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This study adapted a statistical probabilistic anatomical map of the brain for single photon emission computed tomography images of depressive end-stage renal disease patients. This research aimed to investigate the relationship between symptom clusters, disease severity, and cerebral blood flow. Twenty-seven patients （16 males, 11 females） with stages 4 and 5 end-stage renal disease were enrolled, along with 25 healthy controls. All patients underwent depressive mood assessment and brain single photon emission computed tomography. The statistical probabilistic anatomical map images were used to calculate the brain single photon emission computed tomography counts. Asymmetric index was acquired and Pearson correlation analysis was performed to analyze the correlation between symptom factors, severity, and regional cerebral blood flow. The depression factors of the Hamilton Depression Rating Scale showed a negative correlation with cerebral blood flow in the left amygdale. The insomnia factor showed negative correlations with cerebral blood flow in the left amygdala, right superior frontal gyrus, right middle frontal gyrus, and left middle frontal gyrus. The anxiety factor showed a positive correlation with cerebral glucose metabolism in the cerebellar vermis and a negative correlation with cerebral glucose metabolism in the left globus pailidus, right inferior frontal gyrus, both temporal poles, and left parahippocampus. The overall depression severity （total scores of Hamilton Depression Rating Scale） was negatively correlated with the statistical probabilistic anatomical map results in the left amygdala and right inferior frontal gyrus. In conclusion, our results demonstrated that the disease severity and extent of cerebral blood flow quantified by a probabilistic brain atlas was related to various brain areas in terms of the overall severity and symptom factors in end-stage renal disease patients.

针对小样本条件下多元时间序列之间的关联分析问题, 提出了一种小样本概率关联度模型。使用样本函数或样本统计量代替样本构造累积经验分布函数并计算概率关联系数。当比较序列样本容量n < 5, 使用样本顺序统计量的经验分布函数计算概率关联系数; 当比较序列样本容量n≥5, 使用Bootstrap方法重抽样扩充样本后再构造经验分布函数计算概率关联系数, 改进了概率关联度模型中关联系数的计算方法, 解决了小样本条件下这一类多元数据关联分析问题。证明了小样本概率关联度模型满足的基本性质, 给出了小样本概率关联分析的基本步骤, 仿真案例和实际应用验证了文中模型的正确性和有效性。