article: Fatigue due to physical exertion is a ubiquitous phenomenon in everyday life and especially common in a range of neurological diseases. While the effect of fatigue on limiting skill execution are well known, its influence on learning new skills is unclear. This is of particular interest as it is common practice to train athletes, musicians or perform rehabilitation exercises up to and beyond a point of fatigue. In a series of experiments, we describe how muscle fatigue, defined as degradation of maximum force after exertion, impairs motor-skill learning beyond its effects on task execution. The negative effects on learning are evidenced by impaired task acquisition on subsequent practice days even in the absence of fatigue. Further, we found that this effect is in part mediated centrally and can be alleviated by altering motor cortex function. Thus, the common practice of training while, or beyond, fatigue levels should be carefully reconsidered, since this affects overall long-term skill learning.
  We know from everyday life that, in order to gain and maintain proficiency, the most critical requirement in a motor skill is practice. Intensive, repetitive training is an essential routine for musicians, artists, surgeons, and athletes. Repetitive practice is also part of rehabilitation approaches to recover function of the motor system and other domains. While repetition improves performance over time, there comes the point when it also causes fatigue and eventual degradation of task execution (Boyas and Guével, 2011; Gandevia et al. , 1995b). Studies investigating fatigue have made a distinction between fatigue as a cognitive phenomenon and fatigue as a neuromuscular phenomenon (Janet, 2012), although this separation can be blurred at times (Kuppuswamy, 2017). In neurological conditions, for instance, fatigue has been described as an overall state linked to changes in motor cortex excitability (Kuppuswamy et al. , 2015). Here, we use the term fatigue to describe the degradation of maximal force output induced through voluntary physical exertion of task-relevant muscles. Surprisingly little is known about the effects of muscle fatigue on the acquisition of motor skills. The existing literature regarding motor learning under fatigue is mostly limited to a few studies from the 1970–90 s with contradictory results (for a comprehensive overview see Janet, 2012). While some studies have reported that participants are unable to learn a motor task under fatigue conditions (Carron and Ferchuk, 1971, Thomas et al. , 1975), others have not found fatigue to be detrimental to motor learning (Cotten et al. , 1972 Alderman, 1965, Spano and Burke, 1976). One often overlooked key challenge in studying motor learning under fatigue is the so-termed ‘performance-learning’ distinction (Cahill et al. , 2001; Kantak and Winstein, 2012): Performance is usually defined as a temporary effect; for example, how skillful a movement is executed during one training session. In contrast, learning can only be inferred indirectly from performance, by measuring differences in performance over time or tasks (Kantak and Winstein, 2012). This distinction is essential because experimental conditions that affect performance do not necessarily have to affect learning. For example, while the performance of rats in the absence of a motivational cue seemed to show no learning in a maze task despite repeated practice, providing a food reward uncovered that they indeed had learned the right path nonetheless (Tolman and Honzik, 1930). Thus, it is necessary to separate decreased task performance under fatigue with true effects of fatigue on motor learning. Here, we address this issue by disentangling the effect of muscle fatigue on learning a motor skill from the performance confounder. In experiment 1 (N = 38), we asked healthy individuals to learn a sequential pinch force task over two days and showed that, even though participants were only fatigued at Day 1, skill learning was impaired on both days. Interestingly, a subgroup of fatigued subjects (N = 12) took two additional days of training with no fatigue to catch up to the skill performance level of the non-fatigued group. In experiment 2 (N = 20), we tested performance on the untrained, unfatigued hand and demonstrated that participants had impaired skill learning in both the fatigued and unfatigued effector. In experiment 3 (N = 45), we replicated the findings of experiment 1 and tested whether the negative effects of fatigue on learning are centrally mediated. We found that disruptive rTMS to the motor cortex (Cantarero et al. , 2013a; Huang et al. , 2010) partly alleviates the adverse effects of fatigue on skill learning, suggesting a possible role for maladaptive memory formation under fatigued conditions. Finally, in Experiment 4 (N = 18), we investigated if the observed fatigue effects are domain-specific or also present in another task that is cognitive demanding but requires minimal force control. We found that muscle fatigue did not affect the learning of a ten-sequence element task on Day 1 or 2. Altogether, our results provide the first evidence that motor fatigue has a domain-specific lasting adverse effect on skill learning. The findings are significant to professions that rely on intensive physical training to achieve optimal performance. Understanding the effects of fatigue on learning helps the formulation of training and rehabilitation regimens geared to improve motor function.
  In the first experiment, we assessed how muscle fatigue influenced skill learning over multiple days. 38 participants trained in a force-control demanding, isometric pinch task for two days; see Figure 1. While all subjects were instructed to perform an isometric pinch contraction prior to four bouts of training on Day 1, a subset of participants (Fatigue group (FTG), N = 20) performed the contractions until experiencing muscle fatigue (~60% decrement of maximal voluntary contraction. MVC was measured in Newton and monitored by surface electromyographic (EMG) signal). A control group (NoFTG, N = 18) contracted the same muscle group at ~5% of MVC over a matched period of time without experiencing force decline. On Day 2 both groups performed the skill task without the induction of fatigue. Skill learning was indexed by a measure that quantifies shifts in the relationship between movement time and accuracy rate (Reis et al. , 2009). As the relationship between learning rate and skill measure appeared linear, a regression line was fit separately for each day and group. Here the slope of the regression line represents the learning rate (see Materials and methods). Fatigue was reliably induced during Day 1 in the fatigue group, as shown by decrements of MVC over time. Importantly, MVC always stayed above the force level required to execute the task (up to 40% of MVC; see Appendix 1). All participants improved their ability to execute the task on both days; see Figure 1. However, on both days, learning rates for the FTG were significantly reduced when compared to the NoFTG (Day 1: mean slopes NoFTG 0. 169 versus FTG 0. 038; p=0. 01. Day 2: mean slopes NoFTG 0. 339 versus FTG 0. 083; p=0. 03; Figure 1). It is important to note that the lower performance of the fatigued group on Day 1 does not allow to make a direct inference about lower motor learning (e. g. , because of task differences). Nonetheless, on Day 2 the FTG did not even reach the same execution level the NoFTG subjects had at the end of Day 1 (p=0. 01), despite having twice the amount of practice and not been fatigued (i. e. they had similar MVC and were performing within the same force range). Of note, there were no changes in overall learning rate across days for either controls or the fatigued group (Day one versus Day 2 NoFTG: p=0. 21, FTG: p=0. 77), indicating that learning rates remained low in the FTG. A separate analysis of the changes in movement time and percentage of correct trials showed that the effect of fatigue on skill performance was due to more errors in the FTG and not to differences in movement time. Counter-intuitively, this higher rate of errors was due to increased force production in the FTG resulting in target overshoot. This was most prominent for the two lower force targets while performance in the highest force target was similar between groups (see Appendix 1). To assess, how much practice the FTG took to reach similar performance levels as the NoFTG, a subgroup of participants continued training up to four days (NoFTG4D N = 12, FTG4D N = 12). While Day 1 and two showed the same result with lower learning rates for the FTG4D (Day 1: p<0. 01; Day 2: p<0. 01), this group reached similar performance levels to the control group only towards the end of Day 3 and on Day 4 (Day 3: p=0. 07; Day 4: p=0. 09; see Figure 2). Since learning in the FTG was impaired even in the non-fatigued state on Day 2, these results indicate that learning under fatigue conditions has a long-lasting detrimental effect on skill acquisition. Because execution under fatigue is impaired, it is conceivable that this performance confounder masked skill learning. Assessing the transfer of learning to the uninstructed, unfatigued hand provides a unique way to circumvent this challenge. Generalization of motor skills across hands has previously been well characterized, where skill training with one hand results in improved performance in the untrained hand (Camus et al. , 2009; Perez et al. , 2007). Thus, in experiment 2, we measured skill execution in the left hand of a new group of 20 participants before and after training with their right fatigued (FTGTRANSFER, N = 10) or non-fatigued hand (NoFTGTRANSFER, N = 10). Similar to experiment 1, right hand learning rates over the four blocks were lower in participants that performed the task under fatigue compared to controls (mean slope NoFTGTRANSFER 0. 03 versus FTGTRANSFER 0. 008; p=0. 01; Figure 3). As expected, prior to training, the skill measure of the left hand was similar between groups (t18 = −0. 157, p=0. 88). After training with the right hand, performance of the left hand was significantly lower in the FTGTRANSFER compared to the NoFTGTRANSFER (mean ∆block2-block1 NoFTGTRANSFER 0. 133 ± 0. 036 versus FTGTRANSFER 0. 018 ± 0. 003; p<0. 01; Figure 3). Based on the work of Tolman and Honzik (Tolman and Honzik, 1930) differences in skill performance between the fatigued and the control groups could arise from impeded execution in the right hand, which could theoretically mask similar underlying learning rates in both groups. If this would be the case, then the unfatigued effector in both groups should show comparable skill levels. However, our results indicate that fatigue did indeed affect learning and that the performance confounder did not just mask the expression of learning. Importantly, the poor performance in the untrained hand suggests that fatigue impairs central motor-skill learning mechanisms beyond any potential adverse effect within the fatigued effector. To determine whether the effect of fatigue on learning is centrally mediated, we interfered with primary motor cortex processes thought to be involved in skill retention in a new group of participants (Galea et al. , 2011; Muellbacher et al. , 2002; Reis et al. , 2009; Richardson et al. , 2006). To this end, in experiment 3 we used disruptive rTMS (repetitive transcranial magnetic stimulation) over the primary motor cortex (M1) after task training on Day 1 (FTGM1, N = 15) (Cantarero et al. , 2013a; Huang et al. , 2010). To control for potential non-specific effects of rTMS, we also tested a fatigued and a non-fatigued group with TMS applied over the parietal interhemispheric fissure (Pz, according to 10–20 system; FTGSHAM, N = 15 and NoFTGSHAM, N = 10). The permutation test showed that learning rates of both fatigued groups were smaller compared to controls, but similar to each other on Day 1 (mean slope NoFTGSHAM 0. 049, FTGSHAM 0. 02, FTGM1 0. 016; NoFTGSHAM versus FTGSHAM p=0. 01, NoFTGSHAM versus FTGM1 p<0. 01, FTGSHAM versus FTGM1 p=0. 73; Figure 4). On Day 2, consistent with experiment 1 and 2, the learning rate was still smaller in FTGSHAM compared to the NoFTGSHAM control. However, the learning rate of the FTGM1 group was not significantly different from the NoFTGSHAM, but significantly different from FTGSHAM (mean slope NoFTGSHAM 0. 04, FTGSHAM 0. 022, FTGM1 0. 042; NoFTGSHAM versus FTGSHAM p=0. 04, NoFTGSHAM versus FTGM1 p=0. 47, FTGSHAM versus FTGM1 p=0. 03). Of note, comparing learning rates across days within groups, we found a significant difference for FTGM1 (Day one versus Day 2, FTGM1 p=0. 04), but no difference for the other two groups (Day 1 versus Day 2, NoFTGSHAM p=0. 94, FTGSHAM p=0. 88). Together, these results show that disruption of M1 function after training diminished the detrimental effects of fatigue on motor-skill learning. This indicates that the long-lasting effects of fatigue on learning are at least partly centrally mediated and linked to motor memory formation. To determine whether the observed muscle fatigue effects are specific to tasks with high motor control demand (e. g. , accurate force control to complete the sequence) vs. a task with more cognitive demands, we performed an additional control. Here, 18 healthy participants performed a 10-element, finger sequence task by simply pressing the correct key on a computer keyboard after being fatigued (FTGSEQUENCE, N = 9) or not (NoFTGSEQUENCE, N = 9) as done in the prior experiments (for visualization of the study design the Materials and methods and Appendix 1). We found that fatigue prior to the sequence learning did not result in different performance compared to the non-fatigued control group on Day 1 or Day 2. Both groups had less errors in block 4 than block one in both days, with no group difference on either day (Day 1: block: F (3,16) = 4. 474, p=0. 021; group: (F (1,16) = 0. 329, p=0. 574), block*group: (F (3,16) = 1. 458, p=0. 79; Day 2: block: F (3,16) = 5. 363, p=0. 034; group: (F (1,16) = 0. 535, p=0. 475), block*group: (F (3,16) = 2. 603, p=0. 126). The same was true for movement times. Participants decreased their times from block one to block four, but there was no group differences (Day 1: block: F (3,16) = 146. 34, p<0. 001; group: (F (1,16) = 0. 498, p=0. 49), block*group: (F (3,16) = 0. 169, p=0. 972; Day 2: block: F (3,16) = 11. 31, p=0. 004; group: (F (1,16) = 1. 106, p=0. 309), block*group: (F (3,16) = 4. 192, p=0. 057; Figure 5). These results indicate that the detrimental effects of muscle fatigue on learning are specific to skill tasks that required fine force-control, but not in more cognitive-demanding tasks. To ensure that the results from the previous experiments were not due to prolonged physical manifestations of fatigue on Day 2, we fatigued a new group of participants on Day 1 but did not expose them to the pinch force task until the second day (FTGSKILL-DAY2, N = 5). On Day 2, this group showed similar learning rates when compared to the control group on Day 1 (mean slope FTGSKILL-DAY2 0. 049; p=0. 50; see Appendix 1).
  We tested motor learning of a skill task under conditions of fatigue. We found that learning in a fatigued state results in detrimental effects on overall task acquisition. These phenomena are present above and beyond the deleterious consequences of fatigue on performance and appears to be domain-specific, and at least in part, centrally mediated. The deleterious fatigue effect was of such a magnitude that took participants twice as much time to reach the level of performance of individuals who learned the task de novo in non-fatigue conditions. These observations need to be carefully considered when designing training protocols such as in sports or musical performance as well as for rehabilitation programs. While conditions of fatigue during sports or performing arts can occur by chance or by overachieving attitudes, rehabilitation programs are particularly at risk because patients with neurological conditions such as those following stroke or multiple sclerosis frequently experience fatigue.
  A total of 121 healthy participants were recruited from two centers (Johns Hopkins University and University College London). None of the participants suffered from any neurological or psychiatric disorder, nor were they taking any centrally-acting prescribed medication. The experiments were approved by the respective ethics boards at Johns Hopkins School of Medicine Institutional Review Board and the North West London Research Ethics Committee in accordance with the Declaration of Helsinki and written informed consent as well as consent to publish was obtained from all participants (ethics board number 00077792). For the first experiment, the sample size was chosen in line with previously reported effect sizes in motor-skill learning studies (Cantarero et al. , 2013a; Cantarero et al. , 2013b; Reis et al. , 2009). For each experiment, participants were seated in front of a computer monitor and given a force transducer to hold between the thumb and index fingers of their dominant hand. During each trial, participants were instructed to produce isometric pinch presses at different force levels to control the motion of a cursor displayed on the screen. Increasing force resulted in the cursor moving horizontally to the right. Participants were instructed to increase and decrease their pinching force to navigate the cursor through the following sequence: start-gate1-start-gate2-start-gate3-start-gate4-start-end; see Figure 6. The cursor movement followed logarithmic transduction of the applied pinch force as described in previous studies (Reis et al. , 2009). This task has been widely used to study skill learning (e. g. , Reis et al. , 2009; Cantarero et al. , 2013a). It involves two components of learning, speed and accuracy, which we could explore independently (see also Data Analysis and Appendix 1). While the chosen task allows for detailed behavioral assessment of changes underlying learning, due to differences in muscle strengths across fingers involved in the task, the behavioral assessment is potentially less suited to analyze independent mechanisms of peripheral and central fatigue within the hand muscles involved. Participants were seated in front of a computer screen and presented with a horizontal display of five square stimuli (‘G’, ‘H’, ’ J’, ‘B’ or ‘N’) with one highlighted in green. Subjects were instructed to press as fast as possible the corresponding computer key on a desktop keyboard with their index finger. The next element of the sequence was only presented after the correct key response. If an incorrect key response was pressed, the sequence was paused and only resumed once the appropriate key response was made. Each sequence trial started with the presentation of a ‘go’ cue. Participants were exposed to the same 10-element sequence on each trial and had to perform 30 trials in total to complete one of four blocks. Transcranial magnetic stimulation (TMS) was administered with a figure-eight coil (wing diameter = 70 mm) connected to a Magstim 200 stimulator (Magstim, UK). Using TMS, We located the ‘hot-spot’ of the abductor pollicis brevis muscle in the task-relevant hand at rest according to standardized procedures (Chen et al. , 2008; Rossini et al. , 1994). The stimulus intensity that elicited a motor evoked potential (MEP) with a peak-to-peak amplitude of approximately 1 mV was established (Stimulus intensity 1 mV, S1mV) to assess corticomotor excitability. Then 18 MEPs were recorded using the same intensity before the task, directly after the task, and after depotentiation on Day 1 as well as before and after the task at Day 2. The parameters for depotentiation were based on previous reports (Cantarero et al. , 2013a; Huang et al. , 2010), consisted of bursts of three pulses at 50 Hz repeated at 200 ms intervals at an intensity of 70% of the rMT for 10 s. Using the 10–20 electroencephalogram coordinate system, Pz was used as a control stimulation location (SHAM stimulation). Stereotactic neuronavigation (BrainSight, Rogue Research, Montreal, Quebec, Canada) was used to track coil position within sessions. EMG activity from the abductor pollicis brevis muscle was recorded using surface electrodes taped in a belly-tendon orientation. Data were recorded with an AMT-8 (Bortec Biomedical Ltd; sampling rate 5000 Hz, amplification 1000x, band-pass filtering 10–1000 Hz) and saved for offline analysis. For analysis of MVC see Appendix 1. For each trial in the skill task, movement time and error rate were recorded: movement time was defined as the duration from movement onset (forced controlled cursor leaving start position) to reaching the end gate. Error rate was defined as the percentage of trials per block in which participants under- or overshot at least one of the five targets. For the skill learning task, movement time was defined as the duration between the first and the 10th correct key press. Error rate was defined as the percentage of trials per block (number of errorless sequences per block). Movement time and error rate were compared using rmANOVA with the within-subject factor ‘block’ (four levels: b1, b2, b3 and b4) and the between-subject factor ‘group’ (Exp. 1 and 2: two levels, Exp. 3: three levels) for each single day (see Appendix 1). To quantify motor performance, we calculated a skill measure, composed of movement time and error rate. As done in prior studies, the skill measure was calculated as: a= (1- error rate) / [error rate (ln (movement time) b) ], where b is 5. 424 as predefined for this particular task in prior studies (Reis et al. , 2009; Cantarero et al. , 2013b; Mawase et al. , 2017; Spampinato and Celnik, 2017). To study learning (rate of change in performance) during the motor task, we plotted the number of blocks on the x-axis and the skill measured on the y-axis. As the relationship was roughly linear, we fit a robust linear regression model (e. g. , f (x) =c*x + b) for each group; the robust function disregards outliers by estimating an iteratively reweighted least square algorithm. This provided a more parsimonious model than ANOVA, providing a single easily interpretable measure of learning rate given by the slope c. We were particularly interested in measuring and comparing the learning rates between days and groups. When only two blocks were tested (i. e. the left hand in experiment 2) we took the difference between block2 – block1 as a measure of learning rate. Differences in learning rates for each experiment were assessed using a permutation testing procedure. Assuming the null hypothesis of no group difference, participants were randomly re-assigned to the two groups, and the difference in regression coefficients between the resampled groups was computed. This procedure was repeated 10,000 times, allowing us to generate a null distribution for the difference between regression coefficients assuming no group differences. The proportion of resampled values that exceeded the true observed difference was used to compute p-values and determine statistical significance. Under the null hypothesis, the true difference in learning rates between the two groups should lie within the distribution of these randomly generated differences, with extreme values providing evidence against the null hypothesis. Prior to application of any parametric tests, the normality of the dependent variables was assessed using Shapiro-Wilk tests and quantile-quantile plots. A log-transformation was applied to correct for any non-normal data. All ANOVA results were Greenhouse-Geisser corrected if the assumption of sphericity was violated. Student’s t-test was used to assess group differences. Results were considered significant at p<0. 05, and Bonferroni correction was applied to correct for multiple comparisons. All data are expressed as mean ±standard error unless stated otherwise. Statistical analyses were performed using SPSS 22. 0 and custom-written MATLAB routines. Data and custom-written code are available at Branscheidt (2018).
summary: Mastering a new movement requires practice. Intensive and repetitive training is essential for musicians, athletes, or surgeons. It is also important for people undergoing rehabilitation to help them regain normal movements after an illness or injury. Although practice is said to make perfect, there comes the point when it also causes physical fatigue. Fatigue can impair how well a person performs a movement, but its effects on learning a task are less clear. Now, Branscheidt et al. show that being physically fatigued interferes with learning a new movement skill. In the experiments, volunteers were divided in two groups: the first group had to learn a new motor skill after their hand muscles were physically fatigued, the second group learned the same task without being worn out. The fatigued volunteers had a harder time learning a new motor task both on the day of the task and on the following days, even after they had recovered from the fatigue. The same experiment was repeated, but instead of learning a motor task, the volunteers were asked to learn a sequence of keystrokes. The volunteers in both groups learned this new thinking task easily. This suggests that learning new thinking tasks is not affected by physical fatigue. Branscheidt et al. also disrupted memory formation in part of the brain that controls movement after volunteers finished learning the motor task using a technique called repetitive transcranial magnetic stimulation. This eliminated the motor learning deficit in the fatigued group. This may suggest that memories formed after fatigue may impair later motor learning and that physical training or rehabilitation that pushes people to work past fatigue may be counterproductive. Further study of these processes may help to develop better training regimens and rehabilitation methods.
keywords: neuroscience
title: Fatigue induces long-lasting detrimental changes in motor-skill learning