Data from: Dual dimensionality reduction reveals independent encoding of motor features in a muscle synergy for insect flight control

What are the features of movement encoded by changing motor commands? Do motor commands encode movement independently or can they be represented in a reduced set of signals (i.e. synergies)? Motor encoding poses a computational and practical challenge because many muscles typically drive movement, and simultaneous electrophysiology recordings of all motor commands are typically not available. Moreover, during a single locomotor period (a stride or wingstroke) the variation in movement may have high dimensionality, even if only a few discrete signals activate the muscles. Here, we apply the method of partial least squares (PLS) to extract the encoded features of movement based on the cross-covariance of motor signals and movement. PLS simultaneously decomposes both datasets and identifies only the variation in movement that relates to the specific muscles of interest. We use this approach to explore how the main downstroke flight muscles of an insect, the hawkmoth Manduca sexta, encode torque during yaw turns. We simultaneously record muscle activity and turning torque in tethered flying moths experiencing wide-field visual stimuli. We ask whether this pair of muscles acts as a muscle synergy (a single linear combination of activity) consistent with their hypothesized function of producing a left-right power differential. Alternatively, each muscle might individually encode variation in movement. We show that PLS feature analysis produces an efficient reduction of dimensionality in torque variation within a wingstroke. At first, the two muscles appear to behave as a synergy when we consider only their wingstroke-averaged torque. However, when we consider the PLS features, the muscles reveal independent encoding of torque. Using these features we can predictably reconstruct the variation in torque corresponding to changes in muscle activation. PLS-based feature analysis provides a general two-sided dimensionality reduction that reveals encoding in high dimensional sensory or motor transformations.

运动指令的动态变化究竟编码了何种运动特征？运动指令是独立编码运动，还是可通过精简的信号集合（即协同模式）完成运动表征？运动编码问题兼具计算与实践双重挑战：一方面，运动往往由多块肌肉协同驱动；另一方面，同时记录所有运动指令的电生理信号往往难以实现。此外，即便仅需少量离散信号即可激活肌肉，单个运动周期（如步行动作或振翅周期）内的运动变异仍可呈现高维度特性。
本研究采用偏最小二乘（partial least squares, PLS）方法，基于运动信号与运动状态的互协方差提取运动编码特征。该方法可同时对两类数据集实施分解，且仅识别与目标肌肉群相关的运动变异组分。我们利用该方法，探究昆虫烟草天蛾（Manduca sexta）的主要振翅向下飞行肌肉在偏航转向过程中如何编码扭矩信号。实验中，我们在接受宽视野视觉刺激的束缚飞行天蛾中，同步记录其肌肉活动与转向扭矩信号。
本研究旨在探究两个核心问题：这两块肌肉是否以肌肉协同（muscle synergy，即单一活动的线性组合）模式运作，契合其产生左右功率差的预设功能？抑或每块肌肉均独立编码运动变异？研究结果显示，PLS特征分析可高效降低振翅周期内扭矩变异的维度。仅考量振翅平均扭矩时，两块肌肉看似呈现协同模式；但结合PLS特征分析结果后，二者展现出对扭矩的独立编码特性。借助这些特征，我们可基于肌肉激活的变化，预测性地重建对应的扭矩变异信号。基于PLS的特征分析提供了一种通用的双向降维手段，可揭示高维度感知或运动转换过程中的编码机制。