Dimensionality reduction techniques offer a unique perspective on brain state dynamics, in which systems-level activity can be tracked through the engagement of a small number of component trajectories. Used in combination with neuroimaging data collected during the performance of cognitive tasks, these approaches can expose the otherwise latent dimensions upon which the brain reconfigures in order to facilitate cognitive performance. Here, we utilized Principal Component Analysis to transform parcellated BOLD timeseries from an fMRI dataset in which 70 human subjects performed an instruction based visuomotor learning task into orthogonal low-dimensional components. We then used Linear Discriminant Analysis to maximise the mean differences between the low-dimensional signatures of fast-and-slow reaction times and early-and-late learners, while also conserving variance present within these groups. The resultant basis set allowed us to describe meaningful differences between these groups and, importantly, to detail the patterns of brain activity which underpin these differences. Our results demonstrate non-linear interactions between three key brain activation maps with convergent trajectories observed at higher task repetitions consistent with optimization. Furthermore, we show subjects with the greatest reaction time improvements have delayed recruitment of left dorsal and lateral prefrontal cortex, as well as deactivation in parts of the occipital lobe and motor cortex, and that the slowest performers have weaker recruitment of somatosensory association cortex and left ventral visual stream, as well as weaker deactivation in the dorsal lateral prefrontal cortex. Overall our results highlight the utility of a kinematic description of brain states, whereby reformatting data into low-dimensional trajectories sensitive to the subtleties of a task can capture non-linear trends in a tractable manner and permit hypothesis generation at the level of brain states.

降维技术为大脑状态动力学提供了独特的视角，其中系统级活动可以通过少量组件轨迹的参与来跟踪。与在执行认知任务期间收集的神经影像数据结合使用，这些方法可以揭示大脑重新配置的其他潜在维度，以促进认知表现。在这里，我们利用主成分分析将分割的 BOLD 时间序列从 fMRI 数据集中转换为正交低维分量，其中 70 名人类受试者执行基于指令的视觉运动学习任务。然后，我们使用线性判别分析来最大化快慢反应时间和早晚学习者的低维特征之间的平均差异，同时还保留了这些组中存在的差异。由此产生的基础组使我们能够描述这些组之间有意义的差异，重要的是，详细说明支撑这些差异的大脑活动模式。我们的结果证明了三个关键大脑激活图之间的非线性相互作用，在与优化一致的较高任务重复下观察到的收敛轨迹。此外，我们发现反应时间改善最大的受试者延迟了左背侧和外侧前额叶皮层的募集，以及部分枕叶和运动皮层的失活，表现最慢的受试者体感联合皮层和左侧的募集较弱。腹侧视觉流，以及背侧前额叶皮层较弱的失活。