一种基于静息态功能磁共振成像的快速丘脑分割算法

摘要

目的 探讨基于静息态功能磁共振成像(fMRI)的快速有效的丘脑分割方法.方法 静息态fMRI技术是通过测量血氧水平依赖(BOLD)信号的变化间接反映神经元的活动情况.利用丘脑内部的BOLD信号相关并结合聚类分析算法将丘脑进行功能性分割.结果 丘脑被划分为7个区域,同一区域内信号相似度高.此分割结果与利用丘脑-大脑皮层的功能连接强度所得的分割结果相似.结论 静息态fMRI不仅可以分析丘脑-大脑皮层之间的功能连接,还可分析丘脑内部的功能特征.仅利用丘脑内部信息分割丘脑具有运算量小、计算速度快的优点.%Objective To obtain an accurate and effective method for thalamus segmentation based on resting-state functional magnetic resonance imaging (fMRI).Methods Based on the fact that resting-state fMRI technique examined spatial synchronization of spontaneous fluctuations in blood oxygen level-dependent (BOLD) signals indirectly reflect the neuronal and synaptic activity,the in-thalamus BOLD signal correlations were calculated,and then the k-means clustering algorithm was applied to obtain functional connectivity-based thalamus segmentation.Results The thalamus was divided into seven regions.Voxels within the same region were highly correlated with each other.The segmentation result was similar to that divided by functional connectivity between thalamus and the cerebral cortex.Conclusions Resting-state fMRI could provide not only the functional connectivity network between cortical and subcortical brain regions,but also the functional characteristics of thalamus.Segmentation algorithm using only internal information of thalamus shows lower computational complexity and higher processing speed than that based on the functional connectivity between thalamus and the cerebral cortex.