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研究静息状态下脑内中尺度结构的光谱特征。

Investigating the spectral features of the brain meso-scale structure at rest.

作者信息

Iandolo Riccardo, Semprini Marianna, Sona Diego, Mantini Dante, Avanzino Laura, Chiappalone Michela

机构信息

Rehab Technologies Lab, Istituto Italiano di Tecnologia, Genova, Italy.

Pattern Analysis & Computer Vision, Istituto Italiano di Tecnologia, Genova, Italy.

出版信息

Hum Brain Mapp. 2021 Oct 15;42(15):5113-5129. doi: 10.1002/hbm.25607. Epub 2021 Jul 31.

Abstract

Recent studies provide novel insights into the meso-scale organization of the brain, highlighting the co-occurrence of different structures: classic assortative (modular), disassortative, and core-periphery. However, the spectral properties of the brain meso-scale remain mostly unexplored. To fill this knowledge gap, we investigated how the meso-scale structure is organized across the frequency domain. We analyzed the resting state activity of healthy participants with source-localized high-density electroencephalography signals. Then, we inferred the community structure using weighted stochastic block-model (WSBM) to capture the landscape of meso-scale structures across the frequency domain. We found that different meso-scale modalities co-exist and are diversely organized over the frequency spectrum. Specifically, we found a core-periphery structure dominance, but we also highlighted a selective increase of disassortativity in the low frequency bands (<8 Hz), and of assortativity in the high frequency band (30-50 Hz). We further described other features of the meso-scale organization by identifying those brain regions which, at the same time, (a) exhibited the highest degree of assortativity, disassortativity, and core-peripheriness (i.e., participation) and (b) were consistently assigned to the same community, irrespective from the granularity imposed by WSBM (i.e., granularity-invariance). In conclusion, we observed that the brain spontaneous activity shows frequency-specific meso-scale organization, which may support spatially distributed and local information processing.

摘要

最近的研究为大脑的中尺度组织提供了新的见解,突出了不同结构的共同出现:经典的 assortative(模块化)、disassortative 和核心-外围结构。然而,大脑中尺度的频谱特性大多仍未被探索。为了填补这一知识空白,我们研究了中尺度结构在频域中的组织方式。我们使用源定位的高密度脑电图信号分析了健康参与者的静息状态活动。然后,我们使用加权随机块模型(WSBM)推断群落结构,以捕捉频域中中尺度结构的全貌。我们发现不同的中尺度模式共存,并且在频谱上有不同的组织方式。具体而言,我们发现了核心-外围结构的主导地位,但我们也强调了在低频带(<8Hz)中disassortativity的选择性增加,以及在高频带(30 - 50Hz)中assortativity的增加。我们通过识别那些同时(a)表现出最高程度的assortativity、disassortativity和核心-外围性(即参与度),以及(b)无论WSBM所施加的粒度如何(即粒度不变性)都始终被分配到同一群落的脑区,进一步描述了中尺度组织的其他特征。总之,我们观察到大脑自发活动显示出频率特异性的中尺度组织,这可能支持空间分布和局部信息处理。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f10c/8449100/e791f3de73c7/HBM-42-5113-g003.jpg

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