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相关神经网络是通过数据驱动分析确定的情绪、记忆、运动过程抑制的基础。

Related neural networks underlie suppression of emotion, memory, motor processes as identified by data-driven analysis.

机构信息

Department of Psychological and Brain Sciences, University of Louisville, 2301 S, 3rd St., Louisville, KY, 40292, USA.

Insightec Ltd., Chicago, IL, USA.

出版信息

BMC Neurosci. 2023 Aug 24;24(1):44. doi: 10.1186/s12868-023-00812-5.

DOI:10.1186/s12868-023-00812-5
PMID:37620756
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10463822/
Abstract

BACKGROUND

Goal-directed behavior benefits from self-regulation of cognitive and affective processes, such as emotional reactivity, memory retrieval, and prepotent motor response. Dysfunction in self-regulation is a common characteristic of many psychiatric disorders, such as PTSD and ADHD. This study sought to determine whether common intrinsic connectivity networks (ICNs; e.g. default mode network) are involved in the regulation of emotion, motor, and memory processes, and if a data-driven approach using independent component analysis (ICA) would successfully identify such ICNs that contribute to inhibitory regulation.

METHODS

Eighteen participants underwent neuroimaging while completing an emotion regulation (ER) task, a memory suppression (Think/No-Think; TNT) task, and a motor inhibition (Stop Signal; SS) task. ICA (CONN; MATLAB) was conducted on the neuroimaging data from each task and corresponding components were selected across tasks based on interrelated patterns of activation. Subsequently, ICNs were correlated with behavioral performance variables from each task.

RESULTS

ICA indicated a common medial prefrontal network, striatal network, and frontoparietal executive control network, as well as downregulation in task-specific ROIs.

CONCLUSIONS

These results illustrate that common ICNs were exhibited across three distinct inhibitory regulation tasks, as successfully identified through a data-driven approach (ICA).

摘要

背景

目标导向行为得益于认知和情感过程的自我调节，例如情绪反应、记忆检索和优势运动反应。自我调节功能障碍是许多精神疾病的共同特征，如 PTSD 和 ADHD。本研究旨在确定常见的内在连通性网络（ICN；例如默认模式网络）是否参与情绪、运动和记忆过程的调节，以及使用独立成分分析（ICA）的数据分析方法是否能够成功识别有助于抑制调节的此类 ICN。

方法

18 名参与者在完成情绪调节（ER）任务、记忆抑制（Think/No-Think；TNT）任务和运动抑制（Stop Signal；SS）任务时接受神经影像学检查。对每个任务的神经影像学数据进行 ICA（CONN；MATLAB），并根据激活的相关模式在各个任务中选择相应的组件。随后，将 ICN 与每个任务的行为表现变量进行相关分析。

结果

ICA 表明存在一个共同的内侧前额叶网络、纹状体网络和额顶叶执行控制网络，以及特定任务 ROI 的下调。

结论

这些结果表明，通过数据驱动的方法（ICA）成功识别出，三个不同的抑制调节任务中表现出共同的 ICN。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4179/10463822/d98ce64b5632/12868_2023_812_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4179/10463822/551708d6bd54/12868_2023_812_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4179/10463822/cd6ca472d4da/12868_2023_812_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4179/10463822/7242830765a1/12868_2023_812_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4179/10463822/eaa1b2b8a007/12868_2023_812_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4179/10463822/0dc5fe111cba/12868_2023_812_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4179/10463822/04e80883660e/12868_2023_812_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4179/10463822/66aba1993984/12868_2023_812_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4179/10463822/d98ce64b5632/12868_2023_812_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4179/10463822/551708d6bd54/12868_2023_812_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4179/10463822/cd6ca472d4da/12868_2023_812_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4179/10463822/7242830765a1/12868_2023_812_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4179/10463822/eaa1b2b8a007/12868_2023_812_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4179/10463822/0dc5fe111cba/12868_2023_812_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4179/10463822/04e80883660e/12868_2023_812_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4179/10463822/66aba1993984/12868_2023_812_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4179/10463822/d98ce64b5632/12868_2023_812_Fig8_HTML.jpg

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