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Volume 10 (2019): Issue 2 (January 2019)
Special Issue on Mathematical Models and Methods in Biology, Medicine and Physiology. Guest Editors: Michele Piana, Luigi Preziosi

Volume 10 (2019): Issue 1 (February 2019)

Volume 9 (2018): Issue 2 (December 2018)
Special Issue on Mathematical modelling for complex systems: multi-agents methods. Guest Editor: Elena De Angelis

Volume 9 (2018): Issue 1 (February 2018)

Volume 8 (2017): Issue 1 (March 2017)

Volume 7 (2016): Issue 3 (September 2016)
"Special Issue on New Trends in Semi-Lagrangian Methods, Guest Editors: Luca Bonaventura, Maurizio Falcone and Roberto Ferretti

Volume 7 (2016): Issue 2 (June 2016)
Special Issue on Constitutive Equations for Heat Conduction in Nanosystems and Non-equilibrium Processes. Guest Editors: Vito Antonio Cimmelli and David Jou

Volume 7 (2016): Issue 1 (January 2016)
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Journal Details
Format
Journal
eISSN
2038-0909
First Published
15 Dec 2014
Publication timeframe
1 time per year
Languages
English

Search

Volume 10 (2019): Issue 2 (January 2019)
Special Issue on Mathematical Models and Methods in Biology, Medicine and Physiology. Guest Editors: Michele Piana, Luigi Preziosi

Journal Details
Format
Journal
eISSN
2038-0909
First Published
15 Dec 2014
Publication timeframe
1 time per year
Languages
English

Search

7 Articles

Special Issue on Mathematical Models and Methods in Biology, Medicine and Physiology. Guest Editors: Michele Piana, Luigi Preziosi

access type Open Access

Introduction

Published Online: 11 May 2019
Page range: 1 - 1

Abstract

access type Open Access

From dynamics to links: a sparse reconstruction of the topology of a neural network

Published Online: 11 May 2019
Page range: 2 - 11

Abstract

Abstract

One major challenge in neuroscience is the identification of interrelations between signals reflecting neural activity and how information processing occurs in the neural circuits. At the cellular and molecular level, mechanisms of signal transduction have been studied intensively and a better knowledge and understanding of some basic processes of information handling by neurons has been achieved. In contrast, little is known about the organization and function of complex neuronal networks. Experimental methods are now available to simultaneously monitor electrical activity of a large number of neurons in real time. Then, the qualitative and quantitative analysis of the spiking activity of individual neurons is a very valuable tool for the study of the dynamics and architecture of the neural networks. Such activity is not due to the sole intrinsic properties of the individual neural cells but it is mostly the consequence of the direct influence of other neurons. The deduction of the effective connectivity between neurons, whose experimental spike trains are observed, is of crucial importance in neuroscience: first for the correct interpretation of the electro-physiological activity of the involved neurons and neural networks, and, for correctly relating the electrophysiological activity to the functional tasks accomplished by the network. In this work, we propose a novel method for the identification of connectivity of neural networks using recorded voltages. Our approach is based on the assumption that the network has a topology with sparse connections. After a brief description of our method, we will report the performances and compare it to the cross-correlation computed on the spike trains, which represents a gold standard method in the field.

Keywords

  • Neural Networks
  • Sparse reconstruction
  • LASSO method
access type Open Access

Solution of a mathematical model for the treatment of rheumatoid arthritis

Published Online: 11 May 2019
Page range: 12 - 24

Abstract

Abstract

Rheumatoid arthritis is an autoimmune disease of unknown etiology that manifests as a persistent inflammatory synovitis and eventually destroys the joints. The immune system recognizes synovial cells as not self and consequently causes lymphocyte and antibody proliferation that is promoted by the pro-inflammatory cytokines, the most significant being tumor necrosis factor TNF-α. In the treatment of rheumatoid arthritis either monoclonal antibodies or soluble receptors are used to neutralize the TNF-α bioactivity, such as sTNFR2, Etanercept and Infliximab. In [M. Jit et al. Rheumatology 2005;44:323-331] a mathematical model that represents the TNF-α dynamics in the inflamed synovial joint within which locally produced TNF-α can bind to cell-surface receptors was proposed. It consists of four coupled ordinary differential equations, that were integrated numerically assuming a range of estimates of the key parameters. In this paper we complement the previous work by determining the general solution of those equations for specific conditions on the parameters. Then we characterize the behavior of TNF-α in the presence of different inhibitors and also evaluate the inhibitors effectiveness in the treatment of rheumatoid arthritis.

Keywords

  • Rheumatoid arthritis treatment model
  • ordinary differential equations
  • general solution
access type Open Access

An inversion method based on random sampling for real-time MEG neuroimaging

Published Online: 11 May 2019
Page range: 25 - 34

Abstract

Abstract

The MagnetoEncephaloGraphy (MEG) has gained great interest in neurorehabilitation training due to its high temporal resolution. The challenge is to localize the active regions of the brain in a fast and accurate way. In this paper we use an inversion method based on random spatial sampling to solve the real-time MEG inverse problem. Several numerical tests on synthetic but realistic data show that the method takes just a few hundredths of a second on a laptop to produce an accurate map of the electric activity inside the brain. Moreover, it requires very little memory storage. For these reasons the random sampling method is particularly attractive in real-time MEG applications.

Keywords

  • neuroimaging
  • magnetoencephalography
  • source localization
  • inverse problem
  • random sampling
access type Open Access

A simplex method for the calibration of a MEG device

Published Online: 11 May 2019
Page range: 35 - 46

Abstract

Abstract

MagnetoEncephaloGraphy (MEG) devices are helmet–shaped arrays of sensors that measure the tiny magnetic fields produced by neural currents. As they operate at low temperature, they are typically immersed in liquid helium. However, during the cooling process the sensor position and shape can change, with respect to nominal values, due to thermal stress. This implies that an accurate sensor calibration is required before a MEG device is utilized in either neuroscientific research or clinical workflow. Here we describe a calibration scheme developed for the optimal use of a MEG system recently realized at the “Istituto di Cibernetica e Biofisica” of the Italian CNR. To achieve the calibration goal a dedicated magnetic source is used (calibration device) and the geometric parameters of the sensors are determined through an optimisation procedure, based on the Nelder-Mead algorithm, which maximises the correlation coefficient between the predicted and the recorded magnetic field. Then the sensitivity of the sensors is analytically estimated. The developed calibration procedure is validated with synthetic data mimicking a real scenario.

Keywords

  • MagnetoEncephaloGraphy
  • Calibration
  • Calibration Device
  • Nelder-Mead
access type Open Access

Multivariate Regularized Newton and Levenberg-Marquardt methods: a comparison on synthetic data of tumor hypoxia in a kinetic framework

Published Online: 11 May 2019
Page range: 47 - 53

Abstract

Abstract

In this paper we propose a new algorithm to optimize the parameters of a compartmental problem describing tumor hypoxia. The method is based on a multivariate Newton approach, with Tikhonov regularization, and can be easily applied to data with diverse statistical distributions. Here we simulate [18F]fluoromisonidazole Positron Emission Tomography dynamic data of hypoxia of a neck tumor and describe the tracer flow inside tumor with a two-compartments compartmental model. We perform optimization on the parameters of the model via the proposed Multivariate Regularized Newton method and validate it against results obtained with a standard Levenberg-Marquardt approach. The proposed algorithm returns parameters that are closer to the ground truth while preserving the statistical distribution of the data.

Keywords

  • Compartmental analysis
  • Newton methods
  • tumor hypoxia
  • Fmiso-PET
access type Open Access

Extension of tumor fingers: A comparison between an individual-cell based model and a measure theoretic approach

Published Online: 11 May 2019
Page range: 54 - 69

Abstract

Abstract

The invasive capability is fundamental in determining the malignancy of a solid tumor. In particular, tumor invasion fronts are characterized by different morphologies, which result both from cell-based processes (such as cell elasticity, adhesive properties and motility) and from subcellular molecular dynamics (such as growth factor internalization, ECM protein digestion and MMP secretion). Of particular relevance is the development of tumors with unstable fingered morphologies: they are in fact more aggressive and hard to be treated than smoother ones as, even if their invasive depth is limited, they are difficult to be surgically removed. The phenomenon of malignant fingering has been reproduced with several mathematical approaches. In this respect, we here present a qualitative comparison between the results obtained by an individual cell-based model (an extended version of the cellular Potts model) and by a measure-based theoretic method. In particular, we show that in both cases a fundamental role in finger extension is played by intercellular adhesive forces and taxis-like migration.

Keywords

  • tumor fingering
  • cellular Potts model
  • measure theoretic approach
  • hybrid model
7 Articles

Special Issue on Mathematical Models and Methods in Biology, Medicine and Physiology. Guest Editors: Michele Piana, Luigi Preziosi

access type Open Access

Introduction

Published Online: 11 May 2019
Page range: 1 - 1

Abstract

access type Open Access

From dynamics to links: a sparse reconstruction of the topology of a neural network

Published Online: 11 May 2019
Page range: 2 - 11

Abstract

Abstract

One major challenge in neuroscience is the identification of interrelations between signals reflecting neural activity and how information processing occurs in the neural circuits. At the cellular and molecular level, mechanisms of signal transduction have been studied intensively and a better knowledge and understanding of some basic processes of information handling by neurons has been achieved. In contrast, little is known about the organization and function of complex neuronal networks. Experimental methods are now available to simultaneously monitor electrical activity of a large number of neurons in real time. Then, the qualitative and quantitative analysis of the spiking activity of individual neurons is a very valuable tool for the study of the dynamics and architecture of the neural networks. Such activity is not due to the sole intrinsic properties of the individual neural cells but it is mostly the consequence of the direct influence of other neurons. The deduction of the effective connectivity between neurons, whose experimental spike trains are observed, is of crucial importance in neuroscience: first for the correct interpretation of the electro-physiological activity of the involved neurons and neural networks, and, for correctly relating the electrophysiological activity to the functional tasks accomplished by the network. In this work, we propose a novel method for the identification of connectivity of neural networks using recorded voltages. Our approach is based on the assumption that the network has a topology with sparse connections. After a brief description of our method, we will report the performances and compare it to the cross-correlation computed on the spike trains, which represents a gold standard method in the field.

Keywords

  • Neural Networks
  • Sparse reconstruction
  • LASSO method
access type Open Access

Solution of a mathematical model for the treatment of rheumatoid arthritis

Published Online: 11 May 2019
Page range: 12 - 24

Abstract

Abstract

Rheumatoid arthritis is an autoimmune disease of unknown etiology that manifests as a persistent inflammatory synovitis and eventually destroys the joints. The immune system recognizes synovial cells as not self and consequently causes lymphocyte and antibody proliferation that is promoted by the pro-inflammatory cytokines, the most significant being tumor necrosis factor TNF-α. In the treatment of rheumatoid arthritis either monoclonal antibodies or soluble receptors are used to neutralize the TNF-α bioactivity, such as sTNFR2, Etanercept and Infliximab. In [M. Jit et al. Rheumatology 2005;44:323-331] a mathematical model that represents the TNF-α dynamics in the inflamed synovial joint within which locally produced TNF-α can bind to cell-surface receptors was proposed. It consists of four coupled ordinary differential equations, that were integrated numerically assuming a range of estimates of the key parameters. In this paper we complement the previous work by determining the general solution of those equations for specific conditions on the parameters. Then we characterize the behavior of TNF-α in the presence of different inhibitors and also evaluate the inhibitors effectiveness in the treatment of rheumatoid arthritis.

Keywords

  • Rheumatoid arthritis treatment model
  • ordinary differential equations
  • general solution
access type Open Access

An inversion method based on random sampling for real-time MEG neuroimaging

Published Online: 11 May 2019
Page range: 25 - 34

Abstract

Abstract

The MagnetoEncephaloGraphy (MEG) has gained great interest in neurorehabilitation training due to its high temporal resolution. The challenge is to localize the active regions of the brain in a fast and accurate way. In this paper we use an inversion method based on random spatial sampling to solve the real-time MEG inverse problem. Several numerical tests on synthetic but realistic data show that the method takes just a few hundredths of a second on a laptop to produce an accurate map of the electric activity inside the brain. Moreover, it requires very little memory storage. For these reasons the random sampling method is particularly attractive in real-time MEG applications.

Keywords

  • neuroimaging
  • magnetoencephalography
  • source localization
  • inverse problem
  • random sampling
access type Open Access

A simplex method for the calibration of a MEG device

Published Online: 11 May 2019
Page range: 35 - 46

Abstract

Abstract

MagnetoEncephaloGraphy (MEG) devices are helmet–shaped arrays of sensors that measure the tiny magnetic fields produced by neural currents. As they operate at low temperature, they are typically immersed in liquid helium. However, during the cooling process the sensor position and shape can change, with respect to nominal values, due to thermal stress. This implies that an accurate sensor calibration is required before a MEG device is utilized in either neuroscientific research or clinical workflow. Here we describe a calibration scheme developed for the optimal use of a MEG system recently realized at the “Istituto di Cibernetica e Biofisica” of the Italian CNR. To achieve the calibration goal a dedicated magnetic source is used (calibration device) and the geometric parameters of the sensors are determined through an optimisation procedure, based on the Nelder-Mead algorithm, which maximises the correlation coefficient between the predicted and the recorded magnetic field. Then the sensitivity of the sensors is analytically estimated. The developed calibration procedure is validated with synthetic data mimicking a real scenario.

Keywords

  • MagnetoEncephaloGraphy
  • Calibration
  • Calibration Device
  • Nelder-Mead
access type Open Access

Multivariate Regularized Newton and Levenberg-Marquardt methods: a comparison on synthetic data of tumor hypoxia in a kinetic framework

Published Online: 11 May 2019
Page range: 47 - 53

Abstract

Abstract

In this paper we propose a new algorithm to optimize the parameters of a compartmental problem describing tumor hypoxia. The method is based on a multivariate Newton approach, with Tikhonov regularization, and can be easily applied to data with diverse statistical distributions. Here we simulate [18F]fluoromisonidazole Positron Emission Tomography dynamic data of hypoxia of a neck tumor and describe the tracer flow inside tumor with a two-compartments compartmental model. We perform optimization on the parameters of the model via the proposed Multivariate Regularized Newton method and validate it against results obtained with a standard Levenberg-Marquardt approach. The proposed algorithm returns parameters that are closer to the ground truth while preserving the statistical distribution of the data.

Keywords

  • Compartmental analysis
  • Newton methods
  • tumor hypoxia
  • Fmiso-PET
access type Open Access

Extension of tumor fingers: A comparison between an individual-cell based model and a measure theoretic approach

Published Online: 11 May 2019
Page range: 54 - 69

Abstract

Abstract

The invasive capability is fundamental in determining the malignancy of a solid tumor. In particular, tumor invasion fronts are characterized by different morphologies, which result both from cell-based processes (such as cell elasticity, adhesive properties and motility) and from subcellular molecular dynamics (such as growth factor internalization, ECM protein digestion and MMP secretion). Of particular relevance is the development of tumors with unstable fingered morphologies: they are in fact more aggressive and hard to be treated than smoother ones as, even if their invasive depth is limited, they are difficult to be surgically removed. The phenomenon of malignant fingering has been reproduced with several mathematical approaches. In this respect, we here present a qualitative comparison between the results obtained by an individual cell-based model (an extended version of the cellular Potts model) and by a measure-based theoretic method. In particular, we show that in both cases a fundamental role in finger extension is played by intercellular adhesive forces and taxis-like migration.

Keywords

  • tumor fingering
  • cellular Potts model
  • measure theoretic approach
  • hybrid model

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