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The relative contributions of the ipsilateral and contralateral cerebellar cortex and deep nuclei to delay eyeblink conditioning have been debated and are difficult to survey entirely using typical electrophysiological and lesion techniques. To address these issues, we used single-event functional magnetic resonance imaging (fMRI) in the conscious rabbit to visualize the entire cerebellum simultaneously during eyeblink conditioning sessions. Examination of the blood oxygenation level-dependent (BOLD) response to a visual conditioning stimulus early in training revealed significant bilateral learning-related increases in the BOLD response in the anterior interpositus nucleus (IPA) and significant bilateral deactivation in hemispheric lobule VI (HVI) of the cerebellar cortex. Later in training, the BOLD response remained bilateral in the cortex and predominantly ipsilateral in the IPA. Conditioning stimulus-alone trials after conditioning revealed that both sides of HVI were affected similarly but that only the ipsilateral interpositus nucleus was activated. These results suggest that both sides of HVI normally influence the side of the IPA being conditioned and illustrate how fMRI can be used to examine multiple brain regions simultaneously in an awake, behaving animal to discover more rapidly the neural substrates of learning and memory.

We propose an iterative algorithm for enhancing the resolution of monochrome and color image sequences. Various approaches toward motion estimation are investigated and compared. Improving the spatial resolution of an image sequence critically depends upon the accuracy of the motion estimator. The problem is complicated by the fact that the motion field is prone to significant errors since the original high-resolution images are not available. Improved motion estimates may be obtained by using a more robust and accurate motion estimator, such as a pel-recursive scheme instead of block matching, in processing color image sequences, there is the added advantage of having more flexibility in how the final motion estimates are obtained, and further improvement in the accuracy of the motion field is therefore possible. This is because there are three different intensity fields (channels) conveying the same motion information. In this paper, the choice of which motion estimator to use versus how the final estimates are obtained is weighed to see which issue is more critical in improving the estimated high-resolution sequences. Toward this end, an iterative algorithm is proposed, and two sets of experiments are presented. First, several different experiments using the same motion estimator but three different data fusion approaches to merge the individual motion fields were performed. Second, estimated high-resolution images using the block matching estimator were compared to those obtained by employing a pel-recursive scheme. Experiments were performed on a real color image sequence, and performance was measured by the peak signal to noise ratio (PSNR).

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