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Review
. 2015 Jul;16(7):419-29.
doi: 10.1038/nrn3950. Epub 2015 May 28.

Interoceptive predictions in the brain

Lisa Feldman Barrett  1 , W Kyle Simmons  2
Affiliations
Review

Interoceptive predictions in the brain

Lisa Feldman Barrett et al. Nat Rev Neurosci. 2015 Jul.

Abstract

Intuition suggests that perception follows sensation and therefore bodily feelings originate in the body. However, recent evidence goes against this logic: interoceptive experience may largely reflect limbic predictions about the expected state of the body that are constrained by ascending visceral sensations. In this Opinion article, we introduce the Embodied Predictive Interoception Coding model, which integrates an anatomical model of corticocortical connections with Bayesian active inference principles, to propose that agranular visceromotor cortices contribute to interoception by issuing interoceptive predictions. We then discuss how disruptions in interoceptive predictions could function as a common vulnerability for mental and physical illness.

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Figures

Figure 1
Figure 1. Intracortical architecture and intercortical connectivity for predictive coding
Cortical columns are defined by different numbers of layers (also called laminae), with each layer having characteristic cell types and patterns of intracortical and intercortical connectivity. Granular cortex (right) is characterized by six differentiated laminae (layers I–VI), with layer IV containing granule cells, which are excitatory spiny stellate neurons (purple) that amplify and distribute thalamocortical inputs throughout the column. Granular cortex also contains many spiny pyramidal neurons throughout its infragranular and supragranular layers. Pyramidal neurons have: a triangular soma, from which basal dendrites project; an ascending apical dendrite, often with large dendritic tufts in layer I; and a single axon that descends and projects out of the cortical column (sometimes with multiple collaterals). By contrast, agranular cortex (left) does not have a fully expressed layer IV and has a poorly differentiated boundary between layer II and layer III. These upper laminae contain relatively fewer pyramidal neurons than does granular cortex. However, agranular cortex contains relatively greater numbers of large pyramidal neurons in layer V and layer VI than in its upper layers. Despite not having a defined layer IV with granule cells, agranular cortex still receives thalamic projections; however, the sensory information that enters agranular cortex is less amplified and less well redistributed throughout the column than in granular cortex. Dysgranular cortex is found in transition zones between granular and agranular regions and contains a small but defined layer IV and a distinctive (although rudimentary) layer II and layer III. This figure is not intended to be comprehensive but rather highlights laminar and cellular characteristics that are important for understanding the Embodied Predictive Interoception Coding (EPIC) model and its predictions. (For example, in the primate brain, cytoarchitecture varies from posterior to anterior, with posterior columns containing a greater number of cells in layer II and layer III than do anterior columns, and anterior columns containing relatively fewer neurons but a greater number of connections; in addition, the cytoarchitectonic structure varies among different primate species, particularly in the density of connections within the anterior portions of the cortex.) According to the EPIC model, prediction neurons (depicted as green pyramidal neurons) in deep layers of agranular cortex drive active inference by sending sensory predictions via projections (green lines) to supragranular layers of dysgranular and granular sensory cortices. Prediction-error neurons (depicted as red pyramidal neurons) in the supragranular layers of granular cortex compute the difference between the predicted and received sensory signal, and send prediction-error signals via projections (red lines) back to the deep layers of agranular cortical regions. Precision cells (depicted as blue pyramidal neurons) tune the gain on predictions and prediction error dynamically, thereby giving these signals reduced (or, in some cases, greater) weight depending on the relative confidence in the descending predictions or the reliability of incoming sensory signals.
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