Abstract
Fatigue is a universal experience that affects the daily lives of species. Nevertheless, there are disagreements about the definitions and measurement of fatigue. Historically, fatigue was considered a result of energy deficits, but more recently, fatigue is being considered as a decision signal to preserve energy and prevent potential danger. Based on this decision-making model of fatigue, anterior cingulate cortex (ACC), anterior insula (AI) and ventral tegmental area (VTA) may be especially important. ACC is considered as a hub for information synchronisation and decision making. AI, on the other hand, is well known for encoding aversive stimuli. VTA is the conventional site for reward and positive feedback. In our working model, ACC, AI, and VTA would work together during the development of fatigue. To be specific, aversive inputs from AI and positive inputs from VTA would feed into ACC, which unconsciously weighs between costs and gains. One would experience fatigue and cease a task when the costs outweigh the potential gains. We hypothesise that there would be electrophysiological changes in ACC, AI, and VTA during the development of fatigue. To test our hypothesis, we used a novel effort exertion task (with three constructions manipulating reward and effort) to induce fatigue in laboratory rats. During this task we recorded their performance and local field potentials (LFPs) from the three brain regions of interest.
In general, our results revealed some mechanisms underlying the development of fatigue in different settings. Our results suggested that when the expectation of effort was varied, the motivation of animals would decline as the task progressed; however, when the effort was expected, the motivation of animals was sustained. Our results also suggested: 1) AI ACC collaboration in the theta oscillation range during development of fatigue under an effort varied and reward fixed setting; 2) AI and VTA were likely working independently under an effort fixed and reward fixed condition; and 3) VTA ACC communication in the beta oscillation range under the effort fixed but reward varied condition. However, we did not directly measure AI-ACC and VTA-ACC communications, which remain to be investigated in future studies.