Surrogate data analyses of the energy landscape analysis of resting-state brain activity.

IF 3.4 3区 医学 Q2 NEUROSCIENCES
Frontiers in Neural Circuits Pub Date : 2025-03-14 eCollection Date: 2025-01-01 DOI:10.3389/fncir.2025.1500227
Yuki Hosaka, Takemi Hieda, Ruixiang Li, Kenji Hayashi, Koji Jimura, Teppei Matsui
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引用次数: 0

Abstract

The spatiotemporal dynamics of resting-state brain activity can be characterized by switching between multiple brain states, and numerous techniques have been developed to extract such dynamic features from resting-state functional magnetic resonance imaging (fMRI) data. However, many of these techniques are based on momentary temporal correlation and co-activation patterns and merely reflect linear features of the data, suggesting that the dynamic features, such as state-switching, extracted by these techniques may be misinterpreted. To examine whether such misinterpretations occur when using techniques that are not based on momentary temporal correlation or co-activation patterns, we addressed Energy Landscape Analysis (ELA) based on pairwise-maximum entropy model (PMEM), a statistical physics-inspired method that was designed to extract multiple brain states and dynamics of resting-state fMRI data. We found that the shape of the energy landscape and the first-order transition probability derived from ELA were similar between real data and surrogate data suggesting that these features were largely accounted for by stationary and linear properties of the real data without requiring state-switching among locally stable states. To confirm that surrogate data were distinct from the real data, we replicated a previous finding that some topological properties of resting-state fMRI data differed between the real and surrogate data. Overall, we found that linear models largely reproduced the first order ELA-derived features (i.e., energy landscape and transition probability) with some notable differences.

静息状态脑活动能量格局分析的替代数据分析。
静息状态大脑活动的时空动态可以通过多种大脑状态之间的切换来表征,并且已经开发了许多技术来从静息状态功能磁共振成像(fMRI)数据中提取这种动态特征。然而,这些技术中的许多都是基于瞬时时间相关性和共激活模式,仅仅反映了数据的线性特征,这表明这些技术提取的动态特征,如状态切换,可能会被误解。为了检查当使用非基于瞬时时间相关性或共激活模式的技术时是否会发生这种误解,我们研究了基于成对最大熵模型(PMEM)的能量景观分析(ELA),这是一种受统计物理学启发的方法,旨在提取静息状态fMRI数据的多种大脑状态和动态。我们发现,真实数据和替代数据的能量景观形状和一阶跃迁概率相似,这表明这些特征在很大程度上是由真实数据的平稳和线性特性决定的,而不需要在局部稳定状态之间进行状态切换。为了证实替代数据与真实数据不同,我们重复了先前的发现,即静息状态fMRI数据的一些拓扑特性在真实数据和替代数据之间存在差异。总体而言,我们发现线性模型在很大程度上再现了ela衍生的一阶特征(即能源景观和转移概率),但存在一些显著差异。
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来源期刊
CiteScore
6.00
自引率
5.70%
发文量
135
审稿时长
4-8 weeks
期刊介绍: Frontiers in Neural Circuits publishes rigorously peer-reviewed research on the emergent properties of neural circuits - the elementary modules of the brain. Specialty Chief Editors Takao K. Hensch and Edward Ruthazer at Harvard University and McGill University respectively, are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Frontiers in Neural Circuits launched in 2011 with great success and remains a "central watering hole" for research in neural circuits, serving the community worldwide to share data, ideas and inspiration. Articles revealing the anatomy, physiology, development or function of any neural circuitry in any species (from sponges to humans) are welcome. Our common thread seeks the computational strategies used by different circuits to link their structure with function (perceptual, motor, or internal), the general rules by which they operate, and how their particular designs lead to the emergence of complex properties and behaviors. Submissions focused on synaptic, cellular and connectivity principles in neural microcircuits using multidisciplinary approaches, especially newer molecular, developmental and genetic tools, are encouraged. Studies with an evolutionary perspective to better understand how circuit design and capabilities evolved to produce progressively more complex properties and behaviors are especially welcome. The journal is further interested in research revealing how plasticity shapes the structural and functional architecture of neural circuits.
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