Author: Klaas E Stephan1, Zina M Manjaly2, Christoph D Mathys3, Lilian A E Weber4, Saee Paliwal4, Tim Gard5, Marc Tittgemeyer6, Stephen M Fleming3, Helene Haker4, Anil K Seth7, Frederike H Petzschner4
Affiliation: <sup>1</sup> Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH ZurichZurich, Switzerland; Wellcome Trust Centre for Neuroimaging, University College LondonLondon, UK; Max Planck Institute for Metabolism ResearchCologne, Germany.
<sup>2</sup> Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH ZurichZurich, Switzerland; Department of Neurology, Schulthess ClinicZurich, Switzerland.
<sup>3</sup> Wellcome Trust Centre for Neuroimaging, University College London London, UK.
<sup>4</sup> Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich Zurich, Switzerland.
<sup>5</sup> Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH ZurichZurich, Switzerland; Center for Complementary and Integrative Medicine, University Hospital ZurichZurich, Switzerland.
<sup>6</sup> Max Planck Institute for Metabolism Research Cologne, Germany.
<sup>7</sup> Sackler Centre for Consciousness Science, School of Engineering and Informatics, University of Sussex Brighton, UK.
Conference/Journal: Front Hum Neurosci
Date published: 2016 Nov 15
Other:
Volume ID: 10 , Pages: 550 , Special Notes: doi: 10.3389/fnhum.2016.00550. , Word Count: 245
This paper outlines a hierarchical Bayesian framework for interoception, homeostatic/allostatic control, and meta-cognition that connects fatigue and depression to the experience of chronic dyshomeostasis. Specifically, viewing interoception as the inversion of a generative model of viscerosensory inputs allows for a formal definition of dyshomeostasis (as chronically enhanced surprise about bodily signals, or, equivalently, low evidence for the brain's model of bodily states) and allostasis (as a change in prior beliefs or predictions which define setpoints for homeostatic reflex arcs). Critically, we propose that the performance of interoceptive-allostatic circuitry is monitored by a metacognitive layer that updates beliefs about the brain's capacity to successfully regulate bodily states (allostatic self-efficacy). In this framework, fatigue and depression can be understood as sequential responses to the interoceptive experience of dyshomeostasis and the ensuing metacognitive diagnosis of low allostatic self-efficacy. While fatigue might represent an early response with adaptive value (cf. sickness behavior), the experience of chronic dyshomeostasis may trigger a generalized belief of low self-efficacy and lack of control (cf. learned helplessness), resulting in depression. This perspective implies alternative pathophysiological mechanisms that are reflected by differential abnormalities in the effective connectivity of circuits for interoception and allostasis. We discuss suitably extended models of effective connectivity that could distinguish these connectivity patterns in individual patients and may help inform differential diagnosis of fatigue and depression in the future.
Keywords: active inference; allostasis; computational psychiatry; dynamic causal modeling; effective connectivity; homeostasis; multiple sclerosis; predictive coding.
PMID: 27895566 PMCID: PMC5108808 DOI: 10.3389/fnhum.2016.00550