Unbeknownst to the subjects, a third factor was taken into account .... signal was always presented at the same time as an irrelevant shape. (e.g., star, triangle ..... Finally, unpaired Student's t-tests were used to compare the Implicit In-.
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NeuroImage
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journal homepage: www.elsevier.com
Learning stage-dependent effect of M1 disruption on value-based motor decisions Gerard Derosiere∗ , Pierre Vassiliadis, Sophie Demaret, Alexandre Zénon, Julie Duque Institute of Neuroscience, Université catholique de Louvain, 1200, Brussels, Belgium
ARTICLE INFO
ABSTRACT
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The present study aimed at characterizing the impact of M1 disruption on the implementation of implicit value information in motor decisions, at both early stages (during reinforcement learning) and late stages (after consolidation) of action value encoding. Fifty subjects performed, over three consecutive days, a task that required them to select between two finger responses according to the color (instruction) and to the shape (implicit, undisclosed rule) of an imperative signal: considering the implicit rule in addition to the instruction allowed subjects to earn more money. We investigated the functional contribution of M1 to the implementation of the implicit rule in subjects' motor decisions. Continuous theta burst stimulation (cTBS) was applied over M1 either on Day 1 or on Day 3, producing a temporary lesion either during reinforcement learning (cTBSL earning group) or after consolidation of the implicit rule, during decision-making (cTBSD ecision group), respectively. Interestingly, disrupting M1 activity on Day 1 improved the reliance on the implicit rule, plausibly because M1 cTBS increased dopamine release in the putamen in an indirect way. This finding corroborates the view that cTBS may affect activity in unstimulated areas, such as the basal ganglia. Notably, this effect was short-lasting; it did not persist overnight, suggesting that the functional integrity of M1 during learning is a prerequisite for the consolidation of implicit value information to occur. Besides, cTBS over M1 did not impact the use of the implicit rule when applied on Day 3, although it did so when applied on Day 2 in a recent study where the reliance on the implicit rule declined following cTBS (Derosiere et al., 2017). Overall, these findings indicate that the human M1 is functionally involved in the consolidation and implementation of implicit value information underlying motor decisions. However, M1 contribution seems to vanish as subjects become more experienced in using the implicit value information to make their motor decisions.
1. Introduction
Actions constitute one of the most important finality of the central nervous system, allowing animals, including human-beings, to reach context-dependent goals (Derosiere et al., 2017; Hamel-Thibault et al., 2016; Zavala et al., 2015). Importantly, goal-oriented behaviors require selecting suitable actions based on their predicted outcome value, an ability acquired through reinforcement learning (Gluth et al., 2014; Hollon et al., 2014; Luque et al., 2017; Mawase et al., 2017). Converging pieces of evidence indicate that the primary motor cortex (M1) may encode action values during motor decisions. First, transcranial magnetic stimulation (TMS) studies in humans have shown that the amplitude of motor evoked potentials (MEPs) measured during decision-making is shaped by the value of actions to be chosen between (Klein-Flugge and Bestmann, 2012; Klein et al., 2012; Mooshagian et al., 2014). Second, neuroimaging studies have revealed that M1 exhibits phasic neural responses following reward occurrence (Cohen and
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Ranganath, 2007; Lam et al., 2013). Finally, studies in non-human primates and rodents indicate that M1 receives direct functional projections from midbrain dopaminergic structures involved in value-based processes, including the ventral tegmental area (VTA) and the substantia nigra pars compacta (SNc; Luft and Schwarz, 2009; Hosp et al., 2011; Hosp and Luft, 2013; Smith et al., 2013; Kunori et al., 2014). Two of our recent studies suggest that the functional contribution of M1 to value-based motor decisions vary as a function of the learning stage (Zénon et al., 2015; Derosiere et al., 2017). In both studies, participants performed a decision-making task that required them to select between two finger responses according to the color (instruction) and to the shape (implicit, undisclosed rule) of an imperative signal; considering the implicit rule in addition to the instruction allowed subjects to earn more money. In those studies, we specifically investigated the functional contribution of M1 to the implementation of the implicit rule in subjects' motor decisions. Temporary lesions of M1 were produced using continuous theta burst stimulation (cTBS) at different time points during the experiment. In the first study (Zénon et al., 2015),
Corresponding author. CoActions Lab, Institute of Neuroscience, Université catholique Louvain, Av. Mounier, 53 - Bte B1.53.04, 1200, Bruxelles, Belgium.
https://doi.org/10.1016/j.neuroimage.2017.08.075 Received 1 June 2017; Received in revised form 10 August 2017; Accepted 25 August 2017 Available online xxx 1053-8119/ © 2017.
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mance. Participants were financially compensated for their participation and earned additional money depending on their performance on the task (see the Experimental protocol section). The protocol was approved by the institutional review board of the Université catholique de Louvain, Brussels, Belgium, and required written informed consent.
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subjects performed the task for a single day and cTBS was applied before the first block of trials, thus leading to an early dysfunction of M1 – that is, during reinforcement learning. In the second study (Derosiere et al., 2017), subjects practiced the same task for two consecutive days and cTBS occurred in the middle of the second session, thus disrupting M1 after reinforcement learning – that is, once the subjects were just about to implement the implicit information to select their actions, as evident in control subjects. Surprisingly, compared to the data obtained from participants in control groups, cTBS over M1 on Day 1 (Zénon et al., 2015) improved the acquisition of the implicit rule whereas it blocked the implementation of the rule when it was applied on Day 2 (Derosiere et al., 2017). Hence, from these data, M1 cTBS seems to produce opposite effects on the encoding of implicit value information depending on its time of occurrence during practice. Furthermore, in Derosiere et al. (2017), M1 was disrupted once the subjects were just about to implement the implicit rule in their action choices and thus, presumably, when the value-related knowledge was still quite fragile and highly vulnerable to interference. Yet, it is unclear whether M1 would still contribute to the use of implicit value information at later, more stable, learning stages – e.g., after consolidation – during motor decisions. In the present study, we aimed at characterizing the impact of M1 disruption on the implementation of implicit value information in motor decisions, at both early stages (during reinforcement learning) and late stages (after consolidation) of action value encoding. To do so, we recruited a new set of subjects (n = 50) to perform the aforementioned task for three consecutive days. In separate groups of individuals, M1 was disrupted either during reinforcement learning (cTBS applied on Day 1) or once the subjects were proficient at using the implicit value-based rule to make their motor decisions (on Day 3; i.e., after consolidation). In a third control group, a sham stimulation was applied on both days. With this study, we pursued the following goals: (1) to assess the reproducibility of the cTBS boosting effect on Day 1 (Zénon et al., 2015), (2) to investigate the long-term effects of cTBS on Day 1, by considering performance on two additional days and (3) to probe the impact of M1 cTBS on Day 3, after consolidation of the implicit value information. Moreover, we reanalyzed data collected in a group of subjects in Derosiere et al. (2017); [n = 18]) to include a condition in which M1 cTBS was applied on Day 2. This approach allowed us to provide a broad, meta-analytical view of the various effects of M1 disruption on the implementation of implicit value information throughout action value encoding. Some of the results of this study have been previously presented in abstract form (Derosiere et al., 2015a, 2015b).
2.2. Experimental protocol
2.2.1. Experimental design Experiments were conducted in a quiet and dimly-lit room. The subjects were seated at a table in front of a 21-inches cathode ray tube computer screen. The display was gamma-corrected and its refresh rate was set at 100 Hz. The computer screen was positioned at a distance of 70 cm from the subject's eyes and was used to display stimuli during the choice reaction time (RT) task. The left and right forearms were rested upon the surface of the table with the palms facing the table. A computer keyboard was positioned upside down under the dominant (i.e., right) hand with the response keys F8 and F9 under the middle and index fingers, respectively (see Fig. 1A, left side).
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2.2.2. Task The task used in the current experiment has already been exploited in two recent studies (Zénon et al., 2015; Derosiere et al., 2017); it was implemented by means of Matlab 7 (The Mathworks Inc. Natick, Massachusetts, USA) and the Psychophysics toolbox (Brainard, 1997). Subjects were instructed to perform index or middle finger key-presses with the right hand as quickly as possible according to the color of an imperative signal presented on the computer screen. Different tints of green and red were used as color signals (see Fig. 1A, left side). Each tint was obtained by increasing, from a neutral grey tone (RGB, [127, 127, 127]), the strength of the green (G) or the red (R) component of the stimulus (Red Green Blue [RGB] system) by a given saturation value (SATv alue) of 0, 1, 2, 3, 5, 12 or 26. As a result, the colors ranged from clearly green (RGB, [127, 153, 127]) to clearly red (RGB, [153, 127, 127]), with, in between, a set of 10 more ambiguous tints with lower levels of saturation and a neutral gray signal [127, 127, 127]. Hence, we used a total of 13 different RGB colors (SATv alues: −26, −12, −5, −3, −2, −1, 0, 1, 2, 3, 5, 12, 26). In order to avoid a possible color bias on the finger choices, instructions were counterbalanced between subjects. That is, half of the subjects were instructed to respond with the index finger following the display of a green signal and with their middle finger following a red signal; the other half received the opposite instruction. We arbitrarily chose to assign positive SATv alues to the color instructing a middle finger response. Note that for the presentation of the results we use the saturation rank (SATr ank: 0, 1, 2, 3, 4, 5, 6) rather than the SATv alue (see Fig. 1A, left side). After each response, subjects were provided with a feedback of their performance in the form of a monetary reward presented on the computer screen. This reward value was inversely proportional to the RT ((k*ng)/√(RT) [in ms] with “k = 3.462” and “ng”, a number taken randomly from a Gaussian distribution with μ = 30 and σ = 3) and could be easily converted in money: subjects only had to divide the number displayed by 10 to obtain their gain in eurocents for the trial (e.g., a reward value of 20 corresponded to 2 eurocents). The reward value also depended on whether the subjects had respected the instructed color-to-finger assignment. That is, when they responded with the instructed finger (Fingeri nstructed), the “basic score” (i.e., (k*ng)/√(RT)) was further multiplied by 3; it was kept unchanged when they used the other finger (Fingern on-instructed, basic score × 1). Unbeknownst to the subjects, a third factor was taken into account for the computation of the monetary reward. In fact, the color signal could be one of two shapes, a circle or a square, and each shape favored one finger (index or middle finger; the shape-to-finger assign
2. Material and methods 2.1. Participants
50 healthy naive participants were tested in this study. They were randomly assigned to one of three groups (cTBSL earning group [n = 15, 8 women; 22.2 ± 2.7 years old], cTBSD ecision group [n = 18, 10 women; 22.3 ± 2.8 years old] and cTBSC ontrol group [n = 17, 10 women; 22.7 ± 2.5 years old]). In addition, the present paper also involves a reanalysis of data collected by Derosiere et al. (2017) on 18 participants (cTBSD ecisionEarly group [10 women; 23.5 ± 3.0 years old]). Subjects were all asked to answer a medical questionnaire to rule out a potential risk of adverse reactions to TMS. All subjects were right-handed according to the Edinburgh Questionnaire (Oldfield, 1971) and had normal or corrected-to-normal vision; individuals with color blindness were excluded from the study. None of the participants had any neurological disorder or history of psychiatric illness or drug or alcohol abuse, or were on any drug treatments that could influence perfor
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Fig. 1. Value-based motor decision-making task. A. Left: Color saturations and response device. Right: Time course of a typical trial. Each trial started with the presentation of a warning signal, a fixation cross (+), displayed at the center of the screen for 500 ms. This signal indicated the beginning of a trial and was followed by a 500 ms delay period (blank screen). Then, the imperative signal appeared for 50 ms before being obscured by a black mask which remained on the screen until one of the response buttons was pressed. Subjects were asked to respond according to the color of the relevant shape (circle or square) and to ignore the irrelevant one (e.g., star, triangle). The presence of an irrelevant shape forced the subjects to pay attention to the shape information without raising suspicions about its undisclosed reward-biasing role. The latter was either presented above or below the relevant one (circle or square) in a random way. Finally, after the offset of the imperative signal, the visual feedback was presented on the screen for 1000 ms displaying the subject's reward for the preceding trial. In addition, an auditory signal was simultaneously emitted. The higher the reward value was, the louder this auditory signal was. Bottom-right: Representative imperative signals. B. Left: Coefficient parameters used to compute the reward values for responses provided with Fingeri nstructed or Fingern on-instructed in congruent (CT) and incongruent (IT) trials. Note the larger coefficient associated with the Fingeri nstructed when its use is consistent with the implicit rule (CT) compared to when it is not (IT). Similarly, using the Fingern on-instructed also led to larger rewards when it respected the implicit rule in IT compared to CT. Right: Illustration of reward value distributions for a typical reaction time of 400 ms. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
ment was also counterbalanced between subjects). Critically, when a response was provided with the favored finger (Fingerf avored), the basic score was further multiplied by 3; when the subjects used the other finger (Fingern on-favored), the score was kept unchanged (basic score × 1). Hence, a trial could be either “congruent”, when the color-related instruction and the implicit rule called for the same finger response (i.e., when the Fingeri nstructed corresponded to the Fingerf avored), or “incongruent”, when the color and the shape of the signal called for a different response (i.e., when the Fingeri nstructed corresponded to the Fingern on-favored). This implies that in congruent trials, the basic score (described above) was multiplied by 9 when the subjects responded with the Fingeri nstructed, since it also corresponded to the Fingerf avored ([“basic score × 3] × 3”). Oppositely, the basic score was kept unchanged when the response was provided with the Fingern on-instructed in congruent trials (and thus with the Fingern on-favored: “[basic score × 1] × 1”). In incongruent trials, the basic score was always multiplied by 3, regardless of whether they responded with the Fingeri nstructed (“[basic score × 3] × 1”), or the Fingern on-instructed (“[basic score × 1] × 3”, Fig. 1B, left side). Given the above, learning to account for the implicit rule should lead to a greater preference for using the Fingeri nstructed in congruent than in incongruent trials. Hence, most of our analyses focused on the difference in the proportion of Fingeri nstructed responses in congruent versus in incongruent trials.
In order to increase the impact of the implicit rule on the subjects' choices, we provided negative scores in a very specific circumstance. Subjects were told that they would receive a negative score (i.e., they would lose money: “[-basic score × 2]”, Fig. 1B, right side) if they were to make a mistake (i.e., use the Finger n on-instructed) following a color signal with the highest (easiest) SATv alue (R or G+ 26; SATr ank = 6). Yet, in fact, we only penalized these mistakes in congruent trials (CT, turquoise trace in Fig. 1B, right side) but not in incongruent trials (IT, orange trace), thus tolerating Fingern on-instructed responses when they corresponded to a Fingerf avored but not when they corresponded to a Fingern on-favored. The sequence of stimulus presentation is depicted in Fig. 1A (right side). As also evident on the right side of Fig. 1A, the circle or square signal was always presented at the same time as an irrelevant shape (e.g., star, triangle, rectangle, etc.). The irrelevant shape was either presented above or below the relevant shape (circle or square) in a random way. Subjects were asked to respond according to the color of the relevant shape and to ignore the irrelevant one. In a pilot experiment, we had noticed that, in the absence of this irrelevant shape, subjects were wondering why distinct (relevant) shapes (circle and square) were being used. Instead, the present design made them believe that the task was about ignoring the irrelevant shape. Notably the color of the irrelevant shape was also either green or red but the saturation was kept much lower (SATv alue always smaller than R+ 3 or G+ 3) in order to
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5 min. In each subject, cTBS was applied before Block1 on Day 1 and before Block4 on Day 3 (see Fig. 2). At these two time points, cTBS was either applied over the left M1 hand area (M1-LESION) or over the right primary somatosensory cortex (S1) area, 2 cm behind the right M1 area (SHAM; Derosiere et al., 2014, 2017; Alexandre et al., 2015). Subjects never received cTBS on Day 2. Note that applying cTBS halfway through Day 3 (rather than before Block1 , as on Day 1) allowed us to use the Blocks1 -3 on that day to evaluate a potential consolidation of the task overnight from Day 2 to Day 3 in this group of subjects. In addition, it provided the subjects with three additional blocks to practice the task before the cTBS intervention. The SHAM stimulation sessions allowed us to verify that the putative behavioral effects related to M1 cTBS were not due to the tactile and auditory sensations elicited by the stimulation pulses. We chose S1 because this area is very unlikely to be involved in the task used in the present study but elicits cranial sensations that are similar to those occurring during M1 cTBS. Moreover, we opted for S1 in the right hemisphere because we wanted to be consistent with our previous study (Derosiere et al., 2017) where we originally made this choice to prevent the occurrence of interference between S1 cTBS and left M1 functioning. Yet, S1 in the left hemisphere would probably have been a suitable control site as well given the well-known focality of TMS when applied with a figure-of-eight coil (about 10 mm of diameter; Bolognini and Ro, 2010; Brasil-Neto et al., 1992; Maccabee et al., 1990; Thielscher and Kammer, 2002). Consistent with this view, cTBS over right S1 did not impact right M1 activity in the present study, as evident from the analysis of left hand MEPs in the SHAM stimulation sessions (see below). This said, we believe that the side of SHAM stimulation is a minor concern as subjects were completely naïve to the purpose of the study and had no specific expectations regarding the impact of stimulating the left or right hemisphere. They were told upfront that the role of both hemispheres would be investigated.
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limit the interference produced by this distractor. This manipulation was introduced to force the subjects to pay attention to the shape information without raising suspicions about its undisclosed reward-biasing role. Finally, a last undisclosed bias consisted in increasing the range of SATv alues for which a Fingerf avored response was considered as coherent with the color (and thus considered as a Fingeri nstructed) for the computation of the reward value. In an unbiased situation, the theoretical SATv alue at which subjects should switch from using one finger to the other (based on the color signal) coincides with the neutral gray RGB color (R+ 0 and G+ 0). Here, we biased this saturation threshold by shifting it towards a SATv alue of either R+ 2 or G+ 2, as a function of the finger favored by each shape. The R+ 2 shift was used for a shape favoring the Fingeri nstructed for a green color signal (thus considering the Fingerf avored as a Fingeri nstructed for low saturations of red) whereas the G+ 2 bias was used for a shape favoring the Fingeri nstructed for a red signal (thus considering the Fingerf avored as a Fingeri nstructed for low saturations of green). In both cases, this manipulation extended by 2 points the range of SATv alues for which the subjects would have their basic score multiplied by 9 when using the Fingerf avored. Note that subjects could barely detect the color of the signal at these saturation levels. As such, none of them reported noticing the shift. This last bias increased the overall impact of the implicit value-based rule on the subjects' choices (Derosiere et al., 2017). 2.2.3. Blocks and sessions The study included three experimental sessions occurring on three consecutive days. The participants were always tested at the same time of the day to prevent data from being confounded by baseline fluctuations related to different chronobiological states. Each session comprised 6 blocks (Blocks1 -6) of 84 trials (6 congruent and 6 incongruent trials for each SATr ank [n = 7] per Block) and each block lasted about
Fig. 2. Experimental protocol. Top: Time course of the experiment. Subjects came for three sessions (6 blocks each) on three consecutive days (Day 1, Day 2 and Day 3). Continuous Theta Burst Stimulation (cTBS) was applied on Day 1 (before Block1 ) and on Day 3 (before Block4 ), over the left primary motor cortex (M1; M1-LESION) or right primary somatosensory cortex (S1; SHAM stimulation). Bottom: Experimental groups. Participants were either part of the cTBSL earning (green, [n = 15]), cTBSD ecision (blue, [n = 18]), or cTBSC ontrol (black, [n = 17]) group. In the cTBSL earning group, participants received a M1-LESION on Day 1 and a SHAM stimulation on Day 3. In the cTBSD ecision group, participants received a SHAM stimulation on Day 1 and a M1-LESION on Day 3. In the cTBSC ontrol group, participants received a SHAM stimulation on Day 1 and Day 3. Motor Evoked Potentials (MEPs) were elicited by applying single-pulse TMS at different time points throughout Day 1 and Day 3 (Baseline and TMS1 -8). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) 4
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the absence of excitatory changes in right M1 in that situation. Hence, in the cTBSL earning group, MEPs were elicited in the right FDI on Day 1 and in the left FDI on Day 3 (see Fig. 2). In the cTBSD ecision group, they were elicited in the left FDI on Day 1 and in the right FDI on Day 3. Finally, in the cTBSC ontrol group, MEPs were elicited in the left FDI on Days 1 and 3. As evident on Fig. 2, the time points at which MEPs were elicited on Day 1 and Day 3 were comparable in all three groups. On both days, 20 MEPs were elicited at the beginning of the session (Baseline). Then, 15 MEPs were elicited immediately before each Block (TMS1 -7). Notably, TMS4 and TMS5 both occurred before Block4 , to assess motor excitability before and after cTBS on Day 3; TMS1 immediately followed the cTBS train on Day 1. Finally, a last set of 20 MEPs was acquired at the end of the experiment, after Block6 (TMS8 ). Hence, MEPs were probed at 9 different time points (Baseline and TMS1 -8). On Day 1, the TMS1 -8 timings fell 1 min (TMS1 ), 8 min (TMS2 ), 15 min (TMS3 ), 22 min (TMS4 ), 24 min (TMS5 ), 31 min (TMS6 ), 38 min (TMS7 ) and 45 min (TMS8 ) after the cTBS intervention. On Day 3, the four first sets of measures (TMS1 -4) were acquired before the cTBS intervention while the four last TMS timings fell 1 min (TMS5 ), 8 min (TMS6 ), 15 min (TMS7 ) and 22 min (TMS8 ) after the cTBS intervention. On Day 2, two minutes of rest were given between each block, except between Block3 and Block4 where subjects were provided with six minutes of rest, to mimic the time course of the two other sessions on Day 1 and Day 3. Hence, the time course of Blocks was exactly the same on Day 1, Day 2 and Day 3 and comparable across the three different cTBS groups.
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Participants were separated in three groups according to the site of stimulation on Day 1 and 3. A first group received a M1-LESION on Day 1 and a SHAM stimulation on Day 3 (; see Fig. 2, upper panel). Hence, in this group, which we called the cTBSL earning group (n = 15), we disrupted M1 functioning at the very beginning of the experiment. This allowed us to investigate the role of M1 in the reinforcement learning processes at the basis of the acquisition of the implicit value-based rule. Inversely, in a second group, subjects received the SHAM stimulation on Day 1 and the M1-LESION occurred on Day 3, after consolidation of the implicit value-based rule (see Fig. 2, middle panel). Hence, with this group, which we called the cTBSD ecision group (n = 18), we aimed at investigating the role of M1 in the use of the consolidated implicit value information. Finally, a third set of subjects received a SHAM stimulation on both days. Hence, M1 functioning was never disrupted in these subjects (cTBSC ontrol group, n = 17; Fig. 2, lower panel).
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2.2.4. TMS procedure TMS was delivered through a 70 mm figure-of-eight coil connected to a Super Rapid Magstim Stimulator (Magstim Company, Whitland, UK). The coil was placed tangentially on the scalp with the handle oriented towards the back of the head and laterally at a 45° angle away from the midline, approximately perpendicular to the central sulcus. In each subject, M1 was located at the beginning of the sessions on Day 1 and Day 3, by identifying the optimal spot for eliciting MEPs in the right (M1-LESION sessions) or left (SHAM sessions) First Dorsal Interosseous (FDI) muscle. This site (called the “hotspot”) was marked on an electroencephalography cap fitted on the participant's head to provide a reference point of M1 throughout the experimental session (Derosiere et al., 2015c; Duque et al., 2013). We then determined the resting Motor Threshold (rMT) at the hotspot for each participant. The rMT was defined as the minimal TMS intensity required to evoke MEPs of about 50 μV peak-to-peak in the targeted muscle (left or right FDI) on 5 out of 10 consecutive trials. Mean rMT values on Day 1 and Day 3 are shown separately for each of the three groups in Table 1. The cTBS procedure consisted of a series of short TMS trains (three pulses at 50 Hz) repeated every 200 ms for 40 s (600 pulses) at an intensity of 80% of the rMT (Clerget et al., 2012; Zénon et al., 2015; Derosiere et al., 2017). Such an intervention has been shown to inhibit the stimulated cortical area, producing a temporary so-called “virtual lesion” that can last for between 20 (Oberman et al., 2011; Zénon et al., 2015; Derosiere et al., 2017) and 45 min (Huang et al., 2005). MEPs are known to provide a muscle-specific readout of state-changes in the motor system (Bestmann and Duque, 2015; Grandjean et al., 2017; Quoilin and Derosiere, 2015). Hence, in order to monitor the inhibitory effect of cTBS on motor activity, single TMS pulses were applied at 115% of the rMT to elicit MEPs at different time points on Day 1 and Day 3. Again, there was no cTBS on Day 2, so no need to monitor changes in corticospinal excitability. In the M1-LESION sessions, MEPs were always recorded in the right FDI, to evaluate the impact of cTBS over left M1 in that condition. In the SHAM sessions (cTBS over right S1), MEPs were recorded in the left FDI, to control for
2.2.5. MEP amplitudes Electromyography (EMG) was used to measure the peak-to-peak amplitude of MEPs following single-pulse TMS over the contralateral M1. EMG activity was recorded from surface electrodes (Neuroline; Medicotest) placed over the right or the left FDI in the M1-LESION or SHAM sessions, respectively (see Fig. 2), for 1000 ms on each trial, starting 300 ms before the single TMS pulses. The EMG signals were amplified and bandpass filtered on-line (10–500 Hz; NeuroLog; Digitimer), and digitized at 2000 Hz for off-line analysis. MEP amplitudes obtained in each single trial were averaged to obtain a measure of motor excitability at Baseline and at TMS1 -8 for Day 1 and Day 3 (see Fig. 3). In order to prevent contamination of the MEP measurements, trials with background EMG activity greater than 100 μV in the 200-ms window preceding the TMS artifact were excluded from the MEP analysis (Duque et al., 2005; Derosiere et al., 2015c, 2017; Quoilin et al., 2016; Wilhelm et al., 2016). Besides, the MEP data of three subjects (one from each group) had to be removed from our analyses because they presented outlying values (i.e., defined here as values exceeding 2.5 standard deviations (SD) from the mean of their group). The analyses of the MEP data were performed on the remaining pool of subjects (n = 14, n = 17 and n = 17 in the cTBSL earning, the cTBSD ecision and the cTBSC ontrol groups, respectively). As evident on Fig. 3, M1-LESION induced a significant reduction in the amplitude of MEPs elicited at the subsequent time points. That is, MEP amplitudes were reduced for about 20 min after cTBS over M1 and this effect was evident in both the cTBSL earning group (M1-LESION on Day 1) and the cTBSD ecision group (M1-LESION on Day 3). To test for the statistical significance of these effects, we pooled together the MEPs elicited at the four earliest time points (TMS1 -4) and those elicited at the four latest timings (TMS5 -8). These data were analyzed using a three-way repeated-measure ANOVA with DAY (Day 1, Day 3) and TMSE POCH (Baseline, TMS1 -4, TMS5 -8) as within-subject factors and cTBSG ROUP (cTBSL earning, cTBSD ecision, cTBSC ontrol) as a between-subject factor. As expected, the ANOVA revealed a significant DAY × TMSE POCH × cTBSG ROUP interaction on MEP amplitudes (F4 , 88 = 3.05, p = 0.021). Fisher's LSD post-hoc tests showed that MEP
Table 1 Mean resting motor threshold (rMT) and standard error (SE) values (expressed in percentage of the maximum stimulator output) on Day 1 and Day 3 for the three groups of subjects, (cTBSL earning [n = 15], cTBSD ecision [n = 18] and cTBSC ontrol [n = 17]). Day 1
cTBSL earning cTBSD ecision cTBSC ontrol
Day 3
Mean rMT
SE
Mean rMT
SE
60.2 61.4 61.5
3.8 2.2 3.7
60.9 62.2 53.5
3.3 2.1 4.0
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Fig. 3. Mean amplitude (in percentage of Baseline) of Motor Evoked Potentials (MEPs) elicited in the First Dorsal Interosseous (FDI) muscle at Baseline and at each of the following time points (TMS1 -8) before and after continuous Theta Burst Stimulation (cTBS) on Day 1 (left) and on Day 3 (right) in the cTBSL earning (green traces [n = 14]), cTBSD ecision (blue traces [n = 17]) and cTBSC ontrol (black traces [n = 16]) groups. Note the significant MEP suppression after a M1-LESION (i.e., at TMS1 -4 of Day 1 in the cTBSL earning group and at TMS5 -8 of Day 3 in the cTBSD ecision group) but not after a SHAM stimulation (i.e., at all other time points). The histograms represent the mean and standard error (SE) of MEPs pooled across TMS1 -4 and TMS5 -8 on Day 1 and Day 3 for the three different groups of subjects. *: significantly different (p < 0.05). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
amplitudes were significantly reduced following a M1-LESION. As such, in the cTBSL earning group, MEPs were significantly smaller at TMS1 -4 than at Baseline of Day 1 (p = 0.0006). MEPs at TMS1 -4 were also smaller than those elicited at TMS5 -8 on that day suggesting that M1 activity returned to baseline during the second half of the session (p = 0.006). Similarly, in the cTBSD ecision group, MEPs were significantly reduced at TMS5 -8 compared to Baseline and to TMS1 -4 of Day 3 (p = 0.0004 and 0.0001, respectively). We did not observe any significant changes in MEP amplitudes following SHAM stimulations, in any of the three groups (p-values ranging between 0.235 and 0.847). Hence, cTBS over right S1 did not induce any incidental inhibition of right M1 (located 20 mm rostrally to S1), suggesting that the direct impact of cTBS on the underlying cortical tissue was rather focal, as previously demonstrated (Bolognini and Ro, 2010; Brasil-Neto et al., 1992; Maccabee et al., 1990; Thielscher and Kammer, 2002). Based on this result, it is sensible to assume that the lesion induced by left M1 cTBS was also rather focal. Hence, the risk that it directly perturbed activity of the premotor cortex (located 20 mm rostrally; Guyton and Hall, 2015) is low. The same argument holds for more rostral frontal areas and for posterior regions.
circle on the right). Hence, there were four types of trials, each repeated 10 times in a random order (i.e., the whole test consisted of 40 trials). The test required subjects to choose as fast as possible between the shape presented on the left side of the screen (by responding with the index finger) and that presented on the right side (by responding with the middle finger). The most interesting trials were the ones with similar shapes presented on both sides. That is, in these trials one finger response corresponded to the Fingerf avored whereas the other one to the Fingern on-favored (depending on the shape-to-finger assignment used in the main experiment). As such, pilot data revealed that when a subject becomes aware of the undisclosed reward manipulation, he/ she mostly selects the Fingerf avored on these trials. Hence, the proportion of Fingerf avored responses in these trials provides us with a measure of the subject awareness of the undisclosed reward manipulation. The two other trial types (dissimilar shapes) were only used to keep the attention of the subjects away from our primary intention. That is, in those trials the two potential responses either both corresponded to a Fingerf avored or both to a Fingern on-favored and thus, the subject choices on these trials did not provide us with any information relative to the reward manipulation. Hence, only the trials with similar shapes were analyzed. To do so, the proportion of Fingerf avored responses was analyzed using a one-way ANOVA with cTBSG ROUP (cTBSL earning, cTBSD ecision, cTBSC ontrol) as a within-subjects factor. This ANOVA did not reveal any significant effect of the cTBSG ROUP on this dependent variable (F2 , 46 = 0.15, p = 0.859). As such, the proportion of Fingerf avored responses was close to 0.5 in all three groups: it equaled 0.53 ± 0.22, 0.49 ± 0.22, and 0.52 ± 0.17 in the cTBSL earning, cTBSD ecision and cTBSC ontrol, group, respectively. Finally, to further control for the implicit nature of the shape-to-finger assignment, three additional questions were asked to the subjects: 1. “Do you think that responding to a given shape allowed you to get a higher score?”; 2. “Do you think that responding with a given finger allowed you to get a higher score?”; 3. “Do you think that responding to a given shape with a given finger allowed you to get a higher score?”. Subjects were asked to answer these three questions in a written form. Subjects never answered positively to the third question. Taken together, the results confirm that none of the subjects included in the present study became aware of the undisclosed reward manipulation.
2.2.6. Implicit test and questionnaire The subjects were never told about the link between the shape of the signal and the reward contingencies. Hence, we assumed that the shape-to-finger assignment remained implicit throughout the experiment. However, in order to directly control for the implicit nature of this rule, each subject underwent a test at the end of the experiment, immediately after having completed the last block of trials (Block6 ) on Day 3. The test was a modified version of the generative task usually used in the implicit learning literature (Newell and Shanks, 2014) and was exploited in a previous study (Derosiere et al., 2017). It consisted in asking subjects to perform right index or middle finger key-presses (similar responses as in the main experiment) according to an imperative signal. The latter consisted of two gray shapes (circle or square); one was presented on the left side of the computer screen and the other one on the right side. The two shapes could either be the same (circles or squares on both sides) or different (a circle on the left and a square on the right or the reversed configuration: a square on the left and a
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homogeneity of variance using Skewness, Kurtosis and Brown-Forsythe tests. The RTs and the proportion of Fingeri nstructed responses were analyzed using a five-way repeated-measure ANOVA with the factors DAY (Day 1, Day 2, Day 3), BLOCK (Blocks1 -3, Blocks4 -6), CONGRUENCE (congruent and incongruent trials) and SATURATION (SATr ank_0-6) as within-subject factors and cTBSG ROUP (cTBSL earning, cTBSD ecision, cTBSC ontrol) as a between-subjects factor. Then, the Implicit Index was analyzed using a two-way repeated-measure ANOVA with the factors BLOCK (Blocks1 -3, Blocks4 -6; within-subjects factor) and cTBSG ROUP (cTBSL earning, cTBSD ecision, cTBSC ontrol; between-subjects factor). This analysis was run separately for Day 1, Day 2 and Day 3. When appropriate, Fisher's LSD post-hoc tests were used to detect paired differences. Finally, unpaired Student's t-tests were used to compare the Implicit Indices obtained in the cTBSC ontrol group in the Blocks4 -6 of Day 1, Day 2 and Day 3 to the ones obtained in these Blocks in the cTBSL earning, cTBSD ecisionEarly and cTBSD ecision groups, respectively. The significance level was set at p < 0.05; ranges of multiple p-values are presented as p = [min max]. Unless specified, results are expressed as mean ± SE.
2.3. Data processing
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Four subjects were excluded from the behavioral analyses because they did not follow the color-related instruction. They presented an abnormally low proportion of Fingeri nstructed responses (close to 0.5), even following highly saturated signals (SATr ank_4-6), and their RT was much faster than that observed in the other subjects (ranging between 200 and 400 ms). All the analyses were performed on the remaining participants (n = 13, n = 17 and n = 16 in the cTBSL earning, the cTBSD ecision and the cTBSC ontrol groups, respectively). First, the index and middle finger responses were classified according to whether they corresponded to a Fingeri nstructed or a Fingern on-instructed response and according to whether they were provided in a congruent or an incongruent trial. Then, for each session, we pooled together all the data obtained in trials of the first 3 blocks (Blocks1 -3) and all the data obtained in the last 3 blocks (Blocks4 -6). Performance was thus evaluated by considering the proportion of Fingeri nstructed responses (and their RTs) for each SATr ank (SATr ank_0-6) in congruent and incongruent trials, during the first half (Blocks1 -3) or the second half (Blocks4 -6) of each experimental session (Day 1, Day 2 and Day 3) and this, separately for each cTBS group (cTBSL earning, cTBSD ecision and cTBSC ontrol groups). As a second-level analysis, we computed an additional measure to investigate how subjects specifically relied on the implicit rule on each Day and for each cTBS group. This measure, called the Implicit Index, was calculated as the proportion of Fingeri nstructed responses in congruent trials (i.e., when the Fingeri nstructed was also the Fingerf avored by the shape; all SATr ank pooled together) minus the proportion of Fingeri nstructed responses in incongruent trials (i.e., when the Fingeri nstructed was the Fingern on-favored) normalized over the sum of the proportion of Fingeri nstructed responses (in both congruent and incongruent trials). It can be formalized by the following equation:
3. Results
3.1. Proportion of Fingeri nstructed responses
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3.1.1. Main analysis Subjects rapidly learned to follow the color-based instruction, as revealed by a significant DAY × BLOCK interaction on the proportion of Fingeri nstructed responses (F2 , 86 = 13.56, p < 0.00001). Accordingly, the fraction of trials in which the subjects used their Fingeri nstructed increased from Blocks1 -3 to Blocks4 -6 of Day 1 (p < 0.00001) but then remained stable on Day 2 and Day 3 (p = 0.479 and 0.101, respectively). Interestingly though, the ANOVA did not reveal any DAY × BLOCK × cTBSG ROUP interaction (F4 ,86 = 0.28, p = 0.892), suggesting that the interaction described above did not depend on the cTBS group. Hence, M1 cTBS never perturbed the ability to follow the color-based instruction, as evident on Fig. 4 depicting the proportion of Fingeri nstructed responses obtained within each Day, for each triad of Blocks and for each cTBS group. The proportion of Fingeri nstructed responses also depended on the SATURATION of the color signal (F6 , 258 = 601.20, p < 0.00001). As expected, the more the color signal was saturated, the more the subjects responded with the Fingeri nstructed, as confirmed by post-hoc tests revealing significant differences between all levels of saturation (all p-values < 0.00001). Notably, the acquisition of the instruction was not consistent across the different color saturations, as shown by a significant DAY × BLOCK × SATURATION interaction (F1 2, 516 = 1.8, p = 0.046, see Fig. 5A). Subjects learned very fast (within Day 1) to respond with the Fingeri nstructed when they were presented with highly saturated colors (SATr ank_2-6, p-values = [0.00001 0.013] when comparing Blocks1 -3 and Blocks4 -6 of Day 1). In contrast, they did not show any improvement in using the Fingeri nstructed for trials in which the color was barely (or not) discernible (SATr ank_0-1) neither on Day 1 (p-values = [0.152 0.837]), nor on any of the two following Days (p-values = [0.190 0.711]). The implicit rule also influenced the way subjects learned to use the Fingeri nstructed, as shown by the significant DAY × CONGRUENCE interaction (F2 , 86 = 3.49, p = 0.035; see Fig. 5B). On each day, subjects showed a larger preference for using the Fingeri nstructed in congruent (average proportion: 0.746 ± 0.059) than in incongruent trials (0.677 ± 0.094; p-values = [0.031 0.00001]). Yet, this penchant strengthened along with practice, with the proportion of Fingeri nstructed responses progressively increasing over Days in congruent trials (p < 0.003 when comparing Day 3 with Day 1) but remaining un
where Instructed indicates the mean proportion of Fingeri nstructed responses (all SATr ank pooled together). The higher the proportion of Fingeri nstructed responses in congruent trials, and the lower the proportion of Fingeri nstructed responses in incongruent trials, the higher the Implicit Index. In a last analysis, we aimed at providing an overall picture of the short-term effects (within the same day) of M1 disruption as a function of its time of occurrence in our task (Day 1, Day 2 or Day 3). To do so, we included some of the data collected in Derosiere et al. (2017), focusing on the group who received a M1-LESION halfway through Day 2 (called the cTBSD ecisionEarly group here, [n = 18]); the Implicit Index was extracted from Blocks4 -6 of Day 2 in these subjects. This analysis also included data collected on Day 1 (Blocks4 -6) in the cTBSL earning group (M1-LESION on Day 1) and on Day 3 (Blocks4 -6) in the cTBSD ecision group (M1-LESION on Day 3) in the present study. The Implicit Index computed on each of these data sets were compared to those obtained on the same Day in the cTBSC ontrol group of the present study. This analysis thus involved 64 subjects in total (cTBSL earning: [n = 13], cTBSD ecisionEarly: [n = 18], cTBSD ecision: [n = 17], and cTBSC ontrol: [n = 16]). 2.4. Statistical analyses
Statistica software (version 7.0, Statsoft, Oklahoma, United-States) was used for all analyses. All data were examined for normality and
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Fig. 4. Average proportion of Fingeri nstructed responses in the cTBSL earning (green traces [n = 13]), cTBSD ecision (blue traces [n = 17]) and cTBSC ontrol (black traces [n = 16]) groups, from Day 1 (left panel) to Day 3 (right panel). Data from all SATr ank (SATr ank_0-6) and both trial types (congruent/incongruent) are pooled together. *: Significant effect of BLOCK (p < 0.05). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
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(right panel), from Day 1 to Day 3 (upper to lower panels); detailed statistical values are also provided on this Figure. In the cTBSC ontrol group, the proportion of trials in which the subjects used their Fingeri nstructed became progressively larger in congruent than in incongruent trials, reflecting the implementation of the implicit rule in subjects' choices. This penchant mostly emerged in the second part of Day 2 (Blocks4 -6) where the effect of CONGRUENCE became evident for five saturation ranks (significant p-values = [0.0002 0.046]); it was only apparent for one saturation rank (or none of them) in the preceding blocks. On Day 3, the effect of CONGRUENCE consolidated and became even stronger, generalizing to all saturation ranks in Blocks4 -6 of Day 3 (all p-values = [0.00001 0.002]). Subjects in the cTBSD ecision group showed a similar behavior, with the effect of CONGRUENCE emerging mostly in Blocks4 -6 of Day 2 (all saturation; p-values = [0.0001 0.033]) and remaining present for most saturation ranks on Day 3. Importantly, we did not observe any significant change in the CONGRUENCE effect from Blocks1 -3 to Blocks4 -6 on Day 3, despite the fact that a M1-LESION was applied in this group (p-values [0.104 0.761]). Hence, disrupting M1 activity on Day 3 – i.e., following the consolidation of the implicit rule – had no impact on the subjects' choices. Interestingly, results were quite different for the subjects who received a M1-LESION at the beginning of Day 1 (left panel on Fig. 6). As such, in the cTBSL earning group, the CONGRUENCE effect emerged much earlier than in the two other groups; it was already evident for most saturation ranks in Blocks4 -6 of Day 1 (significant p-values = [0.0001 0.027]). However, this effect was short-lasting. Indeed, the CONGRUENCE effect was barely present in Blocks1 -3 of Day 2, reaching significance for only one saturation rank (p = 0.027), similar to the observation made in the two other groups. It then increased again, becoming significant for a majority of saturation ranks in Blocks4 -6 of Day 2 (p-values = [0.00001 0.005]) and remaining stable on Day 3, as in the two other groups.
Fig. 5. A. Average proportion of Fingeri nstructed responses as a function of the SATr ank in Blocks1 -3 (grey traces) and Blocks4 -6 (black traces) on the three consecutive days. Data from all cTBS groups (cTBSL earning, cTBSD ecision and cTBSC ontrol) and both trial types (congruent and incongruent) are pooled together on this figure. B. Mean (and SE) of the proportion of Fingeri nstructed responses as a function of the Day (Day 1, Day 2, Day 3) in congruent (black) and incongruent trials (white). Data from all cTBS groups and all SATr ank (SATr ank_0-6) are pooled together. C. Mean (and SE) of the proportion of Fingeri nstructed responses as a function of the Blocks (Blocks1 -3 and Blocks4 -6) in congruent and incongruent trials. Data from all cTBS groups, SATr ank and Days are pooled together. *: significantly different (p < 0.05).
changed in incongruent trials (p = 0.408). The use of the Fingeri nstructed also increased within each Day, from Blocks1 -3 to Blocks4 -6, but again this was only true for congruent (p < 0.00001) but not for incongruent trials (p = 0.655), as confirmed by a significant BLOCK × CONGRUENCE interaction (F1 , 43 = 8.37, p = 0.006; see Fig. 5C). Taken together, these results indicate that the instructed and implicit rules both influenced the subjects' motor choices. Most importantly, the ANOVA revealed a significant DAY × BLOCK × CONGRUENCE × SATURATION × cTBSG ROUP interaction on the proportion of Fingeri nstructed responses (F2 4, 516 = 1.61, p = 0.035). This suggests that the type of cTBS intervention influenced the way subjects responded to the color signal in congruent and incongruent trials over the course of the experiment. Fig. 6 illustrates the proportion of Fingeri nstructed responses in congruent and incongruent trials, as a function of the saturation rank, in the cTBSL earning group (left panel), the cTBSD ecision group (middle panel) and the cTBSC ontrol group
3.1.2. Analysis of the Implicit Index An Implicit Index was computed to quantify the impact of the implicit rule on the subjects' behavior during the three experimental Days. The larger the Index, the more the subjects relied on the implicit rule. Fig. 7 illustrates the Implicit Index (and detailed statistical values) computed for Day 1 (left panel), Day 2 (middle panel) and Day 3 (right panel). On Day 1, the ANOVA revealed a significant BLOCK × cTBSG ROUP interaction (F2 ,43 = 5.82, p = 0.006): in accordance with the observation made in the main analysis, the Implicit Index increased from Blocks1 -3 to Blocks4 -6 in the cTBSL earning group
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Fig. 6. Proportion of Fingeri nstructed responses as a function of the SATr ank for congruent (full traces) and incongruent trials (dotted traces) in the cTBSL earning (green traces [n = 13]), cTBSD ecision (blue traces [n = 17]) and cTBSC ontrol (black traces [n = 16]) groups, from Day 1 (upper panel) to Day 3 (Lower panel). *: Significant effect of CONGRUENCE (p < 0.05). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
(p = 0.002) but not in the cTBSC ontrol and cTBSD ecision groups (p = 0.337 and 0.742, respectively). As a result, the Implicit Index computed for the Blocks4 -6 of Day 1 was significantly higher in the cTBSL earning than in the cTBSD ecision and cTBSC ontrol groups (p = 0.022 and 0.048, respectively). This finding provides further support for the
idea that disrupting M1 during reinforcement learning increases the reliance on the implicit rule, as shown previously (Zénon et al., 2015). The ANOVA performed on Day 2 revealed a significant main effect of BLOCK (F1 , 43 = 9.98, p = 0.003). As such, the Implicit Index increased from Blocks1 -3 to Blocks4 -6 (p = 0.002). Neither the cTBSG ROUP factor (F2 ,43 = 0.08, p = 0.919), nor the BLOCK × cTBSG ROUP interac
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Fig. 7. Mean (and SE) of Implicit Index values in Blocks1 -3 and Blocks4 -6 of Day 1 (left), Day 2 (middle) and Day 3 (right) in the cTBSL earning (green traces [n = 13]), cTBSD ecision (blue traces [n = 17]) and cTBSC ontrol (black traces [n = 16]) groups. *: Significant effect of BLOCK (p < 0.05). #: Significant effect of cTBSG ROUP (p < 0.05). The small black arrows on the x-axis illustrate the time points at which cTBS was applied. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
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tion (F1 ,43 = 0.04, p = 0.964) were significant on Day 2. This supports the view that subjects in all groups displayed a comparable increase in the use of the implicit value-based rule on Day 2, despite the initial boosting effect observed in the cTBSL earning group on Day 1. Finally, the ANOVA performed on the Day 3 data did not reveal any significant effect. The Implicit Index did not show any further increase from Blocks1 -3 to Blocks4 -6 (main effect of BLOCK: F1 ,43 = 1.27, p = 0.266). In addition, neither the factor cTBSG ROUP (F2 ,43 = 0.10, p = 0.901), nor the BLOCK × cTBSG ROUP interaction (F2 ,43 = 0.52, p = 0.601), were significant. This analysis further indicates that disrupting M1 functioning after a long period of practice (cTBSD ecision group) does not affect the subjects' ability to use the implicit value-based information during their motor decisions. 3.1.3. Additional analysis on Implicit Index including data from Derosiere et al. (2017) In a last analysis, we aimed at providing an overall picture of the short-term effects (within the same day) of M1 disruption as a function of its time of occurrence in our task (Day 1, Day 2 or Day 3). To do so, we included some data collected in Derosiere et al. (2017), as described in the section 2.3. Data processing. Fig. 8 represents the Implicit Indices obtained in the Blocks4 -6 of Day 1, Day 2 and Day 3 in the cTBSL earning, cTBSD ecisionEarly and cTBSD ecision groups, respectively; these indices are compared to those obtained in the cTBSC ontrol group of the present study within the same Days. The t-test performed on Day 1 revealed a significant difference between the Index in the cTBSL earning and cTBSC ontrol groups (t2 7 = −2.08, p = 0.047). Consistent with the preceding analyses, the Implicit Index was larger on Day 1 in the presence of a M1-LESION compared to when M1 functioned normally during reinforcement learning. In contrast, on Day 2, we observed a reversed pattern with a smaller Implicit Index in the presence of a M1-LESION (cTBSD ecisionEarly vs cTBSC ontrol comparison: t3 2 = 2.96, p = 0.005). Finally, the Implicit Index was comparable in the cTBSD ecision and cTBSC ontrol groups on Day 3 (t3 1 = 0.53, p = 0.602), despite the fact that a M1-LESION was induced in the former but not in the latter condition.
Fig. 8. Mean (and SE) of Implicit Index values in Blocks4 -6 of Day 1 (left), Day 2 (middle) and Day 3 (right) in the cTBSL earning (green dot [n = 13]), cTBSD ecisionEarly (orange dot [n = 18]), cTBSD ecision (blue dot [n = 17]) and cTBSC ontrol (black traces [n = 16]) groups. #: Significantly different (p < 0.05). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
remained stable, showing no further reduction from Blocks1 -3 to Blocks4 -6 on the last experimental Day (p = 0.567). As expected, RTs also depended on the degree to which the color of the relevant shape was saturated (main effect of SATURATION: F6 , 258 = 109.29, p < 0.00001): subjects were faster to respond following the highly saturated colors (all p-values ≤ 0.012 when comparing SATr ank_2-6 with SATr ank_0). Interestingly, RTs were also influenced by the CONGRUENCE but this effect depended on the SATURATION (CONGRUENCE × SATURATION interaction: F6 , 258 = 4.01, p = 0.0007, see Fig. 9B). As such, RTs were significantly shorter in congruent than in incongruent trials but this was only true for the three highest levels of saturation (i.e., SATr ank_4-6; p-values = [0.0007 0.001]); RTs were comparable between congruent and incongruent trials in the four other less saturated conditions (i.e., SATr ank_0-3; p-values = [0.078 0.481]). Finally, the ANOVA revealed a significant DAY × CONGRUENCE × SATURATION × cTBSG ROUP interaction (F2 4, 516 = 1.78, p = 0.013). Yet, our Fisher's LSD post-hoc tests failed to reveal any relevant differences in the RTs between the three groups (p-values = [0.701 0.999] when comparing one cTBS group to another within each Day; see Fig. 9C). The interaction was due to the signifi
3.2. RT of Fingeri nstructed responses
The instruction required the subjects to respond as quickly as possible. For this reason, we expected to observe a shortening of the RTs over the course of the experiment. Consistently, we found a significant DAY × BLOCK interaction on the RT data (F2 , 86 = 23.71, p < 0.00001; see Fig. 9A). RTs decreased until Day 3 (all p-values = [0.00001 0.027] when comparing one block set to the preceding one) and then
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Fig. 9. A. Mean (and SE) of Reaction Times (RTs; ms) in Blocks1 -3 (black traces) and Blocks4 -6 (grey traces) of Day 1, Day 2 and Day 3. All cTBS groups (cTBSL earning, cTBSD ecision and cTBSC ontrol), SATr ank (SATr ank_0-6) and trial types (congruent and Incongruent) are pooled together for this figure. B. Mean RTs as a function of the SATr ank in congruent (full traces) and incongruent trials (dotted traces). Again, all other factors (cTBS groups, Days and Blocks) are pooled together. *: Significant effect of SATr ank (p < 0.05); #: Significant effect of CONGRUENCE (p < 0.05). C. Mean RTs as a function of the SATr ank in congruent and incongruent trials in the cTBSL earning (green traces, [n = 13]), cTBSD ecision (blue traces, [n = 17]) and cTBSC ontrol (black traces, [n = 16]) groups; all Blocks are pooled together for each Day (Day 1, Day 2 and Day 3). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
cance of irrelevant comparisons including different saturation ranks in different cTBSG ROUPS or on different days.
als but decrease it in incongruent trials. Most of our analyses focused on this difference in the proportion Fingeri nstructed responses between congruent and incongruent trials, as a marker of the impact of the implicit rule on the subjects' action choices. Critically, cTBS was either applied on Day 1 or on Day 3, producing a temporary lesion of M1 during reinforcement learning (cTBSL earning group) or after consolidation of the implicit rule during decision-making (cTBSD ecision group), respectively. We also reanalyzed data from a previous study (Derosiere et al., 2017) where M1 was disrupted on Day 2 (cTBSD ecisionEarly group) to provide a broad, meta-analytical view of the short-term effects (on the same Day) of M1 disruption on the implementation of the implicit rule throughout practice. Interestingly, the impact of temporary M1 lesions on subjects' motor decisions depended on the Day on which they were produced.
4. Discussion
The present study aimed at characterizing the impact of M1 disruption on the implementation of implicit value information guiding motor decisions at different learning stages. Fifty subjects performed, over three consecutive days, a task that required them to select finger responses according to the color (instruction) and the shape (implicit rule) of an imperative signal. The instruction and the implicit rule were either congruent, calling for a response with the Fingeri nstructed, or incongruent, competing against each other. Hence, considering the implicit rule should increase the use of the Fingeri nstructed in congruent tri
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ing is that the direct impact of cTBS on the functional integrity of M1 during learning impeded the implicit information to consolidate. Indeed, the implicit rule consolidated overnight from Day 2 to Day 3 in the three groups of subjects (no M1 lesion on Day 2), whereas it vanished from Day 1 to Day 2 in the cTBSL earning group (M1 lesion on Day 1), although the performance level reached at the end of Day 1 in the latter group was comparable to that reached at the end of the session on Day 2 in the three groups. Another plausible interpretation is related to the idea that cTBS may have increased the release of dopamine in the striatum. Indeed, while synaptic dopamine is known to facilitate both learning and memory consolidation at certain concentrations, abnormally high concentrations during learning may, on the contrary, negatively impact memory consolidation (de Lima et al., 2011; Grogan et al., 2015). Further investigations are required to better comprehend the role of M1 in the consolidation of implicit value knowledge.
4.1. Consolidation and implementation of the implicit value-based rule along with practice
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When M1 functioned normally over the three days (cTBSC ontrol group), the impact of the implicit rule on the subjects' action choices only became perceptible from the middle of Day 2. Indeed, subjects started to exhibit a clear preference for using the Fingeri nstructed in congruent compared to incongruent trials in Blocks4 -6 of Day 2. These findings are consistent with those gathered in a recent study on another group of control subjects performing the same task (Derosiere et al., 2017). Of note, this penchant then consolidated and remained stable from then on, as evident on Day 3 in the present study. 4.2. Impact of M1 cTBS on day 1 on learning of the implicit rule
4.4. Impact of M1 cTBS on day 3 on the implementation of the implicit rule
When cTBS was applied over M1 before reinforcement learning, subjects exhibited a stronger reliance on the implicit rule on Day 1. That is, participants in the cTBSL earning group already displayed a larger use of the Fingeri nstructed for congruent than incongruent trials in Blocks4 -6 of Day 1. Consistently, the Implicit Index computed on these blocks was higher in the cTBSL earning group than in the cTBSC ontrol group. This effect is very robust as it was also observed on a different group of subjects (n = 17) involved in a previous study (Zénon et al., 2015). We think this result may be understood from a network perspective. Indeed, repetitive TMS is known to alter the activity of a distributed brain network (Bestmann et al., 2004; Briend et al., 2017; Cash et al., 2017; Rastogi et al., 2017). For instance, previous studies have evidenced that repetitive TMS over the frontal lobe (including M1) can evoke a release of dopamine in the striatum in a topological manner: while the stimulation of the lateral prefrontal cortex indirectly increases the extracellular dopamine concentration in the ipsilateral caudate nucleus (Strafella et al., 2001), the stimulation of M1 does so in the putamen (Strafella et al., 2003). Given the facilitatory function of striatal dopamine in reinforcement learning (Soares-Cunha et al., 2016), it is sensible to assume that the boosting effect of M1 cTBS observed on Day 1 in the current study and in Zénon et al. (2015) was due to the indirect impact of the stimulation on the release of dopamine in the putamen. Alternatively, the enhanced reliance on the implicit rule on Day 1 in the cTBSL earning group may be due to the fact that M1 cTBS altered learning of the color-based instruction, leaving more space for the implicit rule in the decision process. However, this hypothesis is not supported by our data given that subjects in the cTBSL earning group improved as well as subjects of the other groups at using the Fingeri nstructed on Day 1. Future studies are required to investigate the neural cause of the boosting effect observed with M1 cTBS on Day 1.
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Another finding of the present study is that M1 disruption did not impact motor decisions when it occurred halfway through Day 3. Indeed, we did not observe any significant difference in the use of the implicit rule between subjects in the cTBSD ecision group and those in the cTBSC ontrol group; the Implicit Indices were comparable in the two groups over the three Days. Subjects in the former group began to rely on the implicit rule in the second half of Day 2 and kept on using it from then on, even in the presence of a virtual M1 lesion during the second half of Day 3. Hence, disrupting M1 in the cTBSD ecision group did not alter the subjects' ability to use the implicit rule to make their motor decisions. This finding contrasts with the results of our recent study in which we observed a reduced reliance on the implicit rule when a similar M1 lesion was realized after the subjects had practiced the exact same task (Derosiere et al., 2017). However, M1 activity was disrupted earlier in our previous study (halfway through Day 2; referred to as the cTBSD ecisionEarly group in the present article) compared to the current experiment. Hence, the findings of the present study suggest that M1 disruption only impacts on the implementation of the implicit rule if it occurs at a time when it is freshly acquired. 4.5. Learning stage-dependent effect of M1 cTBS on the implementation of the implicit rule Recent studies have shown that M1 receives functional projections from major dopaminergic structures (e.g., Hosp et al., 2011; Kunori et al., 2014) and exhibits phasic neural responses following reward occurrence (Cohen and Ranganath, 2007; Lam et al., 2013). In other words, M1 presents key features for being part of a neural network encoding value information to guide motor behavior. What may be the cause of the distinct impact of M1 cTBS on Day 2 and Day 3? One possible interpretation of our results is that M1 becomes progressively less susceptible to cTBS as task-related synapses strengthen over the course of the experiment, maybe through proteostasis (Rosenberg et al., 2014). Another plausible explanation is that the relative contribution of M1 to value-based aspects of motor decisions decreases following consolidation. Similar hypotheses have been proposed in the context of other motor behaviors in other species. In rodents for instance, the contribution of the motor cortex decreases following consolidation of motor sequences (Kawai et al., 2015). In songbirds, the lateral magnocellular nucleus of the anterior nidopallium – a cortex-equivalent structure at the origin of long-lasting changes in downstream circuits during vocal learning (Andalman and Fee, 2009; Turner and Desmurget, 2010) – is crucial for performing freshly acquired motor patterns but not for executing them after consolidation (Bottjer et al., 1984). These results have been interpreted as reflecting a decrease in the involvement of the motor cortex, potentially occurring in parallel with an increased role of
4.3. Impact of M1 cTBS on day 1 on consolidation of the implicit rule
Importantly, the cTBS-related facilitation of implicit learning reported in the previous section was short-lasting. Indeed, performance in the cTBSL earning group went back to “normal” on Day 2 with subjects showing very little reliance on the implicit rule during the first half of the session on Day 2, as observed in the cTBSC ontrol group. Consistently, the Implicit Index showed a substantial decrease from Day 1 to Day 2 in the cTBSL earning group. Then, the influence of the implicit rule became evident again in the second half of Day 2 and persisted on Day 3, similar to the observation made in the cTBSC ontrol group. The absence of consolidation of the implicit material from Day 1 to Day 2 in the cTBSL earning group was not anticipated and could not be predicted from Zénon et al. (2015) as subjects only performed the task for a single day in that previous study. One straightforward interpretation for this find
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the basal ganglia, in the control of motor skills following consolidation (Kawai et al., 2015). Similarly, we propose that while M1 integrity is necessary for consolidating implicit material, its relative contribution may decline following consolidation, potentially in concert with an increased contribution of the basal ganglia, an issue for future investigations.
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Derosiere, G., Billot, M., Ward, E.T., Perrey, S., 2015c. Adaptations of motor neural structures' activity to lapses in attention. Cereb. Cortex 25, 66–74. Derosiere, G., Vassiliadis, P., Demaret, S., Zenon, A., Duque, J., 2015b. Disentangling the involvement of primary motor cortex in value-based reinforcement learning and value-based decision making. Soc. Neurosci. Abstr.620.04. Derosiere, G., Zénon, A., Alamia, A., Duque, J., 2017. Primary motor cortex contributes to the implementation of implicit value-based rules during motor decisions. NeuroImage https://doi.org/10.1016/j.neuroimage.2016.10.010. Derosiere, G., Zenon, A., Alamia, A., Klein, P., Duque, J., 2015a. Contribution of primary motor cortex to perceptual and value-based decision processes. Front. Neurosci. https://doi.org/10.3389/conf.fnins.2015.89.00056, Conference Abstract: 11th National Congress of the Belgian Society for Neuroscience. 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5. Conclusion
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The present work indicates that the effect of M1 cTBS on value-based motor decisions depends on the learning stage at which it is applied. Indeed, when applied before reinforcement learning on Day 1, M1 cTBS improved the reliance on the implicit value-based rule, plausibly because the intervention indirectly increased dopamine release in the putamen. However, this effect was short-lasting; it did not persist overnight, suggesting that M1 disruption during learning altered consolidation of implicit value information (despite the apparent boosting effect). Finally, cTBS over M1 did not impact the use of the implicit rule when applied after consolidation on Day 3, although it did so when M1 was disrupted once the subjects were just about to implement the implicit rule in their action choices, on Day 2. Overall, our findings suggest that the human M1 is functionally involved in the consolidation and implementation of implicit value information underlying motor decisions. However, its contribution seems to vanish as subjects become more experienced in using the implicit rule to make their motor decisions. Further studies are required to improve our comprehension of the role of M1 in value-based motor decisions and to put apart the effects that are due to a direct perturbation of M1 activity through cTBS from those that may be produced by an indirect alteration of areas that are part of the same network. Conflict of interest
The authors declare no competing financial interests. Acknowledgement
This work was supported by grants from the “Fonds Spéciaux de Recherche” (FSR) of the Université Catholique de Louvain, the Belgian National Funds for Scientific Research (FRS-FNRS: MIS F.4512.14) and the “Fondation Médicale Reine Elisabeth” (FMRE). GD was a postdoctoral fellow supported by the FNRS and a Marie Sklodowska-Curie grant (MSCA-CoFund). AZ was a Senior Research Associate supported by INNOVIRIS. References
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