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使用不同的规范连接组对躁狂症进行病灶网络映射。

Lesion network mapping of mania using different normative connectomes.

机构信息

Champalimaud Research and Clinical Centre, Champalimaud Foundation, Lisbon, Portugal.

Department of Psychiatry and Mental Health, Centro Hospitalar de Lisboa Ocidental, Lisbon, Portugal.

出版信息

Brain Struct Funct. 2022 Dec;227(9):3121-3127. doi: 10.1007/s00429-022-02508-8. Epub 2022 May 16.

DOI:10.1007/s00429-022-02508-8
PMID:35575827
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9653320/
Abstract

Lesion network mapping is a neuroimaging technique that explores the network of regions functionally connected to lesions causing a common syndrome. The technique uses resting state functional connectivity from large databases of healthy individuals, i.e., connectomes, and has allowed for important insight into the potential network mechanisms underlying several neuropsychiatric disorders. However, concerns regarding reproducibility have arisen, that may be due to the use of different connectomes, with variable MRI acquisition parameters and preprocessing methods. Here, we tested the impact of using different connectomes on the results of lesion network mapping for mania. We found results were reliable and consistent independent of the connectome used.

摘要

病灶网络映射是一种神经影像学技术,用于探索与导致常见综合征的病灶功能连接的区域网络。该技术使用来自健康个体的大型数据库的静息状态功能连接,即连接组学,这为理解几种神经精神障碍的潜在网络机制提供了重要的见解。然而,人们对可重复性的担忧已经出现,这可能是由于使用了不同的连接组学,具有不同的 MRI 采集参数和预处理方法。在这里,我们测试了使用不同的连接组学对躁狂症病灶网络映射结果的影响。我们发现,无论使用哪个连接组学,结果都是可靠且一致的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a16/9653320/f025fa83c148/429_2022_2508_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a16/9653320/8c409388c61a/429_2022_2508_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a16/9653320/a2bd1660b951/429_2022_2508_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a16/9653320/f025fa83c148/429_2022_2508_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a16/9653320/8c409388c61a/429_2022_2508_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a16/9653320/a2bd1660b951/429_2022_2508_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a16/9653320/f025fa83c148/429_2022_2508_Fig3_HTML.jpg

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本文引用的文献

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Explicit knowledge of task structure is a primary determinant of human model-based action.任务结构的明确知识是人类基于模型行为的主要决定因素。
Nat Hum Behav. 2022 Aug;6(8):1126-1141. doi: 10.1038/s41562-022-01346-2. Epub 2022 May 19.
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Reply: Looking beyond indirect lesion network mapping of prosopagnosia: direct measures required.回复:超越面孔失认症的间接损伤网络映射:需要直接测量。
Brain. 2021 Oct 22;144(9):e76. doi: 10.1093/brain/awab277.
3
Looking beyond indirect lesion network mapping of prosopagnosia: direct measures required.
脑连接中断优化了脑结构与功能之间的关系。
Brain Struct Funct. 2022 Dec;227(9):2893-2895. doi: 10.1007/s00429-022-02585-9.
超越面孔失认症的间接病变网络映射:需要直接测量。
Brain. 2021 Oct 22;144(9):e75. doi: 10.1093/brain/awab276.
4
Brain stimulation and brain lesions converge on common causal circuits in neuropsychiatric disease.脑刺激和脑损伤在神经精神疾病的共同因果回路中汇聚。
Nat Hum Behav. 2021 Dec;5(12):1707-1716. doi: 10.1038/s41562-021-01161-1. Epub 2021 Jul 8.
5
Reply: Lesion network mapping predicts post-stroke behavioural deficits and improves localization.回复:病灶网络图谱可预测中风后的行为缺陷并改善定位。
Brain. 2021 May 7;144(4):e36. doi: 10.1093/brain/awab004.
6
Lesion network mapping predicts post-stroke behavioural deficits and improves localization.病灶网络图谱可预测中风后的行为缺陷并改善定位。
Brain. 2021 May 7;144(4):e35. doi: 10.1093/brain/awab002.
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Lesion network mapping: where do we go from here?病变网络图谱:我们从这里走向何方?
Brain. 2021 Feb 12;144(1):e5. doi: 10.1093/brain/awaa350.
8
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J Clin Invest. 2020 Oct 1;130(10):5209-5222. doi: 10.1172/JCI136096.
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Indirect connectome-based prediction of post-stroke deficits: prospects and limitations.基于间接连接组的中风后缺损预测:前景与局限
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Post-stroke deficit prediction from lesion and indirect structural and functional disconnection.基于病灶和间接结构与功能连接缺失预测卒中后缺损。
Brain. 2020 Jul 1;143(7):2173-2188. doi: 10.1093/brain/awaa156.