Biomedical subjects

Jean-Jacques Bellanger

Publications and source records attributed to Jean-Jacques Bellanger.

4 recordsLinked to original sources

PubMed2006

Quantitative evaluation of linear and nonlinear methods characterizing interdependencies between brain signals.

Brain functional connectivity can be characterized by the temporal evolution of correlation between signals recorded from spatially-distributed regions. It is aimed at explaining how different brain areas interact within networks involved during normal (as in cognitive tasks) or pathological (as in epilepsy) situations. Numerous techniques were introduced for assessing this connectivity. Recently, some efforts were made to compare methods performances but mainly qualitatively and for a special application. In this paper, we go further and propose a comprehensive comparison of different classes of methods (linear and nonlinear regressions, phase synchronization, and generalized synchronization) based on various simulation models. For this purpose, quantitative criteria are used: in addition to mean square error under null hypothesis (independence between two signals) and mean variance computed over all values of coupling degree in each model, we provide a criterion for comparing performances. Results show that the performances of the compared methods are highly dependent on the hypothesis regarding the underlying model for the generation of the signals. Moreover, none of them outperforms the others in all cases and the performance hierarchy is model dependent.

Brain↗

PubMed2005

Time-frequency characterization of interdependencies in nonstationary signals: application to epileptic EEG.

For the past decades, numerous works have been dedicated to the development of signal processing methods aimed at measuring the degree of association between electroencephalographic (EEG) signals. This interdependency parameter, which may be defined in various ways, is often used to characterize a functional coupling between different brain structures or regions during either normal or pathological processes. In this paper, we focus on the time-frequency characterization of the interdependency between signals. Particularly, we propose a novel estimator of the linear relationship between nonstationary signals based on the cross correlation of narrow band filtered signals. This estimator is compared to a more classical estimator based on the coherence function. In a simulation framework, results show that it may exhibit better statistical performances (bias and variance or mean square error) when a priori knowledge about time delay between signals is available. On real data (intracerebral EEG signals), results show that this estimator may also enhance the readability of the time-frequency representation of relationship and, thus, can improve the interpretation of nonstationary interdependencies in EEG signals. Finally, we illustrate the importance of characterizing the relationship in both time and frequency domains by comparing with frequency-independent methods (linear and nonlinear).

Algorithms↗

PubMed2005

Interictal to ictal transition in human temporal lobe epilepsy: insights from a computational model of intracerebral EEG.

In human partial epilepsies and in experimental models of chronic and/or acute epilepsy, the role of inhibition and the relationship between the inhibition and excitation and epileptogenesis has long been questioned. Besides experimental methods carried out either in vitro (human or animal tissue) or in vivo (animals), pathophysiologic mechanisms can be approached by direct recording of brain electrical activity in human epilepsy. Indeed, in some clinical presurgical investigation methods like stereoelectroencephalography, intracerebral electrodes are used in patients suffering from drug resistant epilepsy to directly record paroxysmal activities with excellent temporal resolution (in the order of 1 millisecond). The study of neurophysiologic mechanisms underlying such depth-EEG activities is crucial to progress in the understanding of the interictal to ictal transition. In this study, the authors relate electrophysiologic patterns typically observed during the transition from interictal to ictal activity in human mesial temporal lobe epilepsy (MTLE) to mechanisms (at a neuronal population level) involved in seizure generation through a computational model of EEG activity. Intracerebral EEG signals recorded from hippocampus in five patients with MTLE during four periods (during interictal activity, just before seizure onset, during seizure onset, and during ictal activity) were used to identify the three main parameters of a model of hippocampus EEG activity (related to excitation, slow dendritic inhibition and fast somatic inhibition). The identification procedure used optimization algorithms to minimize a spectral distance between real and simulated signals. Results demonstrated that the model generates very realistic signals for automatically identified parameters. They also showed that the transition from interictal to ictal activity cannot be simply explained by an increase in excitation and a decrease in inhibition but rather by time-varying ensemble interactions between pyramidal cells and local interneurons projecting to either their dendritic or perisomatic region (with slow and fast GABAA kinetics). Particularly, during preonset activity, an increasing dendritic GABAergic inhibition compensates a gradually increasing excitation up to a brutal drop at seizure onset when faster oscillations (beta and low gamma band, 15 to 40 Hz) are observed. These faster oscillations are then explained by the model feedback loop between pyramidal cells and interneurons targeting their perisomatic region. These findings obtained from model identification in human temporal lobe epilepsy are in agreement with some results obtained experimentally, either on animal models of epilepsy or on the human epileptic tissue.

Algorithms↗

PubMed2003

String matching techniques for high-level primitive formation in 2-D vascular imaging.

This paper deals with a so-called "intermediate" description, in other words, the formation of high-level primitives in angiographies. The method is based on an attributed string matching technique capable to capture the shape similarities between low-level primitives (i.e., vessel contours and centerlines). After designing a multiparametric cost function, we propose a multiline pairing algorithm. In order to objectively evaluate its performances, results are first provided on simulated data and then on a set of coronarographic images, where it is shown that anatomically coherent entities like vessel segments and branches can be built, "objects" that can be further individually analyzed for clinical purpose.

Algorithms↗