Encoding of speed and direction of movement in the human supplementary motor area: Laboratory investigation

Department of Neurosurgery, University of California, Los Angeles, California, USA.
Journal of Neurosurgery (Impact Factor: 3.74). 03/2009; 110(6):1304-16. DOI: 10.3171/2008.10.JNS08466
Source: PubMed


The supplementary motor area (SMA) plays an important role in planning, initiation, and execution of motor acts. Patients with SMA lesions are impaired in various kinematic parameters, such as velocity and duration of movement. However, the relationships between neuronal activity and these parameters in the human brain have not been fully characterized. This is a study of single-neuron activity during a continuous volitional motor task, with the goal of clarifying these relationships for SMA neurons and other frontal lobe regions in humans.
The participants were 7 patients undergoing evaluation for epilepsy surgery requiring implantation of intracranial depth electrodes. Single-unit recordings were conducted while the patients played a computer game involving movement of a cursor in a simple maze.
In the SMA proper, most of the recorded units exhibited a monotonic relationship between the unit firing rate and hand motion speed. The vast majority of SMA proper units with this property showed an inverse relation, that is, firing rate decrease with speed increase. In addition, most of the SMA proper units were selective to the direction of hand motion. These relationships were far less frequent in the pre-SMA, anterior cingulate gyrus, and orbitofrontal cortex.
The findings suggest that the SMA proper takes part in the control of kinematic parameters of endeffector motion, and thus lend support to the idea of connecting neuroprosthetic devices to the human SMA.

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    • "We focus especially on the nonlinear characteristics of the hand speed profile and its independence of movement directions. While many BCI studies have typically decoded the hand velocity from neural signals, the hand speed alone could also be decoded from neural signals during point-to-point reaching movements [19] [41] [42]. Hence, we propose to decompose the hand velocity into its speed and direction parameters and decode each parameter independently. "
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    ABSTRACT: Decoding neural signals into control outputs has been a key to the development of brain-computer interfaces (BCIs). While many studies have identified neural correlates of kinematics or applied advanced machine learning algorithms to improve decoding performance, relatively less attention has been paid to optimal design of decoding models. For generating continuous movements from neural activity, design of decoding models should address how to incorporate movement dynamics into models and how to select a model given specific BCI objectives. Considering nonlinear and independent speed characteristics, we propose a hybrid Kalman filter to decode the hand direction and speed independently. We also investigate changes in performance of different decoding models (the linear and Kalman filters) when they predict reaching movements only or predict both reach and rest. Our offline study on human magnetoencephalography (MEG) during point-to-point arm movements shows that the performance of the linear filter or the Kalman filter is affected by including resting states for training and predicting movements. However, the hybrid Kalman filter consistently outperforms others regardless of movement states. The results demonstrate that better design of decoding models is achieved by incorporating movement dynamics into modeling or selecting a model according to decoding objectives.
    BioMed Research International 06/2014; 2014:176857. DOI:10.1155/2014/176857 · 3.17 Impact Factor
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    • "Both the SMA (Fried et al., 1991; Wymbs and Grafton, 2013) and insular cortex (Fink et al., 1997) are involved in movement planning and execution. Although previous studies have reported that SMA neurons encode movement direction in straight reaching movements (Tankus et al., 2009), encoding of more complex movements, such as those inherent in handwriting, has not been thoroughly studied. Here, both areas exhibited above-chance decoding accuracies for single letters when assessed within each scale separately (Figure S4). "
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    ABSTRACT: How does the human motor system encode our incredibly diverse motor repertoire in an efficient manner? One possible way of encoding movements efficiently is to represent them according to their shape/trajectory without regard to their size, by using neural populations that are invariant across scale. To examine this hypothesis, we recorded movement kinematics and functional magnetic resonance imaging (fMRI) while subjects wrote three letters in two different scales. A classification algorithm was trained to identify each letter according to its associated voxel-by-voxel response pattern in each of several motor areas. Accurate decoding of letter identity was possible in primary motor cortex (M1) and anterior intraparietal sulcus (aIPS) regardless of the letter's scale. These results reveal that large, distributed neural populations in human M1 and aIPS encode complex handwriting movements regardless of their particular dynamics and kinematics, in a scale-invariant manner.
    Neuron 01/2014; 81(2):452-62. DOI:10.1016/j.neuron.2013.10.058 · 15.05 Impact Factor
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    • "Although first described in the motor cortex for arm movements in space (Georgopoulos et al., 1982), directional tuning has been found in practically all motor areas where it has been sought for, including premotor cortex (Caminiti et al., 1990b; Fu et al., 1993; Stevenson et al., 2012), human supplementary motor area (Tankus et al., 2009), parietal area 5 (Kalaska et al., 1983; Johnson et al., 1996), parietal area PEc (Battaglia- Mayer et al., 2001; Ferraina et al., 2001), area 7m of the medial wall (Ferraina et al., 1997), parieto-occipital area 6A (Battaglia- Mayer et al., 2000, 2001), external (GPe) and internal (GPi) segments of the globus pallidus (Turner and Anderson, 1997), motor thalamus (Inase et al., 1996), cerebellar cortex (Fortier et al., 1989), and deep cerebellar nuclei (Fortier et al., 1989). An important issue concerns how the directional tuning arises, i.e., what are the relevant synaptic interactions that underlie the shaping of single cell activity to a typically broad tuning function? "
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    ABSTRACT: Motor directional tuning (Georgopoulos et al., 1982) has been found in every brain area in which it has been sought for during the past 30-odd years. It is typically broad, with widely distributed preferred directions and a population signal that predicts accurately the direction of an upcoming reaching movement or isometric force pulse (Georgopoulos et al., 1992). What is the basis for such ubiquitous directional tuning? How does the tuning come about? What are the implications of directional tuning for understanding the brain mechanisms of movement in space? This review addresses these questions in the light of accumulated knowledge in various sub-fields of neuroscience and motor behavior. It is argued (a) that direction in space encompasses many aspects, from vision to muscles, (b) that there is a directional congruence among the central representations of these distributed "directions" arising from rough but orderly topographic connectivities among brain areas, (c) that broad directional tuning is the result of broad excitation limited by recurrent and non-recurrent (i.e., direct) inhibition within the preferred direction loci in brain areas, and (d) that the width of the directional tuning curve, modulated by local inhibitory mechanisms, is a parameter that determines the accuracy of the directional command.
    Frontiers in Neural Circuits 05/2013; 7:92. DOI:10.3389/fncir.2013.00092 · 3.60 Impact Factor
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