英国剑桥大学Isaac Sebenius团队近期取得重要工作进展，他们研究通过大脑MRI对皮层相似性网络进行稳健估计。相关研究成果2023年7月17日在线发表于《自然—神经科学》杂志上。
据介绍，结构相似性是连接体核磁共振成像（MRI）日益关注的焦点。
研究人员提出了Morphometric INverse Divergence（MIND），这是一种基于多个MRI特征的多变量分布之间的差异来评估皮层区域之间受试者内部相似性的新方法。与之前在3个人类数据集和1个猕猴数据集上进行的n>11000次扫描的形态学相似性网络(MSN)方法相比，MIND网络更可靠，更符合皮质细胞结构和对称性，并且与轴突连通性的束迹追踪测量更相关。
源自人类T1加权MRI的MIND网络比源自扩散加权MRI纤维束成像的网络对年龄相关性变化更敏感。皮层区域之间的基因共表达与MIND网络的耦合比与MSN或神经束描记术的耦合更强。MIND网络表型也更具遗传性，尤其是结构分化区域之间的边缘。
总之，MIND网络分析使用现成的MRI为皮层连接组学提供了一个生物学验证的视角。
附：英文原文
Title: Robust estimation of cortical similarity networks from brain MRI
Author: Sebenius, Isaac, Seidlitz, Jakob, Warrier, Varun, Bethlehem, Richard A. I., Alexander-Bloch, Aaron, Mallard, Travis T., Garcia, Rafael Romero, Bullmore, Edward T., Morgan, Sarah E.
Issue&Volume: 2023-07-17
Abstract: Structural similarity is a growing focus for magnetic resonance imaging (MRI) of connectomes. Here we propose Morphometric INverse Divergence (MIND), a new method to estimate within-subject similarity between cortical areas based on the divergence between their multivariate distributions of multiple MRI features. Compared to the prior approach of morphometric similarity networks (MSNs) on n>11,000 scans spanning three human datasets and one macaque dataset, MIND networks were more reliable, more consistent with cortical cytoarchitectonics and symmetry and more correlated with tract-tracing measures of axonal connectivity. MIND networks derived from human T1-weighted MRI were more sensitive to age-related changes than MSNs or networks derived by tractography of diffusion-weighted MRI. Gene co-expression between cortical areas was more strongly coupled to MIND networks than to MSNs or tractography. MIND network phenotypes were also more heritable, especially edges between structurally differentiated areas. MIND network analysis provides a biologically validated lens for cortical connectomics using readily available MRI data.
DOI: 10.1038/s41593-023-01376-7
Source: https://www.nature.com/articles/s41593-023-01376-7
Nature Neuroscience：《自然—神经科学》，创刊于1998年。隶属于施普林格·自然出版集团，最新IF：28.771
官方网址：https://www.nature.com/neuro/
投稿链接：https://mts-nn.nature.com/cgi-bin/main.plex
本期文章：《自然—神经科学》：Online/在线发表
