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Fernando Lopes da Silva

Publications and source records attributed to Fernando Lopes da Silva.

8 recordsLinked to original sources
PubMed2005

Evolving concepts on the pathophysiology of absence seizures: the cortical focus theory.

Four main theories on the pathophysiology of generalized absence seizures have been proposed. The "centrencephalic" theory, proposed in 1954, suggested that discharges originate from a deep-seated diffusely projecting subcortical pacemaker in the midline thalamus. This concept was refined in 1991 with the "thalamic clock" theory, implying that the reticular thalamic nucleus contains the pacemaker cells for the thalamic clock, imposing its rhythm to the cortex. According to other investigators, however, the cortex seems to play a leading role. They suggested that spike-wave discharges have a focal onset in the cortex and are generalized through a rapid propagation. In the "corticoreticular" theory, postulated in 1968, spike-wave discharges are linked to the thalamocortical mechanisms that generate spindles. Rhythmic spindle oscillations generated in the thalamus are transformed into spike-wave discharges when the cortex is hyperexcitable. A 2002 study confirmed in epileptic rats that a functionally intact thalamocortical network is required for the generation of spike-wave discharges. The corticothalamic interrelationships were investigated by means of nonlinear association signal analyses of multiple spike-wave discharges. This showed a consistent focus within the perioral region of the somatosensory cortex. From this focus, seizure activity generalizes rapidly over the cortex. During the first cycles of the seizure the cortex drives the thalamus, while thereafter cortex and thalamus drive each other, thus amplifying and maintaining the rhythmic discharge. In this way the "cortical focus" theory for generalized absence epilepsy bridges cortical and thalamic theories.

Animals↗
PubMed2005

Epileptic transitions: model predictions and experimental validation.

The essence of epilepsy is that a patient displays (long) periods of normal EEG activity (i.e., nonepileptiform) intermingled occasionally with epileptiform paroxysmal activity. The mechanisms of transition between these two types of activity are not well understood. To provide more insight into the dynamics of the neuronal networks leading to seizure generation, the authors developed a computational model of thalamocortical circuits based on relevant patho(physiologic) data. The model exhibits bistability, i.e., it features two operational states, ictal and interictal, that coexist. The transitions between these two states occur according to a Poisson process. An alternative scenario for transitions can be a random walk of network parameters that ultimately leads to a paroxysmal discharge. Predictions of bistable computational model with experimental results from different types of epilepsy are compared.

Adolescent↗
PubMed2004

Functional localization of brain sources using EEG and/or MEG data: volume conductor and source models.

In this overview we examine the basic principles of properties of electroencephalogram and magnetoencephalogram and the corresponding models of sources and of the volume conductor. In particular we show how the dipolar model is anchored in neurophysiological findings and how the different conductivities of the brain and the tissue surrounding it can be estimated. Using these basic models as tools we show how the functional localization of the neural sources of rhythmic activities (alpha and mu rhythms and sleep spindles) and of epileptiform activities can be estimated and integrated with structural data of the brain obtained with MRI.

Brain↗
PubMed2003

In vivo measurement of the brain and skull resistivities using an EIT-based method and realistic models for the head.

In vivo measurements of equivalent resistivities of skull (rho(skull)) and brain (rho(brain)) are performed for six subjects using an electric impedance tomography (EIT)-based method and realistic models for the head. The classical boundary element method (BEM) formulation for EIT is very time consuming. However, the application of the Sherman-Morrison formula reduces the computation time by a factor of 5. Using an optimal point distribution in the BEM model to optimize its accuracy, decreasing systematic errors of numerical origin, is important because cost functions are shallow. Results demonstrate that rho(skull)/rho(brain) is more likely to be within 20 and 50 rather than equal to the commonly accepted value of 80. The variation in rho(brain)(average = 301 omega x cm, SD = 13%) and rho(skull)(average = 12230 omega x cm, SD = 18%) is decreased by half, when compared with the results using the sphere model, showing that the correction for geometry errors is essential to obtain realistic estimations. However, a factor of 2.4 may still exist between values of rho(skull)/rho(brain) corresponding to different subjects. Earlier results show the necessity of calibrating rho(brain) and rho(skull) by measuring them in vivo for each subject, in order to decrease errors associated with the electroencephalogram inverse problem. We show that the proposed method is suited to this goal.

Adult↗
PubMed2003

Epilepsies as dynamical diseases of brain systems: basic models of the transition between normal and epileptic activity.

PURPOSE: The occurrence of abnormal dynamics in a physiological system can become manifest as a sudden qualitative change in the behavior of characteristic physiologic variables. We assume that this is what happens in the brain with regard to epilepsy. We consider that neuronal networks involved in epilepsy possess multistable dynamics (i.e., they may display several dynamic states). To illustrate this concept, we may assume, for simplicity, that at least two states are possible: an interictal one characterized by a normal, apparently random, steady-state of ongoing activity, and another one that is characterized by the paroxysmal occurrence of a synchronous oscillations (seizure). METHODS: By using the terminology of the mathematics of nonlinear systems, we can say that such a bistable system has two attractors, to which the trajectories describing the system's output converge, depending on initial conditions and on the system's parameters. In phase-space, the basins of attraction corresponding to the two states are separated by what is called a "separatrix." We propose, schematically, that the transition between the normal ongoing and the seizure activity can take place according to three basic models: Model I: In certain epileptic brains (e.g., in absence seizures of idiopathic primary generalized epilepsies), the distance between "normal steady-state" and "paroxysmal" attractors is very small in contrast to that of a normal brain (possibly due to genetic and/or developmental factors). In the former, discrete random fluctuations of some variables can be sufficient for the occurrence of a transition to the paroxysmal state. In this case, such seizures are not predictable. Model II and model III: In other kinds of epileptic brains (e.g., limbic cortex epilepsies), the distance between "normal steady-state" and "paroxysmal" attractors is, in general, rather large, such that random fluctuations, of themselves, are commonly not capable of triggering a seizure. However, in these brains, neuronal networks have abnormal features characterized by unstable parameters that are very vulnerable to the influence of endogenous (model II) and/or exogenous (model III) factors. In these cases, these critical parameters may gradually change with time, in such a way that the attractor can deform either gradually or suddenly, with the consequence that the distance between the basin of attraction of the normal state and the separatrix tends to zero. This can lead, eventually, to a transition to a seizure. RESULTS: The changes of the system's dynamics preceding a seizure in these models either may be detectable in the EEG and thus the route to the seizure may be predictable, or may be unobservable by using only measurements of the dynamical state. It is thinkable, however, that in some cases, changes in the excitability state of the underlying networks may be uncovered by using appropriate stimuli configurations before changes in the dynamics of the ongoing EEG activity are evident. A typical example of model III that we discuss here is photosensitive epilepsy. CONCLUSIONS: We present an overview of these basic models, based on neurophysiologic recordings combined with signal analysis and on simulations performed by using computational models of neuronal networks. We pay especial attention to recent model studies and to novel experimental results obtained while analyzing EEG features preceding limbic seizures and during intermittent photic stimulation that precedes the transition to paroxysmal epileptic activity.

Brain↗
PubMed2002

Correlation-based alignment of multichannel signals and application to paroxysmal events.

We propose a new group-theoretical approach to the problem of alignment of time events in multichannel signal recordings. Such an alignment is an essential phase in the classification of transients in electroencephalogram/magnetoencephalogram (MEG) signals. A common reference frame is reconstructed applying a time translation transformation based on delayed mutual correlation functions of the individual events. The method is applied to MEG data sets recorded from epileptic patients showing paroxysmal interictal discharges.

Algorithms↗
PubMedDate not supplied

What does an epileptiform spike look like in MEG? Comparison between coincident EEG and MEG spikes.

Recent investigations suggest that there are differences between the characteristics of EEG and MEG epileptiform spikes. The authors performed an objective characterization of the morphology of epileptiform spikes recorded simultaneously in both EEG and MEG to determine whether they present the same morphologic characteristics. Based on a stepwise approach, the authors performed a computer analysis of EEG and MEG of a set of coincident epileptiform transients selected by a senior clinical neurophysiologist in recordings of three patients with drug-resistant epilepsy. A computer-based algorithm was applied to extract parameters that could be used to describe quantitatively the morphology of the transients, followed by a statistical comparison over the extracted metrics of the EEG and MEG waveforms. EEG and MEG coincident events were statistically different with respect to several morphologic characteristics, such as duration, sharpness, and shape. The differences found appear to be a consequence of MEG signals not being influenced by volume propagation through the tissues with different conductivities that surround the brain, compared with EEG, and of the different orientation of the underlying dipolar sources. The results indicate that visual inspection of MEG spikes and automatic spike-detector algorithms should use criteria adapted to the specific characteristics of the MEG, and not simply those used on conventional EEG.

Adult↗