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Maggie Schnair
This paper is an attempt to extend or augment muscle synergy and motor primitive ideas with task measures. The authors idea is to use information metrics (mutual information, co-information) in 'synergy' creation including task information directly. My reading of the paper is that the framework proposed radically moves from attempts to be analytic in terms of physiology and compositionality with physiological bases, instead into more descriptive ML frameworks that may not support physiological work easily.
Information based separation has been used in muscle synergy analyses using infomax ICA, which is information not variance based at core. Though linear mixing of sources is assumed, minimized mutual information is the basis.
Physiological causal testing of synergy ideas is neglected in the literature reviews in the paper. Although these are in animal work, the clear connection of muscle synergy choices and analyses to physiology is important, and needs to be managed in the new methods proposed. Is any correspondence assumed? Possible?
Second, there is a sampling problem in all synergy analyses. We cannot record all muscles or all task parameters. Examining synergies across multiple tasks seeks 'stationary' compositionality. Including task specific elements may or may not reinforce or give increased coordinative precision to the stationary compositionality.
To me the new methods proposed seem partly orthogonal to the ideas of stable compositionality. The 'synergies' obtained will likely differ, and are more likely to be coordinative control groupings of recurrent task and muscle motifs (based on instrumentation) which may or may not relate to core compositionality in physiology. Is there any expectation that the framework should relate to core compositionality and physiology. This is not clear in the paper as written.
In this study, the authors developed and tested a novel framework for extracting muscle synergies. The approach aims at removing some limitations and constrains typical of previous approaches used in the field. In particular, the authors propose a mathematical formulation that removes constrains of linearity and couple the synergies to their motor outcome, supporting the concept of functional synergies and distinguishing the task-related performance related to each synergy. While some concepts behind this work were already introduced in recent work in the field, the methodology provided here encapsulates all these features in an original formulation providing a step forward with respect to the currently available algorithms. The authors also successfully demonstrated the applicability of their method to previously available datasets of multi-joint movements.
This work proposes a novel framework that addresses physiologically non-verified hypothesis of standard muscle synergy methods: it removes restrictive model assumptions (e.g. linearity, same mixing coefficients) and the reliance on variance-accounted-for (VAF) metrics.
In this work, the effort of the authors aimed at developing the field is clear. It is fundamental to develop novel frameworks for synergy extraction and use them to make them more interpretable and applicable to real scenarios, as well as more adherent to recent findings achieved in motor control and neuroscience that are not reflected in the standard models. At the same time, muscle synergies are being used more and more in research but their impact in practical scenarios is still limited, probably because synergies have rarely been analyzed in a functional context. This paper shows a very in-depth analysis and a novel framework to interpret data that links to the task space from a functional perspective. I also found that the results on the datasets are very well commented but could expand more to show why using this framework is advantageous.
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