Forward and backward walking share the same motor modules and locomotor adaptation strategies.
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ABSTRACT:
SUBMITTER: Zych M
PROVIDER: S-EPMC8405989 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:How do descending inputs from the brain control leg motor circuits to change how an animal walks? Conceptually, descending neurons are thought to function either as command-type neurons, in which a single type of descending neuron exerts a high-level control to elicit a coordinated change in motor output, or through a population coding mechanism, whereby a group of neurons, each with local effects, act in combination to elicit a global motor response. The Drosophila Moonwalker Descending Neurons (MDNs), which alter leg motor circuit dynamics so that the fly walks backwards, exemplify the command-type mechanism. Here, we identify several dozen MDN target neurons within the leg motor circuits, and show that two of them mediate distinct and highly-specific changes in leg muscle activity during backward walking: LBL40 neurons provide the hindleg power stroke during stance phase; LUL130 neurons lift the legs at the end of stance to initiate swing. Through these two effector neurons, MDN directly controls both the stance and swing phases of the backward stepping cycle. These findings suggest that command-type descending neurons can also operate through the distributed control of local motor circuits.
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Project description:Locomotor adaptation is commonly studied using split-belt treadmill walking, in which each foot is placed on a belt moving at a different speed. As subjects adapt to split-belt walking, they reduce metabolic power, but the biomechanical mechanism behind this improved efficiency is unknown. Analyzing mechanical work performed by the legs and joints during split-belt adaptation could reveal this mechanism. Because ankle work in the step-to-step transition is more efficient than hip work, we hypothesized that control subjects would reduce hip work on the fast belt and increase ankle work during the step-to-step transition as they adapted. We further hypothesized that subjects with unilateral, trans-tibial amputation would instead increase propulsive work from their intact leg on the slow belt. Control subjects reduced hip work and shifted more ankle work to the step-to-step transition, supporting our hypothesis. Contrary to our second hypothesis, intact leg work, ankle work and hip work in amputees were unchanged during adaptation. Furthermore, all subjects increased collisional energy loss on the fast belt, but did not increase propulsive work. This was possible because subjects moved further backward during fast leg single support in late adaptation than in early adaptation, compensating by reducing backward movement in slow leg single support. In summary, subjects used two strategies to improve mechanical efficiency in split-belt walking adaptation: a CoM displacement strategy that allows for less forward propulsion on the fast belt; and, an ankle timing strategy that allows efficient ankle work in the step-to-step transition to increase while reducing inefficient hip work.
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Project description:This paper shows that the various computations underlying spatial cognition can be implemented using statistical inference in a single probabilistic model. Inference is implemented using a common set of 'lower-level' computations involving forward and backward inference over time. For example, to estimate where you are in a known environment, forward inference is used to optimally combine location estimates from path integration with those from sensory input. To decide which way to turn to reach a goal, forward inference is used to compute the likelihood of reaching that goal under each option. To work out which environment you are in, forward inference is used to compute the likelihood of sensory observations under the different hypotheses. For reaching sensory goals that require a chaining together of decisions, forward inference can be used to compute a state trajectory that will lead to that goal, and backward inference to refine the route and estimate control signals that produce the required trajectory. We propose that these computations are reflected in recent findings of pattern replay in the mammalian brain. Specifically, that theta sequences reflect decision making, theta flickering reflects model selection, and remote replay reflects route and motor planning. We also propose a mapping of the above computational processes onto lateral and medial entorhinal cortex and hippocampus.
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Project description:Moving about in the world during daily life requires executing and successfully shifting between a variety of functional tasks, such as rising from a chair or bed, walking, turning, and navigating stairs. Moreover, moving about during daily life requires not only navigating between different functional tasks, but also performing these tasks in the presence of mental distractions. However, little is known about underlying neuromuscular control for executing and shifting between these different tasks. In this study, we investigated muscle coordination across walking, turning, and chair transfers by applying motor module (a.k.a. muscle synergy) analysis to the Timed-Up-and-Go (TUG) test with and without a secondary cognitive dual task. We found that healthy young adults recruit a small set of common motor modules across the subtasks of the TUG test and that their composition is robust to cognitive distraction. Instead, cognitive distraction impacted motor module activation timings such that they became more consistent. This work is the first to demonstrate motor module generalization across multiple tasks that are both functionally different and crucial for healthy mobility. Overall, our results suggest that the central nervous system may draw from a "library" of modular control strategies to navigate the variety of movements and cognitive demands required of daily life.
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Project description:BACKGROUND:Promising studies have shown that the gait symmetry of individuals with hemiparesis due to brain lesions, such as stroke, can improve through motor adaptation protocols forcing patients to use their affected limb more. However, little is known about how to facilitate this process. Here we asked if increasing propulsion demands during split-belt walking (i.e., legs moving at different speeds) leads to more motor adaptation and more symmetric gait in survivors of a stroke, as we previously observed in subjects without neurological disorders. METHODS:We investigated the effect of propulsion forces on locomotor adaptation during and after split-belt walking in the asymmetric motor system post-stroke. To test this, 12 subjects in the chronic phase post-stroke experienced a split-belt protocol in a flat and incline session so as to contrast the effects of two different propulsion demands. Step length asymmetry and propulsion forces were used to compare the motor behavior between the two sessions because these are clinically relevant measures that are altered by split-belt walking. RESULTS:The incline session resulted in more symmetric step lengths during late split-belt walking and larger after-effects following split-belt walking. In both testing sessions, subjects who have had a stroke adapted to regain speed and slope-specific leg orientations similarly to young, intact adults. Importantly, leg orientations, which were set by kinetic demands, during baseline walking were predictive of those achieved during split-belt walking, which in turn predicted each individual's post-adaptation behavior. These results are relevant because they provide evidence that survivors of a stroke can generate the leg-specific forces to walk more symmetrically, but also because we provide insight into factors underlying the therapeutic effect of split-belt walking. CONCLUSIONS:Individuals post-stroke at a chronic stage can adapt more during split-belt walking and have greater after-effects when propulsion demands are augmented by inclining the treadmill surface. Our results are promising since they suggest that increasing propulsion demands during paradigms that force patients to use their paretic side more could correct gait asymmetries post-stroke more effectively.
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Project description:Picking up an empty milk carton that we believe to be full is a familiar example of adaptive control, because the adaptation process of estimating the carton's weight must proceed simultaneously with the control process of moving the carton to a desired location. Here we show that the motor system initially generates highly variable behavior in such unpredictable tasks but eventually converges to stereotyped patterns of adaptive responses predicted by a simple optimality principle. These results suggest that adaptation can become specifically tuned to identify task-specific parameters in an optimal manner.
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Project description:We used functional MR imaging (FMRI), a robotic manipulandum and systems identification techniques to examine neural correlates of predictive compensation for spring-like loads during goal-directed wrist movements in neurologically-intact humans. Although load changed unpredictably from one trial to the next, subjects nevertheless used sensorimotor memories from recent movements to predict and compensate upcoming loads. Prediction enabled subjects to adapt performance so that the task was accomplished with minimum effort. Population analyses of functional images revealed a distributed, bilateral network of cortical and subcortical activity supporting predictive load compensation during visual target capture. Cortical regions--including prefrontal, parietal and hippocampal cortices--exhibited trial-by-trial fluctuations in BOLD signal consistent with the storage and recall of sensorimotor memories or "states" important for spatial working memory. Bilateral activations in associative regions of the striatum demonstrated temporal correlation with the magnitude of kinematic performance error (a signal that could drive reward-optimizing reinforcement learning and the prospective scaling of previously learned motor programs). BOLD signal correlations with load prediction were observed in the cerebellar cortex and red nuclei (consistent with the idea that these structures generate adaptive fusimotor signals facilitating cancelation of expected proprioceptive feedback, as required for conditional feedback adjustments to ongoing motor commands and feedback error learning). Analysis of single subject images revealed that predictive activity was at least as likely to be observed in more than one of these neural systems as in just one. We conclude therefore that motor adaptation is mediated by predictive compensations supported by multiple, distributed, cortical and subcortical structures.
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