From the Department of Medical Imaging (R.H., A.S.S., A.S., L.Z.).
Division of Neurology (L.L.), Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.
AJNR Am J Neuroradiol. 2020 Mar;41(3):449-455. doi: 10.3174/ajnr.A6435. Epub 2020 Feb 20.
Graph theory uses structural similarity to analyze cortical structural connectivity. We used a voxel-based definition of cortical covariance networks to quantify and assess the relationship of network characteristics to cognition in a cohort of patients with relapsing-remitting MS with and without cognitive impairment.
We compared subject-specific structural gray matter network properties of 18 healthy controls, 25 patients with MS with cognitive impairment, and 55 patients with MS without cognitive impairment. Network parameters were compared, and predictive value for cognition was assessed, adjusting for confounders (sex, education, gray matter volume, network size and degree, and T1 and T2 lesion load). Backward stepwise multivariable regression quantified predictive factors for 5 neurocognitive domain test scores.
Greater path length ( = -0.28, < .0057) and lower normalized path length ( = 0.36, < .0004) demonstrated a correlation with average cognition when comparing healthy controls with patients with MS. Similarly, MS with cognitive impairment demonstrated a correlation between lower normalized path length ( = 0.40, < .001) and reduced average cognition. Increased normalized path length was associated with better performance for processing ( < .001), learning ( < .001), and executive domain function ( = .0235), while reduced path length was associated with better executive ( = .0031) and visual domains. Normalized path length improved prediction for processing ( = 43.6%, G = 20.9; < .0001) and learning ( = 40.4%, G = 26.1; < .0001) over a null model comprising confounders. Similarly, higher normalized path length improved prediction of average scores (G = 21.3; < .0001) and, combined with WM volume, explained 52% of average cognition variance.
Patients with MS and cognitive impairment demonstrate more random network features and reduced global efficiency, impacting multiple cognitive domains. A model of normalized path length with normal-appearing white matter volume improved average cognitive score prediction, explaining 52% of variance.
图论使用结构相似性来分析皮质结构连通性。我们使用基于体素的皮质协方差网络定义来量化和评估网络特征与认知的关系，在一组伴有或不伴有认知障碍的复发性缓解型多发性硬化症患者中进行了研究。
我们比较了 18 名健康对照者、25 名伴有认知障碍的多发性硬化症患者和 55 名无认知障碍的多发性硬化症患者的个体特定结构灰质网络特性。比较了网络参数，并调整混杂因素（性别、教育程度、灰质体积、网络大小和度数以及 T1 和 T2 病变负荷）后评估了对认知的预测价值。逐步向后多元回归量化了 5 项神经认知域测试评分的预测因子。
与健康对照组相比，路径长度（ = -0.28， <.0057）和归一化路径长度（ = 0.36， <.0004）的差异与平均认知能力相关。同样，伴有认知障碍的多发性硬化症患者的归一化路径长度（ = 0.40， <.001）与认知能力降低之间也存在相关性。归一化路径长度增加与处理（ <.001）、学习（ <.001）和执行域功能（ =.0235）的表现更好相关，而路径长度减少与执行（ =.0031）和视觉域相关。归一化路径长度改善了对处理（ = 43.6%，G = 20.9； <.0001）和学习（ = 40.4%，G = 26.1； <.0001）的预测，优于包含混杂因素的零模型。同样，较高的归一化路径长度改善了对平均认知评分（G = 21.3； <.0001）的预测，与白质体积相结合，解释了 52%的平均认知方差。
伴有认知障碍的多发性硬化症患者表现出更多的随机网络特征和降低的全局效率，影响多个认知域。一个具有正常外观的白质体积的归一化路径长度模型改善了平均认知评分的预测，解释了 52%的方差。
