Can circular inference relate the neuropathological and behavioral aspects of schizophrenia?

Leptourgos, Pantelis; Denève, Sophie; Jardri, Renaud

doi:10.1016/j.conb.2017.08.012

Cited by 25 publications

(27 citation statements)

References 43 publications

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“…For instance, schizophrenia is not defined as a disorder of resting-state FC, but is defined by characteristic symptoms including delusions and disorganized thinking, which may arise from aberrant inference computations (83-85). Behavioral modeling has proposed circuit mechanisms by which false inferences in schizophrenia could arise from imbalanced excitatory-inhibitory interactions in hierarchical processing (86,87). Further research is needed to make connections between circuit-level models and psychological-level models, which may in turn help to link psychiatric biomarkers to symptoms.…”

Section: Applications Of Whole-brain Circuit Models To Schizophreniamentioning

confidence: 99%

Biophysical Modeling of Large-Scale Brain Dynamics and Applications for Computational Psychiatry

Murray

Demirtaş

Antičević

2018

Biological Psychiatry: Cognitive Neuroscience and Neuroimaging

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Noninvasive neuroimaging has revolutionized the study of the organization of the human brain and how its structure and function are altered in psychiatric disorders. A critical explanatory gap lies in our mechanistic understanding of how systems-level neuroimaging biomarkers emerge from underlying synaptic-level perturbations associated with a disease state. We describe an emerging computational psychiatry approach leveraging biophysically based computational models of large-scale brain dynamics and their potential integration with clinical and pharmacological neuroimaging. In particular, we focus on neural circuit models, which describe how patterns of functional connectivity observed in resting-state functional magnetic resonance imaging emerge from neural dynamics shaped by inter-areal interactions through underlying structural connectivity defining long-range projections. We highlight the importance of local circuit physiological dynamics, in combination with structural connectivity, in shaping the emergent functional connectivity. Furthermore, heterogeneity of local circuit properties across brain areas, which impacts large-scale dynamics, may be critical for modeling whole-brain phenomena and alterations in psychiatric disorders and pharmacological manipulation. Finally, we discuss important directions for future model development and biophysical extensions, which will expand their utility to link clinical neuroimaging to neurobiological mechanisms.

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Section: Applications Of Whole-brain Circuit Models To Schizophreniamentioning

confidence: 99%

Biophysical Modeling of Large-Scale Brain Dynamics and Applications for Computational Psychiatry

Murray

Demirtaş

Antičević

2018

Biological Psychiatry: Cognitive Neuroscience and Neuroimaging

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“…Such feedback could also cause multistable perception (i.e. generate a bistable attractor; see Supplementary Figure S5) by temporarily stabilizing the current percept despite the absence of supporting evidence [27].…”

Section: Discussionmentioning

confidence: 99%

“…This model assumes that The NB and WB models predict a slope of 1 and less than 1, respectively, and only the CI model predicts a slope greater than 1. (c) In the CI model, the slope of the log-likelihood/log-posterior curve also depends on the log-prior as a result of the reverberations, indicating an interaction between the two different types of information [27]. Weaker priors are associated with steeper sigmoid curves.…”

Section: Model-based Analysismentioning

confidence: 97%

“…Additionally, as in [26], a cross-term is added to each component, rendering likelihood and prior information completely inseparable. Because of those extra terms, the sensory evidence and prior components become aberrantly correlated, and consequently they generate an interaction (Figure 3c; [27]). Note that the WB model (Figure 4a, middle panel) can be derived from the CI model by removing the reverberated terms, while the NB model (Figure 4a, left panel) by further assuming:…”

Section: ;mentioning

confidence: 99%

“…Interestingly, we also detected a certain amount of circularity in healthy participants, particularly the corruption of sensory evidence by prior information. If circular inference is a more general mechanism than initially predicted, an interesting question arises: is it possible to find evidence of circularity [27] in the perceptual behavior of healthy subjects in the absence of any psychotic experience? Here, we propose that bistability could be an example of percepts induced by such circularity.…”

Section: Introductionmentioning

confidence: 99%

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Circular inference in bistable perception

Leptourgos

Notredame

Eck

et al. 2019

Preprint

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When facing fully ambiguous images, the brain cannot commit to a single percept and instead switches between mutually exclusive interpretations every few seconds, a phenomenon known as bistable perception. Despite years of research, there is still no consensus on whether bistability, and perception in general, is driven primarily by bottom-up or top-down mechanisms. Here, we adopted a Bayesian approach in an effort to reconcile these two theories. Fifty-five healthy participants were exposed to an adaptation of the Necker cube paradigm, in which we manipulated sensory evidence (by shadowing the cube) and prior knowledge (e.g., by varying instructions about what participants should expect to see). We found that manipulations of both sensory evidence and priors significantly affected the way participants perceived the Necker cube. However, we observed an interaction between the effect of the cue and the effect of the instructions, a finding incompatible with Bayes-optimal integration.In contrast, the data were well predicted by a circular inference model. In this model, ambiguous sensory evidence is systematically biased in the direction of current expectations, ultimately resulting in a bistable percept.

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Editorial overview: Computational neuroscience

Fairhall

Machens

2017

Current Opinion in Neurobiology

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Can circular inference relate the neuropathological and behavioral aspects of schizophrenia?

Cited by 25 publications

References 43 publications

Biophysical Modeling of Large-Scale Brain Dynamics and Applications for Computational Psychiatry

Biophysical Modeling of Large-Scale Brain Dynamics and Applications for Computational Psychiatry

Circular inference in bistable perception

Editorial overview: Computational neuroscience

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