A graph neural network approach to investigate brain critical states over neurodevelopment.

IF 3.6 3区 医学 Q2 NEUROSCIENCES
Network Neuroscience Pub Date : 2025-06-27 eCollection Date: 2025-01-01 DOI:10.1162/netn_a_00451
Rodrigo M Cabral-Carvalho, Walter H L Pinaya, João R Sato
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引用次数: 0

Abstract

Recent studies show that functional resting-state dynamics may be modeled by lattice models near criticality, such as the 2D Ising model. The Ising temperature, which is the control parameter dictating the phase transitions of the model, can provide insight into the large-scale dynamics and is being used to better understand different brain states and neurodevelopment. This period is categorized by intricate changes in the microcircuits to consolidate networks. These changes influence the macroscopic brain dynamics and also its functional relations, which can be observed in functional magnetic resonance imaging (fMRI). Therefore, this work investigates neurodevelopment through a novel method to estimate the Ising temperature of the brain from fMRI data using functional connectivity and graph neural networks trained on Ising model networks. The main finding indicates a statistically significant negative correlation between age and temperature for typically developing children (r = -0.48, p < 0.0001) and also children with attention-deficit/hyperactivity disorder (r = -0.49, p < 0.0001). This study suggests that the brain gets distant from criticality as age increases, leading to a more ordered state.

研究神经发育过程中脑临界状态的图神经网络方法。
最近的研究表明,功能静态动力学可以用接近临界的晶格模型来建模,例如二维Ising模型。伊辛温度是决定模型相变的控制参数,它可以提供对大尺度动力学的洞察,并被用来更好地理解不同的大脑状态和神经发育。这一时期是通过微电路的复杂变化来巩固网络的。这些变化影响了宏观脑动力学及其功能关系,可以在功能磁共振成像(fMRI)中观察到。因此,这项工作通过一种新的方法来研究神经发育,该方法使用功能连接和在Ising模型网络上训练的图神经网络,从fMRI数据中估计大脑的Ising温度。主要发现表明,正常发育儿童的年龄和温度之间存在统计学上显著的负相关(r = -0.48, p < 0.0001),注意缺陷/多动障碍儿童的年龄和温度之间也存在统计学上显著的负相关(r = -0.49, p < 0.0001)。这项研究表明,随着年龄的增长,大脑离临界状态越来越远,从而导致更有序的状态。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Network Neuroscience
Network Neuroscience NEUROSCIENCES-
CiteScore
6.40
自引率
6.40%
发文量
68
审稿时长
16 weeks
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