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Giordano F, Rutigliano C, Ugolini C, Iacona E, Ronconi L, Raguseo C, Perillo T, Rosa A, Santoro N, Testoni I. Effect of Music Therapy on Chemotherapy Anticipatory Symptoms in Adolescents: A Mixed Methods Study. J Pain Symptom Manage 2024; 68:e62-e74. [PMID: 38679306 DOI: 10.1016/j.jpainsymman.2024.04.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 04/10/2024] [Accepted: 04/14/2024] [Indexed: 05/01/2024]
Abstract
INTRODUCTION This study explores the feasibility and effects of music therapy on anticipatory nausea and vomiting, stress, pain and quality of life in adolescents undergoing chemotherapy. METHODS This is a single group, quasi-experimental study using pre/post-test design. Participants received four individual sessions of music therapy (MT), led by a certified music therapist. State-Trait-Anxiety Inventory Y-1, and a 0-4 Likert scale for pain, nausea and vomiting were recorded. Heart rate variability (LF/HF) was collected during sessions. PedsQL was collected before and at the end of the trial. Semi-structured interviews were conducted with participants. RESULTS A significant reduction in anxiety, nausea and vomiting, both pre-post single sessions and between different sessions, was observed. A significant increase in LF/HF and PedsQL scores was observed. Relevant themes also emerged from interviews. CONCLUSION Results support the feasibility of introducing music therapy with adolescents undergoing chemotherapy. Perceived stress and psychological/physical wellbeing were improved in participants.
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Affiliation(s)
- Filippo Giordano
- Department of Precision and Regenerative Medicine and Ionian Area (F.G.), University of Bari, Bari, Italy; Pediatric Hemathology-Oncology Unit (F.G., C.R., C.R., A.R., N.S.), University Hospital of Bari, Bari, Italy.
| | - Chiara Rutigliano
- Pediatric Hemathology-Oncology Unit (F.G., C.R., C.R., A.R., N.S.), University Hospital of Bari, Bari, Italy
| | - Caterina Ugolini
- Department of Philosophy (C.U., E.I., I.T.), Sociology, Education and Applied Psychology (FISPPA), University of Padova, Padua, Italy
| | - Erika Iacona
- Department of Philosophy (C.U., E.I., I.T.), Sociology, Education and Applied Psychology (FISPPA), University of Padova, Padua, Italy
| | - Lucia Ronconi
- Computer and Statistical Services, Multifunctional Pole of Psychology (L.R.), University of Padua, Padua, Italy
| | - Celeste Raguseo
- Pediatric Hemathology-Oncology Unit (F.G., C.R., C.R., A.R., N.S.), University Hospital of Bari, Bari, Italy
| | - Teresa Perillo
- Pediatric Hemathology-Oncology Unit (F.G., C.R., C.R., A.R., N.S.), University Hospital of Bari, Bari, Italy
| | - Angarano Rosa
- Pediatric Hemathology-Oncology Unit (F.G., C.R., C.R., A.R., N.S.), University Hospital of Bari, Bari, Italy
| | - Nicola Santoro
- Pediatric Hemathology-Oncology Unit (F.G., C.R., C.R., A.R., N.S.), University Hospital of Bari, Bari, Italy
| | - Ines Testoni
- Department of Philosophy (C.U., E.I., I.T.), Sociology, Education and Applied Psychology (FISPPA), University of Padova, Padua, Italy
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Giordano F, Mitrotti A, Losurdo A, Esposito F, Granata A, Pesino A, Rossini M, Natale P, Dileo V, Fiorentino M, Gesualdo L. Effect of music therapy intervention on anxiety and pain during percutaneous renal biopsy: a randomized controlled trial. Clin Kidney J 2023; 16:2721-2727. [PMID: 38046004 PMCID: PMC10689136 DOI: 10.1093/ckj/sfad246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Indexed: 12/05/2023] Open
Abstract
Background Percutaneous renal biopsy (PRB) may subject patients to emotional distress and pain before and during the biopsy. The aim of this study was to evaluate the effects of complementary/non-pharmacological interventions such as music therapy (MT) on anxiety, pain and satisfaction in renal patients undergoing PRB. Methods A prospective, single-centre, single-blind, randomized controlled two-arm trial was conducted. Patients ≥18 years of age, hospitalized at the Nephrology, Dialysis and Transplantation Unit (Bari, Italy) and scheduled for PRB were screened. Participants were assigned to standard treatment (CG) or to the music therapy (MT) intervention group. Participants in the MT group received standard care and an MT intervention by a certified music therapist qualified in guided imagery and music. The CG patients received the standard of care. MT and CG patients were subjected to identical measurements (pre/post) of the parameters in the State Trait Anxiety Inventory Y1 (STAI-Y1), visual analogue scale for pain (VAS-P) and satisfaction (VAS-S) and heart rate variability. Results A statistically significant difference in the anxiety scores after PRB between MT and CG patients (STAI-Y1 35.4 ± 6.2 versus 42.9 ± 9.0) was observed. MT also had strong and significant effects on VAS-P compared with CG (5.0 ± 1.4 versus 6.3 ± 1.3, respectively; P < .001) and VAS-S (7.8 ± 1.0 versus 6.0 ± 0.9, respectively; P < .001). Decreased activity of the sympathetic nervous system and increased activity of the parasympathetic nervous system was observed after PRB in the MT group. Conclusion Our study supports the use of MT to mitigate the psychological anxiety, pain and sympathetic activation associated with PRB.
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Affiliation(s)
- Filippo Giordano
- Nephrology, Dialysis and Transplantation, Department of Precision and Regenerative Medicine and Ionian Area, University of Bari, Bari, Italy
| | - Adele Mitrotti
- Nephrology, Dialysis and Transplantation, Department of Precision and Regenerative Medicine and Ionian Area, University of Bari, Bari, Italy
| | - Antonia Losurdo
- Nephrology, Dialysis and Transplantation, Department of Precision and Regenerative Medicine and Ionian Area, University of Bari, Bari, Italy
| | | | - Antonio Granata
- Nephrology and Dialysis Unit, “Cannizzaro” Emergency Hospital, Catania, Italy
| | - Alessandra Pesino
- Nephrology, Dialysis and Transplantation, Department of Precision and Regenerative Medicine and Ionian Area, University of Bari, Bari, Italy
| | - Michele Rossini
- Nephrology, Dialysis and Transplantation, Department of Precision and Regenerative Medicine and Ionian Area, University of Bari, Bari, Italy
| | - Patrizia Natale
- Nephrology, Dialysis and Transplantation Unit, Department of Medical and Surgical Sciences, University of Foggia, Foggia, Italy
| | - Vincenzo Dileo
- Nephrology, Dialysis and Transplantation, Department of Precision and Regenerative Medicine and Ionian Area, University of Bari, Bari, Italy
| | - Marco Fiorentino
- Nephrology, Dialysis and Transplantation, Department of Precision and Regenerative Medicine and Ionian Area, University of Bari, Bari, Italy
| | - Loreto Gesualdo
- Nephrology, Dialysis and Transplantation, Department of Precision and Regenerative Medicine and Ionian Area, University of Bari, Bari, Italy
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Pernice R, Faes L, Feucht M, Benninger F, Mangione S, Schiecke K. Pairwise and higher-order measures of brain-heart interactions in children with temporal lobe epilepsy. J Neural Eng 2022; 19. [PMID: 35803218 DOI: 10.1088/1741-2552/ac7fba] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 07/08/2022] [Indexed: 11/11/2022]
Abstract
OBJECTIVE While it is well-known that epilepsy has a clear impact on the activity of both the central nervous system (CNS) and the autonomic nervous system (ANS), its role on the complex interplay between CNS and ANS has not been fully elucidated yet. In this work, pairwise and higher-order predictability measures based on the concepts of Granger causality (GC) and Partial Information Decomposition (PID) were applied on time series of electroencephalographic (EEG) brain wave amplitude and heart rate variability (HRV) in order to investigate directed brain-heart interactions associated with the occurrence of focal epilepsy. APPROACH HRV and the envelopes of δ and α EEG activity recorded from ipsilateral (ipsi-EEG) and contralateral (contra-EEG) scalp regions were analyzed in 18 children suffering from temporal lobe epilepsy monitored during pre-ictal, ictal and post-ictal periods. After linear parametric model identification, we compared pairwise GC measures computed between HRV and a single EEG component with PID measures quantifying the unique, redundant and synergistic information transferred from ipsi-EEG and contra-EEG to HRV. MAIN RESULTS The analysis of GC revealed a dominance of the information transfer from EEG to HRV and negligible transfer from HRV to EEG, suggesting that CNS activities drive the ANS modulation of the heart rhythm, but did not evidence clear differences between δ and α rhythms, ipsi-EEG and contra-EEG, or pre- and post-ictal periods. On the contrary, PID revealed that epileptic seizures induce a reorganization of the interactions from brain to heart, as the unique predictability of HRV originated from the ipsi-EEG for the δ waves and from the contra-EEG for the α waves in the pre-ictal phase, while these patterns were reversed after the seizure. SIGNIFICANCE These results highlight the importance of considering higher-order interactions elicited by PID for the study of the neuro-autonomic effects of focal epilepsy, and may have neurophysiological and clinical implications.
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Affiliation(s)
- Riccardo Pernice
- Department of Engineering, University of Palermo, Viale delle Scienze, Bldg. 9, Palermo, 90128, ITALY
| | - Luca Faes
- Department of Engineering, University of Palermo, Viale delle Scienze, Bldg. 9, Palermo, 90128, ITALY
| | - Martha Feucht
- Epilepsy Monitoring Unit, Department of Child and Adolenscent Neuropsychiatry, University Hospital Vienna, Währinger Gürtel 18-20, Vienna, 1090, AUSTRIA
| | - Franz Benninger
- Department of Child and Adolescent Medicine, University Hospital Vienna, Währinger Gürtel 18-20, Vienna, 1090, AUSTRIA
| | - Stefano Mangione
- Department of Engineering, University of Palermo, Viale delle Scienze, Bldg. 9, Palermo, Sicilia, 90128, ITALY
| | - Karin Schiecke
- Institute of Medical Statistics, Computer Sciences and Documentation, Jena University Hospital, Friedrich Schiller University Jena, Bachstraße 18, Jena, 07743, GERMANY
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González C, Garcia-Hernando G, Jensen EW, Vallverdú-Ferrer M. Assessing rheoencephalography dynamics through analysis of the interactions among brain and cardiac networks during general anesthesia. FRONTIERS IN NETWORK PHYSIOLOGY 2022; 2:912733. [PMID: 36926077 PMCID: PMC10013012 DOI: 10.3389/fnetp.2022.912733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 07/08/2022] [Indexed: 11/13/2022]
Abstract
Cerebral blood flow (CBF) reflects the rate of delivery of arterial blood to the brain. Since no nutrients, oxygen or water can be stored in the cranial cavity due to space and pressure restrictions, a continuous perfusion of the brain is critical for survival. Anesthetic procedures are known to affect cerebral hemodynamics, but CBF is only monitored in critical patients due, among others, to the lack of a continuous and affordable bedside monitor for this purpose. A potential solution through bioelectrical impedance technology, also known as rheoencephalography (REG), is proposed, that could fill the existing gap for a low-cost and effective CBF monitoring tool. The underlying hypothesis is that REG signals carry information on CBF that might be recovered by means of the application of advanced signal processing techniques, allowing to track CBF alterations during anesthetic procedures. The analysis of REG signals was based on geometric features extracted from the time domain in the first place, since this is the standard processing strategy for this type of physiological data. Geometric features were tested to distinguish between different anesthetic depths, and they proved to be capable of tracking cerebral hemodynamic changes during anesthesia. Furthermore, an approach based on Poincaré plot features was proposed, where the reconstructed attractors form REG signals showed significant differences between different anesthetic states. This was a key finding, providing an alternative to standard processing of REG signals and supporting the hypothesis that REG signals do carry CBF information. Furthermore, the analysis of cerebral hemodynamics during anesthetic procedures was performed by means of studying causal relationships between global hemodynamics, cerebral hemodynamics and electroencephalogram (EEG) based-parameters. Interactions were detected during anesthetic drug infusion and patient positioning (Trendelenburg positioning and passive leg raise), providing evidence of the causal coupling between hemodynamics and brain activity. The provided alternative of REG signal processing confirmed the hypothesis that REG signals carry information on CBF. The simplicity of the technology, together with its low cost and easily interpretable outcomes, should provide a new opportunity for REG to reach standard clinical practice. Moreover, causal relationships among the hemodynamic physiological signals and brain activity were assessed, suggesting that the inclusion of REG information in depth of anesthesia monitors could be of valuable use to prevent unwanted CBF alterations during anesthetic procedures.
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Affiliation(s)
- Carmen González
- Biomedical Engineering Research Centre, CIBER of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Universitat Politècnica de Catalunya, Barcelona, Spain.,Research and Development Department, Quantium Medical, Mataró, Spain
| | - Gabriel Garcia-Hernando
- Biomedical Engineering Research Centre, CIBER of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Universitat Politècnica de Catalunya, Barcelona, Spain.,Research and Development Department, Quantium Medical, Mataró, Spain
| | - Erik W Jensen
- Research and Development Department, Quantium Medical, Mataró, Spain
| | - Montserrat Vallverdú-Ferrer
- Biomedical Engineering Research Centre, CIBER of Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Universitat Politècnica de Catalunya, Barcelona, Spain
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5
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Pernice R, Volpes G, Krohova JC, Javorka M, Busacca A, Faes L. Feasibility of Linear Parametric Estimation of Dynamic Information Measures to assess Physiological Stress from Short-Term Cardiovascular Variability . ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:290-293. [PMID: 34891293 DOI: 10.1109/embc46164.2021.9630697] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Extensive efforts have been recently devoted to implement fast and reliable algorithms capable of assessing the physiological response of the organism to physiological stress. In this study, we propose the comparison between model-free and linear parametric methods as regards their ability to detect alterations in the dynamics and in the complexity of cardiovascular and respiratory variability evoked by postural and mental stress. Dynamic entropy (DE) and information storage (IS) measures were calculated on three physiological time-series, i.e. heart period, respiratory volume and systolic arterial pressure, on 61 healthy subjects monitored in resting conditions as well as during head-up tilt and while performing a mental arithmetic task. The results of the comparison suggest the feasibility of DE and IS measures computed from different physiological signals to discriminate among resting and stress states. If compared to the model-free algorithm, the faster linear method appears to be capable of detecting the same (or even more) statistically significant variations of DE or IS between resting and stress conditions, being thus in perspective more suitable for the integration within wearable devices. The computation of entropy indices extracted from multiple physiological signals acquired through wearables will allow a real-time stress assessment on people in daily-life situations.
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Sciaraffa N, Liu J, Aricò P, Flumeri GD, Inguscio BMS, Borghini G, Babiloni F. Multivariate model for cooperation: bridging social physiological compliance and hyperscanning. Soc Cogn Affect Neurosci 2021; 16:193-209. [PMID: 32860692 PMCID: PMC7812636 DOI: 10.1093/scan/nsaa119] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Revised: 06/30/2020] [Accepted: 08/26/2020] [Indexed: 01/06/2023] Open
Abstract
The neurophysiological analysis of cooperation has evolved over the past 20 years, moving towards the research of common patterns in neurophysiological signals of people interacting. Social physiological compliance (SPC) and hyperscanning represent two frameworks for the joint analysis of autonomic and brain signals, respectively. Each of the two approaches allows to know about a single layer of cooperation according to the nature of these signals: SPC provides information mainly related to emotions, and hyperscanning that related to cognitive aspects. In this work, after the analysis of the state of the art of SPC and hyperscanning, we explored the possibility to unify the two approaches creating a complete neurophysiological model for cooperation considering both affective and cognitive mechanisms We synchronously recorded electrodermal activity, cardiac and brain signals of 14 cooperative dyads. Time series from these signals were extracted, and multivariate Granger causality was computed. The results showed that only when subjects in a dyad cooperate there is a statistically significant causality between the multivariate variables representing each subject. Moreover, the entity of this statistical relationship correlates with the dyad’s performance. Finally, given the novelty of this approach and its exploratory nature, we provided its strengths and limitations.
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Affiliation(s)
- Nicolina Sciaraffa
- Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy.,BrainSigns srl, Rome, Italy
| | - Jieqiong Liu
- School of Psychology and Cognitive Science, Shanghai Changning-ECNU Mental Health Center, East China Normal University, Shanghai, China
| | - Pietro Aricò
- Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy.,BrainSigns srl, Rome, Italy.,IRCCS Fondazione Santa Lucia, Neuroelectrical Imaging and BCI Lab, Rome, Italy
| | - Gianluca Di Flumeri
- Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy.,BrainSigns srl, Rome, Italy.,IRCCS Fondazione Santa Lucia, Neuroelectrical Imaging and BCI Lab, Rome, Italy
| | - Bianca M S Inguscio
- BrainSigns srl, Rome, Italy.,Department of Sense Organs, Sapienza University of Rome, Rome, Italy
| | - Gianluca Borghini
- Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy.,BrainSigns srl, Rome, Italy.,IRCCS Fondazione Santa Lucia, Neuroelectrical Imaging and BCI Lab, Rome, Italy
| | - Fabio Babiloni
- Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy.,BrainSigns srl, Rome, Italy.,College of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou Zhejiang Province, People's Republic of China
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Ozimek M, Żebrowski JJ, Baranowski R. Information Flow Between Heart Rhythm, Repolarization, and the Diastolic Interval Series for Healthy Individuals and LQTS1 Patients. Front Physiol 2021; 12:611731. [PMID: 34163369 PMCID: PMC8215390 DOI: 10.3389/fphys.2021.611731] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 04/08/2021] [Indexed: 11/13/2022] Open
Abstract
Using information theoretic measures, relations between heart rhythm, repolarization in the tissue of the heart, and the diastolic interval time series are analyzed. These processes are a fragment of the cardiovascular physiological network. A comparison is made between the results for 84 (42 women) healthy individuals and 65 (45 women) long QT syndrome type 1 (LQTS1) patients. Self-entropy, transfer entropy, and joint transfer entropy are calculated for the three time series and their combinations. The results for self-entropy indicate the well-known result that regularity of heart rhythm for healthy individuals is larger than that of QT interval series. The flow of information depends on the direction with the flow from the heart rhythm to QT dominating. In LQTS1 patients, however, our results indicate that information flow in the opposite direction may occur—a new result. The information flow from the heart rhythm to QT dominates, which verifies the asymmetry seen by Porta et al. in the variable tilt angle experiment. The amount of new information and self-entropy for LQTS1 patients is smaller than that for healthy individuals. However, information transfers from RR to QT and from DI to QT are larger in the case of LQTS1 patients.
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Affiliation(s)
- Mateusz Ozimek
- Cardiovascular Physics Group, Physics of Complex Systems Division, Faculty of Physics, Warsaw University of Technology, Warszawa, Poland
| | - Jan J Żebrowski
- Cardiovascular Physics Group, Physics of Complex Systems Division, Faculty of Physics, Warsaw University of Technology, Warszawa, Poland
| | - Rafał Baranowski
- Cardiovascular Physics Group, Physics of Complex Systems Division, Faculty of Physics, Warsaw University of Technology, Warszawa, Poland.,Institute of Cardiology, Warszawa-Anin, Poland
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8
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Antonacci Y, Minati L, Faes L, Pernice R, Nollo G, Toppi J, Pietrabissa A, Astolfi L. Estimation of Granger causality through Artificial Neural Networks: applications to physiological systems and chaotic electronic oscillators. PeerJ Comput Sci 2021; 7:e429. [PMID: 34084917 PMCID: PMC8157130 DOI: 10.7717/peerj-cs.429] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Accepted: 02/15/2021] [Indexed: 05/13/2023]
Abstract
One of the most challenging problems in the study of complex dynamical systems is to find the statistical interdependencies among the system components. Granger causality (GC) represents one of the most employed approaches, based on modeling the system dynamics with a linear vector autoregressive (VAR) model and on evaluating the information flow between two processes in terms of prediction error variances. In its most advanced setting, GC analysis is performed through a state-space (SS) representation of the VAR model that allows to compute both conditional and unconditional forms of GC by solving only one regression problem. While this problem is typically solved through Ordinary Least Square (OLS) estimation, a viable alternative is to use Artificial Neural Networks (ANNs) implemented in a simple structure with one input and one output layer and trained in a way such that the weights matrix corresponds to the matrix of VAR parameters. In this work, we introduce an ANN combined with SS models for the computation of GC. The ANN is trained through the Stochastic Gradient Descent L1 (SGD-L1) algorithm, and a cumulative penalty inspired from penalized regression is applied to the network weights to encourage sparsity. Simulating networks of coupled Gaussian systems, we show how the combination of ANNs and SGD-L1 allows to mitigate the strong reduction in accuracy of OLS identification in settings of low ratio between number of time series points and of VAR parameters. We also report how the performances in GC estimation are influenced by the number of iterations of gradient descent and by the learning rate used for training the ANN. We recommend using some specific combinations for these parameters to optimize the performance of GC estimation. Then, the performances of ANN and OLS are compared in terms of GC magnitude and statistical significance to highlight the potential of the new approach to reconstruct causal coupling strength and network topology even in challenging conditions of data paucity. The results highlight the importance of of a proper selection of regularization parameter which determines the degree of sparsity in the estimated network. Furthermore, we apply the two approaches to real data scenarios, to study the physiological network of brain and peripheral interactions in humans under different conditions of rest and mental stress, and the effects of the newly emerged concept of remote synchronization on the information exchanged in a ring of electronic oscillators. The results highlight how ANNs provide a mesoscopic description of the information exchanged in networks of multiple interacting physiological systems, preserving the most active causal interactions between cardiovascular, respiratory and brain systems. Moreover, ANNs can reconstruct the flow of directed information in a ring of oscillators whose statistical properties can be related to those of physiological networks.
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Affiliation(s)
- Yuri Antonacci
- Department of Physics and Chemistry “Emilio Segrè”, University of Palermo, Palermo, Italy
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Fondazione Santa Lucia, Rome, Italy
- Department of Computer, Control and Management Engineering “Antonio Ruberti”, University of Rome “La Sapienza”, Rome, Italy
| | - Ludovico Minati
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Trento, Italy
- Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Japan
| | - Luca Faes
- Department of Engineering, University of Palermo, Palermo, Italy
| | - Riccardo Pernice
- Department of Engineering, University of Palermo, Palermo, Italy
| | - Giandomenico Nollo
- Department of Industrial Engineering, University of Trento, Trento, Italy
| | - Jlenia Toppi
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Fondazione Santa Lucia, Rome, Italy
- Department of Computer, Control and Management Engineering “Antonio Ruberti”, University of Rome “La Sapienza”, Rome, Italy
| | - Antonio Pietrabissa
- Department of Computer, Control and Management Engineering “Antonio Ruberti”, University of Rome “La Sapienza”, Rome, Italy
| | - Laura Astolfi
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Fondazione Santa Lucia, Rome, Italy
- Department of Computer, Control and Management Engineering “Antonio Ruberti”, University of Rome “La Sapienza”, Rome, Italy
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9
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Mukli P, Nagy Z, Racz FS, Portoro I, Hartmann A, Stylianou O, Debreczeni R, Bereczki D, Eke A. Two-Tiered Response of Cardiorespiratory-Cerebrovascular Network to Orthostatic Challenge. Front Physiol 2021; 12:622569. [PMID: 33737882 PMCID: PMC7960776 DOI: 10.3389/fphys.2021.622569] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Accepted: 02/08/2021] [Indexed: 12/23/2022] Open
Abstract
Dynamic interdependencies within and between physiological systems and subsystems are key for homeostatic mechanisms to establish an optimal state of the organism. These interactions mediate regulatory responses elicited by various perturbations, such as the high-pressure baroreflex and cerebral autoregulation, alleviating the impact of orthostatic stress on cerebral hemodynamics and oxygenation. The aim of this study was to evaluate the responsiveness of the cardiorespiratory-cerebrovascular networks by capturing linear and nonlinear interdependencies to postural changes. Ten young healthy adults participated in our study. Non-invasive measurements of arterial blood pressure (from that cardiac cycle durations were derived), breath-to-breath interval, cerebral blood flow velocity (BFV, recorded by transcranial Doppler sonography), and cerebral hemodynamics (HbT, total hemoglobin content monitored by near-infrared spectroscopy) were performed for 30-min in resting state, followed by a 1-min stand-up and a 1-min sit-down period. During preprocessing, noise was filtered and the contribution of arterial blood pressure was regressed from BFV and HbT signals. Cardiorespiratory-cerebrovascular networks were reconstructed by computing pair-wise Pearson-correlation or mutual information between the resampled signals to capture their linear and/or nonlinear interdependencies, respectively. The interdependencies between cardiac, respiratory, and cerebrovascular dynamics showed a marked weakening after standing up persisting throughout the sit-down period, which could mainly be attributed to strikingly attenuated nonlinear coupling. To summarize, we found that postural changes induced topological changes in the cardiorespiratory-cerebrovascular network. The dissolution of nonlinear networks suggests that the complexity of key homeostatic mechanisms maintaining cerebral hemodynamics and oxygenation is indeed sensitive to physiological perturbations such as orthostatic stress.
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Affiliation(s)
- Peter Mukli
- Department of Physiology, Semmelweis University, Budapest, Hungary.,Vascular Cognitive Impairment and Neurodegeneration Program, Oklahoma Center for Geroscience and Healthy Brain Aging, Department of Biochemistry and Molecular Biology, University of Oklahoma Health Sciences Center, Oklahoma City, OK, United States.,Institute of Translational Medicine, Semmelweis University, Budapest, Hungary
| | - Zoltan Nagy
- Institute of Translational Medicine, Semmelweis University, Budapest, Hungary
| | | | - Istvan Portoro
- Institute of Translational Medicine, Semmelweis University, Budapest, Hungary
| | - Andras Hartmann
- Institute of Translational Medicine, Semmelweis University, Budapest, Hungary.,Institute for Globally Distributed Open Research and Education (IGDORE), Stockholm, Sweden
| | - Orestis Stylianou
- Department of Physiology, Semmelweis University, Budapest, Hungary.,Institute of Translational Medicine, Semmelweis University, Budapest, Hungary
| | | | - Daniel Bereczki
- Department of Neurology, Semmelweis University, Budapest, Hungary
| | - Andras Eke
- Department of Physiology, Semmelweis University, Budapest, Hungary.,Institute of Translational Medicine, Semmelweis University, Budapest, Hungary.,Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, CT, United States
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Assessment of Outliers and Detection of Artifactual Network Segments Using Univariate and Multivariate Dispersion Entropy on Physiological Signals. ENTROPY 2021; 23:e23020244. [PMID: 33672557 PMCID: PMC7923758 DOI: 10.3390/e23020244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 02/10/2021] [Accepted: 02/12/2021] [Indexed: 11/16/2022]
Abstract
Network physiology has emerged as a promising paradigm for the extraction of clinically relevant information from physiological signals by moving from univariate to multivariate analysis, allowing for the inspection of interdependencies between organ systems. However, for its successful implementation, the disruptive effects of artifactual outliers, which are a common occurrence in physiological recordings, have to be studied, quantified, and addressed. Within the scope of this study, we utilize Dispersion Entropy (DisEn) to initially quantify the capacity of outlier samples to disrupt the values of univariate and multivariate features extracted with DisEn from physiological network segments consisting of synchronised, electroencephalogram, nasal respiratory, blood pressure, and electrocardiogram signals. The DisEn algorithm is selected due to its efficient computation and good performance in the detection of changes in signals for both univariate and multivariate time-series. The extracted features are then utilised for the training and testing of a logistic regression classifier in univariate and multivariate configurations in an effort to partially automate the detection of artifactual network segments. Our results indicate that outlier samples cause significant disruption in the values of extracted features with multivariate features displaying a certain level of robustness based on the number of signals formulating the network segments from which they are extracted. Furthermore, the deployed classifiers achieve noteworthy performance, where the percentage of correct network segment classification surpasses 95% in a number of experimental setups, with the effectiveness of each configuration being affected by the signal in which outliers are located. Finally, due to the increase in the number of features extracted within the framework of network physiology and the observed impact of artifactual samples in the accuracy of their values, the implementation of algorithmic steps capable of effective feature selection is highlighted as an important area for future research.
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Pernice R, Antonacci Y, Zanetti M, Busacca A, Marinazzo D, Faes L, Nollo G. Multivariate Correlation Measures Reveal Structure and Strength of Brain-Body Physiological Networks at Rest and During Mental Stress. Front Neurosci 2021; 14:602584. [PMID: 33613173 PMCID: PMC7890264 DOI: 10.3389/fnins.2020.602584] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 12/16/2020] [Indexed: 12/13/2022] Open
Abstract
In this work, we extend to the multivariate case the classical correlation analysis used in the field of network physiology to probe dynamic interactions between organ systems in the human body. To this end, we define different correlation-based measures of the multivariate interaction (MI) within and between the brain and body subnetworks of the human physiological network, represented, respectively, by the time series of δ, θ, α, and β electroencephalographic (EEG) wave amplitudes, and of heart rate, respiration amplitude, and pulse arrival time (PAT) variability (η, ρ, π). MI is computed: (i) considering all variables in the two subnetworks to evaluate overall brain-body interactions; (ii) focusing on a single target variable and dissecting its global interaction with all other variables into contributions arising from the same subnetwork and from the other subnetwork; and (iii) considering two variables conditioned to all the others to infer the network topology. The framework is applied to the time series measured from the EEG, electrocardiographic (ECG), respiration, and blood volume pulse (BVP) signals recorded synchronously via wearable sensors in a group of healthy subjects monitored at rest and during mental arithmetic and sustained attention tasks. We find that the human physiological network is highly connected, with predominance of the links internal of each subnetwork (mainly η-ρ and δ-θ, θ-α, α-β), but also statistically significant interactions between the two subnetworks (mainly η-β and η-δ). MI values are often spatially heterogeneous across the scalp and are modulated by the physiological state, as indicated by the decrease of cardiorespiratory interactions during sustained attention and by the increase of brain-heart interactions and of brain-brain interactions at the frontal scalp regions during mental arithmetic. These findings illustrate the complex and multi-faceted structure of interactions manifested within and between different physiological systems and subsystems across different levels of mental stress.
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Affiliation(s)
- Riccardo Pernice
- Department of Engineering, University of Palermo, Palermo, Italy
| | - Yuri Antonacci
- Department of Physics and Chemistry “Emilio Segrè,” University of Palermo, Palermo, Italy
| | - Matteo Zanetti
- Department of Industrial Engineering, University of Trento, Trento, Italy
| | | | | | - Luca Faes
- Department of Engineering, University of Palermo, Palermo, Italy
| | - Giandomenico Nollo
- Department of Industrial Engineering, University of Trento, Trento, Italy
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Kaposzta Z, Stylianou O, Mukli P, Eke A, Racz FS. Decreased connection density and modularity of functional brain networks during n-back working memory paradigm. Brain Behav 2021; 11:e01932. [PMID: 33185986 PMCID: PMC7821619 DOI: 10.1002/brb3.1932] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 10/05/2020] [Accepted: 10/18/2020] [Indexed: 12/17/2022] Open
Abstract
INTRODUCTION Investigating how the brain adapts to increased mental workload through large-scale functional reorganization appears as an important research question. Functional connectivity (FC) aims at capturing how disparate regions of the brain dynamically interact, while graph theory provides tools for the topological characterization of the reconstructed functional networks. Although numerous studies investigated how FC is altered in response to increased working memory (WM) demand, current results are still contradictory as few studies confirmed the robustness of these findings in a low-density setting. METHODS In this study, we utilized the n-back WM paradigm, in which subjects were presented stimuli (single digits) sequentially, and their task was to decide for each given stimulus if it matched the one presented n-times earlier. Electroencephalography recordings were performed under a control (0-back) and two task conditions of varying difficulty (2- and 3-back). We captured the characteristic connectivity patterns for each difficulty level by performing FC analysis and described the reconstructed functional networks with various graph theoretical measures. RESULTS We found a substantial decrease in FC when transitioning from the 0- to the 2- or 3-back conditions, however, no differences relating to task difficulty were identified. The observed changes in brain network topology could be attributed to the dissociation of two (frontal and occipitotemporal) functional modules that were only present during the control condition. Furthermore, behavioral and performance measures showed both positive and negative correlations to connectivity indices, although only in the higher frequency bands. CONCLUSION The marked decrease in FC may be due to temporarily abandoned connections that are redundant or irrelevant in solving the specific task. Our results indicate that FC analysis is a robust tool for investigating the response of the brain to increased cognitive workload.
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Affiliation(s)
- Zalan Kaposzta
- Department of Physiology, Semmelweis University, Budapest, Hungary
| | | | - Peter Mukli
- Department of Physiology, Semmelweis University, Budapest, Hungary
| | - Andras Eke
- Department of Physiology, Semmelweis University, Budapest, Hungary
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Antonacci Y, Astolfi L, Nollo G, Faes L. Information Transfer in Linear Multivariate Processes Assessed through Penalized Regression Techniques: Validation and Application to Physiological Networks. ENTROPY (BASEL, SWITZERLAND) 2020; 22:E732. [PMID: 33286504 PMCID: PMC7517272 DOI: 10.3390/e22070732] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Revised: 06/16/2020] [Accepted: 06/26/2020] [Indexed: 01/28/2023]
Abstract
The framework of information dynamics allows the dissection of the information processed in a network of multiple interacting dynamical systems into meaningful elements of computation that quantify the information generated in a target system, stored in it, transferred to it from one or more source systems, and modified in a synergistic or redundant way. The concepts of information transfer and modification have been recently formulated in the context of linear parametric modeling of vector stochastic processes, linking them to the notion of Granger causality and providing efficient tools for their computation based on the state-space (SS) representation of vector autoregressive (VAR) models. Despite their high computational reliability these tools still suffer from estimation problems which emerge, in the case of low ratio between data points available and the number of time series, when VAR identification is performed via the standard ordinary least squares (OLS). In this work we propose to replace the OLS with penalized regression performed through the Least Absolute Shrinkage and Selection Operator (LASSO), prior to computation of the measures of information transfer and information modification. First, simulating networks of several coupled Gaussian systems with complex interactions, we show that the LASSO regression allows, also in conditions of data paucity, to accurately reconstruct both the underlying network topology and the expected patterns of information transfer. Then we apply the proposed VAR-SS-LASSO approach to a challenging application context, i.e., the study of the physiological network of brain and peripheral interactions probed in humans under different conditions of rest and mental stress. Our results, which document the possibility to extract physiologically plausible patterns of interaction between the cardiovascular, respiratory and brain wave amplitudes, open the way to the use of our new analysis tools to explore the emerging field of Network Physiology in several practical applications.
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Affiliation(s)
- Yuri Antonacci
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, 00185 Rome, Italy;
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Fondazione Santa Lucia, 00179 Rome, Italy
| | - Laura Astolfi
- Department of Computer, Control and Management Engineering, Sapienza University of Rome, 00185 Rome, Italy;
- Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Fondazione Santa Lucia, 00179 Rome, Italy
| | - Giandomenico Nollo
- Department of Industrial Engineering, University of Trento, 38123 Trento, Italy;
| | - Luca Faes
- Department of Engineering, University of Palermo, 90128 Palermo, Italy;
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Pernice R, Zanetti M, Nollo G, De Cecco M, Busacca A, Faes L. Mutual Information Analysis of Brain-Body Interactions during different Levels of Mental stress .. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:6176-6179. [PMID: 31947253 DOI: 10.1109/embc.2019.8856711] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
In this work, we analyze brain-heart interactions during different mental states computing mutual information (MI) between the dynamic activity of different physiological systems. In 18 healthy subjects monitored in a relaxed resting state and during a mental arithmetic and a serious game task, multichannel EEG, one lead ECG, respiration and blood volume pulse were collected via wireless non-invasive biosensors. From these signals, synchronous 300-second time series were extracted measuring brain activity via the δ, θ, α, and β EEG power, and activity of the body district via the ECG R-R interval η, the respiratory amplitude ρ and the pulse arrival time π. MI was computed using a linear estimator: (i) between {η,ρ,π} and {δ,θ,α,β}, to measure overall brain-body interactions; (ii) between each time series and the others of the same district, to measure information shared within a district; and (iii) between each time series of a district and all series of the other district, to evaluate individual contributions to the information shared between brain and body. Results document the existence of statistically significant brain-body interactions, with high MI values involving mainly the η body dynamics and the δ and β brain dynamics. State-dependent variations were mostly relevant to the MI of the brain system involving δ, θ, α during mental arithmetic, and α and β during serious game. Thus, MI can be useful to detect correlated activity within and between brain and body systems monitored simultaneously during different mental states.
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Martins A, Pernice R, Amado C, Rocha AP, Silva ME, Javorka M, Faes L. Multivariate and Multiscale Complexity of Long-Range Correlated Cardiovascular and Respiratory Variability Series. ENTROPY 2020; 22:e22030315. [PMID: 33286089 PMCID: PMC7516773 DOI: 10.3390/e22030315] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 03/09/2020] [Accepted: 03/09/2020] [Indexed: 11/16/2022]
Abstract
Assessing the dynamical complexity of biological time series represents an important topic with potential applications ranging from the characterization of physiological states and pathological conditions to the calculation of diagnostic parameters. In particular, cardiovascular time series exhibit a variability produced by different physiological control mechanisms coupled with each other, which take into account several variables and operate across multiple time scales that result in the coexistence of short term dynamics and long-range correlations. The most widely employed technique to evaluate the dynamical complexity of a time series at different time scales, the so-called multiscale entropy (MSE), has been proven to be unsuitable in the presence of short multivariate time series to be analyzed at long time scales. This work aims at overcoming these issues via the introduction of a new method for the assessment of the multiscale complexity of multivariate time series. The method first exploits vector autoregressive fractionally integrated (VARFI) models to yield a linear parametric representation of vector stochastic processes characterized by short- and long-range correlations. Then, it provides an analytical formulation, within the theory of state-space models, of how the VARFI parameters change when the processes are observed across multiple time scales, which is finally exploited to derive MSE measures relevant to the overall multivariate process or to one constituent scalar process. The proposed approach is applied on cardiovascular and respiratory time series to assess the complexity of the heart period, systolic arterial pressure and respiration variability measured in a group of healthy subjects during conditions of postural and mental stress. Our results document that the proposed methodology can detect physiologically meaningful multiscale patterns of complexity documented previously, but can also capture significant variations in complexity which cannot be observed using standard methods that do not take into account long-range correlations.
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Affiliation(s)
- Aurora Martins
- Faculdade de Ciências, Universidade do Porto, Rua Campo Alegre, 4169-007 Porto, Portugal; (A.M.); (C.A.); (A.P.R.)
- Centro de Matemática da Universidade do Porto (CMUP), 4169-007 Porto, Portugal
| | - Riccardo Pernice
- Department of Engineering, University of Palermo, Viale delle Scienze, Bldg. 9, 90128 Palermo, Italy;
- Correspondence:
| | - Celestino Amado
- Faculdade de Ciências, Universidade do Porto, Rua Campo Alegre, 4169-007 Porto, Portugal; (A.M.); (C.A.); (A.P.R.)
| | - Ana Paula Rocha
- Faculdade de Ciências, Universidade do Porto, Rua Campo Alegre, 4169-007 Porto, Portugal; (A.M.); (C.A.); (A.P.R.)
- Centro de Matemática da Universidade do Porto (CMUP), 4169-007 Porto, Portugal
| | - Maria Eduarda Silva
- Faculdade de Economia, Universidade do Porto, Rua Dr. Roberto Frias, 4169-007 Porto, Portugal;
- Centro de Investigação e Desenvolvimento em Matemática e Aplicações (CIDMA)
| | - Michal Javorka
- Department of Physiology, Comenius University in Bratislava, Jessenius Faculty of Medicine, Mala Hora 4C, 03601 Martin, Slovakia;
- Biomedical Center Martin, Comenius University in Bratislava, Jessenius Faculty of Medicine, Mala Hora 4C, 03601 Martin, Slovakia
| | - Luca Faes
- Department of Engineering, University of Palermo, Viale delle Scienze, Bldg. 9, 90128 Palermo, Italy;
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Saleem S, Saeed A, Usman S, Ferzund J, Arshad J, Mirza J, Manzoor T. Granger causal analysis of electrohysterographic and tocographic recordings for classification of term vs. preterm births. Biocybern Biomed Eng 2020. [DOI: 10.1016/j.bbe.2020.01.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Boonstra TW, Faes L, Kerkman JN, Marinazzo D. Information decomposition of multichannel EMG to map functional interactions in the distributed motor system. Neuroimage 2019; 202:116093. [DOI: 10.1016/j.neuroimage.2019.116093] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Revised: 07/12/2019] [Accepted: 08/09/2019] [Indexed: 01/21/2023] Open
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