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. 2021 Apr 30;36(3):27. doi: 10.1007/s10539-021-09801-6

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© The Author(s) 2021

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 2 — Neuroendocrine regulation of immunity via the Hypothalamic–Pituitary–Adrenal axis. In this graphic, we show the predicted pathogen concentration signalled by IFNγ derived from Th1-lymphocytes. This is subtracted from the complement pathway activation (playing the role of sensory data) detected by macrophages to give a prediction error (ε_y) represented by the IL-12 levels released by macrophages. Intuitively, the presence of unanticipated pathogens prompts an increase in macrophage activation. This prediction error may be resolved in two ways. The first is to decrease the amount of pathogen through phagocytosis and oxidative killing. The second is to increase the Th1 response (μ_x) to update predictions (IFNγ) so that they are consistent with the presence of pathogen. The degree to which the Th1 response is increased depends upon two things. The first is prior beliefs about the amount of pathogen expected. Deviation from this prior is indicated by the prediction error (ε_x), which may be intrinsic to T-cell populations. The second is the precision or inverse variance associated with the predicted pathogen concentration. If the variance is assumed to be very high, the effect of the prediction error (ε_y) on the expectation (μ_x) is attenuated. Here, we have assumed the expected variance is a function of some variable v whose expectation (μ_v) is signalled by cortisol from the adrenal cortex. This means that, when cortisol is high, the Th1 response to macrophage-derived cytokines is more limited. To update beliefs about variance, we can penalise deviations from a prior value as before (ε_v), but the prediction error from y has to be handled more carefully. As variance is a second order statistic (the expectation of a squared quantity), we need to square the prediction error (as shown in the hypothalamus) and compare this to the current estimate of the variance. These (respectively) account for the cytokines released by macrophages and detected by the hypothalamus, and for the negative feedback from the adrenal cortex to the anterior pituitary—shown as the point at which the square prediction error and variance are compared (with ξ_v representing their ratio)