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通过一种新型点过程分析揭示大规模脑功能磁共振成像动力学中的临界性

Criticality in large-scale brain FMRI dynamics unveiled by a novel point process analysis.

作者信息

Tagliazucchi Enzo, Balenzuela Pablo, Fraiman Daniel, Chialvo Dante R

机构信息

Departamento de Física, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires Buenos Aires, Argentina.

出版信息

Front Physiol. 2012 Feb 8;3:15. doi: 10.3389/fphys.2012.00015. eCollection 2012.

Abstract

Functional magnetic resonance imaging (fMRI) techniques have contributed significantly to our understanding of brain function. Current methods are based on the analysis of gradual and continuous changes in the brain blood oxygenated level dependent (BOLD) signal. Departing from that approach, recent work has shown that equivalent results can be obtained by inspecting only the relatively large amplitude BOLD signal peaks, suggesting that relevant information can be condensed in discrete events. This idea is further explored here to demonstrate how brain dynamics at resting state can be captured just by the timing and location of such events, i.e., in terms of a spatiotemporal point process. The method allows, for the first time, to define a theoretical framework in terms of an order and control parameter derived from fMRI data, where the dynamical regime can be interpreted as one corresponding to a system close to the critical point of a second order phase transition. The analysis demonstrates that the resting brain spends most of the time near the critical point of such transition and exhibits avalanches of activity ruled by the same dynamical and statistical properties described previously for neuronal events at smaller scales. Given the demonstrated functional relevance of the resting state brain dynamics, its representation as a discrete process might facilitate large-scale analysis of brain function both in health and disease.

摘要

功能磁共振成像(fMRI)技术为我们理解大脑功能做出了重大贡献。当前的方法基于对大脑血氧水平依赖(BOLD)信号中逐渐且连续变化的分析。与该方法不同,最近的研究表明,仅通过检查相对较大幅度的BOLD信号峰值就能获得等效结果,这表明相关信息可以浓缩在离散事件中。本文进一步探讨了这一观点,以展示如何仅通过此类事件的时间和位置来捕捉静息状态下的脑动力学,即从时空点过程的角度。该方法首次允许根据从fMRI数据导出的序参量和控制参量来定义一个理论框架,其中动力学状态可被解释为对应于一个接近二阶相变临界点的系统。分析表明,静息大脑大部分时间处于此类转变的临界点附近,并表现出由先前在较小尺度上描述的神经元事件相同的动力学和统计特性所支配的活动雪崩。鉴于静息状态脑动力学已被证明的功能相关性,将其表示为离散过程可能有助于对健康和疾病状态下的大脑功能进行大规模分析。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff8e/3274757/a82e039319ed/fphys-03-00015-g001.jpg

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