Finger tremor can be voluntarily reduced during a tracking task

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Research Report

Finger tremor can be voluntarily reduced during a tracking task Jean-François Daneault, Benoit Carignan, Christian Duval⁎ Département de Kinanthropologie, Université du Québec à Montréal, 141 Avenue du Président-Kennedy, Montréal, Québec, Canada H2X 1Y4

A R T I C LE I N FO

AB S T R A C T

Article history:

Introduction. It is a well-known fact that physiological tremor has a deleterious effect on

Accepted 10 November 2010

small and precise movements. We recently showed that the amplitude of postural

Available online 27 November 2010

physiological tremor can be voluntarily reduced. Whether this is also applicable to tremor during small movements has not been explored. Objective. This study aims to characterize

Keywords:

tremor during movement, assess whether it is possible to reduce tremor amplitude during

Physiological tremor

voluntary movements, and quantify any changes in accuracy that may result from this

Modulation

modulation. Methods. Finger tremor was measured in 12 healthy volunteers using a laser

Laser

displacement sensor and recorded during (A) a postural physiological tremor condition, (B) a

Kinetic tremor

slow target tracking task, and (C) the same tracking task while trying to reduce tremor

Dual task

amplitude. Results. The tremor characteristics such as distribution of power within the power spectrum remained similar during movement when compared to the static postural condition. Tremor amplitude was significantly reduced when participants attempted to do so. However, this reduction was accompanied with a systematic increase in error. Finally, mean error was significantly higher when the target line moved at higher velocity. Discussion. Our results demonstrate that tremor remains present during movement and that its amplitude can be voluntarily modulated. However, attempting to voluntarily reduce the amplitude of that tremor during movement is not an efficient way to improve tracking performance. © 2010 Elsevier B.V. All rights reserved.

1.

Introduction

Physiological tremor is a small involuntary movement having sinusoidal properties that is present in every limb during a postural task (Elble and Koller, 1990). Those oscillations have been shown to stem from neural activity generated within the central nervous system as well as mechanical properties of the limb being examined (Conway et al., 1995; Elble, 1995; Koster et al., 1998; Lamarre et al., 1975; Llinas, 1984; Marsden et al., 1969; McAuley et al., 1997; Stiles and Randall, 1967; Vaillancourt and Newell, 2000; van Buskirk et al., 1966; Yap and Boshes, 1967). Oscillations are spread throughout the power spectrum, up to 40 Hz. The resulting amplitude of all

these involuntary oscillations is usually below 0.25 mm in healthy individuals (Carignan et al., 2009, 2010). We recently demonstrated, however, that the majority of tremor amplitude is located below 4 Hz when the oscillations are examined in displacement (Carignan et al., 2010; Duval and Jones, 2005). Interestingly, it has been shown that there are oscillations at the same frequencies as postural physiological tremor during finger movement (Goodman and Kelso, 1983; Legros et al., 2010; Vallbo and Wessberg, 1993; Wessberg and Vallbo, 1995, 1996). Some authors have described these oscillations as a biphasic motor output reflecting the descending motor command (Vallbo and Wessberg, 1993; Wessberg and Vallbo, 1996). On the other hand, since these oscillations have similar

⁎ Corresponding author. Fax: +11 514 987 6616 E-mail address: [email protected] (C. Duval). 0006-8993/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.brainres.2010.11.047

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properties as tremor, others have suggested that it is tremor per se and classified it as kinetic tremor (Deuschl et al., 1998). Despite the small amplitude of these oscillations, they have been shown to have a deleterious effect on the performance of small and precise movements, such as during microsurgery (Harwell and Ferguson, 1983). Indeed, a field of research is intent on elaborating mechanical means to reduce tremor during microsurgical procedures (Ang et al., 2004; Cernat et al., 2006; Choi et al., 2007; Gomez-Blanco et al., 2000; Riviere et al., 1998). We recently demonstrated that the amplitude of postural physiological tremor of the index finger can be voluntarily reduced (Carignan et al., 2009; Daneault et al., 2010). While the properties of slow and precise finger movements have been extensively studied in the past (Kakuda, 2000; Vallbo and

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Wessberg, 1993; Wessberg and Vallbo, 1995, 1996; Wessberg and Kakuda, 1999), the possibility that tremor amplitude can be reduced during these accurate movements has not yet been explored. Accordingly, the main goal of the present study was to verify whether it is possible to voluntarily reduce finger tremor amplitude during small finger movements. We also took the opportunity to characterize the power spectrum of tremor during movement and assess the impact of any voluntary attempt of tremor amplitude reduction on movement accuracy.

2.

Results

Fig. 1 (top) illustrates the average absolute power spectrum of all conditions in displacement, velocity, and acceleration.

Fig. 1 – Illustration of tremor power spectrums. Shown are the postural condition (black), the tracking condition (light gray), and the tremor modulation condition (dark gray). Top left: tremor displacement raw power spectrum. Top middle: tremor velocity raw power spectrum. Top right: tremor acceleration raw power spectrum. Bottom left: tremor displacement relative power spectrum where each point on the graph represents one 150th of total power. Bottom middle: tremor velocity relative power spectrum where each point on the graph represents one 150th of total power. Bottom right: tremor acceleration relative power spectrum where each point on the graph represents one 150th of total power. Note that for each spectrum, mean power ± SE at every 0.2 Hz is displayed for all trials within the specified condition.

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Note that power spectrum has similar shapes regardless of condition within each modality, albeit power amplitude differs. Also in Fig. 1 (bottom) are graphs illustrating the relative power at each frequency in displacement, velocity, and acceleration. Note that, as for the absolute power, the shape of the relative power spectrum is also similar regardless of modality. Fig. 2 shows mean tremor amplitude, median power frequency, and power dispersion for all conditions. Mean tremor amplitude in the postural condition was 0.17±0.06 mm, while it was 0.55 ± 0.07 mm in the tracking condition and 0.45± 0.07 mm in the tremor modulation condition. Analysis of variance (ANOVA) revealed a significant condition effect. Post hoc analysis showed that tremor amplitude was significantly lower in the postural condition than in both other conditions (p < 0.05). In addition, tremor amplitude was systematically and significantly lower in the tremor modulation condition than in the tracking condition (p < 0.05). Mean median power frequency was 9.70± 1.45 Hz in the postural condition, 6.45±0.78 Hz in the tracking condition, and 6.59 ± 0.94 Hz in the tremor modulation condition. ANOVA revealed a significant condition effect. Post hoc analysis showed that median power frequency was significantly higher in the postural condition compared to both other conditions (p < 0.05). Mean power dispersion was 13.00± 1.25 Hz for the postural condition, 9.50±0.89 Hz for the tracking condition, and 9.88 ± 1.31 Hz for the tremor modulation condition. ANOVA revealed a significant condition effect. Post hoc analysis showed that power dispersion was significantly higher in the postural condition compared to both other conditions (p < 0.05). EMG activity (percentage of maximal voluntary contraction) for the extensor digitorum communis and flexor digitorum superficialis was 5.31 ± 3.02% and 3.03 ± 2.07% in the postural condition, 10.0 ± 5.0% and 4.6 ± 2.5% in the tracking condition, and 8.2± 4.9% and 5.5 ± 4.8% in the tremor modulation condition. ANOVA revealed that there was a significant difference

between conditions for EMG activity of the extensor digitorum communis. Post hoc analysis showed that EMG activity of the extensor digitorum communis was significantly lower in the postural condition compared to the tracking condition (p < 0.05). No significant differences were observed for EMG activity of the flexor digitorum superficialis between conditions (p > 0.05). Note that there were no significant differences in EMG activity between the tracking and the tremor modulation conditions. These results confirm that co-contraction was not used by participants as a strategy to reduce tremor amplitude. Fig. 3 shows the mean tremor amplitude for all conditions in frequency bands of interest. Mean tremor amplitude in the 0- to 4-Hz frequency band was 0.14 ± 0.05 mm for the postural condition, 0.50 ± 0.06 mm for the tracking condition, and 0.42 ± 0.06 mm for the tremor modulation condition. ANOVA revealed a significant condition effect (p < 0.05). Post hoc analysis showed that mean tremor amplitude was different between each condition for the 0- to 4-Hz frequency band. Mean tremor amplitude in the 4- to 8-Hz band was 0.07 ± 0.02 mm in the postural condition, 0.18 ± 0.04 mm in the tracking condition, and 0.15 ± 0.03 mm in the tremor modulation condition. ANOVA revealed a significant condition effect (p < 0.05). Post hoc analysis showed that mean tremor amplitude was different between each condition for the 4- to 8-Hz frequency band. Mean tremor amplitude in the 8- to 12-Hz frequency band was 0.04 ± 0.01 mm for the postural condition, 0.08 ± 0.02 mm for the tracking condition, and 0.07 ± 0.02 mm for the tremor modulation condition. ANOVA revealed a significant condition effect (p < 0.05). Post hoc analysis showed that mean tremor amplitude was different between each condition for the 8- to 12-Hz frequency band. Mean tremor amplitude in the 16- to 30-Hz frequency band was 0.02 ± 0.005 mm in the postural condition, 0.03 ± 0.003 mm in the tracking condition, and 0.03 ± 0.004 mm in the tremor modulation condition. ANOVA revealed a significant condition effect (p < 0.05). Post hoc analysis showed that mean tremor amplitude was significantly lower in the

Fig. 2 – Characteristics of tremor. Shown are amplitude, median power frequency, and power dispersion for the postural condition (black), the tracking condition (light gray), and the tremor modulation condition (dark gray). Left: mean tremor amplitude in all conditions. Middle: mean median power frequency in all conditions. Right: mean power dispersion in all conditions. Power dispersion represents the width of a frequency band containing 68% of total power centered at the median power frequency.

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Fig. 3 – Illustration of tremor amplitude within frequency bands of interest. Shown for each frequency bands are the postural condition (black), the tracking condition (light gray), and the tremor modulation condition (dark gray). Top left: mean tremor amplitude within the 0- to 4-Hz frequency band for all conditions. Top right: mean tremor amplitude within the 4- to 8-Hz frequency band for all conditions. Bottom left: mean tremor amplitude within the 8- to 12-Hz frequency band for all conditions. Bottom right: mean tremor amplitude within the 16- to 30-Hz frequency band for all conditions.

postural condition compared to both other conditions in the 16to 30-Hz frequency band. Fig. 4 illustrates an example of the deviation from the target line (error) observed over time in the tracking and tremor modulation conditions for one participant. Also illustrated is the mean deviation (±95% confidence interval) from the target line observed over time in the tracking and tremor modulation conditions for all participants. Fig. 5A shows mean error from target line for all participants in the tracking and tremor modulation conditions. ANOVA revealed a significant condition effect (p < 0.05). Post hoc analysis revealed that mean error from target line in the tracking condition (0.39 ± 0.08 mm) was significantly lower than in the tremor modulation condition (0.65 ± 0.14 mm) (p < 0.05). Furthermore, increased error in the tremor modulation condition was observed systematically for every participant. Fig. 5B shows mean error from target line in the tracking

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and tremor modulation conditions, while taking into consideration the velocity of the target line (fast vs. slow). ANOVA revealed a significant group effect (p < 0.05). Indeed, in both conditions, post hoc analysis showed that mean error was significantly higher when the target line moved at higher velocity (tracking condition: p < 0.05; tremor modulation condition: p < 0.05). ANOVA also revealed a significant condition effect (p < 0.05). In fact, post hoc analysis showed that compared to the tracking condition, mean error remained significantly higher in the tremor modulation condition regardless of target velocity. In order to confirm that changes in feedback were not responsible for the results obtained in the current study, we tested an additional 6 participants with a protocol designed to assess the impact of changing the feedback on the amplitude of postural index finger tremor (results in Fig. 6). In this protocol, tremor was recorded in four conditions. (D) Postural control condition where postural physiological tremor was recorded in the same way as in condition A. (E) Tracking control condition where postural physiological tremor was recorded in the same way as in condition A but a feedback, same one shown in condition B, was presented to the participants on the computer screen. (F) Tracking 5× condition where postural physiological tremor was recorded in the same way as in condition E but the participants' tremor, multiplied five times, was added online to the same feedback shown in condition E. (G) Tracking 10× condition where postural physiological tremor was recorded in the same way as in condition E but the participants' tremor, multiplied ten times, was added online to the same feedback shown in condition E. Each condition was performed 4 times, and a rest period of 60 s was allotted between trials. Fig. 6 shows mean tremor amplitude, mean median power frequency, and mean power dispersion for the additional four conditions. Mean tremor amplitude was 0.19 ± 0.08 mm for the postural control condition, 0.20 ± 0.07 mm for the tracking control condition, 0.18 ± 0.05 mm for the tracking 5× condition, and 0.18 ± 0.08 mm for the tracking 10× condition. ANOVA revealed that there was no significant condition effect. Mean median power frequency was 8.7 ± 1.5 Hz for the postural control condition, 8.5 ± 1.3 Hz for the tracking control condition, 8.7 ± 1.5 Hz for the tracking 5× condition, and 8.9 ± 1.5 Hz for the tracking 10× condition. ANOVA revealed that there was no significant condition effect. Mean power dispersion was 11.8 ± 2.2 Hz for the postural control condition, 11.9 ± 1.9 Hz for the tracking control condition, 12.3 ± 2.5 Hz for the tracking 5× condition, and 12.1 ± 2.3 Hz for the tracking 10× condition. ANOVA revealed that there was no significant condition effect. Thus, this confirms that results observed in the current study were not due to changes in feedback.

3.

Discussion

Our results demonstrate for the first time that the amplitude of finger tremor during slow purposeful movements can be voluntarily reduced. Of interest is that the modulation attempt did not modify the spectral characteristics of tremor but only its amplitude. This reduction of tremor amplitude was the result of voluntary modulation of tremor, not a

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Fig. 4 – Deviation from the target line observed over time in the tracking and tremor modulation conditions. Left: two trials taken from one participant (top: tracking condition, bottom: tremor modulation condition). The zero line represents the target line, and the line plot represents the error from the target line (finger position minus target position). Right: mean deviation from the target line (±95% confidence interval) observed over time (top: tracking condition, bottom: tremor modulation condition). Note that error is higher during modulation attempts and that larger errors coincide with higher target velocity (dark gray line represents high target velocity while black line represents low target velocity) (see Fig. 5 for reference).

consequence of a change in feedback characteristics. How this reduction in amplitude is translated within the different frequency bands composing tremor and its effect on movement accuracy is discussed below. Note, however, that this study examined whether modulation of involuntary oscillations was possible at a specific signal-to-noise ratio. Whether this ability and its consequences would apply for tasks of different signal-to-noise ratio is beyond the scope of this paper.

3.1. Tremor properties during postural and slow movements

Fig. 5 – Mean error amplitude during the tracking and tremor modulation conditions over the entire signal and for both fast and slow sections of the target line. Left: mean error amplitude over the entire signal for both tracking and tremor modulation conditions. Right: mean error amplitude for both tracking and tremor modulation conditions during fast and slow portions of the signal. In fast sections, the target line was moving between 1.36 and 4.34 mm s− 1. In slow sections, the line moved between 0 and 1.36 mm s− 1. Note that participants spent as much time in the fast sections as in the slow sections.

It has been argued that oscillations seen during movement represent a biphasic motor output reflecting the descending motor command (Vallbo and Wessberg, 1993; Wessberg and Vallbo, 1996). However, there are pieces of strong evidence suggesting that these oscillations are in fact tremor, such as suggested by Deuschl et al. (1998). First, the examination of the power spectrum of each condition, regardless of being in displacement, velocity or acceleration, will reveal that they are of similar shape. Second, it can be modulated as well as postural physiological tremor. Third, the bi-directionality of these oscillations around the trajectory of the voluntary movement argues against the possibility that they represent incremental steps or corrections during that movement. The similitude between postural tremor and tremor detected during movement in the present experiment suggests a possible common origin. The latter point will need however to be confirmed with further investigation. Our results also show that tremor amplitude increased during movement. Since we believe that tremor generating

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Fig. 6 – Characteristics of tremor related to different feedbacks. Shown are amplitude, median power frequency, and power dispersion for the postural control condition (black), the tracking control condition (gray), the tracking 5× condition (dark gray), and the tracking 10× condition (light gray). Left: mean tremor amplitude in all conditions. Middle: mean median power frequency in all conditions. Right: mean power dispersion in all conditions. Power dispersion represents the width of a frequency band containing 68% of total power centered at the median power frequency.

signals are continually present regardless of movement, we can then postulate that this increase in amplitude could come from a slight change in gain applied to the signals during movement. Several mechanisms could come into effect to produce this modulation. One possible mechanism that could mediate this change in gain is a modulation of reflex activity. Indeed, the majority of tremor amplitude is located in the low frequencies (Carignan et al., 2010), which have been suggested to stem from reflex activities (van Buskirk et al., 1966; Yap and Boshes, 1967), and it has been demonstrated that changes in reflex activity can modulate physiological tremor amplitude (Sowman and Turker, 2007). Another possible mechanism that could account for this phenomenon is a change in unfused motor-unit activity. Since movement inextricably involves changes in motor-unit activity from posture, variations in firing rate and motor-unit recruitment could modify unfused motor-unit activity, which has been implicated in lowfrequency tremor oscillation generation (De Luca and Erim, 1994). Of course, these hypotheses do not preclude the fact that this change in tremor amplitude could be caused by a combination of these and other mechanisms. This remains to be determined by other investigations. Finally, this raises the question as to whether enhanced physiological tremor could merely be the result of a deregulation of that gain through similar mechanisms.

3.2.

Tremor modulation

We recently demonstrated quantitatively that the bulk of postural physiological tremor oscillations are located in lower frequencies, i.e., below 4 Hz, when displacement is examined (Carignan et al., 2010; Duval and Jones, 2005). In fact, we calculated that when oscillations located higher than 3.5 Hz were removed from the original physiological tremor oscillation amplitude, 87% of the signal remains. In a previous study where participants attempted to reduce their tremor, most of the reduction occurred in low-frequency components

(Carignan et al., 2009), thus indicating that the solution to solving the tremor problem during precise movements could lie with the voluntary control of these low frequencies. Our current results show that although amplitude reduction can be observed in the 0-to 4-Hz, 4- to 8-Hz, and 8- to 12-Hz frequency bands during the tremor modulation condition, the majority of tremor amplitude reduction occurred in the 0- to 4-Hz frequency band. These findings are in accordance with previous results (Carignan et al., 2009; Daneault et al., 2010). This suggests that the mechanism to voluntarily reduce postural physiological tremor amplitude and tremor during a purposeful movement will have a similar impact on tremor oscillations.

3.3. Impact of voluntary tremor amplitude modulation during finger movement As mentioned above, tremor was shown to have an adverse effect on micro-movement precision (Harwell and Ferguson, 1983). It would then have been logical that a voluntary reduction of tremor amplitude would improve movement accuracy. Readers should note, however, that the movement performed in this study and those performed during microsurgical procedures such as those examined by Harwell and Ferguson (1983) are not the same. Thus, this study is only the first step towards better understanding tremor control during microsurgery and care should be taken when interpreting the results. Keeping this in mind, nonetheless, a systematic and significant improvement in tremor amplitude during the tremor modulation condition was observed and actually led to a systematic and significant increase in deviation from target, which amounts to an increase in error. It is then reasonable to assert that although tremor has an adverse effect on accuracy during small movements of the finger, attempting to voluntarily reduce tremor had an even more deleterious effect. Of course, one could argue that the reduced signal-to-noise ratio due to the tremor amplification hindered participants from following the target. This would make

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sense, given that it takes longer to detect a direction change of the target stimulus if the noise is increased. However, we believe that the signal-to-noise ratio of 7.5:1 after tremor amplification did not hinder the ability of subjects to detect changes in target direction. A more plausible explanation for the increased error in the modulation condition could then be the bottleneck effect observed during dual tasks. According to this theory, perceptual and response operations occur in parallel but a delay is observed during a central decision stage involved in sensory and motor coordination (Pashler, 1994; Sigman and Dehaene, 2008) or motor–motor coordination (De Jong, 1993; Meyer and Kieras, 1997). Here, in the tracking condition, participants were told to simply follow a moving target using finger movement. During the tremor modulation condition, however, participants were explicitly told to try and reduce tremor amplitude as they followed the target. Thus, this became a dual task where tremor amplitude reduction was the primary task, and following the target, the secondary task. Consequently, the sensory information and/or motor command necessary for tremor modulation probably superseded the motor command to follow the target, hence creating errors. Note that the main goal of the present study was to assess whether modulation of tremor during movement was feasible, and what would be the impact of such modulation on movement accuracy. Accordingly, the experiment was designed with that purpose in mind. We must acknowledge however that we did not design it as a comparison between simple and dual task. This limits our ability to make definite conclusions about the impact of our results on dual-task interference. Nonetheless, this study demonstrated that voluntary control of tremor amplitude during a finger tracking task is possible, albeit with some consequences.

3.4. task

Impact of target velocity on error during a tracking

Another interesting aspect demonstrated by our results is that the speed of movement also had an effect on accuracy, i.e., there was increased error with increased speed. Indeed, during the tracking condition, error was significantly higher when the target was moving at greater velocity. This effect was even more evident during the tremor modulation condition. The results obtained during the tracking condition are in accordance with the theory that the movements performed during such a task are composed of discrete submovements that are of invariant duration but of increasing amplitude with increased velocity (Pasalar et al., 2005; Roitman et al., 2004; Selen et al., 2006). Indeed, the increased amplitude of the small sub-movements makes it more difficult to precisely follow the target. In addition, it has recently been shown that lower target speed allows for more visually guided feedback corrections (Selen et al., 2006). Thus, our results further support the idea that when performing highly precise maneuvers, controlling movement speed might be more beneficial to accuracy than trying to voluntarily reduce tremor. It is not impossible however that one could learn to perform both tasks efficiently, hence reducing tremor amplitude while preserving accuracy. Conversely, a mechanistic limit could be inherent to the motor system as online

control of visually guided movements can be described as intermittent (Fishbach et al., 2005, 2007; Novak et al., 2003) or continuous with dead zones (Hanneton et al., 1997; Miall et al., 1993; Wolpert et al., 1992). As mentioned above, tracking tasks are composed of discrete sub-movements. It was shown that the optimal control strategy when accuracy is required would be to consistently undershoot the target and use discrete corrections to attempt to meet the precision criteria (Engelbrecht et al., 2003; Fishbach et al., 2005; Harris, 1995). Then, this type of control strategy might limit the ability to voluntarily reduce error beyond a given threshold. Nonetheless, attempting to voluntarily reduce the amplitude of that tremor during movement is not an efficient way to improve tracking performance.

3.5. Lingering questions relating to voluntary modulation of physiological tremor This study is the third in a line of projects examining the ability to voluntarily reduce tremor amplitude. The two prior studies demonstrated that postural physiological tremor amplitude can be voluntarily reduced (Carignan et al., 2009; Daneault et al., 2010). Here, we demonstrated that kinetic physiological tremor amplitude can also be voluntarily reduced. Interestingly, in all three studies, the majority of amplitude reduction occurred in a frequency band located below 4 Hz. Most of the power of finger tremor, when displacement is examined, is located within the lowfrequency components (Duval and Jones, 2005; Halliday and Redfearn, 1956; Stiles and Randall, 1967). Indeed, we calculated that more than 90% of tremor amplitude is located below 4 Hz when examining finger displacement (Carignan et al., 2010). Currently, the origin of these frequencies is not well defined, which may partly be explained by the fact that accelerometers have been the preferred tool to assess tremor. Indeed, accelerometers have a tendency to emphasize high-frequency components in detriment of low-frequency ones. As a result, most studies characterizing tremor components have emphasized on the origins of higher frequencies because of better resolution of their instruments within these frequency bands. Thus, more studies have explored the origin of high-frequency tremor components than low-frequency ones. Nonetheless, there are some studies that examined low frequencies. For example, some have postulated that the ballistocardiac effect might be minimally involved in the generation of low-frequency oscillations during posture (Marsden et al., 1969; Morrison and Newell, 2000), while others have implicated unfused motor-unit activity (De Luca and Erim, 1994), or sensorimotor control processes (Morrison et al., 2006). We recently showed that a reduction of low-frequency finger tremor oscillation amplitude was not accompanied by changes in heart rate and respiration characteristics (Daneault et al., 2010). Also, strategies used by participants to modulate tremor amplitude varied greatly, which prevented us from identifying possible mechanisms involved in tremor modulation. It is now clear that identifying a clear and definite origin of oscillations below 4 Hz in healthy adults is essential to determine how subjects modulate their physiological tremor.

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3.6.

Conclusion

Our results demonstrate that tremor remains present during movement and that its amplitude can be voluntarily modulated. However, the dual task of following the target and simultaneously trying to reduce tremor will generate increased error. Thus, attempting to voluntarily reduce the amplitude of that tremor during movement is not an efficient way to improve tracking performance.

4.

Experimental procedures

4.1.

Participants

Twelve participants (mean 25.2 ± 3.9 years) (7 females) agreed to be part of this experimentation by signing the written

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consent form (approved by the Université du Québec à Montréal Ethics Board). All participants were right handed (according to the Edinburgh Handedness test) and were selected from a data bank of participants having already shown to be able to reduce postural physiological tremor amplitude (Carignan et al., 2010). Approximately 1 year separated the two experiments.

4.2.

Study design

As we did in previous studies (Carignan et al., 2009; Duval and Jones, 2005; Duval et al., 1997, 2000, 2005, 2006), tremor was measured on the right index finger using a laser displacement sensor (LDS 90/40, LMI Technologies, The Netherlands). Participants were instructed to avoid co-contraction of forearm muscles as a means to stabilize the finger since it is known that it would increase tremor amplitude (Carignan et al., 2009;

Fig. 7 – Illustration of the transformation applied to the feedback signal during the tremor modulation condition. (A) A depiction of the raw signal generated by moving the finger up and down. Note that the signal-to-noise ratio, where the voluntary movement is the signal and the tremor is the noise, was 30:1. (B) A hi-pass filter is applied online to the signal to isolate the tremor signal. Applying this online filter introduces a small delay of 70 ms to the tremor signal. (C) The tremor signal is then multiplied by a factor of 5. (D) The resulting tremor signal is then added back to the original signal recorded from the laser displacement sensor. After this transformation, the signal-to-noise ratio is now 7.5:1. Note that although a delay was introduced to the tremor signal in B, the original signal to which it is added in D is not filtered. Consequently, the feedback of finger position during the tracking task has no delay, while an inconsequential delay of 70 ms is introduced to the tremor feedback, which oscillates around the position signal.

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Daneault et al., 2010). Accordingly, electromyographic (EMG) recordings of the extensor digitorum communis and flexor digitorum superficialis were used to assess muscular activity with pre-amplified bipolar surface electrodes connected to an amplifier (Therapeutics Unlimited Model 544 System, Therapeutics Unlimited Inc, Iowa City, IA). Note that the electrodes used were plastic-encased and thus, inter-electrode distance remained constant between participants at 19 mm. Participants were seated on a chair while their arm was resting on a custom-designed foam padded support to isolate finger movement by allowing unhindered movement at the metacarpophalangeal joint. A very light and thin piece of white cardboard was placed on the fingernail of the index finger to increase the reflective surface for the laser beam. EMG and finger position data were recorded using Data Acquisition System Laboratory (DASYLab, National Instruments Ireland Resources Limited; measX GmbH & Co. KG, Germany) and sampled at a rate of 2048 Hz. Tremor was recorded in three conditions. (A) Postural condition where postural physiological tremor was recorded. Participants were asked to keep their finger in a horizontal position for 60 s while they looked at a blank computer screen placed at eye level, 120 cm in front of them. (B) Tracking condition where tremor during a low-amplitude finger movement was recorded. Participants were asked to match a vertically moving computer-generated target line with a line they controlled through up and down movement of their index finger. Range of motion of the index finger was 17 mm, which translated into a total movement range of 190 mm on the computer screen. The target line was composed of three sine waves having a frequency of 0.1, 0.085, and 0.054 Hz, respectively. There was also a phase shift of 160 and 140° between the second and third sine waves compared to the first one, respectively. Accordingly, the target moved at a mean velocity of 1.55 mm s− 1 with peaks at 4.34 mm s− 1. Note that during this condition, finger tremor was practically invisible to the participant during finger movement task due to a high signalto-noise ratio (the signal being the voluntary movement, the noise being the tremor). (C) Tremor modulation condition where participants were asked to accomplish the same task as in the tracking condition but this time they were asked to reduce tremor, which was made visible through amplification. The reduction of signal-to-noise ratio was done by first isolating tremor online using a hi-pass filter at 2 Hz. This application of an online filter introduces a delay of 70 ms to the tremor signal. The introduction of this delay had no significant impact on the results presented in this study as seen in the Results section. Then, the tremor signal was multiplied by five and subsequently added to the finger movement. Consequently, the signal-to-noise ratio was modified from 30:1 in the kinetic control condition to 7.5:1 in the modulation condition. As a result, tremor became visible, without modifying finger movement characteristics (Fig. 7). Note that finger movement was not filtered, and thus, no delay was introduced to the feedback of finger position during the tracking task but only to the tremor signal, which oscillates around the position signal. Four trials of 60 s were done for all conditions. A rest period of 60 s was allotted between trials. Lastly, a maximal contraction test was performed where participants were asked to raise their finger against a resistance using maximal

strength. The same was done for flexion of the finger. Two trials were done for flexion and extension. The goal was to normalize EMG activity to maximal voluntary contraction.

4.3.

Data analysis

Tremor analysis was performed with the S-Plus software (Mathsoft, Seattle, Washington USA). The data recorded by the laser were down-sampled to 256 Hz using a moving average. Prior to analysis, finger displacement data were filtered in order to preserve frequencies of interest below 30 Hz and to remove voluntary movements that are located below 1 Hz (high- and low-pass filters using a FFT-inverse FFT method; tapered from 0.9 to 1 Hz and 29.9 to 30 Hz, with a ramp of width 0.1 Hz). The amplitude of tremor was then calculated by applying a root–mean–square on the filtered displacement time series. The resulting value was then multiplied by 2*(2)0,5 to obtain a better representation of finger displacement. Each trial was separated into 5-s epochs on which amplitude was calculated, and the amplitude retained for one trial was the average of all 5-s epochs for that trial. Median power frequency and power dispersion (represents the width of a frequency band containing 68% of total power centered at the median power frequency) were computed in the same way but on the velocity power spectrum by differentiation of the displacement signal. This was done by simply computing the change in x over time for every data point. Computing methods are described in more details elsewhere (Carignan et al., 2010; Duval and Jones, 2005). Error was measured by averaging the absolute value of the deviation from target at every point over the entire signal. Then, error was calculated in the same way but for parts of the signal with slow and fast velocity (0 to 1.36 mm s− 1 and 1.36 to 4.34 mm s− 1, respectively). Equal time was spent in each slow and fast velocity section. Fig. 8 shows the path the finger had to follow.

Fig. 8 – Illustration of the path the finger had to follow. Solid lines represent the slow sections when the target line moved between 0 and 1.36 mm s− 1 and the dashed lines represent the fast sections where the target line moved between 1.36 and 4.34 mm s− 1. Note that the zero line shows when the finger was parallel to the floor. Also of note, changes in directions are inevitably associated with reduction of velocity such that the majority of the slow sections are located when these course changes occur.

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EMG data were down-sampled to 1024 Hz and filtered to preserve frequencies of interest located between 20 and 256 Hz using the same filtering method described above. Each trial was separated into 5-s epochs, and the amplitude retained for one trial was the average of every 5-s epoch. The amplitude of the EMG signal was calculated using a root– mean–square and was transformed into percentage of maximal voluntary contraction.

4.4.

Statistical analysis

All data and graph are expressed as mean ±95% confidence interval unless otherwise specified. One-way ANOVA with repeated measures was used to determine whether there were changes in total tremor amplitude and EMG activity between conditions. One-way ANOVA on rank with repeated measures was used to determine whether there were changes in median power frequency and power dispersion between conditions because the data did not demonstrate equal variance. Tukey's post hoc was used to determine which comparisons yielded significant differences. A paired t-test was performed to determine whether there were significant changes in mean error amplitude and error amplitude for different velocity sections between the tracking and tremor modulation conditions. Threshold for significance was set at p < 0.05 prior to data collection. Since no previous study had examined modulation of physiological tremor amplitude during voluntary movement, sample size was determined after pilot testing of 6 participants had occurred. For those participants, results showed a significant difference in tremor amplitude between the tracking and tremor modulation conditions (p = 0.010) with power equal to 0.874. We then proceeded to double our sample size to 12 participants, giving a significant difference in tremor amplitude (p < 0.001; Power = 0.996).

Acknowledgments The authors of the present study wish to thank the participants who agreed to be part of the study. This research was funded by the Natural Science and Engineering Research Council of Canada through a Master's scholarship (Carignan) and operating grant (Duval). Dr. Duval is also supported by a Fonds de la Recherche en Santé du Québec salary grant.

REFERENCES

Ang, W.T., Pradeep, P.K., Riviere, C.N., 2004. Active tremor compensation in microsurgery. Conf. Proc. IEEE Eng. Med. Biol. Soc. 4, 2738–2741. Carignan, B., Daneault, J.F., Duval, C., 2009. The amplitude of physiological tremor can be voluntarily modulated. Exp. Brain Res. 194, 309–316. Carignan, B., Daneault, J.F., Duval, C., 2010. Quantifying the importance of high frequency components on the amplitude of physiological tremor. Exp. Brain Res. 202, 299–306. Cernat, R., Matei, C.E., Olteanu, L., Riviere, C.N., Dumitras, D.C., 2006. Acousto-optic compensation of tremor for use in a

173

handheld laser microsurgical instrument. Conf. Proc. IEEE Eng. Med. Biol. Soc. 1, 3862–3864. Choi, D.Y., Sandoval, R., MacLachlan, R.A., Ho, L., Lobes, L.A., Riviere, C.N., 2007. Test of tracing performance with an active handheld micromanipulator. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2007, 3638–3641. Conway, B.A., Halliday, D.M., Farmer, S.F., Shahani, U., Maas, P., Weir, A.I., Rosenberg, J.R., 1995. Synchronization between motor cortex and spinal motoneuronal pool during the performance of a maintained motor task in man. J. Physiol. 489 (Pt 3), 917–924. Daneault, J.F., Carignan, B., Duval, C., 2010. Bilateral effect of a unilateral voluntary modulation of physiological tremor. Clin. Neurophysiol. 121, 734–743. De Jong, R., 1993. Multiple bottlenecks in overlapping task performance. J. Exp. Psychol. Hum. Percept. Perform. 19, 965–980. De Luca, C.J., Erim, Z., 1994. Common drive of motor units in regulation of muscle force. Trends Neurosci. 17, 299–305. Deuschl, G., Bain, P., Brin, M., 1998. Consensus statement of the Movement Disorder Society on Tremor. Ad Hoc Scientific Committee. Mov. Disord. 13 (Suppl 3), 2–23. Duval, C., Jones, J., 2005. Assessment of the amplitude of oscillations associated with high-frequency components of physiological tremor: impact of loading and signal differentiation. Exp. Brain Res. 163, 261–266. Duval, C., Beuter, A., Gauthier, S., 1997. Fluctuations in tremor at rest and saccadic eye movements in subjects with Parkinsons disease. Parkinsonism Relat. Disord. 3, 195–205. Duval, C., Panisset, M., Bertrand, G., Sadikot, A.F., 2000. Evidence that ventrolateral thalamotomy may eliminate the supraspinal component of both pathological and physiological tremors. Exp. Brain Res. 132, 216–222. Duval, C., Strafella, A.P., Sadikot, A.F., 2005. The impact of ventrolateral thalamotomy on high-frequency components of tremor. Clin. Neurophysiol. 116, 1391–1399. Duval, C., Panisset, M., Strafella, A.P., Sadikot, A.F., 2006. The impact of ventrolateral thalamotomy on tremor and voluntary motor behavior in patients with Parkinson's disease. Exp. Brain Res. 170, 160–171. Elble, R.J., 1995. Mechanisms of physiological tremor and relationship to essential tremor. In: Handbook of tremor disorders. Vol., L.J. Findley, W.C. Koller, ed.^eds. Dekker, New York, pp. 51-62. Elble, R.J., Koller, W.C., 1990. Tremor., Vol., Johns Hopkins University Press, Baltimore. Engelbrecht, S.E., Berthier, N.E., O'Sullivan, L.P., 2003. The undershoot bias: learning to act optimally under uncertainty. Psychol. Sci. 14, 257–261. Fishbach, A., Roy, S.A., Bastianen, C., Miller, L.E., Houk, J.C., 2005. Kinematic properties of on-line error corrections in the monkey. Exp. Brain Res. 164, 442–457. Fishbach, A., Roy, S.A., Bastianen, C., Miller, L.E., Houk, J.C., 2007. Deciding when and how to correct a movement: discrete submovements as a decision making process. Exp. Brain Res. 177, 45–63. Gomez-Blanco, M., Riviere, C.N., Khosla, P.K., 2000. Intraoperative tremor monitoring for vitreoretinal microsurgery. Stud. Health Technol. Inform. 70, 99–101. Goodman, D., Kelso, J.A., 1983. Exploring the functional significance of physiological tremor: a biospectroscopic approach. Exp. Brain Res. 49, 419–431. Halliday, A.M., Redfearn, J.W., 1956. An analysis of the frequencies of finger tremor in healthy subjects. J. Physiol. 134, 600–611. Hanneton, S., Berthoz, A., Droulez, J., Slotine, J.J., 1997. Does the brain use sliding variables for the control of movements? Biol. Cybern. 77, 381–393. Harris, C.M., 1995. Does saccadic undershoot minimize saccadic flight-time? A Monte–Carlo study. Vision Res. 35, 691–701.

174

BR A I N R ES E A RC H 1 3 7 0 ( 2 01 1 ) 1 6 4 –17 4

Harwell, R.C., Ferguson, R.L., 1983. Physiologic tremor and microsurgery. Microsurgery 4, 187–192. Kakuda, N., 2000. Response of human muscle spindle afferents to sinusoidal stretching with a wide range of amplitudes. J. Physiol. 527 (Pt 2), 397–404. Koster, B., Lauk, M., Timmer, J., Winter, T., Guschlbauer, B., Glocker, F.X., Danek, A., Deuschl, G., Lucking, C.H., 1998. Central mechanisms in human enhanced physiological tremor. Neurosci. Lett. 241, 135–138. Lamarre, Y., Joffroy, A.J., Dumont, M., De Montigny, C., Grou, F., Lund, J.P., 1975. Central mechanisms of tremor in some feline and primate models. Can. J. Neurol. Sci. 2, 227–233. Legros, A., Marshall, H.R., Beuter, A., Gow, J., Cheung, B., Thomas, A.W., Prato, F.S., Stodilka, R.Z., 2010. Effects of acute hypoxia on postural and kinetic tremor. Eur. J. Appl. Physiol. 110, 109–119. Llinas, R., 1984. Rebound excitation as the physiological basis for tremor: a biophysical study of the oscillatory properties of mammalian central neurons in vitro. In: Movement disorders: tremor. Vol., L.J. Findley, R. Capildeo, ed.^eds. Macmillan, London. Marsden, C.D., Meadows, J.C., Lange, G.W., Watson, R.S., 1969. The role of the ballistocardiac impulse in the genesis of physiological tremor. Brain 92, 647–662. McAuley, J.H., Rothwell, J.C., Marsden, C.D., 1997. Frequency peaks of tremor, muscle vibration and electromyographic activity at 10 Hz, 20 Hz and 40 Hz during human finger muscle contraction may reflect rhythmicities of central neural firing. Exp. Brain Res. 114, 525–541. Meyer, D.E., Kieras, D.E., 1997. A computational theory of executive cognitive processes and multiple-task performance: part 1. Basic mechanisms. Psychol. Rev. 104, 3–65. Miall, R.C., Weir, D.J., Stein, J.F., 1993. Intermittency in human manual tracking tasks. J. Mot Behav. 25, 53–63. Morrison, S., Newell, K.M., 2000. Postural and resting tremor in the upper limb. Clin. Neurophysiol. 111, 651–663. Morrison, S., Mills, P., Barrett, R., 2006. Differences in multiple segment tremor dynamics between young and elderly persons. J. Gerontol. A Biol. Sci. Med. Sci. 61, 982–990. Novak, K.E., Miller, L.E., Houk, J.C., 2003. Features of motor performance that drive adaptation in rapid hand movements. Exp. Brain Res. 148, 388–400. Pasalar, S., Roitman, A.V., Ebner, T.J., 2005. Effects of speeds and force fields on submovements during circular manual tracking in humans. Exp. Brain Res. 163, 214–225.

Pashler, H., 1994. Dual-task interference in simple tasks: data and theory. Psychol. Bull. 116, 220–244. Riviere, C.N., Rader, R.S., Thakor, N.V., 1998. Adaptive canceling of physiological tremor for improved precision in microsurgery. IEEE Trans. Biomed. Eng. 45, 839–846. Roitman, A.V., Massaquoi, S.G., Takahashi, K., Ebner, T.J., 2004. Kinematic analysis of manual tracking in monkeys: characterization of movement intermittencies during a circular tracking task. J. Neurophysiol. 91, 901–911. Selen, L.P., van Dieen, J.H., Beek, P.J., 2006. Impedance modulation and feedback corrections in tracking targets of variable size and frequency. J. Neurophysiol. 96, 2750–2759. Sigman, M., Dehaene, S., 2008. Brain mechanisms of serial and parallel processing during dual-task performance. J. Neurosci. 28, 7585–7598. Sowman, P.F., Turker, K.S., 2007. Mandibular tremor during isometric contractions. Arch. Oral Biol. 52, 353–356. Stiles, R.N., Randall, J.E., 1967. Mechanical factors in human tremor frequency. J. Appl. Physiol. 23, 324–330. Vaillancourt, D.E., Newell, K.M., 2000. Amplitude changes in the 8–12, 20–25, and 40 Hz oscillations in finger tremor. Clin. Neurophysiol. 111, 1792–1801. Vallbo, A.B., Wessberg, J., 1993. Organization of motor output in slow finger movements in man. J. Physiol. 469, 673–691. van Buskirk, C., Wolbarsht, M.L., Stecher Jr., K., 1966. The nonnervous causes of normal physiologic tremor. Neurology 16, 217–220. Wessberg, J., Kakuda, N., 1999. Single motor unit activity in relation to pulsatile motor output in human finger movements. J. Physiol. 517 (Pt 1), 273–285. Wessberg, J., Vallbo, A.B., 1995. Coding of pulsatile motor output by human muscle afferents during slow finger movements. J. Physiol. 485 (Pt 1), 271–282. Wessberg, J., Vallbo, A.B., 1996. Pulsatile motor output in human finger movements is not dependent on the stretch reflex. J. Physiol. 493 (Pt 3), 895–908. Wolpert, D.M., Miall, R.C., Winter, J.L., Stein, J.F., 1992. Evidence for an error deadzone in compensatory tracking. J. Mot Behav. 24, 299–308. Yap, C.B., Boshes, B., 1967. The frequency and pattern of normal tremor. Electroencephalogr. Clin. Neurophysiol. 22, 197–203.