Cerebellar climbing fibers signal flexible, rapidly adapting reward predictions.

Classical models of cerebellar computation posit that climbing fibers (CFs) operate according to supervised learning rules, correcting movements by signaling the occurrence of motor errors. However, recent findings suggest that in some behaviors, CF activity can exhibit features that resemble the instructional signals necessary for reinforcement learning, namely reward prediction errors (rPEs). Despite these initial observations, many key properties of reward-related CF responses remain unclear, thus limiting our understanding of how they operate to guide cerebellar learning.

Free-choice high-fat diet consumption reduces lateral hypothalamic GABAergic activity, without disturbing neural response to sucrose drinking in mice.

Nutrition can influence the brain and affect its regulation of food intake, especially that of high-palatable foods. We hypothesize that fat and sugar have interacting effects on the brain, and the lateral hypothalamus (LH) is a prime candidate to be involved in this interaction. The LH is a heterogeneous area, crucial for regulating consummatory behaviors, and integrating homeostatic and hedonic needs.

Disruption of lateral hypothalamic calorie detection by a free choice high fat diet.

During the last few decades, the consumption of low-calorie sweeteners, as a substitute for caloric sweeteners, has sharply increased. Although research shows that caloric versus low-calorie sweeteners can have differential effects on the brain, it is unknown which neuronal populations are responsible for detecting the difference between the two types of sweeteners. Using in vivo two-photon calcium imaging, we investigated how drinking sucrose or sucralose (a low-calorie sweetener) affects the activity of glutamatergic neurons in the lateral hypothalamus.

Quantitative analysis of in-vivo microbubble distribution in the human brain.

Microbubbles (MB) are widely used as contrast agents to perform contrast-enhanced ultrasound (CEUS) imaging and as acoustic amplifiers of mechanical bioeffects incited by therapeutic-level ultrasound. The distribution of MBs in the brain is not yet fully understood, thereby limiting intra-operative CEUS guidance or MB-based FUS treatments. In this paper we describe a robust platform for quantification of MB distribution in the human brain, allowing to quantitatively discriminate between tumoral and normal brain tissues and we provide new information regarding real-time cerebral MBs distribution.