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REPORT

Role of Voluntary Drive in Encoding an Elementary Motor Memory

Human Cortical Physiology Section, National Institutes of Health, Bethesda, Maryland

Submitted 12 February 2004; accepted in final form 11 September 2004

	ABSTRACT

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Motor training consisting of repetitive thumb movements results in encoding of motor memories in the primary motor cortex. It is not known if proprioceptive input originating in the training movements is sufficient to produce this effect. In this study, we compared the ability of training consisting of voluntary (active) and passively-elicited (passive) movements to induce this form of plasticity. Active training led to successful encoding accompanied by characteristic changes in corticomotor excitability, while passive training did not. These results support a pivotal role for voluntary motor drive in coding motor memories in the primary motor cortex.

	INTRODUCTION

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Motor training results in use-dependent plasticity thought to underlie improvements in motor performance (Nudo 2003; Ziemann et al. 2001) and in encoding of motor memories (Butefisch et al. 2000, 2002, 2004; Classen et al. 1998; Donchin et al. 2002; Sawaki et al. 2002; Shadmehr and Holcomb 1997). These memory traces encode the kinematic details of the practiced movements and may contribute to skill acquisition (Classen et al. 1998).

Training consisting of motor imagination, even in the absence of actual movements, results in improved performance (Denis 1985) and in cortical reorganization (Pascual-Leone et al. 1995). Therefore it is possible that perceptual aspects originating in actual training movements are sufficient to encode a motor memory. Supporting this proposal is the finding that peripheral-nerve electrical stimulation, which delivers repetitive afferent volleys to the cortical representation of the stimulated body part, results in increased excitability of the stimulated body part representation in the human motor cortex (Kaelin-Lang et al. 2002; Ridding et al. 2000; Stefan et al. 2000, 2002). In patients with brain lesions, stimulation of peripheral nerves in the absence of motor training, may even lead to improved muscle strength in the stimulated body part (Conforto et al. 2002; Struppler et al. 1996). Rehabilitative treatments consisting of performing passive movements in functionally coherent settings are often used when patients with brain lesions are too weak to move (Hummelsheim et al. 1994). On the other hand, a recent study showed that motor training is clearly more effective than training consisting of passively elicited movements in inducing an increase in motor performance as well as cortical reorganization (Lotze et al. 2003). There is less information available on training characteristics required to store the kinematics of a movement and thus to encode a motor memory in humans. To address this issue, we studied the influence of voluntary motor drive and proprioceptive input originated in passively induced movements on encoding a motor memory in the primary motor cortex (Butefisch et al. 1999, 2000; Classen et al. 1998; Sawaki et al. 2002).

	METHODS

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Subjects

Nine healthy right-handed volunteers (7 males, 2 females) aged 23–43 yr (mean: 34 yr) with no history of neurological or psychiatric diseases participated in this study and underwent a careful neurological examination. Handedness was evaluated by the Edinburgh inventory (Oldfield 1971). The protocol was approved by the National Institute of Neurological Disorders and Stroke Investigational Review Board, and all subjects gave written informed consent.

Paradigm

We measured the ability of two different forms of motor training (passive and active) to influence the direction of thumb movements elicited by noninvasive stimulation of the contralateral motor cortex using transcranial magnetic stimulation (TMS) using a previously described paradigm (Butefisch et al. 1999, 2000; Classen et al. 1998; Sawaki et al. 2002). Subjects sat on a chair firmly connected to a frame designed to immobilize the head and to keep the stimulating magnetic coil in a constant position relative to the head. Head and coil stability were monitored with a three-dimensional laser system. The subject's right arm was immobilized in a semipronated position in a molded armrest with the four long fingers supported and the thumb entirely free to move. Thumb movements were recorded with a two-dimensional accelerometer mounted on the proximal phalanx of the thumb (Kistler Instrument, Amherst, NY). Subjects included in this study fulfilled the following strict inclusion criteria in a separate session before the study: ability of TMS to elicit isolated thumb movements in the absence of movements of any other digits, wrist or arm; consistent (reproducible) direction of TMS-evoked movements in the baseline condition.

TMS

TMS was delivered from a custom-built Cadwell magnetoelectric stimulator (Cadwell Laboratories, Kennewick, WA) through a figure-8 magnetic coil. TMS intensity and coil position were first determined to elicit consistent thumb movements and were kept constant throughout the experiment (Butefisch et al. 2000; Sawaki et al. 2002). Resting motor threshold (rMT) and motor-evoked potentials (MEP) to TMS were measured as previously described (Butefisch et al. 2000; Sawaki et al. 2002). Before and after training, 60 TMS stimuli were delivered at 0.1 Hz to the optimal scalp position to elicit thumb movements. The baseline TMS-evoked thumb movement direction was defined for each subject and each training session as the mean angle of all 60 TMS-evoked movements before training (Butefisch et al. 2000; Sawaki et al. 2002).

EMG recordings

Surface electromyographic (EMG) activity was recorded from the abductor pollicis brevis (APB) and extensor pollicis brevis (EPB) muscles using 9-mm round surface electrodes in a belly-tendon arrangement. EMG signals were amplified and band-pass filtered between 10 Hz and 2 kHz using a Dantec counterpoint electromyograph (Dantec Electronics, Skovlunde, Denmark). The signals were then fed to a Labview-based data-acquisition system (National Instruments, Austin, TX), digitized at a frequency of 5 kHz and stored for off-line data analysis. Subjects' relaxation was closely monitored by continuous high-gain surface EMG-signal on an oscilloscope and auditory feedback. Trials with background EMG activity were discarded from further analysis. Muscles mediating movements in the training direction were labeled as "agonist" and those mediating movements in the opposite direction as "antagonist." Additionally, we measured changes in MEP_agonist/MEP_antagonist ratios. An increased ratio as a function of training would reflect differential excitability changes in agonist and antagonist muscle groups (Butefisch et al. 2000).

Kinematic recordings

Acceleration signals were recorded from a two-dimensional accelerometer positioned over the thumb, digitized at 3,000 Hz, amplified, band-pass filtered between 1 and 100 Hz, stored for off-line data analysis, and monitored on-line to secure training consistency. The direction of TMS-evoked and of voluntary thumb movements was calculated from the first-peak acceleration vectors (converted to "g") of each single movement on a two-dimensional plane using a two-channel accelerometer (flexion-extension on the vertical axis and abduction-adduction on the horizontal axis). Motor training kinematics were monitored by measuring the dispersion of thumb training movement directions and the magnitude of the first peak acceleration of these movements.

Motor training

Subjects participated in two randomly ordered experimental sessions (active or passive training) separated on average by 2.5 wk. An additional separate control experiment was performed. In the active training session (n = 9), subjects practiced voluntary, brisk thumb movements paced by an acoustic signal in a direction opposite to the baseline TMS-evoked thumb movement direction for 30 min (1 Hz) (Classen et al. 1998). After each voluntary movement, the thumb returned to the start position by relaxation, as confirmed by EMG monitoring. Monitoring accuracy and consistency of training was done on-line using the acceleration signal. If necessary, verbal feedback was provided and the subjects were encouraged to perform better. In the passive training session (n = 9), the same experimenter moved the subject's thumb passively and briskly in a direction opposite to the baseline TMS-evoked movement direction for 30 min (1 Hz). Each passive movement was paired with presentation of the same acoustic signal as in the active training session. Monitoring of movement kinematics was implemented as in the active training session and similar verbal feedback was provided if the subject was not relaxed. A previous study has demonstrated that there is no carry-over effect between repeated active training sessions (Classen et al. 1998). Additionally, TMS stimuli (n = 60) were applied before, and after a control session (n = 6) in the absence of any form of training. Endpoint measure of the study: To describe the training effects on TMS-evoked movement directions, we defined a training target zone (TTZ) as a window of ±20° centered on the training direction (Butefisch et al. 2000; Sawaki et al. 2002). The endpoint measure of this study was the increase in the proportion of TMS-evoked movements that fell within the TTZ after training (Butefisch et al. 2000; Sawaki et al. 2002). By design, training was in a direction opposite to the baseline TMS-evoked movement direction (Fig. 1, C and D). Therefore the proportion of TMS-evoked movements within the TTZ before training was relatively small.

View larger version (26K): [in this window] [in a new window]   FIG. 1. A: percentage increase in the number of transcranial magnetic stimulation (TMS)-evoked movements in the training target zone as a function of training type. Note that active training () led to a significant increment in this parameter relative to baseline, while passive training did not (*). B: angular difference between training movements and posttraining TMS-evoked movement directions. Note the small angular difference in the active condition (), whereas in the passive condition they were fundamentally different (*). C and D: radar plots in a representative subject (length of each axis line = 12 counts). 0° () represents the training direction (in most cases, 0°: extension, 90°: abduction and 270°: adduction). In the passive session (C), directions of TMS-evoked movements were similar before () and after () training. In contrast, in the active session (D), directions of TMS-evoked movements after training () matched the training d irection, approximately 180° opposite to the baseline direction () (*: P < 0.05).

Off-line data analysis of motor kinematics and voluntary movements for all trials was done by an investigator blind to training type. Statistical analysis was done using "GB-Stat" software (Dynamic Microsystems, Silver Spring, MD). Changes in the proportion of TMS-evoked movements in the TTZ (our endpoint measure), rMT, MEP-amplitude agonist-antagonist ratios (MEP_agonist/MEP_antagonist) (see Butefisch et al. 2000), dispersion of training movement and the magnitude of the first peak acceleration of these movements between the passive and the active session were analyzed using paired Wilcoxon signed-rank test. Dispersion of training movement directions "r" (Batschelet 1981; Butefisch et al. 2000; Sawaki et al. 2002) ranges between 1 (all acceleration vectors with exactly the same direction) and 0 (no preferential direction at all). Additionally, training effects within individual were analyzed using ² tests (2 x 2 table) with Fisher's exact test. MEP amplitude changes within session were analyzed using a two-way repeated-measure ANOVA with factors muscle (agonist and antagonist) and time (before and after training). We also compared MEP_agonist/MEP_antagonist ratios across sessions with a two-way repeated-measures ANOVA with main factors training (active, passive, control) and time (before and after training). Homogeneity of variance was confirmed by the Hartley test. Data are expressed as means ± SE and results are considered significant if P < 0.05.

	RESULTS

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Kinematic properties of training movements (magnitude of 1st-peak acceleration and dispersion of training movement directions) were comparable across conditions (Table 1). Variability of TMS-evoked movement directions was also comparable at baseline in the two sessions (0.60 ± 0.06, range: 0.32–0.91, and 0.67 ± 0.07, range: 0.27–0.80, NS) and did not change significantly with training.

The proportion of TMS-evoked movements in the TTZ increased significantly after active (14.9 ± 5.5%, z = –2.7, P < 0.01) but not after passive (–0.8 ± 2.0%, z = –0.3, NS) training relative to baseline (Fig. 1, A and B). Additionally, the increase in TMS-evoked movements in the TTZ was larger after active than after passive training (Wilcoxon rank test, z = –2.4, P < 0.05). TMS-evoked thumb movement directions correlated with the trained movement direction after active but not after passive training (correlation between the mean angle of the posttraining TMS-evoked movements calculated for each subject with the mean angle of the training movement in the same individual, Pearson correlation coefficient active: r = 0.66, P = 0.05; passive: r = 0.24, P > 0.5, n = 9). Conversely, the angular difference between the direction of training movements and the direction of TMS-evoked thumb movements matched closely only after active training (Fig. 1, C and D).

The proportion of TMS-evoked movements in the TTZ in the absence of training (control condition) was similar in two determinations separated by 30 min (difference: –1.9%±2.0%, z = –0.7, NS), demonstrating the reproducibility of the endpoint measure. Individual subject analysis showed that the proportion of TMS-evoked movements in the TTZ increased with active training in all nine individuals (significantly in 5, P < 0.05) but not with passive training nor in the absence of training in the control session.

Corticomotor excitability

Corticomotorneuronal excitability preceding training sessions was comparable as expressed by the baseline rMT (FPB; active session: 48 ± 2.3% of stimulator's output, passive session: 46.8 ± 1.3%, z = –0.3, NS; EPB; active session: 47.1 ± 2.2%, passive session: 45.8 ± 1.2%, NS), and by the TMS intensities required to evoke thumb movements (active session: 57 ± 2.5%; passive session: 56.1 ± 2.2%, z = –0.4, NS). Within-session analysis showed that in the active training session the factors time and muscle were not significant, whereas the time x muscle interaction was (2-way ANOVA, F = 5.5, df = 1, P = 0.03, Table 2, Fig. 2).

View larger version (40K): [in this window] [in a new window]   FIG. 2. Motor-evoked potentials (MEP) amplitudes in agonist () and antagonist () muscles with active and passive training. Note the differential effect of active training on both corticomotoneuronal excitability targeting agonist and antagonist muscles.

	DISCUSSION

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The main result of this study is that active motor training led to encoding of a motor memory in the primary motor cortex, consistent with previous reports (Butefisch et al. 2000; Classen et al. 1998; Sawaki et al. 2002), whereas passive training did not. Voluntary drive is associated with changes in EEG (Deecke and Kornhuber 1977) and also in brain activation patterns (Lotze et al. 2003). Overall, similar neural substrates are activated in association with active and passive motions (Nelles et al. 1999; Weiller et al. 1996). These commonalities led to the proposal that passive training could be as successful as active motor training in eliciting cortical plasticity and motor learning (Carel et al. 2000). One previous study addressed this hypothesis and demonstrated that voluntary motor drive is required for motor learning and also appears to be influential in modulating corticomotor excitability (Lotze et al. 2003). Our present results indicate that active training is more effective than passive training in encoding a motor memory in the primary motor cortex, a process that is not associated with overt performance improvements or learning but that may be involved as a prerequisite for skill acquisition (Classen et al. 1998).

MEP amplitudes, which convey information on the magnitude of corticomotor excitability targeting specific muscle groups, were larger in muscles agonist to the training motions and smaller in muscles antagonistic to the training motions with active but not with passive training. Additionally, the MEP_agonist/MEP_antagonist ratio increased only with active training. Therefore active training led to a differential modulation of corticomotor excitability, enhanced in muscles agonistic to the training motions and depressed in muscles antagonistic to the training motions (Butefisch et al. 2000; Classen et al. 1998; Sawaki et al. 2002). It is likely that this differential modulation of corticomotor excitability represents the neurophysiological correlate of the newly encoded motor memory. On the other hand, passive training resulted in decreased corticomotor excitability in both agonist and antagonist muscle groups, a phenomenon that could be due to habituation following repetitive passive motions or to the active will to relax, as shown in sensory modalities (Fahle et al. 1995).

Similarities between both training types included movement kinematics, total number of movements, and the attention paid to the training motions, whereas the main difference was the absence of voluntary motor drive during passive training. It is conceivable that additional minor, undetected differences existed between active and passive training. If present, it is unlikely that they resulted in the marked differences in memory encoding encountered in this study.

While these results underline differences in the ability of active and passive motor training alone to encode a novel motor memory, it is conceivable that longer-lasting or more numerous sessions or the combination of passive training with motor imagery (Li et al. 2004) or brain stimulation (Butefisch et al. 2004) could be more effective. Additionally, proprioceptive input that is directly relevant to motor performance or motor responses may elicit more prominent plastic changes (Kaelin-Lang et al. 2002). Such a proposal would be consistent with the documented influence of somatosensory input on motor cortical function (Mima et al. 1999; Ridding et al. 2000; Stefan et al. 2000, 2002; Weiller et al. 1996). For example, passively elicited movements are associated with increased blood flow in regions similar to those activated during performance of voluntary movements (i.e., primary motor cortex) (Nelles et al. 1999; Weiller et al. 1996). Additionally, somatosensory input elicited by stimulating peripheral nerves modulates corticomotor excitability in both humans (Kaelin-Lang et al. 2002; Lewis et al. 2001; Ridding et al. 2000) and animals (Lemon and Porter 1976; Sanes et al. 1992) and can possibly contribute to recovery of motor function following cortical lesions such as stroke (Conforto et al. 2002).

In summary, our results underline the role of voluntary drive in encoding an elementary motor memory and raise the hypothesis of a modulatory role of propioceptive input in shaping this process. Additionally, these results suggest that to influence more effectively neurorehabilitative processes, passive manipulations may require longer application times or combined interventional approaches.

	GRANTS

TOP ABSTRACT INTRODUCTION METHODS RESULTS DISCUSSION GRANTS ACKNOWLEDGMENTS REFERENCES

 
A. Kaelin-Lang was partially supported by a grant from the Swiss Parkinson Foundation.

	ACKNOWLEDGMENTS

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We thank D. Schoenberg for skillful editing.

Present address of A. Kaelin-Lang: Neurology Dept., University Hospital, Inselspital, Bern, Switzerland.

	FOOTNOTES

 
The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Address for reprint requests and other correspondence: L. G. Cohen, National Institutes of Health, Bldg.10, Room 5N234, 10 Center Dr., MSC 1430, Bethesda, MD, 20892 (E-mail: COHENL{at}ninds.nih.gov)

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This Article Abstract Full Text (PDF) All Versions of this Article: 93/2/1099    most recent 00143.2004v2 00143.2004v1 Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in Web of Science Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Web of Science (10) Citing Articles via Google Scholar Google Scholar Articles by Kaelin-Lang, A. Articles by Cohen, L. G. Search for Related Content PubMed PubMed Citation Articles by Kaelin-Lang, A. Articles by Cohen, L. G.