Modulation of aperiodic EEG activity provides sensitive index of cognitive state changes during working memory task

Reviewer #1 (Public review):

Summary:

Frelih et al. investigated both periodic and aperiodic activity in EEG during working memory tasks. In terms of periodic activity, they found post-stimulus decreases in alpha and beta activity, while in terms of aperiodic activity, they found a bi-phasic post-stimulus steepening of the power spectrum, which was weakly predictive of performance. They conclude that it is crucial to properly distinguish between aperiodic and periodic activity in event-related designs as the former could confound the latter. They also add to the growing body of research highlighting the functional relevance of aperiodic activity in the brain.

Strengths:

This is a well-written, timely paper that could be of interest to the field of cognitive neuroscience, especially to researchers investigating the functional role of aperiodic activity. The authors describe a well-designed study that looked at both the oscillatory and non-oscillatory aspects of brain activity during a working memory task. The analytic approach is appropriate, as a state-of-the-art toolbox is used to separate these two types of activity. The results support the basic claim of the paper that it is crucial to properly distinguish between aperiodic and periodic activity in event-related designs as the former could confound the latter. They also add to the growing body of research highlighting the functional relevance of aperiodic activity in the brain. Commendably, the authors include replications of their key findings on multiple independent data sets.

Weaknesses:

The authors also claim that their results speak to the interplay between oscillatory and non-oscillatory activity, and crucially, that task-related changes in the theta frequency band - often attributed to neural oscillations in the field - are in fact only a by-product of non-oscillatory changes. I believe these claims are too bold and are not supported by compelling evidence in the paper. Some control analyses - e.g., contrasting the scalp topographies of purported theta and non-oscillatory effects - could help strengthen the latter argument, but it may be safest to simply soften these two claims.

In terms of the methodology used, I suggest the authors make it clearer to readers that the primary results were obtained on a sample of middle-aged-to-older-adults, some with subjective cognitive complaints, and note that while stimulus-locked event-related potentials (ERPs) were removed from the data prior to analyses, response-locked ERPs were not. This could potentially confound aperiodic findings. Contrasting the scalp topographies of response-related ERPs and the identified aperiodic components, especially the latter one, could bring some clarity here too.

I also found certain parts of the introduction to be somewhat confusing.

Reviewer #2 (Public review):

Summary:

In this manuscript, Frelih et al investigate the relationship between aperiodic neural activity, as measured by EEG, and working memory performance, and compare this to the more commonly analyzed periodic, and in particular theta, measures that are often associated with such tasks. To do so, they analyze a primary dataset of 57 participants engaging in an n-back task, as well as a replication dataset, and use spectral parameterization to measure periodic and aperiodic features of the data, across time. In doing so, they find both periodic and aperiodic features that relate to the task dynamics, but importantly the aperiodic component appears to explain away what otherwise looks like theta activity in a more traditional analysis. This study, therefore, helps to establish that aperiodic activity is a task-relevant dynamic feature in working memory tasks, and may be the underlying change in many other studies that reported 'theta' changes but did not use methods that could differentiate periodic and aperiodic features.

Strengths:

Key strengths of this paper include that it addresses an important question - that of properly adjudicating which features of EEG recordings relate to working memory tasks - and in doing so provides a compelling answer, with important implications for considering prior work and contributing to understanding the neural underpinnings of working memory. I do not find any significant faults or errors with the design, analysis, and main interpretations as presented by this paper, and as such, find the approach taken to be valid and well-enacted. The use of multiple variants of the working memory task, as well as a replication dataset significantly strengthens this manuscript, by demonstrating a degree of replicability and generalizability. This manuscript is also an important contribution to motivating best practices for analyzing neuro-electrophysiological data, including in relation to using baselining procedures.

Weaknesses:

Overall, I do not find any obvious weaknesses in this manuscript and its analyses that challenge the key results and conclusions. There are some minor reporting notes, on the methods and conclusions that I believe could be improved (details in the suggestions for authors). One aspect that could be improved is that while the figures demonstrate the main findings convincingly, the results as written could have more detailed quantifications of the analyzed effects (including, for example, more on the model results, effect sizes, and quantifications of the different features), in order to more fully report the dynamics of the analyzed features and to provide the reader with more information on the findings.

Reviewer #3 (Public review):

Summary:

Using a specparam (1/f) analysis of task-evoked activity, the authors propose that "substantial changes traditionally attributed to theta oscillations in working memory tasks are, in fact, due to shifts in the spectral slope of aperiodic activity." This is a very bold and ambitious statement, and the field of event-related EEG would benefit from more critical assessments of the role of aperiodic changes during task events. Unfortunately, the data shown here does not support the main conclusion advanced by the authors.

Strengths:

The field of event-related EEG would benefit from more critical assessments of the role of aperiodic changes during task events. The authors perform a number of additional control analyses, including different types of baseline correction, ERP subtraction, as well as replication of the experiment with two additional datasets.

Weaknesses:

The authors did not first show that their first task successfully evoked theta power, nor that specparam is capable of quantifying the background around a short theta burst, nor that theta effects are different between baseline corrected vs. spectral parameterized quantifications.

Author Response:

We would like thank reviewers for your comprehensive and insightful reviews of our manuscript. We highly value your constructive comments and suggestions and are preparing revisions that will enhance both the clarity and robustness of our study. Below is an outline of the changes we will implement in response to the points you raised.

All three reviewers expressed concerns regarding the robustness of our conclusions about the relationship between task-related theta activity and aperiodic changes. We will revise the manuscript to present these conclusions more cautiously, stating that the findings indicate a potential contribution of aperiodic activity to what is traditionally interpreted as theta activity. While our results emphasize the importance of distinguishing between periodic and aperiodic components, further research is necessary to fully understand this relationship. We will conduct additional control analyses, including a comparison of the scalp topographies of theta and aperiodic components, to better understand the relationship between aperiodic and periodic (theta) activity.

In response to Reviewer #1's request for greater transparency in our reporting of methodological details, we will provide key clarifications. We will add a clear statement noting that the primary results are based on data from middle-aged to older adults, some of whom had subjective cognitive complaints (SCC). However, it is important to note that no differences were observed between the SCC group and the control group regarding periodic or aperiodic changes in power. Additionally, the main findings were replicated in a sample of middle-aged adults.

To address potential confounding factors, we will include an analysis contrasting response-related ERPs with the identified aperiodic components. However, we do not entirely agree with the assertion that this will necessarily clarify the results. ERPs are not inherently distinct from aperiodic (or periodic) activity; they may reflect changes in aperiodic (or periodic) power. In our view, examining aperiodic and periodic power, ERPs, or time-frequency decomposition with baseline correction provides different perspectives on the same data. Nonetheless, the combined analyses and their results are intended to guide future researchers toward the most suitable approach for interpreting this data.

Reviewer #3 raised concerns regarding the task's effectiveness in evoking theta power and the ability of spectral parameterization method (specparam) to adequately quantify background activity around theta bursts. To address these concerns, we will include additional visualizations demonstrating that the task reliably elicited theta (and delta) activity. Regarding the reviewer's concerns about specparam and theta bursts, it is important to clarify that specparam, in the form we used, does not incorporate time information; rather, it can be applied to any power spectral density (PSD), independent of how the PSD is derived. Specparam’s performance depends on the methods used to estimate frequency content. For time-frequency decomposition, we employed superlets (https://doi.org/10.1038/s41467-020-20539-9), which have been shown to resolve short bursts of activity more effectively than other methods. To our knowledge, superlets provide the highest resolution in terms of both time and frequency. Moreover, to improve stability, we performed spectral parameterization on trial-averaged power (in contrast to the approach in https://doi.org/10.7554/eLife.77348). Nonetheless, we will conduct a simulation to test whether specparam can reliably resolve low-frequency peaks over the 1/f activity.

Reviewer #2 suggested that the manuscript would benefit from a more detailed account of the effects. In response, we will include more detailed quantifications of the analyzed effects, such as model error and R² values.

We believe that the planned revisions will strengthen the manuscript and address the primary concerns raised by the reviewers. We sincerely appreciate your thoughtful feedback and look forward to submitting an improved version of the manuscript soon.

Once again, thank you for your time and expertise in reviewing our work.

Sincerely,

Andraž Matkovič & Tisa Frelih