Long term evolution of fast ripples during epileptogenesis.

Objective.Fast ripples (FRs) have received considerable attention in the last decade since they represent an electrophysiological biomarker of the epileptogenic zone (EZ). However, the real dynamics underlying the occurrence, amplitude, and time-frequency content of FRs generation during epileptogenesis are still not well understood. This work aims at characterizing and explaining the evolution of these features.Approach.Intracortical electroencephalographic signals recorded in a kainate mouse model of temporal lobe epilepsy were processed in order to compute specific FR features. Then realistic physiologically based computational modeling was employed to explore the different elements that can explain the mechanisms of epileptogenesis and simulate the recorded FR in the early and late latent period.Main results.Results indicated that continuous changes of FR features are mainly portrayed by the epileptic (pathological) tissue size and synaptic properties. Furthermore, the microelectrodes characteristics were found to dramatically affect the observability and spectral/temporal content of FRs. Consequently, FRs evolution seems to mirror the continuous pathophysiological mechanism changes that occur during epileptogenesis as long as the microelectrode properties are taken into account.Significance.Our study suggests that FRs can account for the pathophysiological changes which might explain the EZ generation and evolution and can contribute in the treatment plan of pharmaco-resistant epilepsies.

The influence of biophysical parameters in a biomechanical model of cortical folding patterns.

Abnormal cortical folding patterns, such as lissencephaly, pachygyria and polymicrogyria malformations, may be related to neurodevelopmental disorders. In this context, computational modeling is a powerful tool to provide a better understanding of the early brain folding process. Recent studies based on biomechanical modeling have shown that mechanical forces play a crucial role in the formation of cortical convolutions. However, the effect of biophysical parameters in these models remain unclear. In this paper, we investigate the effect of the cortical growth, the initial geometry and the initial cortical thickness on folding patterns. In addition, we not only use several descriptors of the folds such as the dimensionless mean curvature, the surface-based three-dimensional gyrification index and the sulcal depth, but also propose a new metric to quantify the folds orientation. The results demonstrate that the cortical growth mode does almost not affect the complexity degree of surface morphology; the variation in the initial geometry changes the folds orientation and depth, and in particular, the slenderer the shape is, the more folds along its longest axis could be seen and the deeper the sulci become. Moreover, the thinner the initial cortical thickness is, the higher the spatial frequency of the folds is, but the shallower the sulci become, which is in agreement with the previously reported effects of cortical thickness.

On early brain folding patterns using biomechanical growth modeling.

Abnormal cortical folding patterns may be related to neurodevelopmental disorders such as lissencephaly and polymicrogyria. In this context, computational modeling is a powerful tool to provide a better understanding of the early brain folding process. Recent studies based on biomechanical modeling have shown that mechanical forces play a crucial role in the formation of cortical convolutions. However, the correlation between simulation results and biological facts, and the effect of physical parameters in these models remain unclear. In this paper, we propose a new brain longitudinal length growth model to improve brain model growth. In addition, we investigate the effect of the initial cortical thickness on folding patterns, quantifying the folds by the surface-based three-dimensional gyrification index and a spectral analysis of gyrification. The results tend to show that the use of such biomechanical models could highlight the links between neurodevelopmental diseases and physical parameters.