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  1. Author Response

    Reviewer #2 (Public Review):

    Chylinski et al. investigate sleep EEG properties in a cohort of older individuals, to test how sleep microarchitecture is linked to amyloid burden and memory changes over time, which is important for understanding the evolution of neurodegenerative disease. They report that the temporal coupling of spindles to a specific slow wave type, which they term 'slow switchers', is correlated with A-beta and predictive of subsequent memory decline years later. Strengths of the study are the extensive sleep phenotyping, relatively large cohort, and the acquisition of a follow-up cognitive timepoint two years later. The effect sizes are small, which may be expected due to the nature of this scientific question. The analyses are interesting, but some additional analyses and reporting would be beneficial in the methods and results, particularly the analyses focused on differentiating SW types.

    We thank the reviewer for their comments and suggestions which we address in the following lines.

    Main issues:

    1. The EEG signal processing and analysis methods need additional details. A coincidence of slow wave peaks and spindles is defined as 'co-occurrence' - within what time window do the two events have to co-occur to be considered coincident?

    SW and spindles were detected automatically through published approaches. If the initiation of a spindle was located within the detected period of a SW, both events were considered as co-occurring. The phase of the coupling was set as the moment of the ignition of the spindle with respect to the down and up states of the SWs. We modified the methods to provide more details on these aspects (PAGES 17-18): “After detection of SW and spindles, analysis of their coincidence was performed. A coincidence was defined as to occurrence of the ignition of a spindle within the time frame of a SW: SW ignition at zero µV = phase 0°, SW maximum hyperpolarisation = π/2, zero crossing = π, SW maximum depolarisation = 3π/2, SW termination at zero µV = 2π. This criterion was used on slow and fast switchers.”

    1. In Fig. 1, the analysis does not control for the fact that slow switcher SWs will have a longer time period before the peak than spindles. Fig. 1b's result that more spindles occur in the same phase period could be partially explained by the fact that this phase simply takes a longer period of time for slow switcher SWs (i.e. greater chance of having a spindle if it takes 5x as much time to get from phase -1 to 0, as suggested in Fig. 1c). A control analysis is needed to account for this.

    The duration of the entire SW cycle (zero >> down state >> zero >> up state >> zero) is shorter for SW of overall faster frequency while the duration of the down-to-up-state transition will be shorter in fast switchers. Yet, whether a precise phase of coupling occurs does not depend on the overall frequency or transition frequency of the SW. It could potentially affect the shape of the distribution of phase which could form a plateau. The fact that we find a narrow peak of coupling phase for slow switchers pleads against this bias. The fact that distribution of coupling phase is much broader suggests that spindles do not really co-occur at a particular phase of the SW (yet distribution is not uniform, please see next comment). The former figure 1c consisted in a circular heatmap of the phase distribution of spindles onto SW and did not relate to duration between phases for each slow wave type. We removed this circular display as it was redundant with former figure 1b. Please note that figures 1 and 2 were merged in a single figure 1. We further invite the reviewer to read our response to Essential comment 1) on a related matter dealing with SW type duration.

    1. The green shading in Fig. 1c seems to suggest some phase-coupling for fast switchers too, so it would be appropriate to add a statistic for the statement "no such preferred coupling was detected for fast switcher SWs".

    As already mentioned, we removed fig. 1c from the revised version of the manuscript. We thank the reviewer for this insightful comment. Our initial submission included statistics to demonstrate that coupling phase was different between SW types. Based on visual inspection of the distribution we concluded that only slow switcher SWs showed a preferential coupling phase, but we did not actually test this assumption. We now test it and find that both distributions are not uniform, meaning that, although spindle coupling onto fast switcher SWs is much more widespread, is not random. It is important to note that this result does not interfere with our main finding that is that only spindle coupling onto slow switcher SWs is associated with Ab and memory.

    We corrected the results section according to this new result (PAGE 8):”We assessed whether spindles showed a preferential phase of anchoring with both slow and fast switcher SWs. Qualitative appreciation of the distributions suggests that there is no preferential phase of anchoring of spindles onto the fast switcher SWs while spindle initiation onto slow switcher SWs would show a clear preferred phase (Figure 1e). Watson U² tests indicate, however, that the phase of anchoring onto slow and fast SWs are both non-uniformly distributed (see methods; slow switcher SWs: U² = 904.29, p <0.001; fast switcher SWs: U² = 136.76, p <0.001), i.e. they both show some phase preference. Importantly, further statistical analysis with Watson’s U² test showed that the distribution of spindles anchoring phase was significantly different between slow and fast switcher SWs (U² = 71.143, p <0.001).”

    We also modified the discussion accordingly (PAGE 12): “Three present results confirm that the two types of SW –slow and fast switchers – behave differentially. First, sleep spindles show a difference in their preferential coupling with the transition period from down-to-up state of the slow and fast switcher SWs. While spindles occurring concomitantly to slow switchers SWs show a clear preference to the late part of the depolarisation phase, spindles co-occurring with fast switcher SWs show more widespread phase of coupling (that is still not random/uniformly distributed).”

    We further modified the method section accordingly (PAGE 20): ”We further assessed whether the distribution of spindle onset on the phase of SWs per type was different from a uniform distribution. For each SW type, we generated series of uniformly distributed random values composed of the same number of values spanning the same ranges. Watson’s non-parametric two-sample U² test compared this random series to the actual values.”

    1. The precise implementation of the main statistical tests is a bit unclear in the Methods. When stated "slow wave spindle coupling" is an independent variable, what precisely is in the variable? Is it the phase of the coupling? Is it the proportion of SWs with a spindle for one individual?

    We used the cosine of the individual averaged phase of coupling of the initial part of the spindle within the SW cycle. Cosine were preferred to accommodate the circular nature of a phase detection where the end of SW cycle can also be the beginning of the next SW cycle (using phase of coupling in degrees rather than cosine value did not alter the statistical outputs of our analyses.

    We modified the methods to provide more details on these aspects (PAGES 17-18): “After detection of SW and spindles, analysis of their coincidence was performed. A coincidence was defined as to occurrence of the ignition of a spindle within the time frame of a SW: SW ignition at zero µV = phase 0°, SW maximum hyperpolarisation = π /2, zero crossing = π, SW maximum depolarisation = 3π/2, SW termination at zero µV = 2 π. This criterion was used on slow and fast switchers.” And PAGE 20: “The phase of spindle-SW coupling was set as the phase of the onset of the spindle on the SW converted to its cosine value, to deal with the circularity of the phase variable and perform linear statistics (analysis using the phase in degrees yielded the same outcome).”

    1. Given the small effect size reported for slow switcher SWs, it seems a potential reason for not finding the same result in fast switcher SWs is that there are ~4 times fewer fast switcher SWs. Even if fast switcher SWs had the same size as the underlying effect, is this sample size sufficient to detect it? Is it possible that the difference in the slow wave types reflects the different number of events in each group? Since the analysis does not directly test for a difference between fast and slow (but rather detects a significant effect with slow SWs, and fails to detect it with a smaller number of fast SWs, which does not specifically test for a difference between the two), it seems there is still additional evidence needed if aiming to draw conclusions about these fast and slow SWs being different.

    Please refer to the second main issue above regarding the potential influence of the number of SW types.

    Reviewer #3 (Public Review):

    Strengths:

    • EEG analyses are novel, extensive, and carefully done.
    • Inclusion of baseline amyloid PET is a strength.
    • There is great interest in the transition from normal cognition to cognitive impairment in the earliest stages of disease, and therefore this study population is quite relevant.

    We thank the reviewer for acknowledging the interest of our work and for raising important issues.

    Weaknesses:

    1. The abstract isn't clear regarding the number of participants supporting the principal conclusions. The conclusion RE amyloid was based on the stated n=100, while the one concerning cognitive decline was based only on a subset of n=66.

    We have modified the abstract to make it clear that only 66 individuals took part to the longitudinal assessment.

    1. In the statistical methods, the authors' stated primary analyses were 1) coupling of spindles to slow switching slow waves and 2) coupling of spindles to fast switching slow waves, neither of which has anything specific to do with cognition or dementia. They adjusted these two analyses for 2 comparisons with a threshold of p=0.025. The remainder of the analyses are considered by the authors to be exploratory and therefore not to require adjustment for multiple comparisons. However, in the abstract, the stated goal of the study is to investigate "whether 22 the coupling of spindles and slow waves are associated with early amyloid-beta (Aβ) brain burden, a hallmark of AD neuropathology, and cognitive change over 2 years". This doesn't align with the stated primary analyses in the statistical methods. Moreover, it suggests at a minimum 2 primary outcomes (amyloid burden and cognitive change), and 2 predictors (spindle-slow-switch phase, and spindle-fast-switch phase) for 4 primary analyses that need to be corrected for, resulting in a p-value threshold of 0.05/4 = 0.0125. Neither of the study's primary conclusions (1. that earlier occurrence of spindles on slow-depolarization slow waves is associated with higher prefrontal Ab burden p=0.014 and 2. that earlier occurrence of spindles on slow-depolarization slow waves is associated with greater longitudinal memory decline p=0.032) meets this cutoff. This is even if we disregard the many other comparisons that were made (in the study, there are at least 3 outcomes of interest - baseline cognition, baseline amyloid, and change in cognition) and many EEG predictors examined. Indeed, if we consider all the analyses performed in this study (3 outcomes as above [amyloid, baseline cognition, change in cognition] x 7-8 different EEG measures = 24 comparisons) the 2 significant results at p<0.05 are not all that much more than would be expected by chance.

    We thank the reviewer for raising this important issue. Please refer to Essential comment 3) for full details regarding this point.

    1. It is not 100% clear how the authors selected specifically phase angles between spindles and slow waves (rather than, for instance, percent coincidence, or dispersion of phase angle as a measure of the "tightness" of coupling) as their primary predictors. If these were looked at they would require even more extensive adjustment for multiple comparisons.

    Other metrics could indeed be envisaged but they would raise multiple comparison issues. Our rationale is that it is the timing of spindle and SW co-occurrence (or coupling) that matters for optimal information exchange during sleep. As reported at the beginning of the result section a substantial part of spindle and SW co-occur meaning we are not focusing on a marginal aspect of sleep microstructure. In addition, previous studies (Bouchard et al. 2021) reported that the phase of spindle to SW coupling changes in ageing when it is established that ageing is associated with important sleep changes. This further supports that we are focussing on a relevant aspect of sleep microstructure. Note that we do not observe an effect of age on spindle-SW coupling phase, most likely because of the limited age-range of our sample (50-69y)

    We modified the methods to provide more details on these aspects (PAGES 17-18): “After detection of SW and spindles, analysis of their coincidence was performed. A coincidence was defined as to occurrence of the ignition of a spindle within the time frame of a SW: SW ignition at zero µV = phase 0°, SW maximum hyperpolarisation = π /2, zero crossing = π, SW maximum depolarisation = 3π/2, SW termination at zero µV = 2π. This criterion was used on slow and fast switchers.” And PAGE 20: “The phase of spindle-SW coupling was set as the phase of the onset of the spindle on the SW converted to its cosine value, to deal with the circularity of the phase variable and perform linear statistics (analysis using the phase in degrees yielded the same outcome).”

    1. The authors conclude that their findings suggest that "altered coupling of sleep microstructure elements, key to its mnesic function, contributes to poorer brain and cognitive trajectories in ageing." In their discussion, they do acknowledge that this sort of causal inference is not possible based on the non-interventional nature of this study. Indeed, it is certainly plausible that differences in the phase relationship between spindles and slow waves, rather than being contributors to cognitive decline, may instead be markers of early AD-related brain changes, not picked up on by amyloid PET (e.g. amyloid oligomers, or non-amyloid processes) that are the proximate cause of 2-year cognitive decline.

    We modified the text to temper our statement and state that spindle-SW coupling and cognition could be sensitive to a similar causal factor (PAGE 15): “Finally, given that our protocol does not include manipulation of the coupling of the spindles onto the SWs, it precludes any inference on the causality of one aspect onto the other. It may be that cognition and the coupling of spindle and SWs are sensitive to the same age-related or AD-related phenomenon (e.g. presence of amyloid oligomers, or non-amyloid processes, that would go mostly undetected using common PET scan Aβ radioligand).

    Together, our findings reveal that the timely occurrence of spindles onto a specific type of SWs showing a relative preservation in ageing may play an important role in ageing trajectory, both at the cognitive level and with regards to structural brain integrity. These findings may help to unravel early links between sleep, AD-related pathophysiology and cognitive trajectories in ageing and warrants future clinical trials attempting at manipulating sleep microstructure or Aβ protein accumulation.”

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  2. Evaluation Summary

    This paper is of interest to neuroscientists studying sleep, memory, and neurodegeneration. The authors found that an altered pattern of brain wave during NREM sleep, changes in the coupling of spindles and slow waves, correlates with amyloid burden and predicts memory decline over time in healthy older individuals. The results suggest that sleep brain waves may be a useful tool in identifying older adults at risk for future cognitive impairment in the earliest stage.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 agreed to share their name with the authors.)

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  3. Reviewer #1 (Public Review):

    This study investigated potential links between sleep structure elements and Alzheimer's disease (AD) development in healthy individuals in the late midlife to capture early signs of cognitive decline. Full polysomnography sleep recording and EEG analysis showed that slow waves are classified into two types (slow and fast switchers) and spindles are preferentially coupled only to slow switcher slow waves. The authors revealed that among sleep parameters including sleep duration and SW density, only this spindle-slow switcher SW coupling showed a negative correlation with Aβ burden in mPFC. Furthermore, the follow-up memory test revealed that uncoupling of spindle and slow switcher SWs is predictive of a memory worsening over 2 years. Therefore, this study successfully identified an early marker of Aβ deposit in mPFC and cognitive decline, which may help earlier diagnosis of AD.

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  4. Reviewer #2 (Public Review):

    Chylinski et al. investigate sleep EEG properties in a cohort of older individuals, to test how sleep microarchitecture is linked to amyloid burden and memory changes over time, which is important for understanding the evolution of neurodegenerative disease. They report that the temporal coupling of spindles to a specific slow wave type, which they term 'slow switchers', is correlated with A-beta and predictive of subsequent memory decline years later. Strengths of the study are the extensive sleep phenotyping, relatively large cohort, and the acquisition of a follow-up cognitive timepoint two years later. The effect sizes are small, which may be expected due to the nature of this scientific question. The analyses are interesting, but some additional analyses and reporting would be beneficial in the methods and results, particularly the analyses focused on differentiating SW types.

    Main issues:

    The EEG signal processing and analysis methods need additional details. A coincidence of slow wave peaks and spindles is defined as 'co-occurrence' - within what time window do the two events have to co-occur to be considered coincident?

    In Fig. 1, the analysis does not control for the fact that slow switcher SWs will have a longer time period before the peak than spindles. Fig. 1b's result that more spindles occur in the same phase period could be partially explained by the fact that this phase simply takes a longer period of time for slow switcher SWs (i.e. greater chance of having a spindle if it takes 5x as much time to get from phase -1 to 0, as suggested in Fig. 1c). A control analysis is needed to account for this.

    The green shading in Fig. 1c seems to suggest some phase-coupling for fast switchers too, so it would be appropriate to add a statistic for the statement "no such preferred coupling was detected for fast switcher SWs".

    The precise implementation of the main statistical tests is a bit unclear in the Methods. When stated "slow wave spindle coupling" is an independent variable, what precisely is in the variable? Is it the phase of the coupling? Is it the proportion of SWs with a spindle for one individual?

    Given the small effect size reported for slow switcher SWs, it seems a potential reason for not finding the same result in fast switcher SWs is that there are ~4 times fewer fast switcher SWs. Even if fast switcher SWs had the same size as the underlying effect, is this sample size sufficient to detect it? Is it possible that the difference in the slow wave types reflects the different number of events in each group? Since the analysis does not directly test for a difference between fast and slow (but rather detects a significant effect with slow SWs, and fails to detect it with a smaller number of fast SWs, which does not specifically test for a difference between the two), it seems there is still additional evidence needed if aiming to draw conclusions about these fast and slow SWs being different.

    Read the original source
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  5. Reviewer #3 (Public Review):

    Strengths:
    - EEG analyses are novel, extensive, and carefully done.
    - Inclusion of baseline amyloid PET is a strength.
    - There is great interest in the transition from normal cognition to cognitive impairment in the earliest stages of disease, and therefore this study population is quite relevant.

    Weaknesses:
    - The abstract isn't clear regarding the number of participants supporting the principal conclusions. The conclusion RE amyloid was based on the stated n=100, while the one concerning cognitive decline was based only on a subset of n=66.
    - In the statistical methods, the authors' stated primary analyses were 1) coupling of spindles to slow switching slow waves and 2) coupling of spindles to fast switching slow waves, neither of which has anything specific to do with cognition or dementia. They adjusted these two analyses for 2 comparisons with a threshold of p=0.025. The remainder of the analyses are considered by the authors to be exploratory and therefore not to require adjustment for multiple comparisons. However, in the abstract, the stated goal of the study is to investigate "whether 22 the coupling of spindles and slow waves are associated with early amyloid-beta (Aβ) brain burden, a hallmark of AD neuropathology, and cognitive change over 2 years". This doesn't align with the stated primary analyses in the statistical methods. Moreover, it suggests at a minimum 2 primary outcomes (amyloid burden and cognitive change), and 2 predictors (spindle-slow-switch phase, and spindle-fast-switch phase) for 4 primary analyses that need to be corrected for, resulting in a p-value threshold of 0.05/4 = 0.0125. Neither of the study's primary conclusions (1. that earlier occurrence of spindles on slow-depolarization slow waves is associated with higher prefrontal Ab burden p=0.014 and 2. that earlier occurrence of spindles on slow-depolarization slow waves is associated with greater longitudinal memory decline p=0.032) meets this cutoff. This is even if we disregard the many other comparisons that were made (in the study, there are at least 3 outcomes of interest - baseline cognition, baseline amyloid, and change in cognition) and many EEG predictors examined. Indeed, if we consider all the analyses performed in this study (3 outcomes as above [amyloid, baseline cognition, change in cognition] x 7-8 different EEG measures = 24 comparisons) the 2 significant results at p<0.05 are not all that much more than would be expected by chance.
    - It is not 100% clear how the authors selected specifically phase angles between spindles and slow waves (rather than, for instance, percent coincidence, or dispersion of phase angle as a measure of the "tightness" of coupling) as their primary predictors. If these were looked at they would require even more extensive adjustment for multiple comparisons.
    - The authors conclude that their findings suggest that "altered coupling of sleep microstructure elements, key to its mnesic function, contributes to poorer brain and cognitive trajectories in ageing." In their discussion, they do acknowledge that this sort of causal inference is not possible based on the non-interventional nature of this study. Indeed, it is certainly plausible that differences in the phase relationship between spindles and slow waves, rather than being contributors to cognitive decline, may instead be markers of early AD-related brain changes, not picked up on by amyloid PET (e.g. amyloid oligomers, or non-amyloid processes) that are the proximate cause of 2-year cognitive decline.

    Read the original source
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