Objective: Food intake is regulated by factors that modulate caloric requirements as well as food's reinforcing properties. In this study, we measured brain glucose utilization to an olfactory stimulus (bacon scent), and we examined the role of food restriction and genetic predisposition to obesity on such brain metabolic activity. Methods: Zucker obese (Ob) and lean (Le) rats were divided into four groups: (1) Ob ad-libitum fed, (2) Ob food restricted (70% of ad libitum), (3) Le ad-libitum fed and (4) Le food restricted. Rats were scanned using m-positron emission tomography and 2-[ 18 F]-fluoro-2-deoxy-D-glucose under two conditions: (1) baseline scan (no stimulation) and (2) challenge scan (food stimulation, FS). Results: FS resulted in deactivation of the right and left hippocampus. Ob rats showed greater changes with FS than Le rats (deactivation of hippocampus and activation of the medial thalamus) and Ob but not Le animals deactivated the frontal cortex and activated the superior colliculus. Access to food resulted in an opposite pattern of metabolic changes to the food stimuli in olfactory nucleus (deactivated in unrestricted and activated in restricted) and in right insular/parietal cortex (activated in unrestricted and deactivated in restricted). In addition, restricted but not unrestricted animals activated the medial thalamus. Conclusions: The greater changes in the Ob rats suggest that leptin modulates the regional brain responses to a familiar food stimulus. Similarly, the differences in the pattern of responses with food restriction suggest that FS is influenced by access to food conditions. The main changes with FS occurred in the hippocampus, a region involved in memory, the insular cortex, a region involved with interoception (perception of internal sensations), the medial thalamus (region involved in alertness) and in regions involved with sensory perception (olfactory bulb, olfactory nucleus, occipital cortex, superior colliculus and parietal cortex), which corroborates their relevance in the perception of food.
Because of these difficulties, the results from automatic registration methods have to be visually inspected to detect failures. We propose a method to automate this validation process. Two reference images from the dataset are selected by an expert user avoiding images with poor contrast, animal movement or low quality, and both are co-registered using anatomical landmarks. All the remaining images in the dataset are then registered to every reference with an automatic two-step algorithm based on Mutual Information. The known transformation relating both references allows measuring the registration consistency, which is a good estimator of the accuracy of the alignment process, for every image in the dataset. This value can be used to assess the quality of the registration and therefore detect the incorrect results. We have applied this validation process on a large dataset of 120 FDG-PET rat brain images obtained with a rotating PET scanner. The registration consistency was calculated for every image in the dataset and values below 1.65 mm (PET image resolution) were considered as successful registrations. 116 images were correctly registered with an average error of 0.839 mm, while in four images the proposed method detected a registration failure. Two of these failures were due to very low image quality and these studies were discarded from the study, while the other two were correctly aligned after applying a manual pre-alignment step. Our approach requires minimal user interaction and provides automatic assessment of the registration error, making it unnecessary to visually inspect and check every registration result.
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