2026
Journal article
Open Access
Robustness of complexity estimation in event-driven signals against accuracy of event detection method
Cafiso Marco, Paradisi Paolo
Complexity has gained recent attention in machine learning for its ability to extract synthetic information from large datasets. Complex dynamical systems are characterized by temporal complexity associated with intermittent birth–death events of self-organizing behavior. These rapid transition events (RTEs) can be modeled as a stochastic point process on the time axis, with inter-event times (IETs) revealing rich dynamics. In particular, IETs with power-law distribution mark a departure from the Poisson statistics and indicate the presence of nontrivial complexity that is quantified by the power-law exponent μ of the IET distribution. However, detection of RTEs in noisy signals remains a challenge, since false positives can obscure the statistical structure of the underlying process. In this paper, we address the problem of quantifying the effect of the event detection tool on the accuracy of complexity estimation. This is reached through a systematic evaluation of the Event-Driven Diffusion Scaling (EDDiS) algorithm, a tool exploiting event-driven diffusion to estimate temporal complexity. After introducing the event detection method RTE-Finder (RTEF), we assess the performance of the RTEF-EDDiS pipeline using event-driven synthetic signals. The reliability of the RTEF is found to strongly depend on parameters such as the percentile and the number of false positives can be much higher than the number of genuine complex events. Despite this, we found that the complexity estimation is quite robust with respect to the rate of false positives. In many of the studied cases, the second moment scaling H appears to even improve as the rate of false positives increases, reaching estimation errors of about 4−7%.Source: CHAOS, SOLITONS AND FRACTALS, vol. 208 (issue 118264)
DOI: 10.1016/j.chaos.2026.118264
DOI: 10.2139/ssrn.5342885
DOI: 10.48550/arxiv.2506.06168
Project(s): Future Artificial Intelligence Research - Spoke 1 “Human-centered AI”
Metrics:
Cafiso Marco, Paradisi Paolo
Complexity has gained recent attention in machine learning for its ability to extract synthetic information from large datasets. Complex dynamical systems are characterized by temporal complexity associated with intermittent birth–death events of self-organizing behavior. These rapid transition events (RTEs) can be modeled as a stochastic point process on the time axis, with inter-event times (IETs) revealing rich dynamics. In particular, IETs with power-law distribution mark a departure from the Poisson statistics and indicate the presence of nontrivial complexity that is quantified by the power-law exponent μ of the IET distribution. However, detection of RTEs in noisy signals remains a challenge, since false positives can obscure the statistical structure of the underlying process. In this paper, we address the problem of quantifying the effect of the event detection tool on the accuracy of complexity estimation. This is reached through a systematic evaluation of the Event-Driven Diffusion Scaling (EDDiS) algorithm, a tool exploiting event-driven diffusion to estimate temporal complexity. After introducing the event detection method RTE-Finder (RTEF), we assess the performance of the RTEF-EDDiS pipeline using event-driven synthetic signals. The reliability of the RTEF is found to strongly depend on parameters such as the percentile and the number of false positives can be much higher than the number of genuine complex events. Despite this, we found that the complexity estimation is quite robust with respect to the rate of false positives. In many of the studied cases, the second moment scaling H appears to even improve as the rate of false positives increases, reaching estimation errors of about 4−7%.Source: CHAOS, SOLITONS AND FRACTALS, vol. 208 (issue 118264)
DOI: 10.1016/j.chaos.2026.118264
DOI: 10.2139/ssrn.5342885
DOI: 10.48550/arxiv.2506.06168
Project(s): Future Artificial Intelligence Research - Spoke 1 “Human-centered AI”
Metrics:
See at:
arXiv.org e-Print Archive 
| Chaos Solitons & Fractals | CNR IRIS | www.sciencedirect.com | doi.org 
| doi.org | CNR IRIS
2026
Conference article
Open Access
Advancements in Artificial Intelligence and Computer Vision for Biomedical Applications @SI-Lab
Marco Cafiso, Andrea Carboni, Claudia Caudai, Sara Colantonio, Francesco Conti, Mario D’acunto, Said Daoudagh, Giulio Del Corso, Riccardo Del Gratta, Danila Germanese, Giacomo Ignesti, Gianmarco Lazzini, Giuseppe Riccardo Leone, Barbara Leporini, Massimo Magrini, Massimo Martinelli, Davide Moroni, Ali Reza Omrani, Francesca Pardini, Maria Antonietta Pascali, Paolo Paradisi, Laura Sebastiaini, Marco Tampucci, Valeria Tateo, Federico Volpini
This paper summarizes recent research at the Signals and Images Lab (ISTI-CNR) leveraging artificial intelligence, machine learning, and computer vision to address complex biomedical challenges. We highlight advancements in trustworthy, explainable algorithmic foundations alongside their practical clinical applications across diagnostic imaging, physiological signal analysis and neuromotor rehabilitation.
Marco Cafiso, Andrea Carboni, Claudia Caudai, Sara Colantonio, Francesco Conti, Mario D’acunto, Said Daoudagh, Giulio Del Corso, Riccardo Del Gratta, Danila Germanese, Giacomo Ignesti, Gianmarco Lazzini, Giuseppe Riccardo Leone, Barbara Leporini, Massimo Magrini, Massimo Martinelli, Davide Moroni, Ali Reza Omrani, Francesca Pardini, Maria Antonietta Pascali, Paolo Paradisi, Laura Sebastiaini, Marco Tampucci, Valeria Tateo, Federico Volpini
This paper summarizes recent research at the Signals and Images Lab (ISTI-CNR) leveraging artificial intelligence, machine learning, and computer vision to address complex biomedical challenges. We highlight advancements in trustworthy, explainable algorithmic foundations alongside their practical clinical applications across diagnostic imaging, physiological signal analysis and neuromotor rehabilitation.
2025
Other
Open Access
SI-Lab Annual Research Report 2024
Awais Ch Muhammad, Baiamonte A., Benassi A., Berti A., Bertini G., Buongiorno R, Bulotta D., Cafiso M., Carboni A., Carloni G., Caudai C., Colantonio S., Conti F., Daoudagh S., Del Corso G., Fusco G., Galesi G., Germanese D., Gravili S., Ignesti G., Kuruoglu E. E., Lazzini G., Leone G. R., Leporini B., Magrini M., Martinelli M., Omrani Ali Reza, Pachetti E., Papini O., Paradisi P., Pardini F., Pascali M. A., Pieri G., Reggiannini M., Righi M., Salerno E., Salvetti O., Scozzari A., Sebastiani L., Straface S., Tampucci M., Tarabella L., Tonazzini A., Moroni D.
The Signal & Images Laboratory (SI-Lab) is an interdisciplinary research group in computer vision, signal analysis, intelligent vision systems and multimedia data understanding. It is part of the Institute of Information Science and Technologies (ISTI) of the National Research Council of Italy (CNR). This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2024.DOI: 10.32079/isti-ar-2025/002
Metrics:
Awais Ch Muhammad, Baiamonte A., Benassi A., Berti A., Bertini G., Buongiorno R, Bulotta D., Cafiso M., Carboni A., Carloni G., Caudai C., Colantonio S., Conti F., Daoudagh S., Del Corso G., Fusco G., Galesi G., Germanese D., Gravili S., Ignesti G., Kuruoglu E. E., Lazzini G., Leone G. R., Leporini B., Magrini M., Martinelli M., Omrani Ali Reza, Pachetti E., Papini O., Paradisi P., Pardini F., Pascali M. A., Pieri G., Reggiannini M., Righi M., Salerno E., Salvetti O., Scozzari A., Sebastiani L., Straface S., Tampucci M., Tarabella L., Tonazzini A., Moroni D.
The Signal & Images Laboratory (SI-Lab) is an interdisciplinary research group in computer vision, signal analysis, intelligent vision systems and multimedia data understanding. It is part of the Institute of Information Science and Technologies (ISTI) of the National Research Council of Italy (CNR). This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2024.DOI: 10.32079/isti-ar-2025/002
Metrics:
2025
Other
Open Access
ISTI-day 2025 Proceedings
Del Corso G., Pedrotti A., Federico G., Gennaro C., Carrara F., Amato G., Di Benedetto M., Gabrielli E., Belli D., Matrullo Zoe, Miori V., Tolomei Gabriele, Waheed T., Marchetti E., Calabrò Antonello., Rossetti G., Stella Massimo, Cazabet Rémy, Abramski K., Cau E., Citraro S., Failla A., Mesina V., Morini V., Pansanella V., Colantonio S., Germanese D., Pascali M. A., Bianchi L., Messina N., Falchi F., Barsellotti L., Pacini G., Cassese M., Puccetti G., Esuli A., Volpi L., Moreo Alejandro, Sebastiani F., Sperduti G., Nguyen Dong, Broccia G., Ter Beek M. H., Ferrari A., Massink M., Belmonte Gina, Ciancia V., Papini O., Canapa G., Catricalà B., Manca M., Paternò F., Santoro C., Zedda E., Gallo S., Maenza S., Mattioli A., Simeoli L., Rucci D., Carlini E., Dazzi P., Kavalionak H., Mordacchini M., Rulli C., Muntean Cristina Ioana, Nardini F. M., Perego R., Rocchietti G., Lettich F., Renso C., Pugliese C., Casini G., Haldimann Jonas, Meyer Thomas, Assante M., Candela L., Dell'Amico A., Frosini L., Mangiacrapa F., Oliviero A., Pagano P., Panichi G., Peccerillo B., Procaccini M., Mannocci A., Manghi P., Lonetti F., Kang Dongjae, Di Giandomenico F., Jee Eunkyoung, Lazzini G., Conti F., Scopigno R., D'Acunto M., Moroni D., Cafiso M., Paradisi P., Callieri M., Pavoni G., Corsini M., De Falco A., Sala F., Saraceni Q., Gattiglia Gabriele
ISTI-Day is an annual information and networking event organized by the Institute of Information Science and Technologies "A. Faedo" (ISTI) of the Italian National Research Council (CNR). This event features an opening talk of the Director of the Dept. DIITET (Emilio F. Campana) as well as an overview of the Institute's activities presented by the ISTI Director (Roberto Scopigno). Those institutional segments are complemented by dedicated presentations and round tables featuring former staff members, as well as internal and external collaborators. To foster a network of knowledge and collaboration among newcomers, the 2025 ISTI Day edition also includes a large poster session that provides a comprehensive overview of current research activities. Each of the 13 laboratories contributes 1–3 posters, highlighting the most innovative work and offering early-career researchers a platform for discussion. Thus these proceedings include the posters selected for ISTI-Day 2025, reflecting the diverse and innovative nature of the Institute's research.
Del Corso G., Pedrotti A., Federico G., Gennaro C., Carrara F., Amato G., Di Benedetto M., Gabrielli E., Belli D., Matrullo Zoe, Miori V., Tolomei Gabriele, Waheed T., Marchetti E., Calabrò Antonello., Rossetti G., Stella Massimo, Cazabet Rémy, Abramski K., Cau E., Citraro S., Failla A., Mesina V., Morini V., Pansanella V., Colantonio S., Germanese D., Pascali M. A., Bianchi L., Messina N., Falchi F., Barsellotti L., Pacini G., Cassese M., Puccetti G., Esuli A., Volpi L., Moreo Alejandro, Sebastiani F., Sperduti G., Nguyen Dong, Broccia G., Ter Beek M. H., Ferrari A., Massink M., Belmonte Gina, Ciancia V., Papini O., Canapa G., Catricalà B., Manca M., Paternò F., Santoro C., Zedda E., Gallo S., Maenza S., Mattioli A., Simeoli L., Rucci D., Carlini E., Dazzi P., Kavalionak H., Mordacchini M., Rulli C., Muntean Cristina Ioana, Nardini F. M., Perego R., Rocchietti G., Lettich F., Renso C., Pugliese C., Casini G., Haldimann Jonas, Meyer Thomas, Assante M., Candela L., Dell'Amico A., Frosini L., Mangiacrapa F., Oliviero A., Pagano P., Panichi G., Peccerillo B., Procaccini M., Mannocci A., Manghi P., Lonetti F., Kang Dongjae, Di Giandomenico F., Jee Eunkyoung, Lazzini G., Conti F., Scopigno R., D'Acunto M., Moroni D., Cafiso M., Paradisi P., Callieri M., Pavoni G., Corsini M., De Falco A., Sala F., Saraceni Q., Gattiglia Gabriele
ISTI-Day is an annual information and networking event organized by the Institute of Information Science and Technologies "A. Faedo" (ISTI) of the Italian National Research Council (CNR). This event features an opening talk of the Director of the Dept. DIITET (Emilio F. Campana) as well as an overview of the Institute's activities presented by the ISTI Director (Roberto Scopigno). Those institutional segments are complemented by dedicated presentations and round tables featuring former staff members, as well as internal and external collaborators. To foster a network of knowledge and collaboration among newcomers, the 2025 ISTI Day edition also includes a large poster session that provides a comprehensive overview of current research activities. Each of the 13 laboratories contributes 1–3 posters, highlighting the most innovative work and offering early-career researchers a platform for discussion. Thus these proceedings include the posters selected for ISTI-Day 2025, reflecting the diverse and innovative nature of the Institute's research.
See at:
CNR IRIS | www.isti.cnr.it | CNR IRIS
2025
Other
Metadata Only Access
Sistema di Dance Movement Analysis: Notazione di Laban
Generali Edoardo, Said Daoudagh, Edoardo Generali, Paolo Paradisi
L'analisi e la rappresentazione del movimento umano costituiscono una sfida complessa che coinvolge la ricerca scientifica e le arti performative. In questo contesto, la Notazione Laban rappresenta un linguaggio simbolico efficace per descrivere direzione, livello e qualità dei movimenti, trovando applicazione anche nella Danza Movimento Terapia (DMT). Il lavoro ha avuto come obiettivo la progettazione e lo sviluppo di LABANOTS (LABan Assisted NOTation Service), un sistema software per l'analisi automatica e l'etichettatura semiautomatica dei movimenti corporei secondo la Notazione Laban, con particolare attenzione agli arti superiori. Il sistema si basa su un'architettura modulare composta da un Core Framework, dedicato all'elaborazione dei dati e all'assegnazione dei segni Laban, e una Interfaccia Grafica (GUI) per la gestione e la visualizzazione delle analisi. Tra le tecniche implementate figurano l'uso dell'algoritmo RANSAC per la stima del pavimento, il modello ZoeDepth per la ricostruzione tridimensionale, il calcolo di grandezze cinematiche per l'individuazione dei keyframe e l'assegnazione dei segni Laban tramite coordinate sferiche. I test effettuati hanno confermato la correttezza delle funzionalità principali e l'efficacia dell'approccio proposto, evidenziando tuttavia limiti nella stima della profondità. Miglioramenti futuri in quest'area potranno incrementare ulteriormente l'affidabilità e la precisione del sistema. LABANOTS rappresenta un primo passo verso strumenti di analisi automatizzata del movimento applicabili alla DMT. I futuri sviluppi riguarderanno il miglioramento dei modelli di profondità, l'integrazione di tecniche di intelligenza artificiale più avanzate e l'estensione dell'analisi alle restanti parti del corpo non ancora considerate.
Generali Edoardo, Said Daoudagh, Edoardo Generali, Paolo Paradisi
L'analisi e la rappresentazione del movimento umano costituiscono una sfida complessa che coinvolge la ricerca scientifica e le arti performative. In questo contesto, la Notazione Laban rappresenta un linguaggio simbolico efficace per descrivere direzione, livello e qualità dei movimenti, trovando applicazione anche nella Danza Movimento Terapia (DMT). Il lavoro ha avuto come obiettivo la progettazione e lo sviluppo di LABANOTS (LABan Assisted NOTation Service), un sistema software per l'analisi automatica e l'etichettatura semiautomatica dei movimenti corporei secondo la Notazione Laban, con particolare attenzione agli arti superiori. Il sistema si basa su un'architettura modulare composta da un Core Framework, dedicato all'elaborazione dei dati e all'assegnazione dei segni Laban, e una Interfaccia Grafica (GUI) per la gestione e la visualizzazione delle analisi. Tra le tecniche implementate figurano l'uso dell'algoritmo RANSAC per la stima del pavimento, il modello ZoeDepth per la ricostruzione tridimensionale, il calcolo di grandezze cinematiche per l'individuazione dei keyframe e l'assegnazione dei segni Laban tramite coordinate sferiche. I test effettuati hanno confermato la correttezza delle funzionalità principali e l'efficacia dell'approccio proposto, evidenziando tuttavia limiti nella stima della profondità. Miglioramenti futuri in quest'area potranno incrementare ulteriormente l'affidabilità e la precisione del sistema. LABANOTS rappresenta un primo passo verso strumenti di analisi automatizzata del movimento applicabili alla DMT. I futuri sviluppi riguarderanno il miglioramento dei modelli di profondità, l'integrazione di tecniche di intelligenza artificiale più avanzate e l'estensione dell'analisi alle restanti parti del corpo non ancora considerate.
See at:
CNR IRIS
2025
Conference article
Open Access
Leveraging AI for Signal and Image Analysis in Medicine and Health
Marco Cafiso, Andrea Carboni, Claudia Caudai, Sara Colantonio, Francesco Conti, Mario D’acunto, Said Daoudagh, Giulio Del Corso, Danila Germanese, Giacomo Ignesti, Gianmarco Lazzini, Giuseppe Riccardo Leone, Massimo Magrini, Davide Moroni, Francesca Pardini, Maria Antonietta Pascali, Paolo Paradisi, Federico Volpini
The integration of artificial intelligence (AI) into the medical domain is driving innovation and progress in healthcare. This paper summarizes the research activities that a multidisciplinary research group within the Signals and Images Lab of the Institute of Information Science and Technologies of the National Research Council of Italy is carrying out to explore the great potential of AI in several applications, e.g., in the analysis of biomedical data, and in the development of tools for enhancing trustworthiness and reliability of AI based systems. From cancer diagnosis and grading, to the analysis of body physiological signals to improve the understanding of dance movement therapy as an approach to healthy aging, this work highlights the paradigm shift that AI has brought into medicine and healthcare.Source: CEUR WORKSHOP PROCEEDINGS, vol. 4121. Trieste, June 23-24, 2025
Marco Cafiso, Andrea Carboni, Claudia Caudai, Sara Colantonio, Francesco Conti, Mario D’acunto, Said Daoudagh, Giulio Del Corso, Danila Germanese, Giacomo Ignesti, Gianmarco Lazzini, Giuseppe Riccardo Leone, Massimo Magrini, Davide Moroni, Francesca Pardini, Maria Antonietta Pascali, Paolo Paradisi, Federico Volpini
The integration of artificial intelligence (AI) into the medical domain is driving innovation and progress in healthcare. This paper summarizes the research activities that a multidisciplinary research group within the Signals and Images Lab of the Institute of Information Science and Technologies of the National Research Council of Italy is carrying out to explore the great potential of AI in several applications, e.g., in the analysis of biomedical data, and in the development of tools for enhancing trustworthiness and reliability of AI based systems. From cancer diagnosis and grading, to the analysis of body physiological signals to improve the understanding of dance movement therapy as an approach to healthy aging, this work highlights the paradigm shift that AI has brought into medicine and healthcare.Source: CEUR WORKSHOP PROCEEDINGS, vol. 4121. Trieste, June 23-24, 2025
See at:
ceur-ws.org | CNR IRIS | CNR IRIS
2025
Journal article
Open Access
Nondimensionalisations of the Langevin equation and small-parameter limits
Hottovy S., Paradisi P., Pagnini G.
Inspired by the success of dimensional analysis in continuum mechanics and nondimensional numbers such as Péclet, Schmidt, Lewis, and Prandtl, we introduce the study of the Brownian motion nondimensional parameter M = conservativeforce / nonconservativeforce . In particular, we nondimensionalise the Langevin equation for a Brownian particle of mass m moving in a fluid of viscosity γ with diffusivity coefficient σ under the action of a conservative force in the form of a harmonic oscillator of constant k. We identify all the possible time-scales T , and the associated length-scales L , on the basis of the nondimensional parameter M = m k γ − 2 . Through this nondimensionalisation we study the small-parameter limits with respect to M α and we identify five different regimes due to α , in opposition to the three identified by the standard analysis on the basis of the time-scales: T 1 = m / γ , T 2 = m / k , and T 3 = γ / k . Because of the ratio-type definition of M the leading force is established by the sign of α . For very short time-scales ( α > 0 ) , the particle is trapped at its initial condition. For short times ( α = 0 ) , the particle has the same dynamics of a free Brownian particle. That is, the action of the conservative force is negligible. For intermediate time ( − 1 < α < 0 ) , the particle diffuses as a Wiener process, that is as a free Brownian particle at large elapsed times. The over-damped timescale ( α = − 1 ) is a critical timescale where the dynamics are given by the Smoluchowski-Kramers approximation. For long times ( α < − 1 ) , the particle is trapped at the bottom of the potential well.Source: PHYSICS OF FLUIDS, vol. 37 (issue 3), pp. 037101-1-037101-7
DOI: 10.1063/5.0253896
Metrics:
Hottovy S., Paradisi P., Pagnini G.
Inspired by the success of dimensional analysis in continuum mechanics and nondimensional numbers such as Péclet, Schmidt, Lewis, and Prandtl, we introduce the study of the Brownian motion nondimensional parameter M = conservativeforce / nonconservativeforce . In particular, we nondimensionalise the Langevin equation for a Brownian particle of mass m moving in a fluid of viscosity γ with diffusivity coefficient σ under the action of a conservative force in the form of a harmonic oscillator of constant k. We identify all the possible time-scales T , and the associated length-scales L , on the basis of the nondimensional parameter M = m k γ − 2 . Through this nondimensionalisation we study the small-parameter limits with respect to M α and we identify five different regimes due to α , in opposition to the three identified by the standard analysis on the basis of the time-scales: T 1 = m / γ , T 2 = m / k , and T 3 = γ / k . Because of the ratio-type definition of M the leading force is established by the sign of α . For very short time-scales ( α > 0 ) , the particle is trapped at its initial condition. For short times ( α = 0 ) , the particle has the same dynamics of a free Brownian particle. That is, the action of the conservative force is negligible. For intermediate time ( − 1 < α < 0 ) , the particle diffuses as a Wiener process, that is as a free Brownian particle at large elapsed times. The over-damped timescale ( α = − 1 ) is a critical timescale where the dynamics are given by the Smoluchowski-Kramers approximation. For long times ( α < − 1 ) , the particle is trapped at the bottom of the potential well.Source: PHYSICS OF FLUIDS, vol. 37 (issue 3), pp. 037101-1-037101-7
DOI: 10.1063/5.0253896
Metrics:
See at:
CNR IRIS | pubs.aip.org | CNR IRIS
2025
Journal article
Open Access
A biologically plausible model of astrocyte-neuron networks in random and hub-driven connectivity
Salzano G., Paradisi P., Cataldo E.
Recent research studies in brain neural networks are highlighting the involvement of glial cells, in particular astrocytes, in synaptic modulation, memory formation, and neural synchronization, a role that has often been overlooked. Thus, theoretical models have begun incorporating astrocytes to better understand their functional impact. Additionally, the structural organization of neuron-neuron, astrocyte-neuron and astrocyte-astrocyte connections plays a crucial role in network dynamics. Starting from a recently published astrocyte-neuron network model with neuron-neuron random connectivity, we provide an extensive evaluation of this same model, focusing on astrocytic dynamics, neuron-astrocyte connectivity, and spatial distribution of inhibitory neurons. We propose refinements to the model with the aim of improving the biological plausibility of the above described characteristics of the model. To assess the interplay between astrocytes and network topology, we compare four configurations: neural networks with and without astrocytes, each under random and hub-driven connectivity. Simulations are conducted using the Brian2 simulator, providing insights into how astrocytes and structural heterogeneity jointly influence neural dynamics. Our findings contribute to a deeper understanding of neuron-glia interactions and the impact of network topology on astrocyte-neuron network dynamics. In particular, while finding an expected decrease of neural firing activity due to astrocyte calcium dynamics, we also found that hub-driven topology trigger a much higher firing rate with respect to the random topology, even having this last one a much higher number of neuron-neuron connections.Source: NEURAL NETWORKS, vol. 194 (issue 108111)
DOI: 10.1016/j.neunet.2025.108111
Metrics:
Salzano G., Paradisi P., Cataldo E.
Recent research studies in brain neural networks are highlighting the involvement of glial cells, in particular astrocytes, in synaptic modulation, memory formation, and neural synchronization, a role that has often been overlooked. Thus, theoretical models have begun incorporating astrocytes to better understand their functional impact. Additionally, the structural organization of neuron-neuron, astrocyte-neuron and astrocyte-astrocyte connections plays a crucial role in network dynamics. Starting from a recently published astrocyte-neuron network model with neuron-neuron random connectivity, we provide an extensive evaluation of this same model, focusing on astrocytic dynamics, neuron-astrocyte connectivity, and spatial distribution of inhibitory neurons. We propose refinements to the model with the aim of improving the biological plausibility of the above described characteristics of the model. To assess the interplay between astrocytes and network topology, we compare four configurations: neural networks with and without astrocytes, each under random and hub-driven connectivity. Simulations are conducted using the Brian2 simulator, providing insights into how astrocytes and structural heterogeneity jointly influence neural dynamics. Our findings contribute to a deeper understanding of neuron-glia interactions and the impact of network topology on astrocyte-neuron network dynamics. In particular, while finding an expected decrease of neural firing activity due to astrocyte calcium dynamics, we also found that hub-driven topology trigger a much higher firing rate with respect to the random topology, even having this last one a much higher number of neuron-neuron connections.Source: NEURAL NETWORKS, vol. 194 (issue 108111)
DOI: 10.1016/j.neunet.2025.108111
Metrics:
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CNR IRIS | www.sciencedirect.com | CNR IRIS
2025
Conference article
Open Access
Assessment of dance movement therapy outcomes: a preliminary proposal
Daoudagh S., Ignesti G., Moroni D., Sebastiani L., Paradisi P.
Context: Dance Movement Therapy (DMT) is a therapeutic modality that utilizes movement to promote holistic well-being. Current DMT assessment methods rely heavily on the subjective judgment of the DMT professional. Objective: Our research aims to develop a framework composed of different components with specific functionalities that can be integrated with the DMT modality to improve the objectivity and efficiency of DMT evaluations. Method: The DMT framework consists of an experimental protocol for data collection and a reference-supporting architecture that includes components for video analysis, physiological signal management, and evaluation tools. Artificial Intelligence (AI) based human pose estimation techniques are also employed to derive the DMT participants’ poses during the DMT sessions for more reliable movement analysis. Results: Our preliminary results consist of demonstrating the effectiveness of the AI-based pose estimation tool, YOLO-NAS-Pose, in accurately detecting participants’ poses. Conclusion: The proposed framework offers a promising approach to improving DMT practices by integrating and leveraging AI-based human pose estimation to evaluate participants’ movement in the DMT setting objectively. Future research will focus on refining the framework and developing user-friendly tools for widespread adoption in real DMT contexts.Source: COMMUNICATIONS IN COMPUTER AND INFORMATION SCIENCE, vol. 2371, pp. 382-395. Porto, Portogallo, 21-22/11/2024
DOI: 10.1007/978-3-031-83845-3_23
Project(s): Tuscany Health Ecosystem
Metrics:
Daoudagh S., Ignesti G., Moroni D., Sebastiani L., Paradisi P.
Context: Dance Movement Therapy (DMT) is a therapeutic modality that utilizes movement to promote holistic well-being. Current DMT assessment methods rely heavily on the subjective judgment of the DMT professional. Objective: Our research aims to develop a framework composed of different components with specific functionalities that can be integrated with the DMT modality to improve the objectivity and efficiency of DMT evaluations. Method: The DMT framework consists of an experimental protocol for data collection and a reference-supporting architecture that includes components for video analysis, physiological signal management, and evaluation tools. Artificial Intelligence (AI) based human pose estimation techniques are also employed to derive the DMT participants’ poses during the DMT sessions for more reliable movement analysis. Results: Our preliminary results consist of demonstrating the effectiveness of the AI-based pose estimation tool, YOLO-NAS-Pose, in accurately detecting participants’ poses. Conclusion: The proposed framework offers a promising approach to improving DMT practices by integrating and leveraging AI-based human pose estimation to evaluate participants’ movement in the DMT setting objectively. Future research will focus on refining the framework and developing user-friendly tools for widespread adoption in real DMT contexts.Source: COMMUNICATIONS IN COMPUTER AND INFORMATION SCIENCE, vol. 2371, pp. 382-395. Porto, Portogallo, 21-22/11/2024
DOI: 10.1007/978-3-031-83845-3_23
Project(s): Tuscany Health Ecosystem
Metrics:
See at:
CNR IRIS | link.springer.com | CNR IRIS | CNR IRIS
2024
Conference article
Open Access
Temporal complexity of a Hopfield-type neural model in random and scale-free graphs
Cafiso M., Paradisi P.
The Hopfield network model and its generalizations were introduced as a model of associative, or contentaddressable, memory. They were widely investigated both as an unsupervised learning method in artificial intelligence and as a model of biological neural dynamics in computational neuroscience. The complexity features of biological neural networks have attracted the scientific community’s interest for the last two decades. More recently, concepts and tools borrowed from complex network theory were applied to artificial neural networks and learning, thus focusing on the topological aspects. However, the temporal structure is also a crucial property displayed by biological neural networks and investigated in the framework of systems displaying complex intermittency. The Intermittency-Driven Complexity (IDC) approach indeed focuses on the metastability of self-organized states, whose signature is a power-decay in the inter-event time distribution or a scaling behaviour in the related event-driven diffusion processes. The investigation of IDC in neural dynamics and its relationship with network topology is still in its early stages. In this work, we present the preliminary results of an IDC analysis carried out on a bio-inspired Hopfield-type neural network comparing two different connectivities, i.e., scale-free vs. random network topology. We found that random networks can trigger complexity features similar to that of scale-free networks, even if with some differences and for different parameter values, in particular for different noise levelsDOI: 10.5220/0013007600003837
DOI: 10.48550/arxiv.2406.12895
Project(s): FAIR “Future Artificial Intelligence Research”, Spoke-8: Pervasive AI.
Metrics:
Cafiso M., Paradisi P.
The Hopfield network model and its generalizations were introduced as a model of associative, or contentaddressable, memory. They were widely investigated both as an unsupervised learning method in artificial intelligence and as a model of biological neural dynamics in computational neuroscience. The complexity features of biological neural networks have attracted the scientific community’s interest for the last two decades. More recently, concepts and tools borrowed from complex network theory were applied to artificial neural networks and learning, thus focusing on the topological aspects. However, the temporal structure is also a crucial property displayed by biological neural networks and investigated in the framework of systems displaying complex intermittency. The Intermittency-Driven Complexity (IDC) approach indeed focuses on the metastability of self-organized states, whose signature is a power-decay in the inter-event time distribution or a scaling behaviour in the related event-driven diffusion processes. The investigation of IDC in neural dynamics and its relationship with network topology is still in its early stages. In this work, we present the preliminary results of an IDC analysis carried out on a bio-inspired Hopfield-type neural network comparing two different connectivities, i.e., scale-free vs. random network topology. We found that random networks can trigger complexity features similar to that of scale-free networks, even if with some differences and for different parameter values, in particular for different noise levelsDOI: 10.5220/0013007600003837
DOI: 10.48550/arxiv.2406.12895
Project(s): FAIR “Future Artificial Intelligence Research”, Spoke-8: Pervasive AI.
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arXiv.org e-Print Archive | CNR IRIS | ncta.scitevents.org | doi.org | doi.org | Archivio della Ricerca - Università di Pisa | CNR IRIS
2024
Journal article
Open Access
Reply to Comment on ‘Anomalous diffusion originated by two Markovian hopping-trap mechanisms’
Vitali S., Paradisi P., Pagnini G.
Reply to V P Shkilev.Source: JOURNAL OF PHYSICS. A, MATHEMATICAL AND THEORETICAL, vol. 57 (issue 14), pp. 1-3
DOI: 10.1088/1751-8121/ad329e
Metrics:
Vitali S., Paradisi P., Pagnini G.
Reply to V P Shkilev.Source: JOURNAL OF PHYSICS. A, MATHEMATICAL AND THEORETICAL, vol. 57 (issue 14), pp. 1-3
DOI: 10.1088/1751-8121/ad329e
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2024
Other
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Sistema di dance movement analysis: tracking ed estrazione di features
Cerri Lorenzo, Cerri L., Carta A., Daoudagh S., Paradisi P.
Cerri Lorenzo, Cerri L., Carta A., Daoudagh S., Paradisi P.
2023
Other
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THE D.3.2.1 - AA@THE User needs, technical requirements and specifications
Pratali L, Campana M G, Delmastro F, Di Martino F, Pescosolido L, Barsocchi P, Broccia G, Ciancia V, Gennaro C, Girolami M, Lagani G, La Rosa D, Latella D, Magrini M, Manca M, Massink M, Mattioli A, Moroni D, Palumbo F, Paradisi P, Paternò F, Santoro C, Sebastiani L, Vairo C
Deliverable D3.2.1 del progetto PNRR Ecosistemi ed innovazione - THE
Pratali L, Campana M G, Delmastro F, Di Martino F, Pescosolido L, Barsocchi P, Broccia G, Ciancia V, Gennaro C, Girolami M, Lagani G, La Rosa D, Latella D, Magrini M, Manca M, Massink M, Mattioli A, Moroni D, Palumbo F, Paradisi P, Paternò F, Santoro C, Sebastiani L, Vairo C
Deliverable D3.2.1 del progetto PNRR Ecosistemi ed innovazione - THE
2023
Journal article
Open Access
Morning naps architecture and mentation recall complexity
Sebastiani L, Barcaro U, Paradisi P, Frumento P, Faraguna U
Mentation reports were collected after spontaneous awakenings from morning naps in 18 healthy participants, and associations between sleep stages duration and complexity of recalled mentation were investigated. Participants were continuously recorded with polysomnography and allowed to sleep for a maximum of 2 hr. Mentation reports were classified according to both their complexity (1-6 scale) and their perceived timing of occurrence (Recent or Previous Mentation with respect to the final awakening). The results showed a good level of mentation recall, including different types of mentation with lab-related stimuli. N1 + N2 duration was positively related to the complexity of Previous Mentation recall, while rapid eye movement sleep duration was negatively related. This suggests that the recall of complex mentation, such as dreaming with a plot, occurring far from awakening may depend on the length of N1 + N2. However, the duration of sleep stages did not predict the complexity of Recent Mentation recall. Nevertheless, 80% of participants who recalled Recent Mentation had a rapid eye movement sleep episode. Half of the participants reported incorporating lab-related stimuli in their mentation, which positively correlated with both N1 + N2 and rapid eye movement duration. In conclusion, nap sleep architecture is informative about the complexity of dreams perceived as having occurred early during the sleep episode, but not about those perceived as recent.Source: JOURNAL OF SLEEP RESEARCH (PRINT), vol. 32 (issue 5), pp. e13915-1-e13915-10
DOI: 10.1111/jsr.13915
Metrics:
Sebastiani L, Barcaro U, Paradisi P, Frumento P, Faraguna U
Mentation reports were collected after spontaneous awakenings from morning naps in 18 healthy participants, and associations between sleep stages duration and complexity of recalled mentation were investigated. Participants were continuously recorded with polysomnography and allowed to sleep for a maximum of 2 hr. Mentation reports were classified according to both their complexity (1-6 scale) and their perceived timing of occurrence (Recent or Previous Mentation with respect to the final awakening). The results showed a good level of mentation recall, including different types of mentation with lab-related stimuli. N1 + N2 duration was positively related to the complexity of Previous Mentation recall, while rapid eye movement sleep duration was negatively related. This suggests that the recall of complex mentation, such as dreaming with a plot, occurring far from awakening may depend on the length of N1 + N2. However, the duration of sleep stages did not predict the complexity of Recent Mentation recall. Nevertheless, 80% of participants who recalled Recent Mentation had a rapid eye movement sleep episode. Half of the participants reported incorporating lab-related stimuli in their mentation, which positively correlated with both N1 + N2 and rapid eye movement duration. In conclusion, nap sleep architecture is informative about the complexity of dreams perceived as having occurred early during the sleep episode, but not about those perceived as recent.Source: JOURNAL OF SLEEP RESEARCH (PRINT), vol. 32 (issue 5), pp. e13915-1-e13915-10
DOI: 10.1111/jsr.13915
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Journal of Sleep Research | CNR IRIS | onlinelibrary.wiley.com | ISTI Repository | CNR IRIS
2023
Journal article
Open Access
Changes over the night in REM-sleep microstructure. A hypothesis of similarity to changes in dream features reported in the literature
Barcaro U, Magrini M, Paradisi P
The research aimed at a quantitative description of the changes in REM-sleep microstructure throughout the night. For this purpose, sleep recordings available on the public "PhysioNet" website were analyzed from a chronobiological perspective. This approach was suggested by the fact that two important properties of REM sleep determine its microstructure: the alternation between phasic and tonic microstates, and the presence of Slow Eye Movements in addition to Rapid Eye Movements. Although the examined recordings did not include data about dreams, a significant result of our analyses was the close similarity between the observed highly statistically significant changes in the microstructure and changes in basic dream features that are amply reported in the literature, including recall, word count, vividness and emotional content.Source: INTERNATIONAL JOURNAL OF DREAM RESEARCH, vol. 16 (issue 1)
DOI: 10.11588/ijodr.2023.1.84936
Metrics:
Barcaro U, Magrini M, Paradisi P
The research aimed at a quantitative description of the changes in REM-sleep microstructure throughout the night. For this purpose, sleep recordings available on the public "PhysioNet" website were analyzed from a chronobiological perspective. This approach was suggested by the fact that two important properties of REM sleep determine its microstructure: the alternation between phasic and tonic microstates, and the presence of Slow Eye Movements in addition to Rapid Eye Movements. Although the examined recordings did not include data about dreams, a significant result of our analyses was the close similarity between the observed highly statistically significant changes in the microstructure and changes in basic dream features that are amply reported in the literature, including recall, word count, vividness and emotional content.Source: INTERNATIONAL JOURNAL OF DREAM RESEARCH, vol. 16 (issue 1)
DOI: 10.11588/ijodr.2023.1.84936
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2023
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Event-based complexity in turbulence
Paradisi P, Cesari R
Since the studies of Kolmogorov and Oboukhov in 1941, the problem of in- termittent large velocity excursions was recognized to be one of the most intriguing and elusive aspects of turbulent flows. While many efforts were devoted since 1960s to the magnitude intermittency, related to the statistics of the large increments in the tur- bulent signals, the attention towards the so-called clustering intermittency has started to increase only in the last two decades, even if some pioneering studies were carried in previous years. The low attention towards the clustering intermittency is somewhat surprising, as intermittency itself is essentially defined as an alternance of extended qui- escent periods/regions and short/small high activity periods/zones. It is then natural to characterize intermittency by means of events marked along the dependent variable axes, whatever time or space. Conversely, the concept of crucial events and of temporal complexity, related to non-trivial clustering properties of these same events, has been proposed as a general interpretative framework for the investigation of complex systems with metastable self-organizing dynamics. Without any claim to being complete, in this chapter we give a review of event-based complexity approaches used in literature for the description of turbulence in the plane- tary boundary layer. The main goal is to put a first bridge between turbulence studies exploiting event-based complexity approaches, such as clustering exponents and classi- cal Oboukhov intermittency exponents, and studies about intermittent complex systems, where concepts and ideas developed in the fields of non-equilibrium statistical physics, probability theory and dynamical system theory are jointly exploited.
Paradisi P, Cesari R
Since the studies of Kolmogorov and Oboukhov in 1941, the problem of in- termittent large velocity excursions was recognized to be one of the most intriguing and elusive aspects of turbulent flows. While many efforts were devoted since 1960s to the magnitude intermittency, related to the statistics of the large increments in the tur- bulent signals, the attention towards the so-called clustering intermittency has started to increase only in the last two decades, even if some pioneering studies were carried in previous years. The low attention towards the clustering intermittency is somewhat surprising, as intermittency itself is essentially defined as an alternance of extended qui- escent periods/regions and short/small high activity periods/zones. It is then natural to characterize intermittency by means of events marked along the dependent variable axes, whatever time or space. Conversely, the concept of crucial events and of temporal complexity, related to non-trivial clustering properties of these same events, has been proposed as a general interpretative framework for the investigation of complex systems with metastable self-organizing dynamics. Without any claim to being complete, in this chapter we give a review of event-based complexity approaches used in literature for the description of turbulence in the plane- tary boundary layer. The main goal is to put a first bridge between turbulence studies exploiting event-based complexity approaches, such as clustering exponents and classi- cal Oboukhov intermittency exponents, and studies about intermittent complex systems, where concepts and ideas developed in the fields of non-equilibrium statistical physics, probability theory and dynamical system theory are jointly exploited.
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2023
Other
Open Access
D3.2.1: AA@THE User needs, technical requirements and specifications
Lorenza Pratali, Franca Delmastro, Mattia Campana, Flavio Di Martino, Loreto Pescosolido, Paolo Barsocchi, Giovanna Broccia, Vincenzo Ciancia, Claudio Gennaro, Michele Girolami, Gabriele Lagani, Diego Latella, Massimo Magrini, Marco Manca, Mieke Massink, Andrea Mattioli, Davide Moroni, Filippo Palumbo, Paolo Paradisi, Fabio Paternò, Laura Sebastiani, Claudio Vairo, Carmelina Santoro, Davide La Rosa
The objective of this deliverable is to compile a comprehensive report that describes the user needs, requirements, and technical specifications necessary to successfully implement the pilot study. To achieve this, it is crucial to establish contacts with specific associations and medical experts, which, collaboratively, will help to establish exclusion and inclusion criteria for the target population of healthy adults. Furthermore, another related objective is to define the different categories of users that will interact with the system and their specific needs. This holistic approach will ensure that the system is designed and developed to satisfy the diverse needs of the users and be aligned with the goals of the project. To achieve the milestone M3.2.1, we made significant progresses in the definition of the pilot study for the AA@THE subproject. One of our key achievements is the successful description of users’ needs, requirements, and technical specifications necessary for the study. We worked closely with both a specialized association of personal trainers for Adapted Physical Activity (APA) for older adults, already active in the area of Pisa, and the medical partner who played a crucial role in providing valuable insights and expertise to establish exclusion and inclusion criteria for the target population of healthy adults. In this milestone, we also defined the activities and services that we intend to offer. Specifically, we plan to provide technological systems aimed at monitoring physical and cognitive training processes, as well as stability evaluations, by instrumenting a gym dedicated to active and healthy ageing, which is located within the CNR research area in Pisa. Additionally, we will conduct sleep, nutrition, and sedentary assessments at the volunteers' homes. Furthermore, we successfully defined the different user categories involved in the study. To facilitate the recruitment process and people engagement, on January 17th 2023, we organized an open day in collaboration with the gym association where we presented the overall objectives of the project and we collected feedbacks from a group of healthy adults over 65, already involved in APA training. This allowed us to gain a comprehensive understanding of the users' specific needs in terms of system interactions, thus establishing the system requirements and technical specifications of the AA@THE ecosystem. In parallel, a specific action on “Automatic Support of Medical Image Analysis” has been initiated by members of the “Formal Methods and Tools” group at CNR-ISTI. Such an action aims at leveraging Formal Methods in Computer Science, Logic and Model Checking to augment state-of-the-art machine learning techniques for automatic medical image analysis, enabling end-users to make specific assumptions on the level of accountability and affordability of the system. The methodology is based on a strict intertwining between theory and experimentation, with the development of new theoretical foundations for model reduction and efficient model checking, and experimentation and finalization of a graphical user interface that is being evaluated from the points of view of usability and of cognitive load. Moreover, the design and implementation of a suitable GUI able to support the analysis of medical images has been conducted and tested with small groups of people derived from the hospital in Lucca. The proposed GUI prototype has been evaluated from a cognitive point of view in order to allow easy employment with little training, for general practitioners and caregivers who may lack the technical skills required to use fully-fledged medical imaging programs.Project(s): Tuscany Health Ecosystem
Lorenza Pratali, Franca Delmastro, Mattia Campana, Flavio Di Martino, Loreto Pescosolido, Paolo Barsocchi, Giovanna Broccia, Vincenzo Ciancia, Claudio Gennaro, Michele Girolami, Gabriele Lagani, Diego Latella, Massimo Magrini, Marco Manca, Mieke Massink, Andrea Mattioli, Davide Moroni, Filippo Palumbo, Paolo Paradisi, Fabio Paternò, Laura Sebastiani, Claudio Vairo, Carmelina Santoro, Davide La Rosa
The objective of this deliverable is to compile a comprehensive report that describes the user needs, requirements, and technical specifications necessary to successfully implement the pilot study. To achieve this, it is crucial to establish contacts with specific associations and medical experts, which, collaboratively, will help to establish exclusion and inclusion criteria for the target population of healthy adults. Furthermore, another related objective is to define the different categories of users that will interact with the system and their specific needs. This holistic approach will ensure that the system is designed and developed to satisfy the diverse needs of the users and be aligned with the goals of the project. To achieve the milestone M3.2.1, we made significant progresses in the definition of the pilot study for the AA@THE subproject. One of our key achievements is the successful description of users’ needs, requirements, and technical specifications necessary for the study. We worked closely with both a specialized association of personal trainers for Adapted Physical Activity (APA) for older adults, already active in the area of Pisa, and the medical partner who played a crucial role in providing valuable insights and expertise to establish exclusion and inclusion criteria for the target population of healthy adults. In this milestone, we also defined the activities and services that we intend to offer. Specifically, we plan to provide technological systems aimed at monitoring physical and cognitive training processes, as well as stability evaluations, by instrumenting a gym dedicated to active and healthy ageing, which is located within the CNR research area in Pisa. Additionally, we will conduct sleep, nutrition, and sedentary assessments at the volunteers' homes. Furthermore, we successfully defined the different user categories involved in the study. To facilitate the recruitment process and people engagement, on January 17th 2023, we organized an open day in collaboration with the gym association where we presented the overall objectives of the project and we collected feedbacks from a group of healthy adults over 65, already involved in APA training. This allowed us to gain a comprehensive understanding of the users' specific needs in terms of system interactions, thus establishing the system requirements and technical specifications of the AA@THE ecosystem. In parallel, a specific action on “Automatic Support of Medical Image Analysis” has been initiated by members of the “Formal Methods and Tools” group at CNR-ISTI. Such an action aims at leveraging Formal Methods in Computer Science, Logic and Model Checking to augment state-of-the-art machine learning techniques for automatic medical image analysis, enabling end-users to make specific assumptions on the level of accountability and affordability of the system. The methodology is based on a strict intertwining between theory and experimentation, with the development of new theoretical foundations for model reduction and efficient model checking, and experimentation and finalization of a graphical user interface that is being evaluated from the points of view of usability and of cognitive load. Moreover, the design and implementation of a suitable GUI able to support the analysis of medical images has been conducted and tested with small groups of people derived from the hospital in Lucca. The proposed GUI prototype has been evaluated from a cognitive point of view in order to allow easy employment with little training, for general practitioners and caregivers who may lack the technical skills required to use fully-fledged medical imaging programs.Project(s): Tuscany Health Ecosystem
2022
Journal article
Open Access
Anomalous diffusion originated by two Markovian hopping-trap mechanisms
Vitali S, Paradisi P, Pagnini G
We show through intensive simulations that the paradigmatic features of anomalous diffusion are indeed the features of a (continuous-time) random walk driven by two different Markovian hopping-trap mechanisms. If p (0, 1/2) and 1 - p are the probabilities of occurrence of each Markovian mechanism, then the anomalousness parameter ? (0, 1) results to be ? ? 1 - 1/{1 + log[(1 - p)/p]}. Ensemble and single-particle observables of this model have been studied and they match the main characteristics of anomalous diffusion as they are typically measured in living systems. In particular, the celebrated transition of the walker's distribution from exponential to stretched-exponential and finally to Gaussian distribution is displayed by including also the Brownian yet non-Gaussian interval.Source: JOURNAL OF PHYSICS. A, MATHEMATICAL AND THEORETICAL (PRINT), vol. 55 (issue 224012)
DOI: 10.1088/1751-8121/ac677f
DOI: 10.48550/arxiv.2204.06276
Metrics:
Vitali S, Paradisi P, Pagnini G
We show through intensive simulations that the paradigmatic features of anomalous diffusion are indeed the features of a (continuous-time) random walk driven by two different Markovian hopping-trap mechanisms. If p (0, 1/2) and 1 - p are the probabilities of occurrence of each Markovian mechanism, then the anomalousness parameter ? (0, 1) results to be ? ? 1 - 1/{1 + log[(1 - p)/p]}. Ensemble and single-particle observables of this model have been studied and they match the main characteristics of anomalous diffusion as they are typically measured in living systems. In particular, the celebrated transition of the walker's distribution from exponential to stretched-exponential and finally to Gaussian distribution is displayed by including also the Brownian yet non-Gaussian interval.Source: JOURNAL OF PHYSICS. A, MATHEMATICAL AND THEORETICAL (PRINT), vol. 55 (issue 224012)
DOI: 10.1088/1751-8121/ac677f
DOI: 10.48550/arxiv.2204.06276
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arXiv.org e-Print Archive | Journal of Physics A Mathematical and Theoretical | CNR IRIS | iopscience.iop.org | ISTI Repository | doi.org | CNR IRIS
2022
Journal article
Open Access
Synchronization between music dynamics and heart rhythm is modulated by the musician's emotional involvement: a single case study
Sebastiani L, Mastorci F, Magrini M, Paradisi P, Pingitore A
In this study we evaluated heart rate variability (HRV) changes in a pianist, playing in a laboratory, to investigate whether HRV changes are guided by music temporal features or by technical difficulty and/or subjective factors (e.g., experienced effort). The pianist was equipped with a wearable telemetry device for ECG recording during the execution of 4 classical and 5 jazz pieces. From ECG we derived the RR intervals series (tachogram), and, for each piece, analyzed HRV in the time (RR, RMSSD, Stress Index) and frequency domains (Total spectral power) and performed non-linear analysis (Multiscale Entropy). We also studied the correlation (Pearson) between the time course of music volume envelope and tachogram. Results showed a general reduction of parasympathetic and an increase of sympathetic activity, with the greatest changes during the classical pieces execution, the pianist appraised as more demanding than the jazz ones. The most marked changes occurred during the most technically/emotionally demanding piece, and correlation analysis revealed a negative association between music volume envelope time course and tachogram only for this piece, suggesting a modulation of the limbic system on the synchronization between heart rhythm and music temporal features. Classical music was also associated with the increase of entropy (1st scale) with respect to rest, indicating its effectiveness in driving flexible, healthy, heart dynamics. In conclusion, HRV seems modulated not only by the music temporal features, but also by the pianist's emotional involvement, which is greatly influenced, in a non-trivial manner, by the technical demands and musician expertise.Source: FRONTIERS IN PSYCHOLOGY, vol. 13 (issue 908488)
DOI: 10.3389/fpsyg.2022.908488
Metrics:
Sebastiani L, Mastorci F, Magrini M, Paradisi P, Pingitore A
In this study we evaluated heart rate variability (HRV) changes in a pianist, playing in a laboratory, to investigate whether HRV changes are guided by music temporal features or by technical difficulty and/or subjective factors (e.g., experienced effort). The pianist was equipped with a wearable telemetry device for ECG recording during the execution of 4 classical and 5 jazz pieces. From ECG we derived the RR intervals series (tachogram), and, for each piece, analyzed HRV in the time (RR, RMSSD, Stress Index) and frequency domains (Total spectral power) and performed non-linear analysis (Multiscale Entropy). We also studied the correlation (Pearson) between the time course of music volume envelope and tachogram. Results showed a general reduction of parasympathetic and an increase of sympathetic activity, with the greatest changes during the classical pieces execution, the pianist appraised as more demanding than the jazz ones. The most marked changes occurred during the most technically/emotionally demanding piece, and correlation analysis revealed a negative association between music volume envelope time course and tachogram only for this piece, suggesting a modulation of the limbic system on the synchronization between heart rhythm and music temporal features. Classical music was also associated with the increase of entropy (1st scale) with respect to rest, indicating its effectiveness in driving flexible, healthy, heart dynamics. In conclusion, HRV seems modulated not only by the music temporal features, but also by the pianist's emotional involvement, which is greatly influenced, in a non-trivial manner, by the technical demands and musician expertise.Source: FRONTIERS IN PSYCHOLOGY, vol. 13 (issue 908488)
DOI: 10.3389/fpsyg.2022.908488
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2022
Other
Open Access
SI-Lab annual research report 2021
Righi M, Leone G R, Carboni A, Caudai C, Colantonio S, Kuruoglu E E, Leporini B, Magrini M, Paradisi P, Pascali M A, Pieri G, Reggiannini M, Salerno E, Scozzari A, Tonazzini A, Fusco G, Galesi G, Martinelli M, Pardini F, Tampucci M, Berti A, Bruno A, Buongiorno R, Carloni G, Conti F, Germanese D, Ignesti G, Matarese F, Omrani A, Pachetti E, Papini O, Benassi A, Bertini G, Coltelli P, Tarabella L, Straface S, Salvetti O, Moroni D
The Signal & Images Laboratory is an interdisciplinary research group in computer vision, signal analysis, intelligent vision systems and multimedia data understanding. It is part of the Institute of Information Science and Technologies (ISTI) of the National Research Council of Italy (CNR). This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2021.DOI: 10.32079/isti-ar-2022/003
DOI: https://doi.org/10.32079/isti-ar-2022/003
Metrics:
Righi M, Leone G R, Carboni A, Caudai C, Colantonio S, Kuruoglu E E, Leporini B, Magrini M, Paradisi P, Pascali M A, Pieri G, Reggiannini M, Salerno E, Scozzari A, Tonazzini A, Fusco G, Galesi G, Martinelli M, Pardini F, Tampucci M, Berti A, Bruno A, Buongiorno R, Carloni G, Conti F, Germanese D, Ignesti G, Matarese F, Omrani A, Pachetti E, Papini O, Benassi A, Bertini G, Coltelli P, Tarabella L, Straface S, Salvetti O, Moroni D
The Signal & Images Laboratory is an interdisciplinary research group in computer vision, signal analysis, intelligent vision systems and multimedia data understanding. It is part of the Institute of Information Science and Technologies (ISTI) of the National Research Council of Italy (CNR). This report accounts for the research activities of the Signal and Images Laboratory of the Institute of Information Science and Technologies during the year 2021.DOI: 10.32079/isti-ar-2022/003
DOI: https://doi.org/10.32079/isti-ar-2022/003
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CNR IRIS | ISTI Repository | CNR IRIS