- Open Access
Can motor volition be extracted from the spinal cord?
© Prasad and Sahin; licensee BioMed Central Ltd. 2012
Received: 28 October 2011
Accepted: 24 May 2012
Published: 19 June 2012
Spinal cord injury (SCI) results in the partial or complete loss of movement and sensation below the level of injury. In individuals with cervical level SCI, there is a great need for voluntary command generation for environmental control, self-mobility, or computer access to improve their independence and quality of life. Brain-computer interfacing is one way of generating these voluntary command signals. As an alternative, this study investigates the feasibility of utilizing descending signals in the dorsolateral spinal cord tracts above the point of injury as a means of generating volitional motor control signals.
In this work, adult male rats were implanted with a 15-channel microelectrode array (MEA) in the dorsolateral funiculus of the cervical spinal cord to record multi-unit activity from the descending pathways while the animals performed a reach-to-grasp task. Mean signal amplitudes and signal-to-noise ratios during the behavior was monitored and quantified for recording periods up to 3 months post-implant. One-way analysis of variance (ANOVA) and Tukey’s post-hoc analysis was used to investigate signal amplitude stability during the study period. Multiple linear regression was employed to reconstruct the forelimb kinematics, i.e. the hand position, elbow angle, and hand velocity from the spinal cord signals.
The percentage of electrodes with stable signal amplitudes (p-value < 0.05) were 50% in R1, 100% in R2, 72% in R3, and 85% in R4. Forelimb kinematics was reconstructed with correlations of R2 > 0.7 using tap-delayed principal components of the spinal cord signals.
This study demonstrated that chronic recordings up to 3-months can be made from the descending tracts of the rat spinal cord with relatively small changes in signal characteristics over time and that the forelimb kinematics can be reconstructed with the recorded signals. Multi-unit recording technique may prove to be a viable alternative to single neuron recording methods for reading the information encoded by neuronal populations in the spinal cord.
There are over 2.5 million people living with spinal cord injury (SCI) worldwide with over 250,000 cases of SCI patients in the USA alone . Depending on the level of injury, these individuals can be either quadriplegic (no sensation in the upper and lower limbs) or paraplegic (no sensation below waist level) . The mean life expectancy of those surviving the initial injury is over 40 years [3, 4] as a result of which there are considerable costs associated with primary care and loss of income. Additionally, there are thousands of new patients each year with other neurodegenerative disorders. Neuroprosthetic development has been driven and motivated by the above numbers in the last few decades for improving the quality of daily life for these individuals [5–8]. Current advances in the neuroprosthetic technology have restored communication and control in animal models and human subjects [9, 10]. However, the current state of brain-machine interface (BMI) technology suffers from major issues such as long term stability, low information rate, and high electrode counts needed that has limited the translation of technology from research setting to clinical and household environment. In this study, we propose a novel method of extracting volitional signals from the descending tracts of the spinal cord dorsolateral column above the point of injury and utilizing them as control signals for a spinal cord-computer interface. This study proposes that accessing signals at the spinal level could resolve the stability and information rate issues and perhaps reduce the number of electrodes required due to the unique features of spinal cord neuroanatomy.
Several studies have confirmed that multiple brain areas are involved in any given behavior [11–19]. The prediction accuracy of a BMI depends not only on the number of neurons sampled from multiple cortical areas but also on the type of motor parameter being predicted . Accurate prediction of limb kinematics becomes a challenge if only one cortical area is tapped. There is also a limit on the number of electrodes that can be implanted in a subject, which needs to be increased in order to expand the repertoire of a BMI to include a wide variety of tasks. Due to the nature of distributed coding principle  in the brain, the spinal cord may provide a favorable alternative  for tapping neural signals and using them as command signals.
Reports in the spinal cord injury literature indicate that the proximal sides of the motor fibers are still functional after an injury that severs the spinal cord. The distal portions of the axons go through “Wallerian” degeneration. However, the time course of degeneration for the proximal corticospinal tract (CST) is much slower and a significant portion of the fibers is preserved even years after injury [23–26]. Investigations of corticospinal axons have shown variable amounts of retrograde degeneration in several species after transection of the medullary pyramids [27–29]. Fishman  reported that spinal cord sections within a few spinal segments of the injury were grossly depleted of CST axons. However, the number of axons was close to normal at a distance rostral to the injury, regardless of the duration after the lesion. Clinical studies have also shown that tetraplegic patients were able to voluntarily modulate primary motor cortex (M1) spiking activity several years after injury [30, 31]. All these reports suggest that descending motor signals may be accessible from the spinal cord segments above the point of injury.
The axons carrying these descending signals from different brain areas are tightly bundled together in the spinal cord tracts that innervate various regions of the body depending upon the cord level. The spinal cord contains two major descending systems: the lateral and the medial systems. The medial descending system is mainly involved in posture related activities by integrating vestibular, visual, and somatosensory information [32–34]. The lateral system, consisting of the corticospinal tract (CST) and the rubrospinal tract (RST), is involved in producing skilled forelimb movements and constitute the main pathways for motor control across species [35–52]. A single high-density microelectrode array (MEA) implanted in the cervical region would be able to record motor signals both from the CST and the RST in humans because the two tracks intermingle as they descend in the dorsolateral funiculus. In the rat, however, unlike the humans the major component of the CST travels separately from the RST in the ventral most portion of the dorsal funiculus of the spinal cord [43, 53–55].
In this study, descending signals of the RST in the dorsolateral funiculus from the rat cervical cord were recorded to investigate the feasibility of using these signals as a means of generating command control. The main focus of this study was to demonstrate the functional characteristics, i.e. signal amplitudes, signal-to-noise ratio, and decoding characteristics of the neural signals recorded for periods lasting up to 3 months, and thereby presenting evidence for potential use of these signals in the context of brain-machine interfacing. Stability of signals was assessed based on mean signal amplitudes and signal-to-noise ratios. Forelimb kinematics such as hand position, elbow angle, and hand velocity were also reconstructed using the neural signals to demonstrate that the information encoded in these signals is correlated to the motor function.
All procedures were approved by the Institutional Animal Care and Use Committee (IACUC), Rutgers University, Newark, NJ. Four adult (4–5 months old) male Long Evans rats, all weighing between 300–350 g were used for this study. All animals were trained to reach and grasp food pellets (Bio-Serv, NJ) through a small aperture prior to electrode implantation surgery. The dimensions of the clear plexi-glass training box were 20 x 20 x 20 cm. The aperture was a 3 x 3 cm opening in the box wall about 2.5 cm from the floor and the right-hand side wall. Food pellets were dropped one at a time by the experimenter during each trial. The animals were kept on a feed-restricted diet and then maintained at approximately 85% of their initial body weight during the study period. There was no restriction on their water intake at any time and they were fed normally during weekends. Animal training started approximately ten days after the beginning of food restriction. The animals were placed in the training box for 2–3 sessions for acclimatization after which food was presented as a reward. The animals were considered trained when they attained 90% success level in food reaching, grasping, and retrieval through the aperture, which usually required 8–10 training sessions each lasting approximately 30 minutes. All animals in this study were right-handed.
Custom-made 15-channel microelectrode arrays (MEA) (Blackrock Microsystems, Inc, UT) were used in this study. Electrode shanks were custom arranged in a 5 x 3 matrix and the total dimensions of the MEA were 2 x 1.2 mm. Each shank was 1 mm long, 80 μm thick at the base and tapered to a fine tip. The shank tips were coated by the manufacturer with platinum (approximately 50 μm) that served as the recording surface. Electrical connection to the headstage amplifier was made via a micro-connector (Omnetics, MN).
The impedances were initially found to be low immediately and a few days after surgery but they returned to near ex-vivo values after 10–14 days and did not show a major change comparable to the initial drop for the rest of the study period. The initial instability in electrode impedances has been reported because of the acute tissue response due to stabbing injury caused by implantation [80–84]. As the tissue encapsulation begins to occur around the microelectrode tips by the second week, the implant becomes more stable and impedances return to their pre-implant values.
A 32-channel, 100-gain headstage amplifier (Triangle Biosystems, Inc) was used to interface the MEA with the data acquisition card (National Instruments PCI 6259) and the computer. A trial consisted of five seconds of neural data as well as video images acquired simultaneously using a Matlab program. Neural data and video images were time stamped using the functions in Matlab’s data and image acquisition toolboxes. Neural signals were acquired at a sampling rate of 30 kHz. Video frames were captured with a camcorder from the right side of the rat at 30 frames/s and then deinterlaced to form movies at 60 fields/s.
At the end of the study period, the animals were anesthetized and perfused with heparinized saline followed by 4% neutral buffered formaldehyde (NBF), freshly prepared from p-formaldehyde. The spinal cord segment harboring the electrode array was explanted and further fixed for 24 hours in the NBF and then rinsed in phosphate buffered saline, pH 7.4. The fixed cord segment was trimmed to size under stereo microscope and embedded in paraffin. For sectioning, the paraffin block was oriented such that coronal sections could be taken parallel to the MEA substrate. Five 6 micron serial sections were taken for staining at 20 micron intervals until the MEA shanks could be seen through the remaining paraffin block. Serial sections were stained with Luxol fast blue-cresyl violet (LFB) for evaluation of myelin and other cellular components of tissue reaction.
Immunostaining with anti-GFAP (glial fibrillary acidic protein) was used for evaluating astrocytic activation. The sections and the remaining block were deparaffinized and subjected to antigen retrieval using heat in a computer controlled pressure cooker (Pick Cell Laboratory Retriever) for immuno-staining. The tissue sections were rinsed with phosphate buffered saline (PBS) once. The sections were fixed with 100μL 4% paraformaldehyde for 20 minutes at room temperature followed by a rinse with PBS. The sections were then blocked using 100μL PBS containing 0.1% triton X-100, 10% normal goat serum (Invitrogen, PCN 5000), and 1% bovine serum albumin (BSA) (Fisher Scientific, BP671-1) at room temperature for 45 minutes. This was followed by rinsing the sections with 50μL 0.1% BSA in PBS for 10 minutes and incubating with 40μL PBS overnight at 4°C containing 1:25 rabbit-anti-GFAP (100 μg/mL) (Invitrogen 18–0063) as the primary antibody. The sections were then rinsed with 50μL 0.1% BSA in PBS for 10 minutes followed by incubating with 40μL PBS at room temperature for one hour containing 1:200 goat-anti-rabbit IgG (Alexa Fluor 488, Invitrogen A11008) as the secondary antibody. The sections were then rinsed with 50μL 0.1% BSA in PBS twice for 10 minutes each before putting the cover slip on the slides. Stained sections were evaluated by fluorescence microscopy or bright field microscopy as appropriate.
Data analysis and results
Raw neural signals
Data analysis was performed using MATLAB (Mathworks, MA). Signals were recorded and referenced with respect to one of the shanks on the array and a Pt wire tied to the skull screw was connected to the data acquisition board ground. The reference electrode could alternatively be placed on the spinal cord surface. However, a differential recording with respect to this electrode would be contaminated with sensory signals from the spinocerebellar tract located near the surface. The configuration described above effectively removed the common-mode signals from distant sources such as skeletal muscles, the heart, and the brain.
The difference in signal characteristics was further investigated by the power spectral density (PSD) of the raw neural and EMG signals during the representative trial (Figure 3V). The PSDs show significant power at the lower end of the spectrum (f < 200 Hz) and decrease by frequency. The power spectrum for EMG activity is significantly higher than that of the neural activity and contains frequency components above 2 kHz.
Signal amplitude stability
In order to quantify signal amplitude stability over time, signals were rectified and averaged within the ~500 ms window that the behavior occurred in each trial. Mean and standard deviations of the signal amplitudes were calculated across multiple trials and sessions. One-way analysis of variance (ANOVA) was then performed between the mean amplitudes of all trials within different sessions to investigate signal amplitude stability during the study period. Tukey’s post-hoc analysis was then used to determine the sessions where the mean amplitudes differed significantly.
Decoding characteristics of the signals
The rat’s shoulder, elbow, and hand were marked manually (using the black-white patterns on the animal’s fur as landmarks) in each video frame retrospectively to track the rat’s arm position during the behavioral task. Cosine formula was used to calculate the elbow angle from the distance measurements between the three markers. The relative hand position was calculated by subtracting the x- and y- coordinates of the elbow from that of the hand. The vectoral velocity of the hand was found with respect to the elbow by differentiating the hand position. Finally, rectified-averaged versions of the principal neural components were down sampled to match the sampling rate of the kinematic variables and used in reconstructing them.
The response was modeled as a linear combination of regressor variables (multiple linear regression model), plus a random error ϵ. The regressors in the model were the 50 ms and 100 ms tap delays of the first three principal components. The Matlab’s regress function computed the β coefficient estimates of the above model (1). The success of regression was evaluated by the coefficient of determination (R2 values) between the reconstructed and measured kinematic variables.
Measured and reconstructed kinematic variables are plotted in Figure 7B from a representative trial. In general, the lower frequency components were represented with higher fidelity. This analysis was extended to all animals. Figure 7C shows that the forelimb kinematics were successfully reconstructed from the spinal cord signals with correlation coefficients of R2 > 0.7 in all animals, which strongly suggest a causal relation between the spinal cord signals and the forelimb kinematics.
In this work, we investigated a novel approach for a human-machine interface where the activity of descending tracts in the spinal cord is proposed as a means of generating motor control signals. The main focus of this study was to show: 1) the feasibility of long-term recording from the spinal cord descending tracts, 2) that these signals can be recorded with similar fidelity without deterioration of the signal-to-noise ratio after the first few weeks of acute tissue response, and 3) show that the information encoded in these signals are correlated to the motor function. Rousche and Normann  have shown reliable recordings from the cat sensory cortex for several months using arrays. Recent studies reported that stable signals could be recorded over long term using these electrodes in non-human primates [58, 59] and humans . However, all of these investigations involved electrodes implanted in the cerebral cortex. This study proposes an alternative method for human-machine interfacing by shifting the paradigm from the brain to the spinal cord.
Whishaw  and Sacrey  have shown similarities in forelimb movements and velocity profiles between humans and rats, suggesting that the rat is a suitable model to study the neural control of forelimbs. The RST at the cervical level contains information required for controlling forelimb movements in rats and other species [35–41]. The same implant site in the dorsolateral funiculus of the human spinal cord would primarily record the neural activity of the lateral corticospinal tract (rather than the RST), which is the primary descending pathway in humans. Segments C5-C6 were chosen as the implant site for recording RST signals in rats since it has been reported that RST axons and its collaterals present in C5-T1 spinal cord segments terminate onto the motor neuron pools at these levels that are involved in the control of the forelimb movements . Note that electrode implants at a higher level may have compromised diaphragm function leading to respiratory complications.
The dorsolateral funiculus occupies a large portion of the cervical spinal cord across species. In the rat, the RST tract is found in the dorsolateral funiculus just underneath a thin layer of sensory tract, i.e. dorsal spinocerebellar tract and thus accessible with electrodes of 1 mm length. A surface electrode would not be suitable as it would be recording mostly sensory information. Therefore, commercially available MEAs with penetrating shanks were used as the electrode of choice. The rat spinal cord is too small for implantation of a 100-channel standard Blackrock array. A custom-made 15-channel array with 1 mm electrode shanks was selected instead. Increasing the number of channels would have enabled us to sample a larger set of signals, but it would also increase the size of the MEA hence the trauma to the spinal cord.
Microelectrodes implanted into the white matter of the spinal cord are surrounded by a dense population of axons. Multi-unit activities recorded by these micro-electrodes may thus be insensitive to micro motions. This may be the most important factor for the stability of the recordings. The outcome measures to evaluate long-term stability in the recordings were the variability of the SNR and the mean amplitude of the signal during behavior. The mean signal amplitudes and SNRs were found to vary during the implant duration (Figure 5). However, the signals do not deteriorate substantially or disappear after the acute tissue response, or change in amplitude during the recording time, some of which are common problems with single neuron recordings from the cortex. Some signal amplitude change can also be attributed to the variations in the way the rats perform the behavior. The SNR values in general were low in our case, which may be due to the background neural activity that was considered as the baseline noise in our analysis. The signal mean for the duration of the entire behavior is a more robust parameter to quantify the activity rather than the signal peak, which fluctuates substantially from trial to trial. Using the standard deviations of signal and noise also produced modest yet robust values for SNR.
It is still debated whether the cortical area M1 encodes forelimb dynamics [63–69] or kinematics such as position, velocity, and acceleration [70–73]. Vargas-Irwin  showed that M1 encodes sufficient information to reconstruct some of the kinematic features such as joint angles of arm, wrist, and hand during a reach-and-grasp task. Miller and Sinkjaer  showed that the magnocellular portion of the red nucleus (RNm), where the RST originates  encodes the dynamics of limb muscle activity. The spinal cord circuitry is responsible for integrating these signals to activate various groups of muscles. Therefore, it is plausible that the signals in these spinal cord motor tracts encode both dynamic and kinematic parameters of a voluntary movement.
In this study, we concentrated only on forelimb kinematics. The forelimb motor function was hypothesized to have a stronger correlation to the spinal cord motor signals since these signals are closer to the target muscles in the signal pathway than the higher centers in the brain. The degree of success in reconstructing limb kinematics, determined by the correlation coefficient, was the outcome measure. High degrees of correlation were obtained for all the reconstructed kinematic functions in all the animals suggesting that some aspects of the forelimb kinematics are encoded by the spinal cord signals.
The performance of any neural prostheses would depend on the reproducibility of the control signals. The size of the implanted electrode, insertion technique, and the glial response [77–85] are some of the major factors that determine the stability of the interface over the long term. Recent reports by Bamford  showed that spinal cord tissue tolerated microwire implants up to one month post-implant. However, Utah arrays used here were significantly larger and rigid in structure compared to the single microwires. Despite the evoked immune response and the tissue encapsulation observed around the electrode shanks, animals used in the study were not at any time during the recording period paralyzed due to the implant. Paralysis was observed in some cases where the spinal cord was damaged during the surgery. Those animals were euthanized and no data were included in the analysis from them. The immune response also did not significantly degrade the neural signals during our recording period as shown by moderate changes in the mean signal amplitude between the first and the last recording sessions (Figure 5).
Limitations of the study
One of the major limitations of the study so far has been the inability to record for more than three months. This is mainly due to the lack of a suitable electrode for the rat spinal cord. The use of surface electrodes is attractive because of reduced invasiveness compared to the penetrating electrodes but the anatomical locations of the spinal cord motor tracts does not allow us to use these electrodes. The signals recorded from a surface electrode would contain sensory contamination from the spinocerebellar tracts residing close to the spinal cord surface on the dorsolateral side. Penetrating Utah MEAs were used for study since they were commercially available and the electrode shanks were arranged in well-defined positions. There was also not a large variation between electrode sizes and impedances due the fabrication process. These MEAs were able to record stable signals in our study up to 3 months. However, histological studies showed that they caused significant damage around the implant site. These MEAs also had a solid and thick base which contorted the shape of the cord as seen in histological pictures.
We tested custom-made 25 μm diameter Pt-Ir microwire electrodes implanted bilaterally in a separate study . Using these electrodes, we were able to show that the contralateral cord did not contain observable activity during a task (Figure 4). These microwires were individually inserted into the cord and the surgical procedure was significantly more tedious and longer than MEA implants. However, the histological analysis in these animals showed that the glial scarring and the tissue response were significantly lesser compared to the MEA implants. Only mild astrocytosis and gliosis were observed in the microwire implants. We have not been able to record for more than four weeks in these microwire implanted animals because of wire breakage.
Another major limitation of this study was that the reaching kinematics was reconstructed within individual trials and not generalizable across a set of test trials using the beta coefficients from a training set. The variation in beta coefficients might be because of the slight differences in how the animal reaches for the food pellet between trials. Coefficient variations may have also been due to the use of a single camera to capture the forelimb kinematics in one plane only. Our laboratory has started to use a three camera system with faster frame grabbing rate to improve the behavioral imaging.
Despite the limitations of this study, the results shown here can have major implications in the field of brain-machine interfacing by providing an alternative target site for accessing control signals. In this study, we have demonstrated the feasibility of chronically recording spinal cord descending signals in the motor tracts. We have also shown that forelimb parameters could be successfully reconstructed from spinal cord signals suggesting the information encoded in the spinal cord descending pathways can be utilized as an alternate implant site for tapping into volitional signals. Multi-unit recording method in general may provide more robust signals than the single neuron recordings as commonly used in the central nervous system.
This study was supported by a NIH grant R21 NIH/NICDS HD056963-01 A2 and a graduate fellowship to Abhishek Prasad from New Jersey Commission on Spinal Cord Research. The authors would like to thank Jonathan Groth, Gokhan Ordek, and Nicholas Sachs, PhD for their help during surgery and data collection. The authors would like to thank the reviewers for their comments and suggestions.
- DeVivo MJ: Epidemiology of traumatic spinal cord injury. In Spinal Cord Medicine. Edited by: Kirshblum K, Campagnolo DL, DeLisa JA. Lippincott Williams & Wilkins, Philadelphia; 2002:69-81.Google Scholar
- Schwab ME, Bartholdi D: Degeneration and regeneration of axons in the lesioned spinal cord. Physiol Rev 1996, 76: 319-370.PubMedGoogle Scholar
- Samsa GP, Patrick CH, Feussner JR: Long-term survival of veterans with traumatic spinal cord injury. Arch Neurol 1993, 50: 909-914.View ArticlePubMedGoogle Scholar
- DeVivo MJ, Krause JS, Lammertse DP: Recent trends in mortality and causes of death among persons with spinal cord injury. Arch Phys Med Rehabil 1999, 80: 1411-1419.View ArticlePubMedGoogle Scholar
- Brown-Triolo DL, Roach MJ, Nelson K, Triolo RJ: Consumer perspectives on mobility: Implications for neuroprosthesis design. J Rehabil Res Dev 2002, 39: 659-669.PubMedGoogle Scholar
- Sadowsky C, Volshteyn O, Schultz L, McDonald JW: Spinal Cord Injury. Disabil Rehabil 2002, 24: 680-687.View ArticlePubMedGoogle Scholar
- Peckham PH, Knutson JS: Functional electrical stimulation for neuromuscular applications. Annual Review of Biomedical Engineering 2005, 7: 327-360.View ArticlePubMedGoogle Scholar
- Hirschfeld H: Motor control of every day motor tasks: Guidance for neurological rehabilitation. Physiol Behav 2007, 92: 161-166.View ArticlePubMedGoogle Scholar
- Simeral JD, Kim SP, Black MJ, Donoghue JP, Hochberg LR: Neural control of cursor trajectory and click by a human with tetraplegia 1000 days after implant of an intracortical microelectrode array. J Neural Eng 2011., 8: 10.1088/1741-2560/8/2/025027Google Scholar
- Hochberg LR, Serruya MD, Friehs GM, Mukand JA, Saleh M, Caplan AH, Branner A, Chen D, Penn RD, Donoghue JP: Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 2006, 442: 164-171.View ArticlePubMedGoogle Scholar
- Kalaska JF, Scott SH, Cisek P, Sergio LE: Cortical control of reaching movements. Curr Opin Neurobiol 1997, 7: 849-859.View ArticlePubMedGoogle Scholar
- Covey E: Neural population coding and auditory temporal pattern analysis. Physiol Behav 2000, 69: 211-220.View ArticlePubMedGoogle Scholar
- Wessberg J, Stambaugh CR, Kralik JD, Beck PD, Laubach M, Chapin JK, Kim J, Biggs SJ, Srinivasan MA, Nicolelis MAL: Real-time prediction of hand trajectory by ensembles of cortical neurons in primates. Nature 2000, 408: 361-365.View ArticlePubMedGoogle Scholar
- Wessberg J, Nicolelis MA: Optimizing a linear algorithm for real-time robotic control using chronic cortical ensemble recordings in monkeys. J Cogn Neurosci 2004, 16: 1022-1035.View ArticlePubMedGoogle Scholar
- Santucci DM, Kralik JD, Lebedev MA, Nicolelis MA: Frontal and parietal cortical ensembles predict single-trial muscle activity during reaching movements in primates. Eur J Neurosci 2005, 22: 1529-1540.View ArticlePubMedGoogle Scholar
- Averbeck BB, Latham PE, Pouget A: Neural correlations, population coding and computation. Nat Rev Neurosci 2006, 7: 358-366.View ArticlePubMedGoogle Scholar
- Costa RM, Lin SC, Sotnikova TD, Cyr M, Gainetdinov RR, Caron MG, Nicolelis MA: Rapid alterations in corticostriatal ensemble coordination during acute dopaminedependent motor dysfunction. Neuron 2006, 52: 359-369.View ArticlePubMedGoogle Scholar
- Dzirasa K, Ribeiro S, Costa R, Santos LM, Lin SC, Grosmark A, Sotnikova TD, Gainetdinov RR, Caron MG, Nicolelis MA: Dopaminergic control of sleep-wake states. J Neurosci 2006, 26: 10577-10589.View ArticlePubMedGoogle Scholar
- Simon SA, de Araujo IE, Gutierrez R, Nicolelis MA: The neural mechanisms of gustation: a distributed processing code. Nat Rev Neurosci 2006, 7: 890-901.View ArticlePubMedGoogle Scholar
- Carmena JM, Lebedev MA, Crist RE, O'Doherty JE, Santucci DM, Dimitrov DF, Patil PG, Henriquez CS, Nicolelis MAL: Learning to control a brain-machine interface for reaching and grasping by primates. PLoS Biol 2003., 1: 10.1371/journal.pbio.0000042Google Scholar
- Nicolelis MAL, Lebedev MA: Principles of neural ensemble physiology underlying the operation of brain-machine interfaces. Nat Rev Neurosci 2009, 10: 530-540.View ArticlePubMedGoogle Scholar
- Prasad A, Sahin M: Characterization of neural activity recorded from the descending tracts of the rat spinal cord. Frontiers in Neuroprosthetics 2010., 4: 10.3389/fnins.2010.00021Google Scholar
- Bronson R, Gilles FH, Hall J, Hedley-Whyte ET: Long term post-traumatic retrograde corticospinal degeneration in man. Hum Pathol 1978, 9: 602-607.View ArticlePubMedGoogle Scholar
- Feringa ER, Vahlsing HL, Smith BE: Retrograde transport in corticospinal neurons after spinal cord transection. Neurology 1983, 33: 478-482.View ArticlePubMedGoogle Scholar
- Fishman PS: Retrograde changes in the corticospinal tract of posttraumatic paraplegics. Arch Neurol 1987, 44: 1082-1084.View ArticlePubMedGoogle Scholar
- McBride RL, Feringa ER, Garver MK, Williams JK: Retrograde transport of Fluoro-Gold in corticospinal and rubrospinal neurons 10 and 20 weeks after T-9 spinal cord transection. Exp Neurol 1990, 108: 83-85.View ArticlePubMedGoogle Scholar
- Lassek AM: The pyramidal tract: A study of retrograde degeneration in the monkey. Arch Neurol Psychiatry 1942, 48: 561-567.View ArticleGoogle Scholar
- Lance JW: Behaviour of pyramidal axons following section. Brain 1954, 77: 314-324.View ArticlePubMedGoogle Scholar
- Kalil K, Schneider GE: Retrograde cortical and axonal changes following lesions of the pyramidal tract. Brain Res 1975, 89: 15-27.View ArticlePubMedGoogle Scholar
- Kennedy PR, Bakay RAE: Restoration of neural output from a paralyzed patient by a direct brain connection. Neuroreport 1998, 9: 1707-1711.View ArticlePubMedGoogle Scholar
- Hochberg LR, Donoghue JP: Sensors for brain-computer interfaces: Options for turning thought into action. IEEE Eng Med Biol Mag 2006, 25: 32-38.View ArticlePubMedGoogle Scholar
- Kuypers HGJM: The descending pathways to the spinal cord, their anatomy and function. In Organization of the spinal cord. Volume 11 (Progress in Brain Research). Edited by: Eccles JC, Schade JP. Elsevier Science, ; 1964:178-202. 10.1016/s0079-6123(08)64048-0View ArticleGoogle Scholar
- Squire LR, Bloom FE, McConnell SK, Roberts JL, Spitzer NC, Zigmond MJ (Eds): Fundamental Neuroscience In Fundamental Neuroscience. 2nd edition. Elsevier Science, San Diego, CA; 2003. ISBN number: 978-0126603033Google Scholar
- Kandel E, Schwartz J, Jessell T (Eds): Principles of Neural Science In Principles of Neural Science. McGraw Hill; 2005. ISBN number: 978-0838577011Google Scholar
- Gibson AR, Houk JC, Kohlerman NJ: Relation between red nucleus discharge and movement parameters in trained macaque monkeys. J Physiol 1985, 358: 551-570.PubMed CentralView ArticlePubMedGoogle Scholar
- Gibson AR, Houk JC, Kohlerman NJ: Magnocellular red nucleus activity during different types of limb movement in the macaque monkey. J Physiol 1985, 358: 527-549.PubMed CentralView ArticlePubMedGoogle Scholar
- Daniel H, Billard JM, Angaut P, Batini C: The interposito-rubrospinal system. Anatomical tracing of a motor control pathway in the rat. Neurosci Res 1987, 5: 87-112.View ArticlePubMedGoogle Scholar
- Robinson FR, Houk JC, Gibson AR: Limb specific connections of the cat magnocellular red nucleus. J Comp Neurol 1987, 257: 553-577.View ArticlePubMedGoogle Scholar
- Houk JC: Red nucleus: role in motor control. Curr Opin Neurobiol 1991, 1: 610-615.View ArticlePubMedGoogle Scholar
- Jarratt H, Hyland B: Neuronal activity in rat red nucleus during forelimb reach-to- grasp movements. Neuroscience 1999, 88: 629-642.View ArticlePubMedGoogle Scholar
- Jiang MC, Alheid GF, Nunzi MG, Houk JC: Cerebellar input to magnocellular neurons in the red nucleus of the mouse: synaptic analysis in horizontal brain slices incorporating cerebello-rubral pathways. Neuroscience 2002, 110: 105-121.View ArticlePubMedGoogle Scholar
- Craven J: Major ascending and descending tracts in the spinal cord. Anaesthesia and Intensive Care Medicine 2008, 9: 1-2.View ArticleGoogle Scholar
- Brown LT: Projections and termination of the corticospinal tract in rodents. Exp Brain Res 1971, 13: 432-450.View ArticlePubMedGoogle Scholar
- Brown LT: Rubrospinal projections in the rat. J Comp Neurol 1974, 154: 169-187.View ArticlePubMedGoogle Scholar
- Armand J: The origin, course and terminations of corticospinal fibers in various mammals. Prog Brain Res 1982, 57: 329-360.View ArticlePubMedGoogle Scholar
- Houk JC, Gibson AR, Harvey CF, Kennedy PR, Van Kan PLE: Activity of primate magnocellular red nucleus related to hand and finger movements. Behav Brain Res 1988, 28: 201-206.View ArticlePubMedGoogle Scholar
- Ten Donkelaar HJ: Evolution of the red nucleus and rubrospinal tract. Behav Brain Res 1988, 28: 9-20.View ArticlePubMedGoogle Scholar
- Canedo A: Primary motor cortex influences on the descending and ascending systems. Prog Neurobiol 1997, 51: 287-335.View ArticlePubMedGoogle Scholar
- Whishaw IQ, Gorny B, Sarna J: Paw and limb use in skilled and spontaneous reaching after pyramidal tract, red nucleus and combined lesions in the rat: Behavioral and anatomical dissociations. Behav Brain Res 1998, 93: 167-183.View ArticlePubMedGoogle Scholar
- Iwaniuk AN, Whishaw IQ: On the origin of skilled forelimb movements. Trends Neurosci 2000, 23: 372-376.View ArticlePubMedGoogle Scholar
- Van Kan PLE, McCurdy ML: Role of primate magnocellular red nucleus neurons in controlling hand preshaping during reaching to grasp. J Neurophysiol 2001, 85: 1461-1478.PubMedGoogle Scholar
- Van Kan PLE, McCurdy ML: Contribution of primate magnocellular red nucleus to timing of hand preshaping during reaching to grasp. J Neurophysiol 2002, 87: 1473-1487.PubMedGoogle Scholar
- Miller MW: The origin of corticospinal projection neurons in rat. Exp Brain Res 1987, 67: 339-351.View ArticlePubMedGoogle Scholar
- Terashima T: Anatomy, development and lesion-induced plasticity of rodent corticospinal tract. Neurosci Res 1995, 22: 139-161.View ArticlePubMedGoogle Scholar
- Raineteau O, Fouad K, Bareyre FM, Schwab ME: Reorganization of descending motor tracts in the rat spinal cord. Eur J Neurosci 2002, 16: 1761-1771.View ArticlePubMedGoogle Scholar
- Prasad A, Sahin M: Chronic recordings from the rat spinal cord descending tracts with microwires. 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC'11) 2011.Google Scholar
- Rousche PJ, Normann RA: Chronic recording capability of the utah intracortical electrode array in cat sensory cortex. J Neurosci Methods 1998, 82: 1-15.View ArticlePubMedGoogle Scholar
- Suner S, Fellows MR, Vargas-Irwin C, Nakata GK, Donoghue JP: Reliability of signals from a chronically implanted, silicon-based electrode array in non-human primate primary motor cortex. IEEE Trans Neural Syst Rehabil Eng 2005, 13: 524-541.View ArticlePubMedGoogle Scholar
- Dickey AS, Suminski A, Amit Y, Hatsopoulos NG: Single-unit stability using chronically implanted multielectrode arrays. J Neurophysiol 2009, 102: 1331-1339.PubMed CentralView ArticlePubMedGoogle Scholar
- Whishaw IQ, Pellis SM, Gorny BP: Skilled reaching in rats and humans: Evidence for parallel development or homology. Behav Brain Res 1992, 47: 59-70.View ArticlePubMedGoogle Scholar
- Sacrey LAR, Alaverdashvili M, Whishaw IQ: Similar hand shaping in reaching-for- food (skilled reaching) in rats and humans provides evidence of homology in release, collection, and manipulation movements. Behav Brain Res 2009, 204: 153-161.View ArticlePubMedGoogle Scholar
- Kuchler M, Fouad K, Weinmann O, Schwab ME, Raineteau O: Red nucleus projections to distinct motor neuron pools in the rat spinal cord. J Comp Neurol 2002, 448: 349-359.View ArticlePubMedGoogle Scholar
- Bennett KMB, Lemon RN: Corticomotoneuronal contribution to the fractionation of muscle activity during precision grip in the monkey. J Neurophysiol 1996, 75: 1826-1842.PubMedGoogle Scholar
- Cheney PD, Mewes K, Fetz EE: Encoding of motor parameters by corticomotoneuronal (CM) and rubromotoneuronal (RM) cells producing postspike facilitation of forelimb muscles in the behaving monkey. Behav Brain Res 1988, 28: 181-191.View ArticlePubMedGoogle Scholar
- Smith AM, Hepp Reymond MC, Wyss UR: Relation of activity in precentral cortical neurons to force and rate of force change during isometric contractions of finger muscles. Exp Brain Res 1975, 23: 315-332.View ArticlePubMedGoogle Scholar
- Taira M, Boline J, Smyrnis N, Georgopoulos AP, Ashe J: On the relations between single cell activity in the motor cortex and the direction and magnitude of three-dimensional static isometric force. Exp Brain Res 1996, 109: 367-376.View ArticlePubMedGoogle Scholar
- Evarts EV: Relation of pyramidal tract activity to force exerted during voluntary movement. J Neurophysiol 1968, 31: 14-27.PubMedGoogle Scholar
- Scott SH: Comparison of onset time and magnitude of activity for proximal arm muscles and motor cortical cells before reaching movements. J Neurophysiol 1997, 77: 1016-1022.PubMedGoogle Scholar
- Boline J, Ashe J: On the relations between single cell activity in the motor cortex and the direction and magnitude of three-dimensional dynamic isometric force. Exp Brain Res 2005, 167: 148-159.View ArticlePubMedGoogle Scholar
- Georgopoulos AP, Kalaska JF, Caminiti R, Massey JT: On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex. J Neurosci 1982, 2: 1527-1537.PubMedGoogle Scholar
- Georgopoulos AP, Caminiti R, Kalaska JF: Static spatial effects in motor cortex and area 5: Quantitative relations in a two-dimensional space. Exp Brain Res 1984, 54: 446-454.View ArticlePubMedGoogle Scholar
- Moran DW, Schwartz AB: Motor cortical representation of speed and direction during reaching. J Neurophysiol 1999, 82: 2676-2692.PubMedGoogle Scholar
- Paninski L, Fellows MR, Hatsopoulos NG, Donoghue JP: Spatiotemporal Tuning of Motor Cortical Neurons for Hand Position and Velocity. J Neurophysiol 2004, 91: 515-532.View ArticlePubMedGoogle Scholar
- Vargas-Irwin CE, Shakhnarovich G, Yadollahpour P, Mislow JMK, Black MJ, Donoghue JP: Decoding complete reach and grasp actions from local primary motor cortex populations. J Neurosci 2010, 30: 9659-9669.PubMed CentralView ArticlePubMedGoogle Scholar
- Miller LE, Sinkjaer T: Primate red nucleus discharge encodes the dynamics of limb muscle activity. J Neurophysiol 1998, 80: 59-70.PubMedGoogle Scholar
- Murray HM, Gurule ME: Origin of the rubrospinal tract of the rat. Neurosci Lett 1979, 14: 19-23.View ArticlePubMedGoogle Scholar
- Schmidt S, Horch K, Normann R: Biocompatibility of silicon-based electrode arrays implanted in feline cortical tissue. J Biomed Mater Res 1993, 27: 1393-1399.View ArticlePubMedGoogle Scholar
- Moss J, Ryder T, Aziz TZ, Graeber MB, Bain PG: Electron microscopy of tissue adherent to explanted electrodes in dystonia and Parkinson's disease. Brain 2004, 127: 2755-2763.View ArticlePubMedGoogle Scholar
- Fawcett JW, Asher RA: The glial scar and central nervous system repair. Brain Res Bull 1999, 49: 377-391.View ArticlePubMedGoogle Scholar
- Liu X, McCreery DB, Carter RR, Bullara LA, Yuen TGH, Agnew WF: Stability of the interface between neural tissue and chronically implanted intracortical microelectrodes. IEEE Trans Rehabil Eng 1999, 7: 315-326.View ArticlePubMedGoogle Scholar
- Shain W, Spataro L, Dilgen J, Haverstick K, Retterer S, Isaacson M, Saltzman M, Turner JN: Controlling cellular reactive responses around neural prosthetic devices using peripheral and local intervention strategies. IEEE Trans Neural Syst Rehabil Eng 2003, 11: 186-188.View ArticlePubMedGoogle Scholar
- Biran R, Martin DC, Tresco PA: Neuronal cell loss accompanies the brain tissue response to chronically implanted silicon microelectrode arrays. Exp Neurol 2005, 195: 115-126.View ArticlePubMedGoogle Scholar
- Biran R, Martin DC, Tresco PA: The brain tissue response to implanted silicon microelectrode arrays is increased when the device is tethered to the skull. J Biomed Mater Res A 2007, 82: 169-178.View ArticlePubMedGoogle Scholar
- Polikov VS, Tresco PA, Reichert WM: Response of brain tissue to chronically implanted neural electrodes. J Neurosci Methods 2005, 148: 1-18.View ArticlePubMedGoogle Scholar
- Winslow BD, Tresco PA: Quantitative analysis of the tissue response to chronically implanted microwire electrodes in rat cortex. Biomaterials 2010, 31: 1558-1567.View ArticlePubMedGoogle Scholar
- Bamford JA, Todd KG, Mushahwar VK: The effects of intraspinal microstimulation on spinal cord tissue in the rat. Biomaterials 2010, 3: 5552-5563.View ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.