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Assessing Corticospinal Excitability During Goal-Directed Reaching Behavior

Published: December 2, 2022 doi: 10.3791/64238


Reaching is a fundamental skill that allows humans to interact with the environment. Several studies have aimed to characterize reaching behavior using a variety of methodologies. This paper offers an open-source application of transcranial magnetic stimulation to assess the state of corticospinal excitability in humans during reaching task performance.


Reaching is a widely studied behavior in motor physiology and neuroscience research. While reaching has been examined using a variety of behavioral manipulations, there remain significant gaps in the understanding of the neural processes involved in reach planning, execution, and control. The novel approach described here combines a two-dimensional reaching task with transcranial magnetic stimulation (TMS) and concurrent electromyography (EMG) recording from multiple muscles. This method allows for the noninvasive detection of corticospinal activity at precise time points during the unfolding of reaching movements. The example task code includes a delayed response reaching task with two possible targets displayed ± 45° off the midline. Single pulse TMS is delivered on the majority of task trials, either at the onset of the preparatory cue (baseline) or 100 ms prior to the imperative cue (delay). This sample design is suitable for investigating changes in corticospinal excitability during reach preparation. The sample code also includes a visuomotor perturbation (i.e., cursor rotation of ± 20°) to investigate the effects of adaptation on corticospinal excitability during reach preparation. The task parameters and TMS delivery can be adjusted to address specific hypotheses about the state of the motor system during reaching behavior. In the initial implementation, motor evoked potentials (MEPs) were successfully elicited on 83% of TMS trials, and reach trajectories were recorded on all trials.


Goal-directed reaching is a fundamental motor behavior that allows humans to interact with and manipulate the external environment. The study of reaching in the fields of motor physiology, psychology, and neuroscience has produced rich and extensive literature that includes a variety of methodologies. Early studies of reaching used direct neural recordings in non-human primates to investigate neural activity at the level of single neurons1,2. More recent studies have investigated reaching using behavioral paradigms that employ sensorimotor adaptation to explore the nature of motor learning and control3,4,5. Such behavioral tasks combined with functional magnetic resonance imaging and electroencephalography can measure whole brain activity during reaching in humans6,7. Other studies have applied online TMS to investigate various features of reach preparation and execution8,9,10,11,12,13,14. However, there remains a need for an open-source and flexible approach that combines the behavioral assessment of reaching with TMS. While the utility of combining TMS with behavioral protocols is very well established15, here, we specifically examine the application of TMS within the context of reaching using an open-source approach. This is novel in that other groups who have published using this combination of methods have not made their tools readily available, prohibiting direct replication. This open-source approach facilitates replication, data sharing, and the possibility of multi-site studies. Additionally, should others wish to pursue novel research questions with similar tools, the open-source code can act as a launch pad for innovation, as it is readily adaptable.

TMS offers a noninvasive means of probing the motor system at precisely controlled time points16. When applied over the primary motor cortex (M1), TMS can elicit a measurable deflection in the electromyogram of a targeted muscle. The amplitude of this voltage wave, known as the motor evoked potential (MEP), provides an index of the momentary excitability state of the corticospinal (CS) pathway-a resultant analog of all excitatory and inhibitory influences on the CS pathway17. In addition to providing a reliable within-subject measurement of intrinsic CS excitability, TMS can be combined with other behavioral or kinematic metrics to investigate the relationships between CS activity and behavior in a temporally precise manner. Many studies have utilized a combination of TMS and electromyography (EMG) to address a variety of questions about the motor system, particularly since this combination of methods makes it possible to investigate MEPs under a vast array of behavioral conditions15. One area where this has proven particularly useful is in the study of action preparation, most often through the study of single-joint movements18. However, there are comparatively fewer TMS studies of naturalistic multi-joint movements such as reaching.

The current goal was to design a delayed-response reaching task that includes behavioral kinematics, online single-pulse TMS administration, and simultaneous EMG recording from multiple muscles. The task includes a two-dimensional point-to-point reaching paradigm with online visual feedback using a horizontally oriented monitor such that visual feedback matches reach trajectories (i.e., a 1:1 relationship during veridical feedback and no transformation between visual feedback and motion). The current design also includes a set of trials with a visuomotor perturbation. In the provided example, this is a 20° rotational shift in the cursor feedback. Previous studies have used a similar reaching paradigm to address questions about the mechanisms and computations associated with sensorimotor adaptation19,20,21,22,23,24,25. Furthermore, this approach makes it possible to assess motor system excitability dynamics at precise time points during online motor learning.

Because reaching has proven to be a fruitful behavior for investigating learning/adaptation, assessing CS excitability in the context of this behavior has enormous potential to shed light on the neural substrates involved in these behaviors. These may include local inhibitory influences, changes in tuning properties, the timing of neural events, etc., as have been established in non-human primate research. However, these features have been more difficult to quantify in humans and clinical populations. Neural dynamics can also be investigated in the absence of overt movement in humans using the combined TMS and EMG approach (i.e., during the preparation of movement or at rest).

The tools presented are open-source, and the code is easily adaptable. This novel paradigm will produce important insights into the mechanisms involved in the preparation, execution, termination, and adaptation of reaching movements. Moreover, this combination of methods has the potential to uncover relationships between electrophysiology and reaching behavior in humans.

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All methods detailed here were performed in compliance with IRB protocol and approval (University of Oregon IRB protocol number 10182017.017). Informed consent was obtained from all subjects.

1. Reaching apparatus

  1. Place a large graphics tablet flat on a desktop.
  2. Use an adjustable 80-20 aluminum frame to position the task monitor 6-8 in above the tablet in parallel, with the screen facing upward (for a blueprint, check here: https://github.com/greenhouselab/Reach_TMS and Supplementary Figure 1).
    NOTE: This setup allows for participants to reach across the tablet and acquire targets presented on the task monitor while occluding vision of their reaching arm.
  3. Use the setup described in Kim et al.3 as a reference.

2. Machine interfaces

  1. Connect the tablet to the computer via a USB port. Connect the task monitor to the computer via the HDMI port. Connect the rear TMS port to the computer via a DB-9 cable.
  2. Connect the EMG system to the computer via a PCI-6220 card DAQ. Connect the photodiode to the EMG system via a BNC cable.

3. Photodiode sensor

  1. Attach a photodiode sensor to the BNC cable. Secure the photodiode sensor with tape to the top-right corner of the task monitor, with the sensor facing the screen, ≤ 1 cm away.
    NOTE: This will record the timing of stimuli presented on the task monitor as analog data in an independent input channel.

4. Software

  1. Download VETA Toolbox26 (https://github.com/greenhouselab/Veta) for MATLAB 2018 to interface with the hardware for data collection.
  2. Download the reaching task code (https://github.com/greenhouselab/Reach_TMS) developed for the control of experimental parameters and interfacing with the tablet.

5. Participant screening and informed consent

  1. Screen the subject for contraindications to TMS. Exclusion criteria include a personal or family history of seizure, headache, brain trauma, fainting, chronic stress or anxiety, problems with sleep, and any neuroactive medication. Additional exclusion criteria include any metal implants in the brain or skull and any recreational drug or alcohol use in the 24 h prior to testing. Inclusion criteria included right-handedness and age between 18 and 35 years.
  2. Provide a written explanation of the procedure and associated risks, clarifying any further questions the participant may have.
  3. Obtain informed consent from participants.

6. Subject setup

  1. Position the subject in a comfortable chair facing the tablet. Ensure that the knees are flexed to 90° with the legs under the desk.
  2. Prepare the skin and place EMG electrodes.
    1. Use fine-grain sandpaper to gently abrade the skin at the site of the right first dorsal interossei (FDI), extensor carpi radialis, and anterior deltoid muscles, as well as the C4 prominence at the base of the neck, to detect electrical artifacts produced by the TMS pulse.
      NOTE: Muscle recording sites can be adapted based on user needs.
    2. Swab each abraded area with an alcohol prep pad once per electrode site to clean.
    3. Place one EMG electrode on each site. Ensure that the electrodes run perpendicular to the muscle fibers. Place the ground electrode on the bony prominence of the right elbow.
    4. Secure each electrode with medical tape.
  3. Check the quality of the EMG recording. Use the VETA toolbox to visualize all EMG traces and ensure they are free of artifacts. If EMG traces are noisy, ensure the ground is properly placed and that all electrodes make proper contact with the skin.

7. Transcranial magnetic stimulation

  1. Turn on the TMS machine.
  2. Find the TMS hot spot of the right FDI muscle via stimulation of the left M1.
    1. Place the coil ~5 cm lateral and 2 cm anterior to the vertex of the head, oriented ~45° off the midline.
    2. Administer TMS pulses once every 4 s while repositioning the coil in increments of approximately 5 mm in the anterior-posterior and medial-lateral plane.
    3. Beginning at 30% maximum stimulator output, gradually increase the TMS intensity by 2% increments until MEPs are observed.
    4. Once the optimum location is identified, at which MEPs can be reliably elicited on the majority (~75%) of pulses at the lowest possible stimulator intensity, determine the resting motor threshold (RMT) by finding the intensity level that produces MEPs with a peak-to-peak amplitude of >50 µV on five out of 10 pulses.
    5. Mark the position by gently placing thin strips of reflective tape on the participant's head along the perimeter of the coil. Maintain coil positioning either by manually holding the coil or using a stand to support it.

8. Reaching task setup

  1. Put a Velcro glove on the right hand of the participant to facilitate a relaxed power grip posture.
  2. Attach the stylus to the glove and advise the subject to keep the hand relaxed between reaching movements.
  3. Communicate the task instructions, which are as follows: Guide the cursor to the home position on the bottom of the screen. You will see a cue at one of two target locations. When the target fills in with color, reach through the target as fast and as accurately as possible. Then return to the home position. Indicate locations of home positions, cues, and targets (Figure 1A).
  4. Coach the participant to slice through targets with the stylus as quickly and as accurately as possible. Turn off the lights in the task room to obscure the participant's vision of arm movements and improve the visibility of the task monitor.

9. Task design

  1. Control visual stimulus presentation with Psychtoolbox 3.0 in Matlab 2018 (Supplementary Coding File 1).
  2. Use the following parameters to match the current data: 20 practice trials; 270 test trials; TMS on 4/5 of test trials; TMS either coincides with the preparatory cue onset (baseline TMS) or 100 ms before the imperative cue (delay TMS) with equal frequency; 1/10 of total trials are catch trials, in which the imperative cue does not appear; the home position is a circle with a 2 cm radius positioned in the bottom center of the workspace; two circular targets with 1 cm radius are positioned 15 cm from the home position at +45° and -45° away from the midline.
  3. Set the event order and durations as follows: preparatory cue at 900 ms and imperative cue at 900 ms.

10. TMS administration

  1. The VETA toolbox concurrently administers TMS and records EMG https://github.com/greenhouselab/Veta.
  2. Control the timing of the TMS pulses with the VETA toolbox to coincide with the chosen behavioral events (i.e., the onset of the preparatory cue or 100 ms preceding target onset).
  3. Deliver TMS with sufficient frequency to ensure a sufficient number of MEPs for analysis.
    NOTE: As written, the task code will deliver a TMS pulse on 4/5 of total trials either at the onset of the preparatory cue-to elicit baseline MEPs-or 100 ms before the imperative cue-to elicit delayed MEPs. Parameters can be adjusted in the code according to user needs. Trials without TMS can be used to evaluate behavioral performance in the absence of TMS. This is useful for determining any possible influence of TMS on performance.

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Representative Results

Successful execution of the described methods includes the recording of tablet data, EMG traces, and reliable elicitation of MEPs. An experiment was completed that included 270 test trials with TMS delivered on 4/5 of the trials (216 trials).

Data were collected from 16 participants (eight females; eight males) aged 25 ± 10 years, all of whom self-reported as right-handed. We assessed the effectiveness of the visual perturbation on behavioral performance by deriving a learning function for one representative participant. These data are presented in Figure 1B and show that the participant's hand target error adjusted to the perturbation and washout conditions as expected. We also evaluated the standard deviation of the target error during baseline reaches, which approximated 4.5° (Figure 1B). This is consistent with previous studies24.

One TMS pulse was delivered on each trial. Half of the pulses were delivered at baseline, and half were delivered during a preparatory delay period (Figure 2A). An average of 91 ± 23 baseline and 88 ± 20 delay MEPs were successfully recorded per participant, corresponding to 84% and 81% success rates, respectively. MEPs were counted only when amplitudes exceeded .05 mV. Reach trajectories were successfully captured from the graphics tablet on all trials, excluding catch trials (i.e., trials in which the "go" cue was not presented and trials in which participants either failed to initiate a reach or initiated before the imperative cue).

The average delay period (duration between the preparatory and imperative cue) was 915 ± 0.5 ms (mean ± standard deviation). Baseline TMS was administered 26 ± 8 ms after preparatory cue onset, and delay TMS was 126 ± 3 ms prior to imperative cue onset (Figure 2B). The consistent deviation from the intended TMS administration time in each case indicates that further optimization is needed to account for undesired latencies introduced by hardware or software components. However, the relatively low proportional variance in these latencies suggests these are mostly fixed delays that can be controlled with additional pilot testing and indicate that the timing of events is generally reliable across trials.

Figure 1
Figure 1: Behavioral data collected from the tablet. (A) The workspace includes the home position (dark blue), two targets (cyan), and a representative set of reach trajectories from the pre-exposure block of a single participant. (B) Target error was calculated as the distance in degrees from the endpoint of the reach to the center of the target. Trial bins are the mean of two consecutive trials per bin, and the data are separated by experimental blocks: Pre-exposure (unshaded), exposure (red), washout in the absence of feedback (green), and washout with veridical feedback (unshaded). Please click here to view a larger version of this figure.

Figure 2
Figure 2: Example MEP traces. (A) Representative MEPs and corresponding photodiode trace for both experimental epochs (baseline and delay). (B) Negative baseline MEP latency (-26 ± 8 ms) indicates that the TMS stimulus arrived after the preparatory cue, while positive delay MEP latency (126 ± 3 ms) indicates that the TMS stimulus arrived before the desired time point (100 ms prior to the imperative cue). Latencies are averaged across all participants (n = 16). Please click here to view a larger version of this figure.

Supplementary Figure 1: Blueprint of the reaching apparatus. Please click here to download this File.

Supplementary Coding file 1: Code for visual stimulation. The delayed_reach_TMS.m file contains a task code for controlling the tablet, stimulus presentation, transcranial magnetic stimulation, and electromyography recording. Please click here to download this File.

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The methods outlined above offer a novel approach to studying motor preparation in the context of reaching behaviors. Although reaching represents a popular model task in the study of motor control and learning, there is a need for precisely evaluating the CS dynamics associated with reaching behavior. TMS offers a noninvasive, temporally precise method of capturing CS activity at discrete time points during reaching. The approach described here combines two independent subfields-TMS and reaching-into a single paradigm that involves the simultaneous recording of kinematic and electrophysiological metrics.

While the methods described have the potential to reveal important insights into action control in the context of reaching, there are certain limitations and considerations. Most importantly, the reliability of MEP measurements depends on the stability of the EMG activity prior to TMS administration, as well as the number of MEPs captured27. It is critical that EMG data quality be assessed prior to data collection. For sufficient statistical power, a minimum of 20 MEP measurements per task condition are recommended. Additionally, while changes in the MEP represent a quantitative change in CS excitability, the nature of TMS and the resultant MEP produce a rather crude, summary metric of CS activity, and their causal relationship to behavior should be interpreted with caution15. Furthermore, the graphics tablet requires that the stylus maintain contact with the tablet surface, which limits the range of reaching tasks and grip apertures that can be employed.

Despite the limitations of this specific protocol, the combination of TMS and EMG for indexing motor system excitability during behavioral tasks other than reaching is well established15. Advantages of this combined approach include the ability to measure CS excitability dynamics even in the absence of overt movement, as well as in task-irrelevant muscles. This approach also offers high temporal precision, on the order of milliseconds. Additionally, the protocol described here can be adapted to work with any number of EMG devices that interface directly with a stimulus presentation computer via the listed input/output devices.

Given these advantages, the protocol can help bridge the gap between human and animal studies. A large body of research in non-human primates has examined the electrophysiological mechanisms associated with reaching and motor learning in the context of reaching. Further investigations in humans using the combined TMS and EMG approach can help to bridge non-human electrophysiology and human behavioral findings. Previous studies of MEPs in the context of reaching have shown a facilitatory effect of TMS during reach and grasp preparation when the parietal cortex, premotor cortex, and parietal-M1 circuits were stimulated prior to movement8,14. However, the amplitudes of resting evoked potentials measured with electroencephalography 75 to 150 ms after TMS over the M1 were reduced following force field adapatation13. The nuanced relationship between reaching preparation, adaptation, and changes in CS warrants further investigation. Moreover, by using the same set of tools and methods across laboratories, replication will be more achievable, and this will aid the interpretability of study results.

While the focus here is on TMS of the M1, several studies have utilized dual-site TMS to investigate interactions between cortical areas (e.g., parietal cortex and M1). While many of these studies were conducted during rest, a handful of studies examined cortico-cortical interactions in the context of reach planning and execution. Dual-site TMS showed stimulation of the posterior parietal cortex facilitated M1 excitability at 50 ms and ~100 ms following an auditory "go" cue to initiate a prepared contralateral reach28. Additional methods have been established for dual coil TMS approaches that include applications during goal-directed reach-to-grasp behaviors29. The protocol described here complements these previous studies and methods and can be readily adapted for dual-site TMS studies as well.

The example task code consists of a delayed response task with two potential targets. Parameters such as trial numbers, target and cursor characteristics, visual feedback, and TMS delivery can be adjusted to address a variety of research questions. Data recorded with this approach include behavioral kinematics from the tablet and electrophysiological measurements from the EMG. Preliminary results revealed that TMS and behavioral measurements exhibit reliable timing and sufficient sensitivity to variability in reach directions across trials. These methods and results stand as proof of concept for future investigations into the neural mechanisms of reaching via TMS using this approach.

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All authors declare that there are no conflicts of interest


This research was made possible in part by the generous funding of the Knight Campus Undergraduate Scholars program and the Phil and Penny Knight Foundation


Name Company Catalog Number Comments
2-Port Native PCI Express  StarTech.com RS232 Card with 16950 UART  Must be compatible with desktop computer
Adjustable 80-20 aluminum frame any
Alcohol prep pads any EMG preparation
Bagnoli Bipolar Electrodes Delsys DE 2.1
Bagnoli Reference Electrode Delsys USX2000 2” (5cm) Round
Bagnoli-8 EMG System Delsys
Chair any
Computer monitor for EMG/TMS n/a
Desk any
Desktop Computer Dell xps 8930 RAM: 16 GB, Storage: 1TB, Graphics: 1060 6GB 
EMG electrodes Delsys Sensor Adhesive Interface
Fine grain sandpaper any EMG preparation
Graphics tablet Wacom Intuos-4 XL
Handle of paint roller any to be used as stylus handle, hollowed out center must be large enough for stylus to sit securely inside 
Medical tape any To secure EMG electrodes
PCI-6220 card DAQ National Instruments To interface EMG system
Photodiode Sensor Vishay BPW21R To record timing of task events into EMG trace.
Rear TMS port Magstim Included with TMS machine
Right-handed polyethylene glove any Cut out thumb and index finger of glove to expose FDI muscle
Sensory Adhesive Interface, 2-slot Delsys SC-F01
Stylus Wacom Intuos-4 grip pen
Tablet-to-Computer USB cable  any Included in Tablet purchase
Task Monitor Asus VG248
TMS coil Magstim D70 Remote Coil 7cm diameter, figure-of-eight coil
TMS machine Magstim 200-2
TMS-to-Computer DB9 cable any Connects to PCIe Serial Card
Velcro any To be placed on glove and stylus handle



  1. Georgopoulos, A. P., Kalaska, J. F., Caminiti, R., Massey, J. T. On the relations between the direction of two-dimensional arm movements and cell discharge in primate motor cortex. The Journal of Neuroscience. 2 (11), 1527-1537 (1982).
  2. Georgopoulous, A. P., Schwartz, A. B., Kettner, R. E. Neuronal population coding of movement direction. Science. 233 (4771), 1416-1419 (1986).
  3. Kim, H. E., Morehead, J. R., Parvin, D. E., Moazzezi, R., Ivry, R. B. Invariant errors reveal limitations in motor correction rather than constraints on error sensitivity. Communications Biology. 1, 19 (2018).
  4. Huberdeau, D. M., Krakauer, J. W., Haith, A. M. Dual-process decomposition in human sensorimotor adaptation. Current Opinion in Neurobiology. 33, 71-77 (2015).
  5. Shadmehr, R., Smith, M. A., Krakauer, J. W. Error correction, sensory prediction, and adaptation in motor control. Annual Review of Neuroscience. 33 (1), 89-108 (2010).
  6. Filimon, F., Nelson, J. D., Hagler, D. J., Sereno, M. I. Human cortical representations for reaching: Mirror neurons for execution, observation, and imagery. NeuroImage. 37 (4), 1315-1328 (2007).
  7. Hammon, P. S., Makeig, S., Poizner, H., Todorov, E., de Sa, V. R. Predicting reaching targets from human EEG. IEEE Signal Processing Magazine. 25 (1), 69-77 (2008).
  8. Busan, P., et al. Effect of transcranial magnetic stimulation (TMS) on parietal and premotor cortex during planning of reaching movements. PloS One. 4 (2), 4621 (2009).
  9. Busan, P., et al. Transcranial magnetic stimulation and preparation of visually-guided reaching movements. Frontiers in Neuroengineering. 5, 18 (2012).
  10. Lega, C., et al. The topography of visually guided grasping in the premotor cortex: a dense-transcranial magnetic stimulation (TMS) mapping study. The Journal of Neuroscience. 40 (35), 6790-6800 (2020).
  11. Marigold, D. S., Lajoie, K., Heed, T. No effect of triple-pulse TMS medial to intraparietal sulcus on online correction for target perturbations during goal-directed hand and foot reaches. PloS One. 14 (10), 0223986 (2019).
  12. Savoie, F. -A., Dallaire-Jean, L., Thenault, F., Whittingstall, K., Bernier, P. -M. Single-pulse TMS over the parietal cortex does not impair sensorimotor perturbation-induced changes in motor commands. eNeuro. 7 (2), (2020).
  13. Taga, M., et al. Motor adaptation and internal model formation in a robot-mediated forcefield. Psychoradiology. 1 (2), 73-87 (2021).
  14. Vesia, M., et al. Human dorsomedial parieto-motor circuit specifies grasp during the planning of goal-directed hand actions. Cortex. 92, 175-186 (2017).
  15. Bestmann, S., Krakauer, J. W. The uses and interpretations of the motor-evoked potential for understanding behaviour. Experimental Brain Research. 233 (3), 679-689 (2015).
  16. Rossini, P. M., et al. Non-invasive electrical and magnetic stimulation of the brain, spinal cord, roots and peripheral nerves: Basic principles and procedures for routine clinical and research application. An updated report from an I.F.C.N. Committee. Clinical Neurophysiology. 126 (6), 1071-1110 (2015).
  17. Rothwell, J. C., Thompson, P. D., Boyd, S., Marsden, C. D. Stimulation of the human motor cortex through the scalp. Experimental Physiology. 76 (2), 159-200 (1991).
  18. Bestmann, S., Duque, J. Transcranial magnetic stimulation: decomposing the processes underlying action preparation. The Neuroscientist. 22 (4), 392-405 (2016).
  19. Kim, H. E., Avraham, G., Ivry, R. B. The psychology of reaching: action selection, movement implementation, and sensorimotor learning. Annual Review of Psychology. 72 (1), 61-95 (2021).
  20. McDougle, S. D., Bond, K. M., Taylor, J. A. Explicit and implicit processes constitute the fast and slow processes of sensorimotor learning. The Journal of Neuroscience. 35 (26), 9568-9579 (2015).
  21. McDougle, S. D., Bond, K. M., Taylor, J. A. Implications of plan-based generalization in sensorimotor adaptation. Journal of Neurophysiology. 118 (1), 383-393 (2017).
  22. McDougle, S. D., Ivry, R. B., Taylor, J. A. Taking aim at the cognitive side of learning in sensorimotor adaptation tasks. Trends in Cognitive Sciences. 20 (7), 535-544 (2016).
  23. Morehead, J. R., Qasim, S. E., Crossley, M. J., Ivry, R. Savings upon re-aiming in visuomotor adaptation. The Journal of Neuroscience. 35 (42), 14386-14396 (2015).
  24. Taylor, J. A., Krakauer, J. W., Ivry, R. B. Explicit and implicit contributions to learning in a sensorimotor adaptation task. The Journal of Neuroscience. 34 (8), 3023-3032 (2014).
  25. Tsay, J. S., Kim, H. E., Parvin, D. E., Stover, A. R., Ivry, R. B. Individual differences in proprioception predict the extent of implicit sensorimotor adaptation. Journal of Neurophysiology. 125 (4), 1307-1321 (2021).
  26. Jackson, N., Greenhouse, I. VETA: An open-source matlab-based toolbox for the collection and analysis of electromyography combined with transcranial magnetic stimulation. Frontiers in Neuroscience. 13, 975 (2019).
  27. Goldsworthy, M. R., Hordacre, B., Ridding, M. C. Minimum number of trials required for within- and between-session reliability of TMS measures of corticospinal excitability. Neuroscience. 320, 205-209 (2016).
  28. Koch, G., et al. Functional interplay between posterior parietal and ipsilateral motor cortex revealed by twin-coil transcranial magnetic stimulation during reach planning toward contralateral space. The Journal of Neuroscience. 28 (23), 5944-5953 (2008).
  29. Goldenkoff, E. R., Mashni, A., Michon, K. J., Lavis, H., Vesia, M. Measuring and manipulating functionally specific neural pathways in the human motor system with transcranial magnetic stimulation. Journal of Visualized Experiments. (156), e60706 (2020).
Assessing Corticospinal Excitability During Goal-Directed Reaching Behavior
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Cite this Article

Gomez, I. N., Orsinger, S. R., Kim, H. E., Greenhouse, I. Assessing Corticospinal Excitability During Goal-Directed Reaching Behavior. J. Vis. Exp. (190), e64238, doi:10.3791/64238 (2022).More

Gomez, I. N., Orsinger, S. R., Kim, H. E., Greenhouse, I. Assessing Corticospinal Excitability During Goal-Directed Reaching Behavior. J. Vis. Exp. (190), e64238, doi:10.3791/64238 (2022).

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