Dear Editor, The search for a reliable link between sleep physiology and improvement in memory performance attributed to sleep (i.e., sleep-dependent memory consolidation) has spanned several decades . In the late 1990s, in an attempt to probe causality, researchers began correlating sleep markers (e.g., time in a sleep stage) with memory enhancement over sleep. From these studies, and from corresponding work in animal models (e.g., Ref. ), hypotheses of sleep physiology-dependent memory consolidation in humans were established and have endured. Put simply, it is generally believed that declarative memory consolidation predominantly relies on SWS duration or features (e.g., spectral power in the δ frequency range), motor consolidation predominantly relies on N2 duration or features (e.g., sleep spindles and σ power), and emotional processing or consolidation predominantly relies on REM duration or features (e.g., θ power). However, since the establishment of these hypotheses, countless empirical human investigations have failed to detect hypothesized correlations between sleep and memory consolidation, even when sleep-dependent consolidation has clearly occurred. Moreover, various studies have detected negative correlations when a positive correlation is expected [3–5] or have conducted more correlations than statistically appropriate [6–10], resulting in findings that are likely spurious and thus not credible (the author of this letter is guilty of the latter transgression). Often, when hypothesized correlations are not detected, researchers attempt to find a post hoc explanation for results, either by adjusting hypotheses or by attributing findings to subtle differences or inadequacies in study design [3,8,11]. Although this may not seem to be a substantial issue in the field, null, negative, or spurious correlations are not without cost, as they may lead to time- and resource-consuming experimentation based on results from previous work. Additionally, at times, investigations that do not detect a hypothesized correlation (i.e., a purportedly causal link) between sleep physiology and behavior are considered substandard (e.g., less worthy of a high-impact journal). As such, the continued use of this convention could be detrimental to the field. At academic conferences and through personal correspondence, researchers freely discuss the need to move away from the over-utilization of sleep physiology correlations. Nevertheless, many feel that they are expected to conduct these analyses, either by colleagues or by a manuscript reviewer. Herein, in response to this issue, findings that demonstrate there is not a direct (i.e., 1:1) relationship between sleep physiology and human memory consolidation are discussed. This evidence is presented in an attempt to demonstrate that correlations should not be used to link sleep physiology with sleep-dependent memory consolidation. Subsequently, alternative statistical and methodological approaches are presented. It is hoped that this letter will (1) provide justification for researchers to move away from this convention and (2) propel the field in a more conservative, yet informative, direction. No Detected Correlations: A Statistical Power Issue? Many well-controlled empirical investigations have failed to detect a link between sleep physiology and memory consolidation (i.e., detect no statistically significant correlations). Although it could be proposed that insufficient statistical power (e.g., small sample size) is to blame for null findings, recent work negates this notion. Specifically, a study comprising nearly 1000 participants tested the link between sleep-dependent memory consolidation and sleep characteristics . No correlations between sleep and consolidation were detected, suggesting, almost definitively, that sleep physiology does not solely predict overnight consolidation. This study could and should have been a crucial paradigm shift for the field. However, despite this impressive, rigorous research endeavor, this work has been largely overlooked (to date, this work has been cited only 14 times), and researchers continue to utilize correlation analyses in the same manner. Sleep Alterations Do Not Predict Consolidation Alterations If sleep physiology is directly linked to behavior, populations with characteristic sleep alterations should exhibit sleep-dependent behavioral changes accordingly. Yet that is not the case. For instance, older adults have a marked reduction in SWS but a preservation in N2 . However, in this population, paradoxically, sleep-dependent declarative memory consolidation (a supposed SWS-dependent process) is relatively preserved, whereas procedural memory consolidation (a supposed N2-dependent process) is reduced . These contradictory results suggest that there is no direct relationship between consolidation and sleep physiology, at least an aged population, and that this relationship is more complex than originally surmised. Nocturnal and Nap Characteristics Differentially Predict Behavior Measures of sleep physiology from nocturnal sleep and from mid-day naps differentially predict behavioral correlates. This differentiation has been observed anecdotally and has also been documented. In one set of experiments, the same memory paradigm—within the same lab—produced separate sleep stage correlations for a nocturnal study and a nap study . Specifically, REM was linked to nocturnal consolidation, whereas SWS was linked to nap-dependent consolidation. It is unlikely that the causal influence of sleep varies based on time-of-day. Rather, these findings introduce another intricacy which suggests that sleep stages do not solely predict behavior (circadian rhythmicity, for instance, might play a role) and that there is no 1:1 relationship between sleep physiology and memory consolidation. Potential Next Steps The active effects of sleep on memory are not negated by a lack of correlation. Rather, there are additional factors that need to be considered to develop a comprehensive understanding of the link between sleep and memory. For instance, encoding strength or success  and markers of autonomic nervous system activity  have both been shown to moderate the relationship between sleep markers and consolidation. There is also accumulating evidence for the sequential hypothesis of consolidation (which posits that sleep stage interactions are critical for consolidation ) and the sleep stage cycling hypothesis (which considers the NREM–REM cyclical nature of sleep ). Accounting for these factors could be a critical starting point for future work. Next, an analytic or methodological approach with sound theoretical potential comes from work examining the interaction between state- and trait-specific sleep markers. Trait specificity (e.g., habitual sleep physiology) is rarely considered when seeking a concordance between sleep and behavior. However, a recent study demonstrated that both habitual sleep markers and markers from the assumedly causal night of sleep are important in predicting consolidation . This novel approach has the potential to enhance our understanding of the relationship between EEG brain correlates and memory consolidation. Furthermore, statistically, linear regressions are a more conservative, yet sensible approach than correlations. With this statistical technique, both sleep physiology and other potentially relevant factors (mentioned above) can be included in a single model to create a strong measure of prediction while also reducing the chance of Type I statistical error. Although regressions have previously been utilized in the field, and despite the clear advantages of this statistical approach, they are not commonplace. Additionally, hierarchical linear models, which can be used as an alternative to ANCOVAs, can incorporate several predictor variables while also accounting for covariance between factors. This statistical tool could be useful when comparing populations or experimental groups. Lastly, and perhaps most importantly, establishing a consistent method of reporting results could increase transparency and thoroughness in the field. Given the high number of sleep parameters in use (e.g., sleep stages, oscillatory properties, and cycling), it is not difficult to detect a relationship between sleep and consolidation. Yet, null and conflicting results are often not reported. Reporting relationships between only certain parameters (and not others) allows researchers to present isolated findings without addressing conflicting evidence. To alleviate this issue, in the near future, researchers should work to standardize a set of sleep parameters that are regularly tested and reported. This approach would increase consistency, replicability, and comparability in the field. Conclusion The sleep research field has evolved immensely since the first published link between sleep and memory in 1924 . However, despite the existence of novel tools to probe causality (e.g., cueing), many investigators continue to utilize analytic tools that stagnate progress in the field. Here, critiques of sleep physiology correlations are not meant to point out flawed research approaches. Rather, these correlations have been a critical starting point for future work that is more expansive and comprehensive. Disclosure Statement None declared. Acknowledgments The author thanks Sara Alger and Jared Saletin for their suggestions and amendments. References 1. Verschoor GJ, Holdstock TL. REM bursts and REM sleep following visual and auditory learning. South Afr J Psychol . 1984; 14( 3): 69– 74. Google Scholar CrossRef Search ADS 2. Wilson MA, McNaughton BL. Reactivation of hippocampal ensemble memories during sleep. Science . 1994; 265( 5172): 676– 679. Google Scholar CrossRef Search ADS PubMed 3. Pardilla-Delgado E, Payne JD. The impact of sleep on true and false memory across long delays. Neurobiol Learn Mem . 2017; 137: 123– 133. Google Scholar CrossRef Search ADS PubMed 4. Payne JD, Schacter DL, Propper REet al. The role of sleep in false memory formation. Neurobiol Learn Mem . 2009; 92( 3): 327– 334. Google Scholar CrossRef Search ADS PubMed 5. Scullin MK. Sleep, memory, and aging: the link between slow-wave sleep and episodic memory changes from younger to older adults. Psychol Aging . 2013; 28( 1): 105– 114. Google Scholar CrossRef Search ADS PubMed 6. Albouy G, Fogel S, Pottiez Het al. Daytime sleep enhances consolidation of the spatial but not motoric representation of motor sequence memory. PLoS One . 2013; 8( 1): e52805. Google Scholar CrossRef Search ADS PubMed 7. Mantua J, Mahan KM, Henry OS, Spencer RM. Altered sleep composition after traumatic brain injury does not affect declarative sleep-dependent memory consolidation. Front Hum Neurosci . 2015; 9: 328. Google Scholar CrossRef Search ADS PubMed 8. Piosczyk H, Holz J, Feige Bet al. The effect of sleep-specific brain activity versus reduced stimulus interference on declarative memory consolidation. J Sleep Res . 2013; 22( 4): 406– 413. Google Scholar CrossRef Search ADS PubMed 9. Tamaki M, Matsuoka T, Nittono H, Hori T. Fast sleep spindle (13-15 hz) activity correlates with sleep-dependent improvement in visuomotor performance. Sleep . 2008; 31( 2): 204– 211. Google Scholar CrossRef Search ADS PubMed 10. Tham EK, Lindsay S, Gaskell MG. Markers of automaticity in sleep-associated consolidation of novel words. Neuropsychologia . 2015; 71: 146– 157. Google Scholar CrossRef Search ADS PubMed 11. Nishida M, Nakashima Y, Nishikawa T. Slow sleep spindle and procedural memory consolidation in patients with major depressive disorder. Nat Sci Sleep . 2016; 8: 63– 72. Google Scholar CrossRef Search ADS PubMed 12. Ackermann S, Hartmann F, Papassotiropoulos A, de Quervain DJ, Rasch B. No associations between interindividual differences in sleep parameters and episodic memory consolidation. Sleep . 2015; 38( 6): 951– 959. Google Scholar PubMed 13. Ohayon MM, Carskadon MA, Guilleminault C, Vitiello MV. Meta-analysis of quantitative sleep parameters from childhood to old age in healthy individuals: developing normative sleep values across the human lifespan. Sleep . 2004; 27( 7): 1255– 1273. Google Scholar CrossRef Search ADS PubMed 14. Wilson JK, Baran B, Pace-Schott EF, Ivry RB, Spencer RM. Sleep modulates word-pair learning but not motor sequence learning in healthy older adults. Neurobiol Aging . 2012; 33( 5): 991– 1000. Google Scholar CrossRef Search ADS PubMed 15. Payne JD, Kensinger EA, Wamsley EJet al. Napping and the selective consolidation of negative aspects of scenes. Emotion . 2015; 15( 2): 176– 186. Google Scholar CrossRef Search ADS PubMed 16. Sonni A, Spencer RMC. Sleep protects memories from interference in older adults. Neurobiol Aging . 2015; 36( 7): 2272– 2281. Google Scholar CrossRef Search ADS PubMed 17. Whitehurst LN, Cellini N, McDevitt EA, Duggan KA, Mednick SC. Autonomic activity during sleep predicts memory consolidation in humans. Proc Natl Acad Sci U S A . 2016; 113( 26): 7272– 7277. Google Scholar CrossRef Search ADS PubMed 18. Sara SJ. Sleep to remember. J Neurosci . 2017; 37( 3): 457– 463. Google Scholar CrossRef Search ADS PubMed 19. Lerner I, Lupkin SM, Corter JE, Peters SE, Cannella LA, Gluck MA. The influence of sleep on emotional and cognitive processing is primarily trait- (but not state-) dependent. Neurobiol Learn Mem . 2016; 134 Pt B: 275– 286. Google Scholar CrossRef Search ADS PubMed 20. Jenkins J, Dallenbach K. Obliviscence during sleep and waking. Am J Psychol . 1924; 35( 4): 605– 612. Google Scholar CrossRef Search ADS © Sleep Research Society 2018. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail email@example.com.
SLEEP – Oxford University Press
Published: Feb 1, 2018
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