Cerebellar Cortex 4-12 Hz Oscillations and Unit Phase Relation in the Awake Rat.

Oscillations in the granule cell layer (GCL) of the cerebellar cortex have been related to behavior and could facilitate communication with the cerebral cortex. These local field potential (LFP) oscillations, strong at 4-12 Hz in the rodent cerebellar cortex during awake immobility, should also be an indicator of an underlying influence on the patterns of the cerebellar cortex neuronal firing during rest. To address this hypothesis, cerebellar cortex LFPs and simultaneous single-neuron activity were collected during LFP oscillatory periods in the GCL of awake resting rats. During these oscillatory episodes, different types of units across the GCL and Purkinje cell layers showed variable phase-relation with the oscillatory cycles. Overall, 74% of the Golgi cell firing and 54% of the Purkinje cell simple spike (SS) firing were phase-locked with the oscillations, displaying a clear phase relationship. Despite this tendency, fewer Golgi cells (50%) and Purkinje cell's SSs (25%) showed an oscillatory firing pattern. Oscillatory phase-locked spikes for the Golgi and Purkinje cells occurred towards the peak of the LFP cycle. GCL LFP oscillations had a strong capacity to predict the timing of Golgi cell spiking activity, indicating a strong influence of this oscillatory phenomenon over the GCL. Phase-locking was not as prominent for the Purkinje cell SS firing, indicating a weaker influence over the Purkinje cell layer, yet a similar phase relation. Overall, synaptic activity underlying GCL LFP oscillations likely exert an influence on neuronal population firing patterns in the cerebellar cortex in the awake resting state and could have a preparatory neural network shaping capacity serving as a neural baseline for upcoming cerebellar operations.

Synchronized gamma oscillations (30-50 Hz) in the amygdalo-hippocampal network in relation with seizure propagation and severity.

In this study, we demonstrate that gamma oscillations (30-50 Hz) recorded in the local field potentials (LFP) of the hippocampus are a marker of temporal lobe seizure propagation and that the level of LFP synchrony in the amygdalo-hippocampal network, during these oscillations, is related to the severity of seizures. Sprague-Dawley rats were given a single systemic dose of kainic acid (KA; 6 mg/kg, i.p.) and local field potential activity (1-475 Hz) of the dorsal hippocampus, the amygdala and the neocortex was recorded. Of 135 ictal discharges, 55 (40.7%) involved both limbic structures. We demonstrated that 78.2% of seizures involving both the hippocampus and amygdala showed hippocampal gamma oscillations. Seizure duration was also significantly correlated with the frequency of hippocampal gamma oscillations (r2=0.31, p<0.01) and LFP synchrony in the amygdalo-hippocampal network (r2=0.21, p<0.05). These results suggest that gamma oscillations in the amygdalo-hippocampal network could facilitate long-range synchrony and participate in the propagation of seizures.

Explicit Ringing Removal in Image Deblurring.

In this paper, we present a simple yet effective image deblurring method to produce ringing-free deblurred images. Our work is inspired by the observation that large-scale deblurring ringing artifacts are measurable through a multi-resolution pyramid of low-pass filtering of the blurred-deblurred image pair. We propose to model such a quantification as a convex cost function and minimize it directly in the deblurring process in order to reduce ringing regardless of its cause. An efficient primal-dual algorithm is proposed as a solution to this optimization problem. Since the regularization is more biased toward ringing patterns, the details of the reconstructed image are prevented from over-smoothing. An inevitable source of ringing is sensor saturation which can be detected costlessly contrary to most other sources of ringing. However, dealing with the saturation effect in deblurring introduces a non-linear operator in optimization problem. In this paper, we also introduce a linear approximation as a solution to handling saturation in the proposed deblurring method. As a result of these steps, we significantly enhance the quality of the deblurred images. Experimental results and quantitative evaluations demonstrate that the proposed method performs favorably against state-of-the-art image deblurring methods.

Red Lesion Detection Using Dynamic Shape Features for Diabetic Retinopathy Screening.

The development of an automatic telemedicine system for computer-aided screening and grading of diabetic retinopathy depends on reliable detection of retinal lesions in fundus images. In this paper, a novel method for automatic detection of both microaneurysms and hemorrhages in color fundus images is described and validated. The main contribution is a new set of shape features, called Dynamic Shape Features, that do not require precise segmentation of the regions to be classified. These features represent the evolution of the shape during image flooding and allow to discriminate between lesions and vessel segments. The method is validated per-lesion and per-image using six databases, four of which are publicly available. It proves to be robust with respect to variability in image resolution, quality and acquisition system. On the Retinopathy Online Challenge's database, the method achieves a FROC score of 0.420 which ranks it fourth. On the Messidor database, when detecting images with diabetic retinopathy, the proposed method achieves an area under the ROC curve of 0.899, comparable to the score of human experts, and it outperforms state-of-the-art approaches.

Catching a rat by its edglets.

Computer vision is a noninvasive method for monitoring laboratory animals. In this article, we propose a robust tracking method that is capable of extracting a rodent from a frame under uncontrolled normal laboratory conditions. The method consists of two steps. First, a sliding window combines three features to coarsely track the animal. Then, it uses the edglets of the rodent to adjust the tracked region to the animal's boundary. The method achieves an average tracking error that is smaller than a representative state-of-the-art method.