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Exploring the Abnormal Brain Regions and Abnormal Functional Connections in SZ by Multiple Hypothesis Testing Technique
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作者 Lan Yang Shun Qi +1 位作者 Chen Qiao yanmei kang 《Computer Modeling in Engineering & Sciences》 SCIE EI 2020年第10期215-237,共23页
Schizophrenia(SZ)is one of the most common mental diseases.Its main characteristics are abnormal social behavior and inability to correctly understand real things.In recent years,the magnetic resonance imaging(MRI)tec... Schizophrenia(SZ)is one of the most common mental diseases.Its main characteristics are abnormal social behavior and inability to correctly understand real things.In recent years,the magnetic resonance imaging(MRI)technique has been popularly utilized to study SZ.However,it is still a great challenge to reveal the essential information contained in the MRI data.In this paper,we proposed a biomarker selection approach based on the multiple hypothesis testing techniques to explore the difference between SZ and healthy controls by using both functional and structural MRI data,in which biomarkers represent both abnormal brain functional connectivity and abnormal brain regions.By implementing the biomarker selection approach,six abnormal brain regions and twenty-three abnormal functional connectivity in the brains of SZ are explored.It is discovered that compared with healthy controls,the significantly reduced gray matter volumes are mainly distributed in the limbic lobe and the basal ganglia,and the significantly increased gray matter volumes are distributed in the frontal gyrus.Meanwhile,it is revealed that the significantly strengthened connections are those between the middle frontal gyrus and the superior occipital gyrus,the superior occipital gyrus and the middle occipital gyrus as well as the middle occipital gyrus and the fusiform gyrus,and the rest connections are significantly weakened. 展开更多
关键词 Multiple hypothesis testing SCHIZOPHRENIA magnetic resonance imaging abnormal brain regions abnormal functional connectivity
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Effect of spatially correlated noise on stochastic synchronization in globally coupled FitzHugh–Nagumo neuron systems
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作者 Yange Shao yanmei kang 《Theoretical & Applied Mechanics Letters》 CAS 2014年第1期41-49,共9页
The phenomenon of stochastic synchronization in globally coupled FitzHugh–Nagumo(FHN) neuron system subjected to spatially correlated Gaussian noise is investigated based on dynamical mean-field approximation(DMA... The phenomenon of stochastic synchronization in globally coupled FitzHugh–Nagumo(FHN) neuron system subjected to spatially correlated Gaussian noise is investigated based on dynamical mean-field approximation(DMA) and direct simulation(DS). Results from DMA are in good quantitative or qualitative agreement with those from DS for weak noise intensity and larger system size. Whether the consisting single FHN neuron is staying at the resting state, subthreshold oscillatory regime, or the spiking state, our investigation shows that the synchronization ratio of the globally coupled system becomes higher as the noise correlation coefficient increases, and thus we conclude that spatial correlation has an active effect on stochastic synchronization, and the neurons can achieve complete synchronization in the sense of statistics when the noise correlation coefficient tends to one. Our investigation also discloses that the noise spatial correlation plays the same beneficial role as the global coupling strength in enhancing stochastic synchronization in the ensemble. The result might be useful in understanding the information coding mechanism in neural systems. 展开更多
关键词 stochastic synchronization spatially correlated noise dynamical mean-field approximation
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伽马更新突触输入作用下自适应神经元模型的信噪比增益
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作者 康艳梅 付宇轩 陈亚倩 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2022年第1期148-156,共9页
我们采用自适应漏电积分-放电模型来研究非泊松递质对随机共振及其信噪比增益的影响,并运用事件驱动算法加速模拟过程、研究结果表明,输出信噪比和信噪比增益都会随着伽马分布的形状参数的增加而增加.特别地,当输入信号幅值较大时,由Ga... 我们采用自适应漏电积分-放电模型来研究非泊松递质对随机共振及其信噪比增益的影响,并运用事件驱动算法加速模拟过程、研究结果表明,输出信噪比和信噪比增益都会随着伽马分布的形状参数的增加而增加.特别地,当输入信号幅值较大时,由Gamma噪声诱导的1:1随机锁像揭示出此时发生的是满足频率匹配关系的随机共振现象,并且输出信噪比可以超过输入信噪比,这与Poisson情形显著不同;而当输入信号幅值极弱时,信噪比增益会远远大于1,这是由于发生了噪声诱导的随机共振现象.这些观察结果对于从更现实的突触建模角度理解神经信息处理机制是有意义的. 展开更多
关键词 信噪比增益 输出信噪比 输入信噪比 频率匹配 随机共振 放电模型 伽马分布 形状参数
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