Decoding of muscle activity from the sensorimotor cortex in freely behaving monkeys
Introduction
Significant progress has been made in the development of Brain-Machine Interfaces (BMIs) over the past two decades (Lebedev and Nicolelis, 2017; Moxon and Foffani, 2015). This progress relies heavily on the development of new methodology in the field of neural decoding, in which neuronal activities are translated into signals that can be used to control external devices such as limb prostheses or computers (Lebedev et al., 2008). Currently, movement-related information such as the kinematic parameters of motion, forces, and muscle activity can be accurately decoded from the neuronal activities of behaving animals (Georgopoulos and Carpenter, 2015; Schwartz, 2016). However, previous neural decoding studies using non-human primates have generally been conducted under unnatural conditions, in which most body parts are physically restrained, and only a small subset of movements, such as arm reaching and hand grasping, are studied. Ultimately, the goal of BMI technology is implementation in a non-restrained condition to enable severely disabled individuals to move naturally and freely.
Animal studies have indicated that neuronal coding of limb movement in unconstrained conditions is different from that in constrained conditions. For example, the tuning properties of single neurons in the primary motor cortex (M1) have been reported to vary among movements performed in different working spaces (Aflalo and Graziano, 2006; Caminiti et al., 1990; Griffin et al., 2015) or between movements performed using focal muscles vs. the whole body (Jackson et al., 2007). Thus, it is not clear whether insights obtained from recordings conducted under constrained conditions can be directly applied to the decoding of movement parameters from cortical neuronal activity recorded during more natural movements.
Recent advances in the development of a head-mounted wireless device have demonstrated the possibility of recording neuronal activity in freely-moving animals (Borton et al., 2013; Chestek et al., 2009; Fernandez-Leon et al., 2015; Mavoori et al., 2005; Schwarz et al., 2014). For example, several studies have wirelessly recorded neuronal action potentials from the sensorimotor cortex of rhesus macaques using multi-electrode arrays during arm reaching towards a reward or walking on a treadmill (Capogrosso et al., 2016; Foster et al., 2012, 2014; Rajangam et al., 2016; Schwarz et al., 2014; Yin et al., 2014). In these studies, the modes of motor behavior (e.g., walking or reaching), as well as the specific epoch in each motor behavior (e.g. the flexor or extensor phase during locomotion), were decoded from an ensemble of spiking activities. However, the researchers only examined a small subset of behaviors using specific tasks. Since a hallmark of non-human primates is the rich variety of motor behavior that forms their daily movements (Jaman and Huffman, 2008), neural coding of their natural behaviors can be more informative by recording neuronal signals during free movements in their home cage.
In the present study, we recorded electrocorticograms (ECoGs) from the sensorimotor cortex and electromyograms (EMGs) from forelimb muscles in common marmosets. We used a wireless recording system to record activity as the marmosets moved freely in a cage. The common marmoset is a New World primate species that is considered to be useful as a research subject in the field of neuroscience (Okano et al., 2012; Prins et al., 2014; Walker et al., 2017). The marmoset has several advantages in terms of recording cortical activity during free movement, as follows: 1) as- in humans and rhesus macaques, the cortical sheet in the marmoset is divided into functionally distinct cortical regions (Rosa and Tweedale, 2005); 2) the structure of the marmoset cortex is lissencephalic, which is advantageous for obtaining neuronal signals from the entire cortex using a two-dimensional grid electrode (Komatsu et al., 2017; Newman et al., 2009; Tia et al., 2017); 3) marmosets are much smaller than rhesus macaques, and are thus easier to handle (Prins et al., 2017); 4) the marmoset shows a wide variety of movements in three-dimensional space, such as walking, jumping, and grasping a pole located in the cage (Wang et al., 2014). Thus, we consider the common marmoset to be optimal for our research.
Here, we demonstrated that accurate prediction of EMG from ECoG is possible during natural whole-body movement of marmosets freely moving in a large space, as well as during forced forelimb movement. While high-gamma activity in the M1 contributed most strongly to the prediction of muscle activity in both movement types, a larger area of the sensorimotor cortex contributed to decoding for natural whole-body movement than for forced forelimb movement. These results provide insights that are relevant to future applications of BMI systems in unrestrained individuals.
Section snippets
Materials and methods
We used three adult female monkeys (Callithrix jacchus, weight 380–510 g) in the present study. The experiments were approved by the experimental animal committee of the National Institute of Neuroscience. All animals were cared for and treated humanely in accordance with the institutional guideline for experiments using animals and the NIH guidelines. Before the experiments, the animals were housed with their family with a 12-h light/dark cycle.
Cortical and muscle activities were simultaneously recorded from marmosets
Fig. 2A shows simultaneously recorded forelimb muscle activity and cortical oscillatory activity that occurred while a monkey pulled the lever. EMG data from proximal and distal forelimb muscles showed the following profile: activity of Del had a single peak around the time at which the monkey pulled the lever, activity of TB showed the first peak around movement onset and a second around the time at which the lever was pulled, there was almost no activity of ECR, and activity of FDS showed a
Discussion
In the present study, we simultaneously recorded cortical and muscle activities in freely moving marmosets. We demonstrated that muscle activity during forced forelimb movement could be decoded from ECoG signals recorded over the sensorimotor cortex of marmosets, as is the case with other species (Nakanishi et al., 2017; Shin et al., 2012). We also found that the model could be used to successfully reconstruct muscle activity recorded during natural whole-body movement. For both forced and
Declarations of interest
None.
Acknowledgments
This work was supported by the program for Brain Mapping by Integrated Neurotechnologies for Disease Studies (Brain/MINDS) from the Japan Agency for Medical Research and Development, AMED, Japan.
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