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Simon KC, Cadle C, Nakra N, Nagel MC, Malerba P. Age-associated sleep spindle characteristics in Duchenne muscular dystrophy. SLEEP ADVANCES : A JOURNAL OF THE SLEEP RESEARCH SOCIETY 2024; 5:zpae015. [PMID: 38525359 PMCID: PMC10960605 DOI: 10.1093/sleepadvances/zpae015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 12/18/2023] [Indexed: 03/26/2024]
Abstract
Brain oscillations of non-rapid eye movement sleep, including slow oscillations (SO, 0.5-1.5 Hz) and spindles (10-16 Hz), mirror underlying brain maturation across development and are associated with cognition. Hence, age-associated emergence and changes in the electrophysiological properties of these rhythms can lend insight into cortical development, specifically in comparisons between pediatric populations and typically developing peers. We previously evaluated age-associated changes in SOs in male patients with Duchenne muscular dystrophy (DMD), finding a significant age-related decline between 4 and 18 years. While primarily a muscle disorder, male patients with DMD can also have sleep, cognitive, and cortical abnormalities, thought to be driven by altered dystrophin expression in the brain. In this follow-up study, we characterized the age-associated changes in sleep spindles. We found that age-dependent spindle characteristics in patients with DMD, including density, frequency, amplitude, and duration, were consistent with age-associated trends reported in the literature for typically developing controls. Combined with our prior finding of age-associated decline in SOs, our results suggest that SOs, but not spindles, are a candidate intervention target to enhance sleep in patients with DMD.
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Affiliation(s)
- Katharine C Simon
- Department of Pediatrics, School of Medicine, University of California, Irvine, Irvine, CA, USA
- Pulmonology Department, Children’s Hospital of Orange County, Orange, CA, USA
| | - Chelsea Cadle
- Center for Biobehavioral Health, Abigail Wexner Research Institute, Nationwide Children’s Hospital, Columbus, OH, USA
| | - Neal Nakra
- Pulmonology Department, Children’s Hospital of Orange County, Orange, CA, USA
| | - Marni C Nagel
- Department of Pediatric Psychology, Children’s Hospital of Orange County, Orange, CA, USA
| | - Paola Malerba
- Center for Biobehavioral Health, Abigail Wexner Research Institute, Nationwide Children’s Hospital, Columbus, OH, USA
- Department of Pediatrics, School of Medicine, The Ohio State University, Columbus, OH, USA
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2
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Chen S, He M, Brown RE, Eden UT, Prerau MJ. Individualized temporal patterns dominate cortical upstate and sleep depth in driving human sleep spindle timing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.22.581592. [PMID: 38464146 PMCID: PMC10925076 DOI: 10.1101/2024.02.22.581592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Sleep spindles are critical for memory consolidation and strongly linked to neurological disease and aging. Despite their significance, the relative influences of factors like sleep depth, cortical up/down states, and spindle temporal patterns on individual spindle production remain poorly understood. Moreover, spindle temporal patterns are typically ignored in favor of an average spindle rate. Here, we analyze spindle dynamics in 1008 participants from the Multi-Ethnic Study of Atherosclerosis using a point process framework. Results reveal fingerprint-like temporal patterns, characterized by a refractory period followed by a period of increased spindle activity, which are highly individualized yet consistent night-to-night. We observe increased timing variability with age and distinct gender/age differences. Strikingly, and in contrast to the prevailing notion, individualized spindle patterns are the dominant determinant of spindle timing, accounting for over 70% of the statistical deviance explained by all of the factors we assessed, surpassing the contribution of slow oscillation (SO) phase (~14%) and sleep depth (~16%). Furthermore, we show spindle/SO coupling dynamics with sleep depth are preserved across age, with a global negative shift towards the SO rising slope. These findings offer novel mechanistic insights into spindle dynamics with direct experimental implications and applications to individualized electroencephalography biomarker identification.
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Affiliation(s)
- Shuqiang Chen
- Graduate Program for Neuroscience, Boston University, Boston, MA, USA
| | - Mingjian He
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ritchie E. Brown
- VA Boston Healthcare System and Harvard Medical School, Department of Psychiatry, West Roxbury, MA, USA
| | - Uri T. Eden
- Department of Mathematics and Statistics, Boston University, Boston, MA, USA
| | - Michael J. Prerau
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
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3
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Santa Cruz Mercado LA, Lee JM, Liu R, Deng H, Johnson JJ, Chen AL, He M, Chung ER, Bharadwaj KM, Houle TT, Purdon PL, Liu CA. Age-Dependent Electroencephalogram Features in Infants Under Spinal Anesthesia Appear to Mirror Physiologic Sleep in the Developing Brain: A Prospective Observational Study. Anesth Analg 2023; 137:1241-1249. [PMID: 36881544 DOI: 10.1213/ane.0000000000006410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2023]
Abstract
BACKGROUND Infants under spinal anesthesia appear to be sedated despite the absence of systemic sedative medications. In this prospective observational study, we investigated the electroencephalogram (EEG) of infants under spinal anesthesia and hypothesized that we would observe EEG features similar to those seen during sleep. METHODS We computed the EEG power spectra and spectrograms of 34 infants undergoing infraumbilical surgeries under spinal anesthesia (median age 11.5 weeks postmenstrual age, range 38-65 weeks postmenstrual age). Spectrograms were visually scored for episodes of EEG discontinuity or spindle activity. We characterized the relationship between EEG discontinuity or spindles and gestational age, postmenstrual age, or chronological age using logistic regression analyses. RESULTS The predominant EEG patterns observed in infants under spinal anesthesia were slow oscillations, spindles, and EEG discontinuities. The presence of spindles, observed starting at about 49 weeks postmenstrual age, was best described by postmenstrual age ( P =.002) and was more likely with increasing postmenstrual age. The presence of EEG discontinuities, best described by gestational age ( P = .015), was more likely with decreasing gestational age. These age-related changes in the presence of spindles and EEG discontinuities in infants under spinal anesthesia generally corresponded to developmental changes in the sleep EEG. CONCLUSIONS This work illustrates 2 separate key age-dependent transitions in EEG dynamics during infant spinal anesthesia that may reflect the maturation of underlying brain circuits: (1) diminishing discontinuities with increasing gestational age and (2) the appearance of spindles with increasing postmenstrual age. The similarity of these age-dependent transitions under spinal anesthesia with transitions in the developing brain during physiological sleep supports a sleep-related mechanism for the apparent sedation observed during infant spinal anesthesia.
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Affiliation(s)
- Laura A Santa Cruz Mercado
- From the Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - Johanna M Lee
- From the Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - Ran Liu
- From the Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - Hao Deng
- From the Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - Jasmine J Johnson
- From the Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - Andrew L Chen
- From the Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - Mingjian He
- From the Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Evan R Chung
- From the Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - Kishore M Bharadwaj
- From the Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - Timothy T Houle
- From the Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts
| | - Patrick L Purdon
- From the Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - Chang A Liu
- From the Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
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4
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Montini A, Iranzo A, Cortelli P, Gaig C, Muñoz-Lopetegi A, Provini F, Santamaria J. Scoring sleep in neurodegenerative diseases: A pilot study in the synucleinopathies. Sleep Med 2023; 110:268-286. [PMID: 37678074 DOI: 10.1016/j.sleep.2023.08.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Revised: 08/03/2023] [Accepted: 08/23/2023] [Indexed: 09/09/2023]
Abstract
BACKGROUND Neurodegenerative diseases often alter sleep architecture, complicating the application of the standard sleep scoring rules. There are no recommendations to overcome this problem. Our aim was to develop a scoring method that incorporates the stages previously applied in dementia with Lewy Bodies (DLB), anti-IgLON5 disease, and fatal insomnia, and to test it in patients with alpha-synucleinopathies. METHODS Video-polysomnographies (VPSG) of nine patients (DLB:3, Parkinson's disease (PD):3, and multiple system atrophy (MSA):3) selected for their difficulty in applying standard rules were scored independently by two authors, using additional Sleep/Wake stages. These included Abnormal Wake, Subwake, Undifferentiated NREM sleep (UNREM), Poorly structured N2 (P-S N2) and abnormal REM sleep including REM without atonia (RWA), REM without low-amplitude, mixed-frequency EEG activity (RWL) and REM without rapid eye movements (RWR). RESULTS Patients (4 females) had a median age of 74 (range 63-85). Six patients (all with PD or DLB) had abnormal EEG awake and Subwake stage. UNREM sleep was present in all patients, typically at sleep onset, and was the most common sleep stage in five. P-S N2 was recorded only in the three patients with MSA. Periods of normal and abnormal NREM coexisted in three patients. RWA was the predominant REM subtype, RWR occurred mainly in patients with MSA and RWL in those with DLB. Six patients had brief REM episodes into NREM sleep which we termed "Encapsulated RBD". CONCLUSION Our scoring system allows an accurate description of the complex sleep-wake changes in patients with alpha-synucleinopathies.
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Affiliation(s)
- Angelica Montini
- Department of Biomedical and NeuroMotor Sciences (DiBiNeM), University of Bologna, Bologna, Italy.
| | - Alex Iranzo
- Sleep Disorders Center, Neurology Service, Hospital Clínic de Barcelona, Barcelona, Spain; Clinical Neurophysiology Group, Institut D'Investigacions Biomediques August Pi I Sunyer (IDIBAPS), Barcelona, Spain; CIBERNED CB06/05/0018-ISCIII, Spain; Universitat de Barcelona, Barcelona, Spain.
| | - Pietro Cortelli
- Department of Biomedical and NeuroMotor Sciences (DiBiNeM), University of Bologna, Bologna, Italy; IRCCS Istituto Delle Scienze Neurologiche di Bologna, Bologna, Italy.
| | - Carles Gaig
- Sleep Disorders Center, Neurology Service, Hospital Clínic de Barcelona, Barcelona, Spain; Clinical Neurophysiology Group, Institut D'Investigacions Biomediques August Pi I Sunyer (IDIBAPS), Barcelona, Spain; CIBERNED CB06/05/0018-ISCIII, Spain; Universitat de Barcelona, Barcelona, Spain.
| | - Amaia Muñoz-Lopetegi
- Sleep Disorders Center, Neurology Service, Hospital Clínic de Barcelona, Barcelona, Spain; Clinical Neurophysiology Group, Institut D'Investigacions Biomediques August Pi I Sunyer (IDIBAPS), Barcelona, Spain; CIBERNED CB06/05/0018-ISCIII, Spain.
| | - Federica Provini
- Department of Biomedical and NeuroMotor Sciences (DiBiNeM), University of Bologna, Bologna, Italy; IRCCS Istituto Delle Scienze Neurologiche di Bologna, Bologna, Italy.
| | - Joan Santamaria
- Emeritus Consultant and Researcher, Hospital Clínic of Barcelona and Biomedical Research Institute (IDIBAPS), Spain.
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5
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He M, Das P, Hotan G, Purdon PL. Switching state-space modeling of neural signal dynamics. PLoS Comput Biol 2023; 19:e1011395. [PMID: 37639391 PMCID: PMC10491408 DOI: 10.1371/journal.pcbi.1011395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 09/08/2023] [Accepted: 07/28/2023] [Indexed: 08/31/2023] Open
Abstract
Linear parametric state-space models are a ubiquitous tool for analyzing neural time series data, providing a way to characterize the underlying brain dynamics with much greater statistical efficiency than non-parametric data analysis approaches. However, neural time series data are frequently time-varying, exhibiting rapid changes in dynamics, with transient activity that is often the key feature of interest in the data. Stationary methods can be adapted to time-varying scenarios by employing fixed-duration windows under an assumption of quasi-stationarity. But time-varying dynamics can be explicitly modeled by switching state-space models, i.e., by using a pool of state-space models with different dynamics selected by a probabilistic switching process. Unfortunately, exact solutions for state inference and parameter learning with switching state-space models are intractable. Here we revisit a switching state-space model inference approach first proposed by Ghahramani and Hinton. We provide explicit derivations for solving the inference problem iteratively after applying a variational approximation on the joint posterior of the hidden states and the switching process. We introduce a novel initialization procedure using an efficient leave-one-out strategy to compare among candidate models, which significantly improves performance compared to the existing method that relies on deterministic annealing. We then utilize this state inference solution within a generalized expectation-maximization algorithm to estimate model parameters of the switching process and the linear state-space models with dynamics potentially shared among candidate models. We perform extensive simulations under different settings to benchmark performance against existing switching inference methods and further validate the robustness of our switching inference solution outside the generative switching model class. Finally, we demonstrate the utility of our method for sleep spindle detection in real recordings, showing how switching state-space models can be used to detect and extract transient spindles from human sleep electroencephalograms in an unsupervised manner.
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Affiliation(s)
- Mingjian He
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Proloy Das
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Anesthesia, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California, United States of America
| | - Gladia Hotan
- Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR), Singapore
| | - Patrick L. Purdon
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, Massachusetts, United States of America
- Harvard Medical School, Boston, Massachusetts, United States of America
- Department of Anesthesia, Perioperative and Pain Medicine, Stanford University School of Medicine, Stanford, California, United States of America
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6
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Stokes PA, Rath P, Possidente T, He M, Purcell S, Manoach DS, Stickgold R, Prerau MJ. Transient oscillation dynamics during sleep provide a robust basis for electroencephalographic phenotyping and biomarker identification. Sleep 2022; 46:6701543. [PMID: 36107467 PMCID: PMC9832519 DOI: 10.1093/sleep/zsac223] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 08/30/2022] [Indexed: 01/19/2023] Open
Abstract
Transient oscillatory events in the sleep electroencephalogram represent short-term coordinated network activity. Of particular importance, sleep spindles are transient oscillatory events associated with memory consolidation, which are altered in aging and in several psychiatric and neurodegenerative disorders. Spindle identification, however, currently contains implicit assumptions derived from what waveforms were historically easiest to discern by eye, and has recently been shown to select only a high-amplitude subset of transient events. Moreover, spindle activity is typically averaged across a sleep stage, collapsing continuous dynamics into discrete states. What information can be gained by expanding our view of transient oscillatory events and their dynamics? In this paper, we develop a novel approach to electroencephalographic phenotyping, characterizing a generalized class of transient time-frequency events across a wide frequency range using continuous dynamics. We demonstrate that the complex temporal evolution of transient events during sleep is highly stereotyped when viewed as a function of slow oscillation power (an objective, continuous metric of depth-of-sleep) and phase (a correlate of cortical up/down states). This two-fold power-phase representation has large intersubject variability-even within healthy controls-yet strong night-to-night stability for individuals, suggesting a robust basis for phenotyping. As a clinical application, we then analyze patients with schizophrenia, confirming established spindle (12-15 Hz) deficits as well as identifying novel differences in transient non-rapid eye movement events in low-alpha (7-10 Hz) and theta (4-6 Hz) ranges. Overall, these results offer an expanded view of transient activity, describing a broad class of events with properties varying continuously across spatial, temporal, and phase-coupling dimensions.
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Affiliation(s)
- Patrick A Stokes
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
| | - Preetish Rath
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA,Department of Computer Science, Tufts University, Medford, MA, USA
| | - Thomas Possidente
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA
| | - Mingjian He
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, MA, USA,Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA,Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Shaun Purcell
- Department of Psychiatry, Brigham and Women’s Hospital, Boston, MA, USA
| | - Dara S Manoach
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Robert Stickgold
- Department of Psychiatry, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Michael J Prerau
- Corresponding author. Michael J. Prerau, Brigham and Women's Hospital, Division of Sleep and Circadian Disorders, 221 Longwood Avenue, Boston, MA, 02115, USA.
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7
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Malerba P, Whitehurst L, Mednick SC. The space-time profiles of sleep spindles and their coordination with slow oscillations on the electrode manifold. Sleep 2022; 45:6603295. [PMID: 35666552 PMCID: PMC9366646 DOI: 10.1093/sleep/zsac132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 05/19/2022] [Indexed: 11/17/2022] Open
Abstract
Sleep spindles are important for sleep quality and cognitive functions, with their coordination with slow oscillations (SOs) potentially organizing cross-region reactivation of memory traces. Here, we describe the organization of spindles on the electrode manifold and their relation to SOs. We analyzed the sleep night EEG of 34 subjects and detected spindles and SOs separately at each electrode. We compared spindle properties (frequency, duration, and amplitude) in slow wave sleep (SWS) and Stage 2 sleep (S2); and in spindles that coordinate with SOs or are uncoupled. We identified different topographical spindle types using clustering analysis that grouped together spindles co-detected across electrodes within a short delay (±300 ms). We then analyzed the properties of spindles of each type, and coordination to SOs. We found that SWS spindles are shorter than S2 spindles, and spindles at frontal electrodes have higher frequencies in S2 compared to SWS. Furthermore, S2 spindles closely following an SO (about 10% of all spindles) show faster frequency, shorter duration, and larger amplitude than uncoupled ones. Clustering identified Global, Local, Posterior, Frontal-Right and Left spindle types. At centro-parietal locations, Posterior spindles show faster frequencies compared to other types. Furthermore, the infrequent SO-spindle complexes are preferentially recruiting Global SO waves coupled with fast Posterior spindles. Our results suggest a non-uniform participation of spindles to complexes, especially evident in S2. This suggests the possibility that different mechanisms could initiate an SO-spindle complex compared to SOs and spindles separately. This has implications for understanding the role of SOs-spindle complexes in memory reactivation.
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Affiliation(s)
- Paola Malerba
- Battelle Center for Mathematical Medicine, The Research Institute at Nationwide Children’s Hospital , Columbus, OH , USA
- School of Medicine, The Ohio State University , Columbus, OH , USA
| | - Lauren Whitehurst
- Department of Psychology, University of Kentucky , Lexington, KY , USA
| | - Sara C Mednick
- Department of Cognitive Science, University of California Irvine , Irvine, CA , USA
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8
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Nicolas J, King BR, Levesque D, Lazzouni L, Coffey EBJ, Swinnen S, Doyon J, Carrier J, Albouy G. Sigma oscillations protect or reinstate motor memory depending on their temporal coordination with slow waves. eLife 2022; 11:73930. [PMID: 35726850 PMCID: PMC9259015 DOI: 10.7554/elife.73930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 06/07/2022] [Indexed: 11/18/2022] Open
Abstract
Targeted memory reactivation (TMR) during post-learning sleep is known to enhance motor memory consolidation but the underlying neurophysiological processes remain unclear. Here, we confirm the beneficial effect of auditory TMR on motor performance. At the neural level, TMR enhanced slow wave (SW) characteristics. Additionally, greater TMR-related phase-amplitude coupling between slow (0.5–2 Hz) and sigma (12–16 Hz) oscillations after the SW peak was related to higher TMR effect on performance. Importantly, sounds that were not associated to learning strengthened SW-sigma coupling at the SW trough. Moreover, the increase in sigma power nested in the trough of the potential evoked by the unassociated sounds was related to the TMR benefit. Altogether, our data suggest that, depending on their precise temporal coordination during post learning sleep, slow and sigma oscillations play a crucial role in either memory reinstatement or protection against irrelevant information; two processes that critically contribute to motor memory consolidation.
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Affiliation(s)
- Judith Nicolas
- Department of Movement Sciences, KU Leuven, Leuven, Belgium
| | - Bradley R King
- Department of Health and Kinesiology, Unversity of Utah, Salt Lake City, United States
| | - David Levesque
- Center for Advanced Research in Sleep Medicine, Universite de Montreal, Montreal, Canada
| | - Latifa Lazzouni
- Department of Neurology and Neurosurgery, McGill University, Montreal, Canada
| | | | | | - Julien Doyon
- Department of Neurology and Neurosurgery, McGill University, Montréal, Canada
| | - Julie Carrier
- Centre for Advanced Research in Sleep Medicine, Université de Montréal, Montreal, Canada
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9
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Guo D, Thomas RJ, Liu Y, Shea SA, Lu J, Peng CK. Slow wave synchronization and sleep state transitions. Sci Rep 2022; 12:7467. [PMID: 35523989 PMCID: PMC9076647 DOI: 10.1038/s41598-022-11513-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Accepted: 04/15/2022] [Indexed: 11/08/2022] Open
Abstract
Spontaneous synchronization over large networks is ubiquitous in nature, ranging from inanimate to biological systems. In the human brain, neuronal synchronization and de-synchronization occur during sleep, with the greatest degree of neuronal synchronization during slow wave sleep (SWS). The current sleep classification schema is based on electroencephalography and provides common criteria for clinicians and researchers to describe stages of non-rapid eye movement (NREM) sleep as well as rapid eye movement (REM) sleep. These sleep stage classifications have been based on convenient heuristic criteria, with little consideration of the accompanying normal physiological changes across those same sleep stages. To begin to resolve those inconsistencies, first focusing only on NREM sleep, we propose a simple cluster synchronization model to explain the emergence of SWS in healthy people without sleep disorders. We apply the empirical mode decomposition (EMD) analysis to quantify slow wave activity in electroencephalograms, and provide quantitative evidence to support our model. Based on this synchronization model, NREM sleep can be classified as SWS and non-SWS, such that NREM sleep can be considered as an intrinsically bistable process. Finally, we develop an automated algorithm for SWS classification. We show that this new approach can unify brain wave dynamics and their corresponding physiologic changes.
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Affiliation(s)
- Dan Guo
- Center for Dynamical Biomarkers, MA, 02067, Sharon, USA
| | - Robert J Thomas
- Division of Pulmonary, Critical Care & Sleep, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA
| | - Yanhui Liu
- Olera Technologies, Inc., CA, 94022, Los Altos, USA
| | - Steven A Shea
- Oregon Institute of Occupational Health Sciences, Oregon Health & Science University, Portland, OR, 97239, USA
| | - Jun Lu
- Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA
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10
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Gombos F, Bódizs R, Pótári A, Bocskai G, Berencsi A, Szakács H, Kovács I. Topographical relocation of adolescent sleep spindles reveals a new maturational pattern in the human brain. Sci Rep 2022; 12:7023. [PMID: 35487959 PMCID: PMC9054798 DOI: 10.1038/s41598-022-11098-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 04/18/2022] [Indexed: 11/23/2022] Open
Abstract
Current theories of human neural development emphasize the posterior-to-anterior pattern of brain maturation. However, this scenario leaves out significant brain areas not directly involved with sensory input and behavioral control. Suggesting the relevance of cortical activity unrelated to sensory stimulation, such as sleep, we investigated adolescent transformations in the topography of sleep spindles. Sleep spindles are known to be involved in neural plasticity and in adults have a bimodal topography: slow spindles are frontally dominant, while fast spindles have a parietal/precuneal origin. The late functional segregation of the precuneus from the frontoparietal network during adolescence suggests that spindle topography might approach the adult state relatively late in development, and it may not be a result of the posterior-to-anterior maturational pattern. We analyzed the topographical distribution of spindle parameters in HD-EEG polysomnographic sleep recordings of adolescents and found that slow spindle duration maxima traveled from central to anterior brain regions, while fast spindle density, amplitude and frequency peaks traveled from central to more posterior brain regions. These results provide evidence for the gradual posteriorization of the anatomical localization of fast sleep spindles during adolescence and indicate the existence of an anterior-to-posterior pattern of human brain maturation.
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Affiliation(s)
- Ferenc Gombos
- Laboratory for Psychological Research, Pázmány Péter Catholic University, 1 Mikszáth Kálmán Sq., Budapest, 1088, Hungary.,Adolescent Development Research Group, Hungarian Academy of Sciences - Pázmány Péter Catholic University, Budapest, 1088, Hungary
| | - Róbert Bódizs
- Institute of Behavioural Sciences, Semmelweis University, Budapest, 1089, Hungary.,National Institute of Clinical Neurosciences, Budapest, 1145, Hungary
| | - Adrián Pótári
- Adolescent Development Research Group, Hungarian Academy of Sciences - Pázmány Péter Catholic University, Budapest, 1088, Hungary
| | - Gábor Bocskai
- Laboratory for Psychological Research, Pázmány Péter Catholic University, 1 Mikszáth Kálmán Sq., Budapest, 1088, Hungary.,Doctoral School of Mental Health Sciences, Semmelweis University, Üllői st. 26, Budapest, 1085, Hungary
| | - Andrea Berencsi
- Adolescent Development Research Group, Hungarian Academy of Sciences - Pázmány Péter Catholic University, Budapest, 1088, Hungary.,Institute for the Methodology of Special Needs Education and Rehabilitation, Bárczi Gusztáv Faculty of Special Needs Education, Eötvös Loránd University, Budapest, 1097, Hungary
| | - Hanna Szakács
- Laboratory for Psychological Research, Pázmány Péter Catholic University, 1 Mikszáth Kálmán Sq., Budapest, 1088, Hungary.,Doctoral School of Mental Health Sciences, Semmelweis University, Üllői st. 26, Budapest, 1085, Hungary
| | - Ilona Kovács
- Laboratory for Psychological Research, Pázmány Péter Catholic University, 1 Mikszáth Kálmán Sq., Budapest, 1088, Hungary. .,Adolescent Development Research Group, Hungarian Academy of Sciences - Pázmány Péter Catholic University, Budapest, 1088, Hungary. .,Institute of Cognitive Neuroscience and Psychology, Research Centre for Natural Sciences, Budapest, 1117, Hungary.
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Adra N, Sun H, Ganglberger W, Ye EM, Dümmer LW, Tesh RA, Westmeijer M, Cardoso MDS, Kitchener E, Ouyang A, Salinas J, Rosand J, Cash SS, Thomas RJ, Westover MB. Optimal spindle detection parameters for predicting cognitive performance. Sleep 2022; 45:zsac001. [PMID: 34984446 PMCID: PMC8996023 DOI: 10.1093/sleep/zsac001] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 12/07/2021] [Indexed: 01/07/2023] Open
Abstract
STUDY OBJECTIVES Alterations in sleep spindles have been linked to cognitive impairment. This finding has contributed to a growing interest in identifying sleep-based biomarkers of cognition and neurodegeneration, including sleep spindles. However, flexibility surrounding spindle definitions and algorithm parameter settings present a methodological challenge. The aim of this study was to characterize how spindle detection parameter settings influence the association between spindle features and cognition and to identify parameters with the strongest association with cognition. METHODS Adult patients (n = 167, 49 ± 18 years) completed the NIH Toolbox Cognition Battery after undergoing overnight diagnostic polysomnography recordings for suspected sleep disorders. We explored 1000 combinations across seven parameters in Luna, an open-source spindle detector, and used four features of detected spindles (amplitude, density, duration, and peak frequency) to fit linear multiple regression models to predict cognitive scores. RESULTS Spindle features (amplitude, density, duration, and mean frequency) were associated with the ability to predict raw fluid cognition scores (r = 0.503) and age-adjusted fluid cognition scores (r = 0.315) with the best spindle parameters. Fast spindle features generally showed better performance relative to slow spindle features. Spindle features weakly predicted total cognition and poorly predicted crystallized cognition regardless of parameter settings. CONCLUSIONS Our exploration of spindle detection parameters identified optimal parameters for studies of fluid cognition and revealed the role of parameter interactions for both slow and fast spindles. Our findings support sleep spindles as a sleep-based biomarker of fluid cognition.
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Affiliation(s)
- Noor Adra
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Clinical Data Animation Center (CDAC), Boston, MA, USA
- Henry and Allison McCance Center for Brain Health at Mass General, Boston, MA, USA
| | - Haoqi Sun
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Clinical Data Animation Center (CDAC), Boston, MA, USA
- Henry and Allison McCance Center for Brain Health at Mass General, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Wolfgang Ganglberger
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Clinical Data Animation Center (CDAC), Boston, MA, USA
- Henry and Allison McCance Center for Brain Health at Mass General, Boston, MA, USA
| | - Elissa M Ye
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Clinical Data Animation Center (CDAC), Boston, MA, USA
- Henry and Allison McCance Center for Brain Health at Mass General, Boston, MA, USA
| | - Lisa W Dümmer
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Clinical Data Animation Center (CDAC), Boston, MA, USA
- Henry and Allison McCance Center for Brain Health at Mass General, Boston, MA, USA
- University of Groningen, Groningen, The Netherlands
| | - Ryan A Tesh
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Clinical Data Animation Center (CDAC), Boston, MA, USA
- Henry and Allison McCance Center for Brain Health at Mass General, Boston, MA, USA
| | - Mike Westmeijer
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Clinical Data Animation Center (CDAC), Boston, MA, USA
| | - Madalena Da Silva Cardoso
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Clinical Data Animation Center (CDAC), Boston, MA, USA
- Henry and Allison McCance Center for Brain Health at Mass General, Boston, MA, USA
| | - Erin Kitchener
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Clinical Data Animation Center (CDAC), Boston, MA, USA
- Henry and Allison McCance Center for Brain Health at Mass General, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - An Ouyang
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Clinical Data Animation Center (CDAC), Boston, MA, USA
- Henry and Allison McCance Center for Brain Health at Mass General, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Joel Salinas
- Harvard Medical School, Boston, MA, USA
- Department of Neurology, Center for Cognitive Neurology, New York University Grossman School of Medicine, New York, NY, USA
| | - Jonathan Rosand
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Clinical Data Animation Center (CDAC), Boston, MA, USA
- Henry and Allison McCance Center for Brain Health at Mass General, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Sydney S Cash
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Robert J Thomas
- Harvard Medical School, Boston, MA, USA
- Department of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - M Brandon Westover
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Clinical Data Animation Center (CDAC), Boston, MA, USA
- Henry and Allison McCance Center for Brain Health at Mass General, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
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Lechat B, Scott H, Naik G, Hansen K, Nguyen DP, Vakulin A, Catcheside P, Eckert DJ. New and Emerging Approaches to Better Define Sleep Disruption and Its Consequences. Front Neurosci 2021; 15:751730. [PMID: 34690688 PMCID: PMC8530106 DOI: 10.3389/fnins.2021.751730] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Accepted: 09/16/2021] [Indexed: 01/07/2023] Open
Abstract
Current approaches to quantify and diagnose sleep disorders and circadian rhythm disruption are imprecise, laborious, and often do not relate well to key clinical and health outcomes. Newer emerging approaches that aim to overcome the practical and technical constraints of current sleep metrics have considerable potential to better explain sleep disorder pathophysiology and thus to more precisely align diagnostic, treatment and management approaches to underlying pathology. These include more fine-grained and continuous EEG signal feature detection and novel oxygenation metrics to better encapsulate hypoxia duration, frequency, and magnitude readily possible via more advanced data acquisition and scoring algorithm approaches. Recent technological advances may also soon facilitate simple assessment of circadian rhythm physiology at home to enable sleep disorder diagnostics even for “non-circadian rhythm” sleep disorders, such as chronic insomnia and sleep apnea, which in many cases also include a circadian disruption component. Bringing these novel approaches into the clinic and the home settings should be a priority for the field. Modern sleep tracking technology can also further facilitate the transition of sleep diagnostics from the laboratory to the home, where environmental factors such as noise and light could usefully inform clinical decision-making. The “endpoint” of these new and emerging assessments will be better targeted therapies that directly address underlying sleep disorder pathophysiology via an individualized, precision medicine approach. This review outlines the current state-of-the-art in sleep and circadian monitoring and diagnostics and covers several new and emerging approaches to better define sleep disruption and its consequences.
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Affiliation(s)
- Bastien Lechat
- Adelaide Institute for Sleep Health, Flinders University, Bedford Park, SA, Australia
| | - Hannah Scott
- Adelaide Institute for Sleep Health, Flinders University, Bedford Park, SA, Australia
| | - Ganesh Naik
- Adelaide Institute for Sleep Health, Flinders University, Bedford Park, SA, Australia
| | - Kristy Hansen
- Adelaide Institute for Sleep Health, Flinders University, Bedford Park, SA, Australia
| | - Duc Phuc Nguyen
- Adelaide Institute for Sleep Health, Flinders University, Bedford Park, SA, Australia
| | - Andrew Vakulin
- Adelaide Institute for Sleep Health, Flinders University, Bedford Park, SA, Australia
| | - Peter Catcheside
- Adelaide Institute for Sleep Health, Flinders University, Bedford Park, SA, Australia
| | - Danny J Eckert
- Adelaide Institute for Sleep Health, Flinders University, Bedford Park, SA, Australia
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