Relationship between cognitive dysfunction and brain structural network changes in patients with white matter lesions based on diffusion tensor imagin
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摘要:
目的 探究基于磁共振弥散张量成像技术的脑白质病变患者认知功能障碍与脑结构网络变化的关系。 方法 纳入2017年1月~2022年5月我院120例脑白质病变患者作为研究对象，根据神经心理学评估结果将患者分为认知功能正常组（n=48）、轻度认知功能障碍组（n=44）和痴呆组（n=28），所有患者接受3.0T磁共振检查，测量白质高信号体积，并行弥散张量成像，构建脑结构网络。比较3组患者脑室旁、深部及总白质高信号体积，脑结构网络参数[全局效率、局部效率、最短路径、集聚系数、标准化聚类系数（γ）、近似标准特征路径长度（λ）、小世界属性值（σ）]，并采用Pearson相关性分析脑结构网络参数与蒙特利尔认知评估量表（MoCA）评分的相关性。 结果 认知功能正常组、轻度认知功能障碍组、痴呆组的脑室旁、深部白质高信号体积及总体积均依次增加，差异有统计学意义（P < 0.05）；痴呆组全局效率、局部效率、集聚系数、γ、λ、σ均小于认知功能正常组和轻度认知功能障碍组，最短路径大于认知功能正常组和轻度认知功能障碍组，差异有统计学意义（P < 0.05）；Pearson相关性分析显示，MoCA评分与最短路径呈负相关关系，与全局效率、局部效率、集聚系数、γ、λ、σ呈正相关关系（P < 0.05）。 结论 脑白质病变患者认知功能障碍与白质高信号体积及脑结构网络属性改变有关。 Abstract:Objective To observe the relationship between cognitive dysfunction and brain structural network changes in patients with white matter lesions based on magnetic resonance diffusion tensor imaging. Methods A total of 120 patients with white matter lesions in our hospital from January 2010 to February 2020 were enrolled as the research objects. According to the neuropsychological evaluation results, they were divided into normal cognitive function group (n=48), mild cognitive dysfunction group (n=44) and dementia group (n=28). All patients underwent 3.0T magnetic resonance examination to measure white matter high- signal volume and diffusion tensor imaging imaging to construct brain structural network. The para-ventricular, deep and total white matter high-signal volume, brain structure network parameters [global efficiency, local efficiency, shortest path, clustering coefficient, normalized clustering coefficient (γ), approximate standard characteristic path length (λ), small-world attribute value (σ)] were compared among the three groups. Pearson correlation was used to analyze the correlation between brain structural network parameters and MoCA score. Results The hyperintensity volume and total volume of paraventricular and deep white matter in normal cognitive function group, mild cognitive dysfunction group and dementia group were increased successively, and the differences among groups were statistically significant (P < 0.05). The global efficiency, local efficiency, agglomeration coefficient, γ, λ and σ of dementia group were smaller than those of normal cognitive function group and mild cognitive dysfunction group, and the shortest path of dementia group was larger than those of normal cognitive function group and mild cognitive dysfunction group, and the differences were statistically significant (P < 0.05). Pearson correlation analysis showed that the MoCA score was negatively correlated with the shortest path, but positively correlated with the global efficiency, local efficiency, agglomeration coefficient, γ, λ and σ (P < 0.05). Conclusion Cognitive dysfunction in white matter lesions patients is associated with white matter high signal volume and changes in brain structural network properties. -
表 1 三组患者一般资料比较
Table 1. Comparison of general data among the three groups
Index NC group (n=48) MCI group (n=44) D group (n=28) χ2/F/Z P Gender [n(%)] 0.080 0.962 Male 27（56.25） 26（59.09） 16（57.14） Female 21（43.75） 18（41.91） 12（42.86） Age (years, Mean±SD) 65.27±9.33 65.62±8.15 66.09±6.52 0.986 0.917 Culture level [n(%)] 0.120 0.998 Junior high school and below 19（39.58） 18（15.91） 11（39.29） High school/technical secondary school 21（43.75） 19（43.18） 13（46.43） College degree or above 8（16.67） 7（15.91） 4（14.29） Hypertension [n(%)] 12（25.00） 9（20.45） 6（21.43） 0.300 0.862 Diabetes [n(%)] 6（12.50） 4（9.09） 5（17.86） 1.200 0.548 Hyperlipemia [n(%)] 7（14.58） 5（11.36） 5（17.86） 0.600 0.739 MoCA score (Mean±SD) 28.02±1.79 23.02±2.16a 18.27±2.24ab 208.708 < 0.001 MoCA: Montreal cognitive assessment; MCI: Mild cognitive impairment; D: Dementia. aP < 0.05 vs NC group; bP < 0.05 vs MCI group. 
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表 2 三组患者白质高信号体积比较
Table 2. Comparison of white matter high-signal volume among the three groups (mm3, Mean±SD)
Groups Paraventricular nucleus Deep Bulk volume NC group (n=48) 1863.78±392.60 957.74±197.47 2821.52±590.07 MCI group(n=44) 3527.93±437.09a 1525.62±300.74a 5053.55±737.83a D group(n=28) 6152.80±505.77ab 2605.53±418.57ab 8758.33±924.34ab F 851.393 268.981 580.275 P < 0.001 < 0.001 < 0.001 aP < 0.05 vs NC group; bP < 0.05 vs MCI group.
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表 3 三组患者脑结构网络参数比较
Table 3. Comparison of brain structure network parameters among the three groups (Mean±SD)
Groups Global efficiency Component efficiency Shortest path Clustering coefficient λ γ σ NC group (n=48) 0.41±0.04 0.55±0.05 2.54±0.52 0.40±0.05 1.19±0.03 3.89±0.71 3.27±0.70 MCI group(n=44) 0.38±0.03a 0.53±0.06 2.46±0.33 0.38±0.04 1.15±0.02a 3.86±0.44 3.36±0.52 D group(n=28) 0.35±0.06ab 0.46±0.07ab 2.97±0.49ab 0.34±0.03ab 1.02±0.04ab 2.94±±0.70ab 2.88±0.72ab F 18.124 21.409 21.409 17.719 299.248 24.324 5.064 P < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.008 aP < 0.05 vs NC group; bP < 0.05 vs MCI group. γ: Normalized clustering coefficient; λ: Approximate standard characteristic path length; σ: Small-world attribute value.
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表 4 白质高信号体积、脑结构网络参数与MoCA评分相关性分析
Table 4. Correlation analysis of white matter high-signal volume, brain structural network parameters and MoCA score
Index MoCA score r P Global efficiency 0.621 < 0.001 Component efficiency 0.605 < 0.001 Shortest path -0.612 < 0.001 Clustering coefficient 0.628 < 0.001 λ 0.508 0.010 γ 0.483 0.017 σ 0.495 0.014
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