流形衍生的梯度将癫痫患者的皮质形态与基因表达联系起来

Yang Siqi, Meng Yao, Zhiqiang Zhang, Liao Wei
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引用次数: 0

摘要

扩散嵌入技术提供了将大脑组织原理映射到一个称为梯度的单一流形的方法,这有助于我们理解多尺度大脑特征是如何相互关联的。这些联系可以帮助我们理解神经机制,并进一步拓宽我们对结构与功能关系的认识。本研究利用多元梯度研究了92例遗传性广泛性癫痫伴全身性强直-阵挛发作(GTCS)患者和116例健康对照者的宏观形态学与皮质基因表达的关系。采用扩散图嵌入方法估计形态相似性连接组的梯度,该连接组通过5个皮层特征描述区域间的相似性。结果显示,GTCS的MS梯度在组间存在显著差异,主要涉及高级网络。MS梯度的变化与癫痫危险基因的表达谱呈正相关。
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Manifold-Derived Gradients Link the Cortical Morphology to Gene Expression in Patients with Epilepsy
Diffusion embedding techniques offer the means to map brain organizational principles into a single manifold, called gradients, which help us understand how multi-scale brain features are related to each other. These links can help us understand the neural mechanism, and further broaden our insight into the relation between structure and function. In this study, manifold gradient was leveraged to study the link of macroscopic morphology to cortical gene expression in 92 genetic generalized epilepsy with generalized tonic-clonic seizures (GTCS), and 116 healthy controls. Diffusion map embedding was used to estimate the gradients of morphometric similarity (MS) connectome, which described the interregional similarity through five cortical features. The results revealed significant between-group differences in the MS gradient in GTCS, mainly involved in high-level networks. And the changes in MS gradient showed a positively correlation between expression profile of epileptic risk genes.
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