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. 2013 Apr 8:7:23.
doi: 10.3389/fncom.2013.00023. eCollection 2013.

Neural bases of hand synergies

Marco Santello  1 , Gabriel Baud-Bovy, Henrik Jörntell
Affiliations

Neural bases of hand synergies

Marco Santello et al. Front Comput Neurosci. .

Abstract

The human hand has so many degrees of freedom that it may seem impossible to control. A potential solution to this problem is "synergy control" which combines dimensionality reduction with great flexibility. With applicability to a wide range of tasks, this has become a very popular concept. In this review, we describe the evolution of the modern concept using studies of kinematic and force synergies in human hand control, neurophysiology of cortical and spinal neurons, and electromyographic (EMG) activity of hand muscles. We go beyond the often purely descriptive usage of synergy by reviewing the organization of the underlying neuronal circuitry in order to propose mechanistic explanations for various observed synergy phenomena. Finally, we propose a theoretical framework to reconcile important and still debated concepts such as the definitions of "fixed" vs. "flexible" synergies and mechanisms underlying the combination of synergies for hand control.

Keywords: degrees of freedom; manipulation; motor cortex; premotor neurons.

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Figures

Figure 1
Figure 1
Left: muscular and skeletal architecture of the hand. Note the multi-joint tendons of the extrinsic finger flexor muscle (m. flexor digitorum profundus). Right: simplified model of a hand grasping an object with three fingertips (tripod grasp). The bones are only vaguely visible in the background, whereas the tendons are indicated in red. Only a subset of the tendons/muscle acting on each finger is included in this model (Holzbaur et al., 2005).
Figure 2
Figure 2
Contacts in precision grasps. Left: example of multi-digit (tripod) grasp invovling only contacts with the fingertips. Contacts are represented by their friction cones. Right: hand-object contacts in the pen grasp. The friction cones at the extremity of the pen represent the contact with the table. The computation of the contacts and graphical rendering was done with Grasp-It, an open-source software dedicated to the analysis of grasps (Miller and Allen, 2004).
Figure 3
Figure 3
Schematic organization of neural inputs from premotor neurons to spinal motor nuclei of two hand muscles. Black neuron represents the inhibitory interneurons, which exist in high numbers.
Figure 4
Figure 4
Schematic representation of synergies. The gray rectangles represent the dynamic system formed by pools of premotor neurons. By definition, a synergy would correspond to a stable state of the dynamical system. (A) The solid curve within the rectangle represents the potential field of the system, which is controlled by the descending motor commands. The dashed curve corresponds to another potential field that might be established by a different set of motor commands. Once a synergy is enabled, the current state (black sphere) of the system converges toward the stable state of the system, which establishes a specific coordination pattern or synergy between the alpha-MNs belonging to different motor nuclei. (B) Example of a synergy involving a larger number of spinal motor nuclei. (C) Example of compartmentalization of premotor neuronal pool allowing for the simultaneous activation of several synergies. The divergent connections from premotor to alpha-MNs combine together the contributions of these pools. (D) Effect of learning. Blue curves represent a set of unstable strategies. A slight variation in the motor commands could easily change the dynamic of the system (see dashed curves). The red and green curves represent two stable synergies, in which the impact on the system dynamics of small variations of the motor commands is smaller (dashed curves). A narrow valley (red curve) would correspond to a fixed synergy while a wider valley (green curve) would correspond to a more flexible synergy.
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