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浙江大学学报（理学版）
浙江大学学报（理学版）  2021, Vol. 48 Issue (1): 124-130    DOI: 10.3785/j.issn.1008-9497.2021.01.017
心理学     
脑自发性神经振荡低频振幅表征脑功能网络静息态信息流
孟静1,2, 刘子涵3, 李锐1,2
1.中国科学院心理健康重点实验室（中国科学院心理研究所），北京 100101
2.中国科学院大学心理学系， 北京 100049
3.北京印刷学院印刷与包装工程学院，北京 102600
Amplitude of low-frequency fluctuation characterizes resting-state activity flow over functional connectivity brain networks
MENG Jing1,2, LIU Zihan3, LI Rui1,2
1.CAS Key Laboratory of Mental Health, Institute of Psychology, Beijing 100101, China
2.Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
3.Department of Printing Engineering, Beijing Institute of Graphic Communication, Beijing 102600, China
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摘要： 已有研究表明，任务诱发的局部脑区激活可通过全脑功能网络中脑区间的神经活动信息流（activity flow,AF）预测，然而静息态下自发脑神经振荡与AF的关系仍不清楚。本文旨在研究静息态自发神经活动是否也反映脑区间在功能连接(functional connectivity,FC)路径上的信息传播。用来自千人脑功能连接组计划中的197名健康被试的静息态功能磁共振成像（RS-fMRI）数据，计算全脑160个感兴趣区的自发性神经振荡低频振幅（amplitude of low-frequency fluctuation，ALFF），采用相关性分析和多元回归分析2种方法计算脑区间FC；基于AF模型，通过ALFF与FC的加权和估计汇聚到目标脑区的AF，利用Pearson相关性分析在全脑层面和默认网络脑区层面ALFF与AF之间的空间相关性。结果表明，在全脑层面和默认网络（default-mode network，DMN）脑区层面，AF与ALFF的分布模式显著相关；用多元回归改进FC估计可改善预测效果。低频振幅不仅体现脑区局部的神经振荡和功能情况，同时也反映自发性脑神经活动通过FC通道在脑区间进行信息交互。
关键词： 功能磁共振;  静息态;  低频振幅;  活动信息流;  功能连接    
Abstract: Previous studies have revealed that the task-evoked regional activations can be predicted by interareal activity flow (AF) in brain-wide functional connections.However,little is known about the relationship between regional spontaneous activity and the AF during the resting-state.Here,we aim to investigate if the resting-state reflects communications between brain regions via intrinsic functional connectivity (FC) pathways.The resting-stage functional magnetic resonance imaging (RS-fMRI) data of 197 participants from 1 000 Functional Connectomes Project was used to calculate the amplitude of low-frequency fluctuation (ALFF) for 160 regions of interests,and the FC between these regions was explored using Pearson correlations and multiple regression methods.Based on the AF model,the AF aggregated to each region was then calculated as the FC-weighted sums of the ALFF of all other regions. Pearson correlation analysis was finally performed to examine the spatial correlation between the distribution of ALFF and AF across all 160 regions and default-mode network (DMN) regions.The results show that the ALFF pattern is significantly correlated with the spatial distribution of the AF across both whole brain and the DMN; the FC calculated by multiple regression can improve the predictive effect.Our findings suggest that the ALFF reflects not only the neural oscillations and functions in local regions but also the transmission and stream of spontaneous activity among distributed regions via resting-state FC pathways.
Key words: amplitude of low-frequency fluctuation    functional connectivity    resting-state    functional magnetic resonance imaging    activity flow
收稿日期: 2019-11-25 出版日期: 2021-01-20
CLC:  G 44  
基金资助: 国家自然科学基金资助项目(61673374)；国家重点研发计划项目(2018YFC2000303).
通讯作者: ORCID:http://orcid.org/0000-0002-4101-5415，E-mail：lir@psych.ac.cn.     E-mail: lir@psych.ac.cn
作者简介: 孟静（1991—），ORCID:http://orcid.org/0000-0002-6478-3513，女，硕士，工程师，主要从事发展与教育心理学研；
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引用本文:

孟静, 刘子涵, 李锐. 脑自发性神经振荡低频振幅表征脑功能网络静息态信息流[J]. 浙江大学学报（理学版）, 2021, 48(1): 124-130.

MENG Jing, LIU Zihan, LI Rui. Amplitude of low-frequency fluctuation characterizes resting-state activity flow over functional connectivity brain networks. Journal of Zhejiang University (Science Edition), 2021, 48(1): 124-130.

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https://www.zjujournals.com/sci/CN/10.3785/j.issn.1008-9497.2021.01.017    或    https://www.zjujournals.com/sci/CN/Y2021/V48/I1/124

1 包尚联.脑功能成像物理学［M］.郑州：郑州大学出版社，2006：1-97. BAO S L.Physics of Brain Functional Imaging［M］.Zhengzhou：Zhengzhou University Press，2006：1-97.
2 BISWAL B，YETKIN F Z，HAUGHTON V M，et al.Functional connectivity in the motor cortex of resting human brain using echo-planar MRI［J］.Magnetic Resonance in Medicine，1995，34（4）：537-541.
3 ROSAZZA C，MINATI L.Resting-state brain networks：Literature review and clinical applications［J］.Neurological Sciences，2011，32（5）：773-785.DOI： 10.1007/s10072-011-0636-y
4 LYU H，WANG Z，TONG E，et al.Resting-state functional MRI：Everything that nonexperts have always wanted to know［J］.American Journal of Neuroradiology，2018，39（8）：1390-1399.DOI：10.3174/ajnr.A5527
5 ZANG Y F，HE Y，ZHU C Z，et al.Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI［J］.Brain and Development，2007，29（2）：83-91.DOI：10.1016/j.braindev.2006.07.002
6 肖娟，常静玲，高颖.基于静息态网络神经精神类疾病的脑机制研究进展［J］.中国康复理论与实践，2013（8）：743-747.DOI：10.3969/j.issn.1006-9771. 2013.08.008 XIAO J，CHANG J L，GAO Y.Advance in brain mechanism in nervous and mental diseases based on resting-state networks （review）［J］.Chinese Journal of Rehabilitation Theory and Practice，2013（8）：743-747. DOI：10.3969/j.issn.1006-9771.2013.08.008
7 孙源，韩新焕，郁芸，等.静息态功能磁共振成像在神经内科疾病中的应用［J］.南京医科大学学报（自然科学版），2018，38（10）：1477-1480.DOI：10.7655/NYDXBNS20181033 SUN Y，HAN X H，YU Y，et al.Application of resting-state functional magnetic resonance imaging in neurological disease［J］.Acta Universitatis Medicinalis Nanjing（Natural Science），2018，38（10）：1477-1480. DOI：10.7655/NYDXBNS2018 1033
8 COLE M W，ITO T，BASSETT D S，et al. Activity flow over resting-state networks shapes cognitive task activations［J］.Nature Neuroscience，2016，19（12）：1718-1726.
9 TOMASI D ，VOLKOW N D.Association between brain activation and functional connectivity［J］.Cerebral Cortex，2018，29（5）：1-13.DOI：10.1093/cercor/bhy077
10 NICO U F D，BINYAM N，ALEXANDER L C，et al.Prediction of individual brain maturity using fMRI［J］. Science，2010，329（5997）：1358-1361. DOI：10.1126/science.1194144
11 MASCALI D，DINUZZO M，GILI T，et al.Intrinsic patterns of coupling between correlation and amplitude of low-frequency fMRI fluctuations are disrupted in degenerative dementia mainly due to functional disconnection［J］. PLoS One，2015，10（4）：e0120988. DOI：10.1371/journal.pone.0120988
12 LENHARD W，LENHARD A.Hypothesis Tests for Comparing Correlations［M］. Bibergau：Psychometrica，2014.
13 DEWANG M，ZHONGXIANG D，WENBIN J，et al.Low-frequency fluctuations of the resting brain：High magnitude does not equal high reliability［J］.PLoS One，2015，10（6）：1-20.
14 李雨，舒华.默认网络的神经机制、功能假设及临床应用［J］.心理科学进展，2014，22（2）：234-249. DOI：10.3724/SP.J.1042.2014.00234 LI Y，SHU H.The brain mechanisms and functional hypothesis of default mode network and its clinical application［J］.Advances in Psychological Science，2014，22（2）：234-249.DOI：10.3724/SP.J.1042. 2014.00234
15 LI R，CHEN K，FLEISHER AS，et al.Large-scale directional connections among multi resting-state neural networks in human brain：A functional MRI and Bayesian network modeling study［J］. NeuroImage，2011，56（3）：1035-1042. DOI：10.1016/j.neuroimage.2011.03.010
16 FU Z N，TU Y H，DI X，et al.Characterizing dynamic amplitude of low-frequency fluctuation and its relationship with dynamic functional connectivity：An application to schizophrenia［J］.NeuroImage，2018，180（B）：619-631.DOI：10.1016/j.neuroimage.2017. 09.035
17 GOEBEL R ，ROEBROECK A，KIM D S，et al.Investigating directed cortical interactions in time-resolved fMRI data using vector autoregressive modeling and Granger causality mapping［J］. Magnetic Resonance Imaging，2003，21（10）：1251-1261. DOI：10.1016/j.mri.2003.08.026
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