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自闭症成人中分层预测过程的神经相关性。

Neural correlates of hierarchical predictive processes in autistic adults.

机构信息

Department of Brain and Cognition, Leuven Brain Institute, KU Leuven, 3000, Leuven, Belgium.

Leuven Autism Research (LAuRes), KU Leuven, 3000, Leuven, Belgium.

出版信息

Nat Commun. 2023 Jun 19;14(1):3640. doi: 10.1038/s41467-023-38580-9.

DOI:10.1038/s41467-023-38580-9
PMID:37336874
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10279690/
Abstract

Bayesian theories of autism spectrum disorders (ASD) suggest that atypical predictive mechanisms could underlie the autistic symptomatology, but little is known about their neural correlates. Twenty-six neurotypical (NT) and 26 autistic adults participated in an fMRI study where they performed an associative learning task in a volatile environment. By inverting a model of perceptual inference, we characterized the neural correlates of hierarchically structured predictions and prediction errors in ASD. Behaviorally, the predictive abilities of autistic adults were intact. Neurally, predictions were encoded hierarchically in both NT and ASD participants and biased their percepts. High-level predictions were following activity levels in a set of regions more closely in ASD than NT. Prediction errors yielded activation in shared regions in NT and ASD, but group differences were found in the anterior cingulate cortex and putamen. This study sheds light on the neural specificities of ASD that might underlie atypical predictive processing.

摘要

贝叶斯自闭症谱系障碍(ASD)理论表明,非典型的预测机制可能是自闭症症状的基础,但对于它们的神经相关性知之甚少。26 名神经典型(NT)和 26 名自闭症成年人参与了一项 fMRI 研究,他们在易变的环境中进行了联想学习任务。通过反转感知推理模型,我们描述了 ASD 中层次结构预测和预测误差的神经相关性。行为上,自闭症成年人的预测能力完好无损。神经上,预测在 NT 和 ASD 参与者中都以层次结构的方式进行编码,并使他们的感知产生偏差。在 ASD 中,高级预测比 NT 更紧密地跟随一组区域中的活动水平。预测误差在 NT 和 ASD 中产生了共同的激活,但在前扣带皮层和纹状体中发现了组间差异。这项研究揭示了可能是自闭症异常预测处理的神经特异性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b4b/10279690/2a7ecd89823f/41467_2023_38580_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b4b/10279690/8a5c629ff8b1/41467_2023_38580_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b4b/10279690/0dd616431b88/41467_2023_38580_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b4b/10279690/2a7ecd89823f/41467_2023_38580_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b4b/10279690/8a5c629ff8b1/41467_2023_38580_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b4b/10279690/dabd04786cfc/41467_2023_38580_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b4b/10279690/9d1001a3b2d2/41467_2023_38580_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b4b/10279690/6fa3082f29e6/41467_2023_38580_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b4b/10279690/0dd616431b88/41467_2023_38580_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6b4b/10279690/2a7ecd89823f/41467_2023_38580_Fig6_HTML.jpg

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Disentangling predictive processing in the brain: a meta-analytic study in favour of a predictive network.
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