Loss of integration of brain networks after sleep deprivation relates to the worsening of cognitive functions

  1. Pesoli Matteo
  2. Rucco Rosaria
  3. Liparoti Marianna
  4. Lardone Anna
  5. D’Aurizio Giula
  6. Minino Roberta
  7. Troisi Lopez Emahnuel
  8. Paccone Antonella
  9. Granata Carmine
  10. Sorrentino Giuseppe
  11. Mandolesi Laura
  12. Sorrentino Pierpaolo

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Abstract

The topology of brain networks changes according to environmental demands and can be described within the framework of graph theory. We hypothesized that 24-hours long sleep deprivation (SD) causes functional rearrangements of the brain topology so as to impair optimal communication, and that such rearrangements relate to the performance in specific cognitive tasks, namely the ones specifically requiring attention. Thirty-two young men underwent resting-state MEG recording and assessments of attention and switching abilities before and after SD. We found loss of integration of brain network and a worsening of attention but not of switching abilities. These results show that brain network changes due to SD affect switching abilities, worsened attention and induce large-scale rearrangements in the functional networks.

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  1. eLife

    ###Reviewer #3:

    The study by Pesoli et al. uses MEG acquisition in sleep deprived participants in order to explore the functional integration derived from MEG source reconstructed connectivity and its potential link to attentive functions. The study is well conducted with an appropriate size to explore global graph measures derived from MEG connectivity.

    1. My major concern is that the authors' main claim that MEG connectivity is correlated to attentive function has at best very weak support from the presented data. Though the authors claim in the methods that all analysis were FDR corrected the correlational analysis linking behavior to MEG connectivity does report uncorrected values. E.g. the correlation between Alpha-MEG Degree of Right superior Occipital gyrus bases on a statistical test on 90ROIs x 5 frequency bands x 2 nodal metrics which would result in a Bonferroni threshold of p=0.05/900, the reported p=0.009 is by orders larger than this threshold. This problem applies (on different levels) to all correlations reported in Fig. 6. In order to limit the amount of false positives more stringent statistical thresholding would be needed to analyze the link between connectivity and behavior (a good starting point to solve this issue can be [Makin et al. 2019, elife]). Related to this issue: the hypothesis 'such topological rearrangements would relate to cognitive performance' is highly underdetermined and the authors could stress the strong exploratory character of this study more in both abstract and introduction.

    2. The link to previous literature unclear for the connectivity measure used (Phase Linearity Measurement): the authors should shortly address in a paragraph what we should expect e.g when comparing the measure to more frequently used connectivity measures such as amplitude envelope coupling or coherence (Colclough et al. 2016, NeuroImage). What are the differences of the used measure and why did the authors choose this measure instead of a more frequently used measure?

    3. I was generally missing a consistent definition of the term integration: why did the authors choose the selected graph metrics to measure integration and how do the graph metrics show that the brain loses integration (like they state in the title of the article). The use of all graph measures should be clearly motivated: why did the authors choose these measures and what are they planning to measure to support their hypothesis?

    4. 'In particular, with regards to TS, median reaction times (in ms; median RT) to both repetition and switch trials, and angular transformations of the proportion of errors resulting from the two experimental sessions were submitted to two-factor repeated-measures ANOVA, instead, SC as well as all dependent variables obtained from LCT (number of hits and number of rows completed), were submitted to paired t-test.' This sentence is difficult to understand, I did not understand why in one case you use only posthoc t-tests and in the other case an ANOVA.

    5. Data availability: 'All data generated or analyzed during this study are included in the manuscript and supporting files.', the authors should include a more detailed description of where the interested reader can find data and code. Is it available on request or will it be provided in a repository?

  2. eLife

    ###Reviewer #2:

    This study employs the use of MEG to incorporate both spatial and temporal strengths of previous fMRI and EEG studies to uncover the effects of sleep deprivation on brain function. While the motivation is clear, there are some issues with methodology and the writing is difficult to understand in many places.

    Introduction:

    1. L32-33 This sentence is not clear - 'neuroimaging techniques allowing us to overcome the concept of specific control vs. a distributed property'. Can you use a term like 'distinguish' or 'clarify'?

    2. L56-68 It would be better to talk about overall function of neural oscillations (SWA and spindles) during sleep on executive function and memory consolidation (systems consolidation/synaptic downscaling theories), rather than 'increases', as your study does not augment SWA per se. In fact sleep deprivation does augment SWA in the subsequent recovery period as an indicator of sleep pressure/intensity but we wouldn't consider this as beneficial.

    3. L100 - Can you briefly explain here why these tasks were chosen - e.g. if they have been used in prior SD work with other imaging modalities.

    Results:

    1. L-173 - you're not really comparing between two groups... should read conditions

    2. L204-216 - correlation assumes independence of observations, here you are combining both T0 and T1 conditions and combining them in 1 plot. This is problematic, also if you split these, some relationships look like they are going in opposite directions (e.g. Fig. 6b). Why not correlate change scores (brain/behavior) with each other?

    Discussion:

    1. L277 - There is a lot of discussion about the loss of integration measures during SD, however, the leaf fraction which is supposed to indicate integration of the networks is not significant between conditions.

    2. L252 - Most of the manuscript is set up for the reader to expect that SD would primarily affect frontal lobes and top-down cognition. However, the findings here are somewhat opposite - occipital regions associated with processing of visual stimuli are the ones that show altered diameter and degree metrics - but the authors claim that bottom up processing does not suffer from the effects of SD (L294). These findings need to be reconciled, and also with prior work.

    3. L293 - even if task engagement were a factor, we would not typically expect that participants would perform better after SD (maintained performance might be possible). This could suggest a practice effect at play here - since the first session was always the well-rested session.

    Methods:

    1. L315 - Can you show in a table descriptives for the actigraphic assessments of sleep the night before the experiment?

    2. L378 - disjoint sentence

    3. L400 - what does 'on the letter a beamforming procedure was performed' mean?

    4. L436 - there appears to be no counterbalancing across conditions here as all participants completed T0 first before T1. This could lead to practice effects confounding some of the interpretations. There is a statement about reduction of learning effects using different parallel forms from the LCT (L330) but it is not clear what this means. Can you show within each session (rested/SD) whether or not you see improvements in performance as the task progressed?

  3. eLife

    ###Reviewer #1:

    In this study, 34 participants underwent 24 hours of sleep deprivation. They performed two tasks (letter cancellation and task switching) before and after sleep deprivation. Graph metrics were computed based on resting-MEG data. The authors showed that participants performed worse in the letter cancellation task after sleep deprivation, but performed better in task switching after sleep deprivation. They showed that certain graph metrics were changed after sleep deprivation and some of these metrics were correlated with task performance changes in task switching, but not letter cancellation.

    1. I think it's quite worrisome that participants actually performed better at task switching after sleep deprivation. I wonder if there's a serious flaw in the experimental procedure. One possibility is practice effect since participants performed the task before they were sleep deprived and then performed the task again after sleep deprivation.

    2. While the minimal spanning tree (MST) has been used in some papers, it seems to me that the resulting tree might be sensitive to noise. Besides, such pruning does not seem biologically plausible. I would suggest the authors repeat their analyses using more standard approaches, while taking into account potential pitfalls ( https://www.sciencedirect.com/science/article/pii/S105381191730109X )

    3. False discovery rate was not reported.

    4. It's unclear the sequence of experimental procedure. Perhaps I missed it but were the tasks performed before or after the MEG/MRI acquisition? I only knew the tasks were not performed during MEG because the authors mentioned in the discussion that "the brain measures are made at rest and not during the execution of the task." Seems pretty important to mention this more prominently in the manuscript.

    5. The title states that "Loss of integration of brain networks after one night of sleep deprivation underlies worsening of attentive functions". However, the authors' results contradict the title, since network measures did not correlate with worse letter cancellation task (LCT) performance, but correlated with better task switching performance! The same issue is present in the abstract, where the authors state that "brain network changes due to SD selectively impaired attention", yet the authors reported that "LCT performance and NASA score were not correlated with topological data".

    6. It's hard to follow the results section without first reading the methods section. This is fine if the methods section was before the results section. However, in this manuscript, the results section was before the methods section. Therefore, the authors should provide more methodological overview in the results section. For example, graph theoretic terms like BC and Diameter in Alpha were used in the results section with no explanation.

  4. 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 2 of the manuscript.

    ###Summary:

    This study utilizes MEG to study the effects of sleep deprivation on functional network integration, attention and task-switching. The strength of this study is that this is perhaps the first MEG sleep deprivation dataset and thus, the community would benefit from this data. However, the reviewers felt that there were potentially serious issues with the study design and statistical analyses. More specifically, the improvement in task switching performance after sleep deprivation might simply be due to practice effects. Without counterbalancing T0 and T1, it is unclear how this issue could be resolved. Furthermore, there were concerns about the pooling of T0 and T1 conditions in the correlations with KSS and task performance, as well as issues with multiple comparisons correction.

  5. Version published to 10.1101/2020.07.15.197590 on bioRxiv