Shane D. McKeon,Finnegan Calabro,Ryan V. Thorpe,Alethia de la Fuente,Will Foran,Ashley C. Parr,Stephanie R. Jones,Beatriz Luna
Age-related differences in transient gamma band activity during working memory maintenance through adolescence
Electroencephalogram,Adolescent development,Gamma frequency band,Spectral events,Working memory
Adolescence is a stage of development characterized by neurodevelopmental specialization of cognitive processes. In particular, working memory continues to improve through adolescence, with increases in response accuracy and decreases in response latency continuing well into the twenties. Human electroencephalogram (EEG) studies indicate that gamma oscillations (35–65 Hz) during the working memory delay period support the maintenance of mnemonic information guiding subsequent goal-driven behavior, which decrease in power with development. Importantly, recent electrophysiological studies have shown that gamma events, more so than sustained activity, may underlie working memory maintenance during the delay period. However, developmental differences in gamma events during working memory have not been studied. Here, we used EEG in conjunction with a novel spectral event processing approach to investigate age-related differences in transient gamma band activity during a memory guided saccade (MGS) task in 164 10- to 30-year-olds. Total gamma power was found to significantly decrease through adolescence, replicating prior findings. Results from the spectral event pipeline showed age-related decreases in the mean power of gamma events and trial-by-trial power variability across both the delay period and fixation epochs of the MGS task. In addition, we found that while event number decreased with age during the fixation period, the developmental decrease during the delay period was more dramatic, resulting in an increase in event spiking from fixation to delay in adolescence but not adulthood. While average power of the transient gamma events was found to mediate age-related differences in total gamma power in the fixation and delay periods, the number of gamma events was related to total power in only the delay period, suggesting that the power of gamma events may underlie the sustained gamma activity seen in EEG literature while the number of events may directly support age-related improvements in working memory maintenance. Our findings provide compelling new evidence for mechanistic changes in neural processing characterized by refinements in neural function as behavior becomes optimized in adulthood.
Age-related differences in transient gamma band activity during working memory maintenance through adolescence

Adolescence is a stage of development characterized by neurodevelopmental specialization of cognitive processes. In particular, working memory continues to improve through adolescence, with increases in response accuracy and decreases in response latency continuing well into the twenties. Human electroencephalogram (EEG) studies indicate that gamma oscillations (35–65 Hz) during the working memory delay period support the maintenance of mnemonic information guiding subsequent goal-driven behavior, which decrease in power with development. Importantly, recent electrophysiological studies have shown that gamma events, more so than sustained activity, may underlie working memory maintenance during the delay period. However, developmental differences in gamma events during working memory have not been studied. Here, we used EEG in conjunction with a novel spectral event processing approach to investigate age-related differences in transient gamma band activity during a memory guided saccade (MGS) task in 164 10- to 30-year-olds. Total gamma power was found to significantly decrease through adolescence, replicating prior findings. Results from the spectral event pipeline showed age-related decreases in the mean power of gamma events and trial-by-trial power variability across both the delay period and fixation epochs of the MGS task. In addition, we found that while event number decreased with age during the fixation period, the developmental decrease during the delay period was more dramatic, resulting in an increase in event spiking from fixation to delay in adolescence but not adulthood. While average power of the transient gamma events was found to mediate age-related differences in total gamma power in the fixation and delay periods, the number of gamma events was related to total power in only the delay period, suggesting that the power of gamma events may underlie the sustained gamma activity seen in EEG literature while the number of events may directly support age-related improvements in working memory maintenance. Our findings provide compelling new evidence for mechanistic changes in neural processing characterized by refinements in neural function as behavior becomes optimized in adulthood.

Working memory continues to improve through adolescence into adulthood (Luna et al., 2015; Larsen and Luna, 2018) in parallel with brain maturation (Larsen and Luna, 2018; Giedd, 2004; Gogtay et al., 2004) and optimization of brain function (Montez et al., 2017; Simmonds et al., 2017). Behavioral studies using verbal and visuospatial tasks have consistently shown that working memory accuracy and latency continue to improve well into the twenties (Montez et al., 2017; Simmonds et al., 2017; Alloway et al., 2006; Luna et al., 2004; Geier et al., 2009). Supporting this cognitive maturation, fMRI studies have found that while brain regions involved in working memory are online by childhood, there is a continued refinement and integration of specialized regions that lead to stabilized neural activity and an improvement in behavioral performance, such as latency and accuracy (Montez et al., 2017; Simmonds et al., 2017). In particular, there is substantial evidence that in addition to overall improvements in working memory performance through adolescence, there are significant increases in the reliability with which individuals performed these functions, seen as a reduction in the variability of trial-to-trial accuracy and latency (Montez et al., 2017; Simmonds et al., 2017). Previous developmental electroencephalogram (EEG) studies have shown robust trends in declining power during resting state EEG across early childhood, middle childhood, and into adolescence (Anderson and Perone, 2018). More specifically, age-related decreases in absolute power have been found to decrease in the slow wave (0.5 – 7.5 Hz) (Matousek and Petersén, 1973), alpha (8 – 12 Hz), and beta (12.5 – 34.5 Hz) bands in the frontal, temporal, parietal, and occipital lobes (Whitford et al., 2007; Gasser et al., 1988), as well as the theta (3.5 – 7.5 Hz) (Bowers et al., 2018), and gamma (30 – 70 Hz) (Tierney et al., 2013) bands. In terms of relative power, there have been age-related increases in the alpha (Cragg et al., 2011; Dustman, 1996), and beta (Cragg et al., 2011) bands, as well as a redistribution from lower to higher frequency bands with posterior regions reaching adult levels of power before frontal regions (Marek et al., 2018). Human EEG and animal electrophysiological data have identified that specific aspects of these neural signals, including gamma oscillations, support working memory processes (Tierney et al., 2013; Wang et al., 2021), particularly in the dorsolateral prefrontal cortex (DLPFC) (Simmonds et al., 2017; Segalowitz and Davies, 2004) where gamma power during working memory tasks was increased compared to baseline (Wang et al., 2021). Historically, these gamma oscillations have been characterized as reflecting sustained activity during the maintenance epoch of working memory tasks in both non-human primate and human studies (Jones, 2016; Fuster and Alexander, 1971; Lundqvist et al., 2018; Goldman-Rakic, 1995; Miller et al., 1996). However, recent human and non-human primate studies have shown that at the trial level, neural activity occurs in burst-like events, defined as transient events of neural activity where not only are the amplitude and frequency important, but the timing, duration, and rate of the events may play a key role in supporting higher order cognitive functions (Jones, 2016; Quinn et al., 2019; Shin et al., 2017; Lundqvist et al., 2016; Hirschmann et al., 2020; M. Lundqvist et al., 2018). Working memory delay activity has since been shown to be non-stationary, with spiking occurring sporadically (Lundqvist et al., 2016; M. Lundqvist et al., 2018; Lundqvist et al., 2022). Between these active states, working memories may be stored as temporary changes in synaptic weights through gamma band associated spiking (Lundqvist et al., 2018; Lundqvist et al., 2016; M. Lundqvist et al., 2018; Lundqvist et al., 2011). However, while EEG provides a means for assessing working memory-dependent gamma events, no studies to our knowledge have yet characterized in developmental populations the trial-level spectral events associated with the maturation of working memory. Here, we build off previous EEG studies using established averaging approaches of sustained activity by investigating whether non-stationary working memory activity can provide insight into the underlying mechanisms of working memory during adolescent development. We apply a novel EEG analysis pipeline (Neymotin et al., 2020) to define the developmental trajectory of gamma band events at the trial level, their association with working memory throughout adolescence into young adulthood, and their relationship with traditional EEG analyses of sustained activity. These results demonstrate aspects of the spectral events themselves which account for prior findings of decreased overall gamma power, while highlighting novel developmental differences in the rate of task-dependent gamma events which may constitute a distinct developmentally sensitive process supporting the maturation of working memory maintenance.

One hundred and sixty-four participants (87 assigned female at birth), between 10 and 32 years of age participated in this study. Participants were recruited from the community and were excluded if they had a history of head injury with loss of consciousness, a non-correctable vision problem, a history of substance abuse, a learning disability, or a history of major psychiatric or neurologic conditions in themselves or a first-degree relative. Participants were also excluded if they reported any MRI contraindications, such as non-removable metal in their body given other neuroimaging aspects of the broader project. Participants or the parents of minors gave informed consent with those under 18 years of age providing assent. Participants received payment for their participation. All experimental procedures were approved by the University of Pittsburgh Institutional Review Board and complied with the Code of Ethics of the World Medical Association (Declaration of Helsinki, 1964).

Participants performed a memory guided saccade (MGS) task to assess working memory (see Fig. 1). The trial began with fixation to a blue cross for 1 s. The participant was then presented with a peripheral cue in an unknown location along the horizontal midline (12.5 or 22.2° from central fixation to left or right of center), where they performed a visually guided saccade (VGS) to the target and maintained fixation. Once the cue disappeared, the participant returned their gaze to the central fixation point and fixated for a variable delay epoch (6–10 s) during which they were to maintain the location of the peripheral target in working memory. Once the central fixation disappeared the participant performed a memory guided saccade to the recalled location of the previous target. The trial ended when participants were presented with a white fixation cross that served as the ITI (1.5–15 s). Participants performed 3 runs of the MGS task, each containing 20 trials. Task performance was assessed based on horizontal electrooculogram (hEOG) channels recorded from facial muscles (see acquisition details below). At the start of the session, participants performed a calibration procedure in which they fixated a series of 20 dots sequentially, positioned along the horizontal midline and spanning the width of the screen. These were used to generate a calibration curve relating hEOG voltage to horizontal screen position. Eye position during the MGS task was used to derive output measures using this calibration data by aligning hEOG signals to different task triggers. These were used to calculate VGS & MGS response latencies, as the time difference between the beginning of the recall epoch and the initiation of the VGS and MGS eye movements respectively, and saccadic accuracy, measured as the closest fixation point during the recall period to the fixated location during the initial visually guided fixation epoch.

Concurrent EOG and high-impedance EEG was recorded with a Biosemi ActiveTwo with Actiview software (Minimitter, Bend, OR) 64-channel EEG system, in accordance with the international 10–20 system (Neymotin et al., 2020), located in an electromagnetically shielded room while participants performed the MGS task (described above). The task stimuli were presented on a computer 80 cm from the subject, and head position was maintained using a chin rest. Initial data were sampled at 512 Hz and down sampled to 150 Hz during preprocessing. Data were referenced to external electrodes corresponding to the mastoids due to their proximity to the scalp and low signal recording. An initial bandpass filter was set to 0.5–75 Hz. We used a revised version of an established protocol (https://sccn.ucsd.edu/wiki/Makoto’s_preprocessing_pipeline, retrieved April 23, 2020) for preprocessing compatible with EEGLAB (Delorme and Makeig, 2004). This protocol removes flatline channels (maximum tolerated flatline duration: 8 s), low-frequency drifts, noisy channels (defined as more than 5 standard deviations from the average channel signal), large amplitude artifacts, and incomplete segments of data (Jung et al., 2000; Hipp and Siegel, 2013). Deleted channels were replaced with interpolated data from surrounding electrodes. Continuous signals were divided into epochs, time-locked to stimulus onset (~1–3 s). Data epochs were cleaned with an amplitude threshold of −500 to 500μV to exclude eye blinks. The resulting data were referenced to the average of all electrodes. As a final preprocessing step, independent component analysis (ICA) was performed to identify eye-blink artifacts and remove their contribution to the data. We confirm that gamma bursts are not present due to eye movement by plotting the spectrograms from the EOG channels (Fig. 5).

Spectral power was computed for every electrode from the 3000 – 4000 ms window of the delay epoch of the task to avoid artifact from preceding eye movements and preparation from an imminent response, and from the 1 second inter-trial fixation epoch. Power spectra was derived from a fast Fourier transform (FFT). Power (μV2) was then calculated for the gamma frequency band (35–65 Hz). Power from all 6 electrodes representing the left and right DLPFCs (F3, F4, F5, F6, F7, and F8) were averaged together. The resulting measure is referred to as “total” gamma band power throughout the manuscript.

Spectral data were computed for every electrode from the 3000 – 4000 ms window of the delay epoch of the task to avoid artifact from preceding eye movements and preparation from an imminent response, and from the 1 second inter-trial fixation epoch. The spectrograms of the data were calculated from 20 to 70 Hz by convolving the signals with a complex Morlet wavelet (Qin and Shen, 2000) of the form for each frequency of interest f0, where . The normalization factor was and the constant m, defining the compromise between time and frequency resolution, was 7, consistent with previous literature (Shin et al., 2017). Time-frequency representations of power (TFR) were calculated as the squared magnitude of the complex wavelet-convolved data. The TFR was normalized to the median power value for each frequency band, derived from all the power values within the identified stimulus windows of the delay and fixation epochs. Normalized TFR values were calculated in factors of median (FOM) for each frequency, separately for each participant/trial.

Average gamma power (μV2) was first calculated by averaging the time-frequency representations of power (TFR) across time and frequency for all participants. Analyses were limited to electrodes that had well defined activity in the gamma band of adult participants during the MGS task. To accomplish this, we identified electrodes with gamma activity present by computing total power from the time- and frequency-band averaged TFR across adult subjects (18+ years of age) for each electrode. Power values were plotted across brain topography to highlight regions of increased power. Using this information, coupled with previous knowledge of regions involved with working memory (Simmonds et al., 2017; Segalowitz and Davies, 2004), electrodes corresponding to the right (F4, F6, and F8) and left (F3, F5, and F7) DLPFC were selected for further analysis (see Results, and Fig 4). To verify the presence of gamma band activity, spectrograms for each electrode were calculated from 20 to 70 Hz.

Gamma events were defined as local maxima in the trial-by-trial TFR matrix for each frequency value at the maxima that fell within the gamma band (35 – 65 Hz) and the power exceeded a cutoff of 6x the median power (Shin et al., 2017). This threshold is determined for each individual subject and is epoch specific. Local maxima were found using the Matlab function ‘imregionalmax’. Custom software for identifying transient events and event features is written in Matlab and available at https://github.com/jonescompneurolab/SpectralEvents. Trial average and trial-by-trial variability of each feature were calculated by using the average and standard deviation of each measure (event power, number, and duration) across trials for the delay and fixation epochs separately.

Gamma event power was defined as the normalized TFR value (units of FOM) at the local maximum that defines each event. The trial mean event power was defined as the power of all events averaged in the 3000 – 4000 ms window of the delay epoch and the 0 – 1000 ms window of the fixation epoch.

Gamma event number was calculated as the number of gamma events in the 3000 – 4000 ms window of the delay epoch and the 0 – 1000 ms window of the fixation epoch.

Event duration and frequency span are calculated as full- width- half-maximum (FWHM) from the event maxima in the time and frequency domain, respectively. All spectral measures (power, number of events, duration of events, and variability of each) were averaged across electrodes F3, F5, and F7 to create average spectral event measures for the left DLPFC, and F4, F6, and F8 for the right DLPFC based on the region of interest analysis performed on the total TFR power of each epoch window.

To examine age-related effects in behavioral measures, including accuracy and response latency, generalized additive mixed models (GAMMs) were implemented using the R package mgcv (Wood, 2017). Preliminary outlier detection was conducted on a trial level basis. Express saccades of less than 100 ms, believed to be primarily driven by subcortical systems (Luna et al., 2008), were excluded. Position error measures greater than 23° from the target were excluded as implausible since they exceeded the width of the screen. The remaining trials for each participant were combined, creating average performance measures and trial-by-trial variability measures for each participant. Finally, for group level analyses, outlier detection was performed, excluding participants more than +/−2 SDs away from the mean. A separate model was performed for each of the behavioral measurements: MGS accuracy, MGS latency, and VGS latency, as well as the trial-by-trial variability of each measure. To correct for multiple comparisons between the six behavioral measures, Bonferroni correction was employed which resulted in a corrected alpha of 0.008 (p = 0.05/6 = 0.008). For all behavioral measures showing significant age-related effects, we performed analyses to identify specific periods of significant age-related differences. To do so, a posterior simulation was performed on the first derivative of the GAM model fits. Consistent with previous work (Wood, 2017; Simmonds et al., 2014; Calabro et al., 2020), 10,000 simulated GAMM fits and their derivatives (generated at age intervals of 0.1 years) were computed from a multivariate normal distribution; vector means and covariance of which corresponded to the fitted GAM parameters. Confidence intervals (95%) were generated from the resulting derivatives. The periods of age-related growth were derived from the ages corresponding to when the confidence interval did not include zero (p < 0.05). We used a similar GAMM approach to assess age-related trends in the average power derived from the FFT and for gamma band spectral event properties. In each, we first tested for age by epoch interactions and if the interaction term was not significant, it was removed from the model and epoch was included as a covariate. Values greater than +/−2 SDs away from the variable mean were excluded. Next, a separate model was performed for each of the spectral event measurements: power, duration, number of events per trial, the trial-by-trial variability of each measure. We first tested for age by hemisphere and age by epoch interactions on each measure, and in the event that the interaction term was not significant, it was removed from the model and hemisphere and epoch were included as covariates. Trials greater than +/−2 SDs away from the variable mean were excluded. To correct for the six spectral event measures, a corrected p value of 0.008 was used. Subjects above 25 were removed as we were underpowered to detect developmental effects. To assess relationships between the significant age-related EEG activity and behavior, each delay epoch spectral measure in which we found significant age effects was compared with the memory guided saccade behavioral measures (accuracy, measured in degrees from the correct target location, and response latency), again using GAMs. To correct for the resulting four comparisons, a Bonferroni-corrected p value of 0.012 was used as the criterion for significance. To test for relationships between the age-related differences in the spectral event measures and age-related differences in total power, mediation analyses were implemented using the R package mediation (Tingley et al., 2014). Unstandardized indirect effects were computed for each of 1000 bootstrapped samples, and the 95% confidence interval was computed by determining the indirect effects at the 2.5th and 97.5th percentiles.

Consistent with our prior findings using the MGS task (Montez et al., 2017; Luna et al., 2008; Montez et al., 2019), behavioral performance improved with age for all MGS metrics including increased accuracy (F = 40.89, p = 1.2e-15; Fig. 2A), decreased response latency (F = 21.25, p = 0.01; Fig. 2B), and decreased trial-to-trial variability in both accuracy (F = 48.93, p = 8.82e-07; Fig. 2C) and response latency (F = 61.48, p = 1.2e-15; Fig. 2D). Significant developmental improvements were found to occur throughout adolescence (11–22 years of age) for MGS accuracy and between 11 and 21 years of age for trial-by-trial variability for MGS accuracy (Fig. 2A and 2C). MGS latency was found to have significant growth rates in early adolescence (11–13 years of age, Fig. 2B), while decreases in MGS variability in latency continued into the twenties (Fig. 2D).

Given our hypothesis based on prior literature that gamma power would decrease with age (Tierney et al., 2013), we first identified the presence of gamma band power in adults (age range 18 – 32, N = 87) to characterize the extent of activity in a mature cohort. Average gamma power was calculated across our adult cohort and plotted across brain topography to select regions of interest for investigating age-related differences in transient gamma events during the working memory delay and fixation epochs. Fig. 3 depicts the presence of gamma power across all electrodes of adults during the 3000–4000 ms time window of the delay epoch, with hotspots identified in bilateral DLPFC. Based on this result, and coupled with prior work of neural signals in the DLPFC supporting working memory (Geier et al., 2009; M. Lundqvist et al., 2018, bilateral DLPFC regions (right: F4, F6 and F8, left: F3, F5, and F7) were selected for further analysis. Given apparent differences in the magnitude of gamma power between left and right DLPFC, in subsequent statistical analyses we include terms for hemisphere and age by hemisphere interactions.

Total gamma power was derived from the 3000–4000 ms of the delay period and from the 1 second inter-trial fixation epoch using the FFT from the left and right DLPFC. We found that power significantly decreased with age (F = 4.76, p = 0.03; Fig. 4). Age by epoch (delay and fixation) interactions were not significant (p value), thus both delay and fixation epochs were included in the model and epoch was modelled as a covariate.

Based on evidence indicating that gamma bursts support maintenance of information in working memory (Lundqvist et al., 2018; Lundqvist et al., 2016; M. Lundqvist et al., 2018), we were interested in characterizing age-effects through adolescence in gamma events at the single trial level. Due to non-negative power spectral values, average power across trials can create a continuous band of activity, appearing as a sustained rhythm, even if the trial level response is better described by a series of discrete gamma events. To assess this in our data, we first visualized trial-by-trial gamma events during the same 3000–4000 ms time window of the delay epoch used in the total power analysis above (see representative trials shown in Fig 5). These results demonstrate a pattern in which gamma band activity emerges from a series of transient increases in power (i.e., events) on individual trials, occurring at different moments within the epoch across trials. The presence of event-like gamma activity in the trial level spectrograms suggests the temporal dynamics of these events may be critical to understanding their contributions to working memory (Shin et al., 2017), and provide a more mechanistic description of developmental differences.

Spectral measures were computed across all trials for all participants spanning the full age range of our sample (10–32yo). Mean gamma event power significantly decreased across adolescence across both hemispheres (F = 29.75, p < 0.0001; Fig. 6A) and had a main effect of epoch (F = 15.26, p = 0.001). Gamma event power variability showed decreases across age (F = 15.51, p = 0.001; Fig. 6D). Event number was found to significantly decrease across adolescence (F = 11.73, p = 0.005; Fig. 6B) and has a significant uncorrected age-by-epoch interaction (F = 3.19, puncorrected = 0.02). Event duration variability increased with age (F = 16.34, p = 0.0004; Fig. 6F). As no age-by-hemisphere interactions were found, Fig. 6 depicts fits for the averaged left and right DLPFC. Additional statistics on all associations can be found in Table 1. Additionally, we show that developmental differences in spectral measures, such as trial power and event number, are not specific to the DLPFC but are globally present (Figure S3). An additional analysis was conducted on a control electrode, FT9, to see if these developmental effects would be present in a region not involved in working memory. In this control electrode, gamma trial power decreased significantly across age (F = 26.53, p = 7.06e-07; Figure S4C) as was prevalent across electrodes, but importantly it did not exhibit an age-by-epoch interaction (F = 0.025, p = 0.994). The number of gamma events across adolescence also decreased significantly across age (F = 13.08, p = 0.0004; Figure S4D) but similarly did not show a significant age-by-epoch interaction (F = 0.26, p = 0.856). Furthermore, we investigated whether the results within the 3–4 s window of the delay period are generalizable to the other 1 s segments of the delay period. There were no age-by-interval interactions for neither trial power (2–3s: F = 1.5, p > 0.05, 3–4s: F = 0.33, p > 0.05, 4–5s: F = 1.64, p > 0.05, 5–6s: F = 2.1, p > 0.05) nor event number (2–3s: F = 1.2, p > 0.05, 3–4s: F = 0.7, p > 0.05, 4–5s: F = 1.33, p > 0.05, 5–6s: F = 0.87, p > 0.05) for any of the five segments (Figure S5).

There were no significant associations between trial power and MGS accuracy. There were no significant associations between trial power variability and accuracy variability, nor event number and accuracy. No associations were found between event number variability and accuracy variability. All spectral measures vs accuracy measures can be seen in supplemental figure S1. Further statistics can be found in supplement Table 1. No associations between trial power and response latency nor trial power variability and response latency variability were found. No associations between event number and latency nor event number variability and response latency variability were found. Event duration and response latency did not have a significant relation. Event duration variability and response latency variability did not have a significant relation. All spectral measures vs latency measures can be seen in supplemental figure S2. Further statistics can be found in supplement Table 1.

We conducted mediation analyses to test whether the spectral event measurements exhibiting significant age-related differences (i.e., power and number of events) could account for age-related differences seen in the total gamma power measures in both the delay and fixation periods of the MGS task. Mediation analyses tested whether the relationship between age and total power was mediated by each spectral measure to determine the contribution of the spectral event measures to total gamma power. The effect of age on total power was partially mediated via event power in the fixation epoch (Figure 7; ACME = 0.0018, 95% CI [0.000637, 0], p < 2e-16) epoch, and within the delay epoch (Figure 7; ACME = −0.0015, 95% CI [−0.003, 0], p = 0.010). The number of gamma events partially mediated age-related differences in total power in the delay epoch (Figure 7; ACME = −0.001, 95% CI [−0.002, 0], p = 0.048) but not the fixation (Figure 7; ACME = −0.0004, 95% CI [−0.002, 0], p = 0.49) epoch. Mediation analysis to investigate whether spectral event power in the fixation epoch accounts for the age-related differences in total power in our control region (FT9) were not significant (ACME = −0.0004, 95% CI [−0.0023, 0], p = 0.70; Figure S4A). Similarly, a mediation analysis to investigate whether the number of spectral events in the delay epoch accounts for the age-related differences in total power in the control region was also not significant (ACME = −0.0012, 95% CI [−0.003, 0], p = 0.13; Figure S4B).

In this study we aimed to investigate the neural activity underlying working memory maintenance across adolescence. Consistent with previous literature (Luna et al., 2015; Larsen and Luna, 2018), working memory performance improved into adulthood including increased accuracy and decreased response latency to make a goal-directed response. We focused on understanding age-related differences in trial level transient events of activity during the delay period of a working memory task (Lundqvist et al., 2018; Lundqvist et al., 2016; M. Lundqvist et al., 2018; Lundqvist et al., 2022; Powanwe and Longtin, 2019; Karvat et al., 2020), as opposed to sustained activity, as recent evidence has suggested its importance for understanding mechanisms underlying working memory delay period dynamics (Jones, 2016; Shin et al., 2017; Lundqvist et al., 2016; Miskovic et al., 2015; Sherman et al., 2016). It is traditionally understood that sustained neural activity during working memory delay periods underlies the retention of the information in working memory. However, newer non-human primate evidence suggests that the sustained nature of this signal may arise due to the averaging of delay period activity across trials, while trial-level activity reveals transient neural events (Jones, 2016) which support working memory maintenance. In this study, we leveraged the direct assessment of neural processing provided by scalp EEG, primarily reflecting postsynaptic cortical pyramidal cell activity (Biasiucci et al., 2019; Louis et al., 2016), with high temporal resolution that can characterize signal variability across timescales and frequency bands. Specifically, we investigated age-related effects from childhood to adulthood in the spectral events within the gamma frequency band, which has been found to support working memory maintenance (Lundqvist et al., 2018; Lundqvist et al., 2016), and investigated their associations with working memory maintenance, and assessed whether transient events of activity at the trial level underlie age-related differences in sustained activity and could thus further inform the underlying mechanisms of the development of working memory through adolescence. To determine regions of interest for investigating age-related differences in transient gamma events during the working memory delay and fixation epochs, average gamma power was calculated across our adult cohort and plotted across brain topography. Right and left DLPFC regions of interest were selected, both for the presence of gamma band activity as seen in Fig. 3, and for the previously reported involvement of the DLPFC in working memory tasks (Fuster and Alexander, 1971; Miller and Orbach, 1972; Bauer and Fuster, 1976; Funahashi et al., 1993; Funahashi et al., 1989; Kubota and Niki, 1971). Our results show that total gamma power, derived from the fast Fourier transform (FFT), decreases through adolescence into young adulthood (Fig. 4), consistent with previous studies of gamma band activity (Tierney et al., 2013). Such age-related decreases have also been reported in the beta (Whitford et al., 2007) and theta (Bowers et al., 2018) bands, suggesting that decreases in oscillatory power may be a common developmental pattern. Interestingly, we found that on individual trials gamma band activity was present in discrete, brief events, and only when averaging across trials did more sustained activity patterns emerge (Fig. 5). This finding is consistent with previous studies that have shown that beta and gamma events occur during encoding and then reappear during the delay period of working memory tasks (Lundqvist et al., 2016; M. Lundqvist et al., 2018; Lundqvist et al., 2022). Expanding upon the developmental decreases seen in the gamma band across trials, we then investigated age-related differences in the temporal dynamics of these transient gamma band spectral measures. We confirm gamma events were not due to eye movements as we removed them using Independent Component Analysis (ICA) and we found a lack of substantial overlap in their timing (Fig. 5). We found that mean gamma event power, event power variability, the number of events, and the duration variability of an event, all significantly decreased through adolescence, in both the delay period and fixation epochs of the working memory task (Fig. 6). These results are in agreement with studies showing decreases in power through adolescence across the cortex (Whitford et al., 2007; Cragg et al., 2011; Boord et al., 2007; Lüchinger et al., 2011; Määttä et al., 2019; Segalowitz et al., 2010; Thatcher et al., 2008). Furthermore, previous human EEG studies have characterized elevated gamma frequency power in local field potentials during working memory maintenance (Pesaran et al., 2002; Howard et al., 2003; Jensen et al., 2007; Honkanen et al., 2015), that decreases across adolescence, suggesting optimization of encoding and maintenance circuitry (Wang et al., 2021). Variability in EEG signals has been found to decrease in parallel to developmental decreases in behavioral variability (Franziska et al., 2019), and stabilization of neural signaling occurs in parallel to maturation of structural (Simmonds et al., 2013) and functional (Calabro et al., 2019; Fareri et al., 2015; Gabard-Durnam et al., 2014; Jalbrzikowski et al., 2017; van Duijvenvoorde et al., 2019) connectivity. Thus, decreases in variability may reflect stabilization of neural function and behavior. The developmental differences seen in both the delay and fixation epochs suggest that basic dynamics of gamma oscillations specialize through adolescence regardless of cognitive demands. Myelination and synaptic pruning across association cortex at this time, and in prefrontal cortex in particular (Whitford et al., 2007; Tierney et al., 2013; Miskovic et al., 2015; Segalowitz et al., 2010; Moisala et al., 2018), may underlie optimization of information processing resulting in decreased need to engage mechanisms supported by gamma oscillations into adulthood. Importantly, gamma event power significantly mediated the differences we identified in total gamma band power throughout the fixation and delay epochs (Fig. 7), suggesting that this may provide a mechanistically specific driver of these frequently reported developmental differences in gamma band power regardless of cognitive state. However, during the delay period, when the participant is actively engaging working memory, only the number of spectral events mediates the total gamma power in the delay period (Fig. 7). Age effects were strongest for trial power, trial power variability, event number, and event duration variability. However, given the correlation among these measures, future work is necessary to further disentangle the relative contributions of these measures at baseline to overall developmental processes. Interestingly, we also identified developmental differences in the number of gamma events in both the delay and fixation epochs. However, unlike event power and duration, the number of gamma events (i.e., the number of events per second) exhibited a significant uncorrected age by epoch interaction, reflecting that the number of gamma events in the delay period of the working memory task decreased more drastically with age than during the fixation period. These results may suggest that there is a baseline decrease in the number of gamma events with age, as indicated by the decrease of events in the fixation epoch, possibly due to synaptic pruning (Anderson and Perone, 2018; Whitford et al., 2007; Tierney et al., 2013; Segalowitz et al., 2010; Cho et al., 2015) or reduction in spontaneous activity (Larsen and Luna, 2018; Miskovic et al., 2015). Overall, gamma function decreases with age for delay and fixation, which may reflect decreases in stochastic activity. Given that there is greater gamma function before adulthood during delay compared to fixation, this could also reflect neural variability, which we have found is associated with working memory variability and decreases through development into adulthood (Montez et al., 2019). Additionally, the age-related decrease in the number of gamma events during the working memory delay period may reflect the need for an amplification of gamma events in adolescence to perform the working memory task reflecting greater effort. By adulthood, when working memory performance is optimal, decreased gamma function is sufficient including less overall stochastic function in either fixation or delay (Fig. 6B). Previous work has shown the presence of gamma events during working memory decoding (Lundqvist et al., 2016), ramping up in anticipation of WM readout (Lundqvist et al., 2018), and have suggested that events represent attractor states that correspond to working memory information (Lundqvist et al., 2016). Importantly, in addition to showing task-dependence and epoch specificity, which was not observed for event power or duration, the number of gamma events was only related to total gamma power during the delay period and not during fixation. Together, these results suggest that the rate of gamma events may represent a developmental feature of gamma band processing that is distinct from previously reported differences in gamma power. Thus, our results support that the relative age-related decrease in gamma events specific to the working memory maintenance period may thus be an important and novel component of developmental refinements in working memory performance. However, a limitation of our study is that while these differences in gamma events were specific to the working memory maintenance period, they did not explain individual differences in performance, and thus further work is necessary to clarify their specific behavioral contribution. To investigate whether the spectral event measures were unique to the DLPFC, we looked at age-related differences in the gamma trial power and gamma event number across all scalp electrodes (Figure S3). We found that the age-related decreases seen in the DLPFC were present globally. However, control analyses performed on a posterior temporal electrode, FT9, indicated that age and task epoch effects were specific to regions involving frontal channels, such as the DLPFC (Figure S4C&D). Furthermore, we ran additional mediation analyses to test whether the partial mediations observed in the DLPFC were present in the FT9 electrode. Interestingly, we found that the FT9 spectral event power in the fixation epoch did not account for the age-related differences in the total power, nor did the number of events in the FT9 electrode account for the age-related differences in total power (Figure S4A&B). These results suggest global developmental attenuation of gamma function and specialization of gamma function in frontal cortex supporting working memory performance. Basic working memory function is available in infancy (Diamond, 1985), and at the trial level, adult level performance can be attained even in childhood, though greater variability across trials results in decreased mean performance (Montez et al., 2017). Thus, these results suggest that the core mechanisms of working memory are already available in adolescence. Stabilization of neural signaling occurs in parallel to maturation of structural (Simmonds et al., 2013) and functional (Calabro et al., 2019; Fareri et al., 2015; Gabard-Durnam et al., 2014; Jalbrzikowski et al., 2017; van Duijvenvoorde et al., 2019) connectivity. Decreases in variability may reflect stabilization of neural function and behavior. Variability in gamma events during maturation in adolescence may serve as an adaptive exploratory approach (Montez et al., 2017), which by adulthood has already reached optimal performance and may be more related to variability in attention. The results observed here in adolescence are in accord with neurophysiological data showing that saccade reaction times are strongly correlated with firing activity (Hanes and Schall, 1996; Dean et al., 2011; Roitman and Shadlen, 2002), with higher rates of neuronal firing being linked to faster behavioral responses (Hanes and Schall, 1996; Roitman and Shadlen, 2002). Together, these results suggest that from adolescence to adulthood there may be a process of specialization in which greater neural function may support best performance in adolescence, followed by a shift to less neural activity being required for adult level optimal executive function. We focused on understanding age-related differences in trial level activity as recent evidence indicates its importance for understanding mechanisms underlying working memory delay period dynamics (Jones, 2016; Shin et al., 2017; Lundqvist et al., 2016; Miskovic et al., 2015; Sherman et al., 2016). Thus, we sought to investigate whether trial level transient events of activity underlined the traditionally observed sustained activity. Our mediation analyses suggested as much, with trial level transient event power partially mediating the age-related differences in total sustained power in the fixation and delay epochs. Whereas the number of events in the delay period partially mediated the total power. Recent work shows compelling evidence that transient events during delay periods support maintenance of mnemonic information through the delay period (Lundqvist et al., 2022). More specifically, previous work has shown that working memory activity is not stationary, and it has been hypothesized that information is conveyed as spiking in short attractor states and held by synaptic changes in-between states (Lundqvist et al., 2016). Our findings that trial level gamma activity decreases with development provides compelling new evidence for mechanistic changes in neural processing characterized by refinements in neural function as behavior becomes optimized in adulthood. This study is limited by a cross-sectional cohort, which impede inferences regarding within-subject developmental effects, and future studies leveraging longitudinal cohorts will be necessary to ascertain whether age-related differences in EEG measures support developmental improvements in working memory performance. Further, while we did not find associations between spectral events measures and performance, this may not be surprising as working memory involves a wide circuitry of neuronal activity. Future work utilizing a working memory task with variable load conditions may provide further insight into the behavioral contributions of gamma events since gamma oscillations have been found to be correlated with working memory load (Howard et al., 2003; Barr et al., 2009; Barr et al., 2014). An additional possible limitation is that the threshold for detecting spectral events is determined for each individual and epoch. With a cross sectional study assessing age, the threshold may be changing with age as well. We find that the median power of events, the measure that the threshold is based on, is decreasing with age, which would result in age-related increases in events. However, results indicate age-related decreases in events indicating that changes in burst threshold with age is not accounting for the age-related differences we have observed. However, our results show fewer spectral events with age and thus we do not believe this methodological limitation is contributing to our results. Together, these results suggest that critical refinements in neural function underlie improvements in working memory from adolescence to adulthood, reflecting the ability to readily and consistently engage mechanisms to employ existing cognitive processes, that during adolescence may leverage increases in neural dynamics and variability to improve performance at the trial level. Characterizing neural mechanisms that underlie normative development can inform impaired trajectories, including psychiatric disorders, where working memory is predominantly affected such as in psychosis (Frydecka et al., 2014; Gold et al., 2019) and depression (Rock et al., 2014; Roca et al., 2015), which emerge during the adolescent period (Paus et al., 2008).

As Dr. Luna and her lab have done throughout our studies, results will be made available to other scientists to further probe our understanding of normative development. Findings will be presented at major meetings relevant to our area of study including: Flux-The International Congress for Integrative Developmental Cognitive Neuroscience, Society for Neuroscience, Organization for Human Brain Mapping, Cognitive Neuroscience Society, and Biological Psychiatry as well as other conferences when the theme includes our area of research (American Psychological Association, Society for Research in Child Development, Society for Research on Adolescence). Results will also be disseminated by Dr. Luna’s continuous participation in a variety of related congresses (e.g., Society for Adolescent Health and Medicine); policy related collaborations (e.g., Institute of Medicine – National Academy of Sciences; informing AMA briefs presented to the Supreme Court); and invited lectureships at national and international universities. Dr. Luna is also a responsible contributor to media coverage on human development issues (National Geographic, PBS Brains on Trials, Washington Post, New York Times, as well as smaller pertinent media outlets, such as local newspapers and radio shows). Dr. Luna also presents on a yearly basis to different local high schools and organizations interested in adolescent development. The Laboratory of Neurocognitive Development also has a website where information regarding findings are posted. Finally, Dr. Luna will continue to share her results in Big Data projects where she has been an organizing member (ADHD200, ABIDE, OpenfMRI, and CoRR) whose aim is to make neuroimaging data available to the scientific community to perform innovative analyses further advancing our understanding. Data and code will be made available upon request.