Previous studies indicated that physical characteristics of food influence satiety, but the relative importance of the oral, gastric, and intestinal behaviors of the food is unclear. The aim of this study was to investigate the satiating effects of 2 types of alginates, which gel weakly or strongly on exposure to acid, compared with guar gum whose viscosity is unaffected by acid. Subjects (n = 12; 3 men, 9 women) ingested a 325-mL sweetened, milk-based meal replacer beverage on 4 separate occasions, either alone as a control or including 1% by weight alginate or guar gum. Intragastric gelling, gastric emptying, and meal dilution were assessed by serial MRI while satiety was recorded for 4 h. MR images showed that all of the meals became heterogeneous in the stomach except for guar, which remained homogeneous. The alginate meals formed lumps in the stomach, with the strong-gelling alginate producing the largest volume. Although gastric emptying was similar for all 4 meals, the sense of fullness at the same gastric volume was significantly greater for all 3 viscous meals than for the control. Compared with the control meal, the strong-gelling alginate (P = 0.031) and guar (P = 0.041) meals increased fullness at 115 min, and the strong-gelling alginate decreased hunger by the 115-min (P = 0.041) and 240-min (P = 0.041) time points. Agents that gel on contact with acid may be useful additions to weight-reducing diets. We hypothesize that this effect is due to distension in the gastric antrum and/or altered transport of nutrients to the small intestine in the lumps.

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The purpose of this work was to highlight the neurological differences between the MR resting state networks of a group of children with ADHD (pre-treatment) and an age-matched healthy group. Results were obtained using different image analysis techniques. A sample of n = 46 children with ages between 6 and 12 years were included in this study (23 per cohort). Resting state image analysis was performed using ReHo, ALFF and ICA techniques. ReHo and ICA represent connectivity analyses calculated with different mathematical approaches. ALFF represents an indirect measurement of brain activity. The ReHo and ICA analyses suggested differences between the two groups, while the ALFF analysis did not. The ReHo and ALFF analyses presented differences with respect to the results previously reported in the literature. ICA analysis showed that the same resting state networks that appear in healthy volunteers of adult age were obtained for both groups. In contrast, these networks were not identical when comparing the healthy and ADHD groups. These differences affected areas for all the networks except the Right Memory Function network. All techniques employed in this study were used to monitor different cerebral regions which participate in the phenomenological characterization of ADHD patients when compared to healthy controls. Results from our three analyses indicated that the cerebellum and mid-frontal lobe bilaterally for ReHo, the executive function regions in ICA, and the precuneus, cuneus and the clacarine fissure for ALFF, were the “hubs” in which the main inter-group differences were found. These results do not just help to explain the physiology underlying the disorder but open the door to future uses of these methodologies to monitor and evaluate patients with ADHD.

Mature neocortex adapts to altered sensory input by changing neural activity in cortical circuits. The underlying cellular mechanisms remain unclear. We used blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) to show reorganization in somatosensory cortex elicited by altered whisker sensory input. We found that there was rapid expansion followed by retraction of whisker cortical maps. The cellular basis for the reorganization in primary somatosensory cortex was investigated with paired electrophysiological recordings in the periphery of the expanded whisker representation. During map expansion, the chance of finding a monosynaptic connection between pairs of pyramidal neurons increased 3-fold. Despite the rapid increase in local excitatory connectivity, the average strength and synaptic dynamics did not change, which suggests that new excitatory connections rapidly acquire the properties of established excitatory connections. During map retraction, entire excitatory connections between pyramidal neurons were lost. In contrast, connectivity between pyramidal neurons and fast spiking interneurons was unchanged. Hence, the changes in local excitatory connectivity did not occur in all circuits involving pyramidal neurons. Our data show that pyramidal neurons are recruited to and eliminated from local excitatory networks over days. These findings suggest that the local excitatory connectome is dynamic in mature neocortex.

Rodents vary the frequency of whisking movements during exploratory and discriminatory behaviors. The effect of whisking frequency on whisker cortical maps was investigated by simulating whisking at physiological frequencies and imaging the whisker representations with blood oxygen level-dependent (BOLD) functional magnetic resonance imaging. Repetitive deflection of many right-sided whiskers at 10 Hz evoked a positive BOLD response that extended across contralateral primary somatosensory cortex (SI) and secondary somatosensory cortex (SII). In contrast, synchronous deflection of 2 adjacent whiskers (right C1 and C2) at 10 Hz evoked separate positive BOLD responses in contralateral SI and SII that were predominantly located in upper cortical layers. The positive BOLD responses were separated and partially surrounded by a negative BOLD response that was mainly in lower cortical layers. Two-whisker representations varied with the frequency of simulated whisking. Positive BOLD responses were largest with 7-Hz deflection. Negative BOLD responses were robust at 10 Hz but were weaker or absent with 7-Hz or 3-Hz deflection. Our findings suggest that sensory inputs attributable to the frequency of whisking movements modify whisker cortical representations.

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Accuracy of glioma grading is fundamental for the diagnosis, treatment planning and prognosis of patients. The purpose of this work was to develop a low-cost and easy-to-implement classification model which distinguishes low-grade gliomas (LGGs) from high-grade gliomas (HGGs), through texture analysis applied to conventional brain MRI. Different combinations of MRI contrasts (T 1Gd and T 2) and one segmented glioma region (necrotic and non-enhancing tumor core, NCR/NET) were studied. Texture features obtained from the gray level size zone matrix (GLSZM) were calculated. An under-sampling method was proposed to divide the data into different training subsets and subsequently extract complementary information for the creation of distinct classification models. The sensitivity, specificity and accuracy of the models were calculated, and the best model explicitly reported. The best model included only three texture features and reached a sensitivity, specificity and accuracy of 94.12%, 88.24% and 91.18%, respectively. According to the features of the model, when the NCR/ NET region was studied, HGGs had a more heterogeneous texture than LGGs in the T 1Gd images, and LGGs had a more heterogeneous texture than HGGs in the T 2 images. These novel results partially contrast with results from the literature. The best model proved to be useful for the classification of gliomas. Complementary results showed that the heterogeneity of gliomas depended on the MRI contrast studied. The chosen model stands out as a simple, low-cost, easy-to-implement, reproducible and highly accurate glioma classifier. Importantly, it should be accessible to populations with reduced economic and scientific resources.

No abstract