Effects of junk-food on food-motivated behavior and nucleus accumbens glutamate plasticity; insights into the mechanism of calcium-permeable AMPA receptor recruitment.

In rats, eating obesogenic diets increases calcium-permeable AMPA receptor (CP-AMPAR) transmission in the nucleus accumbens (NAc) core, and enhances food-motivated behavior. Interestingly, these diet-induced alterations in NAc transmission are pronounced and sustained in obesity-prone (OP) male rats and absent in obesity-resistant (OR) populations. However, effects of diet manipulation on food motivation, and the mechanisms underlying this NAc plasticity in OPs is unknown. Using male selectively-bred OP and OR rats, we assessed food-motivated behavior following ad lib access to chow (CH), junk-food (JF), or 10d of JF followed by a return to chow diet (JF-Dep). Motivation for food was greater in OP than OR rats, as expected. However, JF-Dep only produced enhancements in food-seeking in OP groups, while continuous JF access reduced food-seeking in both OPs and ORs. Additionally, optogenetic, chemogenetic, and pharmacological approaches were used to examine NAc CP-AMPAR recruitment following diet manipulation and ex vivo treatment of brain slices. Reducing excitatory transmission in the NAc was sufficient to recruit CP-AMPARs to synapses in OPs, but not ORs. In OPs, JF-induced increases in CP-AMPARs occurred in mPFC-, but not BLA-to-NAc inputs. Together results show that diet differentially affects behavioral and neural plasticity in obesity susceptible populations. We also identify conditions for acute recruitment of NAc CP-AMPARs; these results suggest that synaptic scaling mechanisms contribute to NAc CP-AMPAR recruitment. Overall, this work helps elucidate how diet interacts with obesity susceptibility to influence food-motivated behavior and extends our fundamental understanding of NAc CP-AMPAR recruitment.

Obesity- and diet-induced plasticity in systems that control eating and energy balance.

In April 2023, the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), in partnership with the National Institute of Child Health and Human Development, the National Institute on Aging, and the Office of Behavioral and Social Sciences Research, hosted a 2-day online workshop to discuss neural plasticity in energy homeostasis and obesity. The goal was to provide a broad view of current knowledge while identifying research questions and challenges regarding neural systems that control food intake and energy balance. This review includes highlights from the meeting and is intended both to introduce unfamiliar audiences with concepts central to energy homeostasis, feeding, and obesity and to highlight up-and-coming research in these areas that may be of special interest to those with a background in these fields. The overarching theme of this review addresses plasticity within the central and peripheral nervous systems that regulates and influences eating, emphasizing distinctions between healthy and disease states. This is by no means a comprehensive review because this is a broad and rapidly developing area. However, we have pointed out relevant reviews and primary articles throughout, as well as gaps in current understanding and opportunities for developments in the field.

Quantifying social roles in multi-animal videos using subject-aware deep-learning.

Analyzing social behaviors is critical for many fields, including neuroscience, psychology, and ecology. While computational tools have been developed to analyze videos containing animals engaging in limited social interactions under specific experimental conditions, automated identification of the social roles of freely moving individuals in a multi-animal group remains unresolved. Here we describe a deep-learning-based system - named LabGym2 - for identifying and quantifying social roles in multi-animal groups. This system uses a subject-aware approach: it evaluates the behavioral state of every individual in a group of two or more animals while factoring in its social and environmental surroundings. We demonstrate the performance of subject-aware deep-learning in different species and assays, from partner preference in freely-moving insects to primate social interactions in the field. Our subject-aware deep learning approach provides a controllable, interpretable, and efficient framework to enable new experimental paradigms and systematic evaluation of interactive behavior in individuals identified within a group.