Graph theory analysis reveals an assortative pain network vulnerable to attacks.
Graph theory analysis reveals an assortative pain network vulnerable to attacks.
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DOI：
10.1038/s41598-023-49458-7
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发表时间：
2023-12-11
影响因子：
4.6
通讯作者：
Scherrer, Gregory
中科院分区：
文献类型：
--
作者：
Chen, Chong;Tassou, Adrien;Morales, Valentina;Scherrer, Gregory
疼痛体验的神经基质被描述为连接的大脑区域的密集网络。然而，这些大脑区域的连接模式仍然难以捉摸，阻碍了对疼痛如何从结构连接中出现的更深入理解。在这里，我们采用图论系统地描述了一个全面的疼痛网络的架构，包括皮层和皮层下的大脑区域。这个结构性脑网络由49个节点组成，这些节点表示与疼痛相关的脑区，这些节点由代表其相对传入和传出轴突投射强度的边缘连接。在这个网络中，63%的大脑区域共享相互连接，反映了一个密集的网络。聚类系数（clustering coefficient）是衡量相邻节点连接概率的指标，表明疼痛网络中的大脑区域倾向于聚集在一起。社区检测，在复杂网络中发现内聚群体的过程，成功地揭示了两个已知的子网络，专门介导的感觉和情感成分的疼痛，分别。分类性分析评估了节点与具有相似特征的其他节点连接的趋势，表明疼痛网络是可分类的。最后，鲁棒性，即复杂网络对故障和扰动的抵抗力，表明疼痛网络显示出高度的容错性（局部故障很少影响网络承载的全局信息），但容易受到攻击（选择性地删除枢纽节点会严重改变网络的连通性）。综上所述，图论分析揭示了大脑中处理伤害性信息的结构性疼痛网络。此外，该网络易受攻击的脆弱性提供了通过针对网络中连接最多的大脑区域来减轻疼痛的可能性。
The neural substrate of pain experience has been described as a dense network of connected brain regions. However, the connectivity pattern of these brain regions remains elusive, precluding a deeper understanding of how pain emerges from the structural connectivity. Here, we employ graph theory to systematically characterize the architecture of a comprehensive pain network, including both cortical and subcortical brain areas. This structural brain network consists of 49 nodes denoting pain-related brain areas, linked by edges representing their relative incoming and outgoing axonal projection strengths. Within this network, 63% of brain areas share reciprocal connections, reflecting a dense network. The clustering coefficient, a measurement of the probability that adjacent nodes are connected, indicates that brain areas in the pain network tend to cluster together. Community detection, the process of discovering cohesive groups in complex networks, successfully reveals two known subnetworks that specifically mediate the sensory and affective components of pain, respectively. Assortativity analysis, which evaluates the tendency of nodes to connect with other nodes that have similar features, indicates that the pain network is assortative. Finally, robustness, the resistance of a complex network to failures and perturbations, indicates that the pain network displays a high degree of error tolerance (local failure rarely affects the global information carried by the network) but is vulnerable to attacks (selective removal of hub nodes critically changes network connectivity). Taken together, graph theory analysis unveils an assortative structural pain network in the brain that processes nociceptive information. Furthermore, the vulnerability of this network to attack presents the possibility of alleviating pain by targeting the most connected brain areas in the network.
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影响因子：
7.4
作者：
Borsook D;Youssef AM;Simons L;Elman I;Eccleston C
通讯作者：
Eccleston C
DOI：
10.1177/1073858409349902
发表时间：
2010-04
期刊：
The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry
影响因子：
--
作者：
Borsook D;Sava S;Becerra L
通讯作者：
Becerra L
影响因子：
--
作者：
D'Angelo, Egidio
通讯作者：
D'Angelo, Egidio
影响因子：
2.5
作者：
DONG, WK;RYU, H;WAGMAN, IH
通讯作者：
WAGMAN, IH
DOI：
10.1073/pnas.96.14.7705
发表时间：
1999-07-06
影响因子：
11.1
作者：
Bushnell, MC;Duncan, GH;Carrier, B
通讯作者：
Carrier, B