Department of Psychiatry, University of Cambridge, Cambridge, UK.
Department of Computer Science and Technology, University of Cambridge, Cambridge, UK.
Nat Neurosci. 2023 Aug;26(8):1461-1471. doi: 10.1038/s41593-023-01376-7. Epub 2023 Jul 17.
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.
结构相似性是连接组磁共振成像（MRI）的一个新兴关注点。在这里，我们提出了形态测量反散度（MIND），这是一种基于多个 MRI 特征的多变量分布之间的散度来估计皮质区域内个体间相似性的新方法。与之前基于 n > 11000 次扫描的三个人类数据集和一个猕猴数据集的形态测量相似网络（MSN）的方法相比，MIND 网络更可靠，与皮质细胞构筑学和对称性更一致，与轴突连接的示踪测量更相关。从人类 T1 加权 MRI 得出的 MIND 网络比 MSN 或源自扩散加权 MRI 示踪的网络更能敏感地反映与年龄相关的变化。皮质区之间的基因共表达与 MIND 网络的相关性比与 MSN 或示踪的相关性更强。MIND 网络表型也更具遗传性，特别是在结构分化区域之间的边缘。MIND 网络分析为使用现成的 MRI 数据进行皮质连接组学提供了一个具有生物学验证的视角。
