Biomedical subjects

Mauro Ursino

Publications and source records attributed to Mauro Ursino.

At least 19 recordsLinked to original sources

PubMed2026

Granger connectivity and graph-theoretical analysis of scalp EEG across the preictal to ictal transition for presurgical evaluation.

OBJECTIVE: To assess the feasibility of estimating lateralization and localization of the epileptogenic zone (EZ) in temporal and extratemporal lobe epilepsy by combining Electric Source Imaging (ESI) with functional connectivity analysis of high-density EEG from the preictal to the ictal phase. METHODS: Adults with drug-resistant focal epilepsy and at least one recorded seizure during 40- or 64 channels EEG monitoring were retrospectively included. Granger causality and hubness centrality were computed over the 10-s preictal interval and the first 5 s of the ictal period, with ictal onset defined as the first EEG change identified by experienced epileptologists. The reference standard for EZ localization was based on resective surgical outcome or stereo-EEG findings. RESULTS: Thirteen patients (7 females; median age 35 years) were included. Connectivity analyses showed higher concordance with clinical findings during the preictal phase than during the ictal phase for both lateralization (91% vs 46%) and localization (73% vs 27%). Performance was highest in temporal (7/7 lateralization; 6/7 localization) and frontal lobe epilepsy (2/2 for both), and lower in parieto-occipital epilepsy (1/2 and 0/2, respectively). In two cases with poor surgical outcome or no surgical indication, connectivity findings were discordant with clinical estimates. CONCLUSIONS: Connectivity analysis across the preictal to ictal transition provides relevant lateralizing and localizing information, particularly in temporal and frontal lobe epilepsy, and may reveal clinically meaningful discordance. SIGNIFICANCE: Integrating high-density EEG, ESI, and functional connectivity during the phase preceding the first EEG change may support non-invasive presurgical evaluation.

Humans↗

PubMed2007

Comparison of different cortical connectivity estimators for high-resolution EEG recordings.

The aim of this work is to characterize quantitatively the performance of a body of techniques in the frequency domain for the estimation of cortical connectivity from high-resolution EEG recordings in different operative conditions commonly encountered in practice. Connectivity pattern estimators investigated are the Directed Transfer Function (DTF), its modification known as direct DTF (dDTF) and the Partial Directed Coherence (PDC). Predefined patterns of cortical connectivity were simulated and then retrieved by the application of the DTF, dDTF, and PDC methods. Signal-to-noise ratio (SNR) and length (LENGTH) of EEG epochs were studied as factors affecting the reconstruction of the imposed connectivity patterns. Reconstruction quality and error rate in estimated connectivity patterns were evaluated by means of some indexes of quality for the reconstructed connectivity pattern. The error functions were statistically analyzed with analysis of variance (ANOVA). The whole methodology was then applied to high-resolution EEG data recorded during the well-known Stroop paradigm. Simulations indicated that all three methods correctly estimated the simulated connectivity patterns under reasonable conditions. However, performance of the methods differed somewhat as a function of SNR and LENGTH factors. The methods were generally equivalent when applied to the Stroop data. In general, the amount of available EEG affected the accuracy of connectivity pattern estimations. Analysis of 27 s of nonconsecutive recordings with an SNR of 3 or more ensured that the connectivity pattern could be accurately recovered with an error below 7% for the PDC and 5% for the DTF. In conclusion, functional connectivity patterns of cortical activity can be effectively estimated under general conditions met in most EEG recordings by combining high-resolution EEG techniques, linear inverse estimation of the cortical activity, and frequency domain multivariate methods such as PDC, DTF, and dDTF.

Analysis of Variance↗

PubMed2006

Use of a neural mass model for the analysis of effective connectivity among cortical regions based on high resolution EEG recordings.

Assessment of brain connectivity among different brain areas during cognitive or motor tasks is a crucial problem in neuroscience today. Aim of this work is to use a neural mass model to assess the effect of various connectivity patterns in cortical electroencephalogram (EEG) power spectral density, and investigate the possibility to derive connectivity circuits from EEG data. To this end, a model of an individual region of interest (ROI) has been built as the parallel arrangement of three populations, each described as in Wendling et al. (Eur J Neurosci 15:1499-1508, 2002). Connectivity among ROIs includes three parameters, which specify the strength of connection in the different frequency bands. The following main steps have been followed: (1) we analyzed how the power spectral density (PSD) is significantly modified by the kind of coupling hypothesized among the ROIs; (2) with the model, and using an automatic fitting procedure, we looked for a simple connectivity circuit able to reproduce PSD of cortical EEG in three ROIs during a finger-movement task. The estimated parameters represent the strength of connections among the ROIs in the different frequency bands. Cortical EEGs were computed with an inverse propagation algorithm, starting from measurement performed with 96 electrodes on the scalp. The present study suggests that the model can be used as a simulation tool, able to mimic the effect of connectivity on EEG. Moreover, it can be used to look for simple connectivity circuits, able to explain the main features of observed cortical PSD. These results may open new prospectives in the use of neurophysiological models, instead of empirical models, to assess effective connectivity from neuroimaging information.

Brain Mapping↗

PubMed2006

Direction selectivity of simple cells in the primary visual cortex: comparison of two alternative mathematical models. II: Velocity tuning and response to moving bars.

The two models of direction selectivity, presented in a previous paper, are used to investigate the response of simple cells to moving bars with different length, luminance and orientation. Most results agree with experimental data reported in the literature. However, a striking difference between the models is observable after a reduction in bar length. The antiphase model predicts that the optimal direction of movement for a short bar is equal to the optimal direction for a long bar, whereas the in-phase model predicts that the two optimal directions are orthogonal. This difference may allow experimental discrimination between the two models.

Animals↗

PubMed2006

Direction selectivity of simple cells in the primary visual cortex: comparison of two alternative mathematical models. I: response to drifting gratings.

Two models of a single hypercolumn in the primary visual cortex are presented, and used for the analysis of direction selectivity in simple cells. The two models differ as to the arrangement of inhibitory connections: in the first ("antiphase model") inhibition is in phase opposition with excitation, but with a similar orientation tuning; in the second ("in-phase model"), inhibition is in phase with excitation, but with broader orientation tuning. Simulation results, performed by using drifting gratings with different orientations, and different spatial and temporal frequencies, show that both models are able to explain the origin of direction preference of simple cells.

Computer Simulation↗

PubMed2006

A neural mass model for the simulation of cortical activity estimated from high resolution EEG during cognitive or motor tasks.

Neural mass models have been used for many years to study the macroscopic dynamics of neural populations in a simple and computationally inexpensive way. In this paper, we modified a model proposed by Wendling et al. [Wendling F, Bartolomei F, Bellanger JJ, Chauvel P. Epileptic fast activity can be explained by a model of impaired GABAergic dendritic inhibition. Eur J Neurosci 2002;15:1499-508] to simulate EEG power spectral density (PSD) in some regions of interest (ROIs) during simple tasks (finger movement or working memory tests). The work consists of two subsequent stages: (1) in the first we evaluated the role of some model parameters (i.e., average gain of synapses and their time constants) in affecting power spectral density. This analysis confirmed the possibility to simulate various EEG rhythms (in the alpha, beta and gamma frequency ranges) by modifying just the time constants of the synapses. The position of the individual rhythms (i.e., the corresponding peaks in the PSD) can be finely tuned acting on the average gain of fast inhibitory synapses. This analysis suggested that a single neural mass model produces a unimodal spectrum, which can be finely adjusted, but cannot mimic the overall complexity of EEG in an entire cortical area. (2) Hence, in the second stage we built a model of a ROI by combining three neural mass models arranged in parallel. With this model, and using an automatic fitting procedure, we carefully reproduced the PSD of cortical EEG in several ROIs during finger movement, and their temporal changes during a working memory task, by estimating nine parameters. The estimated parameters represent the excitation of each population (mean value and variance of exogenous input noise) and the average gain of fast inhibitory synapses. Cortical EEGs were computed with an inverse propagation algorithm, starting from measurement performed with a high number of electrodes on the scalp (46-96). Results show that the proposed model is able to mimic PSD of cortical activity acting on a few parameters, which represent external activation and short-time synaptic changes. This information may be exploited to reach a quantitative summary of electrical activity in ROIs during a task, and to derive information on connectivity, starting from non-invasive EEG measurements.

Algorithms↗

PubMed2006

Object segmentation and recovery via neural oscillators implementing the similarity and prior knowledge gestalt rules.

Object recognition requires the solution of the binding and segmentation problems, i.e., grouping different features to achieve a coherent representation. Synchronization of neural activity in the gamma-band, associated with gestalt perception, has often been proposed as a putative mechanism to solve these problems, not only as to low-level processing, but also in higher cortical functions. In the present work, a network of Wilson-Cowan oscillators is used to segment simultaneous objects, and recover an object from partial or corrupted information, by implementing two gestalt rules: similarity and prior knowledge. The network consists of H different areas, each devoted to representation of a particular feature of the object, according to a topological organization. The similarity law is realized via lateral intra-area connections, arranged as a "Mexican-hat". Prior knowledge is realized via inter-area connections, which link properties belonging to a previously memorized object. A global inhibitor allows segmentation of several objects avoiding interference. Simulation results, performed using three simultaneous input objects, show that the network is able to detect an object even in difficult conditions (i.e., when some features are absent or shifted with respect to the original one). Moreover, the trade-off between sensitivity (capacity to detect true positives) and specificity (capacity to reject false positives) can be controlled acting on the extension of lateral synapses (i.e., on the level of accepted similarity). Finally, the network can also deal with correlated objects, i.e., objects which have some common features. Simulations performed using a different number of objects (2, 3, 4 or 5) suggest that the network is able to segment and recall up to four objects, but the oscillation frequency must increase, the lower the number of objects simultaneously present. The model, although quite simpler compared with neurophysiology, may represent a theoretical framework for the analysis of the relationships between object representation, memory, learning, and gamma-band activity. In particular, it extends previous studies on autoassociative memory since it exploits not only oscillatory dynamics, but also a topological organization of features.

Gestalt Theory↗

PubMed2006

Travelling waves and EEG patterns during epileptic seizure: analysis with an integrate-and-fire neural network.

Epilepsy is characterized by paradoxical patterns of neural activity. They may cause different types of electroencephalogram (EEG), which dynamically change in shape and frequency content during the temporal evolution of seizure. It is generally assumed that these epileptic patterns may originate in a network of strongly interconnected neurons, when excitation dominates over inhibition. The aim of this work is to use a neural network composed of 50 x 50 integrate-and-fire neurons to analyse which parameter alterations, at the level of synapse topology, may induce network instability and epileptic-like discharges, and to study the corresponding spatio-temporal characteristics of electrical activity in the network. We assume that a small group of central neurons is stimulated by a depolarizing current (epileptic focus) and that neurons are connected via a Mexican-hat topology of synapses. A signal representative of cortical EEG (ECoG) is simulated by summing the membrane potential changes of all neurons. A sensitivity analysis on the parameters describing the synapse topology shows that an increase in the strength and in spatial extension of excitatory vs. inhibitory synapses may cause the occurrence of travelling waves, which propagate along the network. These propagating waves may cause EEG patterns with different shape and frequency, depending on the particular parameter set used during the simulations. The resulting model EEG signals include irregular rhythms with large amplitude and a wide frequency content, low-amplitude high-frequency rapid discharges, isolated or repeated bursts, and low-frequency quasi-sinusoidal patterns. A slow progressive temporal variation in a single parameter may cause the transition from one pattern to another, thus generating a highly non-stationary signal which resembles that observed during ECoG measurements. These results may help to elucidate the mechanisms at the basis of some epileptic discharges, and to relate rapid changes in EEG patterns with the underlying alterations at the network level.

Computer Simulation↗

PubMed2006

A wavelet based method for automatic detection of slow eye movements: a pilot study.

Electro-oculographic (EOG) activity during the wake-sleep transition is characterized by the appearance of slow eye movements (SEM). The present work describes an algorithm for the automatic localisation of SEM events from EOG recordings. The algorithm is based on a wavelet multiresolution analysis of the difference between right and left EOG tracings, and includes three main steps: (i) wavelet decomposition down to 10 detail levels (i.e., 10 scales), using Daubechies order 4 wavelet; (ii) computation of energy in 0.5s time steps at any level of decomposition; (iii) construction of a non-linear discriminant function expressing the relative energy of high-scale details to both high- and low-scale details. The main assumption is that the value of the discriminant function increases above a given threshold during SEM episodes due to energy redistribution toward higher scales. Ten EOG recordings from ten male patients with obstructive sleep apnea syndrome were used. All tracings included a period from pre-sleep wakefulness to stage 2 sleep. Two experts inspected the tracings separately to score SEMs. A reference set of SEM (gold standard) were obtained by joint examination by both experts. Parameters of the discriminant function were assigned on three tracings (design set) to minimize the disagreement between the system classification and classification by the two experts; the algorithm was then tested on the remaining seven tracings (test set). Results show that the agreement between the algorithm and the gold standard was 80.44+/-4.09%, the sensitivity of the algorithm was 67.2+/-7.37% and the selectivity 83.93+/-8.65%. However, most errors were not caused by an inability of the system to detect intervals with SEM activity against NON-SEM intervals, but were due to a different localisation of the beginning and end of some SEM episodes. The proposed method may be a valuable tool for computerized EOG analysis.

Adult↗

PubMed2006

Assessing cortical functional connectivity by partial directed coherence: simulations and application to real data.

The aim of this paper is to test a technique called partial directed coherence (PDC) and its modification (squared PDC; sPDC) for the estimation of human cortical connectivity by means of simulation study, in which both PDC and sPDC were studied by analysis of variance. The statistical analysis performed returned that both PDC and sPDC are able to estimate correctly the imposed connectivity patterns when data exhibit a signal-to-noise ratio of at least 3 and a length of at least 27 s of nonconsecutive recordings at 250 Hz of sampling rate, equivalent, more generally, to 6750 data samples.

Algorithms↗

PubMed2006

A modeling study of bilirubin kinetics during Molecular Adsorbent Recirculating System sessions.

This work presents a quantitative description, by means of a mathematical model, of bilirubin removal during Molecular Adsorbent Recirculating System sessions. The model includes four compartments: two for the patient, and two for the albumin circuit. Equations in each compartment express mass preservation, mass exchange between compartments, and bilirubin-albumin binding kinetics. Model development and validation are based on in vivo data of bilirubin concentration acquired in eight sessions at different times during the session. The accuracy of the model in reproducing real data is high (error in blood = -0.3 +/- 0.93 mg/dL), if three parameters, representing the depurative efficacy of the system (the dialysance of the blood filter and the initial and final clearance of the depurative elements in the albumin circuit), are estimated on each single session. However, model accuracy is only slightly deteriorated (error in blood = -0.4 +/- 0.99 mg/dL) if a single set of parameters (fixing the three parameters at their mean values) is adopted. These results suggest that the model may be used a priori (i.e., using a single set of parameters) to achieve a satisfactory prediction of the overall bilirubin removal, as well as a posteriori for the estimation of device parameters. The latter use may allow the investigation of the dependence of these parameters on the operative and clinical conditions, in the effort to arrive at a rationalization and optimization of the treatment.

Algorithms↗

PubMed2005

Dependence of visual cell properties on intracortical synapses among hypercolumns: analysis by a computer model.

The role of intracortical synapses in affecting the property of visual cells is investigated by means of an original mathematical model of cortical circuitry in V1. The model represents a compromise between computational simplicity and physiological reliability. The model incorporates four different inputs into a cortical cell: thalamic input from the lateral geniculate nucleus, according to an even Gabor function; short-range inhibition confined within the hypercolumn; a long-range excitation, which emphasizes the properties of the input; and a long-range inhibition. In the model we assume that all cells receive a similar thalamic input, which differs simply according to its position in the retina and orientation preference. Simulations were performed, with different parameter values, to assess the main characteristics of cell response (i.e., the width and locations of subregions in the receptive field (RF), orientation tuning curve, and response to drifting and counterphase gratings) as a function of the strength and extension of intracortical excitatory synapses. Results suggest that, if intracortical excitation is confined within the hypercolumn, the cells exhibit the same properties as simple cells, both with regards to the width and shape of the RF, orientation tuning curve, and response to drifting and counterphase gratings. By contrast, if excitatory synapses extend beyond the hypercolumn with sufficient strength, the cells exhibit the typical characteristics of complex cells. A progressive shift from complex to simple cells can be realized with a monotonic variation in parameters. Simulations are also performed with a hierarchical model, to suggest possible experiments able to discriminate the present recurrent mechanism from the classical hierarchical one. Results support the assumptions of previous simpler models (Chance et al., 1999) and may help to understand and assess the role of intracortical synapses in rigorous quantitative terms.

Algorithms↗

PubMed2004

Comparison of different models of orientation selectivity based on distinct intracortical inhibition rules.

Aim of this work is to present simple models of orientation selectivity in the visual cortex, which do not require massive computational effort. Three different models are compared, in order to gain deeper insight into the structure of cortical circuits generating inhibitory signals. All models represent a single hypercolumn. They differ as to the arrangement of inhibitory connections: in the first ("antiphase inhibition model") inhibition is in phase opposition with excitation, but with a similar orientation tuning; in the second ("in-phase inhibition model"), inhibition is in phase with excitation, but with larger orientation tuning. In these two models the orientation width of inhibition increases with contrast. Finally, a third model ("center-surround model") assumes that inhibition comes from the same cells providing excitation, hence the inhibition tuning is contrast-independent. All models, with suitable values of the intracortical synapse parameters, are able to mimic experimental results in the literature. A few differences are evident between the "center-surround model" and the other two, especially as to the dependence of cortical cell response on spatial frequency. The models can represent practical tools to test hypotheses on the disposition of cortical synapses avoiding massive computational efforts.

Contrast Sensitivity↗

PubMed2004

Opioid-induced respiratory depression: a mathematical model for fentanyl.

In this paper, respiratory depressant effects of fentanyl are described quantitatively by a mathematical model. The model is an extension of a previous one, which reproduces the human ventilatory control system on a physiological basis. It includes the following: three compartments for gas storage and exchange (lungs, body tissue, and brain tissue); the main mechanisms involved in ventilation control (peripheral chemoreceptors, central chemoreceptors, and the central hypoxic depression); and local blood flow regulation. The effects of fentanyl on the respiratory system include a decrease in peripheral and central chemoreceptor gains on ventilation and a direct inhibition of respiratory neural activity. All parameters in the model were chosen according to the literature. The model is able to reproduce the ventilatory effects of fentanyl in several conditions: 1) constant levels of fentanyl; 2) after a bolus injection; 3) at fixed levels of P(ETCO2); and 4) after artificial ventilation. According to the model, in spontaneously breathing subjects, minute ventilation depends on two opposing actions: fentanyl inhibitory influences, which depress ventilation, reducing oxygen tension and increasing CO2 tension, and the consequent activation of chemoreceptors, which stimulates ventilation. Simulations of anesthetized patients resuming spontaneous breathing after artificial ventilation demonstrate the risk of prolonged apnea and tissue hypoxemia. A safe transition can be achieved by increasing patient PCO2 toward the end of artificial ventilation, because an advanced chemoreceptor stimulation is produced, which promptly counteracts fentanyl-induced inhibition at cessation of artificial ventilation.

Adaptation, Physiological↗

PubMed2004

Interaction among humoral and neurogenic mechanisms in ventilation control during exercise.

The control of ventilation during exercise is analyzed by a mathematical model. It represents a significant improvement of our previous work, including the effect of potassium on peripheral chemoreceptors, a fast and a slow neurogenic component, and a more accurate description of blood flow control. Moreover, the feedforward contributions (potassium and neurogenic) have been incorporated within a larger model of feedback ventilatory control by central and peripheral chemoreceptors to O2 and CO2 changes, and the interaction among the different mechanisms in ventilation control have been analyzed during moderate (100 W) and severe (300 W) exercise. Results suggest that ventilatory changes during exercise can be mostly ascribed to feedforward neurogenic mechanisms, which ensure quite constant levels of arterial oxygen and CO2 pressure, thus avoiding the involvement of feedback mechanisms. Potassium plays a mild role in ventilation control during steady-state conditions. However, it may have a role in the transient phase after the onset of severe exercise, allowing a faster attainment of the final steady-state ventilation level. In agreement with the physiological literature, the model evidences the presence of three distinct kinetic components in V(E), which ensue from the temporal superimposition (sometimes antagonistic sometimes synergistic) among the several incorporated mechanisms.

Action Potentials↗

PubMed2004

Modeling segmentation of a visual scene via neural oscillators: fragmentation, discovery of details and attention.

The present study analyses the problem of binding and segmentation of a visual scene by means of a network of neural oscillators, laying emphasis on the problems of fragmentation, perception of details at different scales and spatial attention. The work is based on a two-layer model: a second layer of Wilson-Cowan oscillators is inhibited by information from the first layer. Moreover, the model uses a global inhibitor (GI) to segment objects. Spatial attention consists of an excitatory input, surrounded by an inhibitory annulus. A single object is identified by synchronous oscillatory activity of neural groups. The main idea of this work is that segmentation of objects at different detail levels can be achieved by linking parameters of the GI (i.e. the threshold and the inhibition strength) with the dimension of the zone selected by attention and with the dimension of the smaller objects to be detected. Simulations show that three possible kinds of behavior can be attained with the model, through proper choice of the GI parameters and attention input: (i) large objects in the visual scene are perceived, while small details are suppressed; (ii) large objects are perceived, while details are assembled together to constitute a single 'noise term'; (iii) if attention is focused on a smaller area and the GI parameters modulated accordingly (i.e. the threshold and attention strength are reduced) details are individually perceived as separate objects. These results suggest that the GI and attention may represent two concurrent aspects of the same attentive mechanism, i.e. they should work together to provide flexible management of a visual scene at different levels of detail.

Attention↗

PubMed2003

Binding and segmentation of multiple objects through neural oscillators inhibited by contour information.

Temporal correlation of neuronal activity has been suggested as a criterion for multiple object recognition. In this work, a two-dimensional network of simplified Wilson-Cowan oscillators is used to manage the binding and segmentation problem of a visual scene according to the connectedness Gestalt criterion. Binding is achieved via original coupling terms that link excitatory units to both excitatory and inhibitory units of adjacent neurons. These local coupling terms are time independent, i.e., they do not require Hebbian learning during the simulations. Segmentation is realized by a two-layer processing of the visual image. The first layer extracts all object contours from the image by means of "retinal cells" with an "on-center" receptive field. Information on contour is used to selectively inhibit Wilson-Cowan oscillators in the second layer, thus realizing a strong separation among neurons in different objects. Accidental synchronism between oscillations in different objects is prevented with the use of a global inhibitor, i.e., a global neuron that computes the overall activity in the Wilson-Cowan network and sends back an inhibitory signal. Simulations performed in a 50 x 50 neural grid with 21 different visual scenes (containing up to eight objects + background) with random initial conditions demonstrate that the network can correctly segment objects in almost 100% of cases using a single set of parameters, i.e., without the need to adjust parameters from one visual scene to the next. The network is robust with reference to dynamical noise superimposed on oscillatory neurons. Moreover, the network can segment both black objects on white background and vice versa and is able to deal with the problem of "fragmentation."The main limitation of the network is its sensitivity to static noise superimposed on the objects. Overcoming this problem requires implementation of more robust mechanisms for contour enhancement in the first layer in agreement with mechanisms actually realized in the visual cortex.

Artifacts↗

PubMed2003

Short-term autonomic control of cardiovascular function: a mini-review with the help of mathematical models.

In this work the main aspects of the short-term regulation of the cardiovascular system are reviewed and critically discussed, laying special emphasis on the role of the autonomic neural mechanisms involved, on their mutual interrelationships and complex integration. All these aspects are summarized with the help of mathematical models developed by the authors in past years. The main characteristics of the uncontrolled system (i.e., the heart and vessels) and of the efferent neural branches (sympathetic and vagal) working on it are first described. Then, the afferent pathways which participate in feedback mechanisms (baroreceptors, chemoreceptors, lung-stretch receptors, direct CNS response), and the feedforward mechanisms anticipating cardiovascular requirements are introduced, and their role discussed with reference to various cardiovascular perturbations (hemorrhage or posture changes, hypoxia, asphyxia, dynamic exercise). Analysis of physiological data via mathematical equations, and results of computer simulations, emphasize the great complexity, richness and variability of the autonomic cardiovascular control, including redundant mechanisms and antagonistic requirements. The use of mathematical models is essential to capture this richness, and to summarize apparent contradictory data into a coherent and comprehensive theoretical setting.

Animals↗