Changes in interictal spike features precede the onset of temporal lobe epilepsy.

International audienceOBJECTIVE: One cornerstone event during epileptogenesis is the occurrence of the first spontaneous seizure (SZ1). It is therefore important to identify biomarkers of the network alterations leading to SZ1. In experimental models of temporal lobe epilepsy (TLE), interictal-like activity (ILA) precedes SZ1 by several days. The goal of this study was to determine whether ILA dynamics bore electrophysiological features signaling the impeding transition to SZ1. METHODS: Experimental TLE was triggered by pilocarpine- or kainic acid-induced status epilepticus (SE). Continuous electroencephalographic recordings were performed 7 days before and up to 40 days after SE. The amplitude and duration of the spike and wave components of interictal spikes were analyzed. RESULTS: Two types of interictal spikes were distinguished: type 1, with a spike followed by a long-lasting wave, and type 2, with a spike without wave. The number, amplitude, and duration of type 1 spikes started to decrease, whereas the number of type 2 spikes increased, several days before SZ1, reaching their minimum/maximum values just before SZ1. INTERPRETATION: The change in ILA pattern could constitute a predictive biomarker of SZ1. The mechanisms underlying these dynamic modifications and their functional impact are discussed in the context of the construction of an epileptogenic network

Deep learning model for automatic segmentation of lungs and pulmonary metastasis in small animal MR images

Lungs are the most frequent site of metastases growth. The amount and size of pulmonary metastases acquired from MRI imaging data are the important criteria to assess the efficacy of new drugs in preclinical models. While efficient solutions both for MR imaging and the downstream automatic segmentation have been proposed for human patients, both MRI lung imaging and segmentation in preclinical animal models remains challenging due to the physiological motion (respiratory and cardiac movements), to the low amount of protons in this organ and to the particular challenge of precise segmentation of metastases. As a consequence post-mortem analysis is currently required to obtain information on metastatic volume. In this work, we have developed a complete methodological pipeline for automated analysis of lungs and metastases in mice, consisting of an MR sequence for image acquisition and a deep learning method for automatic segmentation of both lungs and metastases. On one hand, we optimized an MR sequence for mouse lung imaging with high contrast for high detection sensitivity. On the other hand we developed DeepMeta, a multiclass U-Net 3+ deep learning model to automatically segment the images. To assess if the proposed deep learning pipeline is able to provide an accurate segmentation of both lungs and pulmonary metastases, we have longitudinally imaged mice with fast- and slow-growing metastasis. Fifty-five balb/c mice were injected with two different derivatives of renal carcinoma cells. Mice were imaged with a SG-bSSFP (self-gated balanced steady state free precession) sequence at different time points after the injection of cancer cells. Both lung and metastases segmentations were manually performed by experts. DeepMeta was trained to perform lung and metastases segmentation based on the resulting ground truth annotations. Volumes of lungs and of pulmonary metastases as well as the number of metastases per mouse were measured on a separate test dataset of MR images. Thanks to the SG method, the 3D bSSFP images of lungs were artifact-free, enabling the downstream detection and serial follow-up of metastases. Moreover, both lungs and metastases segmentation was accurately performed by DeepMeta as soon as they reached the volume of ∼ 0.02 m m 3 . Thus we were able to distinguish two groups of mice in terms of number and volume of pulmonary metastases as well as in terms of the slow versus fast patterns of growth of metastases. We have shown that our methodology combining SG-bSSFP with deep learning, enables processing of the whole animal lungs and is thus a viable alternative to histology alone