Materials and Methods<\/h2>\n\n\n\nParticipants<\/h3>\n\n\n\n
Sample size was estimated based on our previous study (Rodriguez-Larios, Elshafei, Wiehe, & Haegens, 2022), in which we reported condition-related modulations in behavior during a working-memory task with effect sizes between 0.51 and 0.59 (Cohen\u2019s\u00a0d<\/em>). Assuming similar effect sizes, our sample size calculation ranged between 25 and 33 participants. We collected data from 31 participants, taking into account that approximately 10% of EEG data would be discarded due to uncorrectable artifacts.<\/p>\n\n\n\n Thirty-one healthy adult participants (13 male) took part in the experiment. The mean age was 32.5 years (SD<\/em>\u00a0= 8.5). Participants reported normal or corrected-to-normal vision and no history of neurologic or psychiatric diagnosis. Informed consent procedure and study design were approved by the Institutional Review Board (IRB) of the New York State Psychiatric Institute (Nr. 8001). Participants were compensated for their participation (at 25 USD per hour). Since 4 participants were excluded from the analysis due to uncorrectable EEG artifacts or technical problems during data acquisition, our final sample size was 27.<\/p>\n\n\n\n Participants performed a visual working-memory task while EEG was recorded (see\u00a0Figure 1<\/a>A). First a fixation cross was presented for 1 s. Then, participants were presented with one (or three) angles in a circle. Following a delay period of 3 s, a cue was presented for 1 s. The cue was either \u2018stay\u2019, meaning that the correct answer was the presented angle, or \u2018switch\u2019, indicating that the correct answer was the opposite angle. After the instruction, a response mapping diagram was shown, indicating which button number (1 to 8) corresponded to which angle. This response map was randomized in each trial. After each response, participants received feedback based on their answer (a green circle for correct answers and a red circle for incorrect answers). Participants performed four blocks of 48 trials in approximately 1 hour (192 trials per participant).<\/p>\n\n\n\n Figure 1<\/strong><\/p>\n\n\n\n Spatial Working Memory Task and Behavioral Performance.\u00a0(A)<\/strong>\u00a0Participants were asked to remember the angle of one or three visual stimuli (Load 1 or Load 3). Based on the subsequent instruction, they had to report the presented angle (Stay) or its opposite (Switch) using a computer keyboard. The color in the figure codes for the correct answer in this exemplary trial when the instruction was \u2018Stay\u2019 (orange; answer 8) and \u2018Switch\u2019 (green; answer 4).\u00a0(B)<\/strong>\u00a0Participants showed significantly lower accuracy and higher response times in the condition Load 3 Switch relative to the other three conditions (Load 1 Stay, Load 1 Switch, Load 3 Stay). On each boxplot, the central red line indicates the median, and the bottom and top edges of the box indicate the 25th<\/sup>\u00a0and 75th<\/sup>\u00a0percentiles, respectively. The whiskers extend to the most extreme data points not considered outliers.<\/em><\/p>\n\n\n\n 96-electrode scalp EEG was collected using the BrainVision actiCAP system (Brain Products GmbH, Munich, Germany) with a sampling rate of 500 Hz. Electrodes were labeled according to the international 10-20 system and the reference electrode during the recording was Cz. Amplification and digitalization of the EEG signal was done through an actiCHamp DC amplifier (Brain Products GmbH, Munich, Germany) linked to BrainVision Recorder software (version 2.1, Brain Products GmbH, Munich, Germany). \ufeffVertical (VEOG) and horizontal (HEOG) eye movements were recorded by placing additional bipolar electrodes above and below the left eye (VEOG) and next to the left and right eye (HEOG). In addition, electrocardiogram (ECG) was also recorded using bipolar electrodes.<\/p>\n\n\n\n Pre-processing was performed in MATLAB R2021a using custom scripts and functions from EEGLAB (Delorme & Makeig, 2004) and Fieldtrip (Oostenveld, Fries, Maris, & Schoffelen, 2011) toolboxes. Data were first resampled to 250 Hz and filtered between 1 and 40 Hz. Noisy electrodes were automatically detected (EEGLAB function clean_channels) and interpolated. A mean of 9.7 channels (SD<\/em>\u00a0= 6.5) were interpolated. EEG data were re-referenced to the common average and independent component analysis (runica algorithm) was performed. An automatic component rejection algorithm (Iclabel) was employed to discard components associated with muscle activity, eye movements, heart activity or channel noise (threshold = 0.8; see (Pion-Tonachini, Kreutz-Delgado, & Makeig, 2019)). In addition, components with an absolute correlation with HEOG, VEOG or ECG channels higher than 0.8 were discarded. The mean number of rejected components was 18.1 (SD\u00a0<\/em>= 5.7). Furthermore, Artifact Subspace Reconstruction (ASR) was employed to correct for abrupt noise with a cutoff value of 20\u00a0SD\u00a0<\/em>(Chang, Hsu, Pion-Tonachini, & Jung, 2020).<\/p>\n\n\n\n Single trial EEG data (3 second windows) were transformed to the time-frequency domain using 6-cycles Morlet wavelets (as implemented in the matlab function BOSC_tf) (Whitten, Hughes, Dickson, & Caplan, 2011). In order to avoid edge artifacts (Torrence & Compo, 1998), we excluded timepoints corresponding to epoch boundaries (3 cycles of each Morlet wavelet, which is equivalent to the first and last 70-200 ms). No temporal smoothing was used, the frequency resolution was 1 Hz, and the analyzed band ranged from 1 to 40 Hz. Amplitude at each frequency was extracted from the real component of the convolution between the EEG signal and the family of wavelets. We used an estimate of 1\/f aperiodic activity as amplitude threshold to detect oscillatory bursts (see\u00a0Figure 2A). Aperiodic activity was estimated per electrode and participant by fitting a straight line in log\u2013log space to the average EEG frequency spectrum after excluding frequencies forming the maximum peak (Caplan, Bottomley, Kang, & Dixon, 2015;\u00a0Goyal et al., 2020;\u00a0Kosciessa, Grandy, Garrett, & Werkle-Bergner, 2020). Oscillatory bursts were defined as time points in which the amplitude at a specific frequency exceeded the estimate of aperiodic activity for at least a cycle. In order to rule out the possibility that the detected oscillatory bursts artifactually originated from broadband changes or from a different rhythm at another frequency, only oscillatory bursts that formed the peak with the greatest prominence of the 1\/f-subtracted frequency spectrum were selected (see\u00a0Figure 2B). Using this algorithm, four burst parameters were estimated for the beta range (15-40 Hz): i) amplitude: mean prominence of the peaks of the detected bursts after subtracting aperiodic activity, ii) duration: mean number of cycles of detected oscillatory bursts, iii) rate: mean number of oscillatory bursts, and iv) frequency: mean peak frequency of detected oscillatory bursts (see\u00a0Figure 2C, for topography of each of the extracted parameters when averaging across conditions and participants). The MATLAB code of the beta burst algorithm and supplementary figures can be accessed here: https:\/\/osf.io\/fkhnb\/?view_only=13d1c0ac76574be6a1ba7f300fdd98e<\/a><\/p>\n\n\n\n Figure 2<\/strong><\/p>\n\n\n\n Beta Burst Detection Algorithm and Topography of Beta Burst Parameters.\u00a0(A)<\/strong>\u00a0Time-frequency representation of a trial. The estimate of aperiodic activity (in grey) was used as power threshold to detect beta bursts.\u00a0(B)<\/strong>\u00a0Depiction of the time-frequency representation of the same trial after subtraction of the estimate of 1\/f aperiodic activity (left panel), and frequency spectra of two exemplary beta bursts (right panels). In order to rule out the possibility that detected beta bursts are caused by the non-sinusoidal properties of lower frequency rhythms, only bursts that show a maximum spectral peak in the beta range were selected. An example of a selected burst is depicted in green and an example of a rejected burst is depicted in red.\u00a0(C)<\/strong>\u00a0Topographical representation of each of the extracted burst parameters (frequency, amplitude, rate and duration) across participants and conditions. Color indicates the average value per electrode.<\/em><\/p>\n\n\n\n For the behavioral analysis, a two-way repeated-measures ANOVA and post-hoc\u00a0t-<\/em>tests were performed with the JASP software (Love et al., 2019). Cognitive load (3 items vs 1 item) and memory manipulation (switch vs stay instructions) were defined as factors. This analysis was performed for accuracy and reaction times separately. The effect size was estimated with eta squared (\u03b72<\/sup>\u00a0<\/em>) and Bonferroni correction was applied for multiple comparisons.<\/p>\n\n\n\n For the EEG data, a cluster-based permutation statistical test (Maris & Oostenveld, 2007) was used to assess condition-related differences in each beta burst parameter. This test controls for the type I error rate arising from multiple comparisons using a non-parametric Montecarlo randomization and taking into account the dependency of the data. First, cluster-level test statistics are estimated in the original data and in 1,000 permutations of the data. Cluster-level test-statistics are defined as the sum of\u00a0t<\/em>-values with the same sign across adjacent electrodes that are above a specified threshold (i.e., 97.5th quantile of a\u00a0t<\/em>-distribution). Then, the cluster-level statistics from the original data were evaluated using the reference distribution obtained by taking the maximum cluster\u00a0t<\/em>-value of each permutation. Cluster-corrected\u00a0p<\/em>-values are defined as the proportion of random partitions whose cluster-level test statistic exceeded the one obtained in the observed data. Significance level for the cluster permutation test was set to 0.025 (corresponding to a false alarm rate of 0.05 in a two-sided test). Paired-samples\u00a0t<\/em>-test was chosen as the first-level statistic to compare experimental conditions (Delay vs Fixation, Load 3 vs Load 1 and Switch vs Stay), and the Pearson correlation coefficient was chosen to assess correlations between each of the beta bursts parameters and performance (reaction time and accuracy). The effect size was estimated for each significant cluster with Cohen\u2019s\u00a0d<\/em>.<\/p>\n\n\n\n In order to assess the frequency-specificity of the results in each beta burst parameter, we performed the same statistical tests in a frequency-resolved manner. For each experimental condition, we averaged the output of the beta burst algorithm (amplitude, duration and rate) across electrodes forming previously identified significant clusters. Then, we compared conditions at each frequency (15-40 Hz in steps of 1 Hz) using paired samples\u00a0t<\/em>-tests. The obtained\u00a0p<\/em>-values were corrected for multiple comparisons using the False Discovery Rate method (FDR) (Benjamini & Hochberg, 1995).<\/p>\n\n\n\n Repeated measures ANOVA on accuracy revealed a significant main effect of cognitive load (F<\/em>(1,26) = 14.05;\u00a0p<\/em>\u00a0< .001;\u00a0\u03b72<\/sup>\u00a0<\/em>= 0.23), a significant main effect of memory manipulation (F<\/em>(1,26) = 11.34;\u00a0p<\/em>\u00a0< 0.001;\u00a0\u03b72<\/sup>\u00a0<\/em>= 0.048) and a significant cognitive load by memory manipulation interaction (F<\/em>(1,26) = 16.44;\u00a0p<\/em>\u00a0< .001;\u00a0\u03b72<\/sup>\u00a0<\/em>= 0.070). Post-hoc\u00a0t<\/em>-tests showed that cognitive load only affected accuracy in the memory manipulation condition (lower accuracy in Load 3 Switch relative to Load 1 Switch;\u00a0t<\/em>(26) = 5.17;\u00a0pbonf<\/sub>\u00a0<\/em>< .001) while memory manipulation only affected accuracy when load was high (lower accuracy in Load 3 Switch relative to Load 3 Stay;\u00a0t<\/em>(26) = 5.24;\u00a0pbonf<\/sub>\u00a0<\/em>< .001). In addition, participants showed significantly less accuracy in high load with memory manipulation condition (Load 3 Switch) relative to the low load without memory manipulation condition (Load 1 Stay;\u00a0t<\/em>(26) = 4.86;\u00a0pbonf<\/sub>\u00a0<\/em>< .001; see\u00a0Figure 1B, left panel for descriptives of accuracy per condition using boxplots, showing significant differences between conditions with asterisks).<\/p>\n\n\n\n Similarly, repeated measures ANOVA on reaction time revealed a significant main effect of cognitive load (F<\/em>(1,26) = 13.15;\u00a0p<\/em>\u00a0= .001;\u00a0\u03b72<\/sup>\u00a0<\/em>= 0.11), a significant main effect of memory manipulation (F<\/em>(1,26) = 79.27;\u00a0p<\/em>\u00a0< .001;\u00a0\u03b72<\/sup>\u00a0<\/em>= 0.24) and a significant cognitive load by memory manipulation interaction (F<\/em>(1,26) = 98.82;\u00a0p<\/em>\u00a0< .001;\u00a0\u03b72<\/sup>\u00a0<\/em>= 0.26). Post-hoc t-tests showed that cognitive load only affected response times in the memory manipulation condition (lower reaction time in Load 3 Switch relative to Load 1 Switch;\u00a0t<\/em>(26) = -8.02;\u00a0pbonf<\/sub>\u00a0<\/em>< .001) while memory manipulation only affected response times when load was high (lower reaction time in Load 3 Switch relative to Load 3 Stay;\u00a0t<\/em>(26) = -13.29;\u00a0pbonf<\/sub>\u00a0<\/em>< .001). Participants also showed significantly higher response times in high load with memory manipulation condition (Load 3 Switch) relative to the low load without memory manipulation condition (Load 1 Stay;\u00a0t<\/em>(26) = -7.66;\u00a0pbonf<\/sub>\u00a0<\/em>< .001; see\u00a0Figure 1B, right panel for descriptives of reaction time per condition using boxplots, showing significant differences between conditions with asterisks).<\/p>\n\n\n\n Contrasting the first delay window with the fixation window, we assessed beta burst modulations associated with memory retention and found a significant decrease in beta burst amplitude (tcluster<\/sub>\u00a0<\/em>= -341.30;\u00a0pcluster<\/sub>\u00a0<\/em>< .001;\u00a0d<\/em>\u00a0= 0.40; see\u00a0Figure 3A, for the topographical distribution of\u00a0t<\/em>-values and the descriptives per condition across significant electrodes), a significant decrease in beta burst duration (tcluster<\/sub>\u00a0<\/em>= -35.82;\u00a0pcluster<\/sub>\u00a0<\/em>= .002;\u00a0d<\/em>\u00a0= 0.40;\u00a0Figure 3B) and significant increase in beta burst frequency (tcluster<\/sub>\u00a0<\/em>= 87.56;\u00a0pcluster<\/sub>\u00a0<\/em>= .002;\u00a0d<\/em>\u00a0= 0.33;\u00a0Figure 3C). The topographical distribution of significant modulations was widespread for burst amplitude and burst frequency. On the other hand, the effect on burst duration was limited to centro-parietal electrodes. No significant differences were found for beta burst rate (pcluster<\/sub>\u00a0> .05<\/em>;\u00a0Figure 3D). The average frequency spectra of beta bursts in each condition across electrodes are depicted in\u00a0Figure 3E.<\/p>\n\n\n\n Figure 3<\/strong><\/p>\n\n\n\n Changes in Beta Burst Parameters During Memory Retention.\u00a0(A-D)\u00a0<\/strong>The left panel depicts the topographical distribution of\u00a0t-values for the comparison Delay 1 vs Fixation for each beta burst parameter. Asterisks indicate significant clusters at\u00a0p\u00a0< 0.025. The right panel shows individual values (in grey) and boxplots of significant clusters per condition. When no significant clusters are present, boxplots depict the average across electrodes. On each boxplot, the central red line indicates the median, and the bottom and top edges of the box indicate the 25th<\/sup>\u00a0and 75th<\/sup>\u00a0percentiles, respectively. The whiskers extend to the most extreme data points not considered outliers.\u00a0(E)<\/strong>\u00a0Mean frequency spectrum of detected beta bursts per condition. For visualization purposes, the 1\/f corrected spectra of the detected beta bursts were averaged within conditions and across electrodes after z-scoring over the frequency dimension. Shaded areas reflect variability across subjects (standard error).\u00a0(F)<\/strong>\u00a0Mean waveform shape of detected beta bursts. For visualization purposes, the time course of the detected beta bursts was averaged across conditions and across electrodes after they were aligned to their maximum peak (timepoint 0). Shaded areas reflect standard error across subjects.<\/em><\/p>\n\n\n\n Next, we contrasted beta burst parameters across load conditions. Cognitive load was associated with a significant decrease in beta burst amplitude (tcluster<\/sub>\u00a0<\/em>= -176.49;\u00a0pcluster<\/sub>\u00a0<\/em>< 0.001;\u00a0d<\/em>\u00a0= 0.32; see\u00a0Figure 4A for the topographical distribution of\u00a0t<\/em>-values and the descriptives per condition across significant electrodes), a significant decrease in beta burst duration (tcluster<\/sub>\u00a0<\/em>= -248.39;\u00a0pcluster<\/sub>\u00a0<\/em>< .001;\u00a0d<\/em>\u00a0= 0.67;\u00a0Figure 4B), a significant increase in beta burst frequency (tcluster<\/sub>\u00a0<\/em>= 165.20;\u00a0pcluster<\/sub>\u00a0<\/em>< .001;\u00a0d<\/em>\u00a0= 0.36;\u00a0Figure 4C) and a significant increase in beta burst rate (tcluster<\/sub>\u00a0<\/em>= 87.56;\u00a0pcluster<\/sub>\u00a0<\/em>= .008;\u00a0d<\/em>\u00a0= 0.19;\u00a0Figure 4D). The topographical distribution of significant modulations was widespread for burst amplitude, duration and frequency, while effects on beta burst rates were more constrained to central electrodes. The average frequency spectra of beta bursts in each condition across electrodes are depicted in\u00a0Figure 4E.<\/p>\n\n\n\n Figure 4<\/strong><\/p>\n\n\n\n Changes in Beta Burst Parameters Associated With Memory Load.<\/em>\u00a0(A-D)\u00a0<\/strong>The left panel depicts the topographical distribution of\u00a0t<\/em>-values for the comparison Load 3 vs Load 1 for each beta burst parameter. Asterisks indicate significant clusters at\u00a0p<\/em>\u00a0< 0.025. The right panel shows individual values (in grey) and boxplots of significant clusters per condition. On each boxplot ,the central red line indicates the median, and the bottom and top edges of the box indicate the 25th<\/sup>\u00a0and 75th<\/sup>\u00a0percentiles, respectively. The whiskers extend to the most extreme data points not considered outliers.\u00a0(E)<\/strong>\u00a0Mean frequency spectrum of detected beta bursts per condition. For visualization purposes, the 1\/f corrected spectra of the detected beta bursts were averaged within conditions and across electrodes after\u00a0z<\/em>-scoring over the frequency dimension. Shaded areas reflect variability across subjects (standard error).\u00a0(F)<\/strong>\u00a0Mean waveform shape of detected beta bursts. For visualization purposes, the time course of the detected beta bursts was averaged across conditions and across electrodes after they were aligned to their maximum peak (timepoint 0). Shaded areas reflect standard error across subjects.<\/p>\n\n\n\n In order to assess the effect of memory manipulation on beta burst parameters, we contrasted switch and stay conditions during the second memory delay. Memory manipulation was associated with a significant decrease in beta burst amplitude (tcluster<\/sub>\u00a0<\/em>= 75.41;\u00a0pcluster<\/sub>\u00a0<\/em>= .002;\u00a0d<\/em>\u00a0= 0.18; see\u00a0Figure 5A, for the topographical distribution of\u00a0t<\/em>-values and the descriptives per condition across significant electrodes), a significant decrease in beta burst duration (tcluster<\/sub>\u00a0<\/em>= -103.00;\u00a0pcluster<\/sub>\u00a0<\/em>< .001;\u00a0d<\/em>\u00a0= 0.40;\u00a0Figure 5B), a significant increase in beta burst frequency (tcluster<\/sub>\u00a0<\/em>= 29.55;\u00a0pcluster<\/sub>\u00a0<\/em>< .001;\u00a0d<\/em>\u00a0= 0.30;\u00a0Figure 5C) and a significant increase in beta burst rate (tcluster<\/sub>\u00a0<\/em>= 52.30;\u00a0pcluster<\/sub>\u00a0<\/em>= .006;\u00a0d<\/em>\u00a0= 0.11;\u00a0Figure 5D). The topographical distribution of significant modulations was centro-parietal for burst amplitude, widespread for burst duration and limited to posterior electrodes for burst frequency and burst rate. The average frequency spectra of beta bursts in each condition across electrodes are depicted in\u00a0Figure 5E.<\/p>\n\n\n\n Figure 5<\/strong><\/p>\n\n\n\n Changes in Beta Burst Parameters Associated With Memory Manipulation.\u00a0(A-D)<\/strong>\u00a0The left panel depicts the topographical distribution of t-values for the comparison of conditions Switch and Stay for each beta burst parameter. Asterisks indicate significant clusters at\u00a0p\u00a0< 0.025. The right panel shows individual values (in grey) and boxplots of significant clusters per condition. On each boxplot, the central red line indicates the median, and the bottom and top edges of the box indicate the 25th<\/sup>\u00a0and 75th<\/sup>\u00a0percentiles, respectively. The whiskers extend to the most extreme data points not considered outliers.\u00a0(E)<\/strong>\u00a0Mean frequency spectrum of detected beta bursts per condition. For visualization purposes, the 1\/f corrected spectra of the detected beta bursts were averaged within conditions and across electrodes after z-scoring over the frequency dimension. Shaded areas reflect variability across subjects (standard error).\u00a0(F)<\/strong>\u00a0Mean waveform shape of detected beta bursts. For visualization purposes, the time course of the detected beta bursts was averaged across conditions and across electrodes after they were aligned to their maximum peak (timepoint 0). Shaded areas reflect standard error across subjects.<\/em><\/p>\n\n\n\n Finally, we asked whether beta burst parameters correlated with working-memory performance. Note that this analysis was performed across conditions (i.e., averaging beta burst parameters and performance across all trials). No significant clusters were identified when assessing the relationship between accuracy and each of the four beta burst parameters during the first or second memory delay (all\u00a0pcluster<\/sub>\u00a0<\/sub><\/em>> .05). When assessing interindividual differences in response times, we found a significant positive cluster for beta burst rate during the first delay(tcluster<\/sub>\u00a0<\/em>= 34.90;\u00a0pcluster<\/sub>\u00a0<\/em><\/sub>= .02;\u00a0mean r-value\u00a0<\/sub><\/em>= 0.47). However, further analysis suggested that this correlation was driven by outliers in the data (see Supplementary Figure 5). No significant clusters were identified for the remaining beta burst parameters (all\u00a0pcluster<\/sub>\u00a0<\/em><\/sub>> .05).<\/p>\n\n\n\n The definition of the beta frequency range varies across studies (Spitzer & Haegens, 2017), which could lead to misinterpretation of our results. For this reason, we performed additional analyses to assess the frequency specificity of our results. In short, we performed the same statistical tests as above in a frequency-resolved manner after averaging across electrodes forming significant clusters.<\/p>\n\n\n\n This exploratory analysis revealed that memory load and memory manipulation effects in burst amplitude and duration were more prominent in the lower beta band (<20 Hz), while changes in beta burst rates tended to appear in the upper beta band (>20 Hz; see Supplementary Figures 2 & 3). In contrast, modulations during memory delay (relative to fixation) in burst amplitude and duration were present in most of the beta frequency range (see Supplementary Figures 1 & 4).<\/p>\n\n\n\n Timing is an important aspect of oscillatory activity that is not normally quantified through a burst detection approach. In this way, it is possible that beta bursts occurred in different time periods of the trial in different conditions. To assess this possibility, we quantified the beginning of each beta burst (timing relative to stimulus presentation) and compare their \u2018mean start time\u2019 between conditions. This analysis showed that, in posterior electrodes, beta bursts during the memory delay tended to occur earlier in the trial relative to the fixation period (tcluster<\/sub>\u00a0<\/em>= -72.25;\u00a0pcluster<\/sub>\u00a0<\/em><\/sub>= .009;\u00a0d<\/em>\u00a0= 0.40; Supplementary Figure 5). On the other hand, no significant differences for the memory load or memory manipulation effects(pcluster<\/sub>\u00a0<\/em><\/sub>> .05) and no significant correlations between burst timing and performance were found (pcluster<\/sub>\u00a0<\/em><\/sub>> .05).<\/p>\n\n\n\n Since we define \u201cgenuine\u201d beta bursts as those oscillatory bursts in the beta range that are not accompanied by more pronounced oscillations in lower frequencies, it is possible that the reported modulations are driven by changes in the number of lower frequency bursts. For example, the reported increase in \u201cgenuine\u201d beta burst rates with memory load and memory manipulation could have emerged from a decrease in the number of alpha or theta bursts (as this would result in a relative increase in the number of beta bursts that are not accompanied by a lower-frequency rhythm). In order to rule out this possibility, we assessed condition-related modulations in beta bursts that co-occurred with a more prominent lower-frequency rhythm (i.e., \u201cnon-genuine\u201d beta bursts). Crucially, this analysis showed no significant memory load or memory manipulation effects in \u201cnon-genuine\u201d beta burst rates (see Supplementary Figure 6B-C). Therefore, the reported increases in the rate of \u201cgenuine\u201d beta bursts during memory load and memory manipulation cannot be explained by decreases in the rate of \u201cnon-genuine\u201d beta bursts that co-occurred with lower frequency rhythms.<\/p>\n\n\n\n Interestingly, these control analyses also revealed that unlike \u201cgenuine\u201d beta bursts, \u201cnon-genuine\u201d beta bursts (i.e., those co-occurring with a more prominent lower-frequency rhythm) increased their rate during delay relative to fixation (see Supplementary Figure 6A). The increase in the number of \u201cnon-genuine\u201d beta bursts during the memory delay should be interpreted with caution as they could originate from non-sinusoidalities of lower frequency oscillations. In fact, a visual inspection of the spectrum and waveform shape of \u201cnon-genuine\u201d beta bursts suggests that they reflect alpha activity (~8\u201314 Hz; see Supplementary Figure 6D-E).<\/p>\n\n\n\n Here, we investigated the functional relevance of beta oscillations in human working memory. We collected 96-electrode EEG data while participants performed a spatial working-memory task. Critically, we controlled for the possible influence of lower frequency rhythms with non-sinusoidal properties on beta-band dynamics. Specifically, we developed an algorithm that detects oscillatory bursts in the beta range that do not co-occur in time or space with more prominent rhythms in lower frequencies. Our results show significant modulations in several beta burst parameters during memory retention and manipulation. Both memory load and memory manipulation were associated with decreased beta burst amplitude, decreased beta burst duration, increased beta burst rate and increased beta burst peak frequency. In addition, we found that only beta burst rate showed a significant relation with performance: participants with slower response times tended to have a higher rate of beta bursts during the memory delay. Together, these results show that beta oscillations that cannot be attributed to non-sinusoidal properties of lower frequency rhythms are significantly modulated during working memory and predict performance.<\/p>\n\n\n\n Previous literature on the role of beta oscillations in human working memory focused almost exclusively on amplitude modulations, with seemingly contradictory results (Pavlov & Kotchoubey, 2020). Memory load and memory manipulation have been associated with both increases (Chen & Huang, 2016;\u00a0Deiber et al., 2007;\u00a0Tallon-Baudry, Bertrand, Peronnet, & Pernier, 1998) and decreases in beta amplitude (Erickson, Smith, Albrecht, & Silverstein, 2019;\u00a0Nasrawi & Ede, 2022;\u00a0Pavlov & Kotchoubey, 2020;\u00a0Proskovec, Wiesman, Heinrichs-Graham, & Wilson, 2018). There are two main difficulties that make these previous results hard to interpret. First, given the burst-like nature of beta oscillations (Ede, Quinn, Woolrich, & Nobre, 2018;\u00a0Jones, 2016), changes in amplitude as estimated with conventional analyses could be confounded by changes in other parameters such as rate or duration of beta bursts (Donoghue, Schaworonkow, & Voytek, 2022). Secondly, putative modulations in beta amplitude could also be produced artifactually due to changes in a lower frequency rhythm with non-sinusoidal properties (Cole & Voytek, 2017;\u00a0Schaworonkow & Nikulin, 2019). Here, we control for the influence of these two factors when estimating beta amplitude, and show that beta decreases with cognitive load and during memory manipulation. Thus, our beta burst algorithm might help reconcile previous results if applied to other data sets (see Methods for access to the analysis code). By controlling for the possible influence of lower frequency rhythms and separating the contribution of different beta burst parameters, we can robustly assess whether previous inconsistencies in the literature are due to the analytical approach and\/or to other factors such as the modality or difficulty of the adopted working-memory task.<\/p>\n\n\n\n The here reported modulations in the amplitude and frequency of beta oscillations can be interpreted as changes in cortical excitability. On the one hand, the amplitude of oscillations in the alpha\/beta range in humans has been associated with decreased broadband high frequency activity (BHA) (Iemi et al., 2022), which is thought to provide a measure of local neuronal excitability (Leszczy\u0144ski et al., 2020;\u00a0Ray & Maunsell, 2011). On the other hand, modelling work suggests that increases in the peak frequency of neural oscillations are accompanied by increases in spiking activity of individual neurons (Mierau, Klimesch, & Lefebvre, 2017). In light of these studies, a decrease in beta amplitude and increase in beta frequency with memory load and during memory manipulation might be interpreted as a general increase in the excitability of task-relevant cortical areas. The topographical differences between load and manipulation effects (i.e., manipulation effects are more posterior; see Figures 4\u20135) could then be due to the recruitment of different areas for these two cognitive operations (Jablonska et al., 2020;\u00a0Veltman, Rombouts, & Dolan, 2003).<\/p>\n\n\n\n Based on our results and previous theoretical accounts (Jensen & Mazaheri, 2010;\u00a0Klimesch et al., 2007;\u00a0Spitzer & Haegens, 2017), we speculate that \u201csustained\u201d (i.e., long duration and low rate) and \u201ctransient\u201d (i.e., short duration and high rate) oscillatory activity might reflect different neural processes. We propose that while sustained oscillations reflect functional inhibition, transient bursts are a cause (or consequence) (Schneider et al., 2021) of a set of task-relevant neural populations being (re)activated (Spitzer & Haegens, 2017), with the specific frequency of oscillatory activity related to the size of the neural populations being \u201cinhibited\u201d or \u201creactivated\u201d (i.e., lower frequencies for bigger networks) (Stein & Sarnthein, 2000). Theta oscillations are an intuitive example of this dual role of neural oscillations in humans, as they appear in the transition to sleep in a more sustained manner (putatively reflecting cortical inhibition) (Canales-Johnson et al., 2020;\u00a0Strijkstra, Beersma, Drayer, Halbesma, & Daan, 2003) and transiently during working-memory tasks in prefrontal areas (which are known to be task-relevant) (M\u00fcller & Knight, 2006). This tentative view might reconcile the seeming discrepancy between reports of beta dynamics that suggest an \u201cinhibitory\u201d role, and those that seem to reflect the formation of neural ensembles (Spitzer & Haegens, 2017). Together, we hypothesize that the here reported decreases in duration and increases in rate of beta bursts during working-memory retention and manipulation are reflective of the transient reactivation of content-specific neural circuits. Although there is some evidence for this function of beta bursts from recordings in monkeys (Buschman, Denovellis, Diogo, Bullock, & Miller, 2012;\u00a0Rassi, Lin, Zhang, Emmerzaal, & Haegens, 2022), the analysis of human intracranial EEG data that includes the simultaneous recording of both local field potentials and spiking activity is necessary to fully assess our predictions.<\/p>\n\n\n\n From the four extracted beta burst parameters, only rate (i.e., the mean number of bursts) was significantly associated with interindividual differences in performance. However, further analysis revealed that this correlation might have been driven by outliers in the data. In this regard, a bigger sample size with greater interindividual variability in performance will be needed to robustly assess the relationship between beta bursts and interindividual differences in working-memory performance.<\/p>\n\n\n\n Finally, it is important to underline that several algorithms to detect oscillatory bursts have been proposed (Bonaiuto et al., 2021;\u00a0Neymotin et al., 2022;\u00a0Seymour, Alexander, & Maguire, 2022;\u00a0Shin, Law, Tsutsui, Moore, & Jones, 2017;\u00a0Whitten et al., 2011), and that each of them offers specific advantages. An important factor for the precise detection of oscillatory bursts is the definition of an amplitude threshold. In line with a recently developed algorithm (Szul et al., 2022), we here use the estimate of aperiodic activity as a power threshold, instead of a global threshold of beta amplitude (Bonaiuto et al., 2021;\u00a0Sherman et al., 2016;\u00a0Shin et al., 2017). This strategy was adopted to also detect low-amplitude oscillatory bursts, which might be of functional relevance (Sch\u00fcrmann & Ba\u015far, 2001). In this regard, note that the algorithm developed here specifically aimed to control for the influence of non-sinusoidal low-frequency rhythms on beta oscillations (Schaworonkow & Nikulin, 2019;\u00a0Schaworonkow, 2023), because, to our knowledge, this factor was not taken into account in previously proposed algorithms. However, other algorithms offer other features, such as the detection of rhythms that change in instantaneous frequency (Neymotin et al., 2022;\u00a0Szul et al., 2022), which might make them more adequate for other research questions.<\/p>\n\n\n\n JRL and SH designed the research. JRL performed the research and analyzed the data. JRL and SH wrote the paper.<\/p>\n\n\n\n This work was supported by NWO Vidi grant 016.Vidi.185.137 and NIH grant R01-MH123679.<\/p>\n\n\n\n The authors declare no conflict of interest.<\/p>\n\n\n\n <\/p>\n\n\n\n\n\n\n <\/p>\n\n\n\n <\/p>\n\n\n\n Angelakis, E.,\u00a0Lubar, J. F.,\u00a0Stathopoulou, S., &\u00a0Kounios, J.\u00a0(2004).\u00a0Peak alpha frequency: An electroencephalographic measure of cognitive preparedness.\u00a0Clinical Neurophysiology<\/em>,\u00a0115<\/em>\u00a0(4),\u00a0887\u2013897.\u00a0https:\/\/doi.org\/10.1016\/j.clinph.2003.11.034<\/a><\/p>\n\n\n\n Baddeley, A.\u00a0(2010).\u00a0Working memory.\u00a0Current Biology<\/em>,\u00a020<\/em>\u00a0(4),\u00a0R136\u2013R140.\u00a0https:\/\/doi.org\/10.1016\/j.cub.2009.12.014<\/a><\/p>\n\n\n\n Benjamini, Y., &\u00a0Hochberg, Y.\u00a0(1995).\u00a0Controlling the false discovery rate: A practical and powerful approach to multiple testing.\u00a0Journal of the Royal Statistical Society: Series B (Methodological)<\/em>,\u00a057<\/em>\u00a0(1),\u00a0289\u2013300.\u00a0https:\/\/doi.org\/10.1111\/j.2517-6161.1995.tb02031.x<\/a><\/p>\n\n\n\n Bonaiuto, J. J.,\u00a0Little, S.,\u00a0Neymotin, S. A.,\u00a0Jones, S. R.,\u00a0Barnes, G. R., &\u00a0Bestmann, S.\u00a0(2021).\u00a0Laminar dynamics of high amplitude beta bursts in human motor cortex.\u00a0NeuroImage<\/em>,\u00a0242<\/em>\u00a0,\u00a0118479.\u00a0https:\/\/doi.org\/10.1016\/J.NEUROIMAGE.2021.118479<\/a><\/p>\n\n\n\n Buschman, T. J.,\u00a0Denovellis, E. L.,\u00a0Diogo, C.,\u00a0Bullock, D., &\u00a0Miller, E. K.\u00a0(2012).\u00a0Synchronous oscillatory neural ensembles for rules in the prefrontal cortex.\u00a0Neuron<\/em>,\u00a076<\/em>\u00a0(4),\u00a0838\u2013846.\u00a0https:\/\/doi.org\/10.1016\/j.neuron.2012.09.029<\/a><\/p>\n\n\n\n Canales-Johnson, A.,\u00a0Beerendonk, L.,\u00a0Blain, S.,\u00a0Kitaoka, S.,\u00a0Ezquerro-Nassar, A.,\u00a0Nuiten, S.,\u00a0\u2026\u00a0Bekinschtein, T. A.\u00a0(2020).\u00a0Decreased alertness reconfigures cognitive control networks.\u00a0The Journal of Neurscience<\/em>,\u00a040<\/em>\u00a0(37),\u00a07142\u20137154.\u00a0https:\/\/doi.org\/10.1523\/JNEUROSCI.0343-20.2020<\/a><\/p>\n\n\n\n Caplan, J. B.,\u00a0Bottomley, M.,\u00a0Kang, P., &\u00a0Dixon, R. A.\u00a0(2015).\u00a0Distinguishing rhythmic from non-rhythmic brain activity during rest in healthy neurocognitive aging.\u00a0NeuroImage<\/em>,\u00a0112<\/em>\u00a0,\u00a0341\u2013352.\u00a0https:\/\/doi.org\/10.1016\/J.NEUROIMAGE.2015.03.001<\/a><\/p>\n\n\n\n Chang, C.-Y.,\u00a0Hsu, S. H.,\u00a0Pion-Tonachini, L., &\u00a0Jung, T. P.\u00a0(2020).\u00a0Evaluation of artifact subspace reconstruction for automatic artifact components removal in multi-channel EEG recordings.\u00a0IEEE Transactions on Biomedical Engineering<\/em>,\u00a067<\/em>\u00a0(4),\u00a01114\u20131121.\u00a0https:\/\/doi.org\/10.1109\/TBME.2019.2930186<\/a><\/p>\n\n\n\n Chen, Y., &\u00a0Huang, X.\u00a0(2016).\u00a0Modulation of alpha and beta oscillations during an n-back task with varying temporal memory load.\u00a0Design and Task<\/h3>\n\n\n\n
![\"Spatial](\"https:\/\/advances.in\/psychology\/wp-content\/uploads\/aip00006_figure1-1024x700.webp\")
<\/a><\/figure>\n\n\n\nEEG Acquisition<\/h3>\n\n\n\n
EEG Preprocessing<\/h3>\n\n\n\n
Beta Burst Detection Algorithm<\/h3>\n\n\n\n
![\"Beta](\"https:\/\/advances.in\/psychology\/wp-content\/uploads\/aip00006_figure2-1024x755.webp\")
<\/a><\/figure>\n\n\n\nStatistical Analysis<\/h3>\n\n\n\n
Results<\/h2>\n\n\n\n
Behavioral Performance<\/h3>\n\n\n\n
Beta Burst Modulations With Memory Retention<\/h3>\n\n\n\n
![\"Changes](\"https:\/\/advances.in\/psychology\/wp-content\/uploads\/aip00006_figure3-1024x496.webp\")
<\/a><\/figure>\n\n\n\nBeta Burst Modulations With Cognitive Load<\/h3>\n\n\n\n
![\"Changes](\"https:\/\/advances.in\/psychology\/wp-content\/uploads\/aip00006_figure4-1024x592.webp\")
<\/a><\/figure>\n\n\n\nBeta Burst Modulations Associated With Memory Manipulation<\/h3>\n\n\n\n
![\"Changes](\"https:\/\/advances.in\/psychology\/wp-content\/uploads\/aip00006_figure5-1024x577.webp\")
<\/a><\/figure>\n\n\n\nRelation Between Beta Bursts and Interindividual Differences in Performance<\/h3>\n\n\n\n
Exploring the Frequency Specificity of Beta Bursts Modulations<\/h3>\n\n\n\n
Assessing Potential Condition-Related Modulations in the Temporal Dynamics of Beta Bursts<\/h3>\n\n\n\n
Controlling for Potential Changes in \u201cNon-Genuine\u201d Beta Bursts<\/h3>\n\n\n\n
Discussion<\/h2>\n\n\n\n
Author Contributions<\/h2>\n\n\n\n
Funding Sources<\/h2>\n\n\n\n
Conflicts of Interest<\/h2>\n\n\n\n
\n
References<\/h2>\n\n\n\n