In a sleepy haze, reaching out and grabbing the coffee cup in front of you seems to happen on autopilot. But your caffeine-deprived brain is working hard.
It’s collecting sensory information and other kinds of feedback – clues about where your arm is in space relative to the mug – and sending it to your motor cortex.
Then, the motor cortex plans the upcoming movement and tells your muscles to make it happen.
New research in mice is examining the role of those feedback signals entering the motor cortex, untangling how and when they’re necessary to guide dexterous movements like grasping.
That’s been a big open question, says study coauthor Britton Sauerbrei, an associate at the Howard Hughes Medical Institute’s Janelia Research Campus.
Some neural circuits can generate rhythmic, patterned output without sustained input. Just as a single nudge from a rider can send a horse into a trot, these “central pattern generators” can help animals walk, swim, and fly without ongoing stimulation. But not the motor cortex, it turns out.
“What we show is the motor cortex is fundamentally different from that,” says Sauerbrei. “You can’t just give the cortex a little kick and have it take off and generate that pattern on its own.” Instead, the motor cortex needs to receive feedback throughout the entire movement, Sauerbrei and his colleagues report December 25, 2019, in Nature.
He and his colleagues trained mice to reach for and grasp a food pellet, a behavior that depends on the motor cortex. In some animals, they turned off the thalamus, a switchboard in the brain that directs sensory information and other kinds of feedback to and from the cortex.
Using high speed video cameras, researchers tracked mice’s arm motions as the animals reached out and grasped a food pellet. Then, they tested how switching off different parts of the brain affected this dexterous movement. Credit: Sauerbrei et al./Nature 2019
When the researchers blocked the signals coming into the motor cortex before the mice began to reach, the animals didn’t initiate movement. And when incoming signals were blocked mid-reach, mice stopped moving their paw closer to the pellet.
The rhythm of those signals also matters, the researchers showed. In another experiment, they stimulated neurons carrying signals from the thalamus to the cortex with different patterns of incoming signals.
The frequency of the stimulation affected the motor cortex output, with fast pulses disrupting mice’s grasping skills.
The signals entering the motor cortex via the thalamus come from all over, and it’s not yet clear which ones are most important for directing movement, says Adam Hantman, a group leader at Janelia and the paper’s senior author.
Inputs to the thalamus include sensory information about the position of the arm, visual information, motor commands from other brain regions, and predictions about the upcoming movement.
Using tools developed by the Janelia project team Thalamoseq, Hantman’s lab plans to switch specific regions of the thalamus on and off to test which inputs are really driving the behavior.
For Hantman, the complexity of understanding these kinds of motor skills is what makes studying them so exciting.
“If you want to understand a behavior, and you think you’re going to study one region, you might be in a tough position,” he says. “You need to understand the whole central nervous system.”
More information: Cortical pattern generation during dexterous movement is input-driven, Nature (2019). DOI: 10.1038/s41586-019-1869-9 , https://nature.com/articles/s41586-019-1869-9
The outputs of motor cortex (M1) are lateralized: most spinal projections influence the contralateral musculature. M1 lesions thus produce contralateral motor deficits (Liu and Rouiller, 1999; Murata et al., 2008; Passingham et al., 1983; Vilensky and Gilman, 2002). Similarly, electrical microstimulation activates contralateral musculature (Kwan et al., 1978; Sessle and Wiesendanger, 1982).
The degree to which computations within M1 are lateralized remains less clear. The corpus callosum connects M1 across hemispheres, yielding the potential for extensive cooperation (Gould et al., 1986; Jenny, 1979; Jones and Wise, 1977). Callosally mediated interactions are readily revealed by paired-pulse TMS protocols and can involve net facilitation or suppression (Ferbert et al., 1992; Hanajima et al., 2001; Meyer et al., 1995).
An obvious role for inter-hemispheric communication is coordination of bimanual movement (Donchin et al., 1998; Haken et al., 1985; Kelso, 1984; Kermadi et al., 1998). Yet, there is evidence that unimanual movements also involve sharing information across hemispheres.
Most physiological studies of unimanual movements have focused on activity contralateral to the moving limb, on the grounds that contralateral activity is most functionally relevant and likely to be most prevalent.
Yet, ipsilateral activity can be robust. During finger movements, ipsilateral single-neuron activity is modest but present (Aizawa et al., 1990; Matsunami and Hamada, 1981; Tanji et al., 1988) and behavior can be decoded from BOLD activation within ipsilateral motor cortex (Berlot et al., 2019; Diedrichsen et al., 2013).
Considerable ipsilateral activity has been reported during movements involving the upper arm, such as reaching to remove food from a drawer (Kermadi et al., 1998; Kazennikov et al., 1999), or performing center-out reaches (Cisek et al., 2003; Donchin et al., 2002; Ganguly et al., 2009; Steinberg et al., 2002).
While the presence of ipsilateral activity is established, the nature of that activity is less clear. Few studies have directly compared neural response patterns when the same movement is performed by one arm versus the other. In premotor areas, delay-period responses can encode information about an upcoming reach (Cisek et al., 2003) or grasp (Michaels and Scherberger, 2018) independently of which arm would subsequently move, suggesting that preparatory activity is largely limb-independent.
However, activity during movement was more limb-dependent, for both premotor cortex and M1 (Cisek et al., 2003). Steinberg et al. (2002) reported similar single-neuron directional tuning in M1 regardless of which arm was moving, yet also found evidence for limb-dependent population-level encoding of direction. Donchin et al. (1998) found that activity during bimanual movements differed from that during unimanual movements, but did not analyze whether response tuning varied between contralateral and ipsilateral movements. Thus, it remains unclear to what degree the pattern of M1 responses – at either the single-neuron or population levels – depends on the limb being used.
If responses are limb-independent then the relationship between hemispheres is necessarily simple: both contain the same information, encoded in the same manner. In contrast, strongly limb-dependent responses would raise additional questions.
Are ‘lower-level’ signals (e.g. those describing muscle activity) more prevalent in the contralateral hemisphere? More generally, which signals are shared across hemispheres? How is activity structured such that only one arm moves when both hemispheres are active?
We investigated these questions using a novel ‘cycling’ task, performed with either the left or right arm. We recorded neural activity from both hemispheres simultaneously. In separate sessions, we recorded muscle activity bilaterally. Individual neurons responded robustly regardless of which arm performed the task.
Yet, responses were strongly limb-dependent: for a given neuron, response patterns pertaining to the two arms were essentially unrelated. Nevertheless, we found no clear evidence that the two hemispheres contained different types of information.
For example, muscle activity could be decoded equally well from contralateral or ipsilateral neural activity. More broadly, any signal that was strongly present in one hemisphere was present (and similarly strong) in the other.
Thus, activity in a given hemisphere contains similar information during movement of one arm versus the other, but that information is distributed very differently across individual neurons. This might appear to yield a paradox: how can M1 be robustly active without driving the contralateral arm?
A solution emerged when we examined population activity: arm-specific signals were partitioned into orthogonal dimensions, allowing a simple decoder to naturally separate signals related to the two arms.