A synergy-based hand control is encoded in human motor cortical areas

• Published in: eLife Vol. 5
• Main Authors: Leo, Andrea, Handjaras, Giacomo, Bianchi, Matteo, Marino, Hamal, Gabiccini, Marco, Guidi, Andrea, Scilingo, Enzo Pasquale, Pietrini, Pietro, Bicchi, Antonio, Santello, Marco, Ricciardi, Emiliano
• Format: Journal Article
• Language: English
• Published: England eLife Science Publications, Ltd 15.02.2016; eLife Sciences Publications Ltd; eLife Sciences Publications, Ltd
• Subjects: Biomechanical Phenomena; Brain mapping; Brain research; Cortex (motor); decoding; Electromyography; encoding; fMRI; Functional magnetic resonance imaging; Hand; Hand - physiology; hand control; Humans; Interfaces; Kinematics; Locomotion; Magnetic Resonance Imaging; Models, Neurological; Motor cortex; Motor Cortex - physiology; Motor task performance; Muscles; Neural circuitry; Neuroimaging; Neuroscience; Physiological aspects; Posture; Principal components analysis; Prosthetics; synergies; fMRI; motor cortex; hand control; neuroscience; encoding; synergies; human; decoding
• ISSN: 2050-084X; 2050-084X
• DOI: 10.7554/eLife.13420

How the human brain controls hand movements to carry out different tasks is still debated. The concept of synergy has been proposed to indicate functional modules that may simplify the control of hand postures by simultaneously recruiting sets of muscles and joints. However, whether and to what extent synergic hand postures are encoded as such at a cortical level remains unknown. Here, we combined kinematic, electromyography, and brain activity measures obtained by functional magnetic resonance imaging while subjects performed a variety of movements towards virtual objects. Hand postural information, encoded through kinematic synergies, were represented in cortical areas devoted to hand motor control and successfully discriminated individual grasping movements, significantly outperforming alternative somatotopic or muscle-based models. Importantly, hand postural synergies were predicted by neural activation patterns within primary motor cortex. These findings support a novel cortical organization for hand movement control and open potential applications for brain-computer interfaces and neuroprostheses. The human hand can perform an enormous range of movements with great dexterity. Some common everyday actions, such as grasping a coffee cup, involve the coordinated movement of all four fingers and thumb. Others, such as typing, rely on the ability of individual fingers to move relatively independently of one another. This flexibility is possible in part because of the complex anatomy of the hand, with its 27 bones and their connecting joints and muscles. But with this complexity comes a huge number of possibilities. Any movement-related task – such as picking up a cup – can be achieved via many different combinations of muscle contractions and joint positions. So how does the brain decide which muscles and joints to use? One theory is that the brain simplifies this problem by encoding particularly useful patterns of joint movements as distinct units or “synergies”. A given task can then be performed by selecting from a small number of synergies, avoiding the need to choose between huge numbers of options every time movement is required. Leo et al. now provide the first direct evidence for the encoding of synergies by the human brain. Volunteers lying inside a brain scanner reached towards virtual objects – from tennis rackets to toothpicks – while activity was recorded from the area of the brain that controls hand movements. As predicted, the scans showed specific and reproducible patterns of activity. Analysing these patterns revealed that each corresponded to a particular combination of joint positions. These activity patterns, or synergies, could even be ‘decoded’ to work out which type of movement a volunteer had just performed. Future experiments should examine how the brain combines synergies with sensory feedback to allow movements to be adjusted as they occur. Such findings could help to develop brain-computer interfaces and systems for controlling the movement of artificial limbs.