风险决策的动态功能网络研究
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摘要
目的研究与青少年风险决策相关的动态脑功能网络特征。材料与方法基于49个对象的静息态功能磁共振数据利用动态窗技术进行了动态功能网络的构建,并使用低频振荡振幅作为特征对风险决策行为进行了预测和网络分析。结果动态功能连接较好地预测了风险决策行为(r=0.3612,P=0.0108),并提取了与之相关的功能网络模式,即与风险决策相关的动态功能连接主要位于网络之间,且默认网络的预测能力最强,然后是两个控制网络即额顶网络和带状盖网络。结论本研究使用动态功能连接较好地预测了风险决策行为,更重要的是从动态功能网络上阐明了风险决策行为的特征。
Objective: This study aims to investigate dynamic network characteristic of risky decision making among adolescents. Materials and Methods: We first obtained rest-state functional magnetic resonance imaging(f MRI) data of 49 subjects; then dynamic functional connectivity networks were constructed using dynamic window for each subject and the fluctuation amplitudes of dynamic functional connectivity were calculated,finally these amplitude values were used as the features of multivariate pattern analysis to predict the risky decision behavior so as to obtain dynamic network characteristic of risky decision making. Results: Spontaneous fluctuation of dynamic functional connectivity could predict the risky decision behavior with good performance(r=0.3612,P=0.0108). Seventeen informational functional connectivities were found with powerful predictive function for risky decision making and they were mainly located among networks. Default network played the most important role for the risky decision behavior among all network modules,then two control network including the cingulo-opercular and frontoparietal network also played important roles. Conclusions: We used dynamic functional connectivity to predict risky decision behavior. What's more,we investigated the dynamic network characteristic of risky decision making.
Objective: This study aims to investigate dynamic network characteristic of risky decision making among adolescents. Materials and Methods: We first obtained rest-state functional magnetic resonance imaging(f MRI) data of 49 subjects; then dynamic functional connectivity networks were constructed using dynamic window for each subject and the fluctuation amplitudes of dynamic functional connectivity were calculated,finally these amplitude values were used as the features of multivariate pattern analysis to predict the risky decision behavior so as to obtain dynamic network characteristic of risky decision making. Results: Spontaneous fluctuation of dynamic functional connectivity could predict the risky decision behavior with good performance(r=0.3612,P=0.0108). Seventeen informational functional connectivities were found with powerful predictive function for risky decision making and they were mainly located among networks. Default network played the most important role for the risky decision behavior among all network modules,then two control network including the cingulo-opercular and frontoparietal network also played important roles. Conclusions: We used dynamic functional connectivity to predict risky decision behavior. What's more,we investigated the dynamic network characteristic of risky decision making.
引文
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[2]Romer D.Adolescent risk taking,impulsivity,and brain development:implications for prevention.Dev Psychobiol,2010,52(3):263-276.
[3]Morris AS,Squeglia LM,Jacobus J,et al.Adolescent brain development:implications for understanding risk and resilience processes through neuroimaging research.J Res Adolesc,2018,28(1):4-9.
[4]Eshel N,Nelson EE,Blair RJ,et al.Neural substrates of choice selection in adults and adolescents:development of the ventrolateral prefrontal and anterior cingulate cortices.Neuropsychologia,2007,45(6):1270-1279.
[5]Camara E,Rodriguez-Fornells A,Munte TF.Functional connectivity of reward processing in the brain.Front Hum Neurosci,2009,2:19.
[6]Allen EA,Damaraju E,Plis SM,et al.Tracking whole-brain connectivity dynamics in the resting state.Cereb Cortex,2014,24(3):663-676.
[7]Shen H,Li Z,Qin J,Liu Q,et al.Changes in functional connectivity dynamics associated with vigilance network in taxi drivers.Neuroimage,2016,124:367-378.
[8]Marusak HA,Calhoun VD,Brown S,et al.Dynamic functional connectivity of neurocognitive networks in children.Human Brain Mapping,2017,38(1):97-108.
[9]Kohno M,Ghahremani DG,Morales AM,et al.Risk-taking behavior:dopamine D2/D3 receptors,feedback,and frontolimbic activity.Cereb Cortex,2015,25(1):236-245.
[10]Qi X,Du X,Yang Y,et al.Decreased modulation by the risk level on the brain activation during decision making in adolescents with internet gaming disorder.Front Behav Neurosci,2015,9:296.
[11]Dosenbach NU,Nardos B,Cohen AL,et al.Prediction of individual brain maturity using f MRI.Science,2010,329(5997):1358-1361.
[12]Leonardi,N.Van De,Ville D.On spurious and real fluctuations of dynamic functional connectivity during rest.Neuroimage,2015,104:430-436.
[13]Finn ES,Shen X,Scheinost D,et al.Functional connectome fingerprinting:identifying individuals using patterns of brain connectivity.Nat Neurosci,2015,18(11):1664-1671.
[14]Calhoun VD,Miller R,Pearlson G,et al.The chronnectome:timevarying connectivity networks as the next frontier in f MRI data discovery.Neuron,2014,84(2):262-274.
[15]Hutchison RM,Morton JB.Tracking the brain's functional coupling dynamics over development.J Neurosci,2015,35(17):6849-6859.
[16]Buckner RL,Andrews-Hanna JR,Schacter DL.The brain's default network:anatomy,function,and relevance to disease.Ann N Y Acad Sci,2008,1124:1-38.
[17]Sylvester CM,Corbetta M,Raichle ME,et al.Functional network dysfunction in anxiety and anxiety disorders.Trends Neurosci,2012,35(9):527-535.
[18]Gordon EM,Laumann TO,Gilmore AW,et al.Precision functional mapping of individual human brains.Neuron,2017,95(4):791-807.
[19]Claus ED,Hutchison KE.Neural mechanisms of risk taking and relationships with hazardous drinking.Alcohol Clin Exp Res,2012,36(6):932-940.
[20]Crowley TJ,Dalwani MS,Mikulich-Gilbertson SK,et al.Risky decisions and their consequences:neural processing by boys with antisocial substance disorder.Plo S One,2010,5(9):e12835.
[21]Claus ED,Ewing SW,Magnam RE,et al.Neural mechanisms of risky decision making in adolescents reporting frequent alcohol and/or marijuana use.Brain Imaging Behav,2017:1-13.