Abstract

Building on evidence for working memory (WM) coding of vibrotactile frequency information in monkey prefrontal cortex, recent electroencephalography studies found frequency processing in human WM to be reflected by quantitative modulations of prefrontal upper beta activity (20–30 Hz) as a function of the to-be-maintained stimulus attribute. This kind of stimulus-dependent activity has been observed across different sensory modalities, suggesting a generalized role of prefrontal beta during abstract WM processing of quantitative magnitude information. However, until now the available empirical evidence for such quantitative WM representation remains critically limited to the retention of periodic stimulus frequencies. In the present experiment, we used retrospective cueing to examine the quantitative WM processing of stationary (intensity) and temporal (duration) attributes of a previously presented tactile stimulus. We found parametric modulations of prefrontal beta activity during cued WM processing of each type of quantitative information, in a very similar manner as had before been observed only for periodic frequency information. In particular, delayed prefrontal beta modulations systematically reflected the magnitude of the retrospectively selected stimulus attribute and were functionally linked to successful behavioral task performance. Together, these findings converge on a generalized role of stimulus-dependent prefrontal beta-band oscillations during abstract scaling of analog quantity information in human WM.

Introduction

It is widely accepted that prefrontal cortex (PFC) plays a key role in working memory (WM), that is, in operations enabling the maintenance and online processing of information that no longer exists in the environment (for reviews, see Miller and D'Esposito 2005; Pasternak and Greenlee 2005; D'Esposito 2007). Studies of human WM, mostly in the visual and verbal domains, routinely reported PFC activity in terms of hemodynamic responses (Wager and Smith 2003; Nee et al. 2012), but also in terms of changes in oscillatory activity as assessed by electro- or magnetoencephalography (EEG/MEG) (e.g., Tallon-Baudry et al. 1998; for review, see Benchenane et al. 2011). Thereby, WM function was typically examined by contrasting conditions of high versus no (or lower) WM processing, yielding insights into task-dependent activity associated with different WM operations and materials (Gazzaley and Nobre 2012).

Another line of research in the somatosensory domain has recently succeeded in delineating direct, stimulus-dependent signatures of WM processing in the human PFC. In particular, complementing evidence for parametric WM coding of vibrotactile frequency information in the monkey PFC (Romo et al. 1999; Barak et al. 2010), human EEG studies found WM processing of different frequencies to be systematically reflected by parametric modulations of right prefrontal oscillatory activity in the upper beta band (20–30 Hz; Spitzer et al. 2010; Spitzer and Blankenburg 2011). Recently, this prefrontal activity has also been observed in other sensory modalities (Spitzer and Blankenburg 2012), suggesting a general role of parametric beta oscillations during WM processing of analog stimulus quantities. However, the currently available empirical evidence for such parametric WM processing remains restricted to tasks that require the maintenance of periodic frequency information, thus leaving open whether modulations of prefrontal beta may represent other stimulus attributes, as well. Moreover, the precise nature of the information, which is parametrically encoded during WM processing of different frequencies, is not clear yet. Periodic stimuli of different temporal frequency, as defined by the number of cyclic repeats per second, differ in their fine-grained temporal properties and might be evaluated in terms of the perceived speed of the stimulus dynamics. However, different stimulus frequencies may also be evaluated with respect to the inferred intensity of stimulation, with faster oscillating stimuli suggesting the presence of higher physical energy. It therefore remains unknown to what extent frequency-dependent parametric modulations of prefrontal beta activity might reflect a unique concept of periodic stimulus frequency, or the processing of temporal structure or physical energy, or a more general abstract representation of sensory quantity.

To shed light on these questions, in the present experiment, we systematically examined the WM processing of temporal (duration) and stationary (intensity) quantitative attributes of to-be-maintained vibrotactile stimuli. We used a modified delayed-match-to-sample (DMTS) task (Fig. 1A), which involved retrospective selection of the task-relevant stimulus attribute for sustained maintenance, allowing us to disambiguate the active WM processing of either stimulus dimension after identical encoding conditions. Examining prefrontal EEG oscillations, we hypothesized that if parametric WM effects (Romo et al. 1999; Spitzer et al. 2010; Spitzer and Blankenburg 2012) were uniquely linked to a proprietary concept of periodic frequencies, no parametric modulations, neither by duration nor by intensity, should be observed. If, on the other hand, frequency-dependent modulations relate to the processing of temporal stimulus properties, then prefrontal beta activity might be modulated—if at all—by stimulus duration, but not by intensity. In contrast, if the previous frequency-dependent modulations intrinsically reflect a scaling of perceived physical energy, independent of its precise temporal structure, then a modulation by intensity only is expected. If however, as proposed recently (Spitzer and Blankenburg 2012), parametric WM processing in the PFC relies on a generalized WM representation of quantity, then prefrontal upper beta may be modulated by either of the 2 stimulus attributes, when their abstract magnitude is actively processed in WM.

Figure 1.

(A) Experimental task. On each trial, a vibrotactile stimulus (s1) of a given intensity and duration was presented at the left index finger. A visual cue instructed subjects to retrospectively focus on s1's intensity (“A”) or duration (“B”) throughout a retention interval (green), for subsequent comparison against the corresponding attribute of s2. (B) Upper left: Time frequency statistical parametric maps of differences in oscillatory activity between the A- and B-tasks, collapsed across all values of s1. Dashed gray rectangles indicate TF window and channels where a significant effect was observed (P < 0.05, FWE). Lower left: Time-course of beta (15–25 Hz) activity changes in the A- and B-tasks, for the channels outlined above. Right: SPM 3D source reconstruction of the difference in beta power between 700 and 1300 ms.

Figure 1.

(A) Experimental task. On each trial, a vibrotactile stimulus (s1) of a given intensity and duration was presented at the left index finger. A visual cue instructed subjects to retrospectively focus on s1's intensity (“A”) or duration (“B”) throughout a retention interval (green), for subsequent comparison against the corresponding attribute of s2. (B) Upper left: Time frequency statistical parametric maps of differences in oscillatory activity between the A- and B-tasks, collapsed across all values of s1. Dashed gray rectangles indicate TF window and channels where a significant effect was observed (P < 0.05, FWE). Lower left: Time-course of beta (15–25 Hz) activity changes in the A- and B-tasks, for the channels outlined above. Right: SPM 3D source reconstruction of the difference in beta power between 700 and 1300 ms.

Materials and Methods

Subjects

Twenty-six healthy volunteers (20–35 years; 12 females and 14 males) participated in the experiment with written informed consent. Two subjects were excluded from analysis due to excessive EEG artifacts and deficient performance in the behavioral task (<60% correct). The study was approved by the Ethical Committee of the Charité University Hospital Berlin and corresponded to the Human Subjects Guidelines of the Declaration of Helsinki.

Stimuli and Behavioral Task

Vibrotactile stimulation of the left index finger was delivered by a 16-dot piezoelectric Braille display (4 × 4 quadratic matrix, 2.5 mm spacing) controlled by a programmable stimulator (Piezostimulator, QuaeroSys, St. Johann, Germany). The pins of the Braille display were simultaneously driven by a 123-Hz sinusoidal carrier signal, which was amplitude modulated by a 33-Hz sinus function, creating the percept of a “flutter” vibration (Romo and Salinas 2003) at 33 Hz. These carrier and modulation frequencies were chosen to minimize the risk of artifact contamination at EEG frequencies <90 Hz and to prevent steady-state evoked responses in the EEG frequency range of main interest (20–30 Hz). The sound of the tactile stimulation was masked by white noise (∼80 dB) played via loudspeakers throughout the entire experiment. None of the participants reported hearing any sound attributable to the tactile stimulation.

An illustration of the experimental protocol is shown in Figure 1A. After a variable prestimulus interval (1000–1500 ms), each trial started with  the presentation of a base stimulus (s1). The amplitude (vibration intensity) of s1 was randomly chosen to be 39.1%, 53.7%, 68.4%, or 83.0% of the stimulator's maximum capacity (corresponding to 1600, 2200, 2800, and 3400 arbitrary amplitude units of 4095 maximally available units). The resulting stimulation intensities were not painful, and ranged between the 26- and 56-fold of the average detection threshold for this type of stimuli (1.5% stimulator capacity, or 60.3 amplitude units; assessed in a follow-up test on 5 of the participants using the method-of-limits procedure). The duration of s1 was randomly chosen to be 350, 450, 550, or 650 ms, independent of s1's intensity. Eight hundred milliseconds after offset of s1, a visual cue (“A” or “B,” randomly selected on each trial) indicated whether subjects should for the remainder of the trial focus on s1's intensity (A-cue) or duration (B-cue). After a 2500-ms retention interval, a comparison stimulus (s2) was presented, which differed from s1 both in intensity (by ±7.3% stimulator capacity, corresponding to ±300 amplitude units) and in duration (by ±150 ms). In the A-cue condition, subjects' task was to indicate whether s2 was weaker or stronger than s1 by pressing a response button with the right hand either once or twice. Analogously, in the B-cue condition, subjects were asked to indicate whether s2 was shorter or longer than s1. After several practice trials, each participant completed 7 blocks of 64 trials, for a total of 224 discrimination trials in each cue condition.

EEG Recording and Analysis

EEG was recorded using a 64-channel active electrode system (ActiveTwo, BioSemi, Amsterdam, Netherlands), with electrodes placed in an elastic cap according to the extended 10-20 system. Individual electrode locations were registered using a stereotactic electrode positioning system (Zebris Medical GmbH, Isny, Germany). Vertical and horizontal eye movements were recorded from 4 additional channels. Signals were digitized at a sampling rate of 2048 Hz, off-line band-pass filtered (1–100 Hz), downsampled to 512 Hz, and average referenced. All analyses were carried out using SPM8 for MEG/EEG (update revision number 4667, 27 February 2012; Wellcome Department of Cognitive Neurology, London, UK: www.fil.ion.ucl.ac.uk/spm/) and custom MATLAB code (The Mathworks, Inc., Natick, USA). The EEG was corrected for eye blinks using calibration data to generate individual artifact coefficients and adaptive spatial filtering (for details, see Ille et al. 2002). Remaining artifacts were rejected by excluding all data containing signal amplitudes >80 µV from analysis.

Spectral Analysis

Time frequency (TF) representations of spectral power (5–48 Hz) in the retention interval (−800–2500 ms, relative to cue onset) were obtained by applying a tapered sliding window fast Fourier transform (FFT) with a Hanning taper and an adaptive time window of 7 cycles length, using the FieldTrip function ft_specest_mtmconvol.m provided with SPM8. This method is very similar to a Morlet wavelet transform (cf. Bruns 2004), but provides additional tapering options for optimum sensitivity at higher frequency ranges. Here, exploratory analysis of higher frequency bands (>48 Hz), using a multitapered FFT, yielded no significant effects. To avoid potential results distortions by systematic stimulus-induced effects in the precue interval (Supplementary Fig. 2), the TF spectra were normalized relative to the average power in the entire data epoch (−800–2500 ms) to represent percentage power changes over time. This normalization approach retains condition-specific differences in the TF spectras' temporal profile only. Control analyses of the average reference spectra used for the normalization showed no systematic differences in any of the reported contrasts (all frequency bins P > 0.05), rendering it unlikely that stationary effects had been masked out in the normalized TF data. Note that subsequent analysis involved direct comparisons of the cue-induced TF profiles between the A- and B-tasks after identical s1 stimulation and encoding conditions (cf. Figs 1 and 2), ruling out a potential contamination of the delay period results by s1-induced activity and/or stimulation artifacts. TF bins for which the sliding FFT window overlapped with s2 onset, at the end of the analysis window, were excluded from analysis (cf. transparent masks in Figs 1 and 2).

Figure 2.

Parametric modulations of prefrontal oscillatory activity by the task-relevant stimulus attribute. (A) Modulations by stimulus intensity. Upper left: Time frequency statistical parametric map of the difference in parametric modulation by s1 intensity during retention in the A-cue condition compared with the B-cue condition, for representative right prefrontal channels outlined by dashed rectangle in upper right. Upper right: Descriptive topographical map (color scaling as in left) of the parametric modulation outlined by dashed rectangle in upper left. Lower left: Time-courses of the parametric modulations by s1 intensity in the 2 task conditions, for the frequency band and channels outlined in upper left. “MI”: maintain intensity (A-cue); “MD”: maintain duration (B-cue). Lower right: 3D source reconstruction of the parametric modulation by intensity in the A-cue condition, for the TF window outlined in upper left. (B) Analogous to A, for the modulation by s1 duration in the B-cue condition compared with the A-cue condition. Source reconstruction failed to produce statistically reliable results for the TF window outlined in upper left. A descriptive illustration of the reconstructed prefrontal source is provided in Supplementary Figure 3.

Figure 2.

Parametric modulations of prefrontal oscillatory activity by the task-relevant stimulus attribute. (A) Modulations by stimulus intensity. Upper left: Time frequency statistical parametric map of the difference in parametric modulation by s1 intensity during retention in the A-cue condition compared with the B-cue condition, for representative right prefrontal channels outlined by dashed rectangle in upper right. Upper right: Descriptive topographical map (color scaling as in left) of the parametric modulation outlined by dashed rectangle in upper left. Lower left: Time-courses of the parametric modulations by s1 intensity in the 2 task conditions, for the frequency band and channels outlined in upper left. “MI”: maintain intensity (A-cue); “MD”: maintain duration (B-cue). Lower right: 3D source reconstruction of the parametric modulation by intensity in the A-cue condition, for the TF window outlined in upper left. (B) Analogous to A, for the modulation by s1 duration in the B-cue condition compared with the A-cue condition. Source reconstruction failed to produce statistically reliable results for the TF window outlined in upper left. A descriptive illustration of the reconstructed prefrontal source is provided in Supplementary Figure 3.

Statistical Analysis

The statistical design was implemented in SPM8, using the general linear model (GLM) applied to each subject's single-trial TF data. To warrant conformity with the GLM under normal error assumptions (Kiebel et al. 2005), the TF data were convolved with a 3 Hz × 500 ms (full-width at half maximum) Gaussian kernel. The GLM design matrix (cf. Supplementary Fig. 1A) consisted of 2 dummy variables specifying the trials' cue condition (A or B), 2 parametric regressors coding the intensity of s1 under the respective cue conditions, and 2 parametric regressors analogously specifying the duration of s1. The vectors coding intensity and duration were zero-centered and normalized. Because across trials, intensity and duration were varied independently of each other, the resulting parametric regressors were orthogonal, allowing us to estimate their respective impact within the same regression model. The model was inverted using restricted maximum-likelihood estimation as implemented in SPM8, yielding beta parameter estimates for each model regressor at each TF bin. TF contrasts of interest were then computed by weighted summation of individual regressors' beta estimates (cf. Supplementary Fig. 1B–D).

The individual contrast spectra were subjected to mass univariate analysis on the group level, using 1-sample t-tests as implemented in SPM8 (for details, see Kiebel et al. 2005; Litvak et al. 2011). This involved computing for each TF bin a t-value that reflected the significance of the contrast. Family-wise errors (FWEs) in TF space were controlled using random field theory (RFT; Worsley et al. 1996; Kilner et al. 2005) to determine the FWE-corrected probability that, at a given channel, a cluster of adjacent significant TF bins may have been obtained by chance. A cluster was thereby defined as a group of adjacent TF bins that all exceeded a threshold of P < 0.005 (uncorrected). RFT approximates the probability that a multidimensional statistical map exceeds some height or extent by chance, under the assumption that the error terms conform to a smooth random field with a Gaussian distribution (Brett et al. 2003). Convolving the normalized data with a Gaussian smoothing kernel prior to analysis ensures this assumption. The FWE-corrected significance of a given cluster (Pcluster) is then determined using the distribution of the expected Euler characteristic, given the smoothness of the map, under the null hypothesis of a continuous random field. Analysis of parametric TF contrasts (Fig. 2, cf. Supplementary Fig. 1C,D) a priori focused on right prefrontal channels near electrode F2, where parametric modulations of upper beta activity were most consistently observed in our previous vibrotactile frequency work. If significant (Pcluster < 0.05, FWE) cue-induced parametric modulations were identified at F2, and/or at least 2 immediately neighboring right prefrontal channels, we continued with analyses of the condition-specific modulations in the respective frequency band, averaged over those adjacent channels showing a similar effect (cf. Fig. 2). Unless noted otherwise, only correct discrimination trials were included in the reported analyses. Where applicable, error trials were analyzed separately.

Source Reconstruction

The sources of EEG activity were modeled using 3-dimensional (3D) source reconstruction as implemented in SPM8 (Friston et al. 2006). For each participant, a forward model was constructed, using an 8196 vertex template cortical mesh coregistered to the individual electrode positions via 3 fiducial markers. The forward model's lead field was computed using the 3-shell boundary element method EEG head model available in SPM8 (Phillips et al. 2007). Prior to model inversion, the data were band-pass filtered around the frequency band of interest. Source estimates were then computed on the canonical mesh using multiple sparse priors (Friston et al. 2008) under group constraints (Litvak and Friston 2008), including the data from all conditions of interest. For TF windows where significant effects were identified in the channel-level analysis, condition-specific TF contrasts were used to summarize oscillatory source power for specific frequency bands and at specific times as 3D images. This entailed convolving the single-trial source activity with a series of Morlet projectors and weighting the average power from each condition with a Gaussian window centered on the time interval of interest. The source reconstructions were then analyzed on the group level using conventional statistical parametric mapping procedures, using a display threshold of P < 0.001 (uncorrected). Additional nonsignificant source reconstruction results are illustrated descriptively in Supplementary Figure 3. The SPM anatomy toolbox (Eickhoff et al. 2005) was used to establish cytoarchitectonic reference where possible.

Results

Behavioral Results

On average, participants correctly discriminated 69.3% of the stimulus intensities in the A-task, compared with 77.6% of the stimulus durations in the B-task (P < 0.001, 2-sided paired t-test). A detailed description of the behavioral data is given in Table 1. As expected by Weber's law (Fechner 1966), discrimination accuracy in the A-task decreased with increasing s1 stimulus intensity (Table 1, upper; P < 0.001, linear trend analysis, Bonferroni corrected). In contrast, accuracy in the B-task was not systematically affected by s1 duration (P > 0.70). Thus, the lower mean accuracy in the A-task compared with the B-task was in particular due to poor discrimination of high s1 intensities, compared with long s1 durations (cf. Table 1). In neither task did accuracy covary with variations in the uncued stimulus dimension (i.e., duration in the A-task, or intensity in the B-task, both P's > 0.05).

Table 1

Behavioral results

Accuracy (%) 
 s1 intensity (a.u.) 1600 2200 2800 3400 Linear slope 
 A (maintain intensity) 74.1 76.9 66.1 60.1 −0.53* 
 B (maintain duration) 75.0 78.3 77.7 79.1 0.12 
 s1 duration (ms) 350 450 550 650  
 A (maintain intensity) 69.2 69.7 67.9 69.9 0.00 
 B (maintain duration) 74.0 80.9 80.1 75.4 0.04 
RT (ms) 
 s1 intensity (a.u.) 1600 2200 2800 3400 Linear slope 
 A (maintain intensity) 660 671 660 632 −9.5 
 B (maintain duration) 754 726 746 758 3.2 
 s1 duration (ms) 350 450 550 650  
 A (maintain intensity) 743 691 603 592 −54.1* 
 B (maintain duration) 794 770 730 688 −35.8* 
Accuracy (%) 
 s1 intensity (a.u.) 1600 2200 2800 3400 Linear slope 
 A (maintain intensity) 74.1 76.9 66.1 60.1 −0.53* 
 B (maintain duration) 75.0 78.3 77.7 79.1 0.12 
 s1 duration (ms) 350 450 550 650  
 A (maintain intensity) 69.2 69.7 67.9 69.9 0.00 
 B (maintain duration) 74.0 80.9 80.1 75.4 0.04 
RT (ms) 
 s1 intensity (a.u.) 1600 2200 2800 3400 Linear slope 
 A (maintain intensity) 660 671 660 632 −9.5 
 B (maintain duration) 754 726 746 758 3.2 
 s1 duration (ms) 350 450 550 650  
 A (maintain intensity) 743 691 603 592 −54.1* 
 B (maintain duration) 794 770 730 688 −35.8* 

Note: Discrimination performance. Upper: Mean s1–s2 discrimination accuracy (% correct) in the A- and B-tasks for each s1 intensity and duration, and linear regression slopes. Asterisks indicate significance of a linear trend (Bonferroni corrected). Lower: Same as upper, for RTs (ms), measured relative to the offset of s2.

Average response times (RTs; measured relative to s2 offset) were longer in the B-task (745 ms) when compared with the A-task (656 ms; P < 0.001). This is expected because duration judgments (B-task) require evaluation of s2 in full length. Likewise, RTs in both tasks decreased with increasing stimulus duration (Table 1, lower; both P's < 0.001), but remained stable across stimulus intensities (both P's > 0.50).

Oscillatory EEG Responses

First, we explored possible differences in overall cue-induced TF activity between the 2 task conditions during cued retention (0–2500 ms, Fig. 1B, left). Statistical analysis indicated that, between 700 and 1300 ms after cue onset, activity in the traditional sensorimotor beta band (15–25 Hz) over right centro-parietal channels (C4, C6, CP4, and CP6) was significantly decreased in the A-task compared with the B-task (Pcluster < 0.05, FWE; maximum at channel CP6, 20 Hz, 1050 ms, t = 4.26). SPM 3D source reconstruction attributed this effect to the right primary somatosensory cortex (SI, areas 3b, 1, and 2; Fig. 1B, right; P < 0.001) contralateral to s1 application (for related lateralization analyses, see Spitzer and Blankenburg 2011). No further differences in overall oscillatory activity during the retention interval were found between the 2 tasks.

Also evident from the time-courses illustrated in Figure 1B, lower; in both memory tasks, sensorimotor beta activity was by tendency decreasing throughout the retention interval, in line with previous findings that tactile WM processing per se is typically accompanied by suppression of beta activity in early somatosensory processing areas (e.g., Spitzer and Blankenburg 2011, 2012). Supplementary inspection of this effect in the present data affirmed that the overall power decrease was attributable to traditional beta-band activity (15–25 Hz) in SI (for details, see Supplementary Fig. 2A).

Stimulus-Dependent Parametric Modulations of Prefrontal Beta Activity

To examine parametric modulations of oscillatory activity as a function of the stimulus attribute processed in WM, we analyzed parametric contrasts reflecting the strength of a linear relation between TF activity and the different properties of s1 (i.e., intensity or frequency, cf. Supplementary Fig. 1). Based on our previous work, we focused on right prefrontal channels near electrode F2, where delayed parametric modulations in the upper beta frequency range (20–30 Hz) have been consistently found during WM maintenance of vibrotactile frequency (Spitzer et al. 2010; Spitzer and Blankenburg 2012, see Spitzer and Blankenburg 2011 for evidence that this type of activity is typically right lateralized).

We first inspected the cue-induced parametric modulation by intensity in the A-task, compared with the modulation by intensity in the B-task (Fig. 2A). This contrast revealed a significant (Pcluster < 0.05, FWE) cue-induced parametric modulation in the upper beta frequency range (20–25 Hz) between 250 and 750 ms after cue onset (maximum at channel F4, 22 Hz, 400 ms, t = 3.88). Note that the right prefrontal scalp topography of this effect (Fig. 2A, upper right) might have been reinforced by a priori channel selection. Figure 2A (lower) illustrates the condition-specific time-courses of the average parametric upper beta-band modulations by s1 intensity. In the A-cue condition, when subjects retrospectively focused on s1 intensity, a sustained modulation by intensity was evident between 250 and 1200 ms after cue onset (all time bins P < 0.05). No evidence for a modulation by s1 intensity was found in the B-cue condition (all time bins P > 0.05). Source reconstruction (Fig. 2A, lower right) attributed the parametric modulation by intensity in the A-cue condition to the inferior frontal gyrus (IFG, area 45, P < 0.001) in the right lateral PFC, which conforms with previous localizations of frequency-dependent WM activity (e.g., Spitzer et al. 2010).

Analogously, we examined the cue-induced parametric modulation by duration in the B-task, compared with the modulation by duration in the A-task (Fig. 2B). This contrast revealed a significant (P < 0.05, FWE) cue-induced parametric modulation that was spectrally and topographically similar to the modulation by intensity reported above (23–29 Hz; maximum at channel F2, 27 Hz, 1550 ms, t = 3.26), but emerged in a later time window (1300–1650 ms). The condition-specific time-courses of the average parametric modulations indicate that upper beta was modulated by s1's duration in the B-cue condition (Fig. 2B, lower, 1300–1700 ms, all time bins P < 0.05) where subjects retrospectively focused on duration, but not in the A-cue condition (all time bins P > 0.05), where subjects focused on intensity. Source analysis of the modulation by duration in the B-task yielded no results that exceeded the display threshold. Below threshold, at a considerably reduced level of significance, the effect was attributed to a very similar source as the modulation by intensity (IFG, area 45, see Supplementary Fig. 3).

A descriptive summary of the grand-average changes in upper beta activity as identified in the above analyses is shown in Figure 3A. The linearity of the group averaged data points may only approximately mirror the group statistical analysis of individual subject linearity derived from single-trial GLM analysis (cf. Fig. 2) and is shown for illustrative purpose only.

Figure 3.

(A) Summary illustration of grand-average cue-induced changes in prefrontal upper beta activity as a function of s1 intensity (left) and as a function of s1 duration (right). (B) Performance-related differences. Left: Parametric modulation by s1 intensity during the maintenance of intensity, computed from correct trials (saturated red) compared with incorrect trials (light red). On incorrect trials, the parametric modulation was significantly reduced (z = 3.59, P < 0.001; resampling statistics based on 1000 iterations). Right: Same as left, showing a similar trend for the parametric modulation by s1 duration during the maintenance of duration (z = 1.72, P < 0.10).

Figure 3.

(A) Summary illustration of grand-average cue-induced changes in prefrontal upper beta activity as a function of s1 intensity (left) and as a function of s1 duration (right). (B) Performance-related differences. Left: Parametric modulation by s1 intensity during the maintenance of intensity, computed from correct trials (saturated red) compared with incorrect trials (light red). On incorrect trials, the parametric modulation was significantly reduced (z = 3.59, P < 0.001; resampling statistics based on 1000 iterations). Right: Same as left, showing a similar trend for the parametric modulation by s1 duration during the maintenance of duration (z = 1.72, P < 0.10).

Parametric Modulations on Error Trials

Concluding our analysis, we examined the potential behavioral relevance of the parametric modulation effects reported above by examining the modulations on correct discrimination trials when compared with error trials. To account for the smaller number of incorrect than correct trials, we used resampling statistics (Voytek et al. 2010; Spitzer and Blankenburg 2012) to estimate a surrogate distribution of regression coefficients computed from subsets of randomly drawn correct trials (matched to the number of error trials available per cue and stimulus conditions). For computational efficiency, the resampling analysis was implemented using simple regression analysis instead of the full GLM model used in the main analysis (cf. Supplementary Fig. 1).

Applied to correct trials, single-trial regression replicated the cue-specific modulations by intensity and duration outlined in Figure 2A,B, lower, respectively (mean t-values 2.97 and 2.51; both P's < 0.05). Applied to error trials, in contrast, the analysis showed neither a modulation by intensity in the A condition (t = −0.37; P > 0.70), nor a modulation by duration in the B condition (t = −0.15; P > 0.80). Resampling statistics based on 1000 iterations indicated that, relative to the surrogate distribution's mean, the observed regression coefficient on incorrect trials was significantly decreased in the intensity task (Fig. 3B, left; 0.0119 vs. −0.0033; z = 3.59, P < 0.001), and showed a trend in the same direction for the duration task (Fig. 3B, right, 0.0204 vs. −0.0019, z = 1.72, P < 0.10), overall indicating a reduction in the stimulus-dependent parametric modulations on error trials, compared with correct trials.

Discussion

The presented findings provide an important extension of previous evidence for parametric WM processing of periodic stimulus frequencies (Romo et al. 1999; Barak et al. 2010; Spitzer et al. 2010; Spitzer and Blankenburg 2011, 2012), by demonstrating that oscillatory delay activity in the PFC can be parametrically driven by other stimulus attributes, as well. In particular, during retention of either intensity- or duration information, we identified stimulus-dependent modulations of similar spectral profiles, with maximum modulation in the upper beta frequency range (20–30 Hz), thereby strongly resembling the frequency-dependent parametric WM effects reported recently (Spitzer et al. 2010; Spitzer and Blankenburg 2011, 2012). Like in the previous work, the present parametric EEG modulations were found to relate to successful DMTS task performance and to critically depend on task demands to actively focus on a singular sensory quantity in WM. In particular, the observation of spectrally and topographically similar prefrontal modulations by stationary and nonstationary stimulus attributes indicates that the previously reported frequency-dependent parametric WM phenomena may not be uniquely linked to any specific temporal or nontemporal properties of periodic frequencies. Together, the present and previous findings corroborate a view of parametric WM processing in the PFC as a generalized mechanism for internal representation of abstract quantity information, which may be derived for manifold perceptual dimensions and/or sensory modalities (cf. Spitzer and Blankenburg 2012).

Although the parametric modulations by intensity and duration were spectrally and topographically similar, the intensity effect showed a slightly more anterior prefrontal scalp distribution, and by tendency covered lower frequencies within the upper beta range (20–30 Hz). Due to limitations in the spatial precision of noninvasive EEG recordings, we cannot conclusively infer whether the minor differences on the scalp level may represent different reflections of the same underlying cortical process, or may reflect a similar encoding of the 2 stimulus attributes in adjacent prefrontal areas, which may have been selectively engaged depending on the cue. At least suggestively, 3D source localization attributed the present prefrontal effects to the IFG in the lateral PFC, which conforms very well with previous source localizations of upper beta modulations during periodic frequency maintenance (Spitzer et al. 2010; Spitzer and Blankenburg 2011, 2012) and with the homolog area found to exhibit quantitative WM coding in monkeys' lateral PFC (Romo et al. 1999; Barak et al. 2010).

A prominent difference between the oscillatory representations of the 2 stimulus attributes was their differential time-course during cued retention, with the modulation by intensity arising considerably earlier than the modulation by duration. Notably, very similar time-courses of upper beta modulations were observed in our previous retro-cue study of vibrotactile frequencies, where a quantitative WM representation of more difficult to-be-maintained information arose very early (at similar times as the present intensity effect), whereas more easily remembered information was parametrically represented only after a delay (at similar times as the present duration effect; cf. Fig. 3. in Spitzer and Blankenburg 2011). This pattern is paralleled by our present findings, where s1–s2 discrimination of intensity appeared more difficult than discrimination of the stimuli's duration (cf. Behavioral Results). We may speculate that, in the retro-cue paradigm, subjects may have put greater emphasis a priori on the more difficult of the 2 stimulus attributes (here: intensity), which may have facilitated an early prefrontal representation of the to-be-maintained information. To test this possibility in a post hoc analysis, we examined the brain–behavior correlation between the early cue-induced modulation by intensity (as identified in Fig. 2A, left) and the individual difference in behavioral A- versus B- task performance, which can be seen as a coarse indicator of the extent to which individual subjects may have prioritized intensity- over duration-encoding. Indeed, a positive correlation was found (RPearson = 0.51, P < 0.01), corroborating that anticipatory prioritization may have contributed to the relatively early onset of the prefrontal representation of intensity information.

Independent from the stimulus-dependent modulations of upper beta (20–30 Hz) in PFC, traditional beta-band power (15–25 Hz) in somatosensory areas was overall decreasing throughout the retention interval (cf. Fig. 1A, lower and Supplementary Fig. 2A), in line with previous findings that tactile WM processing per se is accompanied by suppression of sensorimotor beta oscillations (e.g., Spitzer and Blankenburg 2011, 2012). Here, the suppression in SI was found to be transiently reduced during the maintenance of duration, compared with intensity (cf. Fig. 1B). Beta suppression in SI, in the absence of stimulation, is typically related to tactile attention (e.g., Bauer et al. 2006; van Ede et al. 2010; Spitzer and Blankenburg 2011). The presented pattern of SI activity thus suggests a temporary disengagement of tactile attention during retrospective focusing on s1's duration. Interestingly, this effect was evident just before the quantitative representation of duration in the PFC was established (cf. Fig. 1B vs. Fig. 2B), corroborating that quantitative evaluation of the stimuli's duration involved more abstract, nonsensory processing. Aside from these task-dependent differences, however, like in our previous investigations of vibrotactile frequencies, we found no evidence for any delayed parametric representation of the task-relevant quantitative stimulus attributes in early sensory areas (for a review of similar findings, see Romo and Salinas 2003; but see Harris et al. 2002; Wang et al. 2012). In reverse, aside from the specific stimulus-dependent modulations discussed above, right prefrontal upper beta did not reflect overall (i.e., stimulus-independent) differences between the 2 tasks. Together, the present results converge with previous findings that stimulus-dependent prefrontal upper beta modulations are functionally dissociable from sensorimotor activity in the traditional beta band (Spitzer and Blankenburg 2011, 2012).

Why and how should prefrontal upper beta temporarily increase as a function of the to-be-maintained quantitative stimulus attribute? One potential interpretation might be in terms of increased cognitive demands, as might be inferred from decreased discriminability of greater sensory magnitudes, according to the Weber–Fechner law (Weber 1834). However, such view of the present findings appears unlikely, given that s1–s2 discrimination performances declined with increasing stimulus intensity only, but not with increasing duration (cf. Behavioral Results). Moreover, stimulus-dependent prefrontal modulations during s1 processing were evident only on correct, but not on incorrect trials, in line with recent compelling evidence against any direct relation between right prefrontal beta amplitudes and task demands in similar WM tasks (Spitzer and Blankenburg 2011, 2012). An alternative interpretation may be that, in WM, greater sensory magnitudes might appear more salient. In this light, modulations of prefrontal upper beta could be seen as form of mnemonic bottom-up activity, with stimulus-dependent processing induced by the memory, rather than the actual presence, of specific sensory input.

In terms of a more mechanistic account for the present findings, we propose that the prefrontal modulations in upper beta power are more directly linked to the abstract quantity information, which is processed in WM. Parametric coding of analog sensory quantities has been extensively studied in monkeys (for review, see Romo and Salinas 2003). In particular, during WM maintenance in PFC, such coding was characterized by complex parametric modulations of single cells' firing rates and by dynamic changes of small population states over time (Romo et al. 1999; Barak et al. 2010). The present oscillatory modulations in the human PFC may reflect a large-scale correlate of such a dynamically changing quantitative representation of the task-relevant WM contents (for related evidence, see Haegens et al. 2011; Spitzer and Blankenburg 2011). As such, modulations of prefrontal upper beta may reflect a population-level aspect of the neural code that represents the task-relevant quantitative information in WM. We speculate that the parametric (i.e., continuous) nature of such coding corresponds to an abstract concept of analog quantity, which might be necessary to perform the subsequent quantitative comparison against s2 (for related discussion, see Nieder and Merten 2007; Verguts 2007).

We recently proposed that parametric prefrontal modulations observed in human EEG may particularly indicate active updating of WM with the to-be-maintained stimulus attribute's quantitative value on an abstract internal scale (Spitzer and Blankenburg 2011, 2012). In light of such interpretation, an additional factor may have contributed to the observation that the modulation by s1 duration occurred relatively late in the retention interval. Quantitative scaling of s1's duration may have been achieved only after a mental replay of the entire stimulation period (length: 350–650 ms, see Stimuli and Behavioral Task). Thus, in addition to a potential reorientation to the less-attended stimulus attribute, the retrospective WM processing, or imagery, of time (cf. Coull et al. 2008) may have further delayed the parametric prefrontal representation by duration, compared with the representation by s1's stationary intensity. In both memory tasks, however, like in the previous studies of periodic frequency information (Spitzer and Blankenburg 2011, 2012), the parametric EEG modulations were transient, suggesting that further maintenance of a single quantity value may not necessarily require continuous WM updating throughout the entire retention period.

In a broader context of previous EEG/MEG investigations of human WM, mostly in the visual and auditory domains, “parametric” WM processes have typically been assessed by varying the number of discrete, simultaneously to-be-maintained items (i.e., the WM “load”). Oscillatory WM effects associated with load manipulations included parametric modulations of alpha- (∼8–13 Hz; e.g., Jensen et al. 2002; Tuladhar et al. 2007), theta- (∼4–8 Hz; e.g., Jensen and Tesche 2002), and gamma-band amplitudes (>30 Hz; e.g., Howard et al. 2003; Roux et al. 2012). In particular, delay activity in the theta- and gamma bands has been related to the active maintenance of “sensory” representations, with additional phase–amplitude interactions between the 2 bands supporting the simultaneous representation of multiple items in WM (e.g., Jensen 2006; Sauseng et al. 2009; Axmacher et al. 2010). The present quantitative WM task required the abstraction and maintenance of a single stimulus attribute only, and focal upper beta modulations in PFC selectively reflected the one task-relevant quantity in the current focus of attention (for similar results, see Spitzer and Blankenburg 2011). Based on these basic findings, an avenue for future research can be the integration of analog quantity information on the level of multi-item WM, which may involve more complex interactions between different frequency bands, and/or brain areas.

To conclude, we demonstrated parametric WM processing in the human PFC for different quantitative stimulus attributes, reflected by systematic modulations of upper beta oscillatory activity. The findings extend previous research on WM maintenance of periodic frequencies and suggest a generalized mechanism for the purposeful updating of abstract quantity information in human WM.

Supplementary Material

Supplementary material can be found at: http://www.cercor.oxfordjournals.org/.

Funding

This research was supported by a grant from the German Federal Ministry of Education and Research (BMBF) to F.B.

Notes

We thank R. Auksztulewicz and 2 anonymous reviewers for helpful comments on the manuscript. Conflict of Interest: None declared.

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