Modulations in oscillatory activity with amplitude asymmetry can produce cognitively relevant event-related responses

Hanneke van Dijk; Jurrian van der Werf; Ali Mazaheri; W. Pieter Medendorp; Ole Jensen

doi:10.1073/pnas.0908821107

New Research In

Edited by Nancy J. Kopell, Boston University, Boston, MA, and approved November 18, 2009 (received for review September 16, 2009)

Abstract

Event-related responses and oscillatory activity are typically regarded as manifestations of different neural processes. Recent work has nevertheless revealed a mechanism by which slow event-related responses are created as a direct consequence of modulations in brain oscillations with nonsinusoidal properties. It remains unknown if this mechanism applies to cognitively relevant event-related responses. Here, we investigated whether sustained event-related fields (ERFs) measured during working memory maintenance can be explained by modulations in oscillatory power. In particular, we focused on contralateral delayed activity (CDA) typically observed in working memory tasks in which hemifield specific attention is manipulated. Using magnetoencephalography, we observed sustained posterior ERFs following the presentation of the memory target. These ERFs were systematically lateralized with respect to the hemisphere in which the target was presented. A strikingly similar pattern emerged for modulations in alpha (9–13 Hz) power. The alpha power and ERF lateralization were strongly correlated over subjects. Based on a mechanistic argument pertaining to the nonsinusoidal properties of the alpha activity, we conclude that the ERFs modulated by working memory are likely to be directly produced by the modulations in oscillatory alpha activity. Given that posterior alpha activity typically reflects disengagement, we conclude that the CDA is not attributable to an additive process reflecting memory maintenance per se but, rather, is a consequence of how attentional resources are allocated.

Noninvasive electrophysiological recordings in the human brain are typically carried out by using either electroencephalography (EEG) or magnetoencephalography (MEG). In cognitive studies, data from these techniques are studied by means of event-related responses or by modulations in oscillatory power (1). Event-related responses are recorded by averaging 30 or more trials time-locked to a given stimulus, assuming that oscillatory brain activity that is not phase-locked to the stimulus is “averaged out.” To determine task-related modulations in oscillatory activity, the power is calculated for each trial separately and then averaged. This allows for quantification of stimulus-induced changes in oscillatory activity. The relation between event-related responses and oscillatory activity remains a fundamental question in human electrophysiological research (1).

Recently, it was reported that human oscillatory activity in the alpha band has “amplitude fluctuation asymmetry” (AFA) (2, 3). This refers to a nonsinusoidal property implying that amplitude changes are reflected stronger in the peaks than in the troughs of the ongoing oscillations (or vice versa). As a consequence, the signal distribution is skewed such that the mean covaries with magnitude. This finding challenges the dogma that oscillations are averaged out when event-related responses are calculated. Indeed, Mazaheri and Jensen (3) demonstrated empirically that stimulus-induced asymmetrical amplitude modulations of alpha band oscillations could produce slow event-related responses. Although this finding provided a proof-of-principle, we test in this study if the proposed mechanism can explain the emergence of event-related responses reflecting cognitive processing.

Event-related potentials (ERPs) with a slow time course have been reported to be modulated by a wide range of cognitive tasks (4). Although there are a few proposals for how slow responses are generated, the underlying physiological mechanism remains largely untested (5). Research on visuospatial working memory has revealed important slow sustained event-related responses that reflect memory maintenance and load (6). These responses are lateralized depending on whether the remembered stimulus is presented in the left or right hemifield and called contralateral delayed activity (CDA). Other studies have shown hemifield-specific lateralized activity in the alpha band in both attention and working memory tasks. In these tasks, the alpha power is suppressed in the hemisphere contralateral to the attended stimulus (7–9). Here, we hypothesize that such lateralizations are the mechanism underlying sustained event-related responses because of the fact that the alpha activity during retention has AFA. For example, when the stimulus is presented in the left hemifield, there is a slight increase in alpha activity in a left posterior sensor (Fig. 1A). When the stimulus is presented to the right, the alpha reduction is strong (Fig. 1B). Given that only the peaks of the oscillations are suppressed, whereas the troughs remain unchanged, the resulting sustained evoked responses would show a stronger magnitude for right compared with left stimuli (Fig. 1 C and D). To test this hypothesis, we analyzed MEG data from a spatial working memory task in which subjects remembered the location of stimuli presented in the left or right visual field. The event-related fields (ERFs), modulations in alpha power, and amplitude asymmetry were characterized during the memory interval. We found that both alpha power modulation and amplitude asymmetry correlated with the ERFs. This demonstrates a case in which a cognitively relevant evoked response, the CDA, is likely to be produced by changes in oscillatory brain activity.

Fig. 1.

The working hypothesis explaining how slow ERFs are created by modulations in alpha power with asymmetrical amplitude properties. (A) Target stimulus is presented in the left hemifield at t = 0 s. Only MEG signals measured over the left hemisphere are considered. Alpha activity is slightly increased with respect to the stimulus onset over multiple trials. (B) Stimulus is presented in the right hemifield, resulting in strong alpha suppression over multiple trials. (C) ERFs. Because of the asymmetrical amplitude modulations, the oscillatory alpha activity is not averaged out; only the peaks of the oscillations are modulated and not the troughs. As a consequence, the systematic modulation in alpha power produces a slow sustained field. (D) The stronger the modulation, the stronger is the magnitude of the field. In short, we hypothesized that working memory retention resulting in lateralized alpha power modulation would produce sustained lateralized ERFs—the CDA.

Results

To study the relation between posterior alpha activity and ERFs, we conducted a standard working memory experiment in which subjects had to memorize a spatial target location for a short period of time (10). We recorded the electrophysiological signals using MEG from 18 subjects performing this task. The target stimulus was presented in either the left or right hemifield. After 1.6 s, subjects were instructed to make a saccade toward the remembered location (Fig. 2).

Fig. 2.

Description of the working memory task. After a baseline period of 1.5 s, a visual stimulus (○) was presented in the peripheral left or right hemifield for 0.1 s. Subjects were instructed to remember the position of the visual target. At 1.6 s after stimulus onset, the fixation cross (+) disappeared and the subjects were instructed to make a saccade (arrow) toward the remembered target position.

ERFs Are Lateralized by Working Memory.

Based on the electrooculogram (EOG) recordings, we selected the trials in which subjects followed the task instructions (i.e., saccades were made correctly after the target stimuli). Reaction times for the saccades were 212 ± 72 ms (SEM). Initially, we characterized the ERF of the combined planar gradient in response to the target stimuli. The grand average demonstrated strong sustained ERFs in posterior sensors (Fig. 3). In sensors overlying the left hemisphere, the magnitude of the fields was higher for targets presented in the left hemifield than in the right hemifield (Fig. 3A). The reverse was observed in sensors overlying the right hemisphere (Fig. 3B). The topography of the lateralized ERFs revealed a significant difference in left (increase) and right (decrease) parietooccipital sensors (P = 0.016 and P = 0.022, respectively; Fig. 3C). Thus, we found that our paradigm induced sustained working memory-related ERFs, which are lateralized with respect to the presentation of the target—the CDA.

Fig. 3.

ERFs during the retention interval. (A) Grand average ERFs for target stimuli (stim) presented in the left (blue) and right (red) hemifields. The ERFs were averaged for the left sensors, showing a significant difference between left and right target stimuli. (B) Difference in ERFs (left minus right stimulus) for the left and right significant sensors. (C) Topography of the difference in ERFs during the retention period (0.2–1.2 s). Clusters of sensors significantly different are marked by dots (Left: P = 0.016, Right: P = 0.022).

Alpha Band Power Is Lateralized by Working Memory.

Next, we investigated modulations in oscillatory power with respect to the target stimuli. Time-frequency analysis of power revealed a strong modulation in alpha activity over parietooccipital sensors during the delay period (Fig. 4). When the target was presented in the left hemifield, the left sensors showed an increase in alpha band power, whereas the right sensors showed a decrease (Fig. 4A). The reverse pattern was observed when the target was presented to the right (Fig. 4B). Again, we defined laterality as the difference between power for right and left hemifield stimuli (Fig. 4C and Fig. S1). We found a posterior distribution with significant power enhancements in the ipsilateral hemisphere (left cluster: P = 0.033, right cluster: P = 0.007). These results demonstrate a strong lateralization in alpha band activity with respect to the hemifield of the target stimulus.

Fig. 4.

Time-frequency representations (TFRs) of power during the retention interval. (A) TFRs for the left target stimuli (stim). Data are shown for left and right sensors (Left and Right, respectively) as marked in D. (B) TFRs for right target stimuli. Data are shown for left and right sensors (Left and Right, respectively) as marked in D. (C) Difference in TFRs (left minus right stimuli) for left and right sensors (Left and Right, respectively). (D) Topography of the alpha band (9–13 Hz, 0.2–1.2 s) power with respect to left minus right target stimuli. The dots mark two clusters of sensors with significant differences (Left: P = 0.0333, Right: P = 0.007).

Lateralizations of the ERFs and Alpha Activity Are Highly Correlated.

The sustained ERFs and the alpha power enhancements are both lateralized during the retention of a visuospatial memory item, with strikingly similar scalp topographies (compare Fig. 3C and Fig. 4D). Additionally, the sustained ERFs and alpha power modulations seemed to follow a similar time course (compare Fig. 3A and Fig. S2). We quantified the relation between modulations in ERF amplitudes and alpha power by a correlation analysis (see Experimental Procedures). This revealed a highly significant correlation over subjects between ERFs and alpha power modulation over posterior areas (Fig. 5A; r = 0.92, P < 10^-6). Subsequently, we compared the time course of the alpha band and ERF amplitude for the entire period of −0.2 to 1.2 s by correlating the values for 15 time points in all subjects (see Experimental Procedures). The correlation was highly significant in representative left and right sensors (P < 10^-6; Fig. 5B, Left and Right). The strong correlation between lateralized sustained ERFs and lateralized alpha band power strongly suggests that they reflect the same mechanism.

Fig. 5.

Relation between the sustained ERF and the modulation in alpha power. (A) Correlation over subjects of differences in ERFs and alpha power modulations [left minus right target stimuli (stim)] for a left sensor and a right sensor (Left and Right, respectively). The correlations were highly significant (Left, Right: P < 0.0001; Spearman test, Bonferroni-corrected with respect to number of sensors). The topography (Middle) of the correlation coefficients. (B) Correlation over subjects between the time course-resolved ERFs and alpha power modulations (left minus right target stimuli) for a left sensor and a right sensor (Left and Right, respectively). The topography (Middle) of the correlation between the difference in time courses for ERFs and alpha power modulation.

Lateralized Amplitude Asymmetry.

We then proceeded to quantify the degree of AFA in the alpha band by calculating the AFA_index during retention. This measure estimates the amplitude modulations of the peaks of oscillatory activity relative to the modulations of the troughs (see Experimental Procedures). Note that this measure is independent of the dc offset of the signal (3) (Fig. 2). As shown in Fig. 6A, the AFA_index spectra have strong peaks in the alpha and beta bands over posterior sensors. This clearly demonstrates that AFA is present in the alpha band during the retention of the spatial target. The alpha band AFA_index was strongest in left sensors when the target was presented in the left hemifield, whereas the reverse pattern was observed in right sensors (Fig. 6A). This was confirmed by the topographies of the AFA_index values (Fig. 6B and Fig. S1C). For the difference between the two conditions (left minus right target), the AFA_index shows a lateralized posterior parietooccipital distribution (left cluster: P = 0.012, right cluster: P = 0.002; Fig. 6B) similar to both the alpha band oscillatory activity (Fig. 4D) and the ERFs (Fig. 3C).

Fig. 6.

Amplitude asymmetry calculated during the retention interval (0.2–1.2 s). (A) Frequency spectra of the AFA_index for the left (blue) and right (red) targets for a left posterior sensor (MLO11) with maximum 10-Hz AFA_index and a right sensor (MRO12). (B) Topography of the AFA_index in the alpha band for the difference between the conditions [left minus right target stimuli (stim)].The dots depict the clusters of sensors with significant differences (Left: P = 0.012, Right: P = 0.002). These sensors were used for the spectra in B.

Amplitude Asymmetry Correlates with the Production of ERFs.

Finally, we quantified the correlation between the AFA_index and ERF modulations over subjects. Fig. 7 A and C shows a correlation for left and right sensors (MLO22: r = 0.63, P = 0.008; MRO33: r = 0.47, P = 0.046). The topography of the correlation coefficients revealed a posterior distribution (Fig. 7B). Note that the sensors in which the correlations are significant correspond to the group of sensors in which there was the strongest correlation between alpha and ERF lateralization (Fig. 5A). The correlation between sustained lateralized ERFs and the amplitude asymmetry even further supports the case that the ERFs are produced by modulations in alpha band power.

Fig. 7.

Correlation over subjects between the differences (left minus right stimuli in sustained ERFs and the differences in absolute alpha amplitude asymmetry (AFA_index) during retention. Correlations for a left sensor (A) and a right sensor (C) (Left: r = 0.63, P < 0.008; Right: r = 0.47, P < 0.046). (B) Topography of the correlation coefficients.

Considering the additional peak of the AFA_index in the beta band (15–25 Hz; Fig. 6A), we explored the relation between ERFs and beta power modulations (Fig. S3). We did identify systematic changes in beta lateralization with regard to the hemifield of the target; however, the magnitude of AFA_index modulation was much less than that of the alpha band (Fig. S3B). The beta band activity could potentially be explained as a harmonic frequency resulting from the alpha band activity. While such interactions can contribute to the AFA_index they would not produce ERFs (11). Using a partial correlation analysis, we found no correlation between beta band power and ERF lateralization when controlling for the alpha band power (Fig. S3C). However, the correlation between alpha band power and ERF lateralization when controlling for the beta power (Fig. S3B) remained. We conclude that the lateralization in ERFs is primarily caused by power modulations in the alpha band and is not likely explained by either harmonics or physiological effects in the beta band.

Discussion

In this study, we demonstrated that sustained ERFs related to working memory maintenance are likely to be explained by modulations of oscillatory activity in the alpha band. We identified CDA in posterior ERFs with respect to target stimuli presented in the left or right hemifield, which was strongly correlated (spatially and temporally) with hemispheric lateralization of alpha band power. A measure of the amplitude asymmetry (AFA_index) revealed AFA in the alpha band over posterior areas. The AFA_index correlated with ERF lateralization as well. It was previously proposed that systematic modulations in oscillatory activity with amplitude asymmetry constitute a mechanism for generating ERFs/ERPs (2, 3). We have now found even stronger support for this mechanism and demonstrate that it can account for the generation of cognitively relevant ERFs.

Sustained ERFs Are Likely to Reflect Functional Inhibition.

Although the results of the present study strongly suggest that the measured slow ERFs are produced by modulations in oscillatory power, this does not bring into question the results of previous ERP/ERF research. Rather, our findings provide a unique interpretation for the function of slow ERFs/ERPs. Conventionally, ERFs or ERPs are seen as additive brain responses that reflect engagement of working memory networks. Our findings suggest that the slow ERFs/ERPs reflect a modulation in background oscillatory power, predominantly in the alpha band. Alpha activity has recently been interpreted to reflect functional disengagement of task-irrelevant regions, thereby allocating computational resources to task-relevant regions (12, 13). This is based on the observation that alpha activity increases with working memory load during retention in areas not required for working memory maintenance (13, 14). Furthermore, as also demonstrated in this study, alpha band power increases in the hemisphere that is less relevant for the task at hand (7, 9). Intracranial animal recordings have demonstrated that neuronal firing is constrained to a fraction of the alpha cycle (15, 16). We conclude that the slow ERFs producing the CDA observed in the current study are reflecting functional inhibition/disengagement and not an active working memory process. The same interpretation might hold for the spatial working memory-related lateralized ERPs reported by Vogel and Machizawa (6) and Vogel et al. (17). Interestingly, Drew and Vogel (18) recently observed a very similar CDA during a spatial tracking task. These findings support the notion that CDA reflects a general mechanism for allocation of resources rather than memory maintenance per se.

Physiological Mechanism.

Previously, it has been proposed that the evoked response was generated either by an additive mechanism or by phase resetting of ongoing oscillations (1, 19, 20). The mechanism we here embrace explains ERFs by modulations in power of oscillatory activity that has amplitude asymmetry. The physiological mechanism proposed by Mazaheri and Jensen (3) explains asymmetrical oscillations based on the accepted notion that the magnetic fields measured by MEG are produced mainly by intracellular dendritic currents in pyramidal cells (21). The key argument is that the dominating currents measured by MEG are dendritic, running from the distal synapses to the soma (i.e., in one direction). The measured alpha band activity might therefore be a consequence of bouts of synaptic activity producing inward dendritic currents every 100 ms. It is the modulation of the magnitude of these bouts that produces the AFA, which yields the CDA. Thus, the current findings provide important insight into the physiological substrate of the CDA.

Do Power Modulations of Oscillatory Activity with Amplitude Asymmetry Causally Predict the Emergence of Slow Evoked Components?

One might ask if the generation of slow evoked responses is a causal consequence of modulations in oscillatory activity or if it could be explained by a comodulation of a third origin. First, it should be stressed that systematic modulations in oscillations with amplitude asymmetry inevitably will result in the generation of evoked responses (Fig. 1). Because the oscillatory activity we observe has amplitude asymmetry and is systematically modulated, slow evoked components are bound to emerge. Second, the correlation over subjects between lateralization in evoked responses and alpha activity was very strong (r = 0.92, P < 10^-6; Fig. 5). Thus, the simplest explanation is that the slow lateralized ERFs are a direct consequence of oscillatory modulations in the alpha band. This causal explanation could be tested by pharmacological manipulations modulating the posterior alpha activity (22). The lateralization in alpha activity is most likely a consequence of a slow top-down drive involving regions in the intraparietal sulcus and frontal eye field (23). This drive might be produced by the entrainment of slow delta oscillations determining the excitability of parietooccipital regions in relation to the components of the task (24, 25). In this study, we did not investigate the electrophysiological correlate of such a top-down drive beyond the alpha power modulation.

Does the Mechanism Generalize to Other Frequency Bands or Cognitive Functions?

Although the current study has focused on amplitude asymmetry of posterior alpha activity, the phenomenon might generalize to other brain regions and frequency bands (3). For instance, amplitude asymmetry of the ∼10-Hz somatosensory mu rhythm has been reported (2). Other candidates could be theta oscillations [5–9 Hz, working and long-term memory (11, 26–28)], beta band oscillations [15–25 Hz, motor (29) and language (30) tasks], or gamma band activity [40–200 Hz, visual attention (31, 32) and working memory (8, 14, 33)]. Future work is required to investigate which frequency bands have oscillatory activity with amplitude asymmetry that might explain the generation of evoked responses.

Many of the reported amplitude modulations in ERPs/ERFs in cognitive paradigms are associated with slow evoked components. This is the case for long-term memory encoding (34) and retrieval (35). In the domain of language research, slow ERP effects have been found in relation to syntactic violations (36, 37). In motor research, slow ERPs increasing in anticipation of movements have been reported (38). It would be of great interest to investigate to what extent modulations of oscillatory power can account for some of these phenomena.

Conclusion

We have shown that modulations of oscillatory power with amplitude asymmetry in the alpha band can produce cognitively modulated ERFs in a working memory task. Our findings point to a general mechanism for producing evoked responses that is likely to extend beyond the realm of working memory and alpha band activity.

Experimental Procedures

The data used in this study have been published previously (8) but for a different purpose. Here, we mainly describe the experimental details relevant for the current analysis.

Participants.

Eighteen healthy subjects (3 female and 15 male, mean age: 26 ± 3 years) free of any known sensory, perceptual, or motor disorders volunteered to participate in the experiment. All subjects provided written informed consent according to institutional guidelines of the local ethics committee (CMO Committee on Research Involving Human Subjects, region Arnhem-Nijmegen, The Netherlands).

Stimuli and Design.

Visual stimuli were generated with Presentation 9.10 software (Neurobehavioral Systems, Inc.), presented using a liquid crystal display video projector (SANYO PROxtraX multiverse; 60-Hz refresh rate), and back-projected onto the screen using two front-silvered mirrors. Subjects were instructed to fixate on a centrally presented white cross on a black screen during a period of 1.5 s. As a target, a peripheral white dot was presented for 0.1 s to the left or right of the visual fixation cross at a random visual eccentricity between 9° and 18° and restricted between a −36° and 36° elevation angle relative to the central fixation cross. After a delay of 1.6 s, the fixation cross disappeared, which instructed the subjects to make a saccade toward the remembered location of the target (Fig. 2). The fixation cross appeared again after 0.3 s as an indication for the subject to fixate at the center of the screen until the end of the trial. Each trial lasted 4 s.

Data Recording and Analysis.

Data were recorded using a whole-head MEG system (151 axial gradiometers; VSM/CTF Systems). Subjects were seated upright in the MEG system and instructed to sit comfortably without moving. Before and after the recording session, the head position was measured with respect to the MEG sensor array using localization coils fixed at anatomical landmarks (the nasion and at the left and right ear canals). Bipolar EOGs were recorded to check subjects’ gaze behavior and discard trials with eye blinks offline. MEG and EOG data were low-pass-filtered at 300 Hz and digitized at 1,200 Hz. Before the actual measurements, the EOG signal was calibrated using a nine-point calibration grid. Eye movement recordings in all 18 subjects confirmed that they performed the task correctly. Trials in which subjects broke fixation, made saccades in the wrong direction, or blinked the eyes during the trial were excluded from further analysis. Additionally, trials contaminated with muscle activity or sensor jumps were excluded from further analysis. In total, we included 120 ± 18 trials for left hemifield targets and 118 ± 20 trials for right hemifield targets in the analysis.

For ERF analysis, the data were low-pass-filtered at 80 Hz, and a 0.2-s baseline period prior to target onset was used. Subsequently, the trials were averaged for each condition separately.

Oscillatory power was estimated using a multitaper spectral estimation method (40). A frequency-dependent sliding time window was applied. The length of the window was five cycles (i.e., ΔT = 5/f_o, where f_o is the frequency of interest). The data from each sliding time window were multiplied with three orthogonal Hanning tapers. Subsequently, the data were Fourier-transformed and the power spectral densities were averaged. This procedure resulted in estimates of oscillatory power with plus or minus ∼0.3-Hz f_o frequency smoothing. The data were then baseline-corrected using data in the time window centered 0.25 s before target onset (i.e., no posttarget 10-Hz power was bleeding into the baseline).

Sustained fields and alpha band activity in the delay period (0.2–1.2 s) were compared statistically for the left and right target conditions. To normalize for intersubject variances, we computed the z values for each sensor with respect to left and right targets. This was done by initially comparing the conditions within subjects and calculating t values, which then were transformed into z values (using SPM2; the FIL methods group. These z values represented the normalized difference between left and right targets (and not statistical values). The z values were then tested against the null distribution over subjects using a nonparametric cluster-randomization routine in Fieldtrip (41). This routine controls the type 1 error rate in a situation involving multiple comparisons over sensors (see SI Methods).

The correlation between ERF and alpha band activity in the delay period was assessed by subtracting the delay activity with respect to right and left targets. The Spearman correlation was computed over subjects for each sensor. Sensors with a P value smaller then 0.0003 were accepted as being significantly correlated (Bonferroni-corrected for 151 comparisons). Additionally, we calculated the correlation of the time evolution of the alpha power and the time evolution of the ERF amplitude. This yielded 15 alpha power values and 15 ERF amplitude values for each subject. The alpha band values of all subjects were concatenated into one pool, which then contained 270 power values for each sensor. We performed the same analysis for the ERF amplitude values and then correlated the amplitude values as described previously.

To investigate whether the sustained ERF in the delay period could be a consequence of the amplitude asymmetry in the ongoing oscillations, we determined the AFA_index as proposed by Mazaheri and Jensen (3). To compute this index, the data were initially bandpass-filtered in the full trials at the frequency of interest ±1 Hz. The time points of the peaks and troughs of the oscillatory activity were then identified in the filtered data (0.2–1.2 s). These time points were used to estimate the amplitude values of the peaks (S_peak) and troughs (S_trough) in the non-bandpass-filtered data after applying a 10-ms boxcar smoothing kernel. The amplitude asymmetry was estimated by quantifying the normalized difference in the variance for the peaks and troughs [as described by Mazaheri and Jensen (3)]:An AFA_index close to zero would mean that the peaks and troughs are modulated similarly (as for a sinusoidal “symmetrical” signal). When the AFA_index is larger than zero, this would mean that the peaks are modulated stronger than the troughs, and vice versa for negative values. The AFA_index spectrum was calculated for the range between 5 and 50 Hz (in 1-Hz steps). The difference in the AFA_index for 10 Hz between the left and right target conditions was statistically evaluated using a cluster-randomization routine similar to that described previously.

When analyzing the ERFs, power spectra, and AFA_index, we computed a planar gradiometer representation of the data (42). The planar field gradient approximates the signals measured by planar gradiometers. This is often advantageous in MEG signal processing, because the strongest field of the planar gradient usually is situated above the neural sources (43). The vertical and horizontal components were estimated using the fields of each sensor and its neighboring sensors. The resulting components were then combined using the rms after spectral or ERF analysis. By means of this approach, the strongest signal will be directly above the source. However, information about source orientation will be lost.

The Matlab package Fieldtrip was used for data analysis. This is an open-source toolbox for neurophysiological data analysis, which was developed at the Donders Institute for Brain, Cognition, and Behaviour.

Acknowledgments

We thank Ingrid Nieuwenhuis for providing help with the statistical analysis of the ERF and frequency data. This work was supported by Volkswagen Foundation Grant I/79876; a Netherlands Organization for Scientific Research Rubicon scholarship; and the Netherlands Organization for Scientific Research Grants 864.03.007, 400.04.186, and 452/03.307.

Footnotes

¹To whom correspondence should be addressed at: Institute for Clinical Neuroscience and Medical Psychology, Heinrich-Heine University, Universitätsstr. 1, D-40225 Düsseldorf, Germany. E-mail: hanneke.vandijk{at}med.uni-duesseldorf.de.
Author contributions: J.v.d.W., W.P.M., and O.J. designed research; J.v.d.W. performed research. H.v.D., A.M., and O.J. contributed new reagents/analytic tools; J.v.d.W., H.v.D. analyzed data; and H.v.D. and O.J. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission.
This article contains supporting information online at www.pnas.org/cgi/content/full/0908821107/DCSupplemental.

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(2004) Neural dynamics and the fundamental mechanisms of event-related brain potentials. Cereb Cortex 14:476–483.
OpenUrl Abstract/FREE Full Text
↵
1. Penney TB,
2. Maess B,
3. Busch N,
4. Derrfuss J,
5. Mecklinger A
(2003) Cortical activity reduction with stimulus repetition: a whole-head MEG analysis. Brain Res Cogn Brain Res 16:226–231.
OpenUrl CrossRef PubMed
↵
1. Hämäläinen M,
2. Hari R,
3. Ilmoniemi RJ,
4. Knuutila J,
5. Lounasmaa OV
(1993) Magnetoencephalography—Theory, instrumentation, and applications to noninvasive studies of the working human brain. Rev Mod Phys 65:413–497.
OpenUrl CrossRef
↵
1. Ahveninen J,
2. et al.
(2007) MRI-constrained spectral imaging of benzodiazepine modulation of spontaneous neuromagnetic activity in human cortex. NeuroImage 35:577–582.
OpenUrl CrossRef PubMed
↵
1. Capotosto P,
2. Babiloni C,
3. Romani GL,
4. Corbetta M
(2009) Frontoparietal cortex controls spatial attention through modulation of anticipatory alpha rhythms. J Neurosci 29:5863–5872.
OpenUrl Abstract/FREE Full Text
↵
1. Lakatos P,
2. Karmos G,
3. Mehta AD,
4. Ulbert I,
5. Schroeder CE
(2008) Entrainment of neuronal oscillations as a mechanism of attentional selection. Science 320:110–113.
OpenUrl Abstract/FREE Full Text
↵
1. Schroeder CE,
2. Lakatos P
(2009) Low-frequency neuronal oscillations as instruments of sensory selection. Trends Neurosci 32(1):9–18.
OpenUrl CrossRef PubMed
1. Raghavachari S,
2. et al.
(2006) Theta oscillations in human cortex during a working-memory task: Evidence for local generators. J Neurophysiol 95:1630–1638.
OpenUrl Abstract/FREE Full Text
↵
1. Jensen O,
2. Tesche CD
(2002) Frontal theta activity in humans increases with memory load in a working memory task. Eur J Neurosci 15:1395–1399.
OpenUrl CrossRef PubMed
↵
1. Fell J,
2. et al.
(2003) Rhinal-hippocampal theta coherence during declarative memory formation: interaction with gamma synchronization? Eur J Neurosci 17:1082–1088.
OpenUrl CrossRef PubMed
↵
1. Hari R,
2. Salmelin R
(1997) Human cortical oscillations: A neuromagnetic view through the skull. Trends Neurosci 20:44–49.
OpenUrl CrossRef PubMed
↵
1. Bastiaansen M,
2. Hagoort P
(2006) Oscillatory neuronal dynamics during language comprehension. Prog Brain Res 159:179–196.
OpenUrl CrossRef PubMed
↵
1. Fell J,
2. Fernández G,
3. Klaver P,
4. Elger CE,
5. Fries P
(2003) Is synchronized neuronal gamma activity relevant for selective attention? Brain Res Brain Res Rev 42:265–272.
OpenUrl CrossRef PubMed
↵
1. Fries P,
2. Reynolds JH,
3. Rorie AE,
4. Desimone R
(2001) Modulation of oscillatory neuronal synchronization by selective visual attention. Science 291:1560–1563.
OpenUrl Abstract/FREE Full Text
↵
1. Tallon-Baudry C,
2. Bertrand O,
3. Peronnet F,
4. Pernier J
(1998) Induced gamma-band activity during the delay of a visual short-term memory task in humans. J Neurosci 18:4244–4254.
OpenUrl Abstract/FREE Full Text
↵
1. Takashima A,
2. et al.
(2006) Successful declarative memory formation is associated with ongoing activity during encoding in a distributed neocortical network related to working memory: A magnetoencephalography study. Neuroscience 139:291–297.
OpenUrl CrossRef PubMed
↵
1. Rugg MD,
2. et al.
(1998) Dissociation of the neural correlates of implicit and explicit memory. Nature 392:595–598.
OpenUrl CrossRef PubMed
↵
1. Hagoort P,
2. Brown CM
(2000) ERP effects of listening to speech compared to reading: The P600/SPS to syntactic violations in spoken sentences and rapid serial visual presentation. Neuropsychologia 38:1531–1549.
OpenUrl CrossRef PubMed
↵
1. van den Brink D,
2. Hagoort P
(2004) The influence of semantic and syntactic context constraints on lexical selection and integration in spoken-word comprehension as revealed by ERPs. J Cognit Neurosci 16:1068–1084.
OpenUrl CrossRef PubMed
1. Walter WG,
2. Cooper R,
3. Aldridge VJ,
4. McCallum WC,
5. Winter AL
(1964) Contingent negative variation: An electric sign of sensorimotor association and expectancy in the human brain. Nature 203:380–384.
OpenUrl CrossRef PubMed
↵
1. Percival DB,
2. Walden AT
(1993) Spectral analysis for physical applications: multitaper and conventional univariate techniques (Cambridge UP, Cambridge, UK,).
↵
1. Maris E,
2. Oostenveld R
(2007) Nonparametric statistical testing of EEG- and MEG-data. J Neurosci Methods 164:177–190.
OpenUrl CrossRef PubMed
↵
1. Basitiaansen MC,
2. Knosche TR
(2000) Tangential derivative mapping of axial MEG applied to event-related desynchronization research. Clin Neurophysiol 111(7):1300–1305.
OpenUrl CrossRef PubMed
↵
1. Ahonen AI,
2. et al.
(1993) Sampling theory for neuromagnetic detector arrays. IEEE Trans Biomed Eng 40(9):859–869.
OpenUrl CrossRef PubMed

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Proceedings of the National Academy of Sciences: 107 (2)

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Article Classifications

Biological Sciences
Neuroscience

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Makeig S,
Debener S,
Onton J,
Delorme A
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Lopes Da Silva F
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Machizawa MG
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Foxe JJ,
Wang N,
Simpson GV
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[37] Goldman-Rakic PS

[38] ↵
Nikulin VV,
Linkenkaer-Hansen K,
Nolte G,
Curio G
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Jensen O,
Gelfand J,
Kounios J,
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[49] Jensen O,

[50] Gelfand J,

[51] Kounios J,

[52] Lisman JE

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Jokisch D,
Jensen O
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[54] Jokisch D,

[55] Jensen O

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Bollimunta A,
Chen Y,
Schroeder CE,
Ding M
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[57] Bollimunta A,

[58] Chen Y,

[59] Schroeder CE,

[60] Ding M

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Lorincz ML,
Kékesi KA,
Juhász G,
Crunelli V,
Hughes SW
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[63] Kékesi KA,

[64] Juhász G,

[65] Crunelli V,

[66] Hughes SW

[67] ↵
Vogel EK,
McCollough AW,
Machizawa MG
(2005) Neural measures reveal individual differences in controlling access to working memory. Nature 438:500–503.
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[68] Vogel EK,

[69] McCollough AW,

[70] Machizawa MG

[71] ↵
Drew T,
Vogel EK
(2008) Neural measures of individual differences in selecting and tracking multiple moving objects. J Neurosci 28:4183–4191.
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[72] Drew T,

[73] Vogel EK

[74] ↵
Shah AS,
et al.
(2004) Neural dynamics and the fundamental mechanisms of event-related brain potentials. Cereb Cortex 14:476–483.
OpenUrl Abstract/FREE Full Text

[75] Shah AS,

[76] et al.

[77] ↵
Penney TB,
Maess B,
Busch N,
Derrfuss J,
Mecklinger A
(2003) Cortical activity reduction with stimulus repetition: a whole-head MEG analysis. Brain Res Cogn Brain Res 16:226–231.
OpenUrl CrossRef PubMed

[78] Penney TB,

[79] Maess B,

[80] Busch N,

[81] Derrfuss J,

[82] Mecklinger A

[83] ↵
Hämäläinen M,
Hari R,
Ilmoniemi RJ,
Knuutila J,
Lounasmaa OV
(1993) Magnetoencephalography—Theory, instrumentation, and applications to noninvasive studies of the working human brain. Rev Mod Phys 65:413–497.
OpenUrl CrossRef

[84] Hämäläinen M,

[85] Hari R,

[86] Ilmoniemi RJ,

[87] Knuutila J,

[88] Lounasmaa OV

[89] ↵
Ahveninen J,
et al.
(2007) MRI-constrained spectral imaging of benzodiazepine modulation of spontaneous neuromagnetic activity in human cortex. NeuroImage 35:577–582.
OpenUrl CrossRef PubMed

[90] Ahveninen J,

[91] et al.

[92] ↵
Capotosto P,
Babiloni C,
Romani GL,
Corbetta M
(2009) Frontoparietal cortex controls spatial attention through modulation of anticipatory alpha rhythms. J Neurosci 29:5863–5872.
OpenUrl Abstract/FREE Full Text

[93] Capotosto P,

[94] Babiloni C,

[95] Romani GL,

[96] Corbetta M

[97] ↵
Lakatos P,
Karmos G,
Mehta AD,
Ulbert I,
Schroeder CE
(2008) Entrainment of neuronal oscillations as a mechanism of attentional selection. Science 320:110–113.
OpenUrl Abstract/FREE Full Text

[98] Lakatos P,

[99] Karmos G,

[100] Mehta AD,

[101] Ulbert I,

[102] Schroeder CE

[103] ↵
Schroeder CE,
Lakatos P
(2009) Low-frequency neuronal oscillations as instruments of sensory selection. Trends Neurosci 32(1):9–18.
OpenUrl CrossRef PubMed

[104] Schroeder CE,

[105] Lakatos P

[106] Raghavachari S,
et al.
(2006) Theta oscillations in human cortex during a working-memory task: Evidence for local generators. J Neurophysiol 95:1630–1638.
OpenUrl Abstract/FREE Full Text

[107] Raghavachari S,

[108] et al.

[109] ↵
Jensen O,
Tesche CD
(2002) Frontal theta activity in humans increases with memory load in a working memory task. Eur J Neurosci 15:1395–1399.
OpenUrl CrossRef PubMed

[110] Jensen O,

[111] Tesche CD

[112] ↵
Fell J,
et al.
(2003) Rhinal-hippocampal theta coherence during declarative memory formation: interaction with gamma synchronization? Eur J Neurosci 17:1082–1088.
OpenUrl CrossRef PubMed

[113] Fell J,

[114] et al.

[115] ↵
Hari R,
Salmelin R
(1997) Human cortical oscillations: A neuromagnetic view through the skull. Trends Neurosci 20:44–49.
OpenUrl CrossRef PubMed

[116] Hari R,

[117] Salmelin R

[118] ↵
Bastiaansen M,
Hagoort P
(2006) Oscillatory neuronal dynamics during language comprehension. Prog Brain Res 159:179–196.
OpenUrl CrossRef PubMed

[119] Bastiaansen M,

[120] Hagoort P

[121] ↵
Fell J,
Fernández G,
Klaver P,
Elger CE,
Fries P
(2003) Is synchronized neuronal gamma activity relevant for selective attention? Brain Res Brain Res Rev 42:265–272.
OpenUrl CrossRef PubMed

[122] Fell J,

[123] Fernández G,

[124] Klaver P,

[125] Elger CE,

[126] Fries P

[127] ↵
Fries P,
Reynolds JH,
Rorie AE,
Desimone R
(2001) Modulation of oscillatory neuronal synchronization by selective visual attention. Science 291:1560–1563.
OpenUrl Abstract/FREE Full Text

[128] Fries P,

[129] Reynolds JH,

[130] Rorie AE,

[131] Desimone R

[132] ↵
Tallon-Baudry C,
Bertrand O,
Peronnet F,
Pernier J
(1998) Induced gamma-band activity during the delay of a visual short-term memory task in humans. J Neurosci 18:4244–4254.
OpenUrl Abstract/FREE Full Text

[133] Tallon-Baudry C,

[134] Bertrand O,

[135] Peronnet F,

[136] Pernier J

[137] ↵
Takashima A,
et al.
(2006) Successful declarative memory formation is associated with ongoing activity during encoding in a distributed neocortical network related to working memory: A magnetoencephalography study. Neuroscience 139:291–297.
OpenUrl CrossRef PubMed

[138] Takashima A,

[139] et al.

[140] ↵
Rugg MD,
et al.
(1998) Dissociation of the neural correlates of implicit and explicit memory. Nature 392:595–598.
OpenUrl CrossRef PubMed

[141] Rugg MD,

[142] et al.

[143] ↵
Hagoort P,
Brown CM
(2000) ERP effects of listening to speech compared to reading: The P600/SPS to syntactic violations in spoken sentences and rapid serial visual presentation. Neuropsychologia 38:1531–1549.
OpenUrl CrossRef PubMed

[144] Hagoort P,

[145] Brown CM

[146] ↵
van den Brink D,
Hagoort P
(2004) The influence of semantic and syntactic context constraints on lexical selection and integration in spoken-word comprehension as revealed by ERPs. J Cognit Neurosci 16:1068–1084.
OpenUrl CrossRef PubMed

[147] van den Brink D,

[148] Hagoort P

[149] Walter WG,
Cooper R,
Aldridge VJ,
McCallum WC,
Winter AL
(1964) Contingent negative variation: An electric sign of sensorimotor association and expectancy in the human brain. Nature 203:380–384.
OpenUrl CrossRef PubMed

[150] Walter WG,

[151] Cooper R,

[152] Aldridge VJ,

[153] McCallum WC,

[154] Winter AL

[155] ↵
Percival DB,
Walden AT
(1993) Spectral analysis for physical applications: multitaper and conventional univariate techniques (Cambridge UP, Cambridge, UK,).

[156] Percival DB,

[157] Walden AT

[158] ↵
Maris E,
Oostenveld R
(2007) Nonparametric statistical testing of EEG- and MEG-data. J Neurosci Methods 164:177–190.
OpenUrl CrossRef PubMed

[159] Maris E,

[160] Oostenveld R

[161] ↵
Basitiaansen MC,
Knosche TR
(2000) Tangential derivative mapping of axial MEG applied to event-related desynchronization research. Clin Neurophysiol 111(7):1300–1305.
OpenUrl CrossRef PubMed

[162] Basitiaansen MC,

[163] Knosche TR

[164] ↵
Ahonen AI,
et al.
(1993) Sampling theory for neuromagnetic detector arrays. IEEE Trans Biomed Eng 40(9):859–869.
OpenUrl CrossRef PubMed

[165] Ahonen AI,

[166] et al.

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Modulations in oscillatory activity with amplitude asymmetry can produce cognitively relevant event-related responses

Abstract

Results

ERFs Are Lateralized by Working Memory.

Alpha Band Power Is Lateralized by Working Memory.

Lateralizations of the ERFs and Alpha Activity Are Highly Correlated.

Lateralized Amplitude Asymmetry.

Amplitude Asymmetry Correlates with the Production of ERFs.

Discussion

Sustained ERFs Are Likely to Reflect Functional Inhibition.

Physiological Mechanism.

Do Power Modulations of Oscillatory Activity with Amplitude Asymmetry Causally Predict the Emergence of Slow Evoked Components?

Does the Mechanism Generalize to Other Frequency Bands or Cognitive Functions?

Conclusion

Experimental Procedures

Participants.

Stimuli and Design.

Data Recording and Analysis.

Acknowledgments

Footnotes

References

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Modulations in oscillatory activity with amplitude asymmetry can produce cognitively relevant event-related responses

Abstract

Results

ERFs Are Lateralized by Working Memory.

Alpha Band Power Is Lateralized by Working Memory.

Lateralizations of the ERFs and Alpha Activity Are Highly Correlated.

Lateralized Amplitude Asymmetry.

Amplitude Asymmetry Correlates with the Production of ERFs.

Discussion

Sustained ERFs Are Likely to Reflect Functional Inhibition.

Physiological Mechanism.

Do Power Modulations of Oscillatory Activity with Amplitude Asymmetry Causally Predict the Emergence of Slow Evoked Components?

Does the Mechanism Generalize to Other Frequency Bands or Cognitive Functions?

Conclusion

Experimental Procedures

Participants.

Stimuli and Design.

Data Recording and Analysis.

Acknowledgments

Footnotes

References

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