Brain regions exhibiting significant task-related activation during
Functional magnetic resonance imaging (FMRI) is a powerful tool for exploring the neural basis of sensory control of movement and it has profitably been used to study simple actions like finger-tapping (Rao et al., 1996), compensation for visual feedback distortions during movement (Imamizu et al., 2000) and regulation of isometric force (Peck et al., 2001; Kawato et al., 2003; Vaillancourt et al., 2003). However, use of FMRI to image neural mechanisms contributing to more complex motor tasks like stabilization of limb position in the face of environmental perturbation has been limited because such tasks require mechanically-active, MRI-compatible devices capable to perturb the limb in a controlled manner. Here, we present a case study on the development and validation of a MRI-compatible device specifically for use in studying sensorimotor control in the presence of environmental perturbations. We then demonstrate the device in a functional imaging study of limb posture regulation wherein healthy human subjects stabilized their wrists against predictable and unpredictable loads. In particular, we sought to understand how the brain uses somatosensory information to adjust behavioral strategies for load compensation.
We anticipated that multiple strategies might be used to stabilize the limb against perturbation, and that distinct neural mechanisms would implement these strategies. We therefore hypothesized that at least two distinct neural mechanisms contribute to the stabilization of wrist position in the presence of persistent environmental perturbations. A first mechanism likely mediates the online control of endpoint position (not joint torque) via feedback control. Feedback control attempts to adjust motor commands to cancel deviations of the limb from its desired state. Thus we expect regions contributing to feedback control of wrist position to show increased FMRI BOLD response in the presence of positioning errors, independent of joint torque magnitude. Moreover, temporal variations in the BOLD response in these regions should correlate with variations in wrist position on a moment-by-moment basis. A second mechanism likely monitors performance over a longer timeframe than an online feedback controller. This mechanism would initiate discrete, conditional, corrective actions when feedback control fails to eliminate persistent errors. Regions contributing to this higher-order evaluation of performance should demonstrate BOLD responses that reflect changes in positioning errors with a longer temporal integration period than that used for moment-by-moment feedback control.
Portions of this chapter have appeared previously in separate publications (Suminski et al., 2007b; Suminski et al., 2007a).
2. Development of a MRI-compatible manipulandum
Devices intended for use in MRI environments must: 1) satisfy noise tolerance and size limitations imposed by MR scanner technologies, and 2) be constructed of MR compatible materials (Schenck, 1996; Chinzei et al.). The large static magnetic field generated by the scanner precludes use of ferromagnetic materials that would otherwise be attracted into the scanner bore, compromising safety of both the research subject and the scanner. It is also essential that all actuators and sensors in the device be impervious to rapidly switching imaging gradients and that device operation does not disturb the homogeneity of the magnetic field, which would lead to image distortion. Finally, the device must have a small form factor, capable to fit inside the scanner bore without causing subject discomfort. To date, a small number of such robotic devices have been developed for use in neuroscience research or rehabilitation applications (Suminski et al., 2002; Ganesh et al.,2004; Diedrichsen et al., 2005; Flueckiger et al., 2005; Khanicheh et al., 2005; Gassert et al., 2006; Khanicheh et al., 2008; Yu et al., 2008). In this section we describe the design, performance characteristics and validation of a novel, MR compatible, 1 degree-of-freedom (DOF), pneumatically actuated robot for motor control research (Fig. 1). Our aim was to create a device able to both monitor and perturb wrist motion during FMRI, and to demonstrate device safety and efficacy as a tool for the study of complex motor behavior in human subjects.
2.1. Device design and performance characteristics
We used a pneumatic actuator to exert computer-controlled torques about the wrist because this type of actuator can be MR compatible, small, light-weight and back-driveable. A single bellows-type actuator was enclosed within a curved volume. This actuator transmits force from compressed air to a wall rigidly attached to the device’s handle. Pressurizing the actuator generates an extensor torque about the subject’s right wrist whereas pulling a vacuum within the actuator imparts a flexor torque. Air pressure within the actuator is sensed by a pressure transducer (26PC Series, Honeywell International, Inc., Morristown, NJ), amplified (x25) and low-pass filtered (20Hz cutoff frequency) in hardware. Joint angle is sensed with an optical encoder (HEDM-6540, Agilent Technologies, Inc., Palo Alto, CA) located on the underside of the device. The device monitors wrist position to within 0.05° and wrist torque to within 0.001 Nm. Only the manipulandum, pressure transducer, optical encoder and necessary instrumentation are located within the MR environment; all other control components are located in the scanner control room. The manipulandum can accommodate both right- and left-handed individuals, providing 80° range of motion at the wrist (40° flexion to 40° extension). Pressure within the actuator is regulated by a Proportion Air QB3 electro-pneumatic pressure valve (Proportion-Air Inc., McCordsville, IN). Wrist angle and actuator pressure data are acquired at a rate of 1000 samples per second. Commands to the pressure valve are generated at the same rate.
We implemented a proportional-integral-derivative (PID) controller to improve the ability of the device to quickly and accurately regulate pressure within the pneumatic actuator.
where PC(t) is the commanded actuator pressure in units of psi, e(t) is the difference between the measured and desired actuator pressure in units of psi, KP is the proportional gain, KI is the integral gain, and KD is the derivative gain. Ziegler-Nichols tuning rules were used to tune the controller (Ziegler and Nichols, 1942), yielding the following gain values: KP = 3.3, KI = 14, and KD = 0.055. Under PID control, step response rise times (10% to 90% steady state) were 77ms and 90ms for 1 and 2 PSI step changes, respectively, with modest overshoot (19%; Fig. 2A). Due to time required to transmit air from the MRI control room to the actuator, we observed an average time delay (command onset to 10% steady state) of 62ms for both step responses.
We identified the bandwidth of the closed-loop system by assessing the system’s ability to track changes in commanded actuator pressure having a 1 PSI peak-to-peak ‘chirp’ profile sweeping from 0 to 5Hz. The device is able to track commanded pressure changes within ±15% of the peak pressure up to 1.6 Hz (Fig. 2B). These frequency response characteristics allow the robot to apply torsional spring-like loads about the wrist. By way of demonstration, we commanded the robot to apply two separate position-dependent loads (0.075 and 0.15 Nm/°) and estimated the realized spring constants obtained during 25° flexion/extension movements performed by a representative human subject. The estimated stiffness of the two spring-like loads, obtained by fitting a linear model to the joint torque vs. joint angle data, were 0.059 and 0.134 Nm/° respectively, yielding an average error of 16%. In both cases, the torque-angle relationships were linear, with regression r2 values exceeding 96% and 99% for the 0.075 and 0.15 Nm/° loads, respectively (Fig. 2C).
2.2. MR-compatibility testing
We validated the simultaneous acquisition of manipulandum data and FMRI images by scanning a spherical head phantom both with and without the robotic device in a 3.0T GE Excite HD MR scanner (General Electric Healthcare, Milwaukee, WI). The phantom (Fig. 3A, P; GE Model #: 2359877) was supported within a split transmit/receive quadrature head coil (Fig. 3A, HC; GE Model #: 2376114). A gradient echo, echo planar imaging (EPI) pulse sequence (29 contiguous sagittal slices; echo time (TE) = 25ms, inter-scan period (TR) = 2s, flip angle = 77°, field of view (FOV) = 24cm, 64 x 64 matrix; 3.75x3.75x6 mm spatial resolution) was used to verify: 1) that operation of the robot during scanning does not induce significant artifacts in functional images, and 2) that the robot can measure pressure and joint angle without contamination from gradient switching noise during EPI.
Validation testing used a blocked experimental design (Duration = 270s). During “Motion” states, the computer cycled the device’s handle through a sinusoidal trajectory (0.25 cycles per second) whereas the device remained motionless during “No Motion” states (50% duty cycle; period = 60s). Raw, complex k-space data (I and Q channels) were collected to allow analysis of both magnitude and phase MR images. We quantified the effects of simultaneous operation of the robot and scanner during “Motion” and “No Motion” states by imaging the phantom with the robot at 6 distances from the center of the imaging volume (0.25m, 0.50m, 0.75m, 1.0m, and 1.25m) as well as in a control condition with the robot operating in the scanner control room (∞). The phantom was sampled using 7 equal-volume regions of interest (ROI) distributed within its spherical boundary to test whether the robot induced amplitude and phase anisotropies during scanning.
We computed three measures to assess compatibility of the robot and MR scanner; two evaluated MR signal quality during robot operation in the “Motion” and “No Motion” states and the third evaluated the effects of echo planar imaging on measurements of handle position and actuator pressure. First, we calculated the signal to noise ratio (SNR) within each ROI for each robot-distance condition using the magnitude images:
μROI is the time series average within a given ROI, and σnoise is an estimate of noise obtained by calculating the standard deviation of the time series in the magnitude images in an identically sized ROI located outside the phantom (Fig. 3B; ROI "N"). The factor 0.665 corrected for changes in the statistical distribution of σnoise caused by calculating the magnitude image from the original complex MR data (Haacke et al., 1999). Values of SNR varied across the seven ROIs but were insensitive to the robot’s distance within each ROI (eg. Fig 3C). Three-way ANOVA found main effects of both ROI location (p < 0.0005) and robot distance (p < 0.0005) but no effect of robot motion state (p = 0.929). Comparison of SNR at each of the five distances relative to the control condition (∞) revealed a small but significant 0.64 dB and 0.90 dB
Second, we used the phase images to quantify changes in magnetic field homogeneity induced by robot operation within the scanner suite. We computed the average change in the static magnetic field for each ROI (ΔBROI):
where, ROI is the average change in each ROI’s phase time series with respect to baseline (i.e. ∞), γ is the gyromagnetic ratio, and TE is the echo time of the EPI sequence (Haacke et al., 1999). We then normalized ΔBROI to the magnitude of the static magnetic field (B0 = 3.0T) yielding a unit-less quantity corresponding to the homogeneity of the magnetic field (Δ
Finally, we quantified the effects of echo planar imaging on robot operation by calculating SNR for the actuator pressure (SNRP) and wrist angle (SNRA) signals while the computer drove the robot’s handle through a sinusoidal trajectory:
Root mean squared (RMS) values of actuator pressure and joint angle were calculated during “Motion” and “No Motion” states to approximate signal and noise respectively. Neither joint angle nor pressure SNR varied systematically as a function of robot distance from the center of the scanner bore. Individual two-sample t-tests found no difference in SNRA or SNRP (p > 0.705) when compared to baseline measures obtained when the robot was operated outside the scanning environment (∞).
In conclusion, we have implemented a pneumatically actuated manipulandum that applies controlled joint torques and measures joint angle at the wrist. This device neither degrades fMRI signal quality nor is itself compromised by rapidly switching imaging gradients.
2.3. Comparison of the device with other MR-compatible devices
In the last decade, several robotic devices have been developed for use during MR scanning (see Gassert et al., 2008 for review). The 1 DOF manipulandum we developed compares favorably to other MR-compatible devices used in neuroscience research or rehabilitation applications. For example, the device developed by Hidler et al. (Hidler et al., 2006) only monitors the torque/force generated by a subject whereas our device can simulate dynamic environments by generating controlled torques about the wrist. Other MR-compatible devices can apply dynamic loads using Lorentz coils (Riener et al., 2005), ultrasonic motors (Flueckiger et al., 2005), electrorheological fluids (Khanicheh et al., 2008) or hydrostatic pistons (Gassert et al., 2006). However, in contrast to the device presented by Riener and colleagues, our device does not degrade image quality when operated less than 1m from the scanner’s isocenter. Because the devices presented by Flueckinger (Flueckiger et al., 2005) and Gassert (Gassert et al., 2006) are not backdriveable, they can not simulate realistic dynamic loads during movements requiring rapid changes in direction whereas our device clearly can do so. Recently, Yu, et al. compared 1 DOF MR-compatible devices containing hydraulic and pneumatic actuators (Yu et al., 2008) and concluded that pneumatic actuation was favorable for fast, force controlled applications, whereas hydraulic actuation was best for applications requiring accurate position control (Yu et al., 2008). And while the 2 DOF device presented by Diedrichsen (Diedrichsen et al., 2005) offers the ability to perturb planar reaching movements of the arm, perturbation of proximal limb segments can lead to considerable head motion that must be accounted for during analysis of fMRI data (Diedrichsen and Shadmehr, 2005). In contrast, our current design limits motion to the wrist, which may lead to fewer head motion artifacts in the fMRI dataset.
3. Limb position regulation with proprioceptive feedback
For a first demonstration of the robot's utility, we examined how the brain uses proprioceptive feedback of limb position for the moment-by-moment (i.e. on-line) feedback stabilization of wrist position during a compensatory tracking task. Limb stabilization is important because meaningful interaction with the world frequently requires stabilization of hand-held items (eg. holding a young child’s hand) and/or movement of such objects between stabilized positions or "postures" (eg. turning the steering wheel of a car). At any moment, task performance may be compromised due to environmental perturbations (eg. the car hitting a pothole) requiring corrective action to maintain desired performance.
The central nervous system can employ three strategies acting on different timescales to compensate for errors arising during stabilization. First, it may utilize feedback regulation of joint position via segmental (Sinkjaer and Hayashi, 1989) and transcortical (“long loop”) reflex pathways (Evarts and Tanji, 1976; Strick, 1978; Evarts and Fromm, 1981) to minimize errors. Alternatively, subjects may increase the impedance of the limb via voluntary co-activation (Milner and Cloutier, 1993) of muscles whose actions oppose one another (i.e. antagonist muscles). Finally, subjects may generate discrete, feedforward, corrective movements when feedback mechanisms and impedance regulation fail to adequately reduce perceived errors (Haaland and Harrington, 1989; Fagg et al., 1998). These strategies are not mutually exclusive, but are complementary in two ways. First, they reduce performance errors over different timescales ranging from the short-latency mechanical responses of antagonist coactivation and reflex action to the reduction of persistent errors by discrete adjustment of behavioral goals. Second, they provide the flexibility in motor output needed to respond to task-dependent tradeoffs between accuracy and muscular effort, thus providing the behavioral basis for optimality in human motor control (Todorov and Jordan, 2002; Scott, 2004). Much is yet unknown about the neuromuscular control of limb position (i.e. posture stabilization), including which aspects of environmental perturbation are compensated on a moment-by-moment basis, and what performance criteria might cause a subject to generate a discrete corrective movement during stabilization.
Ten healthy right-handed volunteers (5 female) participated in two experimental sessions performed on separate days. They performed identical wrist stabilization tasks both days. In one session, subjects stabilized the wrist against robotic perturbation while inside a mock MR scanner. This allowed recording of electromyographic (EMG) data from task-relevant muscles. In the other session, subjects performed the experiment while undergoing FMRI scanning in a 1.5T General Electric Signa scanner equipped with a 3-axis local gradient head coil and an elliptical endcapped quadrature radiofrequency head coil. In both sessions, subjects rested supine in the scanner with their head constrained by foam padding to reduce head motion. With arms at their sides, subjects grasped the robot handle with their right hand. The handle and wrist axes of rotation were aligned and the frame of the device was secured to both the subject’s forearm and the inner wall of the scanner bore for support.
3.1. Experimental procedure
Both sessions consisted of a blocked experimental design that alternated between periods of rest and active wrist stabilization. Each stabilization trial was conducted in 5 phases (Fig. 4). During the 30 s prior to stabilization (phase 1), the subject was instructed to relax while the robot held the hand in a comfortable resting posture of 40° flexion (
During each 3-minute imaging run,
3.2. Behavioural correlates of stabilization
We found that that wrist torque perturbations elicited changes in wrist angle (
In summary, the behavioral and electromyographic data revealed that subjects compensated for environmental perturbations using a combination of three readily identifiable strategies. Subjects modulated limb impedance via co-activation of agonist/antagonist muscle pairs spanning the wrist, elbow and shoulder. Correlation analysis found that subjects also invoke both spinal and supraspinal reflexes to compensate for the perturbations. Finally, subjects generated discrete corrective movements to reduce performance errors that likely accumulated due to the lack of visual feedback during stabilization (cf. Wann and Ibrahim, 1992). We next sought to characterize the neural mechanisms contributing to each of these strategies during experiments conducted within the MR scanner.
3.3. Neural correlates of stabilization
Functional images were generated and analyzed within the Analysis of Functional NeuroImages (AFNI) software package (Cox, 1996). The first three images in each run were discarded to allow for equilibration of the magnetic field. Individual run time series were then concatenated and aligned in three-dimensional space using an interactive, linear, least squares method. Voxel-wise multiple linear regression was used to determine the amount of FMRI signal contrast between the two task conditions (
Changes in BOLD signal intensity (relative to rest) correlated with periods of
Using these features, we performed a k-means cluster analysis to identify ROIs demonstrating common patterns of sensitivity to trial type and/or passive movement. Two distinct groups were identified: ROIs that were sensitive to both trial type and passive movement (Cluster I; red regions in Fig. 6A), and those demonstrating increased BOLD activation over the duration of the trial with little sensitivity to passive movement (Cluster II; blue regions in Fig. 6A). Group separation and membership was visualized by plotting the coordinates of each ROI on the axes defined by the features of
|Precentral Gyrus (BA 4,6)||L||-30.1||-19.4||51.3||14166||5.884|
|Medial Frontal Gyrus (BA 6)||B||-0.5||-11.6||48||7856||5.0334|
|Cingulate Gyrus (BA 24,31)|
|Inferior/Superior Parietal Lobule (BA 5,7,40)||L||-30.3||-38.3||56.6||2630||4.7958|
|Precentral Gyrus (BA 6)||R||49.6||-2.4||39||957||4.5288|
|Precentral Gyrus (BA 6)||R||22.3||-15.4||61.2||866||4.2839|
|Middle Temporal Gyrus (BA 39)||R||52.6||-68.9||15.9||596||4.3556|
|Insula (BA 13)||L||-47.5||0.2||8.3||8102||4.8657|
|Superior Temporal Gyrus (BA 22,41)|
|Inferior Parietal Lobule (BA 40)||L||-50.9||-31.5||24.9||7477||4.9995|
|Insula (BA 13)||R||42.7||5.6||4.8||5633||5.0229|
|Superior Temporal Gyrus (BA 22)|
|Inferior Parietal Lobule (BA 40)||R||53.4||-40.6||36.4||5125||4.3849|
|Middle/Inferior Frontal Gyrus (BA 10,46)||R||39.2||38.5||3.5||2668||4.5259|
|Middle/Inferior Frontal Gyrus (BA 10,45,46)||L||-42||29.8||12.5||1334||4.3385|
|Inferior Parietal Lobule (BA 40)||L||-49.1||-53.3||39.9||1079||4.1923|
|Medial Frontal Gyrus (BA 6,8)||B||-3||25.4||41.5||1033||4.156|
|Cingulate Gyrus (BA 32)|
|Superior Frontal Gyrus (BA 8)|
|Superior Frontal Gyrus (BA 6)||R||22.1||13.3||51.6||687||4.5912|
|Middle Frontal Gyrus (BA 10)||L||-38.2||49.2||3.4||582||4.2741|
|Cerebellar Cortex Lobule IV, V, VI||B||5.9||-49.4||-14||9571||5.2885|
|Basal Ganglia and Thalamus||L||-19.7||-17||10.1||6684||5.025|
|L = Left; R = Right; B = Bilateral; BA = Broadman’s Area|
We performed a second BOLD regression analysis to identify brain regions involved in generating the discrete corrective movements observed behaviorally. We limited our investigation to the
We performed a final set of multiple linear regression analyses to identify brain regions explicitly involved in the moment-by-moment and long-term evaluation and correction for kinematic and/or kinetic performance errors. We wished to know whether the activations identified in the preceding analyses were related to compensation for kinematic errors, generation of wrist torques, or both. Here, we performed four separate regressions using input reference functions corresponding to the magnitude of RMS wrist angle errors and RMS torque both on a trial-by-trial and TR-by-TR basis (
Regions demonstrating increased sensitivity to errors that change on a moment-by-moment timescale (
|Inferior Parietal Lobule (BA 40)||L||51.3||29.9||28.4||3599||4.7941|
|Postcentral Gyrus (BA 2)|
|Insula (BA 13)|
|Medial Frontal Gyrus (BA 6)||B||1.5||4.9||50||3056||4.7047|
|Superior Frontal Gyurs (BA 6)||L|
|Cingulate Gyrus (BA 24)||R|
|Insula (BA 13)||R||-41||-8.3||2.9||2245||4.7498|
|Superior Temporal Gyrus (BA 22)|
|Insula (BA 13)||L||47||-0.5||11.8||1986||4.9146|
|Superior Temporal Gyrus (BA 22)|
|Inferior Parietal Lobule (BA 40)||R||-50.4||42||38.7||1706||4.2648|
|Inferior Parietal Lobule (BA 5,40)||L||35.6||38.4||53.3||761||4.2931|
|Inferior Frontal Gyrus (BA 46)||R||-35.7||-34.5||14.9||756||5.0422|
|Middle Frontal Gyurs (BA 10)|
|Precentral Gyrus (BA 6)||R||-46.3||2.3||43||247||4.7634|
|Precentral Gyrus (BA 44)||R||-53.7||-5.6||9.8||180||4.0774|
|Cingulate Gyrus (BA 32)||R||-8.6||-17.8||43.7||152||5.0572|
|Precentral Gyrus (BA 4)||L||28.5||21.6||63.2||115||3.9996|
|Basal Ganglia and Thalamus||L||18.8||14.4||7.1||1036||4.4593|
|Globus Pallidus (Medial/Lateral)|
|Ventral Posterior Lateral|
|Cerebellar Cortex Lobule IV, V, VI||R||-15.4||48.5||-16.1||871||4.2192|
|Inferior Parietal Lobule (BA 40)||L||41.5||54.3||36.6||556||4.302|
|Medial Frontal Gyrus (BA 8)||L||6.9||-20.3||43.6||45||3.9183|
|L = Left; R = Right; B = Bilateral; BA = Broadman’s Area|
4. Summary and future directions
This chapter has described the development and validation of a novel, 1 DOF pneumatically actuated manipulandum. We demonstrated that the device was: 1) capable of generating computer controlled perturbations of movement and 2) compatible with the MR scanner such that performance of neither the device nor image quality was affected by robot operation. We then demonstrated device utility in a study of wrist posture stabilization against environmental perturbation. We provided behavioral evidence that subjects invoke three complementary compensatory responses when stabilizing the wrist in the absence of ongoing visual feedback of task performance. These compensatory responses include feedback regulation via spinal and long-loop reflexes, impedance modulation via antagonist muscle co-activation and feedforward, discrete corrective movements. Analysis of functional neuroimages obtained from the same subjects performing the same tasks revealed two distinct networks that were differentially excited by the task. The first (Cluster I) included a cerebello-thalamo-cortical network previously implicated in the online computation and feedback correction of errors (Marsden et al., 1972; Lee and Tatton, 1975; Evarts and Vaughn, 1978; Marsden et al., 1978; Strick, 1978; Horne and Butler, 1995). BOLD signal changes within these regions were correlated with moment-by-moment fluctuations in state estimation errors (Fig. 7, red regions;
These results highlight the importance of both postural and movement trajectory control mechanisms in peripheral limb stabilization and suggest a possible neural basis for the distinct postural and trajectory (movement) control mechanisms recently isolated during point-to-point arm movements and movement sequences (Ghez et al., 2007; Scheidt and Ghez, 2007). Additional studies are needed to better understand how the brain combines the different control processes to minimize performance errors and how the brain uses information from multiple sensory feedback modalities to optimize limb stabilization and movement control. Such work will be greatly facilitated by the use of mechanically-active tools that apply physical perturbations to the limb while subjects undergo concurrent functional MR imaging.
This work was supported by NSF BES0238442, NIH NCRR M01-RR00058, 2 P01 MH51358, R01 HD053727 and by the Alvin W. and Marion Birnschein Foundation. We thank Kristine Mosier and Steve Rao for constructive feedback on an earlier version of this manuscript, Jeff Goldstein for crafting the manipulandum, Dr. N. Bansal for helpful suggestions regarding the statistical handling of the data, as well as Vanai Roopchansingh, Sally Durgerian, Matthew Verber and the MCW MRI technicians for assistance during FMRI data collection and analysis.
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