Left medial temporal lobe epilepsy (MTLE) is associated with verbal memory impairment usually related to hippocampal damage. We used functional MRI (fMRI) to investigate the patterns of functional activity in healthy volunteers and MTLE patients engaged in verbal episodic memory tasks to look for evidence of a reallocation of verbal memory in epileptic patients. fMRI data were collected from seven MTLE patients with left-sided hippocampal sclerosis and 10 healthy right-handed control subjects on a 3T scanner. Subjects were instructed to learn a list of 17 words (encoding) and then to recall them (retrieval) on successive trials. Healthy volunteers and patients both exhibited bilateral activation (right higher than left) of the parahippocampal gyrus during the retrieval. This effect was more marked in the control subjects. In contrast to the control subjects, patients exhibited consistent and extensive left prefrontal activations in all the memory tasks. These findings show that verbal memory tasks did not involve the same functional patterns in patients and healthy volunteers. This may be interpreted as a dysfunctional response due to the epilepsy and left hippocampal sclerosis, and could reflect the early onset and progressive course of the disease.

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There is growing interest in studying the role of connectivity patterns in brain functions. In recent years, functional brain networks were found to exhibit small-world properties during different brain states. In previous studies, time-independent networks were recovered from long time periods of brain activity. In this paper, we propose an approach, the event-related networks, that allows one to characterize the dynamical evolution of functional brain networks in time-frequency space. We illustrate this approach by characterizing connectivity patterns in magnetoencephalographic signals recorded during a visual stimulus paradigm. When compared with equivalent random and regular networks, the results reveal that functional connectivity varies with time and frequency during the processing of the stimulus, while maintaining a small-world structure. This approach may provide insights into the connectivity of other complex and spatially extended nonstationary systems.

Semiarid landscapes are characterized by vegetated surfaces. Understanding the impact of vegetation on aeolian soil erosion is important for reducing soil erosion or limiting crop damage through abrasion or burial. In the present study, a saltation model fully coupled with a large-eddy simulation airflow model is extended to vegetated landscapes. From this model, the sensitivity of sand erosion to different arrangements and type of plants (shrub versus tree) representative of semiarid landscapes is investigated and the wind erosion reduction induced by plants is quantified. We show that saltation processes over vegetated surfaces have a limited impact on the mean wind statistics, the momentum extracted from the flow by saltating particles being negligible compared to that extracted by plants. Simulated sand erosion patterns resulting from plant distribution, i.e., accumulation and erosion areas, appear qualitatively consistent with previous observations. It is shown that sand erosion reduction depends not only on vegetation cover but also on plant morphology and plant distribution relative to the mean wind direction. A simple shear stress partitioning approach applied in shrub cases gives similar trends of sand erosion reduction as the present model following wind direction and vegetation cover. However, the magnitude of the reduction appears significantly different from one approach to another. Although shrubs trap saltating particles, trees appear more efficient than shrubs to reduce sand erosion. This is explained by the large-scale sheltering effect of trees compared to the local shrub one.