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Modelling brain dynamics by Boolean networks

Scientific Reports

F Bertacchini, C Scuro, P Pantano, E Bilotta

Understanding the relationship between brain architecture and brain function is a central issue in neuroscience. We modeled realistic spatio-temporal patterns of brain activity on a human connectome with a Boolean networks model with the aim of computationally replicating certain cognitive functions as they emerge from the standardization of many fMRI studies, identified as patterns of human brain activity. Results from the analysis of simulation data, carried out for different parameters and initial conditions identified many possible paths in the space of parameters of these network models, with normal (ordered asymptotically constant patterns), chaotic (oscillating or disordered) but also highly organized configurations, with countless spatial–temporal patterns. We interpreted these results as routes to chaos, permanence of the systems in regimes of complexity, and ordered stationary behavior, associating these dynamics to cognitive processes. The most important result of this work is the study of emergent neural circuits, i.e., configurations of areas that synchronize over time, both locally and globally, determining the emergence of computational analogues of cognitive processes, which may or may not be similar to the functioning of biological brain. Furthermore, results put in evidence the creation of how the brain creates structures of remote communication. These structures have hierarchical organization, where each level allows for the emergence of brain organizations which behave at the next superior level. Taken together these results allow the interplay of dynamical and topological roots of the multifaceted brain dynamics to be understood.

A project based learning approach for improving students’ computational thinking skills

Frontiers in Robotics and AI

Francesca Bertacchini, Carmelo Scuro, Pietro Pantano, Eleonora Bilotta

An educational robotics lab has been planned for undergraduate students in an Electronic Engineering degree, using the Project Based Learning (PBL) approach and the NAO robot. Students worked in a research context, with the aim of making the functions of the NAO robot as social and autonomous as possible, adopting in the design process the Wolfram Language (WL), from the Mathematica software. Interfacing the programming environment of the NAO with Mathematica, they solved in part the problem of autonomy of the NAO, thus realizing enhanced functions of autonomous movement, recognition of human faces and speech for improving the system social interaction. An external repository was created to streamline processes and stow data that the robot can easily access. Self-assessment processes demonstrated that the course provided students with useful skills to cope with real life problems.

The influence of an atypical spatial-numerical configuration on the SNARC effect: the role of order in spatial-numerical associations is revealed by context and task demands


Serena Mingolo, Valter Prpic, Eleonora Bilotta, Carlo Fantoni, Tiziano Agostini, Mauro Murgia

To better investigate the role of the order in spatial- numerical associations, we employed an atypical configuration of numerical stimuli as context and three different tasks, each involving different representations that were consistent or inconsistent with the order of the context or unbound to it. According to the observed results, the context shaped a spatial association when the task was based on the same configuration, it produced a conflict when it was inconsistent with the representation evoked by the task, and it did not affect the SNARC effect when it was unbound to the task. Taken together, the results of the present study highlight that spatial-numerical associations can be modulated by the order elicited by the context depending on the tasks.

SARS-CoV-2 emerging complexity and global dynamics

Chaos: An Interdisciplinary Journal of Nonlinear Science

Francesca Bertacchini, Pietro S Pantano, Eleonora Bilotta

The novel SARS-CoV-2 virus, prone to variation when interacting with spatially extended ecosystems and within hosts, can be considered a complex dynamic system. Therefore, it behaves creating several space–time manifestations of its dynamics. However, these physical manifestations in nature have not yet been fully disclosed or understood. Here we show 4D and 2D space–time patterns of the rate of infected individuals on a global scale, giving quantitative measures of transitions between different dynamical behaviors. By slicing the spatiotemporal patterns, we found manifestations of the virus behavior, such as cluster formation and bifurcation. Furthermore, by analyzing morphogenesis processes by entropy, we have been able to detect the virus phase transitions, typical of adaptive biological systems.

Snarcing with a phone: The role of order in spatial-numerical associations is revealed by context and task demands

Journal of Experimental Psychology: Human Perception and Performance

Serena Mingolo, Valter Prpic, Eleonora Bilotta, Carlo Fantoni, Tiziano Agostini, Mauro Murgia

Previous literature on the spatial-numerical association of response codes (SNARC) effect examined which factors modulate spatial-numerical associations. Recently, the role of order in the SNARC effect has been debated, and further research is necessary to better understand its contribution. The present study investigated how the order elicited by the context of the stimuli and by task demands interact. Across three experiments, we presented numbers in the context of a mobile phone keypad, an overlearned numerical display in which the ordinal position of numbers differs from the mental number line. The experiments employed three tasks with different levels of consistency with the order elicited by the context.

Shopping with (out) distancing: modelling the personal space to limit the spread of contagious disease among consumers in retail stores

Journal of Marketing Management

Eleonora Pantano, Gabriele Pizzi, Eleonora Bilotta, Pietro Pantano

This research aims at providing a new model of consumers’ personal space to limit the spread of contagious disease while shopping in person. To this end, it adopts an agents-based simulation approach to model consumers’ movements in the store during COVID-19 pandemic. Findings show the extent to which consumers’ contacts with others increase the risk of contagion, due to the occurrence of social gatherings in certain areas. Specifically, there is a linear correlation between the number of consumers in the store and the number of consumers susceptible to contract the disease. Thus, the personal space from a psychological perception becomes an individual and compulsory boundary to protect consumers from contagious disease.

On the temporal spreading of the SARS-CoV-2

PLoS One

Francesca Bertacchini, Eleonora Bilotta, Pietro S Pantano

The behaviour of SARS-CoV-2 virus is certainly one of the most challenging in contemporary world. Although the mathematical modelling of the virus has made relevant contributions, the unpredictable behaviour of the virus is still not fully understood. To identify some aspects of the virus elusive behaviour, we focused on the temporal characteristics of its course. We have analysed the latency trends the virus has realized worldwide, the outbreak of the hot spots, and the decreasing trends of the pandemic. We found that the spatio-temporal pandemic dynamics shows a complex behaviour. As with physical systems, these changes in the pandemic’s course, which we have called transitional stages of contagion, highlight shared characteristics in many countries. The main results of this work is that the pandemic progression rhythms have been clearly identified for each country, providing the processes and the stages at which the virus develops, thus giving important information on the activation of containment and control measures.

University students’ hangover may affect cognitive research

Frontiers in Psychology

Mauro Murgia, Serena Mingolo, Valter Prpic, Fabrizio Sors, Ilaria Santoro, Eleonora Bilotta, Tiziano Agostini

University students are the most employed category of participants in cognitive research. However, researchers cannot fully control what their participants do the night before the experiments (e.g., consumption of alcohol) and, unless the experiment specifically concerns the effects of alcohol consumption, they often do not ask about it. Despite previous studies demonstrating that alcohol consumption leads to decrements in next-day cognitive abilities, the potential confounding effect of hangover on the validity of cognitive research has never been addressed. To address this issue, in the present study, a test-retest design was used, with two groups of university students: at T0, one group was constituted by hungover participants, while the other group was constituted by non-hungover participants; at T1, both groups were re-tested in a non-hangover state. In particular, the tests used were two versions of a parity judgment task and an arithmetic verification task. The results highlight that: (a) the response times of university students experiencing a hangover are significantly slower than those of non-hangover students and (b) the response times of hungover students are slower than those of the same students when re-tested in a non-hangover state.

Mid-sagittal plane detection for advanced physiological measurements in brain scans

Physiological Measurement

Francesca Bertacchini, Rossella Rizzo, Eleonora Bilotta, Pietro Pantano, Angela Luca, Alessandro Mazzuca, Antonio Lopez, Alzheimer’s Disease Neuroimaging Initiative

The process of diagnosing many neurodegenerative diseases, such as Parkinson’s and progressive supranuclear palsy, involves the study of brain magnetic resonance imaging (MRI) scans in order to identify and locate morphological markers that can highlight the health status of the subject. A fundamental step in the pre-processing and analysis of MRI scans is the identification of the mid-sagittal plane, which corresponds to the mid-brain and allows a coordinate reference system for the whole MRI scan set.

Lesson planning by computational thinking skills in Italian pre-service teachers

Informatics in Education

Lorella Gabriele, Francesca Bertacchini, Assunta Tavernise, Leticia Vaca-Cárdenas, Pietro Pantano, Eleonora Bilotta

In the last years, a growing trend in different educational contexts focused on Computational Thinking (CT) skills acquisition for both in-service teachers and students. But very low attention has been paid to pre-service teachers’ education in regards to CT skills. To solve this issue, an empirical experimentation has been carried out with141 Italian pre-service teachers, that attended at a programming course, with the following aims: 1) provide them the main coding concepts by using Scratch 2.0; 2) offer practical advice on how to design educational applications (apps) to be applied into school context; 3) assess their apps by applying an already existing methodology, useful to give them feedback on their programming expertise and CT skills. 

Axisymmetric solutions for a chemotaxis model of Multiple Sclerosis

Ricerche di matematica

E Bilotta, F Gargano, V Giunta, MC Lombardo, P Pantano, M Sammartino

In this paper we study radially symmetric solutions for our recently proposed reaction–diffusion–chemotaxis model of Multiple Sclerosis. Through a weakly nonlinear expansion we classify the bifurcation at the onset and derive the amplitude equations ruling the formation of concentric demyelinating patterns which reproduce the concentric layers observed in Balò sclerosis and in the early phase of Multiple Sclerosis. 

Eckhaus and zigzag instability in a chemotaxis model of multiple sclerosis

Atti della Accademia Peloritana dei Pericolanti-Classe di Scienze Fisiche, Matematiche e Naturali

Eleonora Bilotta, Francesco Gargano, Valeria Giunta, Maria Carmela Lombardo, Pietro Pantano, Marco Sammartino

We present a theoretical and numerical study of the bifurcations of the stationary patterns supported by a chemotactic model of Multiple Sclerosis (MS). We derive the normal forms of the dynamics which allows to predict the appearance and stabilization of the emerging branches describing the concentric patterns typical of Balo’s sclerosis, a very aggressive variant of MS. Spatial modulation of the Turing-type structures through a zigzag instability is also addressed. The nonlinear stage of the Eckhaus and zigzag instability is investigated numerically: defect-mediated wavenumber adjustments are recovered and the time of occurrence of phase-slips is studied as the system parameters are varied.

Brain-like large scale cognitive networks and dynamics

The European Physical Journal Special Topics

Francesca Bertacchini, Eleonora Bilotta, Maria Carmela Lombardo, Marco Sammartino, Pietro Pantano

A new approach to the study of the brain and its functions known as Human Connectomics has been recently established. Starting from magnetic resonance images (MRI) of brain scans, it is possible to identify the fibers that link brain areas and to build an adjacency matrix that connects these areas, thus creating the brain connectome. The topology of these networks provides a lot of information about the organizational structure of the brain (both structural and functional). Nevertheless this knowledge is rarely used to investigate the possible emerging brain dynamics linked to cognitive functions. In this work, we implement finite state models on neural networks to display the outcoming brain dynamics, using different types of networks, which correspond to diverse segmentation methods and brain atlases. 

A facial emotions recognition application for subjects with Autism Spectrum Disorder

EDULEARN18 Proceedings

Francesca Bertacchini, Lorella Gabriele, Pietro Salvatore Pantano, Diana Olmedo-Vizueta, Angela Giaquinta, Assunta Tavernise, Eleonora Bilotta

Individuals with autism spectrum disorder have difficulties in facial emotion recognition (Joseph, Tanaka, 2002). Although these difficulties have been long investigated using different technologies (Alves et al., 2013; Bertacchini et al., 2013; Kim et al., 2015) results have not still yield a shared identification of all the involved variables as well as common results. In order to investigate this complex problem, an advanced application has been designed and implemented, involving the most useful features that other tools actually present in the market have. In fact, it allows the display of 3D faces expressing the following six basic emotions: Joy, Sadness, Anger, Fear, Disgust, and Surprise

Demyelination patterns in a mathematical model of multiple sclerosis

Journal of mathematical biology

MC Lombardo, R Barresi, E Bilotta, F Gargano, P Pantano, Mml Sammartino

In this paper we derive a reaction-diffusion-chemotaxis model for the dynamics of multiple sclerosis. We focus on the early inflammatory phase of the disease characterized by activated local microglia, with the recruitment of a systemically activated immune response, and by oligodendrocyte apoptosis. The model consists of three equations describing the evolution of macrophages, cytokine and apoptotic oligodendrocytes. The main driving mechanism is the chemotactic motion of macrophages in response to a chemical gradient provided by the cytokines. Our model generalizes the system proposed by Calvez and Khonsari (Math Comput Model 47(7–8):726–742, 2008) and Khonsari and Calvez (PLos ONE 2(1):e150, 2007) to describe Baló’s sclerosis, a rare and aggressive form of multiple sclerosis.

Discovery of Regular Domains in Large DNA Data Sets

Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

Francesca Bertacchini, Eleonora Bilotta, Pietro Pantano

Previous literature on the spatial-numerical association of response codes (SNARC) effect examined which factors modulate spatial-numerical associations. Recently, the role of order in the SNARC effect has been debated, and further research is necessary to better understand its contribution. The present study investigated how the order elicited by the context of the stimuli and by task demands interact. Across three experiments, we presented numbers in the context of a mobile phone keypad, an overlearned numerical display in which the ordinal position of numbers differs from the mental number line. The experiments employed three tasks with different levels of consistency with the order elicited by the context.

Wavefront invasion for a chemotaxis model of multiple sclerosis

Ricerche di Matematica

R Barresi, E Bilotta, F Gargano, MC Lombardo, P Pantano, Mml Sammartino

In this work we study wavefront propagation for a chemotaxis reaction-diffusion system describing the demyelination in Multiple Sclerosis. Through a weakly non linear analysis, we obtain the Ginzburg–Landau equation governing the evolution of the amplitude of the pattern. We validate the analytical findings through numerical simulations. We show the existence of traveling wavefronts connecting two different steady solutions of the equations. The proposed model reproduces the progression of the disease as a wave: for values of the chemotactic parameter below threshold, the wave leaves behind a homogeneous plaque of apoptotic oligodendrocytes. For values of the chemotactic coefficient above threshold, the model reproduces the formation of propagating concentric rings of demyelinated zones, typical of Baló’s sclerosis.

Neuroprotective effect of human mesenchymal stem cells in a compartmentalized neuronal membrane system

Acta Biomaterialia

Antonella Piscioneri, Sabrina Morelli, Maria Mele, Marcello Canonaco, Eleonora Bilotta, Pietro Pantano, Enrico Drioli, Loredana De Bartolo

In this work, we describe the development of a compartmentalized membrane system using neonatal rodent hippocampal cells and human mesenchymal stem cells (hMSCs) to investigate the neuroprotective effects of hMSCs. To elucidate this interaction an in vitro oxygen-glucose deprivation (OGD) model was used that mimics central nervous system insults in vivo. Cells were cultured in a membrane system with a sandwich configuration in which the hippocampal cells were seeded on a fluorocarbon (FC) membrane, and were separated by hMSCs through a semipermeable polyethersulfone (PES) membrane that ensures the transport of molecules and paracrine factors, but prevents cell-to-cell contact. This system was used to simulate a cerebral ischemic damage by inducing OGD for 120 min. 

Fully automated segmentation of the pons and midbrain using human T1 MR brain images

Acta Biomaterialia

Salvatore Nigro, Antonio Cerasa, Giancarlo Zito, Paolo Perrotta, Francesco Chiaravalloti, Giulia Donzuso, Franceso Fera, Eleonora Bilotta, Pietro Pantano, Aldo Quattrone, Alzheimer’s Disease Neuroimaging Initiative

This paper describes a novel method to automatically segment the human brainstem into midbrain and pons, called LABS: Landmark-based Automated Brainstem Segmentation. LABS processes high-resolution structural magnetic resonance images (MRIs) according to a revised landmark-based approach integrated with a thresholding method, without manual interaction.

A cellular neural network methodology for the automated segmentation of multiple sclerosis lesions

Journal of neuroscience methods

Antonio Cerasa, Eleonora Bilotta, Antonio Augimeri, Andrea Cherubini, Pietro Pantano, Giancarlo Zito, Pierluigi Lanza, Paola Valentino, Maria C Gioia, Aldo Quattrone

We present a new application based on genetic algorithms (GAs) that evolves a Cellular Neural Network (CNN) capable of automatically determining the lesion load in multiple sclerosis (MS) patients from magnetic resonance imaging (MRI). In particular, it seeks to identify brain areas affected by lesions, whose presence is revealed by areas of higher intensity if compared to healthy tissue. The performance of the CNN algorithm has been quantitatively evaluated by comparing the CNN output with the expert’s manual delineation of MS lesions. The CNN algorithm was run on a data set of 11 MS patients; for each one a single dataset of MRI images (matrix resolution of 256×256 pixels) was acquired. Our automated approach gives satisfactory results showing that after the learning process the CNN is capable of detecting MS lesions with different shapes and intensities (mean DICE coefficient=0.64).

Evolving Cellular Neural Networks for the Automated Segmentation of Multiple Sclerosis Lesions

Variants of Evolutionary Algorithms for Real-World Applications

Eleonora Bilotta, Antonio Cerasa, Pietro Pantano, Aldo Quattrone, Andrea Staino, Francesca Stramandinoli

This chapter presents an innovative approach for the segmentation of brain images that contain multiple sclerosis (MS) white matter lesions. Quantitative research of Magnetic Resonance Images (MRI), aimed at detecting and studying lesion load and tissue volumes, has turned out to be very useful for the re-evaluation of patients and clinical assessment of therapy. Until now, the standard procedure for this purpose has been the manual delineation of MS lesions, which makes the analysis a time-consuming process. The application presented in this work is a genetic algorithm (GA) that evolves a Cellular Neural Network (CNN) for pattern recognition. This network is capable to automatically segment the brain areas affected by lesions in MRI and also to immediately eliminate the parts of the brain that are not directly connected to the disease (like the skull, the optic nerve, etc.) in the segmentation process.