Hagmann P, Thiran J-P, Jonasson L, Vandergheynst P, Clarke S, Maeder P, Meuli R
Signal Processing Institute, Swiss Federal Institute of Technology, 1015 Lausanne, Switzerland.
Neuroimage. 2003 Jul;19(3):545-54. doi: 10.1016/s1053-8119(03)00142-3.
Several approaches have been used to trace axonal trajectories from diffusion MRI data. If such techniques were first developed in a deterministic framework reducing the diffusion information to one single main direction, more recent approaches emerged that were statistical in nature and that took into account the whole diffusion information. Based on diffusion tensor MRI data coming from normal brains, this paper presents how brain connectivity could be modelled globally by means of a random walk algorithm. The mass of connections thus generated was then virtually dissected to uncover different tracts. Corticospinal, corticobulbar, and corticothalamic tracts, the corpus callosum, the limbic system, several cortical association bundles, the cerebellar peduncles, and the medial lemniscus were all investigated. The results were then displayed in the form of an in vivo brain connectivity atlas. The connectivity pattern and the individual fibre tracts were then compared to known anatomical data; a good matching was found.
已有多种方法用于从扩散磁共振成像（MRI）数据追踪轴突轨迹。如果此类技术最初是在将扩散信息简化为单一主要方向的确定性框架中开发的，那么最近出现了本质上具有统计学意义且考虑了整个扩散信息的方法。基于来自正常大脑的扩散张量MRI数据，本文展示了如何通过随机游走算法对大脑连通性进行全局建模。然后对由此产生的连接集合进行虚拟剖析，以揭示不同的神经束。对皮质脊髓束、皮质延髓束、皮质丘脑束、胼胝体、边缘系统、几条皮质联合束、小脑脚和内侧丘系都进行了研究。然后将结果以活体大脑连通性图谱的形式展示出来。接着将连通性模式和个体纤维束与已知的解剖学数据进行比较，发现匹配良好。
