Institute of Neurosciences and Medicine (INM-1), Research Centre Jülich, Jülich, Germany.
Department of Physics, School of Mathematics and Natural Sciences, Bergische Universität Wuppertal, Wuppertal, Germany.
Science. 2022 Nov 4;378(6619):500-504. doi: 10.1126/science.abq2599. Epub 2022 Nov 3.
A comprehensive description of how neurons and entire brain regions are interconnected is fundamental for a mechanistic understanding of brain function and dysfunction. Neuroimaging has shaped the way to approaching the human brain's connectivity on the basis of diffusion magnetic resonance imaging and tractography. At the same time, polarization, fluorescence, and electron microscopy became available, which pushed spatial resolution and sensitivity to the axonal or even to the synaptic level. New methods are mandatory to inform and constrain whole-brain tractography by regional, high-resolution connectivity data and local fiber geometry. Machine learning and simulation can provide predictions where experimental data are missing. Future interoperable atlases require new concepts, including high-resolution templates and directionality, to represent variants of tractography solutions and estimates of their accuracy.
全面描述神经元和整个脑区如何相互连接，对于理解大脑功能和功能障碍的机制至关重要。神经影像学改变了人们基于弥散磁共振成像和示踪技术研究人类大脑连接的方式。与此同时，极化、荧光和电子显微镜的出现，将空间分辨率和灵敏度提高到了轴突甚至突触水平。新的方法对于通过区域高分辨率连接数据和局部纤维几何形状来告知和约束全脑示踪技术是强制性的。机器学习和模拟可以在缺少实验数据的情况下提供预测。未来的可互操作图谱需要新的概念，包括高分辨率模板和方向性，以表示示踪解决方案的变体及其准确性的估计。
