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本文引用的文献

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Robust circuitry-based scores of structural importance of human brain areas.基于稳健电路的人类脑区结构重要性评分。
PLoS One. 2024 Jan 17;19(1):e0292613. doi: 10.1371/journal.pone.0292613. eCollection 2024.
2
Discovering sex and age implicator edges in the human connectome.发现人类连接组中的性别和年龄暗示边缘。
Neurosci Lett. 2022 Nov 20;791:136913. doi: 10.1016/j.neulet.2022.136913. Epub 2022 Oct 19.
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Introducing and applying Newtonian blurring: an augmented dataset of 126,000 human connectomes at braingraph.org.引入并应用牛顿模糊:在 braingraph.org 上增加了一个包含 126000 个人类连接组的数据集。
Sci Rep. 2022 Feb 23;12(1):3102. doi: 10.1038/s41598-022-06697-4.
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The braingraph.org database with more than 1000 robust human connectomes in five resolutions.braingraph.org数据库拥有超过1000个五种分辨率的可靠人类连接组。
Cogn Neurodyn. 2021 Oct;15(5):915-919. doi: 10.1007/s11571-021-09670-5. Epub 2021 Mar 12.
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The Graph of Our Mind.我们思维的图谱。
Brain Sci. 2021 Mar 8;11(3):342. doi: 10.3390/brainsci11030342.
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The frequent complete subgraphs in the human connectome.人类连接组中的频繁完全子图。
PLoS One. 2020 Aug 20;15(8):e0236883. doi: 10.1371/journal.pone.0236883. eCollection 2020.
7
Good neighbors, bad neighbors: the frequent network neighborhood mapping of the hippocampus enlightens several structural factors of the human intelligence on a 414-subject cohort.好邻居,坏邻居:海马体频繁的网络邻居映射为 414 名受试者队列揭示了几个人类智力的结构因素。
Sci Rep. 2020 Jul 20;10(1):11967. doi: 10.1038/s41598-020-68914-2.
8
The Frequent Network Neighborhood Mapping of the human hippocampus shows much more frequent neighbor sets in males than in females.人类海马体的频繁网络近邻映射显示,男性的近邻集比女性更频繁。
PLoS One. 2020 Jan 28;15(1):e0227910. doi: 10.1371/journal.pone.0227910. eCollection 2020.
9
The frequent subgraphs of the connectome of the human brain.人类大脑连接组的频繁子图
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10
High-resolution directed human connectomes and the Consensus Connectome Dynamics.高分辨率定向人类连接组图谱与共识连接组动力学。
PLoS One. 2019 Apr 16;14(4):e0215473. doi: 10.1371/journal.pone.0215473. eCollection 2019.

人类大脑回路连接的长度和宽度密切相关。

The length and the width of the human brain circuit connections are strongly correlated.

作者信息

Hegedűs Dániel, Grolmusz Vince

机构信息

PIT Bioinformatics Group, Eötvös University, Budapest, H-1117 Hungary.

Uratim Ltd., Budapest, H-1118 Hungary.

出版信息

Cogn Neurodyn. 2025 Dec;19(1):21. doi: 10.1007/s11571-024-10201-1. Epub 2025 Jan 9.

DOI:10.1007/s11571-024-10201-1
PMID:39801908
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11717732/
Abstract

The correlations of several fundamental properties of human brain connections are investigated in a consensus connectome, constructed from 1064 braingraphs, each on 1015 vertices, corresponding to 1015 anatomical brain areas. The properties examined include the edge length, the fiber count, or edge width, meaning the number of discovered axon bundles forming the edge and the occurrence number of the edge, meaning the number of individual braingraphs where the edge exists. By using our previously published robust braingraphs at https://braingraph.org, we have prepared a single consensus graph from the data and compared the statistical similarity of the edge occurrence numbers, edge lengths, and fiber counts of the edges. We have found a strong positive Spearman correlation between the edge occurrence numbers and the fiber count numbers, showing that statistically, the most frequent cerebral connections have the largest widths, i.e., the fiber count. We have found a negative Spearman correlation between the fiber lengths and fiber counts, showing that, typically, the shortest edges are the widest or strongest by their fiber counts. We have also found a negative Spearman correlation between the occurrence numbers and the edge lengths: it shows that typically, the long edges are infrequent, and the frequent edges are short.

摘要

在一个由1064个脑图谱构建的共识连接组中,研究了人类大脑连接的几个基本属性之间的相关性。这些脑图谱中的每一个都有1015个顶点,对应于1015个解剖学脑区。所研究的属性包括边长度、纤维数量(或边宽度,即构成边的已发现轴突束的数量)以及边的出现次数(即边存在的个体脑图谱的数量)。通过使用我们之前在https://braingraph.org上发布的可靠脑图谱,我们从数据中制备了一个单一的共识图,并比较了边的出现次数、边长度和纤维数量的统计相似性。我们发现边的出现次数与纤维数量之间存在很强的正斯皮尔曼相关性,这表明从统计学角度来看,最频繁的脑连接具有最大的宽度,即纤维数量。我们发现纤维长度与纤维数量之间存在负斯皮尔曼相关性,这表明通常情况下,最短的边按其纤维数量来说是最宽或最强的。我们还发现出现次数与边长度之间存在负斯皮尔曼相关性:这表明通常情况下,长边出现的频率低,而频繁出现的边较短。