The involvement of the motor cortex in postural control: a delicate balancing act
Bibliographic record
Abstract
Whenever we move, our brain tunes our postural reflexes so that our balance is maintained. This remarkable task is usually accomplished without any conscious thought on our part. Depending on the task at hand, the output of multiple muscles must be constantly tweaked to maintain balance. How this is accomplished is a mystery that is far from being understood. Part of the complexity of the problem is that posture is controlled by numerous interacting networks such as the spinal cord, cerebellum, cortex and brainstem (Jacobs & Horak, 2007; Deliagina et al. 2008). The control of posture can be divided into two systems. The first is the anticipatory mode, where postural corrections are made prior to movement, and the second is the feedback mode where corrections are made in response to perturbations (Deliagina et al. 2007). The focus of this perspectives article is on the feedback mode, which operates to maintain a dorsal side up orientation in quadrupeds, and an upright orientation in humans. Deliagina and colleagues suggest that the body of quadrupeds can be split into at least two independent postural systems: one that controls the head and neck, and another which controls the trunk. Vestibular and visual input are mainly used to stabilize the head while somatosensory inputs from the limbs are primarily used to correct trunk displacements. With regard to the trunk, local spinal reflexes and long-loop supraspinal pathways are thought to participate in correcting perturbations of the limb. Descending feedback to the spinal cord is attributed to the reticulospinal, vestibulospinal and corticospinal tracts and facilitates the final postural corrective response. The recent work by Karayannidou and colleagues published in this issue of The Journal of Physiology provides insight into how pyramidal tract neurons (PTNs) from the fore and hind limb projections in the primary motor cortex respond to postural changes during two distinct tasks (Karayannidou et al. 2009). PTNs are the main output neurons of the motor cortex and influence activity of motoneurons and interneurons in the ventral horn of the spinal cord. They receive somatosensory input from proprioceptors in the limbs and other areas, including the posterior parietal cortex which integrates several sensory modalities. Consequently, PTNs are well positioned to provide corrective inputs to pools of motoneurons and interneurons within the spinal cord. PTNs are not hard-wired to produce a given response in muscles. PTN discharge can vary between tasks even when the EMG output from the muscles is relatively similar. For example, when monkeys were trained to perform a precision or power grip task, PTNs discharged at a higher rate during the precision grip task (Muir & Lemon, 1983) even though the same muscle activation occurred in both tasks. Drew and colleagues found that PTNs recorded in walking cats increased their rate of firing when a compensatory movement of the forelimb was required to avoid an obstacle (Drew, 1993). Karayannidou and colleagues provide further evidence of the task-dependent nature of PTNs. They designed a task whereby cats were trained to walk on a treadmill that allowed them to tilt their bodies 15 deg to the left or to the right. They then compared this task with one that imposed a similar tilt but which required that the animal remain stationary. The behavioural response to the tilts was essentially identical in both tasks. After the tilt was imposed, there was a marked asymmetry in the hind and forelimbs; the legs on the tilt down side exhibited a longer length, accompanied by an increase in extensor tone. The authors examined whether the PTNs responded in a similar manner following a tilt imposed during walking compared to standing. They found that many PTNs would increase their rate of firing in response to a tilt regardless of the task, while others would only respond during one task and not another. A particularly interesting subgroup responded to tilts in one direction with an animal walking on a treadmill but only responded to tilts in the opposite direction with a stationary animal. Overall, the authors provide compelling data suggesting that PTNs modulate their output to postural perturbations, and that this modulation could depend on the task being performed. What exactly are these PTNs doing? We know that the motor cortex participates in some, but not all, aspects of postural control (Deliagina et al. 2007; Jacobs & Horak, 2007). While it is possible to say that PTNs respond differently depending on the task, their involvement in postural adjustment cannot be ascertained. How then can we make the leap from correlation to causality? This depends on the ability to selectively inactivate groups of PTNs. Certainly, approaches exist whereby genetically identified neurons can be reversibly inactivated (Gosgnach et al. 2006) and techniques are being developed to identify PTNs using genetic approaches (Molyneaux et al. 2005). Combined with calcium imaging of populations of cells in the cortex (Stosiek et al. 2003; Murayama et al. 2007), I expect the tracking and assigning of causality to PTNs will be tractable. While this is unlikely to occur overnight, the exciting discoveries of Karayannidou and colleagues encourage us to intensify our efforts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".