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Sheybani L, Vivekananda U, Rodionov R, Diehl B, Chowdhury FA, McEvoy AW, Miserocchi A, Bisby JA, Bush D, Burgess N, Walker MC. Wake slow waves in focal human epilepsy impact network activity and cognition. Nat Commun 2023; 14:7397. [PMID: 38036557 PMCID: PMC10689494 DOI: 10.1038/s41467-023-42971-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 10/27/2023] [Indexed: 12/02/2023] Open
Abstract
Slow waves of neuronal activity are a fundamental component of sleep that are proposed to have homeostatic and restorative functions. Despite this, their interaction with pathology is unclear and there is only indirect evidence of their presence during wakefulness. Using intracortical recordings from the temporal lobe of 25 patients with epilepsy, we demonstrate the existence of local wake slow waves (LoWS) with key features of sleep slow waves, including a down-state of neuronal firing. Consistent with a reduction in neuronal activity, LoWS were associated with slowed cognitive processing. However, we also found that LoWS showed signatures of a homeostatic relationship with interictal epileptiform discharges (IEDs): exhibiting progressive adaptation during the build-up of network excitability before an IED and reducing the impact of subsequent IEDs on network excitability. We therefore propose an epilepsy homeostasis hypothesis: that slow waves in epilepsy reduce aberrant activity at the price of transient cognitive impairment.
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Affiliation(s)
- Laurent Sheybani
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK
- National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
| | - Umesh Vivekananda
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK
- National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
| | - Roman Rodionov
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK
- National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
| | - Beate Diehl
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK
- National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
| | - Fahmida A Chowdhury
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK
- National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
| | - Andrew W McEvoy
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK
- National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
| | - Anna Miserocchi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK
- National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK
- NIHR University College London Hospitals Biomedical Research Centre, London, UK
| | - James A Bisby
- Division of Psychiatry, University College London, London, UK
| | - Daniel Bush
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK.
| | - Neil Burgess
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK.
- Institute of Cognitive Neuroscience, University College London, London, UK.
| | - Matthew C Walker
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, University College London, London, UK.
- National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK.
- NIHR University College London Hospitals Biomedical Research Centre, London, UK.
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2
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Esfahani MJ, Farboud S, Ngo HVV, Schneider J, Weber FD, Talamini LM, Dresler M. Closed-loop auditory stimulation of sleep slow oscillations: Basic principles and best practices. Neurosci Biobehav Rev 2023; 153:105379. [PMID: 37660843 DOI: 10.1016/j.neubiorev.2023.105379] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 08/25/2023] [Accepted: 08/30/2023] [Indexed: 09/05/2023]
Abstract
Sleep is essential for our physical and mental well-being. During sleep, despite the paucity of overt behavior, our brain remains active and exhibits a wide range of coupled brain oscillations. In particular slow oscillations are characteristic for sleep, however whether they are directly involved in the functions of sleep, or are mere epiphenomena, is not yet fully understood. To disentangle the causality of these relationships, experiments utilizing techniques to detect and manipulate sleep oscillations in real-time are essential. In this review, we first overview the theoretical principles of closed-loop auditory stimulation (CLAS) as a method to study the role of slow oscillations in the functions of sleep. We then describe technical guidelines and best practices to perform CLAS and analyze results from such experiments. We further provide an overview of how CLAS has been used to investigate the causal role of slow oscillations in various sleep functions. We close by discussing important caveats, open questions, and potential topics for future research.
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Affiliation(s)
| | - Soha Farboud
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, the Netherlands
| | - Hong-Viet V Ngo
- Department of Psychology, University of Essex, United Kingdom; Department of Psychology, University of Lübeck, Germany; Center for Brain, Behaviour and Metabolism, University of Lübeck, Germany
| | - Jules Schneider
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, Technische Universität Dresden, Germany; Department of Neurology, University Hospital Carl Gustav Carus, Technische Universität Dresden, Germany
| | - Frederik D Weber
- Donders Institute for Brain, Cognition and Behaviour, Radboudumc, the Netherlands; Department of Sleep and Cognition, Netherlands Institute for Neuroscience, an institute of the Royal Netherlands Academy of Arts and Sciences, Amsterdam, the Netherlands
| | - Lucia M Talamini
- Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands; Amsterdam Brain and Cognition, University of Amsterdam, Amsterdam, the Netherlands
| | - Martin Dresler
- Donders Institute for Brain, Cognition and Behaviour, Radboudumc, the Netherlands.
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3
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Kahn M, Krone LB, Blanco‐Duque C, Guillaumin MCC, Mann EO, Vyazovskiy VV. Neuronal-spiking-based closed-loop stimulation during cortical ON- and OFF-states in freely moving mice. J Sleep Res 2022; 31:e13603. [PMID: 35665551 PMCID: PMC9786831 DOI: 10.1111/jsr.13603] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 02/20/2022] [Accepted: 03/22/2022] [Indexed: 12/30/2022]
Abstract
The slow oscillation is a central neuronal dynamic during sleep, and is generated by alternating periods of high and low neuronal activity (ON- and OFF-states). Mounting evidence causally links the slow oscillation to sleep's functions, and it has recently become possible to manipulate the slow oscillation non-invasively and phase-specifically. These developments represent promising clinical avenues, but they also highlight the importance of improving our understanding of how ON/OFF-states affect incoming stimuli and what role they play in neuronal plasticity. Most studies using closed-loop stimulation rely on the electroencephalogram and local field potential signals, which reflect neuronal ON- and OFF-states only indirectly. Here we develop an online detection algorithm based on spiking activity recorded from laminar arrays in mouse motor cortex. We find that online detection of ON- and OFF-states reflects specific phases of spontaneous local field potential slow oscillation. Our neuronal-spiking-based closed-loop procedure offers a novel opportunity for testing the functional role of slow oscillation in sleep-related restorative processes and neural plasticity.
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Affiliation(s)
- Martin Kahn
- Department of PhysiologyAnatomy and Genetics, University of OxfordOxfordUK,Sleep and Circadian Neuroscience InstituteUniversity of OxfordOxfordUK
| | - Lukas B. Krone
- Department of PhysiologyAnatomy and Genetics, University of OxfordOxfordUK,Sleep and Circadian Neuroscience InstituteUniversity of OxfordOxfordUK,University Hospital of Psychiatry and PsychotherapyUniversity of BernBernSwitzerland,Centre for Experimental NeurologyUniversity of BernBernSwitzerland
| | - Cristina Blanco‐Duque
- Department of PhysiologyAnatomy and Genetics, University of OxfordOxfordUK,Sleep and Circadian Neuroscience InstituteUniversity of OxfordOxfordUK
| | - Mathilde C. C. Guillaumin
- Sleep and Circadian Neuroscience InstituteUniversity of OxfordOxfordUK,Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK,Department of Health Sciences and TechnologyInstitute for NeuroscienceETH, ZurichSwitzerland
| | - Edward O. Mann
- Department of PhysiologyAnatomy and Genetics, University of OxfordOxfordUK
| | - Vladyslav V. Vyazovskiy
- Department of PhysiologyAnatomy and Genetics, University of OxfordOxfordUK,Sleep and Circadian Neuroscience InstituteUniversity of OxfordOxfordUK
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4
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Mirchi N, Warsi NM, Zhang F, Wong SM, Suresh H, Mithani K, Erdman L, Ibrahim GM. Decoding Intracranial EEG With Machine Learning: A Systematic Review. Front Hum Neurosci 2022; 16:913777. [PMID: 35832872 PMCID: PMC9271576 DOI: 10.3389/fnhum.2022.913777] [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: 04/06/2022] [Accepted: 05/31/2022] [Indexed: 11/13/2022] Open
Abstract
Advances in intracranial electroencephalography (iEEG) and neurophysiology have enabled the study of previously inaccessible brain regions with high fidelity temporal and spatial resolution. Studies of iEEG have revealed a rich neural code subserving healthy brain function and which fails in disease states. Machine learning (ML), a form of artificial intelligence, is a modern tool that may be able to better decode complex neural signals and enhance interpretation of these data. To date, a number of publications have applied ML to iEEG, but clinician awareness of these techniques and their relevance to neurosurgery, has been limited. The present work presents a review of existing applications of ML techniques in iEEG data, discusses the relative merits and limitations of the various approaches, and examines potential avenues for clinical translation in neurosurgery. One-hundred-seven articles examining artificial intelligence applications to iEEG were identified from 3 databases. Clinical applications of ML from these articles were categorized into 4 domains: i) seizure analysis, ii) motor tasks, iii) cognitive assessment, and iv) sleep staging. The review revealed that supervised algorithms were most commonly used across studies and often leveraged publicly available timeseries datasets. We conclude with recommendations for future work and potential clinical applications.
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Affiliation(s)
- Nykan Mirchi
- Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Nebras M. Warsi
- Division of Neurosurgery, Hospital for Sick Children, Department of Surgery, University of Toronto, Toronto, ON, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Frederick Zhang
- Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada
| | - Simeon M. Wong
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- Program in Neuroscience and Mental Health, Hospital for Sick Children Research Institute, Toronto, ON, Canada
| | - Hrishikesh Suresh
- Division of Neurosurgery, Hospital for Sick Children, Department of Surgery, University of Toronto, Toronto, ON, Canada
| | - Karim Mithani
- Division of Neurosurgery, Hospital for Sick Children, Department of Surgery, University of Toronto, Toronto, ON, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
| | - Lauren Erdman
- Vector Institute for Artificial Intelligence, MaRS Centre, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Hospital for Sick Children, Toronto, ON, Canada
| | - George M. Ibrahim
- Division of Neurosurgery, Hospital for Sick Children, Department of Surgery, University of Toronto, Toronto, ON, Canada
- Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada
- Program in Neuroscience and Mental Health, Hospital for Sick Children Research Institute, Toronto, ON, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada
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5
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Ferster ML, Da Poian G, Menachery K, Schreiner SJ, Lustenberger C, Maric A, Huber R, Baumann CR, Karlen W. Benchmarking real-time algorithms for in-phase auditory stimulation of low amplitude slow waves with wearable EEG devices during sleep. IEEE Trans Biomed Eng 2022; 69:2916-2925. [PMID: 35259094 DOI: 10.1109/tbme.2022.3157468] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Auditory stimulation of EEG slow waves (SW) during non-rapid eye movement (NREM) sleep has shown to improve cognitive function when it is delivered at the up-phase of SW. SW enhancement is particularly desirable in subjects with low-amplitude SW such as older adults or patients suffering from neurodegeneration such as Parkinson disease (PD). However, existing algorithms to estimate the up-phase suffer from a poor phase accuracy at low EEG amplitudes and when SW frequencies are not constant. We introduce two novel algorithms for real-time EEG phase estimation on autonomous wearable devices. The algorithms were based on a phase-locked loop (PLL) and, for the first time, a phase vocoder (PV). We compared these phase tracking algorithms with a simple amplitude threshold approach. The optimized algorithms were benchmarked for phase accuracy, the capacity to estimate phase at SW amplitudes between 20 and 60 V, and SW frequencies above 1 Hz on 324 recordings from healthy older adults and PD patients. Furthermore, the algorithms were implemented on a wearable device and the computational efficiency and the performance was evaluated on simulated sleep EEG, as well as prospectively during a recording with a PD patient. All three algorithms delivered more than 70% of the stimulation triggers during the SW up-phase. The PV showed the highest capacity on targeting low-amplitude SW and SW with frequencies above 1 Hz. The testing on real-time hardware revealed that both PV and PLL have marginal impact on microcontroller load, while the efficiency of the PV was 4% lower than the PLL. Active auditory stimulation did not influence the phase tracking. This work demonstrated that phase-accurate auditory stimulation can be delivered during home-based sleep interventions with a wearable device also in populations with low-amplitude SW.
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6
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Klinzing JG, Tashiro L, Ruf S, Wolff M, Born J, Ngo HVV. Auditory stimulation during sleep suppresses spike activity in benign epilepsy with centrotemporal spikes. Cell Rep Med 2021; 2:100432. [PMID: 34841286 PMCID: PMC8606903 DOI: 10.1016/j.xcrm.2021.100432] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Revised: 06/12/2021] [Accepted: 09/27/2021] [Indexed: 11/25/2022]
Abstract
Benign epilepsy with centrotemporal spikes (BECTS) is a common form of childhood epilepsy linked to diverse cognitive abnormalities. The electroencephalogram of patients shows focal interictal epileptic spikes, particularly during non-rapid eye movement (NonREM) sleep. Spike formation involves thalamocortical networks, which also contribute to the generation of sleep slow oscillations (SOs) and spindles. Motivated by evidence that SO-spindle activity can be controlled through closed-loop auditory stimulation, here, we show in seven patients that auditory stimulation also reduces spike rates in BECTS. Stimulation during NonREM sleep decreases spike rates, with most robust reductions when tones are presented 1.5 to 3.5 s after spikes. Stimulation further reduces the amplitude of spikes closely following tones. Sleep spindles are negatively correlated with spike rates, suggesting that tone-evoked spindle activity mediates the spike suppression. We hypothesize spindle-related refractoriness in thalamocortical circuits as a potential mechanism. Our results open an avenue for the non-pharmacological treatment of BECTS. Spikes in BECTS epilepsy and sleep spindles may share thalamocortical generation Auditory stimulation during sleep evokes sleep spindles and suppresses spikes Stimulation may reduce spiking by inducing thalamocortical refractoriness
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Affiliation(s)
- Jens G Klinzing
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, 72076 Tübingen, Germany.,Centre for Integrative Neuroscience, University of Tübingen, 72076 Tübingen, Germany.,Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540, USA
| | - Lilian Tashiro
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, 72076 Tübingen, Germany
| | - Susanne Ruf
- University Children's Hospital Tübingen, 72076 Tübingen, Germany
| | - Markus Wolff
- Department of Pediatric Neurology, Vivantes Hospital Neukölln, 12351 Berlin, Germany
| | - Jan Born
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, 72076 Tübingen, Germany.,Centre for Integrative Neuroscience, University of Tübingen, 72076 Tübingen, Germany
| | - Hong-Viet V Ngo
- Institute of Medical Psychology and Behavioral Neurobiology, University of Tübingen, 72076 Tübingen, Germany.,Department of Psychology, University of Lübeck, 23562 Lübeck, Germany
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7
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Moreira CG, Baumann CR, Scandella M, Nemirovsky SI, Leach S, Huber R, Noain D. Closed-loop auditory stimulation method to modulate sleep slow waves and motor learning performance in rats. eLife 2021; 10:e68043. [PMID: 34612204 PMCID: PMC8530509 DOI: 10.7554/elife.68043] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Accepted: 09/29/2021] [Indexed: 12/26/2022] Open
Abstract
Slow waves and cognitive output have been modulated in humans by phase-targeted auditory stimulation. However, to advance its technical development and further our understanding, implementation of the method in animal models is indispensable. Here, we report the successful employment of slow waves' phase-targeted closed-loop auditory stimulation (CLAS) in rats. To validate this new tool both conceptually and functionally, we tested the effects of up- and down-phase CLAS on proportions and spectral characteristics of sleep, and on learning performance in the single-pellet reaching task, respectively. Without affecting 24 hr sleep-wake behavior, CLAS specifically altered delta (slow waves) and sigma (sleep spindles) power persistently over chronic periods of stimulation. While up-phase CLAS does not elicit a significant change in behavioral performance, down-phase CLAS exerted a detrimental effect on overall engagement and success rate in the behavioral test. Overall CLAS-dependent spectral changes were positively correlated with learning performance. Altogether, our results provide proof-of-principle evidence that phase-targeted CLAS of slow waves in rodents is efficient, safe, and stable over chronic experimental periods, enabling the use of this high-specificity tool for basic and preclinical translational sleep research.
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Affiliation(s)
- Carlos G Moreira
- Department of Neurology, University Hospital Zurich, University of ZurichZurichSwitzerland
| | - Christian R Baumann
- Department of Neurology, University Hospital Zurich, University of ZurichZurichSwitzerland
- University Center of Competence Sleep & Health Zurich (CRPP), University of ZurichZurichSwitzerland
- Neuroscience Center Zurich (ZNZ)ZurichSwitzerland
| | - Maurizio Scandella
- Department of Neurology, University Hospital Zurich, University of ZurichZurichSwitzerland
| | - Sergio I Nemirovsky
- Institute of Biological Chemistry, School of Exact and Natural Sciences (IQUIBICEN). CONICET – University of Buenos AiresBuenos AiresArgentina
| | - Sven Leach
- Child Development Center, University Children’s Hospital Zurich, University of ZurichZurichSwitzerland
| | - Reto Huber
- University Center of Competence Sleep & Health Zurich (CRPP), University of ZurichZurichSwitzerland
- Neuroscience Center Zurich (ZNZ)ZurichSwitzerland
- Child Development Center, University Children’s Hospital Zurich, University of ZurichZurichSwitzerland
- Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric Hospital, University of ZurichZurichSwitzerland
| | - Daniela Noain
- Department of Neurology, University Hospital Zurich, University of ZurichZurichSwitzerland
- University Center of Competence Sleep & Health Zurich (CRPP), University of ZurichZurichSwitzerland
- Neuroscience Center Zurich (ZNZ)ZurichSwitzerland
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8
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Stoyell SM, Baxter BS, McLaren J, Kwon H, Chinappen DM, Ostrowski L, Zhu L, Grieco JA, Kramer MA, Morgan AK, Emerton BC, Manoach DS, Chu CJ. Diazepam induced sleep spindle increase correlates with cognitive recovery in a child with epileptic encephalopathy. BMC Neurol 2021; 21:355. [PMID: 34521381 PMCID: PMC8438890 DOI: 10.1186/s12883-021-02376-5] [Citation(s) in RCA: 9] [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: 01/24/2021] [Accepted: 08/31/2021] [Indexed: 12/02/2022] Open
Abstract
BACKGROUND Continuous spike and wave of sleep with encephalopathy (CSWS) is a rare and severe developmental electroclinical epileptic encephalopathy characterized by seizures, abundant sleep activated interictal epileptiform discharges, and cognitive regression or deceleration of expected cognitive growth. The cause of the cognitive symptoms is unknown, and efforts to link epileptiform activity to cognitive function have been unrevealing. Converging lines of evidence implicate thalamocortical circuits in these disorders. Sleep spindles are generated and propagated by the same thalamocortical circuits that can generate spikes and, in healthy sleep, support memory consolidation. As such, sleep spindle deficits may provide a physiologically relevant mechanistic biomarker for cognitive dysfunction in epileptic encephalopathies. CASE PRESENTATION We describe the longitudinal course of a child with CSWS with initial cognitive regression followed by dramatic cognitive improvement after treatment. Using validated automated detection algorithms, we analyzed electroencephalograms for epileptiform discharges and sleep spindles alongside contemporaneous neuropsychological evaluations over the course of the patient's disease. We found that sleep spindles increased dramatically with high-dose diazepam treatment, corresponding with marked improvements in cognitive performance. We also found that the sleep spindle rate was anticorrelated to spike rate, consistent with a competitively shared underlying thalamocortical circuitry. CONCLUSIONS Epileptic encephalopathies are challenging electroclinical syndromes characterized by combined seizures and a deceleration or regression in cognitive skills over childhood. This report identifies thalamocortical circuit dysfunction in a case of epileptic encephalopathy and motivates future investigations of sleep spindles as a biomarker of cognitive function and a potential therapeutic target in this challenging disease.
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Affiliation(s)
- S M Stoyell
- Department of Neurology, Massachusetts General Hospital, 175 Cambridge St, Suite 340, Boston, MA, 02114, USA
| | - B S Baxter
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, 02114, USA
- Harvard Medical School, Boston, MA, 02115, USA
| | - J McLaren
- Department of Neurology, Massachusetts General Hospital, 175 Cambridge St, Suite 340, Boston, MA, 02114, USA
| | - H Kwon
- Department of Neurology, Massachusetts General Hospital, 175 Cambridge St, Suite 340, Boston, MA, 02114, USA
| | - D M Chinappen
- Department of Neurology, Massachusetts General Hospital, 175 Cambridge St, Suite 340, Boston, MA, 02114, USA
| | - L Ostrowski
- Department of Neurology, Massachusetts General Hospital, 175 Cambridge St, Suite 340, Boston, MA, 02114, USA
| | - L Zhu
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - J A Grieco
- Massachusetts General Hospital, Psychology Assessment Center, Boston, MA, 02114, USA
| | - M A Kramer
- Department of Mathematics and Statistics, Boston University, Boston, MA, 02115, USA
| | - A K Morgan
- Massachusetts General Hospital, Psychology Assessment Center, Boston, MA, 02114, USA
| | - B C Emerton
- Massachusetts General Hospital, Psychology Assessment Center, Boston, MA, 02114, USA
| | - D S Manoach
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, 02114, USA
- Harvard Medical School, Boston, MA, 02115, USA
| | - C J Chu
- Department of Neurology, Massachusetts General Hospital, 175 Cambridge St, Suite 340, Boston, MA, 02114, USA.
- Harvard Medical School, Boston, MA, 02115, USA.
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9
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Sousouri G, Krugliakova E, Skorucak J, Leach S, Snipes S, Ferster ML, Da Poian G, Karlen W, Huber R. Neuromodulation by means of phase-locked auditory stimulation affects key marker of excitability and connectivity during sleep. Sleep 2021; 45:6347149. [PMID: 34373925 DOI: 10.1093/sleep/zsab204] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Revised: 07/15/2021] [Indexed: 11/12/2022] Open
Abstract
The propagating pattern of sleep slow waves (high-amplitude oscillations < 4.5 Hz) serves as a blueprint of cortical excitability and brain connectivity. Phase-locked auditory stimulation is a promising tool for the modulation of ongoing brain activity during sleep; however, its underlying mechanisms remain unknown. Here, eighteen healthy young adults were measured with high-density electroencephalography (hd-EEG) in three experimental conditions; one with no stimulation, one with up- and one with down-phase stimulation; ten participants were included in the analysis. We show that up-phase auditory stimulation on a right prefrontal area locally enhances cortical involvement and promotes traveling by increasing the propagating distance and duration of targeted small-amplitude waves. On the contrary, down-phase stimulation proves more efficient at perturbing large-amplitude waves and interferes with ongoing traveling by disengaging cortical regions and interrupting high synchronicity in the target area as indicated by increased traveling speed. These results point out to different underlying mechanisms mediating the effects of up- and down-phase stimulation and highlight the strength of traveling analysis as a sensitive and informative method for the study of connectivity and cortical excitability alterations.
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Affiliation(s)
- Georgia Sousouri
- Child Development Centre and Children's Research Centre, University Children's Hospital Zürich, University of Zurich, Zurich, Switzerland
- Mobile Health Systems Lab, Department of Health Sciences and Technology, ETH Zürich, Zurich, Switzerland
| | - Elena Krugliakova
- Child Development Centre and Children's Research Centre, University Children's Hospital Zürich, University of Zurich, Zurich, Switzerland
| | - Jelena Skorucak
- Child Development Centre and Children's Research Centre, University Children's Hospital Zürich, University of Zurich, Zurich, Switzerland
| | - Sven Leach
- Child Development Centre and Children's Research Centre, University Children's Hospital Zürich, University of Zurich, Zurich, Switzerland
| | - Sophia Snipes
- Child Development Centre and Children's Research Centre, University Children's Hospital Zürich, University of Zurich, Zurich, Switzerland
- Neural Control of Movement Lab, Department of Health Sciences and Technology, ETH Zürich, Zurich, Switzerland
| | - Maria Laura Ferster
- Mobile Health Systems Lab, Department of Health Sciences and Technology, ETH Zürich, Zurich, Switzerland
| | - Giulia Da Poian
- Mobile Health Systems Lab, Department of Health Sciences and Technology, ETH Zürich, Zurich, Switzerland
| | - Walter Karlen
- Mobile Health Systems Lab, Department of Health Sciences and Technology, ETH Zürich, Zurich, Switzerland
| | - Reto Huber
- Child Development Centre and Children's Research Centre, University Children's Hospital Zürich, University of Zurich, Zurich, Switzerland
- Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric Hospital, University of Zürich, Zurich, Switzerland
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10
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Real-Time Excitation of Slow Oscillations during Deep Sleep Using Acoustic Stimulation. SENSORS 2021; 21:s21155169. [PMID: 34372405 PMCID: PMC8347755 DOI: 10.3390/s21155169] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 07/27/2021] [Accepted: 07/28/2021] [Indexed: 11/20/2022]
Abstract
Slow-wave synchronous acoustic stimulation is a promising research and therapeutic tool. It is essential to clearly understand the principles of the synchronization methods, to know their performances and limitations, and, most importantly, to have a clear picture of the effect of stimulation on slow-wave activity (SWA). This paper covers the mentioned and currently missing parts of knowledge that are essential for the appropriate development of the method itself and future applications. Artificially streamed real sleep EEG data were used to quantitatively compare the two currently used real-time methods: the phase-locking loop (PLL) and the fixed-step stimulus in our own implementation. The fixed-step stimulation method was concluded to be more reliable and practically applicable compared to the PLL method. The sleep experiment with chronic insomnia patients in our sleep laboratory was analyzed in order to precisely characterize the effect of sound stimulation during deep sleep. We found that there is a significant phase synchronization of delta waves, which were shown to be the most sensitive metric of the effect of acoustic stimulation compared to commonly used averaged signal and power analyses. This finding may change the understanding of the effect and function of the SWA stimulation described in the literature.
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Fehér KD, Wunderlin M, Maier JG, Hertenstein E, Schneider CL, Mikutta C, Züst MA, Klöppel S, Nissen C. Shaping the slow waves of sleep: A systematic and integrative review of sleep slow wave modulation in humans using non-invasive brain stimulation. Sleep Med Rev 2021; 58:101438. [PMID: 33582581 DOI: 10.1016/j.smrv.2021.101438] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2020] [Revised: 10/14/2020] [Accepted: 11/10/2020] [Indexed: 01/19/2023]
Abstract
The experimental study of electroencephalographic slow wave sleep (SWS) stretches over more than half a century and has corroborated its importance for basic physiological processes, such as brain plasticity, metabolism and immune system functioning. Alterations of SWS in aging or pathological conditions suggest that modulating SWS might constitute a window for clinically relevant interventions. This work provides a systematic and integrative review of SWS modulation through non-invasive brain stimulation in humans. A literature search using PubMed, conducted in May 2020, identified 3220 studies, of which 82 fulfilled inclusion criteria. Three approaches have been adopted to modulate the macro- and microstructure of SWS, namely auditory, transcranial electrical and transcranial magnetic stimulation. Our current knowledge about the modulatory mechanisms, the space of stimulation parameters and the physiological and behavioral effects are reported and evaluated. The integration of findings suggests that sleep slow wave modulation bears the potential to promote our understanding of the functions of SWS and to develop new treatments for conditions of disrupted SWS.
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Affiliation(s)
- Kristoffer D Fehér
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Switzerland
| | - Marina Wunderlin
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Switzerland
| | - Jonathan G Maier
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Switzerland
| | - Elisabeth Hertenstein
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Switzerland
| | - Carlotta L Schneider
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Switzerland
| | - Christian Mikutta
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Switzerland; Privatklinik Meiringen, Meiringen, Switzerland
| | - Marc A Züst
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Switzerland
| | - Stefan Klöppel
- University Hospital of Old Age Psychiatry and Psychotherapy, University of Bern, Switzerland
| | - Christoph Nissen
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Switzerland.
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A Systematic Review of Closed-Loop Feedback Techniques in Sleep Studies-Related Issues and Future Directions. SENSORS 2020; 20:s20102770. [PMID: 32414060 PMCID: PMC7285770 DOI: 10.3390/s20102770] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/03/2020] [Revised: 04/13/2020] [Accepted: 05/10/2020] [Indexed: 01/09/2023]
Abstract
Advances in computer processing technology have enabled researchers to analyze real-time brain activity and build real-time closed-loop paradigms. In many fields, the effectiveness of these closed-loop protocols has proven to be better than that of the simple open-loop paradigms. Recently, sleep studies have attracted much attention as one possible application of closed-loop paradigms. To date, several studies that used closed-loop paradigms have been reported in the sleep-related literature and recommend a closed-loop feedback system to enhance specific brain activity during sleep, which leads to improvements in sleep's effects, such as memory consolidation. However, to the best of our knowledge, no report has reviewed and discussed the detailed technical issues that arise in designing sleep closed-loop paradigms. In this paper, we reviewed the most recent reports on sleep closed-loop paradigms and offered an in-depth discussion of some of their technical issues. We found 148 journal articles strongly related with 'sleep and stimulation' and reviewed 20 articles on closed-loop feedback sleep studies. We focused on human sleep studies conducting any modality of feedback stimulation. Then we introduced the main component of the closed-loop system and summarized several open-source libraries, which are widely used in closed-loop systems, with step-by-step guidelines for closed-loop system implementation for sleep. Further, we proposed future directions for sleep research with closed-loop feedback systems, which provide some insight into closed-loop feedback systems.
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Page J, Lustenberger C, Frӧhlich F. Nonrapid eye movement sleep and risk for autism spectrum disorder in early development: A topographical electroencephalogram pilot study. Brain Behav 2020; 10:e01557. [PMID: 32037734 PMCID: PMC7066345 DOI: 10.1002/brb3.1557] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 11/10/2019] [Accepted: 01/03/2020] [Indexed: 02/05/2023] Open
Abstract
OBJECTIVE Autism spectrum disorder (ASD) is a pervasive neurodevelopmental disorder that emerges in the beginning years of life (12-48 months). Yet, an early diagnosis of ASD is challenging as it relies on the consistent presence of behavioral symptomatology, and thus, many children are diagnosed later in development, which prevents early interventions that could benefit cognitive and social outcomes. As a result, there is growing interest in detecting early brain markers of ASD, such as in the electroencephalogram (EEG) to elucidate divergence in early development. Here, we examine the EEG of nonrapid eye movement (NREM) sleep in the transition from infancy to toddlerhood, a period of rapid development and pronounced changes in early brain function. NREM features exhibit clear developmental trajectories, are related to social and cognitive development, and may be altered in neurodevelopmental disorders. Yet, spectral features of NREM sleep are poorly understood in infants/toddlers with or at high risk for ASD. METHODS The present pilot study is the first to examine NREM sleep in 13- to 30-month-olds with ASD in comparison with age-matched healthy controls (TD). EEG was recorded during a daytime nap with high-density array EEG. RESULTS We found topographically distinct decreased fast theta oscillations (5-7.25 Hz), decreased fast sigma (15-16 Hz), and increased beta oscillations (20-25 Hz) in ASD compared to TD. CONCLUSION These findings suggest a possible functional role of NREM sleep during this important developmental period and provide support for NREM sleep to be a potential early marker for ASD.
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Affiliation(s)
- Jessica Page
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.,Carolina Center for Neurostimulation, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.,Department of Communication Sciences and Disorders, Northwestern University, Evanston, Illinois
| | - Caroline Lustenberger
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.,Department of Health Sciences and Technology, Institute of Movement Sciences and Sport, ETH Zurich, Zurich, Switzerland
| | - Flavio Frӧhlich
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.,Carolina Center for Neurostimulation, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.,Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.,Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.,Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.,Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
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