This paper reviews the foundation for a role of the human anterior insular cortex (AIC) in emotional awareness, defined as the conscious experience of emotions. We first introduce the neuroanatomical features of AIC and existing findings on emotional awareness. Using empathy, the awareness and understanding of other people's emotional states, as a test case, we then present evidence to demonstrate: 1) AIC and anterior cingulate cortex (ACC) are commonly coactivated as revealed by a meta‐analysis, 2) AIC is functionally dissociable from ACC, 3) AIC integrates stimulus‐driven and top‐down information, and 4) AIC is necessary for emotional awareness. We propose a model in which AIC serves two major functions: integrating bottom‐up interoceptive signals with top‐down predictions to generate a current awareness state and providing descending predictions to visceral systems that provide a point of reference for autonomic reflexes. We argue that AIC is critical and necessary for emotional awareness. J. Comp. Neurol. 521:3371‐3388, 2013. © 2013 Wiley Periodicals, Inc.
This paper describes a discrete state-space model and accompanying methods for generative modeling. This model generalizes partially observed Markov decision processes to include paths as latent variables, rendering it suitable for active inference and learning in a dynamic setting. Specifically, we consider deep or hierarchical forms using the renormalization group. The ensuing renormalizing generative models (RGM) can be regarded as discrete homologs of deep convolutional neural networks or continuous state-space models in generalized coordinates of motion. By construction, these scale-invariant models can be used to learn compositionality over space and time, furnishing models of paths or orbits: that is, events of increasing temporal depth and itinerancy. This technical note illustrates the automatic discovery, learning, and deployment of RGMs using a series of applications. We start with image classification and then consider the compression and generation of movies and music. Finally, we apply the same variational principles to the learning of Atari-like games.
Seizure activity is a ubiquitous and pernicious pathophysiology that, in principle, should yield to mathematical treatments of (neuronal) ensemble dynamics - and therefore interventions on stochastic chaos. A seizure can be characterised as a deviation of neural activity from a stable dynamical regime, i.e. one in which signals fluctuate only within a limited range. In silico treatments of neural activity are an important tool for understanding how the brain can achieve stability, as well as how pathology can lead to seizures and potential strategies for mitigating instabilities, e.g. via external stimulation. Here, we demonstrate that the (neuronal) state equation used in Dynamic Causal Modelling generalises to a Fokker-Planck formalism when propagation of neuronal activity along structural connections is considered. Using the Jacobian of this generalised state equation, we show that an initially unstable system can be rendered stable via a reduction in diffusivity (i.e., connectivity that disperses neuronal fluctuations). We show, for neural systems prone to epileptic seizures, that such a reduction can be achieved via external stimulation. Specifically, we show that this stimulation should be applied in such a way as to temporarily mirror epileptic activity in the areas adjoining an affected brain region - thus 'fighting seizures with seizures'. We offer proof of principle using simulations based on functional neuroimaging data collected from patients with idiopathic generalised epilepsy, in which we successfully suppress pathological activity in a distinct sub-network. Our hope is that this technique can form the basis for real-time monitoring and intervention devices that are capable of suppressing or even preventing seizures in a non-invasive manner.
This commentary takes a closer look at how “constructive models of subjective perception,” referred to by Collerton et al. (sect. 2), might contribute to the Perception and Attention Deficit (PAD) model. It focuses on the neuronal mechanisms that could mediate hallucinations, or false inference – in particular, the role of cholinergic systems in encoding uncertainty in the context of hierarchical Bayesian models of perceptual inference (Friston 2002b; Yu & Dayan 2002).
The remarkable resilience of cognitive functions to focal brain damage suggests that multiple neural systems can sustain the same function either via similar mechanisms or by implementing different cognitive strategies. Two classes of ‘multiple neural systems’ can be distinguished based on the time courses of their underlying neural mechanisms. First, multiple degenerate neural systems might be present in the normal brain where they are either co-activated or remain latent during task performance. In this case plastic changes are immediate and rely on unmasking of a pre-existing but functionally latent system. Second, there might be only one system in the normal brain while an alternative system emerges in the pathological brain owing to functional reorganization, i.e. long-term plastic changes induced by the underlying pathological process. We discuss how multiple neural systems can be revealed using (1) intersubject variability or (2) an iterative approach integrating information from lesion and functional imaging studies.
Stimulation of serotonergic neurotransmission by psilocybin has been shown to shift emotional biases away from negative towards positive stimuli. We have recently shown that reduced amygdala activity during threat processing might underlie psilocybin's effect on emotional processing. However, it is still not known whether psilocybin modulates bottom-up or top-down connectivity within the visual-limbic-prefrontal network underlying threat processing. We therefore analyzed our previous fMRI data using dynamic causal modeling and used Bayesian model selection to infer how psilocybin modulated effective connectivity within the visual-limbic-prefrontal network during threat processing. First, both placebo and psilocybin data were best explained by a model in which threat affect modulated bidirectional connections between the primary visual cortex, amygdala, and lateral prefrontal cortex. Second, psilocybin decreased the threat-induced modulation of top-down connectivity from the amygdala to primary visual cortex, speaking to a neural mechanism that might underlie putative shifts towards positive affect states after psilocybin administration. These findings may have important implications for the treatment of mood and anxiety disorders.
