Enhanced simulations of whole-brain dynamics using hybrid resting-state structural connectomes.
Enhanced simulations of whole-brain dynamics using hybrid resting-state structural connectomes.
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DOI：
10.3389/fncom.2023.1295395
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发表时间：
2023
影响因子：
3.2
通讯作者：
Leow, Alex
中科院分区：
文献类型：
--
作者：
Manos, Thanos;Diaz-Pier, Sandra;Fortel, Igor;Driscoll, Ira;Zhan, Liang;Leow, Alex
关键词：
人类大脑由数十亿个神经元和突触连接组成，是一个复杂的网络，协调大脑区域之间兴奋和抑制活动的复杂平衡。兴奋和抑制之间的动态平衡对于调节皮层网络中的神经输入/输出关系以及调节其对刺激的响应的动态范围至关重要。为了使用连接组学来推断这种平衡，我们最近引入了一个基于伊辛模型的计算框架，该模型最初是为了解释铁磁体中的相变而开发的，并提出了一种新的混合静止状态结构连接组（rsSC）。在这里，我们表明，生成模型的基础上的仓本相位振荡器可以用来模拟静态和动态的功能连接体（FC）与rsSC作为耦合权重系数，使模拟的FC对齐以及与观察到的FC相比，模拟传统的结构连接体。
The human brain, composed of billions of neurons and synaptic connections, is an intricate network coordinating a sophisticated balance of excitatory and inhibitory activities between brain regions. The dynamical balance between excitation and inhibition is vital for adjusting neural input/output relationships in cortical networks and regulating the dynamic range of their responses to stimuli. To infer this balance using connectomics, we recently introduced a computational framework based on the Ising model, which was first developed to explain phase transitions in ferromagnets, and proposed a novel hybrid resting-state structural connectome (rsSC). Here, we show that a generative model based on the Kuramoto phase oscillator can be used to simulate static and dynamic functional connectomes (FC) with rsSC as the coupling weight coefficients, such that the simulated FC aligns well with the observed FC when compared with that simulated traditional structural connectome.
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