Searching for an anchor in an unpredictable world: A computational model of obsessive compulsive disorder.

Psychological Review, Journal Year: 2020, Volume and Issue: 127(5), P. 672 - 699

Published: Feb. 27, 2020

In this article, we develop a computational model of obsessive-compulsive disorder (OCD). We propose that OCD is characterized by difficulty in relying on past events to predict the consequences patients' own actions and unfolding possible events. Clinically, corresponds both trusting their (and therefore repeating them), common preoccupation with unlikely chains Critically, idea basis well-developed framework Bayesian brain, where impairment formalized as excessive uncertainty regarding state transitions. illustrate validity using quantitative simulations use these form specific empirical predictions. These predictions are evaluated relation existing evidence, used delineate directions for future research. show how seemingly unrelated findings phenomena can be explained model, including persistent experience were not adequately performed tendency repeat actions; information gathering (i.e., checking); indecisiveness pathological doubt; overreliance habits at expense goal-directed behavior; overresponsiveness sensory stimuli, thoughts, feedback. discuss relationship interaction between our other prominent models OCD, focusing harm-avoidance, not-just-right experiences, or impairments behavior. Finally, outline potential clinical implications suggest lines (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Language: Английский

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Perceptual insensitivity to the modulation of interoceptive signals in depression, anxiety, and substance use disorders

Scientific Reports, Journal Year: 2021, Volume and Issue: 11(1)

Published: Jan. 22, 2021

Abstract This study employed a series of heartbeat perception tasks to assess the hypothesis that cardiac interoceptive processing in individuals with depression/anxiety (N = 221), and substance use disorders 136) is less flexible than healthy 53) context physiological perturbation. Cardiac interoception was assessed via tapping when: (1) guessing allowed; (2) not (3) experiencing an perturbation (inspiratory breath hold) expected amplify sensation. Healthy participants showed performance improvements across three conditions, whereas those and/or disorder minimal improvement. Machine learning analyses suggested individual differences these were negatively related anxiety sensitivity, but explained relatively little variance performance. These results reveal perceptual insensitivity modulation signals evident several common psychiatric disorders, suggesting deficits realm psychopathology manifest most prominently during states homeostatic

Language: Английский

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Neural Mechanisms and Psychology of Psychedelic Ego Dissolution

Pharmacological Reviews, Journal Year: 2022, Volume and Issue: 74(4), P. 876 - 917

Published: Sept. 9, 2022

Neuroimaging studies of psychedelics have advanced our understanding hierarchical brain organization and the mechanisms underlying their subjective therapeutic effects. The primary mechanism action classic is binding to serotonergic 5-HT2A receptors. Agonist activity at these receptors leads neuromodulatory changes in synaptic efficacy that can a profound effect on message-passing brain. Here, we review cognitive neuroimaging evidence for effects psychedelics: particular, influence selfhood subject-object boundaries-known as ego dissolution-surmised underwrite Agonism receptors, located apex cortical hierarchy, may particularly powerful sentience consciousness. These endure well after pharmacological half-life, suggesting neural plasticity play role efficacy. Psychologically, this be accompanied by disarming resistance increases repertoire perceptual hypotheses affords alternate pathways thought behavior, including those undergird selfhood. We consider interaction between neuromodulation through lens predictive coding, which speaks value how make sense world specific predictions about effective connectivity hierarchies tested using functional neuroimaging. SIGNIFICANCE STATEMENT: Classic bind Their agonist efficacy, resulting information processing synthesize an abundance imaging research with psychological interpretations informed framework coding. Moreover, coding suggested offer more sophisticated findings bridging large-scale networks.

Language: Английский

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The fractal brain: scale-invariance in structure and dynamics

Cerebral Cortex, Journal Year: 2022, Volume and Issue: 33(8), P. 4574 - 4605

Published: Sept. 26, 2022

Abstract The past 40 years have witnessed extensive research on fractal structure and scale-free dynamics in the brain. Although considerable progress has been made, a comprehensive picture yet to emerge, needs further linking mechanistic account of brain function. Here, we review these concepts, connecting observations across different levels organization, from both structural functional perspective. We argue that, paradoxically, level cortical circuits is least understood point view perhaps best studied dynamical one. link about scale-freeness fractality with evidence that environment provides constraints may explain usefulness Moreover, discuss behavior exhibits properties, likely emerging similarly organized dynamics, enabling an organism thrive shares same organizational principles. Finally, sparse for try speculate consequences computation. These properties endow computational capabilities transcend current models neural computation could hold key unraveling how constructs percepts generates behavior.

Language: Английский

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The neural coding framework for learning generative models

Nature Communications, Journal Year: 2022, Volume and Issue: 13(1)

Published: April 19, 2022

Neural generative models can be used to learn complex probability distributions from data, sample them, and produce density estimates. We propose a computational framework for developing neural inspired by the theory of predictive processing in brain. According theory, neurons brain form hierarchy which one level expectations about sensory inputs another level. These update their local based on differences between observed signals. In similar way, artificial our predict what neighboring will do, adjust parameters how well predictions matched reality. this work, we show that learned within perform practice across several benchmark datasets metrics either remain competitive with or significantly outperform other functionality (such as variational auto-encoder).

Language: Английский

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