Dissecting muscle synergies in the task space

Reviewer #1 (Public Review):

The proposed study provides an innovative framework for the identification of muscle synergies taking into account their task relevance. State-of-the-art techniques for extracting muscle interactions use unsupervised machine-learning algorithms applied to the envelopes of the electromyographic signals without taking into account the information related to the task being performed. In this work, the authors suggest including the task parameters in extracting muscle synergies using a network information framework previously proposed. This allows the identification of muscle interactions that are relevant, irrelevant, or redundant to the parameters of the task executed.

The proposed framework is a powerful tool to understand and identify muscle interactions for specific task parameters and it may be used to improve man-machine interfaces for the control of prostheses and robotic exoskeletons.

With respect to the network information framework recently published, this work added an important part to estimate the relevance of specific muscle interactions to the parameters of the task executed. However, the authors should better explain what is the added value of this contribution with respect to the previous one, also in terms of computational methods.

In general, the method proposed relies on several hyperparameters and cost functions that have been optimized for the specific datasets. A sensitivity analysis should be performed, varying these parameters and reporting the performance of the framework.

It is not clear how the well-known phenomenon of cross-talk during the recording of electromyographic muscle activity may affect the performance of the proposed technique and how it may bias the overall outcomes of the framework.

Reviewer #2 (Public Review):

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.

This approach is very different from the notions of physiological compositional elements as muscle synergies and motor primitives, and to me seems to really be striving to identify task relevant coordinative couplings. This is a meta problem for more classical analyses. Classical analyses seek compositional elements stable across tasks. These elements may then be explored in causal experiments and generative simulations of coupling and control strategies. The present work does not convince me that the joint 'meta' analysis proposed with task information added is not unmoored from physiology and causal modeling in some important ways. It also neglects publications and methods that might be inconvenient to the new framework.

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?

Questions and concerns with the framework as an overall tool:

First, muscle based motor information sources have influences on different time scales in the task mechanics. Analyses of synergies in the methods proposed will be very much dependent on the number and quality of task variables included and how these are managed. Standardizing and comparing among labs, tasks sets and instrumentation differences is not well enough considered as a problem in this new proposed method toolset, at least in my reading. Will replication, and testing across groups ever be truly feasible in this framework? Muscle based motor information sources have influences on different time scales in the task mechanics. Kinematic analyses, dynamic analyses and force plate analyses of the same task may provide task variables that alter the results in the proposed framework it seems.

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.

It would be useful to explore the approach with a range of neuromechanical models and controllers and simulated data to explore the issues I am raising and convince readers that this analysis framework adds clarity rather than dissolving the generalizability and interpretability of analyses in terms of underlying causal mechanisms.

The authors need to better frame their work in relation to causal analyses if they are claiming links to muscle synergies analyses and claim extension/refinement. Alternatively, these may not be linked, and instead parallel approaches exploring different hypotheses and goals using different organizational data descriptors.
To me this appears a data science tool that may not help any reductionist efforts and leads into less interpretable descriptions of motor control. Not invalid, but sufficiently different that common term use muddies the water.

Reviewer #3 (Public Review):

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.

Preliminary results positively support the scientific soundness of the presented approach and its potential. The added values of the method should be documented more in future work to understand how the presented formulation relates to previous approaches and what novel insights can be achieved in practical scenarios and confirm/exploit the potential of the theoretical findings.

Strengths:

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.

The method is solid and achieves the prescribed objectives at a computational level and in preliminary laboratory data.

A toolbox is available for testing the methods on a larger scale.

The paper is well written and shows a high level of innovation, original content and analysis

Weaknesses:

Task performance variables could be specified in more quantitative definition in future work (e.g.: articular angles rather than a generic starting point- end point).

The paper does not show a comparison with previous approaches (e.g.: NMF) or recently developed approaches (such as MMF).

A discussion of the likely impact of the work on the field, and the utility of the methods and data to the community.

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.

There are some key points for discussion that follow from this paper which can be described more, maybe in future work, and that might contribute to major developments in the field, including:

The understanding of how the separation between relevant (redundant and synergistic) and irrelevant synergies impact on synergy analysis in practical works;

Interpreting how different synergistic organizations described in this work allows to better describe data from real scenarios (e.g.: motor recovery of patients after neurological diseases);

Discussing in detail how the presented findings compare with standard algorithms such as NMF to determine the added value provided with this approach;

Describe how redundant synergies reflect real neural organization and - if their "existence" is confirmed - how they contribute to redesign the concept of muscle synergies and of modular/synergistic control in general.