Many of the movements that humans and other animals make every day are deceptively complex and only appear easy because of extensive practice. For example, picking up an object involves several steps that must be precisely controlled, including reaching towards the item and holding it using the right amount of pressure to not crush it or drop it. Part of the brain called the motor cortex is thought to be important for learning and controlling these skilled movements, but its exact role in these processes is not clear.

A technique called optogenetics allows the roles of individual parts of the brain to be studied by rapidly altering their activity, whilst minimizing the likelihood that the brain will compensate for these changes. By genetically modifying animals to produce light-sensitive channel proteins in certain brain cells, the activity of particular regions of the brain can be controlled by shining light onto them. Guo et al. have now used optogenetics to control the motor cortex as the mice performed a task they had been trained to do – reaching for and picking up a food pellet.

Suddenly shutting down the motor cortex at the start of a trial prevented the mice from starting the task, and shut down part way through the task caused the front limbs of the mice to freeze in midair. However, only the learned, skilled task was frozen by motor cortex shutdown; mice could still move their limbs normally if the motor cortex was instead shut down during routine movements. When the cortex was reactivated, the mice instantly resumed trying to pick up the food pellet.

Unexpectedly, even during rest periods when there was no food pellet and the mice were just waiting for the experiment to begin, turning the motor cortex off and then back on again suddenly caused the mice to perform the complete grabbing motion. This implies that the cortical activity evoked at the end of inactivation acts to trigger the full movement sequence. This was particularly likely to occur if the animal had been deprived of food before the test or was particularly well trained, but did not depend on the position of the limb.

Overall, Guo et al.’s work opens the question of how the instructions that describe the learned movement are encoded within the motor cortex and its downstream networks. Future studies could also investigate how learning a set of movements affects the structure of cortical neurons and their connections, thus suggesting how these memories are stored.

Execution of complex voluntary movements depends on many functions including choosing a behavior, specifying each step, enacting the movements, and learning to perform with increasing skill. Motor control is achieved by the coordinated activity of myriad neural structures, including the cerebral cortex, basal ganglia, cerebellum, and spinal cord. Neurophysiological recordings, inactivation experiments, and stimulation studies have been used to describe the role of cortex in motor control. Cortical neurons display dynamic activity patterns during movement planning and execution, but their functional contribution to skilled action cannot be assessed from recordings alone (Lemon, 1993; Scott, 2003; Evarts, 2011). Previous inactivation experiments have suggested the cortex tunes—rather than initiates and executes—motor programs to achieve dexterous movement (Walker and Fulton, 1938; Lawrence and Kuypers, 1968; Castro, 1972; Passingham et al., 1983; Martin and Ghez, 1991; Whishaw, 2000; Fogassi et al., 2001; Peters et al., 2014). The classical perturbation methods used in these studies lack temporal specificity and allow for the emergence of compensatory mechanisms (but see supplement of Peters et al., 2014). Electrical stimulation of cortex has been shown to be sufficient to elicit complex movements, but it remains difficult to resolve if evoked behaviors are caused by direct cortical neuron stimulation or antidromic activation of inputs to cortex (Ferrier, 1873; Penfield and Boldrey, 1937; Penfield, 1954; Gottlieb et al., 1993; Graziano et al., 2002; Ramanathan et al., 2006; Harrison et al., 2012). Thus the precise role of cortex in skilled movement remains unclear. Here we inhibited cortex with an optogenetic method that minimizes compensation, and selective stimulation of cortical neurons was achieved by the cessation of inhibition.

To test the role of the cortex for control of skilled motor tasks, we developed a multi-step, cued, prehension task in which head-fixed mice reach for a food pellet, grab it, and bring the pellet to the mouth for consumption (Figure 1A, Video 1). This prehension behavior was broken down into six sequential epochs (Lift, Hand open, Grab, Supinate, At mouth, and Chew; Figure 1B and C) that divide the complex movement into components that involve distinct muscle activations. Unlike previous paradigms, our task employed head fixation to reduce postural variability, allow examination of reaction time, and enable future physiological investigations (Whishaw et al., 1991; Osborne and Dudman, 2014). All animals that underwent training learned the task. Reaching for the pellet could be achieved in 5–10 sessions and stable performance (median success rate was 74%, n = 13 animals) could be realized in approximately 15 sessions. We used high-speed video and machine-learning-based tracking to semi-automatically convert filmed movements into hand trajectories (see Materials and methods, Figure 1D and E, Video 2). Stereotypy of head-fixed prehension can be seen in sequential trajectories from a single animal during a behavioral session (Figure 1E).

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While chronic cortical perturbations degrade prehension performance in freely moving animals, such experiments allow the animal sufficient time to learn behavioral strategies to circumvent affected circuits (Whishaw et al., 1991; Whishaw et al., 1993). To avoid this confound, we employed rapid onset, within-trial suppression by light-activating channelrhodopsin (ChR2)-expressing inhibitory neurons (expressing the vesicular GABA transporter [Slc32a1]) in cortex with pulses of light (Figure 2) (Guo et al., 2014). This method presumes that activation of Slc32a1-COP4*H134R/EYFP-positive neurons selectively suppresses cortex, although other possibilities do exist (for evidence against, see methods). Cortical inhibition was limited in space (Figure 2—figure supplement 1) and time by regulating the position and duration of light exposure. To limit compensation, light was delivered on less than 20% of trials during a behavioral session. We first applied inhibition at cue onset to test the role of cortex for behavior initiation. The only effective area of cortical suppression was centered over contralateral forelimb motor cortex (Figure 2B–E, Video 3). Because forelimb motor and sensory cortices are overlapping at this resolution in the rodent, we will refer to this region as sensorimotor (SM) cortex (Ayling et al., 2009).

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Contralateral sensorimotor cortex suppression at the beginning of an individual trial eliminated initiation of the prehension behavior (initiation only occurred in 35 of 459 trials during contralateral sensorimotor cortex inhibition, compared to initiation in 3675 of 3692 control trials, n = 8 animals, Figure 2A and B, Video 3). Prehension reliably occurred after optogenetic cortical suppression was relinquished (initiation in 436 of 467 trials, n = 8 animals), Figure 2B, Video 3). Inhibition of other cortical areas did not affect behavioral initiation rates (ipsilateral sensorimotor (initiation in 124 of 124 trials, n = 3 animals, Figure 2C, Video 4), visual (initiation in 122 of 124 trials, n = 3 animals, Figure 2D), and frontal (initiation in 124 of 124 trials, n = 3 animals, Figure 2E) cortex). In contrast to prehension, voluntary grooming was not impacted by sensorimotor cortical suppression (11/11 trials, n = 4 animals, Video 5). To rule out that initiation failure was due to an inability to process the cue, we activated cortical inhibitory neurons during a cued licking task in which a pellet of food positioned close to the mouth was captured by the tongue; this behavior was not affected by sensorimotor inhibition (Figure 2—figure supplement 2, Videos 6 and 7). During this cued licking task, animals sometimes moved their forelimb from the perch to their mouth/table (2/3 animals, Video 6). While these movements shared some features with the trained prehension behavior, they were not kinematically stereotyped. Contralateral sensorimotor cortical suppression did not prevent these forelimb movements during cued licking (forelimb movement in 44/44 control trials and 20/20 laser trials for two animals, Video 7). These results indicate that contralateral sensorimotor cortex activity is necessary for initiation of a learned, goal-directed, skilled prehension behavior.

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We next tested the role of cortex during the execution of prehension by activating inhibitory neurons at different times after initiation (Videos 8–16). Sensorimotor cortical suppression during the Lift, Hand open, Grab, and Supination components of the movement resulted in a “freezing” of progression towards the target, after a short latency (5/5 animals; Figure 3A–D; Videos 8, 9, 10, 12 and 16). The latency to freezing (between 72–170 ms for a representative animal) implies that this is the timescale over which the cortex commands specific components of the movement (Figure 3A4–D4). Movement during the delay was not simply the result of momentum, since in many cases the movement progressed to the next distinct component before freezing (Figure 3A3–E4). Cortical suppression was not effective at impeding hand movements involved in chewing, suggesting these motor programs do not require sensorimotor cortex (Figure 3E and F, Videos 11 and 13–15), despite involving similar muscle groups. Prolonged suppression of sensorimotor cortex during freezing resulted in a retraction of the limb either to the perch or a characteristic midair position (Figure 3A2–E2, Videos 8, 9, 10 and 12). These results indicate that ongoing contralateral sensorimotor cortex activity is necessary for progressing through the steps of a learned, goal-directed, skilled prehension behavior.

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Cortical suppression-induced behavioral freezing was rapidly reversible; immediately upon cessation of the light, animals again reached/grabbed for the pellet (“rebound prehension”, 8/8 animals, Figure 2B, Figure 3A1–E1, Videos 3, 8–10, 12 and 16). The initiation of rebound prehension had shorter and less variable latencies when compared to control (uninterrupted, tone-cued) prehension (Figure 2A and B, 4A). Once initiated, however, durations of the distinct phases of rebound prehension and the trajectories were not statistically different from control behavior (Figure 4A–C; Figure 4—figure supplement 1). End-point accuracies of control and rebound prehension attempts were similar (4/5 animals, Wilcoxon–Mann–Whitney rank sum, p>0.05; for 1/5 animal difference was significant but median end-point error was only 0.74 +/– 0.52 mm (median +/– median absolute deviation); Figure 4D and E). Interestingly, end-point accuracies were also similar for rebound prehension attempts either beginning from the perch or from a diversity of midair positions (5/5 animals, Wilcoxon–Mann–Whitney rank sum, p>0.05; Figure 4D and E). Therefore, motor programs comparable to natural forms of the behavior follow termination of inhibitory neuron activation in sensorimotor cortex.

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Two possibilities could underlie rebound prehension: the animals could make them voluntarily or they could be a direct consequence of the relief of inhibition. An involuntary nature of rebound prehension was supported by its occurrence even on cued trials when the animal had already grabbed the pellet (Figure 3, Videos 10, 12 and 16). To further test this, we removed both the pellet and the cue to disengage the animal from the task. During these uncued experiments, the animals remained motionless during contralateral sensorimotor cortical inhibition as expected. Surprisingly, immediately (~250 ms) following light cessation, the animals reached and grabbed at the empty pellet tray (initiation in 192/224 trials, n = 8 animals, Figure 4F, Videos 17). Rebound execution did not occur in untrained animals (3/3 animals) and was specific to prehension; no other type of movement was generated following light termination (n = 1434 trials, n = 5 animals). If a pellet was in the target position but the animal was not alerted of its presence, cessation of inhibition frequently generated the full reach-grab-eat sequence (Videos 18). Taken together, these observations indicate that rebound prehension is not the result of ongoing sensory information or the animals’ remembered behavioral state. Rather, termination of inhibitory neuron activation in sensorimotor cortex, in combination with plastic changes induced by training, is sufficient to evoke a complex, learned motor act.

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Rebound prehension showed dependence on a number of factors. Anatomically, no area outside of sensorimotor cortex contralateral to the utilized limb was effective in evoking the behavior in the no cue/no pellet condition (ipsilateral sensorimotor, 0/68 trials, n = 3 animals, Figure 4G; frontal, 0/20 trials, n = 2 animals; visual, 0/21 trials, n = 3 animals). Duration of cortical inhibition was also a critical factor in initiating rebound prehension. At least 2 s (median across animals) of cortical inhibition was required to achieve rebound prehension rates above 90% (n = 4 animals, Figure 4H). Longer durations (4 s was longest tested) of cortical inhibition also reduced the latency variability of prehension initiation (n = 2 animals, Figure 4H). Interestingly, long durations of photostimulation (2 s or more) were specifically associated with prominent rebound spiking in cortical neurons (Figure 4—figure supplement 2). This raises the possibility that rebound spiking in the cortex is responsible for rebound prehension. Rebound prehension itself is also cortically dependent, as reactivation of sensorimotor inhibitory cortical neurons freezes progression of the rebound movement (Videos 19, Figure 4—figure supplement 3). The presence of food in the mouth decreased rebound prehension rates (18/95 trials exhibited rebound; Figure 4I; Videos 11, 13–15). For nearly all of these (93/95) trials, animals were still chewing at light termination. To distinguish whether the reduced rate of rebound prehension was related to motor-program competition (chewing versus prehension) or to motivation (due to consumption of the pellet), we assayed rebound rates for failure trials where animals were chewing without the pellet (29/256 trials, Figure 4I). Prehension generally occurred (21/29 trials) if the light was terminated during pelletless chewing. To further test the role of hunger/satiety, we terminated food deprivation; animals on free food for one day exhibited significantly reduced rates of rebound prehension despite still engaging in the control version of the behavior (Figure 4J). These results suggest that motivational drive, operating along both short (eating) and long (sated) time scales, gates rebound prehension. Therefore, evocation of the learned behavior depends not only on features of the inhibition but also on the satiety of the animal.

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By leveraging the rapid and reversible nature of optogenetic inhibition, we were able to probe the online role of cortex in a learned prehension task. Contrary to the view that cortex is only involved in achieving dexterity (Kawai et al., 2015), we found cortex to be necessary for initiating and executing the dexterous and non-dexterous steps of this skilled movement. The necessity of cortex for initiation and execution is consistent with some interpretations of experiments where cortical stimulation delayed or interrupted voluntary movements in primates (Penfield and Jasper, 1954; Day et al., 1989; Lemon et al., 1995; Churchland and Shenoy, 2007). Apparent disagreements between our work and that described by Kawai et al. may be due to differences in the behavior and/or the perturbation methodology. The forelimb-lever press task in the study by Kawai et al. requires animals to learn a time interval but not the precise spatial position of an external target. In contrast, the prehension behavior studied here requires the animal to learn the position of the pellet and how to precisely execute a reach to that location. This difference suggests that cortex is not required for interval tasks but rather skilled interactions with objects in the environment. An outlier animal described by Kawai et al. provides support for this hypothesis; this animal was unique in that it made precise movements to the lever and showed a significant decrease in performance after cortical perturbation. Additionally, the two experiments also differ in the acuteness of the cortical manipulations; we used rapid onset optogenetic inhibition while Kawai et al. utilized pharmacological and surgical manipulations. The rapidity of optogenetic inhibition may impede compensation mechanisms that ameliorate resulting phenotypes.

We found that termination of inhibitory neuron activation in sensorimotor cortex suffices to drive a full action sequence to the learned target. Previous activation studies of cortex have shown that electrical (Ferrier, 1873; Penfield and Boldrey, 1937; Penfield, 1954; Gottlieb et al., 1993; Graziano et al., 2002; Ramanathan et al., 2006; Bonazzi et al., 2013) and optogenetic (Harrison et al., 2012) stimulation for behaviorally relevant durations produces motor sequences. Rebound prehension differs from these movements in three ways: rebound prehension is a coordinated sequence of movements (reaching, grabbing, and eating) accurately directed to an external goal regardless of starting position, depends on training, and is gated by motivation (Ramanathan et al., 2006; Harrison et al., 2012; Bonazzi et al., 2013). These differences suggest that termination of inhibition evokes a motor engram specifying a learned goal/end-point of a trained behavior (Bernstein, 1967; Todorov and Jordan, 2002). Cortical synapses modified during the learning of skilled actions (Rioult-Pedotti et al., 1998; Xu et al., 2009; Wang et al., 2011) may be responsible for the formation of these motor engrams. Alternatively, the engram could be stored elsewhere, and relief of cortical inhibition could trigger or permit its activation in a downstream network. The ability to specifically elicit a post-manipulation transition of cortical state from quiescence to motor engram activation should enable functional characterization of the neural programs responsible for enacting skilled multi-step behavior.

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Author details

1. Jian-Zhong Guo

   Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

   Contribution

   J-ZG, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

2. Austin R Graves

   Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

   Contribution

   ARG, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

3. Wendy W Guo

   Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

   Contribution

   WWG, Acquisition of data, Analysis and interpretation of data

   Competing interests

   The authors declare that no competing interests exist.

4. Jihong Zheng

   Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

   Contribution

   JZ, Acquisition of data, Analysis and interpretation of data

   Competing interests

   The authors declare that no competing interests exist.

5. Allen Lee

   Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

   Contribution

   AL, Wrote critical software, Analysis and interpretation of data

   Competing interests

   The authors declare that no competing interests exist.

6. Juan Rodríguez-González

   Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

   Contribution

   JR-G, Wrote critical software, Analysis and interpretation of data

   Competing interests

   The authors declare that no competing interests exist.

7. Nuo Li

   Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

   Contribution

   NL, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

8. John J Macklin

   Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

   Contribution

   JJM, Acquisition of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

9. James W Phillips

   Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

   Contribution

   JWP, Acquisition of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

10. Brett D Mensh

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

    Contribution

    BDM, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

11. Kristin Branson

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

    Contribution

    KB, Conception and design, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

12. Adam W Hantman

    Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States

    Contribution

    AWH, Conception and design, Analysis and interpretation of data, Drafting or revising the article

    For correspondence

    hantmana@janelia.hhmi.org

    Competing interests

    The authors declare that no competing interests exist.

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