Alexander Technique exposure and physiological measures of movement planning and execution
Audre Wirtanen1,2 and Harlan Fichtenholtz3
1 Balance Arts Center, (New York City, New York, United States); 2 Bennington College,
Department of Science, Math, and Computing (Bennington, VT, United States); 3 Keane
State College, Department of Psychology (Keane, New Hampshire, United States)
Author contributions: Both authors developed the current study’s task, procedures, and data analysis strategies; A.
Wirtanen analyzed data and wrote the current publication.
*To whom correspondence should be addressed: [email protected]
# of pages # of figures # of tables # of multimedia
28 6 0 0
Word count: Abstract (267), Body (4,286)
Acknowledgements: The authors would like to thank Rebecca Brooks for aiding with recruitment of AT participants,
Rebecca Warzer contributing to the pilot work this study was based upon.
Conflict of interest: The authors have no conflicts of interest that relate to the present work.
Funding sources: Thank you to the Geary Fund at Bennington College and the Bennington College Department of
Science, Math, and Computing for supporting this project.
1 Alexander Technique exposure and physiological measures of movement planning and execution
Audre Wirtanen1,2 and Harlan Fichtenholtz3
1 Balance Arts Center, (New York City, New York, United States); 2 Bennington College,
Department of Science, Math, and Computing (Bennington, VT, United States); 3 Keane
State College, Department of Psychology (Keane, New Hampshire, United States)
1. Abstract
Alexander Technique (AT) is a somatic practice aimed at increasing awareness of one's movement habits in order to undo inefficient neuromuscular patterns that people learn in response to environmental shifts or stressors over time, so that they can move more dynamically and with ease. Practicing AT has therapeutic effects for both clinical and healthy populations but there is no basic research regarding the underlying physiological shifts that AT practice may correlate with. The goal of the current study was to determine the changes in electrophysiological measures of motor preparation and output in people with significant AT exposure.
We examined electrophysiological correlates of motor planning including Event Related
Potentials (ERPs), activity of the deltoid muscle using EMG, and trajectory of a reaching movement with a twin axis goniometer during a version of a Go/NoGo task. AmSat certified practitioners and students with at least one consecutive year of AT experience
(n=10) were compared to participants (n=8) with no AT experience. Individual averages were combined into grand averages within each group, and groups were compared with
2 independent t-tests. Response accuracy of inhibition was calculated with a Chi-square analysis.
AT practice for at least one year significantly correlated with changes in movement planning ERPs in the cerebrum, a different pattern of muscular activation, and changes in the pathway of movement compared to control. In addition, the AT group was more accurate at successfully inhibiting a motor response during STOP trials. This data provides the first electrophysiological evidence of Alexander Technique practice influencing brain activity, and identifies correlations between AT and differences in movement preparation and execution during a unilateral motor response.
2. Author Summary
This study examined the way people with significant exposure to the Alexander Technique plan and make movements. Alexander Technique is a somatic practice that aims to re- educate poor movement patterns over time. Movement preparation in the brain, muscle activity, and the pathway of the movement were recorded during a task that prompted the participant to press a space bar or stay at rest and wait for the next trial.
3. Keywords
Alexander Technique, motor planning, motor execution
4. Introduction
Somatics is a field that studies the body from a first-person perspective. Internal awareness supports the way someone senses and interacts with their environment, and is just as
3 important as external awareness. There are many forms of Somatic practices, but all include a focus on proprioception and sensation of the body in various contexts. Most use mental imagery and movement explorations in addition to internal and external observation. The differences between varying methods is how the body is consciously thought about. The Alexander Technique is a form of somatic practice that teaches correct anatomical awareness and use of the body to undo learned tension or laxity so that people can feel freer mentally and physically in their daily lives.
Alexander Technique
The Alexander Technique (AT) is a somatic practice aimed at identifying and undoing inefficient neuromuscular patterns that develop over time to improve the functioning of the body as a whole. These inefficient neuromuscular patterns are referred to as habits because movement is most often coordinated in that manner or use. To re-train poor habitual motor functions into a more supportive neuromuscular coordination, AT teaches an increased form of awareness in action and inhibition, and emphasizes the relationship between how a person thinks about moving, and how that thinking informs subsequent movement patterns. This process utilizes conscious thought and hands on guidance to alter imbalances in muscular tone, unconscious automatic postural mechanisms, and operation of the whole body when preparing for and executing motor acts [1,2] F.M. Alexander initially described the technique in the 1920s [for example see 1], and while the basic principles have remained central to the practice, multiple lineages of the work exist. AT is well known among performing artists who recognize that the way they use their body affects the quality of their work, but is relatively unknown to other working populations. Most recently, the practice has been used to relieve chronic pain, increase independence, and regain ease and
4 movement ability in various clinical populations. More research needs to be done to understand how and why AT provides these beneficial and therapeutic effects to more diverse populations of people. It is important to review the findings of previous studies implementing AT to put the current study into perspective.
Exposure to the Alexander Technique has been shown to enhance respiratory function by increasing respiratory capacity [3] improve Functional Reach (FR) as an indicator of balance [4] and affect coordination during sit-to-stand movements in normal populations
[for examples see 5,6]. Biomedical interventions using AT have focused on exploring its potential to mitigate signs and symptoms of joint pain, neurodegenerative disease, and include measures of well being in addition to measures of pain and disability. Following AT lessons, subjects with chronic low back pain (LBP) reported less pain and increased dynamic modulation of postural tone [7,8,9]. Other studies correlated AT practice with decreased neck pain, increased self-efficacy [10], and reduced musculature co-contraction and knee pain during early stance and gait in subjects with knee osteoarthritis [11]. Studies specifically utilizing AT as a co-intervention for Parkinson's Disease found improvements in axial rigidity, postural sway and tone, measures of disability, back depression, and attitudes to self [12,13]. In addition, people with Ehlers-Danlos Syndrome (EDS), a genetic connective tissue disease with limited treatment available, are more frequently seeking out
AT lessons to decrease the probability of joint dislocations, and improve their quality of life
[14,15]. These studies and materials suggest that AT influences the way movements are planned and executed, and may contribute to efficiency in automatic and volitional motor output in both normal and clinical populations.
Thus, recent publications have begun to elucidate AT's potential biomedical value in relation to movement and pain [1-15]. However, there are no current studies exploring
5 more fundamental physiological changes associated with AT exposure. A more detailed picture of how the technique may be influencing motor planning and execution is necessary to support its value as intervention in physically compromised populations. This study investigates physiological changes in the motor cortex during motor planning, and features of movement execution during a version of a Go/NoGo task compared to age matched control.
Electrophysiological Correlates of Motor Control
Event Related Potentials (ERPs) preceding movement were recorded to monitor the accumulation of neural activity before subjects made a reaching movement to press a button. ERPs are summed voltage recordings of cortical activity using non-invasive
Electroencephalography (EEG) electrodes positioned on the scalp. These time-based waveforms are the result of several overlapping components that represent mental activation processes, and cannot be deconstructed into separate cellular mechanisms, but indicate changes in brain activity.
The Readiness Potential (RP) is an averaged movement ERP consisting of a phasic negative shift in voltage preceding voluntary unilateral limb movement [16]. RPs are greater in the contralateral motor cortex, and have been confirmed across species by direct subdural recordings from the primary motor cortices and supplementary motor areas in monkeys, humans, and invertebrates [17,18,19,20]. Because greater activation occurs on the contralateral side of the brain corresponding to the limb or part of limb moved, the
Lateralized Readiness Potential (LRP) is a more specific measure of motor preparation. The
LRP measures additional activity from the contralateral motor cortex by subtracting the activity from the ipsilateral cortex [21,22]. It is a relatively small ERP that most often increases in negative amplitude as it gets closer to the motor response threshold and
6 becomes increasingly positive after that threshold is crossed. The negative to positive waveform shift is time-locked to EMG activation, and begins up to 300 ms before movement [23] regardless of response accuracy or latency [24], and pre-chosen or spontaneous selection of body side moved [22] LRPs can also be elicited from sensory cued responses [25].
The LRP has been studied independently to make inferences about the nature of cognitive evaluation processes, and how manipulations affect our information processing system
[26]. It has also been a measure of age related inhibitory control in decision-making (Bryce et al., 2011), response competition and inhibition [28] features of movement disorders and brain injury [for examples see 29,30] and stimulus priming [31]. It is unclear whether force rate or force production of motor output affects LRP production [32,33], but it is generally concluded that LRPs indicate abstract motor planning rather than physical features of movement output.
Most studies cited above infer that the LRP represents the pre-conscious neural choice to move, before we are even aware of movement ourselves, which has ignited debate regarding free will [for example see 34]. Other, more compelling, empirical evidence suggests that the LRP reflects an accumulation of ongoing spontaneous neural activity that grows toward a threshold as the probability of making the movement increases [19,25,35].
In this case, the decision process includes a continuous flow of information to the motor system, and the RP is an example of an increase in partial information toward an action that includes random neural fluctuations [36,37]. The neural buildup so frequently seen in the LRP illustrates the integration of accumulated inputs rather than outputs toward a commitment to move [for example see 38] and once the threshold is crossed an action is initiated.
7 Based on these findings, we chose to look for a movement ERP similar to the Lateralized
Readiness Potential (LRP) as a measure of pre-motor neural accumulation bound to a sensorimotor commitment threshold.
Go/NoGo Task
A version of the Go/ NoGo task was used to prompt subjects to either commit to or inhibit a movement response. The Go/NoGo task has been used to study response inhibition and initiation by looking at differences in various Event Related Potentials (ERPs), recorded from the cortex, involved in decision making and withdrawing [for example see 39]. The task uses stimuli that appear on a screen in front of subjects, in random order, to prompt the correct response. The Go/NoGo task was chosen to investigate response preparation and execution of a motor act when subjects were randomly prompted to alternate between making the response and inhibiting it, and if chosen physiological measures differed in the
Alexander Technique group compared to control.
7. Material and methods
Participants
People with at least 1 year of twice weekly AT lessons were compared to age matched control. A total of 18 subjects were enrolled whose ages ranged from 21-68 years with normal or corrected to normal vision. The AT testing group (n=10, F=8) included
American Society for the Alexander Technique (AmSat) certified practitioners as well as students studying the technique privately, or privately and in a group setting. The control
8 group (n=8, F=6) reported no exposure to Alexander Technique, Feldekrais, or dance experience. Contemporary modern dance training frequently includes exercises derived from somatic techniques, particularly in the New England area. AT practitioners were recruited from the greater New England area, while other qualifying participants were recruited from Bennington College. All participants provided written informed consent, and the project was approved by the Bennington College Committee on Research with Human
Participants.
Procedure
Participants sat at a desk with a computer monitor, keyboard and designated rest position for their dominant hand and completed a version of a Go/ NoGo task. The words REACH and STOP were selected as visual stimuli corresponding to the initiation or inhibition of a unilateral motor response with the subject's dominant arm. When a REACH signal appeared, participants extended their arm from the rest position and pressed the space bar on a keyboard placed directly in front of them and immediately return their hand to the identified rest position. When a STOP signal appeared, participants inhibited their motor response and did not press the space bar, leaving their hand at rest. The testing block consisted of 40 REACH trials and 20 STOP trials. All trials were randomized and each stimulus appeared on the monitor for 500 ms. Fixation crosses appeared in between stimuli and lasted between 1000 and 2000 ms. Fixation cross duration was randomized to avoid anticipatory responses. The keyboard was placed approximately 70-80% the subject's fully extended reach to control for recruitment of compensatory trunk movements. Both the rest area and space bar were in line with the shoulder joint so that reaching and retracting
9 movements took place on a perpendicular axis in relation to the coronal plane of the torso to control for substantial angle deviations during the reaching movement.
Twenty randomized practice trials proceeded the testing block. One of the researchers supervised the practice block and gave feedback regarding response time and accuracy.
Practice trials required an 80% correct response rate in order to move on to testing. No feedback was provided during the testing block.
Physiological measurement
We chose to record movement related potentials that preceded a unilateral motor response similar to the LRP. A single channel electrode was placed above contralateral motor cortex correspondent to the moving arm. Ipsilateral recordings were not subtracted, and therefore our ERP was not fully lateralized. Deltoid muscular activation and the pathway of reaching movements were also recorded as quantitative measures of motor execution.
Cortical activity was recorded through a single channel electroencephalograph (EEG) electrode placed on the skull at either C3 or C4 to measure motor planning activity corresponding to the contralateral arm. The reference was placed on the mastoid bone and the ground electrode on the earlobe, both contralateral to the active recording site. EEG was recorded at 1000Hz and digitally filtered from .5-100Hz and notch filtered around
60Hz. The event related potential (ERP) of interest was the Lateralized Readiness Potential.
However, with a 1 channel EEG device, an un-lateralized ERP was identified within the known LRP timeframe and compared. Movement planning ERP averages consisted of waveforms taking place .5 seconds before the REACH signal appeared and .25 seconds after
10 the initiation of movement. Initiation of movement was identified by an increase of deltoid
EMG above resting baseline.
Muscle output was quantified by measuring electromyogram (EMG) activity of the deltoid muscle. The reference was placed below the deltoid and the ground on the contralateral collarbone. EMG was recorded at 1000 Hz and band pass filtered from 30-500Hz. EMG was rectified, integrated, and averaged 1 second before and after the button press and normalized with the maximum value of each subject.
Pathways of the reaching movements were measured using a twin-axis goniometer (Biopac) secured along the ulna and humorous, with the bendable coil at the elbow joint. The goniometer measured the reaching and retracting movements on a x and y-axis and was secured to the arm using paper tape. Goniometer data was taken in its raw form and averaged as degrees of change at the elbow joint.
8. Results
The data analysis presented in this paper reports physiological measurements of Go trials, and an analysis of inaccurate response frequency during inhibitory trials. All motor response data was averaged for each participant and combined into within group grand averages. Group performance was compared using independent samples t-tests. Inaccurate inhibitory response frequency was compared using a Chi square analysis.
Task performance
11 No differences were seen between the groups in performing this task. Both reaction time
(AT group; M= 961.736, SD= 204.367; CT group; M= 879.06, SD= 212.417; t (18)=
0.847, p= .410, MD= 82.673) and accuracy during REACH trials (AT group; M= 39.90,
SD= 0.32; CT group; M= 40, SD= 0; t (16)= 0.889, p = .387, MD= -0.10) were similar for both groups. During inhibitory trials, task performance did differ significantly between groups. There were more successful inhibitory responses in the AT group (X2= 15.127, p=
.0001). The AT group had 170 successful inhibitions and 29 unsuccessful inhibitions, while the CT group had 115 successful inhibitions and 53 unsuccessful inhibitions. Unsuccessful inhibitions were defined with either EMG activation above baseline or EMG above baseline and degrees of movement change measured by the goniometer.
Electrophysiological correlates of motor preparation
Event-related potentials were calculated for each participant time-locked to the onset of muscle activity, as measured by EMG activity, for successful Go-Trials. Grand averages were computed for each group and the groups were compared using independent samples t-tests.
During the time period 500-196 milliseconds before the movement started, the AT group had a significantly lower ERP amplitude (M= -1.0478, SD= 0.550) compared to CT (M=
0.942, SD= 0.477), t (296)= 33.349, p< .0001, CI= -2.107 to -1.872, MD= -1.989. At
196ms the AT group had a significantly larger increase toward the motor response threshold (M= 0.445, SD= 0.339) while the CT group’s amplitude fluctuated just below the threshold (M= 1.438, SD= 0.161), t (20)= 8.763, p< .0001, CI = -1.23 to -0.757,
MD= -0.994. Following this increase, the AT group’s ERP amplitude was significantly higher until EMG activation (M= 1.383, SD= 0.545) compared to CT (M= 0.924, SD=
0.742), t (172)= 4.649, p<.0001, CI= 0.264 to 0.654, MD= 0.459. In sum, the AT group’s 12 ERP began lower, steeply increased, and remained higher than CT’s as it approached the motor response threshold (see Fig. 1).
Muscle activity
The total duration EMG was above baseline (seconds) during the movement necessary to press the space bar was significantly less in the AT group (M=1.483, SD= 0.287) compared to CT (M=2.206, SD= 0.475), t (16)=4.002, p= .001, CI -1.106 to -0.34, MD= -0.723 (see
Fig. 2). Deltoid muscles in the AT group were active for less time during the arm movement. The average EMG amplitude during the movement did not differ between AT group (M=0.0055, SD= 0.0032) and CT (M= 0.0058, SD= 0.0036), t (16)= 0.192, p=
.849, MD= -0.0003. The maximum deltoid EMG amplitude (millivolts) was just significantly higher in the AT group (M=0.015, SD= 0.006) compared to control (M=
0.009, SD= 0.005), t (16)=2.21, p= .042, CI= 0.0025 to 0.0126, MD= 0.0064 (see Fig.
3).
There was no difference in the time it took from the REACH signal to the maximum EMG value in the AT group (M= 0.859, SD= 0.275) compared to CT (M=0.658, SD= 0.265), t
(16)= 1.564, p= .137, MD= 0.201. In both groups, the maximum EMG amplitude was reached before the retraction.
The return to baseline from the average maximum EMG value took significantly less time in the AT group (M= 1.051, SD= 0.250), with a delta of .73 seconds, compared to CT (M=
1.783, SD= 0.517), t (16)= 3.951, p= .0011, CI= -1.124 to -0.339, MD= -0.731 (see Fig.
4). Following the press of the space bar, deltoid activity returned to baseline on average
49% faster during the movement in the AT group, which indicates less deltoid activation by almost half during the retraction from the space bar.
13
Kinematics measured by twin-axis goniometer
Movement along the X-axis, as measured by the goniometer, was significantly greater in the
AT group (M = -19.46, SD = 1.35) compared to the CT group (M = -16.09, SD = 1.48), t(400) = 23.87, p < .0001, MD = -3.37, CI = -3.65 - -3.09. In addition, movement along the Y-axis was significantly greater in the AT group (M= 8.173, SD= 1.164) compared to
CT (M= 3.413, SD= 1.774), t(639)= 37.15, p< .0001, CI= 4.51 to 5.08, MD= 4.76.
These data show that the AT group’s degree of change of the elbow joint was significantly higher than control on both the x and y planes of movement collected (see Fig. 5).
There was no difference in the time it took to reach maximum elbow extension from the
REACH signal in the AT group (M= 1.14, SD= 0.25) compared to CT (M= 0.98, SD=
0.38), t(16)= 1.61, p= .12, MD= 0.24. Though, the time it took from the maximum extension of the elbow to return to rest was significantly lower in the AT group (M= 0.77,
SD= 0.18) compared to CT (M= 1.38, SD= 0.5), t (17)= 3.52, p= .0028, CI= -0.96 to -
0.24, MD= -0.6 (see Fig. 6).
11. Discussion
Our data show differences in neural and motor functioning between people with AT exposure and controls. A more variable ERP response preparation in the AT group coincided with a different pattern of activation and timing of EMG activity, pathway of motor output, and better accuracy of inhibition during STOP trials compared to controls.
14 Behavior in relation to muscular activation
The AT group’s pathway of movement included a greater rate of change at the elbow joint on both x and y-axes measured my the goniometer, which indicates that the AT group more fully used their joint’s range of motion compared to control. No other joints were kinematically monitored, but in order to reach 70-80% their full arm’s length the CT group may have invoked other joints to complete the movement. A common compensatory strategy for healthy and impaired reaches is to recruit the trunk to extend the distance of an extremity [40]. Trials were not videotaped, so this hypothesis cannot be confirmed within the methods of the current study.
Data from the goniometer also showed that there was no difference in the time it took to reach and physically press the space bar, but the AT group took significantly less time to return back to rest. This occurred even in the context of the AT group generating more extension, indicating that they may have moved in a more efficient way by employing the necessary joints of the extremities which allowed them to return to rest more easily.
Electromyography recordings showed that during REACH trials, EMG activity occurred above baseline for significantly less time in the AT group compared to control. Though, the overall sum of EMG activity was not different between groups. The AT group reached a significantly higher maximum amplitude of muscle activity, and from the EMG max, returned to baseline significantly faster than controls. Taken together, the AT group’s data show a different pattern of muscular activity throughout the movement that is more variable, while the control group’s deltoid EMG was less variable and occurred for a longer duration during REACH trial responses. The AT group may have employed deltoid activity when it was most necessary to aid in the extension away from their center of gravity, and
15 allowed for other musculature to take over following the initial spike- particularly during the return to rest indicated by the fast decrease back to baseline EMG compared to control.
Motor planning ERPs in relation to response accuracy
Our measure of the electrophysiological correlates of motor planning showed that the AT group had significantly lower motor planning ERP amplitude followed by rapid peak that occurred around 200ms pre-movement, and remained significantly higher than the control group prior to EMG activation (above baseline). This increased variability is a measure of differential response preparation [24]. The AT group was more accurate in inhibiting a motor response than the CT group during STOP trials. These data indicate that the AT group had more control over their movements, both initiating the reaching movement and inhibiting the prepotent responses often elicited during the Go/NoGo Task.
Since the trials were randomized, subjects could not predict whether next trial would prompt a button press or an inhibition of movement. Given the larger proportion of REACH trials to STOP trials, control participants seem to have developed an automated response preparation in anticipation of a REACH signal. The control group’s ERP is more often fluctuating above the AT ERP and is therefore closer to the motor response threshold.
Responding to a STOP signal by staying at rest required an increase in recruitment of inhibitory activation processes in the control group, which is also reflected in their number of unsuccessful inhibitions. Their higher accumulation of neural activity just below the response threshold corresponded a higher probability of movement, and combined with random variable fluctuations, may have led to more error when movement was not appropriate.
16 In both groups, the accumulation of ERP activity included spontaneous fluctuations. The control group showed more spontaneous fluctuations occurring at consistent amplitudes closer to the motor response threshold. The AT group accumulated neural activity at a slower rate, showed a higher rate of change when there became a greater probability of crossing the motor response threshold, and remained at a higher amplitude even as ERPs decreased once the threshold was crossed. The control group accumulated neural inputs that consistently varied at a higher probability of movement initiation, even though it was just as important to recruit inhibitory mechanisms of decision-making on a trial-to-trial basis.
One limitation of the current study is the inability to get an exact LRP. Though we recorded from the contralateral hemisphere, we were not able to subtract ERPs from the ipsilateral hemisphere. Our claims are not as strong without this subtraction method commonly seen in previous studies [24]. In addition, our data show a phasic positive amplitude shift followed by negative amplitude shift. Some studies report this difference in polarity even with the common LRP subtraction method [22]. In our view, we may see this difference in polarity because we were not able to compute an exact LRP. Future studies should consider computing an exact LRP, and a testing group with more consistent and longer AT experience.
In sum, ERPs in the AT group were variable, and increased when the motor response was more likely to occur. The CT group ERP activity accumulated at a higher rate and fluctuated just below the motor response threshold even when there was no indication to make a movement, which coincided with a higher rate of error during inhibitory trials.
When a movement was executed, the AT group showed a greater range of motion at the elbow joint, and a quicker return to rest, while the CT group used less range and a returned
17 to rest more slowly. EMG activation patterns differed between groups. The AT group had a higher amplitude spike of EMG activity in the deltoid followed by a quicker return to baseline during the retraction, and that activity pattern occurred above baseline for less time than CT. The CT group had a lower amplitude spike, longer EMG activation in total, and a longer return to baseline activity. There was no difference in the sum of EMG between groups. Therefore, the AT group isn’t employing less activation of the muscle, but rather they are utilizing that activity in a variable, more efficient way.
Taken together, these results suggest that practicing the Alexander Technique may increase movement efficiency. Always being at the edge of movement, including musculature more often, and not fully extending the joints necessary for a task limits one’s ability to respond differently to situations by constantly being near response thresholds and moving in a less coordinated way.
Somatic practices are often overlooked by brain scientists, medical practitioners, and therapists as resources for healthy change and embodiment. And though there is experiential evidence of benefits of somatic work for performers, and some evidence of therapeutic effects for clinical populations, there is no scientific data providing information about the physiological changes that may provide these benefits. This is the first research completed indicating correlative changes in the brain, musculature, and motor output after exposure to the Alexander Technique. It begins to scientifically suggest that consciously practicing body awareness with Alexander Technique changes the way nervous systems initiate, inhibit, and complete unilateral movement.
18 12. Acknowledgments
The authors would like to thank Rebecca Brooks for aiding with recruitment of AT participants, Rebecca Warzer for contributing to the pilot work this study was based upon.
13. Financial support
Thank you to the Geary Fund at Bennington College and the Bennington College
Department of Science, Math, and Computing for supporting this project.
14. Ethical Statement
All of the procedures used during this study were approved by the Bennington College
Committee in Research with Human Participants. All participants were given a clear description of the procedures and provided written informed consent.
15. Authors Contribution
Both authors developed the current study’s task, procedures, and data analysis strategies;
A. Wirtanen analyzed data and wrote the current publication.
16. Statement of interest
The authors have no conflicts of interest that relate to the present work.
19 17. Figure Captions
Figure 1: AT in RED, CT in BLUE. ERP motor preparation recorded from the cortex at C3 or C4, averaged, time-locked to EMG activation above baseline at 0. Graph includes data 500 ms before to 50 ms following EMG activation. AT’s ERP significantly lower 500-196 ms before EMG activation (p< .0001), increased steeply to a significantly higher amplitude (p< .0001) until EMG activation.
Figure 2: Average total time in seconds EMG activity was above baseline during REACH trial responses. AT in RED (M=1.483, SD= 0.287), CT in BLUE (M= 2.206, SD= 0.475). AT group’s time EMG was active significantly less than CT, p= .001.
Figure 3: Maximum EMG amplitude (mV). AT in RED (M=0.015, SD= 0.006), CT in BLUE (M= 0.009, SD= 0.005). AT reached significantly higher amplitude during reaching movement compared to CT, p= .042.
Figure 4: Average time (sec) for EMG to return to baseline from maximum amplitude. AT in RED (M= 1.051, SD= 0.250), CT in BLUE (M= 1.783, SD= 0.517). AT returned to baseline significantly quicker than CT, p= .0011.
Figure 5: Average degrees of change of arm movements during space bar press of both x (negative degrees of change) and y-axis (positive degrees of change). On graph above, x- axis is time in seconds, y-axis degrees of change in raw form. AT group represented in red, CT in blue. At group significantly greater degrees of change on both x and y axes compared to CT, p=< .0001, p< .0001.
Figure 6: Average time taken to return to rest from maximum elbow extension. AT (M= 0.77, SD= 0.18) in RED and CT (M= 1.38, SD= 0.5) in BLUE. AT group returned to rest from maximum extension significantly faster, p= 0.0028.
20 Figure 1
21 Figure 2
22 Figure 3
23 Figure 4
24 Figure 5
25 Figure 6
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