Brain functional imaging in obese and diabetic patients.

Obesity and type 2 diabetes are associated with greater risk of brain damage. Over the last decade, functional imaging techniques (functional magnetic resonance imaging, fMRI, positron emission tomography, PET, electroencephalography, magnetoencephalography, near infrared spectroscopy) have been exploited to better characterize behavioral and cognitive processes, by addressing cerebral reactions to a variety of stimuli or tasks, including hormones and substrates (e.g., glucose, insulin, gut peptides), environmental cues (e.g., presentation of sensory stimuli), and cognitive tasks. Among these techniques, fMRI and PET are most commonly used, and this review focuses on results obtained with these techniques in relation to brain substrate metabolism, appetite control and food intake, and cognitive decline in obesity and type 2 diabetes. The available knowledge indicates that there are a series of cerebral abnormalities associating with, or preceding obesity and type 2 diabetes, including impaired substrate handling, insulin resistance, disruption of inter-organ cross-talk and of resting state networking. Some of these abnormalities are reversed by metabolic interventions, suggesting that they are partly a consequence rather than cause of disease. Therefore, causal implications and mechanisms remain to be determined.

Permutations of functional magnetic resonance imaging classification may not be normally distributed.

A fundamental question that often occurs in statistical tests is the normality of distributions. Countless distributions exist in science and life, but one distribution that is obtained via permutations, usually referred to as permutation distribution, is interesting. Although a permutation distribution should behave in accord with the central limit theorem, if both the independence condition and the identical distribution condition are fulfilled, no studies have corroborated this concurrence in functional magnetic resonance imaging data. In this work, we used Anderson-Darling test to evaluate the accordance level of permutation distributions of classification accuracies to normality expected under central limit theorem. A simulation study has been carried out using functional magnetic resonance imaging data collected, while human subjects responded to visual stimulation paradigms. Two scrambling schemes are evaluated: the first based on permuting both the training and the testing sets and the second on permuting only the testing set. The results showed that, while a normal distribution does not adequately fit to permutation distributions most of the times, it tends to be quite well acceptable when mean classification accuracies averaged over a set of different classifiers is considered. The results also showed that permutation distributions can be probabilistically affected by performing motion correction to functional magnetic resonance imaging data, and thus may weaken the approximation of permutation distributions to a normal law. Such findings, however, have no relation to univariate/univoxel analysis of functional magnetic resonance imaging data. Overall, the results revealed a strong dependence across the folds of cross-validation and across functional magnetic resonance imaging runs and that may hinder the reliability of using cross-validation. The obtained p-values and the drawn confidence level intervals exhibited beyond doubt that different permutation schemes may beget different permutation distributions as well as different levels of accord with central limit theorem. We also found that different permutation schemes can lead to different permutation distributions and that may lead to different assessment of the statistical significance of classification accuracy.

A meta-analytic review of neuroimaging studies of specific phobia to small animals

Introduction: Neuroimaging techniques have been used to identify the neurological bases of phobias. Objective: This meta-review examines functional magnetic resonance imaging studies of individuals with specific animal phobia compared to healthy controls. Method: Searches on Medline, Psycinfo, Academic Search Complete, PubMed, PsycARTICLES, Redalyc, Scopus, and Cochrane databases were conducted. Twenty high quality studies were selected. The effect size estimation was calculated. Results: The random-effects model showed a high overall effect size for both limbic and frontal sites. Data analyses showed greater brain activity in the left amygdala and insular cortex in phobic individuals. We also observed an activation of the fusiform gyrus, the dorsolateral prefrontal cortex left, and the left cingulate cortex, although these areas were less frequently involved. Healthy controls showed high heterogeneity in the brain areas activated by phobic stimuli. Conclusions: These findings suggest the possible existence of a double processing pathway in phobic stimuli: a rapid processing pathway involving limbic areas and a slow pathway involving both limbic and frontal areas

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