支持快速获得新视觉运动技能的结构通路
Structural Pathways Supporting Swift Acquisition of New Visuo-Motor Skills
摘要：
人类技能学习需要大脑区域的分布式网络的精细协调，这些区域通过白质束直接相互连接，以实现有效的信息传输。然而，这些解剖学途径中的个体差异如何影响学习中的个体差异仍然远未被理解。在这里，我们测试的假设，支持任务绩效的结构网络的组织中的个体差异预测人类学习视觉运动技能的速度的个体差异。在6周的时间里，22名健康的成年受试者练习了一项离散序列生产任务，在那里他们根据离散的视觉线索学习了一系列手指运动。我们在间隔约两周的四次MRI扫描期间收集了结构成像数据，并使用确定性纤维束成像，为每个参与者生成结构网络，以识别连接皮质和皮质下大脑区域的流线。我们观察到，连接早期视觉（但不是运动）区域的白质连接性增加与更快的学习速度有关。此外，我们观察到运动和视觉模块之间的多边缘路径的强度也与学习率相关，支持多突触连接在成功技能获取中的作用。我们的研究结果表明，扩散成像和基于tractography的连接的组合可用于预测未来学习能力的个体差异，特别是当与网络科学和图论的方法相结合时。
英文摘要：
Human skill learning requires fine-scale coordination of distributed networks of brain regions that are directly linked to one another by white matter tracts to allow for effective information transmission. Yet how individual differences in these anatomical pathways may impact individual differences in learning remains far from understood. Here, we test the hypothesis that individual differences in the organization of structural networks supporting task performance predict individual differences in the rate at which humans learn a visuo-motor skill. Over the course of 6 weeks, twenty-two healthy adult subjects practiced a discrete sequence production task, where they learned a sequence of finger movements based on discrete visual cues. We collected structural imaging data during four MRI scanning sessions spaced approximately two weeks apart, and using deterministic tractography, structural networks were generated for each participant to identify streamlines that connect cortical and sub-cortical brain regions. We observed that increased white matter connectivity linking early visual (but not motor) regions was associated with a faster learning rate. Moreover, we observed that the strength of multi-edge paths between motor and visual modules was also correlated with learning rate, supporting the role of polysynaptic connections in successful skill acquisition. Our results demonstrate that the combination of diffusion imaging and tractography-based connectivity can be used to predict future individual differences in learning capacity, particularly when combined with methods from network science and graph theory.
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