The use of transcranial magnetic stimulation (TMS) to study human motor control requires the integration of data acquisition systems to control TMS delivery and simultaneously record human behavior. The present manuscript provides a detailed methodology for integrating data acquisition systems for the purpose of investigating human movement via TMS.
Transcranial magnetic stimulation techniques allow for an in-depth investigation into the neural mechanisms that underpin human behavior. To date, the use of TMS to study human movement, has been limited by the challenges related to precisely timing the delivery of TMS to features of the unfolding movement and, also, by accurately characterizing kinematics and kinetics. To overcome these technical challenges, TMS delivery and acquisition systems should be integrated with an online motion tracking system. The present manuscript details technical innovations that integrate multiple acquisition systems to facilitate and advance the use of TMS to study human movement. Using commercially available software and hardware systems, a step-by-step approach to both the hardware assembly and the software scripts necessary to perform TMS studies triggered by specific features of a movement is provided. The approach is focused on the study of upper limb, planar, multi-joint reaching movements. However, the same integrative system is amenable to a multitude of sophisticated studies of human motor control.
Transcranial magnetic stimulation (TMS) is a non-invasive method to stimulate the human cortex.3,5 There are several TMS protocols that are used to understand cortical function such as single and multiple pulses, dual-site stimulation to probe functional connectivity, and repetitive pulses to promote neural plasticity.4,6-8 TMS protocols may also be combined to advance the present understanding of human cortical processes and guide neural rehabilitation strategies. In addition to stimulating the cortex, TMS can also be used to understand sub-cortical function by stimulation of the corticospinal tract or cerebellum.
One of the largest technical challenges currently facing TMS research is the ability to study the role of cortical areas during goal-directed voluntary movement in humans. Several considerations contribute to this technical challenge. First, TMS delivery should be combined with real-time human motion capture. In this way, TMS pulses can be delivered or triggered by features within a movement sequence providing a time-locked approach to study complex movement. Second, integrating TMS delivery and motion capture permits a detailed characterization of complex movement as it unfolds, which will advance the understanding of brain-behavior relationships that underpin motor control. At present, there are no commercially available systems that inclusively integrate TMS and motion capture methodologies. For neuroscientists in the field of motor control, this void typically translates into time consuming, technical challenges to integrate multiple software and hardware data acquisition and delivery systems. This technical limitation has also resulted in sparse research dedicated to the study of dynamic multi-joint movements involving the upper limb. For TMS to advance the field of human motor control, it is imperative that cortical function be probed during complex human movement.
To effectively integrate TMS and motion capture methodologies, the acquisition system must allow real-time simultaneous TMS and motion capture. Second, the system must be suitable to study movement kinematics (i.e., description of the movement), movement kinetics (i.e., forces that cause movement), and muscle activity. Third, the system must be able to synchronize TMS pulses to these movement features, and be triggered by criteria based on complex movement features. Such a system will provide an essential linkage between cortical function and kinematic and kinetics of movement.
This manuscript details a unique approach to integrate methods of TMS and motion capture. This approach allows detailed analysis of the mechanics of complex multi-joint movements, and permits automated control of TMS pulses triggered by specific features of the movement (i.e., kinematics, kinetics, or muscle activity). Further, this data acquisition system allows for TMS and motion capture to be integrated with experimental paradigms that require visuo-motor or sensorimotor tasks. This manuscript details an innovative approach to integrate commonly used motion capture hardware and software systems for the purpose of combining TMS and human movement acquisition and analysis. Data are presented using a sample study of human cortical functioning during planar multi-joint movement. The software scripts required to perform the experiment are available for download.
NOTE: The following protocol can be applied to a variety of experiments. Below are details regarding an experiment that involves a visually guided arm reaching task to one of six spatial targets displayed on a computer monitor. TMS, to probe corticospinal excitability, is triggered by either analog signals emerging from the movement (i.e., EMG or electrogoniometer input) or digital signals generated from the sweep-based data acquisition software. This study was approved by the McMaster Research Ethics Board in accordance with the Declaration of Helsinki. A sample dataset is provided.
1. Hardware/software Requirements
NOTE: Figure 1 displays a schematic of the hardware requirements to integrate TMS and motion capture systems in the context of a computer-controlled visuo-motor experiment.
Figure 1. Hardware Set-up. To allow for the electromagnetic motion capture data to be sent to the sweep-based data acquisition software and the visual stimulus delivery software, first assemble the 4 electromagnetic sensors with the system's console. Connect the system's console to the PC 1 with a 9 pin serial cable. Connect the PC 1 to the PC 2 with a 9 pin serial cable. To allow for TMS delivery, connect the PC 1 with the A/D box with a USB cable and connect a BNC cable between the A/D box and the TMS unit. To allow for EMG recording, connect the EMG leads to the EMG amp and connect the EMG amp to A/D box via BNC cables. Connect the electrogoniometer (Elgon) to the A/D box via a BNC cable to record joint angle changes online. To allow the visual stimulus delivery software to trigger the trial start, connect the PC 2 to the A/D box trigger input via an LPT port to BNC cable. Please click here to view a larger version of this figure.
Figure 2. Arm bracing device. Depicted is a participant placed in the arm bracing device, while a TMS coil is placed on the participant's scalp. Please click here to view a larger version of this figure.
2. Experiment Set-up
Figure 3 displays the results from a single trial. In this trial, Figure 3A shows the initial position of the participant and, after an auditory 'go' cue, the participant moved as quickly and accurately as possible to the target (i.e., the final position). The sweep-based data acquisition software triggered a TMS pulse based on EMG onset in the biceps brachii muscle. This permitted the measure of corticospinal output directed to upper arm muscles to be evaluated at a specific time during performance of the task. Figure 3B displays the MEP obtained from each muscle from the single TMS pulse during EMG onset of this trial. The peak-to-peak amplitude of the MEP from the TMS pulse is measured from each muscle. Alternatively, the area of the MEP could be measured. Changes in the MEP size across different movement phases or movement types indicate changes in corticospinal excitability across different tasks or points in time. Using the integrated approach of motion capture and TMS systems, researchers may quantify neural activity originating from motor cortex at a precise moment during the behavior, such as during EMG onset in this example. Further, there can be a delay inserted between the EMG onset and the triggering of TMS delivery (see the sequencer file on lines 88 to 98 and 109 to 117 to insert this delay) to investigate the time course of corticospinal output that may vary throughout the movement. Importantly, other analog signals such as movement kinematics (joint angle, joint velocity, joint acceleration) and sensory cues (visual, auditory) may also be used to trigger TMS delivery.
Figures 3C and 3D display the angular displacement of the shoulder and elbow joint. Figures 3E and 3F display the angular velocity at the shoulder and elbow joint. Figure 3G and 3H display the kinetics at the shoulder and elbow joints. The blue, green, and red lines are the net, muscle, and bone on bone contact moment, respectively. The corticospinal excitability, directed to each muscle, could then be compared to the different movement outcome measures (i.e., movement kinematics and kinetics). These measures are computed based on the motion capture data and the anthropometric data. Additionally, this set-up allows for time-locked TMS pulses to occur at any point prior to or during the movement and can assess changes in corticospinal excitability in relation to certain features of the movement.
Figure 4 shows example MEPs recorded from the biceps brachii (A) and pectoralis major (C), while reaching to a target that requires both biceps brachii and pectoralis major (E) to be active. Figure 4 also shows MEP recorded from triceps brachii (B) and posterior deltoid (D), while reaching to a target that requires both triceps brachii and posterior deltoid (F) to be active.
Figure 3. Representative results from a single trial. (A) the schematic on the left shows the starting position at the trial beginning, while the schematic on the right shows the end position during the trial. (B) the peak to peak amplitude of the MEP evoked in the upper arm muscles. BB = Biceps Brachii, TB = Triceps Brachii, PM = Pectoralis Major, PD = Posterior Deltoid. (C & D) the angular displacement time profile of the shoulder and elbow joints throughout the trial. The values indicate the rotation (in radians) displaced by a counterclockwise rotation in relation to the right horizontal. An increasing angle indicates flexion, while a decreasing angle indicates extension. (E & F) the angular velocity time profile of the shoulder and elbow joints throughout the trial. (G & H) the moment time profile of the shoulder and elbow joint throughout the trial. The blue line depicts the Net Moment, the red line depicts the Bone on Bone Contact Moment, and the green line depicts the predicted Muscle Moment. Positive values indicate that the moment is acting in the flexor direction (i.e., counterclockwise rotation), while negative values indicate that the moment is acting in the extensor direction (i.e., clockwise rotation). See Supplementary Information 4 for calculation of muscle, bone on bone contact and net moment. Please click here to view a larger version of this figure.
Figure 4. Representative MEPs recorded from upper arm muscles. MEP recorded from biceps brachii (A) and pectoralis major (C), while reaching to a target that requires activity of both biceps brachii and pectoralis major (E). MEP recorded from triceps brachii (B) and posterior deltoid (D), while reaching to a target that requires activity of both triceps brachii and posterior deltoid (F). Please click here to view a larger version of this figure.
The present manuscript details an innovative method to integrate TMS and motion capture systems in the context of a visuo-motor task. To make rapid and meaningful advances in the study of human motor control, it is essential that methodologies allow for precise communication across multiple hardware and software systems. The paradigm presented could be used to study a variety of research interests including the cortical contribution to motor learning, the neurophysiology of motor control, and multi-joint movement control in special populations. For example, we have used this paradigm to study how corticospinal excitability changes with varying magnitudes and directions of interaction torques acting about the shoulder and elbow joints. Interaction torques are present in all “real-world” multi-joint movements and their influence on the movement plan becomes more apparent with faster actions. Individuals with cerebellar ataxia and children with Developmental Coordination Disorder, however, have issues “accounting” for these interaction torques when performing voluntary goal-directed arm movements. The paradigm presented could be used to understand cortical functioning in these populations.
There are several advantages to using the techniques presented. Relative to the high costs associated with a TMS system, the addition of the electromagnetic motion capture system and the necessary software and cables is minimal. Although the demonstrated usage of this system is focused on arm control, it can be adapted to the finger, leg, and even multiple limbs to study increasingly complex movements. Further, future systems can build on this current set-up and be adapted to study three-dimensional movements of the arm or other limbs, and examine human motor control in a variety of contexts. This system also allows the experimenter to have access to corticospinal measurements during multiple phases within a movement sequence, such as the pre-movement, movement onset and later phases of movements, increasing the precision with which these phases can be systematically studied. Although analog signals of EMG activity and joint angle were used in the present demonstration, the software is adaptable to allow any analog signal to trigger TMS pulses throughout the trial. Last, this experimental set-up could be extended for use with paired, multiple, dual-site, and repetitive TMS protocols to allow for advances in the study of brain and behavior.
There are a few critical components of this set-up that require experimenter attention. First, it is very important to locate the motor hotspot to ensure TMS-evoked responses are as focused as possible to evoke activity in the muscles to be studied. To aid with this process, place the coil on the scalp in a location that requires the lowest stimulator intensity to elicit a consistent 1 mV response in the muscle of interest. Second, errors in motion capture sensor placement may create errors in joint kinematics and kinetics calculations. Therefore, the motion capture sensors should be placed over bony landmarks that represent joint centers of rotation. Third, obtain the best representation of each individual’s anthropometric data. Errors of determining anthropometric data and subsequent calculations could cause drastic errors in approximating the joint kinetics. Fourth, the head should remain as motionless as possible during the arm movements and the postural muscle should remain relaxed. Head motion can be prevented by a head rest that is available with most TMS equipment. Activity of postural muscles may alter corticospinal excitability and surface EMG can be used to record this activity. Further, to isolate signals obtained from a single muscle EMG electrode placement should avoid pick-up from neighboring muscles. The reader is referred to the electrode placement guidelines provided.2 Last, we have demonstrated an experiment that is focused on discrete arm movements. There are however, movements that may be too complex to provide accurate information of the phenomenon studied using TMS.
The integrative system includes several large and heavy components, thereby limiting its portability and the opportunity to perform testing in non-laboratory environments. There are limitations of the motion capture system used in this study, such as limited sensors, interference with conductive materials and small pick-up range for the sensors, but the software provided in this study is flexible and can be used with other motion capture systems. A future user of this set-up might have reservations about having an electromagnetic motion capture system concurring with TMS. In a small percentage of the trials, the TMS pulse causes a transient artifact in the motion capture data, but this artifact can easily be smoothed offline (a sweep-based data acquisition script file can be obtained from the corresponding author upon request if necessary). Overall, these limitations are minimal, and do not affect the versatility of this set-up to study a number of uncovered areas in brain and behavior.
The authors have nothing to disclose.
The authors thank funding from the Natural Sciences and Engineering Research Council to AJN.
Polhemus FASTRAK | Polhemus Inc. | 6 degrees of freedom electromagnetic motion tracking device with 4 sensors | |
Presentation | Neurobehavioural Systems Inc. | A fully programmable software for experiments involving data acquisition and stimulus delivery | |
Cutom built Exoskeleton | 80/20 Inc. – The industrial erector set | Varies | Various parts used to build the exoskeleton |
Brainsight | Rogue Research Inc. | Neuronavigation software to track coil position throughout the experiment |