a structural MRI or headshape to provide a convenient visual correspondence to anatomy. These results can also be navigated interactively, with the spatial maps and source time series or spectrogram linked accordingly. Animations can be generated to view the evolution of neural activity over time. NUTMEG can also display brain renderings and perform spatial normalization of functional maps using SPM's engine. As a MATLAB package, the end user may easily link with other toolboxes or add customized functions.

Changes in brain network activity during working memory tasks: a magnetoencephalography study.

In this study, we elucidate the changes in neural oscillatory processes that are induced by simple working memory tasks. A group of eight subjects took part in modified versions of the N-back and Sternberg working memory paradigms. Magnetoencephalography (MEG) data were recorded, and subsequently processed using beamformer based source imaging methodology. Our study shows statistically significant increases in θ oscillations during both N-back and Sternberg tasks. These oscillations were shown to originate in the medial frontal cortex, and further to scale with memory load. We have also shown that increases in θ oscillations are accompanied by decreases in ß and γ band oscillations at the same spatial coordinate. These decreases were most prominent in the 20-40 Hz frequency range, although spectral analysis showed that γ band power decrease extends up to at least 80 Hz. ß/γ Power decrease also scales with memory load. Whilst θ increases were predominately observed in the medial frontal cortex, ß/γ decreases were associated with other brain areas, including nodes of the default mode network (for the N-back task) and areas associated with language processing (for the Sternberg task). These observations are in agreement with intracranial EEG and fMRI studies. Finally, we have shown an intimate relationship between changes in ß/γ band oscillatory power at spatially separate network nodes, implying that activity in these nodes is not reflective of uni-modal task driven changes in spatially separate brain regions, but rather represents correlated network activity. The utility of MEG as a non-invasive means to measure neural oscillatory modulation has been demonstrated and future studies employing this technology have the potential to gain a better understanding of neural oscillatory processes, their relationship to functional and effective connectivity, and their correspondence to BOLD fMRI.

Comparison of functional connectivity in default mode and sensorimotor networks at 3 and 7T.

OBJECT: The objective of this work was to assess functional connectivity measurements at ultra-high field (7T), given BOLD contrast to noise ratio increases with magnetic field strength but physiological noise also increases. MATERIALS AND METHODS: Resting state BOLD data were acquired at 3 and 7T to assess connectivity in the sensorimotor network (SMN) and default mode network (DMN) at different spatial smoothing levels. RESULTS: At 3 and 7T positive correlation is observed between a right sensorimotor seed and left sensorimotor cortex. For the DMN, a seed in posterior cingulate cortex results in a high correlation in inferior parietal lobes and medial prefrontal cortex. We show higher temporal correlation coefficients for both the SMN and DMN at 7T compared to 3T for all smoothing levels. A spatial correlation between connectivity maps revealed no significant differences for the SMN, whilst the DMN showed increased spatial correlation dependent on SNR. The maximum physiological noise contribution was found to be higher at 7T, but noise in both seed and network nodes was not significantly increased, as shown by no significant difference in the spatial correlation of maps following physiological correction. CONCLUSION: 7T can improve spatial specificity of connectivity maps and facilitate measurement of connectivity in areas of lower intrinsic network correlation.

MEG in the macaque monkey and human: distinguishing cortical fields in space and time.

Magnetoencephalography (MEG) is an increasingly popular non-invasive tool used to record, on a millisecond timescale, the magnetic field changes generated by cortical neural activity. MEG has the advantage, over fMRI for example, that it is a direct measure of neural activity. In the current investigation we used MEG to measure cortical responses to tactile and auditory stimuli in the macaque monkey. We had two aims. First, we sought to determine whether MEG, a technique that may have low spatial accuracy, could be used to distinguish the location and organization of sensory cortical fields in macaque monkeys, a species with a relatively small brain compared to that of the human. Second, we wanted to examine the temporal dynamics of cortical responses in the macaque monkey relative to the human. We recorded MEG data from anesthetized monkeys and, for comparison, from awake humans that were presented with simple tactile and auditory stimuli. Neural source reconstruction of MEG data showed that primary somatosensory and auditory cortex could be differentiated and, further, that separate representations of the digit and lip within somatosensory cortex could be identified in macaque monkeys as well as humans. We compared the latencies of activity from monkey and human data for the three stimulation types and proposed a correspondence between the neural responses of the two species. We thus demonstrate the feasibility of using MEG in the macaque monkey and provide a non-human primate model for examining the relationship between external evoked magnetic fields and their underlying neural sources.

Investigating spatial specificity and data averaging in MEG.

This study shows that the spatial specificity of MEG beamformer estimates of electrical activity can be affected significantly by the way in which covariance estimates are calculated. We define spatial specificity as the ability to extract independent timecourse estimates of electrical brain activity from two separate brain locations in close proximity. Previous analytical and simulated results have shown that beamformer estimates are affected by narrowing the time frequency window in which covariance estimates are made. Here we build on this by both experimental validation of previous results, and investigating the effect of data averaging prior to covariance estimation. In appropriate circumstances, we show that averaging has a marked effect on spatial specificity. However the averaging process results in ill-conditioned covariance matrices, thus necessitating a suitable matrix regularisation strategy, an example of which is described. We apply our findings to an MEG retinotopic mapping paradigm. A moving visual stimulus is used to elicit brain activation at different retinotopic locations in the visual cortex. This gives the impression of a moving electrical dipolar source in the brain. We show that if appropriate beamformer optimisation is applied, the moving source can be tracked in the cortex. In addition to spatial reconstruction of the moving source, we show that timecourse estimates can be extracted from neighbouring locations of interest in the visual cortex. If appropriate methodology is employed, the sequential activation of separate retinotopic locations can be observed. The retinotopic paradigm represents an ideal platform to test the spatial specificity of source localisation strategies. We suggest that future comparisons of MEG source localisation techniques (e.g. beamformer, minimum norm, Bayesian) could be made using this retinotopic mapping paradigm.

Relating BOLD fMRI and neural oscillations through convolution and optimal linear weighting.

The exact relationship between neural activity and BOLD fMRI is unknown. However, several recent findings, recorded invasively in both humans and monkeys, show a positive correlation of BOLD to high-frequency (30-150 Hz) oscillatory power changes and a negative correlation to low-frequency (8-30 Hz) power changes arising from cortical areas. In this study, we computed the time series correlation between BOLD GE-EPI fMRI at 7 T and neural activity measures from noninvasive MEG, using a time-frequency beam former for source localisation. A sinusoidal drifting grating was presented visually for 4 s followed by a 20 s rest period in both recording modalities. The MEG time series were convolved with either a measured or canonical haemodynamic response function (HRF) for comparison with the measured BOLD data, and the BOLD data were deconvolved with either a measured or a canonical HRF for comparison with the measured MEG. In the visual cortex, the higher frequencies (mid-gamma=52-75 Hz and high-gamma=75-98 Hz) were positively correlated with BOLD whilst the lower frequencies (alpha=8-12 Hz and beta=12-25 Hz) were negatively correlated with BOLD. Furthermore, regression including all frequency bands predicted BOLD better than stimulus timing alone, although no individual frequency band predicted BOLD as well as stimulus timing. For this paradigm, there was, in general, no difference between using the SPM canonical HRF compared to the subject-specific measured HRF. In conclusion, MEG replicates findings from invasive recordings with regard to time series correlations with BOLD data. Conversely, deconvolution of BOLD data provides a neural estimate which correlates well with measured neural effects as a function of neural oscillation frequency.

The relationship between magnetic and electrophysiological responses to complex tactile stimuli.

BACKGROUND: Magnetoencephalography (MEG) has become an increasingly popular technique for non-invasively characterizing neuromagnetic field changes in the brain at a high temporal resolution. To examine the reliability of the MEG signal, we compared magnetic and electrophysiological responses to complex natural stimuli from the same animals. We examined changes in neuromagnetic fields, local field potentials (LFP) and multi-unit activity (MUA) in macaque monkey primary somatosensory cortex that were induced by varying the rate of mechanical stimulation. Stimuli were applied to the fingertips with three inter-stimulus intervals (ISIs): 0.33s, 1s and 2s. RESULTS: Signal intensity was inversely related to the rate of stimulation, but to different degrees for each measurement method. The decrease in response at higher stimulation rates was significantly greater for MUA than LFP and MEG data, while no significant difference was observed between LFP and MEG recordings. Furthermore, response latency was the shortest for MUA and the longest for MEG data. CONCLUSION: The MEG signal is an accurate representation of electrophysiological responses to complex natural stimuli. Further, the intensity and latency of the MEG signal were better correlated with the LFP than MUA data suggesting that the MEG signal reflects primarily synaptic currents rather than spiking activity. These differences in latency could be attributed to differences in the extent of spatial summation and/or differential laminar sensitivity.

Probabilistic algorithms for MEG/EEG source reconstruction using temporal basis functions learned from data.

We present two related probabilistic methods for neural source reconstruction from MEG/EEG data that reduce effects of interference, noise, and correlated sources. Both methods localize source activity using a linear mixture of temporal basis functions (TBFs) learned from the data. In contrast to existing methods that use predetermined TBFs, we compute TBFs from data using a graphical factor analysis based model [Nagarajan, S.S., Attias, H.T., Hild, K.E., Sekihara, K., 2007a. A probabilistic algorithm for robust interference suppression in bioelectromagnetic sensor data. Stat Med 26, 3886-3910], which separates evoked or event-related source activity from ongoing spontaneous background brain activity. Both algorithms compute an optimal weighting of these TBFs at each voxel to provide a spatiotemporal map of activity across the brain and a source image map from the likelihood of a dipole source at each voxel. We explicitly model, with two different robust parameterizations, the contribution from signals outside a voxel of interest. The two models differ in a trade-off of computational speed versus accuracy of learning the unknown interference contributions. Performance in simulations and real data, both with large noise and interference and/or correlated sources, demonstrates significant improvement over existing source localization methods.

A probabilistic algorithm integrating source localization and noise suppression for MEG and EEG data.

We have developed a novel probabilistic model that estimates neural source activity measured by MEG and EEG data while suppressing the effect of interference and noise sources. The model estimates contributions to sensor data from evoked sources, interference sources and sensor noise using Bayesian methods and by exploiting knowledge about their timing and spatial covariance properties. Full posterior distributions are computed rather than just the MAP estimates. In simulation, the algorithm can accurately localize and estimate the time courses of several simultaneously active dipoles, with rotating or fixed orientation, at noise levels typical for averaged MEG data. The algorithm even performs reasonably at noise levels typical of an average of just a few trials. The algorithm is superior to beamforming techniques, which we show to be an approximation to our graphical model, in estimation of temporally correlated sources. Success of this algorithm using MEG data for localizing bilateral auditory cortex, low-SNR somatosensory activations, and for localizing an epileptic spike source are also demonstrated.

Spatiotemporal integration of tactile information in human somatosensory cortex.

BACKGROUND: Our goal was to examine the spatiotemporal integration of tactile information in the hand representation of human primary somatosensory cortex (anterior parietal somatosensory areas 3b and 1), secondary somatosensory cortex (S2), and the parietal ventral area (PV), using high-resolution whole-head magnetoencephalography (MEG). To examine representational overlap and adaptation in bilateral somatosensory cortices, we used an oddball paradigm to characterize the representation of the index finger (D2; deviant stimulus) as a function of the location of the standard stimulus in both right- and left-handed subjects. RESULTS: We found that responses to deviant stimuli presented in the context of standard stimuli with an interstimulus interval (ISI) of 0.33 s were significantly and bilaterally attenuated compared to deviant stimulation alone in S2/PV, but not in anterior parietal cortex. This attenuation was dependent upon the distance between the deviant and standard stimuli: greater attenuation was found when the standard was immediately adjacent to the deviant (D3 and D2 respectively), with attenuation decreasing for non-adjacent fingers (D4 and opposite D2). We also found that cutaneous mechanical stimulation consistently elicited not only a strong early contralateral cortical response but also a weak ipsilateral response in anterior parietal cortex. This ipsilateral response appeared an average of 10.7 +/- 6.1 ms later than the early contralateral response. In addition, no hemispheric differences either in response amplitude, response latencies or oddball responses were found, independent of handedness. CONCLUSION: Our findings are consistent with the large receptive fields and long neuronal recovery cycles that have been described in S2/PV, and suggest that this expression of spatiotemporal integration underlies the complex functions associated with this region. The early ipsilateral response suggests that anterior parietal fields also receive tactile input from the ipsilateral hand. The lack of a hemispheric difference in responses to digit stimulation supports a lack of any functional asymmetry in human somatosensory cortex.