Summary: The brain naturally implements computational principles and learned rules of life integrated with sensory information to guide motor plans and actions.
Source: Picower Institute for Learning and Memory
Your new apartment is only a few blocks from the bus stop, but today you are late and you see the bus pass in front of you. You start a full sprint. Your goal is to get to the bus as quickly as possible and then stop exactly in front of the doors (which are never exactly in the same place along the sidewalk) to enter before they close. To stop quickly and accurately enough, a new MIT mouse study reveals that the mammalian brain is cleverly wired to implement the principles of computation.
You’d think stopping abruptly at a target after a hard run would be as simple as a reflex, but catching a bus or running to a visually indicated landmark to earn a water reward (like the mouse), is a learned, visually guided, goal-oriented feat.
In such tasks, which are of major interest to the lab of lead author Mriganka Sur, Newton Professor of Neuroscience at the Picower Institute for Learning and Memory at MIT, the crucial decision to switch from behavior ( run) to another (stop) comes from the cerebral cortex, where the brain integrates learned rules of life with sensory information to guide plans and actions.
“The lens is where the cortex comes in,” said Sur, a faculty member in MIT’s Department of Brain and Cognitive Sciences. “Where am I supposed to stop to achieve this goal of getting on the bus.”
And this is also where it gets complicated. Mathematical models of behavior developed by post-doctoral researcher and lead study author Elie Adam predicted that a “stop” signal going directly from the M2 region of the cortex to the brainstem regions, which actually control the legs , would be processed too slowly.
“You have M2 sending a stop signal, but when you model it and go through the math, you find that this signal, on its own, wouldn’t be fast enough to make the animal stop in time,” said Adam, whose work appears in the journal Cell reports.
So how does the brain speed up the process? What Adam, Sur, and co-author Taylor Johns discovered is that M2 sends the signal to an intermediate region called the subthalamic nucleus (STN), which then sends two signals down two separate pathways that converge at new in the brainstem.
Why? Because the difference made by these two signals, one inhibitory and the other excitatory, arriving one after the other shifts the problem of integration, which is a relatively slow addition of inputs, to differentiation , which is a direct recognition of the change. The calculation change implements the stop signal much faster.
Adam’s model using engineering control systems and theory – accurately – predicted the speed needed for proper stopping and this differentiation would be needed to achieve this, but it took a series of anatomical investigations and experimental manipulations to confirm the predictions of the model.
First, Adam confirmed that indeed M2 produced an increase in neural activity only when the mice had to meet their trained goal of stopping at the landmark. It was also shown to send the resulting signals to the STN. Other stops for other reasons did not use this route. Moreover, the artificial activation of the M2-STN pathway caused the mice to stop, and its artificial inhibition caused the mice to overshoot the landmark slightly more often.
The STN must then certainly signal the brainstem, in particular the pedunculopontine nucleus (PPN) in the midbrain locomotor region. But when the scientists looked at neural activity beginning in the M2 and rapidly driving the PPN, they found that different cell types in the PPN responded with different timings. In particular, before the stop, the excitatory cells were active and their activity reflected the speed of the animal during the stops.
Then, looking at the STN, they saw two types of bursts of activity around the stops – one slightly slower than the other – which were transmitted either directly to the PPN by excitation or indirectly via the substantia nigra pars reticulata (SNr) by inhibition. The net result of the interaction of these signals in the PPN was excitation-enhanced inhibition. This sudden change could be quickly found by differentiation to implement the stop.
“An inhibitory overvoltage followed by excitation can create a strong [change of] signal,” On said.
The study is consistent with other recent articles. In collaboration with Picower Institute researcher Emery N. Brown, Adam recently produced a new model of how deep brain stimulation in the STN rapidly corrects motor problems resulting from Parkinson’s disease. And last year, members of Sur’s lab, including Adam, published a study showing how the cortex trumps the brain’s more deeply rooted reflexes in visually guided motor tasks.
Together, these studies contribute to understanding how the cortex can consciously control instinctively hard-wired motor behaviors, but also how important deeper regions, such as the STN, are for the rapid implementation of goal-directed behavior. . A recent lab review explains this.
Adam hypothesized that the “hyperdirect pathway” of cortex-STN communications may have a broader role than rapid cessation of action, potentially extending beyond motor control to other brain functions such as interruptions and changes in thought or mood.
Funding: The JPB Foundation, the National Institutes of Health and the Simons Foundation Autism Research Initiative funded the study.
About this movement, research news in mathematics and neuroscience
Author: David Orenstein
Source: Picower Institute for Learning and Memory
Contact: David Orenstein – Picower Institute for Learning and Memory
Image: Image is credited to Elie Adam/MIT Picower Institute
Original research: Free access.
“Dynamic control of visually guided locomotion by cortico-subthalamic projections” by Mriganka Sur et al. Cell reports
Dynamic control of locomotion visually guided by cortico-subthalamic projections
- We have developed a visually guided locomotion task to study stop signaling
- M2-STN projection sends stop signal on visually guided locomotion stops
- M2-STN activity bidirectionally controls visually guided locomotion stops
- M2-STN to MLR/PPN pathways perform differentiation for rapid control of locomotion
Goal-directed locomotion requires control signals that propagate from higher-order areas to regulate spinal mechanisms. The hyperdirect corticosubthalamic pathway provides a short route for cortical information to reach the locomotor centers of the brainstem.
We developed a task in which fixed-head mice run to a visual landmark, then stop and wait to collect the reward and examined the role of projections from the secondary motor cortex (M2) to the subthalamic nucleus. (STN) in the control of locomotion.
Our results from behavioral modeling, calcium imaging, and optogenetic manipulation suggest that the M2-STN pathway can be recruited during visually guided locomotion to rapidly and precisely control the pedunculopontine nucleus (PPN) of the midbrain locomotor region through the ganglia of the base.
By capturing physiological dynamics through a feedback control model and analyzing neural signals in M2, PPN, and STN, we find that corticosubthalamic projections potentially control PPN activity by differentiating an M2 error signal to ensure dynamic fast input-output.