Decoding the brain state-dependent relationship between pupil dynamics and resting state fMRI signal fluctuation.
Decoding the brain state-dependent relationship between pupil dynamics and resting state fMRI signal fluctuation.
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作者：
Sobczak F;Pais-Roldán P;Takahashi K;Yu X
在各种行为实验中，瞳孔动态可作为大脑认知过程和唤醒状态的生理指标。瞳孔直径的变化反映了神经调节系统驱动的大脑状态波动。静息态功能磁共振成像（rs-fMRI）已被用来识别神经元与瞳孔直径变化相关的整体模式；然而，神经调节核的不同大脑状态依赖性激活模式与瞳孔动力学之间的联系仍有待探索。在这里，我们确定了四组试验，这些试验具有与麻醉大鼠大脑瞳孔直径变化相关的独特活动模式。超越了典型的 rs-fMRI 与瞳孔动力学的相关性分析，我们利用主成分分析 (PCA) 分解了 rs-fMRI 的时空模式，并通过解码方法优化 PCA 成分权重来表征簇特定的瞳孔 -fMRI 关系。这项工作表明，在不同的试验中，瞳孔动力学与不同的神经调节中心紧密耦合，提出了一种新颖的基于 PCA 的解码方法来研究大脑状态依赖的瞳孔 -fMRI 关系。
Pupil dynamics serve as a physiological indicator of cognitive processes and arousal states of the brain across a diverse range of behavioral experiments. Pupil diameter changes reflect brain state fluctuations driven by neuromodulatory systems. Resting-state fMRI (rs-fMRI) has been used to identify global patterns of neuronal correlation with pupil diameter changes; however, the linkage between distinct brain state-dependent activation patterns of neuromodulatory nuclei with pupil dynamics remains to be explored. Here, we identified four clusters of trials with unique activity patterns related to pupil diameter changes in anesthetized rat brains. Going beyond the typical rs-fMRI correlation analysis with pupil dynamics, we decomposed spatiotemporal patterns of rs-fMRI with principal component analysis (PCA) and characterized the cluster-specific pupil–fMRI relationships by optimizing the PCA component weighting via decoding methods. This work shows that pupil dynamics are tightly coupled with different neuromodulatory centers in different trials, presenting a novel PCA-based decoding method to study the brain state-dependent pupil–fMRI relationship.