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Scarciglia A, Catrambone V, Bianco M, Bonanno C, Toschi N, Valenza G. Stochastic brain dynamics exhibits differential regional distribution and maturation-related changes. Neuroimage 2024; 290:120562. [PMID: 38484917 DOI: 10.1016/j.neuroimage.2024.120562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 02/29/2024] [Accepted: 03/01/2024] [Indexed: 03/18/2024] Open
Abstract
Functional magnetic resonance imaging (fMRI) is a powerful non-invasive method for studying brain function by analyzing blood oxygenation level-dependent (BOLD) signals. These signals arise from intricate interplays of deterministic and stochastic biological elements. Quantifying the stochastic part is challenging due to its reliance on assumptions about the deterministic segment. We present a methodological framework to estimate intrinsic stochastic brain dynamics in fMRI data without assuming deterministic dynamics. Our approach utilizes Approximate Entropy and its behavior in noisy series to identify and characterize dynamical noise in unobservable fMRI dynamics. Applied to extensive fMRI datasets (645 Cam-CAN, 1086 Human Connectome Project subjects), we explore lifelong maturation of intrinsic brain noise. Findings indicate 10% to 60% of fMRI signal power is due to intrinsic stochastic brain elements, varying by age. These components demonstrate a physiological role of neural noise which shows a distinct distributions across brain regions and increase linearly during maturation.
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Affiliation(s)
- Andrea Scarciglia
- Department of Information Engineering, School of Engineering, University of Pisa, Italy; Bioengineering and Robotics Research Center E.Piaggio, School of Engineering, University of Pisa, Italy.
| | - Vincenzo Catrambone
- Department of Information Engineering, School of Engineering, University of Pisa, Italy; Bioengineering and Robotics Research Center E.Piaggio, School of Engineering, University of Pisa, Italy
| | - Martina Bianco
- Department of Information Engineering, School of Engineering, University of Pisa, Italy; Department of Mathematics, University of Pisa, Italy
| | | | - Nicola Toschi
- Department of Biomedicine and Prevention, University of Rome "Tor Vergata", Rome, Italy; A.A. Martinos Center for Biomedical Imaging, MGH and Harvard Medical School, Boston, MA, USA
| | - Gaetano Valenza
- Department of Information Engineering, School of Engineering, University of Pisa, Italy; Bioengineering and Robotics Research Center E.Piaggio, School of Engineering, University of Pisa, Italy
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Malandrone F, Catrambone V, Carletto S, Rossini PG, Coletti Moja M, Oliva F, Pagani M, Valenza G, Ostacoli L. Restoring bottom-up communication in brain-heart interplay after trauma-focused psychotherapy in breast cancer patients with post-traumatic stress disorder. J Affect Disord 2024; 351:143-150. [PMID: 38281599 DOI: 10.1016/j.jad.2024.01.172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 05/18/2023] [Accepted: 01/17/2024] [Indexed: 01/30/2024]
Abstract
BACKGROUND The psychological impact of breast cancer (BC) is substantial, with a significant number of patients (up to 32 %) experiencing post-traumatic stress disorder (PTSD). Exploring the emotional aspects of PTSD through the functional brain-heart interplay (BHI) offers valuable insights into the condition. BHI examines the functional interactions between cortical and sympathovagal dynamics. This study aims to investigate changes in functional directional BHI after trauma-focused (TF) psychotherapy, specifically Eye Movement Desensitization and Reprocessing (EMDR), in comparison to treatment as usual (TAU) among BC patients with PTSD. To our knowledge, this study represents the first examination of such changes. METHODS We enrolled thirty BC patients who met the criteria for a PTSD diagnosis, with fourteen receiving EMDR and fifteen receiving TAU over a two- to three-month period. We analyzed changes in the emotional response during a script-driven imagery setting. Quantification of the functional interplay between EEG and sympathovagal dynamics was achieved using the synthetic data generation model (SDG) on electroencephalographic (EEG) and heartbeat series. Our focus was on the difference in the BHI index extracted at baseline and post-treatment. RESULTS We found statistically significant higher coupling in the heart-to-brain direction in patients treated with EMDR compared to controls. This suggests that the flow of information from the autonomic nervous system to the central nervous system is restored following EMDR-induced recovery from PTSD. Furthermore, we observed a significant correlation between improvements in PTSD symptoms and an increase in functional BHI after EMDR treatment. CONCLUSIONS TF psychotherapy, particularly EMDR, appears to facilitate the restoration of the bottom-up flow of interoceptive information, which is dysfunctional in patients with PTSD. The application of BHI analysis to the study of PTSD not only aids in identifying biomarkers of the disorder but also enhances our understanding of the changes brought about by TF treatments.
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Affiliation(s)
- F Malandrone
- Department of Clinical and Biological Sciences, University of Turin, Italy
| | - V Catrambone
- NeuroCardiovascular Intelligence Lab, Department of Information Engineering & Research Centre "E. Piaggio", School of Engineering, University of Pisa, Italy
| | - S Carletto
- Department of Clinical and Biological Sciences, University of Turin, Italy.
| | - P G Rossini
- Department of Clinical and Biological Sciences, University of Turin, Italy
| | - M Coletti Moja
- Neurology Department, Ospedale degli Infermi, Ponderano, Italy
| | - F Oliva
- Department of Clinical and Biological Sciences, University of Turin, Italy
| | - M Pagani
- Institute of Cognitive Sciences and Technologies, Consiglio Nazionale delle Ricerche, Rome, Italy
| | - G Valenza
- NeuroCardiovascular Intelligence Lab, Department of Information Engineering & Research Centre "E. Piaggio", School of Engineering, University of Pisa, Italy
| | - L Ostacoli
- Department of Clinical and Biological Sciences, University of Turin, Italy
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Catrambone V, Candia‐Rivera D, Valenza G. Intracortical brain-heart interplay: An EEG model source study of sympathovagal changes. Hum Brain Mapp 2024; 45:e26677. [PMID: 38656080 PMCID: PMC11041380 DOI: 10.1002/hbm.26677] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 02/18/2024] [Accepted: 03/23/2024] [Indexed: 04/26/2024] Open
Abstract
The interplay between cerebral and cardiovascular activity, known as the functional brain-heart interplay (BHI), and its temporal dynamics, have been linked to a plethora of physiological and pathological processes. Various computational models of the brain-heart axis have been proposed to estimate BHI non-invasively by taking advantage of the time resolution offered by electroencephalograph (EEG) signals. However, investigations into the specific intracortical sources responsible for this interplay have been limited, which significantly hampers existing BHI studies. This study proposes an analytical modeling framework for estimating the BHI at the source-brain level. This analysis relies on the low-resolution electromagnetic tomography sources localization from scalp electrophysiological recordings. BHI is then quantified as the functional correlation between the intracortical sources and cardiovascular dynamics. Using this approach, we aimed to evaluate the reliability of BHI estimates derived from source-localized EEG signals as compared with prior findings from neuroimaging methods. The proposed approach is validated using an experimental dataset gathered from 32 healthy individuals who underwent standard sympathovagal elicitation using a cold pressor test. Additional resting state data from 34 healthy individuals has been analysed to assess robustness and reproducibility of the methodology. Experimental results not only confirmed previous findings on activation of brain structures affecting cardiac dynamics (e.g., insula, amygdala, hippocampus, and anterior and mid-cingulate cortices) but also provided insights into the anatomical bases of brain-heart axis. In particular, we show that the bidirectional activity of electrophysiological pathways of functional brain-heart communication increases during cold pressure with respect to resting state, mainly targeting neural oscillations in theδ $$ \delta $$ ,β $$ \beta $$ , andγ $$ \gamma $$ bands. The proposed approach offers new perspectives for the investigation of functional BHI that could also shed light on various pathophysiological conditions.
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Affiliation(s)
- Vincenzo Catrambone
- Neurocardiovascular Intelligence Laboratory & Department of Information Engineering & Bioengineering and Robotics Research Center, E. Piaggio, School of EngineeringUniversity of PisaPisaItaly
| | - Diego Candia‐Rivera
- Sorbonne Université, Paris Brain Institute (ICM), INRIA, CNRS, INSERM, AP‐HP, Hôpital Pitié‐SalpêtriŕeParisFrance
| | - Gaetano Valenza
- Neurocardiovascular Intelligence Laboratory & Department of Information Engineering & Bioengineering and Robotics Research Center, E. Piaggio, School of EngineeringUniversity of PisaPisaItaly
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Catrambone V, Zallocco L, Ramoretti E, Mazzoni MR, Sebastiani L, Valenza G. Integrative neuro-cardiovascular dynamics in response to test anxiety: A brain-heart axis study. Physiol Behav 2024; 276:114460. [PMID: 38215864 DOI: 10.1016/j.physbeh.2024.114460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 12/08/2023] [Accepted: 01/08/2024] [Indexed: 01/14/2024]
Abstract
Test anxiety (TA), a recognized form of social anxiety, is the most prominent cause of anxiety among students and, if left unmanaged, can escalate to psychiatric disorders. TA profoundly impacts both central and autonomic nervous systems, presenting as a dual manifestation of cognitive and autonomic components. While limited studies have explored the physiological underpinnings of TA, none have directly investigated the intricate interplay between the CNS and ANS in this context. In this study, we introduce a non-invasive, integrated neuro-cardiovascular approach to comprehensively characterize the physiological responses of 27 healthy subjects subjected to test anxiety induced via a simulated exam scenario. Our experimental findings highlight that an isolated analysis of electroencephalographic and heart rate variability data fails to capture the intricate information provided by a brain-heart axis assessment, which incorporates an analysis of the dynamic interaction between the brain and heart. With respect to resting state, the simulated examination induced a decrease in the neural control onto heartbeat dynamics at all frequencies, while the studying condition induced a decrease in the ascending heart-to-brain interplay at EEG oscillations up to 12Hz. This underscores the significance of adopting a multisystem perspective in understanding the complex and especially functional directional mechanisms underlying test anxiety.
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Affiliation(s)
- Vincenzo Catrambone
- Neurocardiovascular Intelligence Laboratory, Department of Information Engineering & Bioengineering and Robotics Research Center E. Piaggio, School of Engineering, University of Pisa, Pisa, Italy.
| | - Lorenzo Zallocco
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Eleonora Ramoretti
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Maria Rosa Mazzoni
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Laura Sebastiani
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy; Institute of Information Science and Technologies A. Faedo, ISTI-CNR, Pisa, Italy
| | - Gaetano Valenza
- Neurocardiovascular Intelligence Laboratory, Department of Information Engineering & Bioengineering and Robotics Research Center E. Piaggio, School of Engineering, University of Pisa, Pisa, Italy
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Scarciglia A, Catrambone V, Bonanno C, Valenza G. Physiological Noise: Definition, Estimation, and Characterization in Complex Biomedical Signals. IEEE Trans Biomed Eng 2024; 71:45-55. [PMID: 37399153 DOI: 10.1109/tbme.2023.3291538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/05/2023]
Abstract
BACKGROUND Nonlinear physiological systems exhibit complex dynamics driven by intrinsic dynamical noise. In cases where there is no specific knowledge or assumption about system dynamics, such as in physiological systems, it is not possible to formally estimate noise. AIM We introduce a formal method to estimate the power of dynamical noise, referred to as physiological noise, in a closed form, without specific knowledge of the system dynamics. METHODOLOGY Assuming that noise can be modeled as a sequence of independent, identically distributed (IID) random variables on a probability space, we demonstrate that physiological noise can be estimated through a nonlinear entropy profile. We estimated noise from synthetic maps that included autoregressive, logistic, and Pomeau-Manneville systems under various conditions. Noise estimation is performed on 70 heart rate variability series from healthy and pathological subjects, and 32 electroencephalographic (EEG) healthy series. RESULTS Our results showed that the proposed model-free method can discern different noise levels without any prior knowledge of the system dynamics. Physiological noise accounts for around 11% of the overall power observed in EEG signals and approximately 32% to 65% of the power related to heartbeat dynamics. Cardiovascular noise increases in pathological conditions compared to healthy dynamics, and cortical brain noise increases during mental arithmetic computations over the prefrontal and occipital regions. Brain noise is differently distributed across cortical regions. CONCLUSION Physiological noise is very part neurobiological dynamics and can be measured using the proposed framework in any biomedical series.
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Abstract
Electroencephalographic (EEG) microstates are brain states with quasi-stable scalp topography. Whether such states extend to the body level, that is, the peripheral autonomic nerves, remains unknown. We hypothesized that microstates extend at the brain-heart axis level as a functional state of the central autonomic network. Thus, we combined the EEG and heartbeat dynamics series to estimate the directional information transfer originating in the cortex targeting the sympathovagal and parasympathetic activity oscillations and vice versa for the afferent functional direction. Data were from two groups of participants: 36 healthy volunteers who were subjected to cognitive workload induced by mental arithmetic, and 26 participants who underwent physical stress induced by a cold pressure test. All participants were healthy at the time of the study. Based on statistical testing and goodness-of-fit evaluations, we demonstrated the existence of microstates of the functional brain-heart axis, with emphasis on the cerebral cortex, since the microstates are derived from EEG. Such nervous-system microstates are spatio-temporal quasi-stable states that exclusively refer to the efferent brain-to-heart direction. We demonstrated brain-heart microstates that could be associated with specific experimental conditions as well as brain-heart microstates that are non-specific to tasks.
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Affiliation(s)
- Vincenzo Catrambone
- Neurocardiovascular Intelligence Laboratory, Bioengineering and Robotics Research Center E. Piaggio, & Department of Information Engineering, School of EngineeringUniversity of PisaPisaItaly
| | - Gaetano Valenza
- Neurocardiovascular Intelligence Laboratory, Bioengineering and Robotics Research Center E. Piaggio, & Department of Information Engineering, School of EngineeringUniversity of PisaPisaItaly
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Ghouse A, Candia-Rivera D, Valenza G. Nonlinear neural patterns are revealed in high frequency functional near infrared spectroscopy analysis. Brain Res Bull 2023; 203:110759. [PMID: 37716513 DOI: 10.1016/j.brainresbull.2023.110759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 07/29/2023] [Accepted: 09/11/2023] [Indexed: 09/18/2023]
Abstract
Functional Near Infrared Spectroscopy (fNIRS) is a useful tool for measuring hemoglobin concentration. Linear theory of the hemodynamic response function supports low frequency analysis (<0.2 Hz). However, we hypothesized that nonlinearities, arising from the complex neurovascular interactions sustaining vasomotor tone, may be revealed in higher frequency components of fNIRS signals. To test this hypothesis, we simulated nonlinear hemodynamic models to explore how blood flow autoregulation changes may alter evoked neurovascular signals in high frequencies. Next, we analyzed experimental fNIRS data to compare neural representations between fast (0.2-0.6 Hz) and slow (<0.2 Hz) waves, demonstrating that only nonlinear representations quantified by sample entropy are distinct between these frequency bands. Finally, we performed group-level distance correlation analysis to show that the cortical distribution of activity is independent only in the nonlinear analysis of fast and slow waves. Our study highlights the importance of analyzing nonlinear higher frequency effects seen in fNIRS for a comprehensive analysis of cortical neurovascular activity. Furthermore, it motivates further exploration of the nonlinear dynamics driving regional blood flow and hemoglobin concentrations.
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Affiliation(s)
- Ameer Ghouse
- Bioengineering and Robotics Research Center "E. Piaggio", School of Engineering, University of Pisa, Italy.
| | - Diego Candia-Rivera
- Sorbonne Université, Paris Brain Institute (ICM), INRIA, CNRS, INSERM, AP-HP, Hôpital Pitié-Salpêtrière, Paris, France
| | - Gaetano Valenza
- Department of Information Engineering, School of Engineering, University of Pisa, Italy; Bioengineering and Robotics Research Center "E. Piaggio", School of Engineering, University of Pisa, Italy.
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De-Pra Y, Catrambone V, van-Wassenhove V, Moscatelli A, Valenza G, Bianchi M. Altering Time Perception in Virtual Reality Through Multimodal Visual-Tactile Kappa Effect. IEEE Trans Haptics 2023; 16:518-523. [PMID: 37099460 DOI: 10.1109/toh.2023.3270639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
The perception of time is highly subjective and intertwined with space perception. In a well-known perceptual illusion, called Kappa effect, the distance between consecutive stimuli is modified to induce time distortions in the perceived inter-stimulus interval that are proportional to the distance between the stimuli. However, to the best of our knowledge, this effect has not been characterized and exploited in virtual reality (VR) within a multisensory elicitation framework. This paper investigates the Kappa effect elicited by concurrent visual-tactile stimuli delivered to the forearm, through a multimodal VR interface. This paper compares the outcomes of an experiment in VR with the results of the same experiment performed in the "physical world", where a multimodal interface was applied to participants' forearm to deliver controlled visual-tactile stimuli. Our results suggest that a multimodal Kappa effect can be elicited both in VR and in the physical world relying on concurrent visual-tactile stimulation. Moreover, our results confirm the existence of a relation between the ability of participants in discriminating the duration of time intervals and the magnitude of the experienced Kappa effect. These outcomes can be exploited to modulate the subjective perception of time in VR, paving the path toward more personalised human-computer interaction.
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Petros NG, Alvarsson-Hjort J, Hadlaczky G, Wasserman D, Ottaviano M, Gonzalez-Martinez S, Carletto S, Scilingo EP, Valenza G, Carli V. Predictors of the Use of a Mental Health-Focused eHealth System in Patients With Breast and Prostate Cancer: Bayesian Structural Equation Modeling Analysis of a Prospective Study. JMIR Cancer 2023; 9:e49775. [PMID: 37698900 PMCID: PMC10523218 DOI: 10.2196/49775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 07/19/2023] [Accepted: 08/21/2023] [Indexed: 09/13/2023] Open
Abstract
BACKGROUND eHealth systems have been increasingly used to manage depressive symptoms in patients with somatic illnesses. However, understanding the factors that drive their use, particularly among patients with breast and prostate cancer, remains a critical area of research. OBJECTIVE This study aimed to determine the factors influencing use of the NEVERMIND eHealth system among patients with breast and prostate cancer over 12 weeks, with a focus on the Technology Acceptance Model. METHODS Data from the NEVERMIND trial, which included 129 patients with breast and prostate cancer, were retrieved. At baseline, participants completed questionnaires detailing demographic data and measuring depressive and stress symptoms using the Beck Depression Inventory-II and the Depression, Anxiety, and Stress Scale-21, respectively. Over a 12-week period, patients engaged with the NEVERMIND system, with follow-up questionnaires administered at 4 weeks and after 12 weeks assessing the system's perceived ease of use and usefulness. Use log data were collected at the 2- and 12-week marks. The relationships among sex, education, baseline depressive and stress symptoms, perceived ease of use, perceived usefulness (PU), and system use at various stages were examined using Bayesian structural equation modeling in a path analysis, a technique that differs from traditional frequentist methods. RESULTS The path analysis was conducted among 100 patients with breast and prostate cancer, with 66% (n=66) being female and 81% (n=81) having a college education. Patients reported good mental health scores, with low levels of depression and stress at baseline. System use was approximately 6 days in the initial 2 weeks and 45 days over the 12-week study period. The results revealed that PU was the strongest predictor of system use at 12 weeks (βuse at 12 weeks is predicted by PU at 12 weeks=.384), whereas system use at 2 weeks moderately predicted system use at 12 weeks (βuse at 12 weeks is predicted by use at 2 weeks=.239). Notably, there were uncertain associations between baseline variables (education, sex, and mental health symptoms) and system use at 2 weeks, indicating a need for better predictors for early system use. CONCLUSIONS This study underscores the importance of PU and early engagement in patient engagement with eHealth systems such as NEVERMIND. This suggests that, in general eHealth implementations, caregivers should educate patients about the benefits and functionalities of such systems, thus enhancing their understanding of potential health impacts. Concentrating resources on promoting early engagement is also essential given its influence on sustained use. Further research is necessary to clarify the remaining uncertainties, enabling us to refine our strategies and maximize the benefits of eHealth systems in health care settings.
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Affiliation(s)
- Nuhamin Gebrewold Petros
- National Centre for Suicide Research and Prevention of Mental Ill-Health, Department of Learning, Informatics, Management and Ethics, Karolinska Institute, Stockholm, Sweden
| | - Jesper Alvarsson-Hjort
- National Centre for Suicide Research and Prevention of Mental Ill-Health, Centre for Health Economics, Informatics, and Health Services Research, Stockholm Health Care Services, Stockholm, Sweden
- Stockholm Centre for Health and Social Change, Department of Psychology, School of Social Sciences, Södertörn University, Stockholm, Sweden
| | - Gergö Hadlaczky
- National Centre for Suicide Research and Prevention of Mental Ill-Health, Department of Learning, Informatics, Management and Ethics, Karolinska Institute, Stockholm, Sweden
- National Centre for Suicide Research and Prevention of Mental Ill-Health, Centre for Health Economics, Informatics, and Health Services Research, Stockholm Health Care Services, Stockholm, Sweden
| | - Danuta Wasserman
- National Centre for Suicide Research and Prevention of Mental Ill-Health, Department of Learning, Informatics, Management and Ethics, Karolinska Institute, Stockholm, Sweden
| | - Manuel Ottaviano
- Life Supporting Technologies, Universidad Politécnica de Madrid, Madrid, Spain
| | | | - Sara Carletto
- Department of Clinical and Biological Sciences, University of Turin, Turin, Italy
| | | | - Gaetano Valenza
- Research Center "E.Piaggio", School of Engineering, University of Pisa, Pisa, Italy
| | - Vladimir Carli
- National Centre for Suicide Research and Prevention of Mental Ill-Health, Department of Learning, Informatics, Management and Ethics, Karolinska Institute, Stockholm, Sweden
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Scarciglia A, Catrambone V, Bonanno C, Valenza G. Characterization of Physiological Noise in Complex Cardiovascular Variability Series. Annu Int Conf IEEE Eng Med Biol Soc 2023; 2023:1-4. [PMID: 38082793 DOI: 10.1109/embc40787.2023.10339997] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
The cardiovascular system can be analyzed using spectral, nonlinear, and complexity metrics. Nevertheless, dynamical noise may significantly impact these quantifiers. To our knowledge, there has been no attempt to quantify the intrinsic cardiovascular system noise driving heartbeat dynamics. To this end, this study presents a novel, model-free framework to define and quantify physiological noise using nonlinear Approximate Entropy profile. The framework was tested using analytical noisy series and then applied to real Heart Rate Variability (HRV) series gathered from a publicly-available dataset of recordings from 19 young and 19 elderly subjects watching the movie "Fantasia". Results suggest that physiological noise may account for over 15% of cardiovascular dynamics and is influenced by aging, with decreased cardiac noise in the elderly compared to the young subjects. Our findings indicate that physiological noise is a crucial factor in characterizing cardiovascular dynamics, and current spectral, nonlinear, and complexity assessments should take into account underlying dynamical noise estimates.
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Catrambone V, Valenza G. A Unified Framework for Investigating Aperiodic and Periodic Components in the Hearbeat Dynamics Spectrum: a Feasibility Study. Annu Int Conf IEEE Eng Med Biol Soc 2023; 2023:1-4. [PMID: 38083473 DOI: 10.1109/embc40787.2023.10340558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Heart Rate Variability (HRV) series is a widely used, non-invasive, and easy-to-acquire time-resolved signal for evaluating autonomic control on cardiovascular activity. Despite the recognition that heartbeat dynamics contains both periodic and aperiodic components, the majority of HRV modeling studies concentrate on only one component. On the one hand, there are models based on self-similarity and 1/f behavior that focus on the aperiodic component; on the other hand, there is the conventional division of the spectral domain into narrow-band oscillations, which considers HRV as a combination of periodic components. Taking inspiration from a recent parametrization of EEG power spectra, here we evaluate the applicability of a unified modeling framework to quantitatively assess heartbeat dynamics spectra as a mixture of aperiodic and periodic components. The proposed model is applied on publicly-available, real HRV series collected during postural changes from 10 healthy subjects. Results show that the proposed modeling effectively characterizes different experimental conditions and may complement HRV standard analysis defined in the frequency domain.
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12
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Catrambone V, Valenza G. Towards the definition of Microstates of the Cortical Brain-Heart Axis. Annu Int Conf IEEE Eng Med Biol Soc 2023; 2023:1-4. [PMID: 38082600 DOI: 10.1109/embc40787.2023.10340338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Brain microstates are defined as states with quasi-stable scalp activity topography and have been widely studied in literature. Whether those states are brain-specific or extend to the body level is unknown yet. We investigate the extension of cortical microstates to the peripheral autonomic nerve, specifically at the brain-heart axis level as a functional state of the central autonomic network. To achieve this, we combined Electroencephalographic (EEG) and heart rate variability (HRV) series from 36 healthy volunteers undergoing a cognitive workload elicitation after a resting state. Our results showed the existence of microstates at the functional brain-heart axis with spatio-temporal and quasi-stable states that exclusively pertained to the efferent direction from the brain to the heart. Some of the identified microstates are specific for neural or cardiovascular frequency bands, while others topographies are recurrent over the EEG and HRV spectra. Furthermore, some of the identified brain-heart microstates were associated with specific experimental conditions, while others were nonspecific to tasks. Our findings support the hypothesis that EEG microstates extend to the brain-heart axis level and may be exploited in future neuroscience and clinical research.
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Catrambone V, Valenza G. Complex Brain-Heart Mapping in Mental and Physical Stress. IEEE J Transl Eng Health Med 2023; 11:495-504. [PMID: 37817820 PMCID: PMC10561752 DOI: 10.1109/jtehm.2023.3280974] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 04/29/2023] [Accepted: 05/25/2023] [Indexed: 10/12/2023]
Abstract
OBJECTIVE The central and autonomic nervous systems are deemed complex dynamic systems, wherein each system as a whole shows features that the individual system sub-components do not. They also continuously interact to maintain body homeostasis and appropriate react to endogenous and exogenous stimuli. Such interactions are comprehensively referred to functional brain-heart interplay (BHI). Nevertheless, it remains uncertain whether this interaction also exhibits complex characteristics, that is, whether the dynamics of the entire nervous system inherently demonstrate complex behavior, or if such complexity is solely a trait of the central and autonomic systems. Here, we performed complexity mapping of the BHI dynamics under mental and physical stress conditions. METHODS AND PROCEDURES Electroencephalographic and heart rate variability series were obtained from 56 healthy individuals performing mental arithmetic or cold-pressure tasks, and physiological series were properly combined to derive directional BHI series, whose complexity was quantified through fuzzy entropy. RESULTS The experimental results showed that BHI complexity is mainly modulated in the efferent functional direction from the brain to the heart, and mainly targets vagal oscillations during mental stress and sympathovagal oscillations during physical stress. CONCLUSION We conclude that the complexity of BHI mapping may provide insightful information on the dynamics of both central and autonomic activity, as well as on their continuous interaction. CLINICAL IMPACT This research enhances our comprehension of the reciprocal interactions between central and autonomic systems, potentially paving the way for more accurate diagnoses and targeted treatments of cardiovascular, neurological, and psychiatric disorders.
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Affiliation(s)
- Vincenzo Catrambone
- Neurocardiovascular Intelligence Laboratory, Bioengineering and Robotics Research Center E. Piaggio, and Department of Information EngineeringSchool of EngineeringUniversity of Pisa56126PisaItaly
| | - Gaetano Valenza
- Neurocardiovascular Intelligence Laboratory, Bioengineering and Robotics Research Center E. Piaggio, and Department of Information EngineeringSchool of EngineeringUniversity of Pisa56126PisaItaly
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Valenza G. Depression as a cardiovascular disorder: central-autonomic network, brain-heart axis, and vagal perspectives of low mood. Front Netw Physiol 2023; 3:1125495. [PMID: 37260560 PMCID: PMC10228690 DOI: 10.3389/fnetp.2023.1125495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 05/04/2023] [Indexed: 06/02/2023]
Abstract
If depressive symptoms are not caused by the physiological effects of a substance or other medical or neurological conditions, they are generally classified as mental disorders that target the central nervous system. However, recent evidence suggests that peripheral neural dynamics on cardiovascular control play a causal role in regulating and processing emotions. In this perspective, we explore the dynamics of the Central-Autonomic Network (CAN) and related brain-heart interplay (BHI), highlighting their psychophysiological correlates and clinical symptoms of depression. Thus, we suggest that depression may arise from dysregulated cardiac vagal and sympathovagal dynamics that lead to CAN and BHI dysfunctions. Therefore, treatments for depression should target the nervous system as a whole, with particular emphasis on regulating vagal and BHI dynamics.
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Alfeo AL, Zippo AG, Catrambone V, Cimino MGCA, Toschi N, Valenza G. From local counterfactuals to global feature importance: efficient, robust, and model-agnostic explanations for brain connectivity networks. Comput Methods Programs Biomed 2023; 236:107550. [PMID: 37086584 DOI: 10.1016/j.cmpb.2023.107550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 04/14/2023] [Accepted: 04/14/2023] [Indexed: 05/03/2023]
Abstract
BACKGROUND Explainable artificial intelligence (XAI) is a technology that can enhance trust in mental state classifications by providing explanations for the reasoning behind artificial intelligence (AI) models outputs, especially for high-dimensional and highly-correlated brain signals. Feature importance and counterfactual explanations are two common approaches to generate these explanations, but both have drawbacks. While feature importance methods, such as shapley additive explanations (SHAP), can be computationally expensive and sensitive to feature correlation, counterfactual explanations only explain a single outcome instead of the entire model. METHODS To overcome these limitations, we propose a new procedure for computing global feature importance that involves aggregating local counterfactual explanations. This approach is specifically tailored to fMRI signals and is based on the hypothesis that instances close to the decision boundary and their counterfactuals mainly differ in the features identified as most important for the downstream classification task. We refer to this proposed feature importance measure as Boundary Crossing Solo Ratio (BoCSoR), since it quantifies the frequency with which a change in each feature in isolation leads to a change in classification outcome, i.e., the crossing of the model's decision boundary. RESULTS AND CONCLUSIONS Experimental results on synthetic data and real publicly available fMRI data from the Human Connect project show that the proposed BoCSoR measure is more robust to feature correlation and less computationally expensive than state-of-the-art methods. Additionally, it is equally effective in providing an explanation for the behavior of any AI model for brain signals. These properties are crucial for medical decision support systems, where many different features are often extracted from the same physiological measures and a gold standard is absent. Consequently, computing feature importance may become computationally expensive, and there may be a high probability of mutual correlation among features, leading to unreliable results from state-of-the-art XAI methods.
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Affiliation(s)
- Antonio Luca Alfeo
- Department of Information Engineering, University of Pisa, Largo Lucio Lazzarino, 1, Pisa, 56126, Italy; Bioengineering & Robotics Research Center E. Piaggio, University of Pisa, Largo Lucio Lazzarino, 1, Pisa, 56126, Italy.
| | - Antonio G Zippo
- Institute of Neuroscience, Consiglio Nazionale delle Ricerche, Via Raoul Follereau, 3, Vedano al Lambro (MB), 20854, Italy
| | - Vincenzo Catrambone
- Department of Information Engineering, University of Pisa, Largo Lucio Lazzarino, 1, Pisa, 56126, Italy; Bioengineering & Robotics Research Center E. Piaggio, University of Pisa, Largo Lucio Lazzarino, 1, Pisa, 56126, Italy
| | - Mario G C A Cimino
- Department of Information Engineering, University of Pisa, Largo Lucio Lazzarino, 1, Pisa, 56126, Italy; Bioengineering & Robotics Research Center E. Piaggio, University of Pisa, Largo Lucio Lazzarino, 1, Pisa, 56126, Italy
| | - Nicola Toschi
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Via Montpellier 1, Roma, 00133, Italy
| | - Gaetano Valenza
- Department of Information Engineering, University of Pisa, Largo Lucio Lazzarino, 1, Pisa, 56126, Italy; Bioengineering & Robotics Research Center E. Piaggio, University of Pisa, Largo Lucio Lazzarino, 1, Pisa, 56126, Italy
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Valenza G. Specific Brain-Heart Axis-Related Pathomechanism in Heart Failure Are Revealed Through Analysis of Cardiovascular Dynamics. J Am Coll Cardiol 2023; 81:e109. [PMID: 36990551 DOI: 10.1016/j.jacc.2022.11.065] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 11/15/2022] [Indexed: 03/31/2023]
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Candia-Rivera D, Norouzi K, Ramsøy TZ, Valenza G. Dynamic fluctuations in ascending heart-to-brain communication under mental stress. Am J Physiol Regul Integr Comp Physiol 2023; 324:R513-R525. [PMID: 36802949 PMCID: PMC10026986 DOI: 10.1152/ajpregu.00251.2022] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2023]
Abstract
Dynamical information exchange between central and autonomic nervous systems, as referred to functional brain-heart interplay, occurs during emotional and physical arousal. It is well documented that physical and mental stress lead to sympathetic activation. Nevertheless, the role of autonomic inputs in nervous system-wise communication under mental stress is yet unknown. In this study, we estimated the causal and bidirectional neural modulations between electroencephalogram (EEG) oscillations and peripheral sympathetic and parasympathetic activities using a recently proposed computational framework for a functional brain-heart interplay assessment, namely the sympathovagal synthetic data generation model. Mental stress was elicited in 37 healthy volunteers by increasing their cognitive demands throughout three tasks associated with increased stress levels. Stress elicitation induced an increased variability in sympathovagal markers, as well as increased variability in the directional brain-heart interplay. The observed heart-to-brain interplay was primarily from sympathetic activity targeting a wide range of EEG oscillations, whereas variability in the efferent direction seemed mainly related to EEG oscillations in the γ band. These findings extend current knowledge on stress physiology, which mainly referred to top-down neural dynamics. Our results suggest that mental stress may not cause an increase in sympathetic activity exclusively as it initiates a dynamic fluctuation within brain-body networks including bidirectional interactions at a brain-heart level. We conclude that directional brain-heart interplay measurements may provide suitable biomarkers for a quantitative stress assessment and bodily feedback may modulate the perceived stress caused by increased cognitive demand.
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Affiliation(s)
- Diego Candia-Rivera
- Department of Information Engineering & Bioengineering and Robotics Research Center E. Piaggio, School of Engineering, University of Pisa, Pisa, Italy
| | - Kian Norouzi
- Department of Applied Neuroscience, Neurons, Inc., Taastrup, Denmark
- Faculty of Management, University of Tehran, Tehran, Iran
| | - Thomas Zoëga Ramsøy
- Department of Applied Neuroscience, Neurons, Inc., Taastrup, Denmark
- Faculty of Neuroscience, Singularity University, Santa Clara, California, United States
| | - Gaetano Valenza
- Department of Information Engineering & Bioengineering and Robotics Research Center E. Piaggio, School of Engineering, University of Pisa, Pisa, Italy
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Nardelli M, Citi L, Barbieri R, Valenza G. Characterization of autonomic states by complex sympathetic and parasympathetic dynamics. Physiol Meas 2023; 44. [PMID: 36787644 DOI: 10.1088/1361-6579/acbc07] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 02/14/2023] [Indexed: 02/16/2023]
Abstract
Assessment of heartbeat dynamics provides a promising framework for non-invasive monitoring of cardiovascular and autonomic states. Nevertheless, the non-specificity of such measurements among clinical populations and healthy conditions associated with different autonomic states severely limits their applicability and exploitation in naturalistic conditions. This limitation arises especially when pathological or postural change-related sympathetic hyperactivity is compared to autonomic changes across age and experimental conditions. In this frame, we investigate the intrinsic irregularity and complexity of cardiac sympathetic and vagal activity series in different populations, which are associated with different cardiac autonomic dynamics. Sample entropy, fuzzy entropy, and distribution entropy are calculated on the recently proposed sympathetic and parasympathetic activity indices (SAI and PAI) series, which are derived from publicly available heartbeat series of congestive heart failure patients, elderly and young subjects watching a movie in the supine position, and healthy subjects undergoing slow postural changes. Results show statistically significant differences between pathological/old subjects and young subjects in the resting state and during slow tilt, with interesting trends in SAI- and PAI-related entropy values. Moreover, while CHF patients and healthy subjects in upright position show the higher cardiac sympathetic activity, elderly and young subjects in resting state showed higher vagal activity. We conclude that quantification of intrinsic cardiac complexity from sympathetic and vagal dynamics may provide new physiology insights and improve on the non-specificity of heartbeat-derived biomarkers.
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Affiliation(s)
- Mimma Nardelli
- Bioengineering and Robotics Research Centre E. Piaggio and Dipartimento di Ingegneria dell'Informazione, University of Pisa, Italy
| | - Luca Citi
- School of Computer Science and Electronic Engineering, University of Essex, United Kingdom
| | - Riccardo Barbieri
- Department of Electronics, Informatics and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Gaetano Valenza
- Bioengineering and Robotics Research Centre E. Piaggio and Dipartimento di Ingegneria dell'Informazione, University of Pisa, Italy
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Pasini I, Perlini C, Donisi V, Mason A, Schweiger V, Secchettin E, Lugoboni F, Valenza G, Del Piccolo L. "INTEGRO INTEGRated Psychotherapeutic InterventiOn" on the Management of Chronic Pain in Patients with Fibromyalgia: The Role of the Therapeutic Relationship. Int J Environ Res Public Health 2023; 20:3973. [PMID: 36900982 PMCID: PMC10002139 DOI: 10.3390/ijerph20053973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 02/18/2023] [Accepted: 02/20/2023] [Indexed: 06/18/2023]
Abstract
Fibromyalgia (FM) is a chronic disease characterized by a heterogeneous set of physical and psychological conditions. The chronic experience of disability felt by patients and the impact on quality of life (QoL) of the disease may worsen the cognitive reappraisal ability and contribute to maintaining an altered pain modulation mechanism. This paper presents the study protocol of an INTEGRated psychotherapeutic interventiOn on the management of chronic pain in patients with fibromyalgia (INTEGRO). The aim of the study is to investigate the efficacy of an integrated psychotherapeutic intervention focused on pain management on QoL and pain perception, in a pilot sample of 45 FM patients with idiopathic chronic pain. The contribution of perceived therapeutic relationship (alliance) and physiological attunement, in both the patient and therapist, will be considered as possible mediators of intervention efficacy. Attachment dimensions, traumatic experiences, difficulties in emotion regulation, mindfulness attitude and psychophysiological profile will also be considered as covariates. The objectives are to evaluate longitudinally if patients will experience an increase in QoL perception (primary endpoint), pain-managing self-efficacy and emotion-regulation abilities as well as a reduction in pain intensity (secondary endpoints), considering the mediating role of perceived therapeutic alliance and physiological attunement in both the patient and therapist.
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Affiliation(s)
- Ilenia Pasini
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, 37134 Verona, Italy
| | - Cinzia Perlini
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, 37134 Verona, Italy
| | - Valeria Donisi
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, 37134 Verona, Italy
| | - Anna Mason
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, 37134 Verona, Italy
| | - Vittorio Schweiger
- Pain Therapy Centre, Department of Surgery, Dentistry, Maternal and Infant Sciences, Verona University Hospital, Policlinico GB Rossi, 37134 Verona, Italy
| | - Erica Secchettin
- Pain Therapy Centre, Department of Surgery, Dentistry, Maternal and Infant Sciences, Verona University Hospital, Policlinico GB Rossi, 37134 Verona, Italy
| | - Fabio Lugoboni
- Unit of Addiction Medicine, Department of Internal Medicine, Integrated University Hospital of Verona, Policlinico GB Rossi, 37134 Verona, Italy
| | - Gaetano Valenza
- Bioengineering and Robotics Research Center “E. Piaggio”, Department of Information Engineering, School of Engineering, University of Pisa, 56122 Pisa, Italy
| | - Lidia Del Piccolo
- Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, 37134 Verona, Italy
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Catrambone V, Valenza G. Nervous–system–wise Functional Estimation of Directed Brain–Heart Interplay through Microstate Occurrences. IEEE Trans Biomed Eng 2023; PP. [PMID: 37022278 DOI: 10.1109/tbme.2023.3240593] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
BACKGROUND The quantification of functional brain-heart interplay (BHI) through analysis of the dynamics of the central and autonomic nervous systems provides effective biomarkers for cognitive, emotional, and autonomic state changes. Several computational models have been proposed to estimate BHI, focusing on a single sensor, brain region, or frequency activity. However, no models currently provide a directional estimation of such interplay at the organ level. OBJECTIVE This study proposes an analysis framework to estimate BHI that quantifies the directional information flow between whole-brain and heartbeat dynamics. METHODS System-wise directed functional estimation is performed through an ad-hoc symbolic transfer entropy implementation, which leverages on EEG-derived microstate series and on partition of heart rate variability series. The proposed framework is validated on two different experimental datasets: the first investigates the cognitive workload performed through mental arithmetic and the second focuses on an autonomic maneuver using a cold pressor test (CPT). RESULTS The experimental results highlight a significant bidirectional increase in BHI during cognitive workload with respect to the preceding resting phase and a higher descending interplay during a CPT compared to the preceding rest and following recovery phases. These changes are not detected by the intrinsic self entropy of isolated cortical and heartbeat dynamics. CONCLUSION This study corroborates the literature on the BHI phenomenon under these experimental conditions and the new perspective provides novel insights from an organ-level viewpoint. SIGNIFICANCE A system-wise perspective of the BHI phenomenon may provide new insights into physiological and pathological processes that may not be completely understood at a lower level/scale of analysis.
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Hakimi N, Shahbakhti M, Sappia S, Horschig JM, Bronkhorst M, Floor-Westerdijk M, Valenza G, Dudink J, Colier WNJM. Estimation of Respiratory Rate from Functional Near-Infrared Spectroscopy (fNIRS): A New Perspective on Respiratory Interference. Biosensors (Basel) 2022; 12:bios12121170. [PMID: 36551137 PMCID: PMC9775029 DOI: 10.3390/bios12121170] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 12/05/2022] [Accepted: 12/08/2022] [Indexed: 05/27/2023]
Abstract
OBJECTIVE Respiration is recognized as a systematic physiological interference in functional near-infrared spectroscopy (fNIRS). However, it remains unanswered as to whether it is possible to estimate the respiratory rate (RR) from such interference. Undoubtedly, RR estimation from fNIRS can provide complementary information that can be used alongside the cerebral activity analysis, e.g., sport studies. Thus, the objective of this paper is to propose a method for RR estimation from fNIRS. Our primary presumption is that changes in the baseline wander of oxygenated hemoglobin concentration (O2Hb) signal are related to RR. METHODS fNIRS and respiratory signals were concurrently collected from subjects during controlled breathing tasks at a constant rate from 0.1 Hz to 0.4 Hz. Firstly, the signal quality index algorithm is employed to select the best O2Hb signal, and then a band-pass filter with cut-off frequencies from 0.05 to 2 Hz is used to remove very low- and high-frequency artifacts. Secondly, troughs of the filtered O2Hb signal are localized for synthesizing the baseline wander (S1) using cubic spline interpolation. Finally, the fast Fourier transform of the S1 signal is computed, and its dominant frequency is considered as RR. In this paper, two different datasets were employed, where the first one was used for the parameter adjustment of the proposed method, and the second one was solely used for testing. RESULTS The low mean absolute error between the reference and estimated RRs for the first and second datasets (2.6 and 1.3 breaths per minute, respectively) indicates the feasibility of the proposed method for RR estimation from fNIRS. SIGNIFICANCE This paper provides a novel view on the respiration interference as a source of complementary information in fNIRS.
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Affiliation(s)
- Naser Hakimi
- Artinis Medical Systems, B.V., Einsteinweg 17, 6662 PW Elst, The Netherlands
- Department of Neonatology, Wilhelmina Children’s Hospital, University Medical Center Utrecht, Lundlaan 6, 3584 EA Utrecht, The Netherlands
| | - Mohammad Shahbakhti
- Artinis Medical Systems, B.V., Einsteinweg 17, 6662 PW Elst, The Netherlands
- Biomedical Engineering Institute, Kaunas University of Technology, K. Barsausko 59, LT-51423 Kaunas, Lithuania
| | - Sofia Sappia
- Artinis Medical Systems, B.V., Einsteinweg 17, 6662 PW Elst, The Netherlands
| | - Jörn M. Horschig
- Artinis Medical Systems, B.V., Einsteinweg 17, 6662 PW Elst, The Netherlands
| | - Mathijs Bronkhorst
- Artinis Medical Systems, B.V., Einsteinweg 17, 6662 PW Elst, The Netherlands
| | | | - Gaetano Valenza
- Bioengineering and Robotics Research Center E. Piaggio and the Department of Information Engineering, School of Engineering, University of Pisa, Via G. Caruso 16, 56122 Pisa, Italy
| | - Jeroen Dudink
- Department of Neonatology, Wilhelmina Children’s Hospital, University Medical Center Utrecht, Lundlaan 6, 3584 EA Utrecht, The Netherlands
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Candia-Rivera D, Sappia MS, Horschig JM, Colier WNJM, Valenza G. Confounding effects of heart rate, breathing rate, and frontal fNIRS on interoception. Sci Rep 2022; 12:20701. [PMID: 36450811 PMCID: PMC9712694 DOI: 10.1038/s41598-022-25119-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 11/24/2022] [Indexed: 12/02/2022] Open
Abstract
Recent studies have established that cardiac and respiratory phases can modulate perception and related neural dynamics. While heart rate and respiratory sinus arrhythmia possibly affect interoception biomarkers, such as heartbeat-evoked potentials, the relative changes in heart rate and cardiorespiratory dynamics in interoceptive processes have not yet been investigated. In this study, we investigated the variation in heart and breathing rates, as well as higher functional dynamics including cardiorespiratory correlation and frontal hemodynamics measured with fNIRS, during a heartbeat counting task. To further investigate the functional physiology linked to changes in vagal activity caused by specific breathing rates, we performed the heartbeat counting task together with a controlled breathing rate task. The results demonstrate that focusing on heartbeats decreases breathing and heart rates in comparison, which may be part of the physiological mechanisms related to "listening" to the heart, the focus of attention, and self-awareness. Focusing on heartbeats was also observed to increase frontal connectivity, supporting the role of frontal structures in the neural monitoring of visceral inputs. However, cardiorespiratory correlation is affected by both heartbeats counting and controlled breathing tasks. Based on these results, we concluded that variations in heart and breathing rates are confounding factors in the assessment of interoceptive abilities and relative fluctuations in breathing and heart rates should be considered to be a mode of covariate measurement of interoceptive processes.
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Affiliation(s)
- Diego Candia-Rivera
- Bioengineering and Robotics Research Center E. Piaggio & Department of Information Engineering, School of Engineering, University of Pisa, 56122, Pisa, Italy.
| | - M Sofía Sappia
- Artinis Medical Systems, B.V., Einsteinweg 17, 6662 PW, Elst, The Netherlands
- Donders Institute for Brain, Behaviour and Cognition, Radboud University Nijmegen, 6525 EN, Nijmegen, The Netherlands
| | - Jörn M Horschig
- Artinis Medical Systems, B.V., Einsteinweg 17, 6662 PW, Elst, The Netherlands
| | - Willy N J M Colier
- Artinis Medical Systems, B.V., Einsteinweg 17, 6662 PW, Elst, The Netherlands
| | - Gaetano Valenza
- Bioengineering and Robotics Research Center E. Piaggio & Department of Information Engineering, School of Engineering, University of Pisa, 56122, Pisa, Italy
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Candia-Rivera D, Catrambone V, Barbieri R, Valenza G. A new framework for modeling the bidirectional interplay between brain oscillations and cardiac sympathovagal activity. Annu Int Conf IEEE Eng Med Biol Soc 2022; 2022:1957-1960. [PMID: 36083927 DOI: 10.1109/embc48229.2022.9871169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The study of functional brain-heart interplay (BHI) aims to describe the dynamical interactions between central and peripheral autonomic nervous systems. Here, we introduce the Sympathovagal Synthetic Data Generation Model, which constitutes a new computational framework for the assessment of functional BHI. The model estimates the bidirectional interplay with novel quantifiers of cardiac sympathovagal activity gathered from Laguerre expansions of RR series (from the ECG), as an alternative to the classical spectral analysis. The main features of the model are time-varying coupling coefficients linking Electroencephalography (EEG) oscillations and cardiac sympathetic or parasympathetic activity, for either ascending or descending direction of the information flow. In this proof-of-concept study, functional BHI is quantified in the direction from-heart-to-brain, on data from 16 human volunteers undergoing a cold-pressor test. Results show that thermal stress induces heart-to-brain functional interplay originating from sympathetic and parasympathetic activities and sustaining EEG oscillations mainly in the δ and γ bands. The proposed computational framework could provide a viable tool for the functional assessment of the causal interplay between cortical and cardiac sympathovagal dynamics.
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Ghouse A, Candia-Rivera D, Valenza G. Multivariate Pattern Analysis of Entropy estimates in Fast- and Slow-Wave Functional Near Infrared Spectroscopy: A Preliminary Cognitive Stress study. Annu Int Conf IEEE Eng Med Biol Soc 2022; 2022:373-376. [PMID: 36085980 DOI: 10.1109/embc48229.2022.9871839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Functional near infrared spectroscopy (fNIRS) is a modality that can measure shallow cortical brain signals and also contains pulsatile oscillations that originate from heartbeat dynamics. In particular, while fNIRS slow waves (0 Hz to 0.6 Hz) refer to the standard hemodynamic signal, fast-wave (0.8 Hz to 3 Hz) fNIRS signals refer to cardiac oscillations. Using a cognitive stress experiment paradigm with mental arithmetic, the aim of this study was to assess differences in cortical activity when using slow-wave or fast-wave fNIRS signals. Furthermore, we aimed to see whether fNIRS fast and slow waves provide different information to discriminate mental arithmetic tasks from baseline. We used data from 10 healthy subjects from an open dataset performing mental arithmetic tasks and assessed fNIRS signals using mean values in the time domain, as well as complexity estimates including sample, fuzzy, and distribution entropy. A searchlight representational similarity analysis with pairwise t-test group analysis was performed to compare the representational dissimilarity matrices of each searchlight center. We found significant representational differences between fNIRS fast and slow waves for all complexity estimates, at different brain regions. On the other hand, no statistical differences were observed for mean values. We conclude that entropy analysis of fNIRS data may be more sensitive than traditional methods like mean analysis at detecting the additional information provided by fast-wave signals for discriminating mental arithmetic tasks and warrants further research.
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Scarciglia A, Catrambone V, Bonanno C, Valenza G. Multiscale partition-based Kolmogorov-Sinai Entropy: a preliminary HRV study on Heart Failure vs. Atrial Fibrillation. Annu Int Conf IEEE Eng Med Biol Soc 2022; 2022:131-134. [PMID: 36085961 DOI: 10.1109/embc48229.2022.9871728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Several approaches for estimating complexity in physiological time series at various time scales have recently been developed, with a special focus on heart rate variability (HRV) series. While numerous multiscale complexity quantifiers have been investigated, a multiscale Kolmogorov-Sinai (K-S) entropy for the characterization of cardiovascular dynamics still has to be properly assessed. In this pilot study, we investigate the Algorithmic Information Content, which is calculated using an effective compression algorithm, to quantify multiscale partition- based K-S entropy on experimental HRV series. Data were gathered from publicly available datasets comprising long-term, unstructured recordings from 10 healthy subjects, as well as 10 patients with congestive heart failure (CHF) and 10 patients with atrial fibrillation. Results show that multiple time scales and domain partitions statistically discern healthy vs. pathological cardiovascular dynamics. We conclude that the proposed multiscale partition-based K-S entropy may constitute a viable tool for the complexity assessment of cardiovascular variability series.
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Catrambone V, Patron E, Gentili C, Valenza G. Complexity Modulation in functional Brain-Heart Interplay series driven by Emotional Stimuli: an early study using Fuzzy Entropy. Annu Int Conf IEEE Eng Med Biol Soc 2022; 2022:2306-2309. [PMID: 36085864 DOI: 10.1109/embc48229.2022.9871938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Increasing attention has recently been devoted to the multidisciplinary investigation of functional brain-heart interplay (BHI), which has provided meaningful insights in neuroscience and clinical domains including cardiology, neurology, clinical psychology, and psychiatry. While neural (brain) and heartbeat series show high nonlinear and complex dynamics, a complexity analysis on BHI series has not been performed yet. To this end, in this preliminary study, we investigate BHI complexity modulation in 17 healthy subjects undergoing a 3-minute resting state and emotional elicitation through standardized image slideshow. Electroencephalographic and heart rate variability series were the inputs of an adhoc BHI model, which provides directional (from-heart-to-brain and from-brain-to-heart) estimates at different frequency bands. A Fuzzy entropy analysis was performed channel-wise on the model output for the two experimental conditions. Results suggest that BHI complexity increases in the emotional elicitation phase with respect to a resting state, especially in the functional direction from the heart to the brain. We conclude that BHI complexity may be a viable computational tool to characterize neurophysiological and pathological states under different experimental conditions.
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Manzoni D, Catrambone V, Valenza G. Causal Symbolic Information Transfer for the Assessment of functional Brain-Heart Interplay through EEG Microstates Occurrences: a proof-of-concept study. Annu Int Conf IEEE Eng Med Biol Soc 2022; 2022:255-258. [PMID: 36086149 DOI: 10.1109/embc48229.2022.9871000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Electroencephalography (EEG) microstates analysis provides a sequence of topographies representing the scalp-related electric field over time, and each microstate is synthetically represented by a symbol. Despite recent advances on functional brain-heart interplay (BHI) assessment, to our knowledge no methodology takes EEG microstates into account to relate the causal heartbeat dynamics. Moreover, standard BHI methods are tailored to a single EEG-channel analysis, neglecting the comprehensive information associated with a multichannel cluster or a whole-brain activity. To overcome these limitations, we devised a novel methodological frame-work for the assessment of functional BHI that exploits the symbolic representation of both EEG microstates and heart rate variability (HRV) series. Directional BHI quantification is then performed through Kullback-Leibler Divergence (KLD) and Transfer Entropy. The proposed methodology is here preliminarily tested on a dataset gathered from healthy subjects undergoing a resting state and a mental arithmetic task. Except for the KLD in the from-brain-to-heart direction, all other estimates showed significant differences between the two experimental conditions. We conclude that the proposed frame-work may promisingly provide novel insights on brain-heart phenomena through a whole-brain symbolic representation.
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Carli V, Petros NG, Hadlaczky G, Vitcheva T, Berchialla P, Bianchi S, Carletto S, Christinaki E, Citi L, Dinis S, Gentili C, Geraldes V, Giovinazzo L, Gonzalez-Martinez S, Meyer B, Ostacoli L, Ottaviano M, Ouakinin S, Papastylianou T, Paradiso R, Poli R, Rocha I, Settanta C, Scilingo EP, Valenza G. The NEVERMIND e-health system in the treatment of depressive symptoms among patients with severe somatic conditions: A multicentre, pragmatic randomised controlled trial. EClinicalMedicine 2022; 48:101423. [PMID: 35706482 PMCID: PMC9092507 DOI: 10.1016/j.eclinm.2022.101423] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 04/08/2022] [Accepted: 04/11/2022] [Indexed: 11/17/2022] Open
Abstract
Background This study assessed the effectiveness of the NEVERMIND e-health system, consisting of a smart shirt and a mobile application with lifestyle behavioural advice, mindfulness-based therapy, and cognitive behavioural therapy, in reducing depressive symptoms among patients diagnosed with severe somatic conditions. Our hypothesis was that the system would significantly decrease the level of depressive symptoms in the intervention group compared to the control group. Methods This pragmatic, randomised controlled trial included 425 patients diagnosed with myocardial infarction, breast cancer, prostate cancer, kidney failure, or lower limb amputation. Participants were recruited from hospitals in Turin and Pisa (Italy), and Lisbon (Portugal), and were randomly assigned to either the NEVERMIND intervention or to the control group. Clinical interviews and structured questionnaires were administered at baseline, 12 weeks, and 24 weeks. The primary outcome was depressive symptoms at 12 weeks measured by the Beck Depression Inventory II (BDI-II). Intention-to-treat analyses included 425 participants, while the per-protocol analyses included 333 participants. This trial is registered in the German Clinical Trials Register, DRKS00013391. Findings Patients were recruited between Dec 4, 2017, and Dec 31, 2019, with 213 assigned to the intervention and 212 to the control group. The sample had a mean age of 59·41 years (SD=10·70), with 44·24% women. Those who used the NEVERMIND system had statistically significant lower depressive symptoms at the 12-week follow-up (mean difference=-3·03, p<0·001; 95% CI -4·45 to -1·62) compared with controls, with a clinically relevant effect size (Cohen's d=0·39). Interpretation The results of this study show that the NEVERMIND system is superior to standard care in reducing and preventing depressive symptoms among patients with the studied somatic conditions. Funding The NEVERMIND project received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement No. 689691.
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Affiliation(s)
- Vladimir Carli
- National Centre for Suicide Research and Prevention of Mental Ill-Health, Karolinska Institutet, Stockholm, Sweden
| | - Nuhamin Gebrewold Petros
- National Centre for Suicide Research and Prevention of Mental Ill-Health, Karolinska Institutet, Stockholm, Sweden
| | - Gergö Hadlaczky
- National Centre for Suicide Research and Prevention of Mental Ill-Health, Karolinska Institutet, Stockholm, Sweden
| | - Tereza Vitcheva
- National Centre for Suicide Research and Prevention of Mental Ill-Health, Karolinska Institutet, Stockholm, Sweden
| | - Paola Berchialla
- Department of Clinical and Biological Sciences, Università degli Studi di Torino, Turin, Italy
| | - Silvia Bianchi
- Research Center "E.Piaggio" and Department of Information Engineering, School of Engineering, Università di Pisa, Pisa, Italy
| | - Sara Carletto
- Department of Neuroscience "Rita Levi Montalcini", Università degli Studi di Torino, Turin, Italy
| | - Eirini Christinaki
- School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK
| | - Luca Citi
- School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK
| | - Sérgio Dinis
- Faculdade de Medicina and CCUL, Universidade de Lisboa, Lisbon, Portugal
| | - Claudio Gentili
- General Psychology Department, Università degli Studi di Padova, Padua, Italy
| | - Vera Geraldes
- Faculdade de Medicina and CCUL, Universidade de Lisboa, Lisbon, Portugal
| | - Lorena Giovinazzo
- Department of Clinical and Biological Sciences, Università degli Studi di Torino, Turin, Italy
| | | | | | - Luca Ostacoli
- Department of Clinical and Biological Sciences, Università degli Studi di Torino, Turin, Italy
| | - Manuel Ottaviano
- Life Supporting Technologies, Universidad Politécnica de Madrid, Madrid, Spain
| | - Silvia Ouakinin
- Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal
| | - Tasos Papastylianou
- School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK
| | | | - Riccardo Poli
- School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK
| | - Isabel Rocha
- Faculdade de Medicina and CCUL, Universidade de Lisboa, Lisbon, Portugal
| | - Carmen Settanta
- Department of Clinical and Biological Sciences, Università degli Studi di Torino, Turin, Italy
| | - Enzo Pasquale Scilingo
- Research Center "E.Piaggio" and Department of Information Engineering, School of Engineering, Università di Pisa, Pisa, Italy
| | - Gaetano Valenza
- Research Center "E.Piaggio" and Department of Information Engineering, School of Engineering, Università di Pisa, Pisa, Italy
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Candia-Rivera D, Catrambone V, Barbieri R, Valenza G. Functional assessment of bidirectional cortical and peripheral neural control on heartbeat dynamics: a brain-heart study on thermal stress. Neuroimage 2022; 251:119023. [PMID: 35217203 DOI: 10.1016/j.neuroimage.2022.119023] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Revised: 02/16/2022] [Accepted: 02/18/2022] [Indexed: 12/12/2022] Open
Abstract
The study of functional brain-heart interplay (BHI) from non-invasive recordings has gained much interest in recent years. Previous endeavors aimed at understanding how the two dynamical systems exchange information, providing novel holistic biomarkers and important insights on essential cognitive aspects and neural system functioning. However, the interplay between cardiac sympathovagal and cortical oscillations still has much room for further investigation. In this study, we introduce a new computational framework for a functional BHI assessment, namely the Sympatho-Vagal Synthetic Data Generation Model, combining cortical (electroencephalography, EEG) and peripheral (cardiac sympathovagal) neural dynamics. The causal, bidirectional neural control on heartbeat dynamics was quantified on data gathered from 26 human volunteers undergoing a cold-pressor test. Results show that thermal stress induces heart-to-brain functional interplay sustained by EEG oscillations in the delta and gamma bands, primarily originating from sympathetic activity, whereas brain-to-heart interplay originates over central brain regions through sympathovagal control. The proposed methodology provides a viable computational tool for the functional assessment of the causal interplay between cortical and cardiac neural control.
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Affiliation(s)
- Diego Candia-Rivera
- Bioengineering and Robotics Research Center E. Piaggio & Department of Information Engineering, School of Engineering, University of Pisa, 56122, Pisa, Italy.
| | - Vincenzo Catrambone
- Bioengineering and Robotics Research Center E. Piaggio & Department of Information Engineering, School of Engineering, University of Pisa, 56122, Pisa, Italy
| | - Riccardo Barbieri
- Department of Electronics, Informatics, and Bioengineering, Politecnico di Milano, 20133, Milano, Italy
| | - Gaetano Valenza
- Bioengineering and Robotics Research Center E. Piaggio & Department of Information Engineering, School of Engineering, University of Pisa, 56122, Pisa, Italy
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Valenza G, Faes L, Toschi N, Barbieri R. Advanced computation in cardiovascular physiology: new challenges and opportunities. Philos Trans A Math Phys Eng Sci 2021; 379:20200265. [PMID: 34689624 DOI: 10.1098/rsta.2020.0265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 07/16/2021] [Indexed: 06/13/2023]
Abstract
Recent developments in computational physiology have successfully exploited advanced signal processing and artificial intelligence tools for predicting or uncovering characteristic features of physiological and pathological states in humans. While these advanced tools have demonstrated excellent diagnostic capabilities, the high complexity of these computational 'black boxes' may severely limit scientific inference, especially in terms of biological insight about both physiology and pathological aberrations. This theme issue highlights current challenges and opportunities of advanced computational tools for processing dynamical data reflecting autonomic nervous system dynamics, with a specific focus on cardiovascular control physiology and pathology. This includes the development and adaptation of complex signal processing methods, multivariate cardiovascular models, multiscale and nonlinear models for central-peripheral dynamics, as well as deep and transfer learning algorithms applied to large datasets. The width of this perspective highlights the issues of specificity in heartbeat-related features and supports the need for an imminent transition from the black-box paradigm to explainable and personalized clinical models in cardiovascular research. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.
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Affiliation(s)
| | - Luca Faes
- University of Palermo, Palermo, Italy
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31
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Saul JP, Valenza G. Heart rate variability and the dawn of complex physiological signal analysis: methodological and clinical perspectives. Philos Trans A Math Phys Eng Sci 2021; 379:20200255. [PMID: 34689622 DOI: 10.1098/rsta.2020.0255] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/14/2021] [Indexed: 06/13/2023]
Abstract
Spontaneous beat-to-beat variations of heart rate (HR) have intrigued scientists and casual observers for centuries; however, it was not until the 1970s that investigators began to apply engineering tools to the analysis of these variations, fostering the field we now know as heart rate variability or HRV. Since then, the field has exploded to not only include a wide variety of traditional linear time and frequency domain applications for the HR signal, but also more complex linear models that include additional physiological parameters such as respiration, arterial blood pressure, central venous pressure and autonomic nerve signals. Most recently, the field has branched out to address the nonlinear components of many physiological processes, the complexity of the systems being studied and the important issue of specificity for when these tools are applied to individuals. When the impact of all these developments are combined, it seems likely that the field of HRV will soon begin to realize its potential as an important component of the toolbox used for diagnosis and therapy of patients in the clinic. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.
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Affiliation(s)
- J Philip Saul
- Department of Pediatrics, School of Medicine, West Virginia University, Morgantown, WV 25606, USA
| | - Gaetano Valenza
- Research Center E. Piaggio and Department of Information Engineering, University of Pisa, Pisa, Italy
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32
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Catrambone V, Barbieri R, Wendt H, Abry P, Valenza G. Functional brain-heart interplay extends to the multifractal domain. Philos Trans A Math Phys Eng Sci 2021; 379:20200260. [PMID: 34689620 PMCID: PMC8543048 DOI: 10.1098/rsta.2020.0260] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 02/12/2021] [Indexed: 05/09/2023]
Abstract
The study of functional brain-heart interplay has provided meaningful insights in cardiology and neuroscience. Regarding biosignal processing, this interplay involves predominantly neural and heartbeat linear dynamics expressed via time and frequency domain-related features. However, the dynamics of central and autonomous nervous systems show nonlinear and multifractal behaviours, and the extent to which this behaviour influences brain-heart interactions is currently unknown. Here, we report a novel signal processing framework aimed at quantifying nonlinear functional brain-heart interplay in the non-Gaussian and multifractal domains that combines electroencephalography (EEG) and heart rate variability series. This framework relies on a maximal information coefficient analysis between nonlinear multiscale features derived from EEG spectra and from an inhomogeneous point-process model for heartbeat dynamics. Experimental results were gathered from 24 healthy volunteers during a resting state and a cold pressor test, revealing that synchronous changes between brain and heartbeat multifractal spectra occur at higher EEG frequency bands and through nonlinear/complex cardiovascular control. We conclude that significant bodily, sympathovagal changes such as those elicited by cold-pressure stimuli affect the functional brain-heart interplay beyond second-order statistics, thus extending it to multifractal dynamics. These results provide a platform to define novel nervous-system-targeted biomarkers. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.
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Affiliation(s)
- Vincenzo Catrambone
- Research Center E.Piaggio, Department of Information Engineering, School of Engineering, University of Pisa, Pisa, Italy
| | - Riccardo Barbieri
- Department of Electronics, Informatics and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Herwig Wendt
- IRIT–ENSEEIHT, Université de Toulouse, CNRS, Toulouse, France
| | - Patrice Abry
- University of Lyon, ENS de Lyon, University Claude Bernard, CNRS, Laboratoire de Physique, Lyon, France
| | - Gaetano Valenza
- Research Center E.Piaggio, Department of Information Engineering, School of Engineering, University of Pisa, Pisa, Italy
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33
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Ghouse A, Faes L, Valenza G. Inferring directionality of coupled dynamical systems using Gaussian process priors: Application on neurovascular systems. Phys Rev E 2021; 104:064208. [PMID: 35030953 DOI: 10.1103/physreve.104.064208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 11/09/2021] [Indexed: 06/14/2023]
Abstract
Dynamical system theory has recently shown promise for uncovering causality and directionality in complex systems, particularly using the method of convergent cross mapping (CCM). In spite of its success in the literature, the presence of process noise raises concern about CCM's ability to uncover coupling direction. Furthermore, CCM's capacity to detect indirect causal links may be challenged in simulated unidrectionally coupled Rossler-Lorenz systems. To overcome these limitations, we propose a method that places a Gaussian process prior on a cross mapping function (named GP-CCM) to impose constraints on local state space neighborhood comparisons. Bayesian posterior likelihood and evidence ratio tests, as well as surrogate data analyses are performed to obtain a robust statistic for dynamical coupling directionality. We demonstrate GP-CCM's performance with respect to CCM in synthetic data simulation as well as in empirical electroencephelography (EEG) and functional near infrared spectroscopy (fNIRS) activity data. Our findings show that GP-CCM provides a statistic that consistently reports indirect causal structures in non-separable unidirectional system interactions; GP-CCM also provides coupling direction estimates in noisy physiological signals, showing that EEG likely causes, i.e., drives, fNIRS dynamics.
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Affiliation(s)
- Ameer Ghouse
- Bioengineering and Robotics Research Centre "E Piaggio" & Department of Engineering, University of Pisa, 56122 Pisa, Italy
| | - Luca Faes
- Department of Engineering, University of Palermo, 90133 Palermo, Italy
| | - Gaetano Valenza
- Bioengineering and Robotics Research Centre "E Piaggio" & Department of Engineering, University of Pisa, 56122 Pisa, Italy
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34
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Sorriento A, Cafarelli A, Valenza G, Ricotti L. Ex-vivo quantitative ultrasound assessment of cartilage degeneration. Annu Int Conf IEEE Eng Med Biol Soc 2021; 2021:2976-2980. [PMID: 34891870 DOI: 10.1109/embc46164.2021.9630198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Osteoarthritis is a common disease that implies joint degeneration and that strongly affects the quality of life. Conventional radiography remains currently the most used diagnostic method, even if it allows only an indirect assessment of the articular cartilage and employ the use of ionizing radiations. A non-invasive, continuous and reliable diagnosis is crucial to detect impairments and to improve the treatment outcomes.Quantitative ultrasound techniques have proved to be very useful in providing an objective diagnosis of several soft tissues. In this study, we propose quantitative ultrasound parameters, based on the analysis of radiofrequency data derived from both healthy and osteoarthritis-mimicking (through chemical degradation) ex-vivo cartilage samples. Using a transmission frequency typically employed in the clinical practice (7.5-15 MHz) with an external ultrasound probe, we found results in terms of reflection at the cartilage surface and sample thickness comparable to those reported in the literature by exploiting arthroscopic transducers at high frequency (from 20 to 55 MHz). Moreover, for the first time, we introduce an objective metric based on the phase entropy calculation, able to discriminate the healthy cartilage from the degenerated one.Clinical Relevance- This preliminary study proposes a novel and quantitative method to discriminate healthy from degenerated cartilage. The obtained results pave the way to the use of quantitative ultrasound in the diagnosis and monitoring of knee osteoarthritis.
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35
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Ghiasi S, Dell'Acqua C, Benvenuti SM, Scilingo EP, Gentili C, Valenza G, Greco A. Classifying subclinical depression using EEG spectral and connectivity measures. Annu Int Conf IEEE Eng Med Biol Soc 2021; 2021:2050-2053. [PMID: 34891691 DOI: 10.1109/embc46164.2021.9630044] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Detecting depression on its early stages helps preventing the onset of severe depressive episodes. In this study, we propose an automatic classification pipeline to detect subclinical depression (i.e., dysphoria) through the electroencephalography (EEG) signal. To this aim, we recorded the EEG signals in resting condition from 26 female participants with dysphoria and 38 female controls. The EEG signals were processed to extract several spectral and functional connectivity features to feed a nonlinear Support Vector Machine (SVM) classifier embedded with a Recursive Feature Elimination (RFE) algorithm. Our recognition pipeline obtained a maximum classification accuracy of 83.91% in recognizing dysphoria patients with a combination of connectivity and spectral measures. Moreover, an accuracy of 76.11% was achieved with only the 4 most informative functional connections, suggesting a central role of cortical connectivity in the theta band for early depression recognition. The present study can facilitate the diagnosis of subclinical conditions of depression and may provide reliable indicators of depression for the clinical community.
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36
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Alfeo AL, Catrambone V, Cimino MGCA, Vaglini G, Valenza G. Recognizing motor imagery tasks from EEG oscillations through a novel ensemble-based neural network architecture. Annu Int Conf IEEE Eng Med Biol Soc 2021; 2021:5983-5986. [PMID: 34892481 DOI: 10.1109/embc46164.2021.9629900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Brain-Computer Interfaces (BCI) provide effective tools aimed at recognizing different brain activities, translate them into actions, and enable humans to directly communicate through them. In this context, the need for strong recognition performances results in increasingly sophisticated machine learning (ML) techniques, which may result in poor performance in a real application (e.g., limiting a real-time implementation). Here, we propose an ensemble approach to effectively balance between ML performance and computational costs in a BCI framework. The proposed model builds a classifier by combining different ML models (base-models) that are specialized to different classification sub-problems. More specifically, we employ this strategy with an ensemble-based architecture consisting of multi-layer perceptrons, and test its performance on a publicly available electroencephalography-based BCI dataset with four-class motor imagery tasks. Compared to previously proposed models tested on the same dataset, the proposed approach provides greater average classification performances and lower inter-subject variability.
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37
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Scarciglia A, Catrambone V, Bonanno C, Valenza G. Quantifying partition-based Kolmogorov-Sinai Entropy on Heart Rate Variability: a young vs. elderly study. Annu Int Conf IEEE Eng Med Biol Soc 2021; 2021:5469-5472. [PMID: 34892363 DOI: 10.1109/embc46164.2021.9630975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
In the last decades, a considerable effort has been devoted to quantify complexity in physiological time series, with a particular focus on heart rate variability (HRV). To this end, exemplary quantifiers including Approximate Entropy and Sample Entropy have successfully been applied by leveraging on statistical approximation and further parametrization through the definition of tolerance and embedding dimension, among others. In this study, we investigate the use of the Algorithmic Information Content, which is estimated through an effective compression algorithm, to quantify partition-based Kolmogorov-Sinai (K-S) entropy on HRV series. We test such a K-S estimate on real data gathered from the Fantasia database, aiming to discern young vs. elderly complex dynamics. Experimental results show that elderly people are associated with a lower HRV complexity and a more predictable behavior, with significantly lower partition-based K-S entropy than the young adults. We conclude that partition-based K-S entropy may effectively be used to investigate pathological conditions in the cardiovascular system, complementing state-of-the-art methods for complexity assessment.
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Cavaliere L, Catrambone V, Bianchi M, Rocha AP, Valenza G. Characterization of upper limb movement-related EEG dynamics through fractional integrated autoregressive modeling. Annu Int Conf IEEE Eng Med Biol Soc 2021; 2021:5987-5990. [PMID: 34892482 DOI: 10.1109/embc46164.2021.9630051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The analysis of electroencephalographic (EEG) series associated with movement performance is important for understanding the cortical neural control on motor tasks. While the existence of long-range correlations in physiological dynamics has been reported in previous studies, such a characterization in EEG series gathered during upper-limb movements has not been performed yet. To this end, here we report on a fractional integrated autoregressive analysis of EEG series during different functional classes of motor actions and resting phase, and data were gathered from 33 healthy volunteers. Results show significant differences in EEG long-range correlations on EEG series from characteristic topography.
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Nardelli M, Catrambone V, Grandi G, Banfi T, Bruno RM, Scilingo EP, Faraguna U, Valenza G. Activation of brain-heart axis during REM sleep: a trigger for dreaming. Am J Physiol Regul Integr Comp Physiol 2021; 321:R951-R959. [PMID: 34704848 DOI: 10.1152/ajpregu.00306.2020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Dreams may be recalled after awakening from sleep following a defined electroencephalographic pattern that involves local decreases in low-frequency activity in the posterior cortical regions. While a dreaming experience implies bodily changes at many organ-, system-, and timescale-levels, the entity and causal role of such peripheral changes in a conscious dream experience are unknown. We performed a comprehensive, causal, multivariate analysis of physiological signals acquired during REM sleep at night, including high-density EEG and peripheral dynamics including electrocardiography and blood pressure. In this preliminary study, we investigated multiple recalls and non-recalls of dream experiences using data from nine healthy volunteers. The aim was not only to investigate the changes in central and autonomic dynamics associated with dream recalls and non-recalls, but also to characterize the central-peripheral dynamical and (causal) directional interactions, and the temporal relations of the related arousals upon awakening. We uncovered a brain-body network that drives a conscious dreaming experience that acts with specific interaction and time delays. Such a network is sustained by the blood pressure dynamics and the increasing functional information transfer from the neural heartbeat regulation to the brain. We conclude that bodily changes play a crucial and causative role in a conscious dream experience during REM sleep.
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Affiliation(s)
- Mimma Nardelli
- Bioengineering and Robotics Research Centre E. Piaggio and Department of Information Engineering, University of Pisa, Italy
| | - Vincenzo Catrambone
- Bioengineering and Robotics Research Centre E. Piaggio and Department of Information Engineering, University of Pisa, Italy
| | - Giulia Grandi
- Department of Translational Research and of New Surgical and Medical Technologies, University of Pisa, Italy
| | - Tommaso Banfi
- Department of Translational Research and of New Surgical and Medical Technologies, University of Pisa, Italy
| | - Rosa Maria Bruno
- INSERM U970 Team 7, Paris Cardiovascular Research Centre - PARCC, University Paris Descartes, Sorbonne Paris Cité, Paris, France
| | - Enzo Pasquale Scilingo
- Bioengineering and Robotics Research Centre E. Piaggio and Department of Information Engineering, University of Pisa, Italy
| | - Ugo Faraguna
- Department of Translational Research and of New Surgical and Medical Technologies, University of Pisa, Italy.,Department of Developmental Neuroscience, IRCCS Fondazione Stella Maris, Pisa, Italy
| | - Gaetano Valenza
- Bioengineering and Robotics Research Centre E. Piaggio and Department of Information Engineering, University of Pisa, Italy
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Bartoletti R, Greco A, Di Vico T, Durante J, Ficarra V, Scilingo EP, Valenza G. Bioelectric Impedance Analysis Test Improves the Detection of Prostate Cancer in Biopsy Candidates: A Multifeature Decision Support System. Front Oncol 2021; 11:555277. [PMID: 34513653 PMCID: PMC8430220 DOI: 10.3389/fonc.2021.555277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Accepted: 07/28/2021] [Indexed: 11/13/2022] Open
Abstract
Prostate cancer (PCa) gold-standard diagnosis relies on prostate biopsy, which is currently overly recommended since other available noninvasive tools such as prostate-specific antigen (PSA) multiparametric MRI (mMRI) showed low diagnostic accuracy or high costs, respectively. The aim of the study was to determine the accuracy of a novel Bioelectric Impedance Analysis (BIA) test endorectal probe for the selection of patients candidate to prostate biopsy and in particular the clinical value of three different parameters such as resistance (R), reactance (Xc), and phase angle (PA) degree. One-hundred twenty-three consecutive candidates to prostate biopsy and 40 healthy volunteers were enrolled. PSA and PSA density (PSAD) determinations, Digital Rectal Examination (DRE), and the novel BIA test were analyzed in patients and controls. A 16-core prostate biopsy was performed after a mMRI test. The study endpoints were to determine accuracy of BIA test in comparison with PSA, PSAD levels, and mMRI and obtain prostate cancer (PCa) prediction by BIA test. The Mann-Whitney U-test, the Wilkoxon rank test, and the Holm-Bonferroni's method were adopted for statistical analyses, and a computational approach was also applied to differentiate patients with PCa from those with benign disease. Combined PSA, PSAD, DRE, and trans-rectal ultrasound test failed to discern patients with PCa from those with benign disease (62.86% accuracy). mMRI PIRADS ≥3 showed a sensitivity of 83% and a specificity of 59%. The accuracy in discerning PCa increased up to 75% by BIA test (sensitivity 63.33% and specificity 83.75%). The novel finger probe BIA test is a cheap and reliable test that may help to improve clinical multifeature noninvasive diagnosis for PCa and reduce unnecessary biopsies.
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Affiliation(s)
- Riccardo Bartoletti
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Alberto Greco
- Department of Information Engineering & Research Center “E. Piaggio”, University of Pisa, Pisa, Italy
| | - Tommaso Di Vico
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Jacopo Durante
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Vincenzo Ficarra
- Department of Human and Paediatric Pathology “Gaetano Barresi” University of Messina, Messina, Italy
| | - Enzo Pasquale Scilingo
- Department of Information Engineering & Research Center “E. Piaggio”, University of Pisa, Pisa, Italy
| | - Gaetano Valenza
- Department of Information Engineering & Research Center “E. Piaggio”, University of Pisa, Pisa, Italy
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Marín-Morales J, Higuera-Trujillo JL, Guixeres J, Llinares C, Alcañiz M, Valenza G. Heart rate variability analysis for the assessment of immersive emotional arousal using virtual reality: Comparing real and virtual scenarios. PLoS One 2021; 16:e0254098. [PMID: 34197553 PMCID: PMC8248697 DOI: 10.1371/journal.pone.0254098] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Accepted: 06/19/2021] [Indexed: 11/18/2022] Open
Abstract
Many affective computing studies have developed automatic emotion recognition models, mostly using emotional images, audio and videos. In recent years, virtual reality (VR) has been also used as a method to elicit emotions in laboratory environments. However, there is still a need to analyse the validity of VR in order to extrapolate the results it produces and to assess the similarities and differences in physiological responses provoked by real and virtual environments. We investigated the cardiovascular oscillations of 60 participants during a free exploration of a real museum and its virtualisation viewed through a head-mounted display. The differences between the heart rate variability features in the high and low arousal stimuli conditions were analysed through statistical hypothesis testing; and automatic arousal recognition models were developed across the real and the virtual conditions using a support vector machine algorithm with recursive feature selection. The subjects' self-assessments suggested that both museums elicited low and high arousal levels. In addition, the real museum showed differences in terms of cardiovascular responses, differences in vagal activity, while arousal recognition reached 72.92% accuracy. However, we did not find the same arousal-based autonomic nervous system change pattern during the virtual museum exploration. The results showed that, while the direct virtualisation of a real environment might be self-reported as evoking psychological arousal, it does not necessarily evoke the same cardiovascular changes as a real arousing elicitation. These contribute to the understanding of the use of VR in emotion recognition research; future research is needed to study arousal and emotion elicitation in immersive VR.
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Affiliation(s)
- Javier Marín-Morales
- Instituto de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, València, Spain
| | - Juan Luis Higuera-Trujillo
- Instituto de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, València, Spain
| | - Jaime Guixeres
- Instituto de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, València, Spain
| | - Carmen Llinares
- Instituto de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, València, Spain
| | - Mariano Alcañiz
- Instituto de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, València, Spain
| | - Gaetano Valenza
- Bioengineering and Robotics Research Centre E Piaggio & Department of Information Engineering, University of Pisa, Pisa, Italy
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Candia-Rivera D, Catrambone V, Barbieri R, Valenza G. Integral pulse frequency modulation model driven by sympathovagal dynamics: Synthetic vs. real heart rate variability. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102736] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Candia-Rivera D, Catrambone V, Valenza G. The role of electroencephalography electrical reference in the assessment of functional brain-heart interplay: From methodology to user guidelines. J Neurosci Methods 2021; 360:109269. [PMID: 34171310 DOI: 10.1016/j.jneumeth.2021.109269] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 06/16/2021] [Accepted: 06/18/2021] [Indexed: 12/25/2022]
Abstract
BACKGROUND The choice of EEG reference has been widely studied. However, the choice of the most appropriate re-referencing for EEG data is still debated. Moreover, the role of EEG reference in the estimation of functional Brain-Heart Interplay (BHI), together with different multivariate modelling strategies, has not been investigated yet. METHODS This study identifies the best methodology combining a proper EEG electrical reference and signal processing methods for an effective functional BHI assessment. The effects of the EEG reference among common average, mastoids average, Laplacian reference, Cz reference, and the reference electrode standardization technique (REST) were explored throughout different BHI methods including synthetic data generation (SDG) model, heartbeat-evoked potentials, heartbeat-evoked oscillations, and maximal information coefficient. RESULTS The SDG model exhibited high robustness between EEG references, whereas the maximal information coefficient method exhibited a high sensitivity. The common average and REST references for EEG showed a good consistency in the between-method comparisons. Laplacian, and Cz references significantly bias a BHI measurement. COMPARISON WITH EXISTING METHODS The use of EEG reference based on a common average outperforms on the use of other references for consistency in estimating directed functional BHI. We do not recommend the use of EEG references based on analytical derivations as the experimental conditions may not meet the requirements of their optimal estimation, particularly in clinical settings. CONCLUSION The use of a common average for EEG electrical reference is concluded to be the most appropriate choice for a quantitative, functional BHI assessment.
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Affiliation(s)
- Diego Candia-Rivera
- Bioengineering and Robotics Research Center E. Piaggio and the Department of Information Engineering, School of Engineering, University of Pisa, Pisa, Italy.
| | - Vincenzo Catrambone
- Bioengineering and Robotics Research Center E. Piaggio and the Department of Information Engineering, School of Engineering, University of Pisa, Pisa, Italy
| | - Gaetano Valenza
- Bioengineering and Robotics Research Center E. Piaggio and the Department of Information Engineering, School of Engineering, University of Pisa, Pisa, Italy
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44
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Averta G, Barontini F, Catrambone V, Haddadin S, Handjaras G, Held JPO, Hu T, Jakubowitz E, Kanzler CM, Kühn J, Lambercy O, Leo A, Obermeier A, Ricciardi E, Schwarz A, Valenza G, Bicchi A, Bianchi M. U-Limb: A multi-modal, multi-center database on arm motion control in healthy and post-stroke conditions. Gigascience 2021; 10:giab043. [PMID: 34143875 PMCID: PMC8212873 DOI: 10.1093/gigascience/giab043] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Revised: 04/26/2021] [Accepted: 05/14/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Shedding light on the neuroscientific mechanisms of human upper limb motor control, in both healthy and disease conditions (e.g., after a stroke), can help to devise effective tools for a quantitative evaluation of the impaired conditions, and to properly inform the rehabilitative process. Furthermore, the design and control of mechatronic devices can also benefit from such neuroscientific outcomes, with important implications for assistive and rehabilitation robotics and advanced human-machine interaction. To reach these goals, we believe that an exhaustive data collection on human behavior is a mandatory step. For this reason, we release U-Limb, a large, multi-modal, multi-center data collection on human upper limb movements, with the aim of fostering trans-disciplinary cross-fertilization. CONTRIBUTION This collection of signals consists of data from 91 able-bodied and 65 post-stroke participants and is organized at 3 levels: (i) upper limb daily living activities, during which kinematic and physiological signals (electromyography, electro-encephalography, and electrocardiography) were recorded; (ii) force-kinematic behavior during precise manipulation tasks with a haptic device; and (iii) brain activity during hand control using functional magnetic resonance imaging.
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Affiliation(s)
- Giuseppe Averta
- Research Center “Enrico Piaggio” and Dipartimento di Ingegneria dell’Informazione, University of Pisa Largo Lucio Lazzarino 1, 56122 Pisa, Italy
- Soft Robotics for Human Cooperation and Rehabilitation, Fondazione Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
| | - Federica Barontini
- Research Center “Enrico Piaggio” and Dipartimento di Ingegneria dell’Informazione, University of Pisa Largo Lucio Lazzarino 1, 56122 Pisa, Italy
- Soft Robotics for Human Cooperation and Rehabilitation, Fondazione Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
| | - Vincenzo Catrambone
- Research Center “Enrico Piaggio” and Dipartimento di Ingegneria dell’Informazione, University of Pisa Largo Lucio Lazzarino 1, 56122 Pisa, Italy
| | - Sami Haddadin
- RSI - Chair of Robotics and Systems Intelligence, Munich School of Robotics and Machine Intelligence, Technical University Munich (TUM), Heßstr. 134, 80797 München, Germany
| | - Giacomo Handjaras
- MoMiLab Research Unit, IMT School for Advanced Studies Lucca, Piazza S. Francesco 19, 55100 Lucca, Italy
| | - Jeremia P O Held
- Division of Vascular Neurology and Neurorehabilitation, Department of Neurology, University of Zurich, Frauenklinikstrasse 26, 8006 Zürich, Switzerland
| | - Tingli Hu
- RSI - Chair of Robotics and Systems Intelligence, Munich School of Robotics and Machine Intelligence, Technical University Munich (TUM), Heßstr. 134, 80797 München, Germany
| | - Eike Jakubowitz
- Laboratory for Biomechanics and Biomaterials (LBB), Department of Orthopaedic Surgery, Hannover Medical School, L384, 30625 Hannover, Germany
| | - Christoph M Kanzler
- Rehabilitation Engineering Laboratory, Institute of Robotics and Intelligent Systems, Department of Health Sciences and Technology, CLA H 1.1 Tannenstrasse 3, 8092 Zurich, Switzerland
| | - Johannes Kühn
- RSI - Chair of Robotics and Systems Intelligence, Munich School of Robotics and Machine Intelligence, Technical University Munich (TUM), Heßstr. 134, 80797 München, Germany
| | - Olivier Lambercy
- Rehabilitation Engineering Laboratory, Institute of Robotics and Intelligent Systems, Department of Health Sciences and Technology, CLA H 1.1 Tannenstrasse 3, 8092 Zurich, Switzerland
| | - Andrea Leo
- MoMiLab Research Unit, IMT School for Advanced Studies Lucca, Piazza S. Francesco 19, 55100 Lucca, Italy
| | - Alina Obermeier
- Laboratory for Biomechanics and Biomaterials (LBB), Department of Orthopaedic Surgery, Hannover Medical School, L384, 30625 Hannover, Germany
| | - Emiliano Ricciardi
- MoMiLab Research Unit, IMT School for Advanced Studies Lucca, Piazza S. Francesco 19, 55100 Lucca, Italy
| | - Anne Schwarz
- Division of Vascular Neurology and Neurorehabilitation, Department of Neurology, University of Zurich, Frauenklinikstrasse 26, 8006 Zürich, Switzerland
| | - Gaetano Valenza
- Research Center “Enrico Piaggio” and Dipartimento di Ingegneria dell’Informazione, University of Pisa Largo Lucio Lazzarino 1, 56122 Pisa, Italy
| | - Antonio Bicchi
- Research Center “Enrico Piaggio” and Dipartimento di Ingegneria dell’Informazione, University of Pisa Largo Lucio Lazzarino 1, 56122 Pisa, Italy
- Soft Robotics for Human Cooperation and Rehabilitation, Fondazione Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy
| | - Matteo Bianchi
- Research Center “Enrico Piaggio” and Dipartimento di Ingegneria dell’Informazione, University of Pisa Largo Lucio Lazzarino 1, 56122 Pisa, Italy
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Catrambone V, Messerotti Benvenuti S, Gentili C, Valenza G. Intensification of functional neural control on heartbeat dynamics in subclinical depression. Transl Psychiatry 2021; 11:221. [PMID: 33854037 PMCID: PMC8046790 DOI: 10.1038/s41398-021-01336-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 03/30/2021] [Indexed: 01/06/2023] Open
Abstract
Subclinical depression (dysphoria) is a common condition that may increase the risk of major depression and leads to impaired quality of life and severe comorbid somatic diseases. Despite its prevalence, specific biological markers are unknown; consequently, the identification of dysphoria currently relies exclusively on subjective clinical scores and structured interviews. Based on recent neurocardiology studies that link brain and cardiovascular disorders, it was hypothesized that multi-system biomarkers of brain-body interplay may effectively characterize dysphoria. Thus, an ad hoc computational technique was developed to quantify the functional bidirectional brain-heart interplay. Accordingly, 32-channel electroencephalographic and heart rate variability series were obtained from 24 young dysphoric adults and 36 healthy controls. All participants were females of a similar age, and results were obtained during a 5-min resting state. The experimental results suggest that a specific feature of dysphoria is linked to an augmented functional central-autonomic control to the heart, which originates from central, frontopolar, and occipital oscillations and acts through cardiovascular sympathovagal activity. These results enable further development of a large set of novel biomarkers for mood disorders based on comprehensive brain-body measurements.
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Affiliation(s)
- Vincenzo Catrambone
- Research Center E. Piaggio & Department of Information Engineering, School of Engineering, University of Pisa, 56126, Pisa, Italy.
| | | | - Claudio Gentili
- grid.5608.b0000 0004 1757 3470Department of General Psychology, University of Padua, 35131 Padua, Italy
| | - Gaetano Valenza
- grid.5395.a0000 0004 1757 3729Research Center E. Piaggio & Department of Information Engineering, School of Engineering, University of Pisa, 56126 Pisa, Italy
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Ghiasi S, Greco A, Faes L, Javorka M, Barbieri R, Scilingo EP, Valenza G. Quantifying multidimensional control mechanisms of cardiovascular dynamics during multiple concurrent stressors. Med Biol Eng Comput 2021; 59:775-785. [PMID: 33665768 DOI: 10.1007/s11517-020-02311-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Accepted: 12/30/2020] [Indexed: 10/22/2022]
Abstract
Heartbeat regulation is achieved through different routes originating from central autonomic network sources, as well as peripheral control mechanisms. While previous studies successfully characterized cardiovascular regulatory mechanisms during a single stressor, to the best of our knowledge, a combination of multiple concurrent elicitations leading to the activation of different autonomic regulatory routes has not been investigated yet. Therefore, in this study, we propose a novel modeling framework for the quantification of heartbeat regulatory mechanisms driven by different neural routes. The framework is evaluated using two heartbeat datasets gathered from healthy subjects undergoing physical and mental stressors, as well as their concurrent administration. Experimental results indicate that more than 70% of the heartbeat regulatory dynamics is driven by the physical stressor when combining physical and cognitive/emotional stressors. The proposed framework provides quantitative insights and novel perspectives for neural activity on cardiac control dynamics, likely highlighting new biomarkers in the psychophysiology and physiopathology fields. A Matlab implementation of the proposed tool is available online.
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Affiliation(s)
- Shadi Ghiasi
- Department of Information Engineering & Research Center E. Piaggio, University of Pisa, Pisa, Italy.
| | - Alberto Greco
- Department of Information Engineering & Research Center E. Piaggio, University of Pisa, Pisa, Italy
| | - Luca Faes
- Department of Engineering, University of Palermo, Palermo, Italy
| | - Michal Javorka
- Department of Physiology and the Biomedical Center Martin, Jessenius Faculty of Medicine, Comenius University in Bratislava, Martin, Slovakia
| | - Riccardo Barbieri
- Department of Electronics, Informatics and Bioengineering, Politecnico di Milano, Milano, Italy
| | - Enzo Pasquale Scilingo
- Department of Information Engineering & Research Center E. Piaggio, University of Pisa, Pisa, Italy
| | - Gaetano Valenza
- Department of Information Engineering & Research Center E. Piaggio, University of Pisa, Pisa, Italy
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Catrambone V, Averta G, Bianchi M, Valenza G. Toward brain-heart computer interfaces: a study on the classification of upper limb movements using multisystem directional estimates. J Neural Eng 2021; 18. [PMID: 33601354 DOI: 10.1088/1741-2552/abe7b9] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 02/18/2021] [Indexed: 12/13/2022]
Abstract
OBJECTIVE Brain-computer interfaces (BCI) exploit computational features from brain signals to perform a given task. Despite recent neurophysiology and clinical findings indicating the crucial role of functional interplay between brain and cardiovascular dynamics in locomotion, heartbeat information remains to be included in common BCI systems. In this study, we exploit the multidimensional features of directional and functional interplay between electroencephalographic and heartbeat spectra to classify upper limb movements into three classes. APPROACH We gathered data from 26 healthy volunteers that performed 90 movements; the data were processed using a recently proposed framework for brain-heart interplay (BHI) assessment based on synthetic physiological data generation. Extracted BHI features were employed to classify, through sequential forward selection scheme and k-nearest neighbors algorithm, among resting state and three classes of movements according to the kind of interaction with objects. MAIN RESULTS The results demonstrated that the proposed brain-heart computer interface (BHCI) system could distinguish between rest and movement classes automatically with an average 90% of accuracy. SIGNIFICANCE Further, this study provides neurophysiology insights indicating the crucial role of functional interplay originating at the cortical level onto the heart in the upper limb neural control. The inclusion of functional BHI insights might substantially improve the neuroscientific knowledge about motor control, and this may lead to advanced BHCI systems performances.
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Affiliation(s)
- Vincenzo Catrambone
- Research Center E. Piaggio, Information Engineering, University of Pisa School of Engineering, Largo L. Lazzarino,1, Pisa, Italy, 56126, ITALY
| | - Giuseppe Averta
- Research Center E. Piaggio, Information Engineering, University of Pisa School of Engineering, Largo L. Lazzarino, 1, Pisa, Italy, 56126, ITALY
| | - Matteo Bianchi
- Research Center E. Piaggio, Information Engineering, University of Pisa School of Engineering, Largo L. Lazzarino, 1, Pisa, Toscana, 56126, ITALY
| | - Gaetano Valenza
- Research Center E. Piaggio, Information Engineering, University of Pisa School of Engineering, Largo L. Lazzarino, 1, Pisa, Toscana, 56126, ITALY
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Dell'Acqua C, Ghiasi S, Messerotti Benvenuti S, Greco A, Gentili C, Valenza G. Increased functional connectivity within alpha and theta frequency bands in dysphoria: A resting-state EEG study. J Affect Disord 2021; 281:199-207. [PMID: 33326893 DOI: 10.1016/j.jad.2020.12.015] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 11/20/2020] [Accepted: 12/05/2020] [Indexed: 02/08/2023]
Abstract
BACKGROUND The understanding of neurophysiological correlates underlying the risk of developing depression may have a significant impact on its early and objective identification. Research has identified abnormal resting-state electroencephalography (EEG) power and functional connectivity patterns in major depression. However, the entity of dysfunctional EEG dynamics in dysphoria is yet unknown. METHODS 32-channel EEG was recorded in 26 female individuals with dysphoria and in 38 age-matched, female healthy controls. EEG power spectra and alpha asymmetry in frontal and posterior channels were calculated in a 4-minute resting condition. An EEG functional connectivity analysis was conducted through phase locking values, particularly mean phase coherence. RESULTS While individuals with dysphoria did not differ from controls in EEG spectra and asymmetry, they exhibited dysfunctional brain connectivity. Particularly, in the theta band (4-8 Hz), participants with dysphoria showed increased connectivity between right frontal and central areas and right temporal and left occipital areas. Moreover, in the alpha band (8-12 Hz), dysphoria was associated with increased connectivity between right and left prefrontal cortex and between frontal and central-occipital areas bilaterally. LIMITATIONS All participants belonged to the female gender and were relatively young. Mean phase coherence did not allow to compute the causal and directional relation between brain areas. CONCLUSIONS An increased EEG functional connectivity in the theta and alpha bands characterizes dysphoria. These patterns may be associated with the excessive self-focus and ruminative thinking that typifies depressive symptoms. EEG connectivity patterns may represent a promising measure to identify individuals with a higher risk of developing depression.
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Affiliation(s)
- Carola Dell'Acqua
- Department of General Psychogy, University of Padua, Via Venezia 8 - 35131, Padua, Italy; Padova Neuroscience Center (PNC), University of Padua, Via Orus 2/B - 35131, Padua, Italy.
| | - Shadi Ghiasi
- Department of Information Engineering & Bioengineering and Robotics Research Center E. Piaggio, School of Engineering, University of Pisa, Pisa, Italy
| | - Simone Messerotti Benvenuti
- Department of General Psychogy, University of Padua, Via Venezia 8 - 35131, Padua, Italy; Padova Neuroscience Center (PNC), University of Padua, Via Orus 2/B - 35131, Padua, Italy
| | - Alberto Greco
- Department of Information Engineering & Bioengineering and Robotics Research Center E. Piaggio, School of Engineering, University of Pisa, Pisa, Italy
| | - Claudio Gentili
- Department of General Psychogy, University of Padua, Via Venezia 8 - 35131, Padua, Italy; Padova Neuroscience Center (PNC), University of Padua, Via Orus 2/B - 35131, Padua, Italy
| | - Gaetano Valenza
- Department of Information Engineering & Bioengineering and Robotics Research Center E. Piaggio, School of Engineering, University of Pisa, Pisa, Italy
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Sorriento A, Poliziani A, Cafarelli A, Valenza G, Ricotti L. A novel quantitative and reference-free ultrasound analysis to discriminate different concentrations of bone mineral content. Sci Rep 2021; 11:301. [PMID: 33432022 PMCID: PMC7801603 DOI: 10.1038/s41598-020-79365-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 12/07/2020] [Indexed: 12/19/2022] Open
Abstract
Bone fracture is a continuous process, during which bone mineral matrix evolves leading to an increase in hydroxyapatite and calcium carbonate content. Currently, no gold standard methods are available for a quantitative assessment of bone fracture healing. Moreover, the available tools do not provide information on bone composition. Whereby, there is a need for objective and non-invasive methods to monitor the evolution of bone mineral content. In general, ultrasound can guarantee a quantitative characterization of tissues. However, previous studies required measurements on reference samples. In this paper we propose a novel and reference-free parameter, based on the entropy of the phase signal calculated from the backscattered data in combination with amplitude information, to also consider absorption and scattering phenomena. The proposed metric was effective in discriminating different hydroxyapatite (from 10 to 50% w/v) and calcium carbonate (from 2 to 6% w/v) concentrations in bone-mimicking phantoms without the need for reference measurements, paving the way to their translational use for the diagnosis of tissue healing. To the best of our knowledge this is the first time that the phase entropy of the backscattered ultrasound signals is exploited for monitoring changes in the mineral content of bone-like materials.
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Affiliation(s)
- A Sorriento
- The BioRobotics Institute, Scuola Superiore Sant'Anna, 56127, Pisa, Italy.
- Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, 56127, Pisa, Italy.
| | - A Poliziani
- The BioRobotics Institute, Scuola Superiore Sant'Anna, 56127, Pisa, Italy
- Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, 56127, Pisa, Italy
| | - A Cafarelli
- The BioRobotics Institute, Scuola Superiore Sant'Anna, 56127, Pisa, Italy
- Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, 56127, Pisa, Italy
| | - G Valenza
- Bioengineerring and Robotics Research Centre E Piaggio, University of Pisa, 56122, Pisa, Italy
- Department of Information Engineering, University of Pisa, 56123, Pisa, Italy
| | - L Ricotti
- The BioRobotics Institute, Scuola Superiore Sant'Anna, 56127, Pisa, Italy
- Department of Excellence in Robotics & AI, Scuola Superiore Sant'Anna, 56127, Pisa, Italy
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Catrambone V, Talebi A, Barbieri R, Valenza G. Time-resolved Brain-to-Heart Probabilistic Information Transfer Estimation Using Inhomogeneous Point-Process Models. IEEE Trans Biomed Eng 2021; 68:3366-3374. [DOI: 10.1109/tbme.2021.3071348] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Vincenzo Catrambone
- Research Center E. Piaggio, Information Engineering, University of Pisa, 9310 Pisa, Toscana, Italy, (e-mail: )
| | - Alireza Talebi
- Research Center E. Piaggio, Information Engineering, University of Pisa, 9310 Pisa, Toscana, Italy, (e-mail: )
| | | | - Gaetano Valenza
- Research Center E. Piaggio, Information Engineering, University of Pisa, 9310 Pisa, Toscana, Italy, (e-mail: )
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