A context-dependent switch from sensing to feeling in the primate amygdala

The skin transmits affective signals that integrate into our social vocabulary. As the socio-affective aspects of touch are likely processed in the amygdala, we compare neural responses to social grooming and gentle airflow recorded from the amygdala and the primary somatosensory cortex of non-human primates. Neurons in the somatosensory cortex respond to both types of tactile stimuli. In the amygdala, however, neurons do not respond to individual grooming sweeps even though grooming elicits autonomic states indicative of positive affect. Instead, many show changes in baseline firing rates that persist throughout the grooming bout. Such baseline fluctuations are attributed to social context because the presence of the groomer alone can account for the observed changes in baseline activity. It appears, therefore, that during grooming, the amygdala stops responding to external inputs on a short timescale but remains responsive to social context (or the associated affective states) on longer time scales

A motor unit-based model of muscle fatigue

Muscle fatigue is a temporary decline in the force and power capacity of skeletal muscle resulting from muscle activity. Because control of muscle is realized at the level of the motor unit (MU), it seems important to consider the physiological properties of motor units when attempting to understand and predict muscle fatigue. Therefore, we developed a phenomenological model of motor unit fatigue as a tractable means to predict muscle fatigue for a variety of tasks and to illustrate the individual contractile responses of MUs whose collective action determines the trajectory of changes in muscle force capacity during prolonged activity. An existing MU population model was used to simulate MU firing rates and isometric muscle forces and, to that model, we added fatigue-related changes in MU force, contraction time, and firing rate associated with sustained voluntary contractions. The model accurately estimated endurance times for sustained isometric contractions across a wide range of target levels. In addition, simulations were run for situations that have little experimental precedent to demonstrate the potential utility of the model to predict motor unit fatigue for more complicated, real-worldapplications. Moreover the model provided insight, into the complex orchestration of MU force contributions during fatigue, that would be unattainable with current experimental approachesAuto21 Network of Centres of Excellence [A506-AWH]; National Institutes of Health [R01NS079147]Open access journal.This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

The role of the amygdala in processing social and affective touch

The amygdala plays a central role in socio-emotional behavior, yet its role in processing affective touch is not well established. Longitudinal studies reveal that touch-deprived infants show later in life exaggerated emotional reactivity related to structural and functional changes in the amygdala. The connectivity of the amygdala is well-suited to process the sensory features and the socio-cognitive dimensions of touch. The convergent processing of bottom-up and top-down touch-related inputs in the amygdala triggers autonomic responses. The positive hedonic value of touch in humans and grooming in non-human primates is correlated with vagal tone and the release of oxytocin and endogenous opioids. Grooming reduces vigilance that has been shown to depend critically on the amygdala. Touch-induced vagal tone and lowered vigilance alter neural activity in the amygdala. Under these circumstances neurons no longer respond to each touch stimulus, rather they appear to signal a sustained functional state in which the amygdala appears decoupled from monitoring the external environment

Fatigue model outputs for a sustained 100% MVC load for 200 s.

<p>(A) Increased excitation in response to fatigue. Force capacity is shown with and without firing rate adaptation and the modeled force remains at the target load until the endurance time. (B) Firing rate of each MU, over the course of the trial. Lines begin when the MU was recruited. Each 20th MU is highlighted and labelled, but all 120 MUs are shown as lighter lines. (C) Force contribution of each MU. (D) Relative force capacity of each MU (normalized to its rested capacity). Note the higher y-axis scale (57) than with the 20% MVC (22), 50% MVC (30), and 80% MVC (50) force.</p

Summary of the motor unit and fatigue model parameters.

<p>(A) Example of a twitch response indicating the peak twitch force (P) and contraction time (CT). (B) Inverse relationship between the modeled contraction times and peak twitch forces across the motor units. Values for every 20th MU are shown. (C) Relationship between normalized firing rate and normalized force. This was modeled to be the same for each motor unit. (D) Direct relationship between modeled peak twitch force and fatigability across motor units.</p

Fatigue model outputs for a sustained 80% MVC load with an endurance time of 14.8 s.

<p>(A) Increased excitation in response to fatigue. Force capacity is shown with and without firing rate adaptation and the modeled force remains at the target load until the endurance time. (B) Firing rate of each MU, over the course of the trial. Lines begin when the MU was recruited. Each 20th MU is highlighted and labelled, but all 120 MUs are shown as lighter lines. (C) Force contribution of each MU. (D) Relative force capacity of each MU (normalized to its rested capacity). Note the higher y-axis scale (50) than with the 20% MVC force (22) and 50% MVC force (30).</p