Circular inference predicts nonuniform overactivation and dysconnectivity in brain-wide connectomes

Bouttier, Vincent; Duttagupta, Suhrit; Jardri, Renaud

doi:10.1016/j.schres.2020.12.045

Cited by 10 publications

(2 citation statements)

References 66 publications

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“…Conversely, an "ascending loop" (also called "climbing loop") is generated when the sensory evidence corrupts the prior, leading to the amplification of the likelihood and an overinterpretation of sensory data. Such circularity could be an important feature of perceptual inference in humans (Leptourgos et al, 2020c(Leptourgos et al, , 2020a, while in extreme cases, it could generate psychotic symptoms, including hallucinations and delusions (Bouttier et al, 2021;Jardri et al, 2017). This idea is in line with related theories that postulate that schizophrenia may result from an impairment in the brain's predictive mechanisms (Adams et al, 2013;Corlett et al, 2019;Fletcher and Frith, 2009;Leptourgos and Corlett, 2020;Powers et al, 2017;Sterzer et al, 2018).…”

Section: The Circular Inference Frameworksupporting

confidence: 58%

From hallucinations to synaesthesia: A circular inference account of unimodal and multimodal erroneous percepts in clinical and drug-induced psychosis

Leptourgos

Bouttier

Jardri

2022

Neuroscience & Biobehavioral Reviews

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Section: The Circular Inference Frameworksupporting

confidence: 58%

From hallucinations to synaesthesia: A circular inference account of unimodal and multimodal erroneous percepts in clinical and drug-induced psychosis

Leptourgos

Bouttier

Jardri

2022

Neuroscience & Biobehavioral Reviews

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“…These effects reemphasize the exaggerated centrality of medial temporal lobe structures in schizophrenia during active task conditions, and they highlight the dynamic nature of inter-group differences in network repertoires as driven by task context. We note that an overwhelming focus on positive results (i.e., significant inter-group differences), frequently obscures appreciation of the subtle nature of effects, even in conditions as debilitating as schizophrenia ( Bouttier, Duttagupta, Denève, & Jardri, 2022 ).…”

Section: Discussionmentioning

confidence: 88%

The topology, stability, and instability of learning-induced brain network repertoires in schizophrenia

Meram

Baajour

Chowdury

et al. 2023

Network Neuroscience

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There is a paucity of graph-theoretic methods applied to task-based data in schizophrenia (SCZ). Tasks are useful for modulating brain network dynamics, and topology. Understanding how changes in task conditions impact inter-group differences in topology can elucidate unstable network characteristics in SCZ. Here, in a group of patients and healthy controls (n = 59 total, 32 SCZ), we used an associative learning task with four distinct conditions (Memory Formation, Post-Encoding Consolidation, Memory Retrieval, and Post-Retrieval Consolidation) to induce network dynamics. From the acquired fMRI time series data, betweenness centrality (BC), a metric of a node’s integrative value was used to summarize network topology in each condition. Patients showed 1) differences in BC across multiple nodes and conditions; 2) decreased BC in more integrative nodes, but increased BC in less integrative nodes; 3) discordant node ranks in each of the conditions, and 4) complex patterns of stability and instability of node ranks across conditions. These analyses reveal that task conditions induce highly variegated patterns of network dys-organization in SCZ. We suggest that the dys-connection syndrome that is schizophrenia, is a contextually evoked process, and that the tools of network neuroscience should be oriented toward elucidating the limits of this dys-connection.

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A normative approach to radicalization in social networks

Bouttier,

Leclercq,

Jardri

et al. 2024

J Comput Soc Sc

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In recent decades, the massification of online social connections has made information globally accessible in a matter of seconds. Unfortunately, this has been accompanied by a dramatic surge in extreme opinions, without a clear solution in sight. Using a model performing probabilistic inference in large-scale loopy graphs through exchange of messages between nodes, we show how circularity in the social graph directly leads to radicalization and the polarization of opinions. We demonstrate that these detrimental effects could be avoided if the correlations between incoming messages could be decreased. This approach is based on an extension of Belief Propagation (BP) named Circular Belief Propagation (CBP) that can be trained to drastically improve inference within a cyclic graph. CBP was benchmarked using data from Facebook© and Twitter©. This approach could inspire new methods for preventing the viral spreading and amplification of misinformation online, improving the capacity of social networks to share knowledge globally without resorting to censorship.

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Circular inference predicts nonuniform overactivation and dysconnectivity in brain-wide connectomes

Cited by 10 publications

References 66 publications

From hallucinations to synaesthesia: A circular inference account of unimodal and multimodal erroneous percepts in clinical and drug-induced psychosis

From hallucinations to synaesthesia: A circular inference account of unimodal and multimodal erroneous percepts in clinical and drug-induced psychosis

The topology, stability, and instability of learning-induced brain network repertoires in schizophrenia

A normative approach to radicalization in social networks

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