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Sanhedrai H, Havlin S, Dvir H. Mechanistic description of spontaneous loss of memory persistent activity based on neuronal synaptic strength. Heliyon 2024; 10:e23949. [PMID: 38223719 PMCID: PMC10787259 DOI: 10.1016/j.heliyon.2023.e23949] [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: 03/23/2023] [Revised: 11/06/2023] [Accepted: 12/16/2023] [Indexed: 01/16/2024] Open
Abstract
Persistent neural activity associated with working memory (WM) lasts for a limited time duration. Current theories suggest that its termination is actively obtained via inhibitory currents, and there is currently no theory regarding the possibility of a passive memory-loss mechanism that terminates memory persistent activity. Here, we develop an analytical-framework, based on synaptic strength, and show via simulations and fitting to wet-lab experiments, that passive memory-loss might be a result of an ionic-current long-term plateau, i.e., very slow reduction of memory followed by abrupt loss. We describe analytically the plateau, when the memory state is just below criticality. These results, including the plateau, are supported by experiments performed on rats. Moreover, we show that even just above criticality, forgetfulness can occur due to neuronal noise with ionic-current fluctuations, yielding a plateau, representing memory with very slow decay, and eventually a fast memory decay. Our results could have implications for developing new medications, targeted against memory impairments, through modifying neuronal noise.
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Affiliation(s)
| | - Shlomo Havlin
- Department of Physics, Bar-Ilan University, Ramat-Gan, Israel
| | - Hila Dvir
- Department of Physics, Bar-Ilan University, Ramat-Gan, Israel
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2
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Abid NUH, Lum Cheng In T, Bottaro M, Shen X, Hernaez Sanz I, Yoshida S, Formentin C, Montagnese S, Mani AR. Application of short-term analysis of skin temperature variability in prediction of survival in patients with cirrhosis. FRONTIERS IN NETWORK PHYSIOLOGY 2024; 3:1291491. [PMID: 38250541 PMCID: PMC10796461 DOI: 10.3389/fnetp.2023.1291491] [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: 09/09/2023] [Accepted: 12/12/2023] [Indexed: 01/23/2024]
Abstract
Background: Liver cirrhosis is a complex disorder, involving several different organ systems and physiological network disruption. Various physiological markers have been developed for survival modelling in patients with cirrhosis. Reduction in heart rate variability and skin temperature variability have been shown to predict mortality in cirrhosis, with the potential to aid clinical prognostication. We have recently reported that short-term skin temperature variability analysis can predict survival independently of the severity of liver failure in cirrhosis. However, in previous reports, 24-h skin temperature recordings were used, which are often not feasible in the context of routine clinical practice. The purpose of this study was to determine the shortest length of time from 24-h proximal temperature recordings that can accurately and independently predict 12-month survival post-recording in patients with cirrhosis. Methods: Forty individuals diagnosed with cirrhosis participated in this study and wireless temperature sensors (iButtons) were used to record patients' proximal skin temperature. From 24-h temperature recordings, different length of recordings (30 min, 1, 2, 3 and 6 h) were extracted sequentially for temperature variability analysis using the Extended Poincaré plot to quantify both short-term (SD1) and long-term (SD2) variability. These patients were then subsequently followed for a period of 12 months, during which data was gathered concerning any cases of mortality. Results: Cirrhosis was associated with significantly decreased proximal skin temperature fluctuations among individuals who did not survive, across all durations of daytime temperature recordings lasting 1 hour or more. Survival analysis showcased 1-h daytime proximal skin temperature time-series to be significant predictors of survival in cirrhosis, whereby SD2, was found to be independent to the Model for End-Stage Liver Disease (MELD) score and thus, the extent of disease severity. As expected, longer durations of time-series were also predictors of mortality for the majority of the temperature variability indices. Conclusion: Crucially, this study suggests that 1-h proximal skin temperature recordings are sufficient in length to accurately predict 12-month survival in patients with cirrhosis, independent from current prognostic indicators used in the clinic such as MELD.
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Affiliation(s)
- Noor-Ul-Hoda Abid
- Network Physiology Laboratory, Division of Medicine, UCL, London, United Kingdom
- UCL Medical School, UCL, London, United Kingdom
| | - Travis Lum Cheng In
- Network Physiology Laboratory, Division of Medicine, UCL, London, United Kingdom
| | - Matteo Bottaro
- Department of Medicine, University of Padova, Padova, Italy
| | - Xinran Shen
- Network Physiology Laboratory, Division of Medicine, UCL, London, United Kingdom
| | - Iker Hernaez Sanz
- Network Physiology Laboratory, Division of Medicine, UCL, London, United Kingdom
| | - Satoshi Yoshida
- Network Physiology Laboratory, Division of Medicine, UCL, London, United Kingdom
| | | | - Sara Montagnese
- Department of Medicine, University of Padova, Padova, Italy
- Chronobiology Section, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom
| | - Ali R. Mani
- Network Physiology Laboratory, Division of Medicine, UCL, London, United Kingdom
- Institute for Liver and Digestive Health (ILDH), Division of Medicine, UCL, London, United Kingdom
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3
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Ju Wang JD, Chen M, Zhang C, Parker J, Saneto R, Ramirez JM. Sleep and Breathing Disturbances in Children With Leigh Syndrome: A Comparative Study. Pediatr Neurol 2022; 136:56-63. [PMID: 36137349 DOI: 10.1016/j.pediatrneurol.2022.08.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 07/24/2022] [Accepted: 08/18/2022] [Indexed: 02/02/2023]
Abstract
BACKGROUND Leigh syndrome (LS) is a progressive neurodegenerative mitochondrial disease characterized by necrotizing lesions affecting different parts of the central nervous system, especially in the brainstem and basal ganglia. Lesions in this area may involve respiratory and sleep centers, resulting in the clinically significant disturbances seen-but poorly characterized-in LS. The purpose of the present study is to characterize and compare the physiologic responses to respiratory disturbances quantified by polysomnography metrics of children with LS with age-sex- and apnea-hypopnea index (AHI)-matched patients with obstructive sleep apnea (OSA), a common clinical population with similar burden of sleep-disordered breathing. METHODS Retrospective comparative study of polysomnographic data from six patients with LS were reviewed and compared with 18 age-sex-AHI-matched patients with OSA, with particular attention to cardiorespiratory and sleep architecture metrics. RESULTS Sleep architecture and stage duration were conserved in LS and OSA groups, but increased wake after sleep onset was seen among the first group. The LS group exhibited both obstructive and central sleep apnea. The group also had significantly greater values of heart rate, ≥3% oxygen desaturation index, and lower values of sleep efficiency, respiratory arousal index, and total sleep time when compared with the OSA group. CONCLUSIONS Patients with LS exhibited significantly more sleep-related cardiorespiratory disturbances and sleep fragmentation when compared with neurotypical children with OSA. Given that these findings are plausibly detrimental to health and development, sleep evaluation in patients with similar conditions should be encouraged for early management.
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Affiliation(s)
- Jia-Der Ju Wang
- Center for Integrative Brain Research, Seattle Children's Research Institute, Seattle, Washington.
| | - Maida Chen
- Division of Pulmonary and Sleep Medicine, Department of Pediatrics, University of Washington School of Medicine, Seattle, Washington
| | | | - Jessica Parker
- Center for Integrative Brain Research, Seattle Children's Research Institute, Seattle, Washington
| | - Russell Saneto
- Division of Pediatric Neurology, Department of Neurology, University of Washington School of Medicine, Seattle, Washington
| | - Jan-Marino Ramirez
- Center for Integrative Brain Research, Seattle Children's Research Institute, Seattle, Washington; Departments of Neurological Surgery, University of Washington School of Medicine, Seattle, Washington; Departments of Pediatrics, University of Washington School of Medicine, Seattle, Washington
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4
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S ME, K B, H D, Ludwig K, N K, T H, H G, M M. A Novel Quantitative Arousal-Associated EEG-Metric to Predict Severity of Respiratory Distress in Obstructive Sleep Apnea Patients. Front Physiol 2022; 13:885270. [PMID: 35812317 PMCID: PMC9257225 DOI: 10.3389/fphys.2022.885270] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Accepted: 05/31/2022] [Indexed: 11/25/2022] Open
Abstract
Respiratory arousals (RA) on polysomnography (PSG) are an important predictor of obstructive sleep apnea (OSA) disease severity. Additionally, recent reports suggest that more global indices of desaturation such as the hypoxic burden, namely the area under the curve (AUC) of the oxygen saturation (SaO2) PSG trace may better depict the desaturation burden in OSA. Here we investigated possible associations between a new metric, namely the AUC of the respiratory arousal electroencephalographic (EEG) recording, and already established parameters as the apnea/hypopnea index (AHI), arousal index and hypoxic burden in patients with OSA. In this data-driven study, polysomnographic data from 102 patients with OSAS were assessed (32 female; 70 male; mean value of age: 52 years; mean value of Body-Mass-Index-BMI: 31 kg/m2). The marked arousals from the pooled EEG signal (C3 and C4) were smoothed and the AUC was estimated. We used a support vector regressor (SVR) analysis to predict AHI, arousal index and hypoxic burden as captured by the PSG. The SVR with the arousal-AUC metric could quite reliably predict the AHI with a high correlation coefficient (0,58 in the training set, 0,65 in the testing set and 0,64 overall), as well as the hypoxic burden (0,62 in the training set, 0,58 in the testing set and 0,59 overall) and the arousal index (0,58 in the training set, 0,67 in the testing set and 0,66 overall). This novel arousal-AUC metric may predict AHI, hypoxic burden and arousal index with a quite high correlation coefficient and therefore could be used as an additional quantitative surrogate marker in the description of obstructive sleep apnea disease severity.
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Affiliation(s)
- Malatantis-Ewert S
- Department of Otorhinolaryngology, Sleep Medicine Center, Medical Center of the University of Mainz, Mainz, Germany
- Movement Disorders and Neurostimulation, Department of Neurology, Biomedical Statistics and Multimodal Signal Processing Unit, Medical Center of the University of Mainz, Mainz, Germany
- *Correspondence: Malatantis-Ewert S,
| | - Bahr K
- Department of Otorhinolaryngology, Sleep Medicine Center, Medical Center of the University of Mainz, Mainz, Germany
| | - Ding H
- Movement Disorders and Neurostimulation, Department of Neurology, Biomedical Statistics and Multimodal Signal Processing Unit, Medical Center of the University of Mainz, Mainz, Germany
| | - Katharina Ludwig
- Department of Otorhinolaryngology, Sleep Medicine Center, Medical Center of the University of Mainz, Mainz, Germany
| | - Koirala N
- Haskins Laboratories, Yale University, New Haven, CT, United States
| | - Huppertz T
- Department of Otorhinolaryngology, Sleep Medicine Center, Medical Center of the University of Mainz, Mainz, Germany
| | - Gouveris H
- Department of Otorhinolaryngology, Sleep Medicine Center, Medical Center of the University of Mainz, Mainz, Germany
| | - Muthuraman M
- Movement Disorders and Neurostimulation, Department of Neurology, Biomedical Statistics and Multimodal Signal Processing Unit, Medical Center of the University of Mainz, Mainz, Germany
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Chen B, Ciria LF, Hu C, Ivanov PC. Ensemble of coupling forms and networks among brain rhythms as function of states and cognition. Commun Biol 2022; 5:82. [PMID: 35064204 PMCID: PMC8782865 DOI: 10.1038/s42003-022-03017-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 12/23/2021] [Indexed: 01/02/2023] Open
Abstract
The current paradigm in brain research focuses on individual brain rhythms, their spatiotemporal organization, and specific pairwise interactions in association with physiological states, cognitive functions, and pathological conditions. Here we propose a conceptually different approach to understanding physiologic function as emerging behavior from communications among distinct brain rhythms. We hypothesize that all brain rhythms coordinate as a network to generate states and facilitate functions. We analyze healthy subjects during rest, exercise, and cognitive tasks and show that synchronous modulation in the micro-architecture of brain rhythms mediates their cross-communications. We discover that brain rhythms interact through an ensemble of coupling forms, universally observed across cortical areas, uniquely defining each physiological state. We demonstrate that a dynamic network regulates the collective behavior of brain rhythms and that network topology and links strength hierarchically reorganize with transitions across states, indicating that brain-rhythm interactions play an essential role in generating physiological states and cognition.
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Affiliation(s)
- Bolun Chen
- Keck Laboratory for Network Physiology, Department of Physics, Boston University, Boston, MA, 02215, USA
| | - Luis F Ciria
- Keck Laboratory for Network Physiology, Department of Physics, Boston University, Boston, MA, 02215, USA
- Mind, Brain and Behaviour Research Center, Department of Experimental Psychology, Faculty of Psychology, University of Granada, Campus de la Cartuja, Granada, 18071, Spain
| | - Congtai Hu
- Keck Laboratory for Network Physiology, Department of Physics, Boston University, Boston, MA, 02215, USA
| | - Plamen Ch Ivanov
- Keck Laboratory for Network Physiology, Department of Physics, Boston University, Boston, MA, 02215, USA.
- Division of Sleep Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
- Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. Georgi Bonchev Str. Block 21, Sofia, 1113, Bulgaria.
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6
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Bach V, Libert JP. Hyperthermia and Heat Stress as Risk Factors for Sudden Infant Death Syndrome: A Narrative Review. Front Pediatr 2022; 10:816136. [PMID: 35498814 PMCID: PMC9051231 DOI: 10.3389/fped.2022.816136] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 03/24/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Heat stress and hyperthermia are common findings in sudden infant death syndrome (SIDS) victims. It has been suggested that thermal stress can increase the risk of SIDS directly via lethal hyperthermia or indirectly by altering autonomic functions. Major changes in sleep, thermoregulation, cardiovascular function, and the emergence of circadian functions occur at the age at which the risk of SIDS peaks-explaining the greater vulnerability at this stage of development. Here, we review the literature data on (i) heat stress and hyperthermia as direct risk factors for SIDS, and (ii) the indirect effects of thermal loads on vital physiological functions. RESULTS Various situations leading to thermal stress (i.e., outdoors temperatures, thermal insulation from clothing and bedding, the prone position, bed-sharing, and head covering) have been analyzed. Hyperthermia mainly results from excessive clothing and bedding insulation with regard to the ambient thermal conditions. The appropriate amount of clothing and bedding thermal insulation for homeothermia requires further research. The prone position and bed-sharing do not have major thermal impacts; the elevated risk of SIDS in these situations cannot be explained solely by thermal factors. Special attention should be given to brain overheating because of the head's major role in body heat losses, heat production, and autonomic functions. Thermal stress can alter cardiovascular and respiratory functions, which in turn can lead to life-threatening events (e.g., bradycardia, apnea with blood desaturation, and glottal closure). Unfortunately, thermal load impairs the responses to these challenges by reducing chemosensitivity, arousability, and autoresuscitation. As a result, thermal load (even when not lethal directly) can interact detrimentally with vital physiological functions. CONCLUSIONS With the exception of excessive thermal insulation (which can lead to lethal hyperthermia), the major risk factors for SIDS appears to be associated with impairments of vital physiological functions when the infant is exposed to thermal stress.
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Affiliation(s)
- Véronique Bach
- PeriTox, UMR_I 01, UPJV/INERIS, Jules Verne University of Picardy, Amiens, France
| | - Jean-Pierre Libert
- PeriTox, UMR_I 01, UPJV/INERIS, Jules Verne University of Picardy, Amiens, France
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7
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Metzner C, Schilling A, Traxdorf M, Schulze H, Krauss P. Sleep as a random walk: a super-statistical analysis of EEG data across sleep stages. Commun Biol 2021; 4:1385. [PMID: 34893700 PMCID: PMC8664947 DOI: 10.1038/s42003-021-02912-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Accepted: 11/23/2021] [Indexed: 11/15/2022] Open
Abstract
In clinical practice, human sleep is classified into stages, each associated with different levels of muscular activity and marked by characteristic patterns in the EEG signals. It is however unclear whether this subdivision into discrete stages with sharply defined boundaries is truly reflecting the dynamics of human sleep. To address this question, we consider one-channel EEG signals as heterogeneous random walks: stochastic processes controlled by hyper-parameters that are themselves time-dependent. We first demonstrate the heterogeneity of the random process by showing that each sleep stage has a characteristic distribution and temporal correlation function of the raw EEG signals. Next, we perform a super-statistical analysis by computing hyper-parameters, such as the standard deviation, kurtosis, and skewness of the raw signal distributions, within subsequent 30-second epochs. It turns out that also the hyper-parameters have characteristic, sleep-stage-dependent distributions, which can be exploited for a simple Bayesian sleep stage detection. Moreover, we find that the hyper-parameters are not piece-wise constant, as the traditional hypnograms would suggest, but show rising or falling trends within and across sleep stages, pointing to an underlying continuous rather than sub-divided process that controls human sleep. Based on the hyper-parameters, we finally perform a pairwise similarity analysis between the different sleep stages, using a quantitative measure for the separability of data clusters in multi-dimensional spaces. To improve our understand of how EEG activity reflects the dynamics of human sleep, Metzner et al. use human EEG data and superstatistical analysis to demonstrate that each sleep stage has a characteristic distribution and temporal correlation function of raw EEG signals. They also show that the hyper-parameters controlling the EEG signals have characteristic, sleep-stage-dependent distributions, which can be exploited for a simple Bayesian sleep stage detection.
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Affiliation(s)
- Claus Metzner
- Neuroscience Lab, Experimental Otolaryngology, University Hospital Erlangen, Erlangen, Germany.
| | - Achim Schilling
- Neuroscience Lab, Experimental Otolaryngology, University Hospital Erlangen, Erlangen, Germany.,Laboratory of Sensory and Cognitive Neuroscience, Aix-Marseille University, Marseille, France.,Cognitive Computational Neuroscience Group, Friedrich-Alexander University Erlangen-Nuremberg, Nuremberg, Germany
| | - Maximilian Traxdorf
- Department of Otorhinolaryngology, Paracelsus Medical University, Nuremberg, Germany
| | - Holger Schulze
- Neuroscience Lab, Experimental Otolaryngology, University Hospital Erlangen, Erlangen, Germany
| | - Patrick Krauss
- Neuroscience Lab, Experimental Otolaryngology, University Hospital Erlangen, Erlangen, Germany.,Cognitive Computational Neuroscience Group, Friedrich-Alexander University Erlangen-Nuremberg, Nuremberg, Germany.,Pattern Recognition Lab, Friedrich-Alexander University Erlangen-Nuremberg, Nuremberg, Germany
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8
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Cardis R, Lecci S, Fernandez LM, Osorio-Forero A, Chu Sin Chung P, Fulda S, Decosterd I, Lüthi A. Cortico-autonomic local arousals and heightened somatosensory arousability during NREMS of mice in neuropathic pain. eLife 2021; 10:65835. [PMID: 34227936 PMCID: PMC8291975 DOI: 10.7554/elife.65835] [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: 12/16/2020] [Accepted: 07/02/2021] [Indexed: 12/22/2022] Open
Abstract
Frequent nightly arousals typical for sleep disorders cause daytime fatigue and present health risks. As such arousals are often short, partial, or occur locally within the brain, reliable characterization in rodent models of sleep disorders and in human patients is challenging. We found that the EEG spectral composition of non-rapid eye movement sleep (NREMS) in healthy mice shows an infraslow (~50 s) interval over which microarousals appear preferentially. NREMS could hence be vulnerable to abnormal arousals on this time scale. Chronic pain is well-known to disrupt sleep. In the spared nerve injury (SNI) mouse model of chronic neuropathic pain, we found more numerous local cortical arousals accompanied by heart rate increases in hindlimb primary somatosensory, but not in prelimbic, cortices, although sleep macroarchitecture appeared unaltered. Closed-loop mechanovibrational stimulation further revealed higher sensory arousability. Chronic pain thus preserved conventional sleep measures but resulted in elevated spontaneous and evoked arousability. We develop a novel moment-to-moment probing of NREMS vulnerability and propose that chronic pain-induced sleep complaints arise from perturbed arousability.
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Affiliation(s)
- Romain Cardis
- Department of Fundamental Neurosciences, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland.,Pain Center, Department of Anesthesiology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Sandro Lecci
- Department of Fundamental Neurosciences, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Laura Mj Fernandez
- Department of Fundamental Neurosciences, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Alejandro Osorio-Forero
- Department of Fundamental Neurosciences, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Paul Chu Sin Chung
- Pain Center, Department of Anesthesiology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Stephany Fulda
- Sleep Medicine Unit, Neurocenter of Southern Switzerland, Civic Hospital (EOC) of Lugano, Lugano, Switzerland
| | - Isabelle Decosterd
- Pain Center, Department of Anesthesiology, Lausanne University Hospital (CHUV), Lausanne, Switzerland
| | - Anita Lüthi
- Department of Fundamental Neurosciences, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
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9
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Yun S, Jeong B. Aberrant EEG signal variability at a specific temporal scale in major depressive disorder. Clin Neurophysiol 2021; 132:1866-1877. [PMID: 34147011 DOI: 10.1016/j.clinph.2021.05.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 04/17/2021] [Accepted: 05/18/2021] [Indexed: 11/28/2022]
Abstract
OBJECTIVE Signal variability is linked to irregularities in time series caused by intrinsic nonlinearities of the neural system and can be measured on variable temporal scales over short time series. By measuring refined complex multiscale permutation entropy (RCMPE) from resting-state electroencephalography (EEG) data, we investigated the presence of a specific range of time scales characterizing major depressive disorder (MDD). METHOD We used an EEG dataset acquired from 22 MDD patients and 22 healthy controls in the eyes-closed (EC) and eyes-open (EO) states available on the PRED + CT website. Signal variability in both the EC and EO states was compared between the two groups, and their relationship to depressive symptom severity was examined. RESULTS In the EC state, the RCMPE was higher in the MDD group than in the control group on a coarse temporal scale, approximately 20-32 ms, at almost all sensors. It also showed a negative correlation with depressive symptom severity on a fine temporal scale, approximately 2-26 ms, in the frontal, right temporal, and left parietal sensor areas in MDD. The EO state revealed a group difference but no relationship with depressive symptom severity. CONCLUSION Our results suggested that the diagnosis of MDD as a trait and the severity of depressive symptoms as a state are linked to EEG signal variability on the coarse temporal scale and the fine scale in the resting state, respectively. SIGNIFICANCE Signal variability reflects different characteristics of depression depending on the temporal scale.
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Affiliation(s)
- Seokho Yun
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - Bumseok Jeong
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.
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10
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Liang X, Xiong J, Cao Z, Wang X, Li J, Liu C. Decreased sample entropy during sleep-to-wake transition in sleep apnea patients. Physiol Meas 2021; 42. [PMID: 33761471 DOI: 10.1088/1361-6579/abf1b2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 03/24/2021] [Indexed: 11/12/2022]
Abstract
Objective. This study aimed to prove that there is a sudden change in the human physiology system when switching from one sleep stage to another and physical threshold-based sample entropy (SampEn) is able to capture this transition in an RR interval time series from patients with disorders such as sleep apnea.Approach. Physical threshold-based SampEn was used to analyze different sleep-stage RR segments from sleep apnea subjects in the St. Vincents University Hospital/University College Dublin Sleep Apnea Database, and SampEn differences were compared between two consecutive sleep stages. Additionally, other standard heart rate variability (HRV) measures were also analyzed to make comparisons.Main results. The findings suggested that the sleep-to-wake transitions presented a SampEn decrease significantly larger than intra-sleep ones (P < 0.01), which outperformed other standard HRV measures. Moreover, significant entropy differences between sleep and subsequent wakefulness appeared when the previous sleep stage was either S1 (P < 0.05), S2 (P < 0.01) or S4 (P < 0.05).Significance. The results demonstrated that physical threshold-based SampEn has the capability of depicting physiological changes in the cardiovascular system during the sleep-to-wake transition in sleep apnea patients and it is more reliable than the other analyzed HRV measures. This noninvasive HRV measure is a potential tool for further evaluation of sleep physiological time series.
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Affiliation(s)
- Xueyu Liang
- The State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, People's Republic of China
| | - Jinle Xiong
- The State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, People's Republic of China
| | - Zhengtao Cao
- Air Force Medical Center, PLA. Beijing, 100142, People's Republic of China
| | - Xingyao Wang
- The State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, People's Republic of China
| | - Jianqing Li
- The State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, People's Republic of China
| | - Chengyu Liu
- The State Key Laboratory of Bioelectronics, School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, People's Republic of China
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11
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Ivanov PC. The New Field of Network Physiology: Building the Human Physiolome. FRONTIERS IN NETWORK PHYSIOLOGY 2021; 1:711778. [PMID: 36925582 PMCID: PMC10013018 DOI: 10.3389/fnetp.2021.711778] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 05/21/2021] [Indexed: 12/22/2022]
Affiliation(s)
- Plamen Ch Ivanov
- Keck Laboratory for Network Physiology, Department of Physics, Boston University, Boston, MA, United States.,Harvard Medical School and Division of Sleep Medicine, Brigham and Women's Hospital, Boston, MA, United States.,Bulgarian Academy of Sciences, Institute of Solid State Physics, Sofia, Bulgaria
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12
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Yang J, Pan Y, Wang T, Zhang X, Wen J, Luo Y. Sleep-Dependent Directional Interactions of the Central Nervous System-Cardiorespiratory Network. IEEE Trans Biomed Eng 2020; 68:639-649. [PMID: 32746063 DOI: 10.1109/tbme.2020.3009950] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
OBJECTIVE We investigated the nature of interactions between the central nervous system (CNS) and the cardiorespiratory system during sleep. METHODS Overnight polysomnography recordings were obtained from 33 healthy individuals. The relative spectral powers of five frequency bands, three ECG morphological features and respiratory rate were obtained from six EEG channels, ECG, and oronasal airflow, respectively. The synchronous feature series were interpolated to 1 Hz to retain the high time-resolution required to detect rapid physiological variations. CNS-cardiorespiratory interaction networks were built for each EEG channel and a directionality analysis was conducted using multivariate transfer entropy. Finally, the difference in interaction between Deep, Light, and REM sleep (DS, LS, and REM) was studied. RESULTS Bidirectional interactions existed in central-cardiorespiratory networks, and the dominant direction was from the cardiorespiratory system to the brain during all sleep stages. Sleep stages had evident influence on these interactions, with the strength of information transfer from heart rate and respiration rate to the brain gradually increasing with the sequence of REM, LS, and DS. Furthermore, the occipital lobe appeared to receive the most input from the cardiorespiratory system during LS. Finally, different ECG morphological features were found to be involved with various central-cardiac and cardiac-respiratory interactions. CONCLUSION These findings reveal detailed information regarding CNS-cardiorespiratory interactions during sleep and provide new insights into understanding of sleep control mechanisms. SIGNIFICANCE Our approach may facilitate the investigation of the pathological cardiorespiratory complications of sleep disorders.
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13
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Kwon Y, Mariani S, Gadi SR, Jacobs Jr DR, Punjabi NM, Reid ML, Azarbarzin A, Wellman AD, Redline S. Characterization of lung-to-finger circulation time in sleep study assessment: the Multi-Ethnic Study of Atherosclerosis. Physiol Meas 2020; 41:065004. [DOI: 10.1088/1361-6579/ab8e12] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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14
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Islam S, Shah V, Gidde STR, Hutapea P, Song SH, Picone J, Kim A. A Machine Learning Enabled Wireless Intracranial Brain Deformation Sensing System. IEEE Trans Biomed Eng 2020; 67:3521-3530. [PMID: 32340930 DOI: 10.1109/tbme.2020.2990071] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
A leading cause of traumatic brain injury (TBI) is intracranial brain deformation due to mechanical impact. This deformation is viscoelastic and differs from a traditional rigid transformation. In this paper, we describe a machine learning enabled wireless sensing system that predicts the trajectory of intracranial brain deformation. The sensing system consists of an implantable soft magnet and an external magnetic sensor array with a sensing volume of 12 × 12 × 4 mm3. Machine learning algorithm predicts the brain deformation by interpreting the magnetic sensor outputs created by the change in position of the implanted soft magnet. Three different machine learning models were trained on calibration data: (1) random forests, (2) k-nearest neighbors, and (3) a multi-layer perceptron-based neural network. These models were validated using both in vitro (a needle inserted into PVC gel) and in vivo (blast exposure to live and dead rat brains) experiments. The in vitro gel deformation predicted by these machine learning models showed excellent agreement with the camera measurements and had absolute error = 138 μm, Fréchet distance = 372 μm with normalized Procrustes disparity = 0.034. The in vivo brain deformation predicted by these models had absolute error = 50 μm, Fréchet distance = 95 μm with normalized Procrustes disparity = 0.055 for dead animal and absolute error = 125 μm, Fréchet distance = 289 μm with normalized Procrustes disparity = 0.2 for live animal respectively. These results suggest that the proposed machine learning enabled sensor system can be an effective tool for measuring in situ brain deformation.
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15
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Dvir H, Guo S, Havlin S, Xin N, Jun T, Li D, Zhifei X, Kang R, Bartsch RP. Central Sleep Apnea Alters Neuronal Excitability and Increases the Randomness in Sleep-Wake Transitions. IEEE Trans Biomed Eng 2020; 67:3185-3194. [PMID: 32149619 DOI: 10.1109/tbme.2020.2979287] [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: 11/06/2022]
Abstract
OBJECTIVE While most studies on Central Sleep Apnea (CSA) have focused on breathing and metabolic disorders, the neuronal dysfunction that causes CSA remains largely unknown. Here, we investigate the underlying neuronal mechanism of CSA by studying the sleep-wake dynamics as derived from hypnograms. METHODS We analyze sleep data of seven groups of subjects: healthy adults (n = 48), adults with obstructive sleep apnea (OSA) (n = 29), adults with CSA (n = 25), healthy children (n = 40), children with OSA (n = 18), children with CSA (n = 73) and CSA children treated with CPAP (n = 10). We calculate sleep-wake parameters based on the probability distributions of wake-bout durations and sleep-bout durations. We compare these parameters with results obtained from a neuronal model that simulates the interplay between sleep- and wake-promoting neurons. RESULTS We find that sleep arousals of CSA patients show a characteristic time scale (i.e., exponential distribution) in contrast to the scale-invariant (i.e., power-law) distribution that has been reported for arousals in healthy sleep. Furthermore, we show that this change in arousal statistics is caused by triggering more arousals of similar durations, which through our model can be related to a higher excitability threshold in sleep-promoting neurons in CSA patients. CONCLUSIONS We propose a neuronal mechanism to shed light on CSA pathophysiology and a method to discriminate between CSA and OSA. We show that higher neuronal excitability thresholds can lead to complex reorganization of sleep-wake dynamics. SIGNIFICANCE The derived sleep parameters enable a more specific evaluation of CSA severity and can be used for CSA diagnosis and monitor CSA treatment.
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Zorko A, Frühwirth M, Goswami N, Moser M, Levnajić Z. Heart Rhythm Analyzed via Shapelets Distinguishes Sleep From Awake. Front Physiol 2020; 10:1554. [PMID: 32009972 PMCID: PMC6978775 DOI: 10.3389/fphys.2019.01554] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2019] [Accepted: 12/10/2019] [Indexed: 12/14/2022] Open
Abstract
Automatically determining when a person falls asleep from easily available vital signals is important, not just for medical applications but also for practical ones, such as traffic safety or smart homes. Heart dynamics and respiration cycle couple differently during sleep and awake. Specifically, respiratory modulation of heart rhythm or respiratory sinus arrhythmia (RSA) is more prominent during sleep, as both sleep and RSA are connected to strong vagal activity. The onset of sleep can be recognized or even predicted as the increase of cardio-respiratory coupling. Here, we employ this empirical fact to design a method for detecting the change of consciousness status (sleep/awake) based only on heart rate variability (HRV) data. Our method relies on quantifying the (self)similarity among shapelets - short chunks of HRV time series - whose "shapes" are related to the respiration cycle. To test our method, we examine the HRV data of 75 healthy individuals recorded with microsecond precision. We find distinctive patterns stable across age and sex, that are not only indicative of sleep and awake, but allow to pinpoint the change from awake to sleep almost immediately. More systematic analysis along these lines could lead to a reliable prediction of sleep.
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Affiliation(s)
- Albert Zorko
- Complex Systems and Data Science Lab, Faculty of Information Studies in Novo Mesto, Novo Mesto, Slovenia
| | | | - Nandu Goswami
- Physiology Division, Otto Loewi Research Center of Vascular Biology, Immunity and Inflammation, Medical University of Graz, Graz, Austria
| | - Maximilian Moser
- Human Research Institute, Weiz, Austria
- Physiology Division, Otto Loewi Research Center of Vascular Biology, Immunity and Inflammation, Medical University of Graz, Graz, Austria
| | - Zoran Levnajić
- Complex Systems and Data Science Lab, Faculty of Information Studies in Novo Mesto, Novo Mesto, Slovenia
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17
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Jansen C, Penzel T, Hodel S, Breuer S, Spott M, Krefting D. Network physiology in insomnia patients: Assessment of relevant changes in network topology with interpretable machine learning models. CHAOS (WOODBURY, N.Y.) 2019; 29:123129. [PMID: 31893662 DOI: 10.1063/1.5128003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Accepted: 12/03/2019] [Indexed: 06/10/2023]
Abstract
Network physiology describes the human body as a complex network of interacting organ systems. It has been applied successfully to determine topological changes in different sleep stages. However, the number of network links can quickly grow above the number of parameters that are typically analyzed with standard statistical methods. Artificial Neural Networks (ANNs) are a promising approach as they are successful in large parameter spaces, such as in digital imaging. On the other hand, ANN models do not provide an intrinsic approach to interpret their predictions, and they typically require large training data sets. Both aspects are critical in biomedical research. Medical decisions need to be explainable, and large data sets of quality assured patient and control data are rare. In this paper, different models for the classification of insomnia-a common sleep disorder-have been trained with 59 patients and age and gender matched controls, based on their physiological networks. Feature relevance evaluation is employed for all methods. For ANNs, the extrinsic interpretation method DeepLift is applied. The results are not identical across methods, but certain network links have been rated as relevant by all or most of the models. While ANNs show less classification accuracy (0.89) than advanced tree-based models (0.92 and 0.93), DeepLift provides an in-depth ANN interpretation with feature relevance scores for individual data samples. The analysis revealed modifications in the pulmonar, ocular, and cerebral subnetworks that have not been described before but are consistent with known findings on the physiological impact of insomnia.
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Affiliation(s)
- Christoph Jansen
- Center for Biomedical Image and Information Processing, HTW Berlin-University of Applied Sciences, Berlin 12459, Germany
| | - Thomas Penzel
- Interdisciplinary Sleep Medicine Center, Charité-Universitäsmedizin Berlin, Berlin 11017, Germany
| | - Stephan Hodel
- Center for Biomedical Image and Information Processing, HTW Berlin-University of Applied Sciences, Berlin 12459, Germany
| | - Stefanie Breuer
- Center for Biomedical Image and Information Processing, HTW Berlin-University of Applied Sciences, Berlin 12459, Germany
| | - Martin Spott
- School of Computing, Communication and Business, HTW Berlin-University of Applied Sciences, Berlin 12459, Germany
| | - Dagmar Krefting
- Department of Medical Informatics, University Medical Center Göttingen, Göttingen 37075, Germany
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18
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Critical Dynamics and Coupling in Bursts of Cortical Rhythms Indicate Non-Homeostatic Mechanism for Sleep-Stage Transitions and Dual Role of VLPO Neurons in Both Sleep and Wake. J Neurosci 2019; 40:171-190. [PMID: 31694962 DOI: 10.1523/jneurosci.1278-19.2019] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2019] [Revised: 09/07/2019] [Accepted: 10/07/2019] [Indexed: 11/21/2022] Open
Abstract
Origin and functions of intermittent transitions among sleep stages, including brief awakenings and arousals, constitute a challenge to the current homeostatic framework for sleep regulation, focusing on factors modulating sleep over large time scales. Here we propose that the complex micro-architecture characterizing sleep on scales of seconds and minutes results from intrinsic non-equilibrium critical dynamics. We investigate θ- and δ-wave dynamics in control rats and in rats where the sleep-promoting ventrolateral preoptic nucleus (VLPO) is lesioned (male Sprague-Dawley rats). We demonstrate that bursts in θ and δ cortical rhythms exhibit complex temporal organization, with long-range correlations and robust duality of power-law (θ-bursts, active phase) and exponential-like (δ-bursts, quiescent phase) duration distributions, features typical of non-equilibrium systems self-organizing at criticality. We show that such non-equilibrium behavior relates to anti-correlated coupling between θ- and δ-bursts, persists across a range of time scales, and is independent of the dominant physiologic state; indications of a basic principle in sleep regulation. Further, we find that VLPO lesions lead to a modulation of cortical dynamics resulting in altered dynamical parameters of θ- and δ-bursts and significant reduction in θ-δ coupling. Our empirical findings and model simulations demonstrate that θ-δ coupling is essential for the emerging non-equilibrium critical dynamics observed across the sleep-wake cycle, and indicate that VLPO neurons may have dual role for both sleep and arousal/brief wake activation. The uncovered critical behavior in sleep- and wake-related cortical rhythms indicates a mechanism essential for the micro-architecture of spontaneous sleep-stage and arousal transitions within a novel, non-homeostatic paradigm of sleep regulation.SIGNIFICANCE STATEMENT We show that the complex micro-architecture of sleep-stage/arousal transitions arises from intrinsic non-equilibrium critical dynamics, connecting the temporal organization of dominant cortical rhythms with empirical observations across scales. We link such behavior to sleep-promoting neuronal population, and demonstrate that VLPO lesion (model of insomnia) alters dynamical features of θ and δ rhythms, and leads to significant reduction in θ-δ coupling. This indicates that VLPO neurons may have dual role for both sleep and arousal/brief wake control. The reported empirical findings and modeling simulations constitute first evidences of a neurophysiological fingerprint of self-organization and criticality in sleep- and wake-related cortical rhythms; a mechanism essential for spontaneous sleep-stage and arousal transitions that lays the bases for a novel, non-homeostatic paradigm of sleep regulation.
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19
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Wang JWJL, Lombardi F, Zhang X, Anaclet C, Ivanov PC. Non-equilibrium critical dynamics of bursts in θ and δ rhythms as fundamental characteristic of sleep and wake micro-architecture. PLoS Comput Biol 2019; 15:e1007268. [PMID: 31725712 PMCID: PMC6855414 DOI: 10.1371/journal.pcbi.1007268] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Accepted: 07/11/2019] [Indexed: 01/08/2023] Open
Abstract
Origin and functions of intermittent transitions among sleep stages, including short awakenings and arousals, constitute a challenge to the current homeostatic framework for sleep regulation, focusing on factors modulating sleep over large time scales. Here we propose that the complex micro-architecture characterizing the sleep-wake cycle results from an underlying non-equilibrium critical dynamics, bridging collective behaviors across spatio-temporal scales. We investigate θ and δ wave dynamics in control rats and in rats with lesions of sleep-promoting neurons in the parafacial zone. We demonstrate that intermittent bursts in θ and δ rhythms exhibit a complex temporal organization, with long-range power-law correlations and a robust duality of power law (θ-bursts, active phase) and exponential-like (δ-bursts, quiescent phase) duration distributions, typical features of non-equilibrium systems self-organizing at criticality. Crucially, such temporal organization relates to anti-correlated coupling between θ- and δ-bursts, and is independent of the dominant physiologic state and lesions, a solid indication of a basic principle in sleep dynamics.
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Affiliation(s)
- Jilin W. J. L. Wang
- Keck Laboratory for Network Physiology, Department of Physics, Boston University, Boston, Massachusetts, United States of America
| | - Fabrizio Lombardi
- Keck Laboratory for Network Physiology, Department of Physics, Boston University, Boston, Massachusetts, United States of America
- Institute of Science and Technology Austria, A-3400 Klosterneuburg, Austria
| | - Xiyun Zhang
- Keck Laboratory for Network Physiology, Department of Physics, Boston University, Boston, Massachusetts, United States of America
| | - Christelle Anaclet
- Department of Neurobiology, University of Massachusetts Medical School, Worcester, Massachusetts, United States of America
- Department of Neurology, Division of Sleep Medicine, Harvard Medical School and Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America
| | - Plamen Ch. Ivanov
- Keck Laboratory for Network Physiology, Department of Physics, Boston University, Boston, Massachusetts, United States of America
- Department of Neurology, Division of Sleep Medicine, Harvard Medical School and Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America
- Harvard Medical School and Division of Sleep Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, United States of America
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20
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Morelli D, Bartoloni L, Rossi A, Clifton DA. A computationally efficient algorithm to obtain an accurate and interpretable model of the effect of circadian rhythm on resting heart rate. Physiol Meas 2019; 40:095001. [PMID: 31437825 DOI: 10.1088/1361-6579/ab3dea] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
OBJECTIVE Wrist-worn wearable devices equipped with heart rate (HR) sensors have become increasingly popular. The ability to correctly interpret the collected data is fundamental to analyse user's well-being and perform early detection of abnormal physiological data. Circadian rhythm is a strong factor of variability in HR, yet few models attempt to accurately model its effect on HR. APPROACH In this paper we present a mathematical derivation of the single-component cosinor model with multiple components that fits user data to a predetermined arbitrary function (the expected shape of the circadian effect on resting HR (RHR)), thus permitting us to predict the user's circadian rhythm component (i.e. MESOR, Acrophase and Amplitude) with a high accuracy. MAIN RESULTS We show that our model improves the accuracy of HR prediction compared to the single component cosinor model (10% lower RMSE), while retaining the readability of the fitted model of the single component cosinor. We also show that the model parameters can be used to detect sleep disruption in a qualitative experiment. The model is computationally cheap, depending linearly on the size of the data. The computation of the model does not need the full dataset, but only two surrogates, where the data is accumulated. This implies that the model can be implemented in a streaming approach, with important consequences for security and privacy of the data, that never leaves the user devices. SIGNIFICANCE The multiple component model provided in this paper can be used to approximate a user's RHR with higher accuracy than single component model, providing traditional parameters easy to interpret (i.e. the same produced by the single component cosinor model). The model we developed goes beyond fitting circadian activity on RHR, and it can be used to fit arbitrary periodic real valued time series, vectorial data, or complex data.
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Affiliation(s)
- Davide Morelli
- Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, Oxford, OX2 6DP, United Kingdom. Biobeats Group LTD, 3 Fitzhardinge Street, London, W1H 6EF, United Kingdom
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21
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Bocaccio H, Pallavicini C, Castro MN, Sánchez SM, De Pino G, Laufs H, Villarreal MF, Tagliazucchi E. The avalanche-like behaviour of large-scale haemodynamic activity from wakefulness to deep sleep. J R Soc Interface 2019; 16:20190262. [PMID: 31506046 PMCID: PMC6769314 DOI: 10.1098/rsif.2019.0262] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Accepted: 08/08/2019] [Indexed: 02/02/2023] Open
Abstract
Increasing evidence suggests that responsiveness is associated with critical or near-critical cortical dynamics, which exhibit scale-free cascades of spatio-temporal activity. These cascades, or 'avalanches', have been detected at multiple scales, from in vitro and in vivo microcircuits to voltage imaging and brain-wide functional magnetic resonance imaging (fMRI) recordings. Criticality endows the cortex with certain information-processing capacities postulated as necessary for conscious wakefulness, yet it remains unknown how unresponsiveness impacts on the avalanche-like behaviour of large-scale human haemodynamic activity. We observed a scale-free hierarchy of co-activated connected clusters by applying a point-process transformation to fMRI data recorded during wakefulness and non-rapid eye movement (NREM) sleep. Maximum-likelihood estimates revealed a significant effect of sleep stage on the scaling parameters of the cluster size power-law distributions. Post hoc statistical tests showed that differences were maximal between wakefulness and N2 sleep. These results were robust against spatial coarse graining, fitting alternative statistical models and different point-process thresholds, and disappeared upon phase shuffling the fMRI time series. Evoked neural bistabilities preventing arousals during N2 sleep do not suffice to explain these differences, which point towards changes in the intrinsic dynamics of the brain that could be necessary to consolidate a state of deep unresponsiveness.
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Affiliation(s)
- H. Bocaccio
- Grupo de Investigación en Neurociencias Aplicadas a las Alteraciones de la Conducta, Instituto de Neurociencias FLENI-CONICET, Buenos Aires, Argentina
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
- Departamento de Física, FCEyN, UBA, e Instituto de Física de Buenos Aires (IFIBA), Buenos Aires, Argentina
| | - C. Pallavicini
- Grupo de Investigación en Neurociencias Aplicadas a las Alteraciones de la Conducta, Instituto de Neurociencias FLENI-CONICET, Buenos Aires, Argentina
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
- Departamento de Física, FCEyN, UBA, e Instituto de Física de Buenos Aires (IFIBA), Buenos Aires, Argentina
| | - M. N. Castro
- Grupo de Investigación en Neurociencias Aplicadas a las Alteraciones de la Conducta, Instituto de Neurociencias FLENI-CONICET, Buenos Aires, Argentina
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
- Departamento de Fisiología, Facultad de Medicina, UBA, Buenos Aires, Argentina
- Departamento Salud Mental, Unidad Docente FLENI, Facultad de Medicina, UBA, Buenos Aires, Argentina
| | - S. M. Sánchez
- Grupo de Investigación en Neurociencias Aplicadas a las Alteraciones de la Conducta, Instituto de Neurociencias FLENI-CONICET, Buenos Aires, Argentina
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
- Departamento de Física, FCEyN, UBA, e Instituto de Física de Buenos Aires (IFIBA), Buenos Aires, Argentina
| | - G. De Pino
- Grupo de Investigación en Neurociencias Aplicadas a las Alteraciones de la Conducta, Instituto de Neurociencias FLENI-CONICET, Buenos Aires, Argentina
- Laboratorio de Neuroimágenes, Departamento de Imágenes, FLENI, Buenos Aires, Argentina
- Escuela de Ciencia y Tecnología (ECyT), Universidad Nacional de San Martín, Argentina
| | - H. Laufs
- Department of Neurology, Christian-Albrechts-University, Kiel, Germany
| | - M. F. Villarreal
- Grupo de Investigación en Neurociencias Aplicadas a las Alteraciones de la Conducta, Instituto de Neurociencias FLENI-CONICET, Buenos Aires, Argentina
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
- Departamento de Física, FCEyN, UBA, e Instituto de Física de Buenos Aires (IFIBA), Buenos Aires, Argentina
| | - E. Tagliazucchi
- National Scientific and Technical Research Council (CONICET), Buenos Aires, Argentina
- Departamento de Física, FCEyN, UBA, e Instituto de Física de Buenos Aires (IFIBA), Buenos Aires, Argentina
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22
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Dvir H, Kantelhardt JW, Zinkhan M, Pillmann F, Szentkiralyi A, Obst A, Ahrens W, Bartsch RP. A Biased Diffusion Approach to Sleep Dynamics Reveals Neuronal Characteristics. Biophys J 2019; 117:987-997. [PMID: 31422824 DOI: 10.1016/j.bpj.2019.07.032] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2019] [Revised: 06/20/2019] [Accepted: 07/19/2019] [Indexed: 01/10/2023] Open
Abstract
We propose a biased diffusion model of accumulated subthreshold voltage fluctuations in wake-promoting neurons to account for stochasticity in sleep dynamics and to explain the occurrence of brief arousals during sleep. Utilizing this model, we derive four neurophysiological parameters related to neuronal noise level, excitability threshold, deep-sleep threshold, and sleep inertia. We provide the first analytic expressions for these parameters, and we show that there is good agreement between empirical findings from sleep recordings and our model simulation results. Our work suggests that these four parameters can be of clinical importance because we find them to be significantly altered in elderly subjects and in children with autism.
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Affiliation(s)
- Hila Dvir
- Department of Physics, Bar-Ilan University, Ramat-Gan, Israel.
| | - Jan W Kantelhardt
- Institute of Physics, Martin-Luther-University Halle-Wittenberg, Halle, Germany
| | - Melanie Zinkhan
- Institute of Clinical Epidemiology, Martin-Luther-University Halle-Wittenberg, Halle, Germany
| | - Frank Pillmann
- Department of Psychiatry and Psychotherapy, Martin-Luther-University Halle-Wittenberg, Halle, Germany
| | - Andras Szentkiralyi
- Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
| | - Anne Obst
- Department of Internal Medicine B, Ernst-Moritz-Arndt University Greifswald, Greifswald, Germany
| | - Wolfgang Ahrens
- Leibniz Institute for Prevention Research and Epidemiology, Bremen, Germany
| | - Ronny P Bartsch
- Department of Physics, Bar-Ilan University, Ramat-Gan, Israel.
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23
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Hu G, Huang X, Jiang T, Yu S. Multi-Scale Expressions of One Optimal State Regulated by Dopamine in the Prefrontal Cortex. Front Physiol 2019; 10:113. [PMID: 30873039 PMCID: PMC6404637 DOI: 10.3389/fphys.2019.00113] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Accepted: 01/30/2019] [Indexed: 11/13/2022] Open
Abstract
The prefrontal cortex (PFC), which plays key roles in many higher cognitive processes, is a hierarchical system consisting of multi-scale organizations. Optimizing the working state at each scale is essential for PFC's information processing. Typical optimal working states at different scales have been separately reported, including the dopamine-mediated inverted-U profile of the working memory (WM) at the system level, critical dynamics at the network level, and detailed balance of excitatory and inhibitory currents (E/I balance) at the cellular level. However, it remains unclear whether these states are scale-specific expressions of the same optimal state and, if so, what is the underlying mechanism for its regulation traversing across scales. Here, by studying a neural network model, we show that the optimal performance of WM co-occurs with the critical dynamics at the network level and the E/I balance at the level of individual neurons, suggesting the existence of a unified, multi-scale optimal state for the PFC. Importantly, such a state could be modulated by dopamine at the synaptic level through a series of U or inverted-U profiles. These results suggest that seemingly different optimal states for specific scales are multi-scale expressions of one condition regulated by dopamine. Our work suggests a cross-scale perspective to understand the PFC function and its modulation.
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Affiliation(s)
- Guyue Hu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Xuhui Huang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing, China
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Shan Yu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
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