Neuronal representation of stand and squat in the primary motor cortex of monkeys
© Ma et al.; licensee BioMed Central. 2015
- Received: 28 May 2014
- Accepted: 26 March 2015
- Published: 9 April 2015
Determining neuronal topographical information in the cerebral cortex is of fundamental importance for developing neuroprosthetics. Significant progress has been achieved in decoding hand voluntary movement with cortical neuronal activity in nonhuman primates. However, there are few successful reports in scientific literature for decoding lower limb voluntary movement with the cortical neuronal firing. We once reported an experimental system, which consists of a specially designed chair, a visually guided stand and squat task training paradigm and an acute neuron recording setup. With this system, we can record high quality cortical neuron activity to investigate the correlation between these neuronal signals and stand/squat movement.
In this research, we train two monkeys to perform the visually guided stand and squat task, and record neuronal activity in the vast areas targeted to M1 hind-limb region, at a distance of 1 mm. We find that 76.9% of recorded neurons (1230 out of 1598 neurons) showing task-firing modulation, including 294 (18.4%) during the pre-response window; 310 (19.4%) for standing up; 104 (6.5%) for the holding stand phase; and 205 (12.8%) during the sitting down. The distributions of different type neurons have a high degree of overlap. They are mainly ranged from +7.0 to 13 mm in the Posterior-Anterior dimension, and from +0.5 to 4.0 mm in Dosal-lateral dimension, very close to the midline, and just anterior of the central sulcus.
The present study examines the neuronal activity related to lower limb voluntary movements in M1 and find topographical information of various neurons tuned to different stages of the stand and squat task. This work may contribute to understanding the fundamental principles of neural control of lower limb movements. Especially, the topographical information suggests us where to implant the chronic microelectrode arrays to harvest the most quantity and highest quality neurons related to lower limb movements, which may accelerate to develop cortically controlled lower limb neuroprosthetics for spinal cord injury subjects.
- Single neuron recording
- Topographical information
- Lower limb movement
Over the past two decades, numbers of researches have been reported in investigation of the relationship between the firing pattern of cortical neurons and the animal’s behavioural performance [1-12]. Using behavioural neurophysiological methods, significant progress has been achieved in the study of topographical information of the cerebral cortex [13-17]. These pioneering studies provide hopes and promises for the development of neuroprostheses for spinal cord/brain injured patients or people suffering from many neural degenerative diseases with limb dyskinesia.
Technical challenges to record cortical unit spikes in conscious behaviour monkey have been presented in the investigation of cortical control of lower limb. For example, microdrivable electrode recording technology requires the limitation of movement of the subject’s head . It is the deficiency of this technology that limits its utility and using range. Quite a few of current research achievements in this area are focused on the upper limb movements [19-32]. For lower limb movements, there are few reports . We have developed a novel experimental system for recording cortical neuronal signals related to stand and squat movement . The system consists of a specially designed chair, a stand and squat task paradigm and a neuron recording setup with microdrivable electrodes. With this system, a monkey can perform visually guided stand and squat while the head is relatively stationary to allow accurate single cortical unit recording. The neuronal firing patterns, the kinematics and muscle activities were recorded simultaneously for analysing the cortical control of lower limb functions.
Determining neuronal topographical information in the cerebral cortex is of fundamental importance for developing neuroprosthetics. In this study, we trained two monkeys to perform a visually-cued stand and squat task and scanned the whole primary motor cortex at a distance of 1 mm, and recorded 1598 firing neurons. This work may contribute to the understanding of the fundamental principles of neural control of lower limb movement. By knowing how cortical neurons modulate their activities to initiate and control the standing and sitting movement we may eventually be able to develop cortically controlled neuroprosthetic systems for spinal cord injury patients.
Spinal cord injuries are particularly tragic for most of the victims are injured in the prime of life. Every year, there are over 350,000 people worldwide who are diagnosed with the tragically debilitating consequences of dysfunction in lower limb motor control, so it is obvious for the significance in searching for effective means of helping these people to regain ability to stand up and walk.
Our data were collected from two male rhesus (Macaca mulatta) monkeys (Monkey H and Monkey V, 4–6 years old) to identify the cortical areas related to lower limb voluntary movements. The animal behavioural training and the actual experimental protocols had been described in detail in our previous paper . The experiments were complied with NIH policy on Humane Care and Use of laboratory animals, and were approved by the institutional Animal Care and Use Committee, Arizona State University, USA.
Each monkey was trained to perform a visually-cued stand and squat task using a well-designed primate chair. The chair was made of stainless steel and acrylic plastic, equipped with a movable pedal. While the head and body were restrained in the chair, the subject can still perform stand and sit movement, via pushing down the pedal with adjustable weight. The pedal was connected to the chair with two inner-set Axletrees, which made it possible to move up and down easily with negligible friction. When the subject wanted to stand up, the pedal can be pushed down to achieve the upright standing posture. When the subject intended to sit down and release the pushing force, the pedal was pulled up by the counter weight through the sliding axles, which were set on the back part of the chair.
Under deep general anaesthesia, 3 headpieces were surgically implanted to allow head restraint. At a second surgery, a single 23×20 mm recording chamber was mounted so as to give access to the primary motor area for recordings in the left hemisphere. After the craniotomy surgery had been made, the monkeys received a full course of antibiotics (20 mg/kg oxytetracycline, i.m.) and analgesic (10 μg/kg buprenorphine, i.m).
Separate surgical procedures were performed to implant fine-wire intramuscular electrodes for electromyogram (EMG) into 6 selected lower limb muscles from each leg, including right and left soleus (RS and LS), right and left tibialis anterior (RTA and LTA), right and left semitendinosus (RST and LST), right and left recutus femoris (RRF and LRF), right and left extensor digitorum longus (REDL and LEDL), and right and left flexor digitorum longus (RFDL and LFDL).
Acute cortical activity and lower-limb electromyogram recordings
Single-unit activities were recorded using a microdrive recording system (Thomas Recording system, Germany). The location of each electrode penetration was determined by triangulation on fiducial markers on the chamber lid, which allowed the stereotaxic position of each new penetration to be calculated. The actual coordination of M1 area in stereotaxic apparatus can be obtained accurately according to the atlas of monkeys’ brain anatomy. Before every penetration, the positions of each electrode were calculated precisely with the method above to ensure that the recording area was within the right range. Lower-limb EMGs were recorded synchronously with a sampling rate of 1000 Hz.
Neural signals corresponding to extracellular action potentials were conditioned and amplified, filtered, digitized and stored using a 64-channel neuron recording system (Plexon Inc., Dallas, TX). About 20 successful trials formed a recording session. Spike sorting was performed to isolate individual neuronal unit on each data session based on the clustering of detected action potential waveforms in principal components (PC) feature spaces using the software tool of Offline Sorter (Plexon Inc., Dallas, TX). Sorted data from each session were imported to Neuroexplorer (Plexon Inc.) for statistical analysis. Peri-event histogram analysis was applied to examine patterns of multiunit activity associated with events of lower limb kinematics in motor tasks, with special attention on: initiation of stand, stand up, stand hold on and sit down during the position control task.
A one-way analysis of variance (ANOVA) with p < 0.05 was applied to classify neurons based on their statistical properties of neural activity (firing rate) in each epoch, compared to the average firing rate during the whole trial. The firing rate of each neuron was computed in a 200 ~ 500 milliseconds time window with 30-millisecond bins.
Density contour of neuronal distribution was calculated at each penetration point as the number of neurons related to event divided by the total number of cells recorded under that penetration. Then the densities for all the penetrations were smoothed by a median filter, and the density contour for neuronal distribution related to events was plotted. The hottest colour represented the highest density.
In (1), each spiking rates input x is convolved with its finite impulse response (FIR) function fim. The subscription im stands for the FIR function of neuron i on muscle m, and ε stands for the residue error, and N is the number of total neuron units involved into the prediction. The values of the FIR function fim need to be estimated from the system inputs and outputs in a train dataset. In model implementation, the spiking rate of each neuron was computed in non-overlapping 20 ms time windows; the EMG signals were detrended, full-wave rectified and then filtered by an order 8 Butterworth low-pass filter with a cut-off frequency of 4 Hz. For each session, we used the first 2/3 trials to train the model, and test it on the remaining 1/3 trials.
We have recorded 1598 units (689 from Monkey H, 909 from Monkey V) that exhibited activity patterns during the task (see Figure 1A). To further in-depth analyses, we classified the neurons for specific action or event coding for control commands.
Units for initiation of stand up and their distribution area in M1
Units for stand up and their distribution area in M1
Units for stand hold and their distribution area in M1
Units for sit down and their distribution area in M1
EMG predictions using event-related neurons
Table 1 described the percentages of sessions where the R2’s are greater than 0.5, and the highest R2’s we got in all sessions, for each EMG. We can see that for all 6 hind limb EMG signals, a large portion of sessions has poor predictive performance by the cortical neuronal activities. However, for each lower limb EMG signal, we still harvested quite a few sessions with high R2 values.
A fundamental issue which should be addressed in priority was to describe the details of topographic information in M1 related to the lower limb functions. Although the spatial mapping of the leg area in M1 has been repeatedly confirmed in the last decades, using different techniques such as microstimulation or fMRI [35-37], the accurate coordinates of brain area for electrode array implantation was still not very specific.
Constructing multi-electrode arrays based BMI required more accurately parameters.
In this work, we identified the cortical areas related to lower movements in M1 areas by acute neuronal recording methods. We found that all these neurons showed a highly overlapping distribution, which suggested that for practical applications of brain-machine interface, the general information for lower limb movements could be obtained together from the same motor cortical region (Coordination: DL: 0.8-3.6; PA: 9–12; Depth: 1–3).
For the initiation of standing up, the distribution areas from coronal view was relatively small. The hot area ranged from +6.3 to +12.3 mm in the Posterior-Anterior dimension, and from +0.4 to 4.0 mm in Dorsal-lateral dimension. The vertical distribute depths were also relatively narrow, ranged from 0.5 mm to 2.5 mm.
For the standing up, the distribution areas in coronal view got much bigger. Most of the units distributed in the range from +8.0 to 16 mm in the Posterior-Anterior dimension, and from +1.6 to 6.0 mm in Dorsal-lateral dimension. The distribution depths in vertical view were also relatively narrow, ranged from 0.5 mm to 3.0 mm from the brain.
The most interesting was that we still found 104 units related to standing holding, most of them distribution in the range from +8.0 to 14 mm in the Posterior-Anterior dimension, and from +1.5 to 4.5 mm in Dorsal-lateral dimension. The distribution depths in vertical view were also relatively narrow, ranged from 0.5 mm to 4.5 mm. It was generally thought that the brain does not participate standing holding, but we recorded some cortical neurons firing during standing holding. It may be caused by the task which is guided by visual cues, which recruited certain numbers of neurons to perform the task.
For the 205 squatting down related units, most of their distribution was in the range from +8.0 to 14 mm in the Posterior-Anterior dimension, and from +1.5 to 7.0 mm in Dorsal-Lateral dimension. The vertical distribution depths were also relatively narrow, ranged from 0.5 mm to 4.5 mm.
The results about lower-limb motor control here had some spatially overlapping functional maps with that of hand and arm control in reach and grasp task (Figure 2). One of possible reasons for causing such overlapping may lie on the difficulties of excluding the neuronal activity associated with fore-limb movements during the tasks. However, distinctions were observable. Lower-limb areas were much closer to the central sulcus and the midline.
The main aim for constructing a model using task-related neurons’ spiking rates to predict the EMG envelop of each muscle in lower-limb was to investigate whether the innervating relationship between cortical neuronal units and lower-limb muscles existed and can be depicted in some determinate form. The results of figure 6 indicated that a relatively straight forward linear model can convey such neuron-muscular innervation relationship quite well, though it was believed the whole cortical motor control process contained a lot of nonlinearity. The successful application of linear model here suggested that the neurons obtained in our acute recording were highly muscular control related.
Future work will include applying the neuron selection method proposed in this paper on chronically recorded neural datasets and developing on-line decoding algorithms. We will implant multiple microelectrode arrays in the area ranged from +9.0 to 12 mm in the Posterior-Anterior dimension, and from +0.8 to 3.6 mm in Dorsal-lateral dimension, and centre of 2.0 mm in depth from the brain surface. It is very close to the middle line and central sulcus. There are rich of blood vessels over this region, which might easily cause accidentally bleeding during implant surgery. More seriously, most of this area is covered by a large sinusoid on the top of middle line. Unlike blood vessels, the sinusoidal membrane is more easily damaged by sharp tools, and the damage is irreparable. It is almost a taboo to do any surgery in this area if we cannot find a way to escape touching the surface of these tissues. To address this issue, we design a special micro-electrode array for chronic cortical recording. With long length of the electrodes, we could implant the arrays at a big angle from the lateral side so as to escape touching the surface of direct top. And because of implant angle, we design the different length of the two raw electrodes (2.5 mm in one row, and 3.5 mm in another row).
The present study has examined the neuronal activity related to lower limb voluntary movements in M1 and found various neurons tuned to different stages of the stand and squat task. Utilizing a multi-input-single output (MISO) linear model, we have successfully predicted EMG signals from the small number of collected neurons. Most importantly, we have obtained the topographic information and could locate these neurons related to lower limb activity. This work may contribute to the understanding of the fundamental principles of neural control of lower limb movements and the further development of cortically controlled lower limb neuroprosthetics.
This work was supported in part by a grant from Taipei Veterans General Hospital, and National Natural Science Foundation of China (31460263, 61233015), and 973 Project of China (2013CB329506). Authors appreciate the collaboration from Jose J. Aguayo and Gabriel Bodeen in the neural control laboratory in Arizona State University.
- Batuev AS, Pirogov AA, Orlov AA. Role of the frontal lobes in integrative activity of the brain in Macaca mulatta. Neurosci Behav Physiol. 1980;10:265–70.View ArticlePubMedGoogle Scholar
- Boch R. Behavioral Modulation of Neuronal-Activity in Monkey Striate Cortex - Excitation in the Absence of Active Central Fixation. Exp Brain Res. 1986;64:610–4.View ArticlePubMedGoogle Scholar
- Dickey AS, Suminski A, Amit Y, Hatsopoulos NG. Single-Unit Stability Using Chronically Implanted Multielectrode Arrays. J Neurophysiol. 2009;102:1331–9.View ArticlePubMed CentralPubMedGoogle Scholar
- Gottlieb Y, Vaadia E, Abeles M. Single Unit-Activity in the Auditory-Cortex of a Monkey Performing a Short-Term Memory Task. Exp Brain Res. 1989;74:139–48.View ArticlePubMedGoogle Scholar
- Liu J, Newsome WT. Correlation between speed perception and neural activity in the middle temporal visual area. J Neurosci. 2005;25:711–22.View ArticlePubMedGoogle Scholar
- Pieper CF, Goldring S, Jenny AB, McMahon JP. Comparative study of cerebral cortical potentials associated with voluntary movements in monkey and man. Electroencephalogr Clin Neurophysiol. 1980;48:266–92.View ArticlePubMedGoogle Scholar
- Riehle A, Requin J. Monkey Primary Motor and Premotor Cortex - Single-Cell Activity Related to Prior Information About Direction and Extent of an Intended Movement. J Neurophysiol. 1989;61:534–49.PubMedGoogle Scholar
- Saarinen T, Laaksonen H, Parviainen T, Salmelin R. Motor cortex dynamics in visuomotor production of speech and non-speech mouth movements. Cereb Cortex. 2006;16:212–22.View ArticlePubMedGoogle Scholar
- Seamans JK, Lapish CC, Durstewitz D. Comparing the prefrontal cortex of rats and primates: insights from electrophysiology. Neurotox Res. 2008;14:249–62.View ArticlePubMedGoogle Scholar
- Serences JT, Boynton GM. The representation of behavioral choice for motion in human visual cortex. The Journal of neuroscience : the official journal of the Society for Neuroscience. 2007;27:12893–9.View ArticleGoogle Scholar
- Thorpe SJ, Rolls ET, Maddison S. The orbitofrontal cortex: neuronal activity in the behaving monkey. Exp Brain Res. 1983;49:93–115.View ArticlePubMedGoogle Scholar
- West RA, Larson CR. Neurons of the anterior mesial cortex related to faciovocal activity in the awake monkey. J Neurophysiol. 1995;74:1856–69.PubMedGoogle Scholar
- Yoshida K, Saito N, Iriki A, Isoda M. Representation of others’ action by neurons in monkey medial frontal cortex. Current biology : CB. 2011;21:249–53.View ArticlePubMedGoogle Scholar
- Westendorff S, Klaes C, Gail A. The cortical timeline for deciding on reach motor goals. The Journal of neuroscience : the official journal of the Society for Neuroscience. 2010;30:5426–36.View ArticleGoogle Scholar
- Yang Z, Heeger DJ, Seidemann E. Rapid and precise retinotopic mapping of the visual cortex obtained by voltage-sensitive dye imaging in the behaving monkey. J Neurophysiol. 2007;98:1002–14.View ArticlePubMed CentralPubMedGoogle Scholar
- Yasuhara T, Matsukawa N, Yu G, Xu L, Mays RW, Kovach J, et al. Behavioral and histological characterization of intrahippocampal grafts of human bone marrow-derived multipotent progenitor cells in neonatal rats with hypoxic-ischemic injury. Cell Transplant. 2006;15:231–8.View ArticlePubMedGoogle Scholar
- Wallis JD, Miller EK. Neuronal activity in primate dorsolateral and orbital prefrontal cortex during performance of a reward preference task. Eur J Neurosci. 2003;18:2069–81.View ArticlePubMedGoogle Scholar
- Zacksenhouse M, Nemets S. Strategies for Neural Ensemble Data Analysis for Brain-Machine Interface (BMI) Applications. In: Nicolelis MAL, editor. Methods for Neural Ensemble Recordings. 2nd ed. Boca Raton (FL): CRC Press; 2008. Frontiers in Neuroscience.Google Scholar
- Bradnam LV, Stinear CM, Byblow WD. Cathodal transcranial direct current stimulation suppresses ipsilateral projections to presumed propriospinal neurons of the proximal upper limb. J Neurophysiol. 2011;105:2582–9.View ArticlePubMedGoogle Scholar
- Bradnam LV, Stinear CM, Lewis GN, Byblow WD. Task-Dependent Modulation of Inputs to Proximal Upper Limb Following Transcranial Direct Current Stimulation of Primary Motor Cortex. J Neurophysiol. 2010;103:2382–9.View ArticlePubMedGoogle Scholar
- Yanagisawa T, Hirata M, Saitoh Y, Kato A, Shibuya D, Kamitani Y, et al. Neural decoding using gyral and intrasulcal electrocorticograms. Neuroimage. 2009;45:1099–106.View ArticlePubMedGoogle Scholar
- Fagg AH, Ojakangas GW, Miller LE, Hatsopoulos NG. Kinetic Trajectory Decoding Using Motor Cortical Ensembles (vol 17, pg 487, 2009). Ieee T Neur Sys Reh. 2009;17:606.View ArticleGoogle Scholar
- Magenes G, Passaglia F, Secco EL. A new approach of multi-d.o.f. prosthetic control. Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society Conference. 2008;2008:3443–6.Google Scholar
- Bradberry TJ, Contreras-Vidal JL, Rong F. Decoding Hand and Cursor Kinematics from Magnetoencephalographic Signals during Tool Use. 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2008;2008:5306–9.View ArticleGoogle Scholar
- Milner TE, Franklin DW, Imamizu H, Kawato M. Central control of grasp: manipulation of objects with complex and simple dynamics. Neuroimage. 2007;36:388–95.View ArticlePubMedGoogle Scholar
- Begliomini C, Caria A, Grodd W, Castiello U. Comparing Natural and Constrained Movements: New Insights into the Visuomotor Control of Grasping. PloS one. 2007;2(10):e1108. doi:10.1371/journal.pone.0001108.View ArticlePubMed CentralPubMedGoogle Scholar
- Ochiai T, Mushiake H, Tanji J. Involvement of the ventral premotor cortex in controlling image motion of the hand during performance of a target-capturing task. Cereb Cortex. 2005;15:929–37.View ArticlePubMedGoogle Scholar
- Shimazu H, Maier MA, Cerri G, Kirkwood PA, Lemon RN. Macaque ventral premotor cortex exerts powerful facilitation of motor cortex outputs to upper limb motoneurons. The Journal of neuroscience : the official journal of the Society for Neuroscience. 2004;24:1200–11.View ArticleGoogle Scholar
- Carmena JM, Lebedev MA, Crist RE, O’Doherty JE, Santucci DM, Dimitrov DF, et al. Learning to control a brain-machine interface for reaching and grasping by primates. PLoS Biol. 2003;1:193–208.View ArticleGoogle Scholar
- Miller LE, Holdefer RN, Houk JC. The role of the cerebellum in modulating voluntary limb movement commands. Arch Ital Biol. 2002;140:175–83.PubMedGoogle Scholar
- Mason CR, Gomez JE, Ebner TJ. Primary motor cortex neuronal discharge during reach-to-grasp: controlling the hand as a unit. Arch Ital Biol. 2002;140:229–36.PubMedGoogle Scholar
- He JP, Ma CL, Herman R. Engineering neural interfaces for rehabilitation of lower limb function in spinal cord injured. P Ieee. 2008;96:1152–66.View ArticleGoogle Scholar
- Janssen WGM, Bussmann HBJ, Stam HJ. Determinants of the sit-to-stand movement: A review. Phys Ther. 2002;82:866–79.PubMedGoogle Scholar
- Ma C, He J. A method for investigating cortical control of stand and squat in conscious behavioral monkeys. J Neurosci Methods. 2010;192:1–6.View ArticlePubMedGoogle Scholar
- Logothetis NK, Pauls J, Augath M, Trinath T, Oeltermann A. Neurophysiological investigation of the basis of the fMRI signal. Nature. 2001;412:150–7.View ArticlePubMedGoogle Scholar
- Matsui T, Tamura K, Koyano KW, Takeuchi D, Adachi Y, Osada T, et al. Direct comparison of spontaneous functional connectivity and effective connectivity measured by intracortical microstimulation: an fMRI study in macaque monkeys. Cereb Cortex. 2011;21:2348–56.View ArticlePubMedGoogle Scholar
- Smirnakis SM, Schmid MC, Weber B, Tolias AS, Augath M, Logothetis NK. Spatial specificity of BOLD versus cerebral blood volume fMRI for mapping cortical organization. J Cereb Blood Flow Metab. 2007;27:1248–61.View ArticlePubMedGoogle Scholar
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