Department of Psychology, Center for Cognitive Brain Imaging, Carnegie Mellon University, 5000 Forbes Ave., Pittsburgh, PA, 15213, USA.
Brain Struct Funct. 2019 Apr;224(3):1345-1357. doi: 10.1007/s00429-019-01838-4. Epub 2019 Feb 6.
The critical role of the hippocampus in human learning has been illuminated by neuroimaging studies that increasingly improve the detail with which hippocampal function is understood. However, the hippocampal information developed with different types of imaging technologies is seldom integrated within a single investigation of the neural changes that occur during learning. Here, we show three different ways in which a small hippocampal region changes as the structures and names of a set of organic compounds are being learned, reflecting changes at the microstructural, informational, and cortical network levels. The microstructural changes are sensed using measures of water diffusivity. The informational changes are assessed using machine learning of the neural representations of organic compounds as they are encoded in the fMRI-measured activation levels of a set of hippocampal voxels. The changes in cortical networks are measured in terms of the functional connectivity between hippocampus and parietal regions. The co-location of these three hippocampal changes reflects that structure's involvement in learning at all three levels of explanation, consistent with the multiple ways in which learning brings about neural change.
神经影像学研究越来越详细地揭示了海马体在人类学习中的关键作用。然而，不同类型的成像技术所获得的海马体信息很少在学习过程中神经变化的单一研究中进行整合。在这里，我们展示了三种不同的方式，即当一组有机化合物的结构和名称被学习时，一个小的海马体区域如何发生变化，反映了微观结构、信息和皮质网络层面的变化。微观结构的变化是通过测量水扩散率来感知的。信息变化是通过对有机化合物的神经表示进行机器学习来评估的，这些表示是在一组海马体体素的 fMRI 测量激活水平中编码的。皮质网络的变化是根据海马体和顶叶区域之间的功能连接来测量的。这三种海马体变化的共定位反映了该结构在所有三个解释层面上的学习参与，这与学习带来神经变化的多种方式一致。
