Neuronal timescales are functionally dynamic and shaped by cortical microarchitecture

  1. Richard Gao
  2. Ruud L. van den Brink
  3. Thomas Pfeffer
  4. Bradley Voytek

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Abstract

Complex cognitive functions such as working memory and decision-making require information maintenance over many timescales, from transient sensory stimuli to long-term contextual cues. While theoretical accounts predict the emergence of a corresponding hierarchy of neuronal timescales, direct electrophysiological evidence across the human cortex is lacking. Here, we infer neuronal timescales from invasive intracranial recordings. Timescales increase along the principal sensorimotor-to-association axis across the entire human cortex, and scale with single-unit timescales within macaques. Cortex-wide transcriptomic analysis shows direct alignment between timescales and expression of excitation- and inhibition-related genes, as well as genes specific to voltage-gated transmembrane ion transporters. Finally, neuronal timescales are functionally dynamic: prefrontal cortex timescales expand during working memory maintenance and predict individual performance, while cortex-wide timescales compress with aging. Thus, neuronal timescales follow cytoarchitectonic gradients across the human cortex, and are relevant for cognition in both short- and long-terms, bridging microcircuit physiology with macroscale dynamics and behavior.

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  1. eLife

    ###Reviewer #4 This is an innovative and very interesting study reporting the correlation of extracted neural timescales and expression of NMDA and GABA_a receptor subunits amongst others.

    Comments:

    -definition of timescale is missing in the introduction. Fast and slow responding to sensory versus cue related information reflects a circular definition of timescales.

    -the results text say that the aperiodic components is interpreted as time scale but not how the inference is made, i.e. what quantity is interpreted as time scale.

    -it is difficult to keep track of which timescales are referred to when in the text, e.g. the authors start referring to neuronal timescales after having discussed ECOG based time scales and spike timescales. It seems important for cleanly separating the source of the timescale to denote them with a unique label depending on the source data that gives rise to them. Why not use a subscript for spike, epiduralECoG, subduralECoG, intracranialLFP, ... ?

    -the article seems to assume that mRNA expression for specific receptor subunits correspond to the density of expression of those receptors. It seems important that this is made explicit (if correct) and that a reference is given that shows this relationship.

    -line 142 refers to "task-free ECoG recordings in macaques" but does not clarify where the data comes from. No reference is provided.

  2. eLife

    ###Reviewer #3 In this paper entitled 'Neuronal timescales are functionally dynamic and shaped by cortical microstructure', Gao et al. use open access databases to address two distinct questions: 1) the relationship between hierarchically organized variations in neuronal timescales and brain gene expression and 2) the effect of task and age onto the neuronal timescales of a given cortical regions.

    Overall, this is a well-designed study and the combination of open access databases is well organized and astutely exploited. I, in particular, very like the analysis that tests whether variations in gene expression still accounts for variations in neuronal timescales when the main gradient effect is regressed out. Below are my comments on the manuscript.

    1. For the non-specialist reader, the concept of neuronal timescales that is central to the paper should be defined more explicitly in the introduction ('neuronal timescales' appear in paragraph 3, while it gets defined in paragraphs 1 and 2).

    2. In figure 2B, some T1w/T2w values are above values of 2, which is not standard. Likewise, several outliers can be observed. This might have impacted the estimation of the regression slope. This slope currently matches the one from Burt et al. 2018, although the data point distribution is different.

    3. Figure 4B is contradicting figure 2C as the evidenced timescale hierarchy is different (comparing PC, PFC and OFC). Please explain.

    4. Figure 4B and 4C, please show actual data points and justify parametric tests.

    5. Figure 4C: how consistent is the increase in delay period timescales across areas within each subject. In other words, is this a general property of the brain, task-related effects resulting in a non-specific adjustment in neuronal timescales or are there regional differences in the reported increase (you might want to exclude the PFC from the analysis to remove task related effects).

    6. The manuscript addresses two distinct aspects of neuronal timescales: their relationship to local microarchitecture and their dynamics as a function of task or age. Although there is obviously a strong inter-relationship between these two aspects, this deserves a more extensive discussion. For example, in relation with the previous point, if local microstructural properties predict neuronal timescales, why is it that timescale changes during the delay seem to be ubiquitous (or are they)? And why should such changes (that are overall in the same range) correlate with subject performance in the PFC but not in the other areas? How does this relate to the aging observations? Although this discussion is bound to be speculative, I think it is important in order to strengthen the link between these two independent avenues of the paper, and to enrich the discussion about the functional role of these dynamic changes in neuronal timescales.

    7. Given the described age-related effect, did the authors check that the different databases they used sampled from subjects with the same age distribution.

    8. Legend of figure 1 is not self-explanatory and a lot of the symbols and information plotted in the figures are not explained. Unfortunately, this information is also missing from the result section.

    9. Figs 3E and 3F are mislabeled as 4E and 4F.

    10. Generally speaking, given that the main text itself is very dense, figure legends should be more self-explanatory. Quite often, figure detail description and contextual information are missing both from the text and the figures. This also applies to the supplementary figures.

  3. eLife

    ###Reviewer #2 Overall, this is an interesting manuscript and a well-done study. The main finding is that neural timescales, as quantified through the decay of the power spectrum, vary over cortical regions and are correlated with genes that regulate ionic and structural properties of neurons. The findings aren't terribly surprising and the computational impact on cognition and aging remains unclear (other than showing differences), but the overall approach is novel and interesting.

    I have an overarching concern, which is that the manuscript is written to be dense yet terse, which makes it harder to read, particularly given the complexity of the analyses. It feels like it was written for a journal with extreme word limitations. The manuscript would be overall improved if the authors would "loosen their belt" and explain the findings and methods in more detail.

    What are "these" limitations on line 96?

    Figure 1e: how is r2=1 when the dots do not fall on the line?

    I'm confused about the description of the methods on page 5. For example, "we can estimate neuronal timescale from the 'characteristic frequency'" which implies a peak in the spectrum. Yet in the next sentence they write that they extract timescale from aperiodic components.

    Page 7: Are these markers also correlated with cell packing density? If so, it's possible that denser neural networks have longer timescales.

    Relatedly, how strongly inter-correlated are these genetic markers across the cortex? The authors mostly take a mass-univariate approach except for showing gene-PC1 in Figure 3a. There isn't enough information shown to evaluate whether the top PC is suitable, or whether this PC comprises many/all gene contributions or is driven by a small number, etc.

    I'm missing the modeling results. They appear as a schematic in figure 1 and are mentioned in the Methods section. Was this model actually used somewhere?

  4. eLife

    ###Reviewer #1

    These findings are a significant advance in comparison to previous work like Murray et al. (2014) and Dotson & Gray (2018 - please cite here) in the sense that brain-wide hierarchy is considered, whereas previous work considered a smaller set of brain areas. Furthermore, several other interesting correlations are reported with timescales. Overall the analyses appear to be of very high quality, providing a standard for similar studies in the future, and the authors carefully considered problems that arise in correcting for dependent samples, which I applaud.

    Some of the claims need further discussion or refinement, in my opinion.

    1. The comparison shown in Figure 2 between spiking time-scale and ECOG time-scale might be problematic, in the sense that the spiking time-scales were taken from the Murray et al. (2014) paper where they were quantified with a different technique. My suggestion would be to quantify time-scales in the same manner as Murray, or maybe there is a convincing argument why this is not a problem.

    2. The correlations shown between transcriptomics and timescales need to be carefully considered. While the authors regress out T1w/T2w residuals, these might just be one structural factor that changes with cortical hierarchy and assumes that the underlying relationships are linear. Hence, it is possible that timescales and gene profiles are correlated with structure but that there is no causal relationship between these genes and timescales. In this sense, the correlation of genes with hierarchy might also yield similar genetic profiles. It would be important to show the correlation of hierarchy with genetic profiles, to see whether this looks different from the correlations that are obtained with timescale.

    3. The authors use T1W/T2W as the measure for cortical hierarchy. This is a gradient-based perspective on cortical hierarchy. However, there are other perspectives on hierarchy that are not gradient-based, but are based on anatomical connectivity, e.g. as pursued by Kennedy and Van Essen (Vezoli et al., 2020, Biorxiv). This needs to be discussed.

    4. The paper does not consider oscillations, which is fine, but the reader is left wondering how oscillations affect these time-scales. Discussion on this aspect would be useful.

    5. Are the rho correlation values corrected for the expected value of the surrogate distribution? That is, are they significantly overestimated due to the dependent samples issue? In this case I would recommend reporting the corrected correlation values, rather than the raw correlation values.

    6. The correlation performed in Figure 4D is a bit unclear to me. Are the different dots+lines participants, or is this a binned correlation? If it is a binned correlation, does that represent a problem for the correlation analysis?

    7. It would be useful in Figure 1/2 to show some examples of ECOG time-scales related to the actual underlying signals and PSDs, rather than just illustrating the technique on simulated data, so that the validity of the technique can be judged.

    8. In general it would be useful to report carefully the N's and the dataset that is used for each analysis, because it is easy to get lost in what is what as the authors analyze a huge number of datasets.

    9. The technique of removing spatial autocorrelations that influence the p-value appears to be sophisticated and well done. In case this analysis poses problems with other reviewers, I would recommend using a cross-validation prediction approach where a subset of subjects is used for training and the other subjects are used for testing.

  5. eLife

    ##Preprint Review

    This preprint was reviewed using eLife’s Preprint Review service, which provides public peer reviews of manuscripts posted on bioRxiv for the benefit of the authors, readers, potential readers, and others interested in our assessment of the work. This review applies only to version 3 of the manuscript.

    ###Summary

    Gao et al. analyze how brain-wide timescales of ECoG signals vary across the cortical hierarchy and relate these timescales to several other aspects of structure, behavior and function. They report the following main findings: 1) Timescales increase with the cortical hierarchy. 2) Time-scales, after regressing out the hierarchical T1w/T2w structure variable, correlate significantly with several genes related to synaptic receptors and ion channels. 3) Time-scales increase with working memory task vs. baseline, and predict working memory performance across subjects. 4) Time-scales decrease with aging, in a region-specific way. These findings are a significant advance in comparison to previous work by considering brain-wide hierarchy at a high spatial and temporal resolution and relating them to behaviour and genetics.

  6. Version published to 10.1101/2020.05.25.115378 on bioRxiv