Greater Cognitive-Motor Interference in Individuals Post-Stroke During More Complex Motor Tasks

The speed-accuracy trade-off of fast movements acts inversely and as such is known as the Fitts's law. The aim of this study is to determine how instep kick (IK) speed grading instructions affect the instep kick speed and accuracy. The primary hypothesis assumes that a complex motor task such as IK has an inverse relation between speed and accuracy, and the secondary hypothesis assumes that the applied speed grading instructions are sensitive. The research involved 13 male players, the average age of 15 years (±1.6). The experimental protocol included the execution of IK at five different speeds, determined by verbal instructions to respondents. For assessment of kicking accuracy, we observed the following dependent variables: mean radial error (MRE), bivariate variable error (BVE), and centroid radial error (CRE). Comparative analysis has shown that higher accuracy (reduced MRE) and kicking consistency (reduced BVE) are achieved under lower kicking speeds, but these effects were not achieved in regard to CRE. Subsequent analyses have shown that MRE has a tendency towards a significant difference between the slowest and fastest kicks (p=0.068-0.075), while in the case of BVE it has been found that there are differences between the slowest and all other speed levels (p≤0.05). The main findings of this study have indicated a partial existence (two of three variables) of an inverse relationship between speed and accuracy in complex motor tasks such as IK (multi-joint and discrete motion).

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Emerging Roles of Filopodia and Dendritic Spines in Motoneuron Plasticity during Development and Disease

Neural Plasticity ◽

10.1155/2016/3423267 ◽

2016 ◽

Vol 2016 ◽

pp. 1-31 ◽

Cited By ~ 17

Author(s):

Refik Kanjhan ◽

Peter G. Noakes ◽

Mark C. Bellingham

Keyword(s):

Dendritic Growth ◽

Gene Knockout ◽

Spine Density ◽

Motor Tasks ◽

Dendritic Trees ◽

Sodium Calcium ◽

Complex Motor ◽

Distribution Shifts ◽

Circuit Formation ◽

Energy Dependent

Motoneurons develop extensive dendritic trees for receiving excitatory and inhibitory synaptic inputs to perform a variety of complex motor tasks. At birth, the somatodendritic domains of mouse hypoglossal and lumbar motoneurons have dense filopodia and spines. Consistent with Vaughn’s synaptotropic hypothesis, we propose a developmental unified-hybrid model implicating filopodia in motoneuron spinogenesis/synaptogenesis and dendritic growth and branching critical for circuit formation and synaptic plasticity at embryonic/prenatal/neonatal period. Filopodia density decreases and spine density initially increases until postnatal day 15 (P15) and then decreases by P30. Spine distribution shifts towards the distal dendrites, and spines become shorter (stubby), coinciding with decreases in frequency and increases in amplitude of excitatory postsynaptic currents with maturation. In transgenic mice, either overexpressing the mutated human Cu/Zn-superoxide dismutase (hSOD1G93A) gene or deficient in GABAergic/glycinergic synaptic transmission (gephyrin, GAD-67, or VGAT gene knockout), hypoglossal motoneurons develop excitatory glutamatergic synaptic hyperactivity. Functional synaptic hyperactivity is associated with increased dendritic growth, branching, and increased spine and filopodia density, involving actin-based cytoskeletal and structural remodelling. Energy-dependent ionic pumps that maintain intracellular sodium/calcium homeostasis are chronically challenged by activity and selectively overwhelmed by hyperactivity which eventually causes sustained membrane depolarization leading to excitotoxicity, activating microglia to phagocytose degenerating neurons under neuropathological conditions.

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Performance in complex motor tasks deteriorates in hyperthermic humans

Temperature ◽

10.1080/23328940.2017.1368877 ◽

2017 ◽

Vol 4 (4) ◽

pp. 420-428 ◽

Cited By ~ 20

Author(s):

Jacob F. Piil ◽

Jesper Lundbye-Jensen ◽

Steven J. Trangmar ◽

Lars Nybo

Keyword(s):

Motor Tasks ◽

Complex Motor

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Haptic fMRI: Accurately estimating neural responses in motor, pre-motor, and somatosensory cortex during complex motor tasks

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society ◽

10.1109/embc.2014.6944017 ◽

2014 ◽

Cited By ~ 7

Author(s):

Samir Menon ◽

Michelle Yu ◽

Kendrick Kay ◽

Oussama Khatib

Keyword(s):

Somatosensory Cortex ◽

Neural Responses ◽

Motor Tasks ◽

Complex Motor

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Mini-Review: The motor deficit of ALS reflects failure to generate muscle synergies for complex motor tasks, not just muscle strength

Neuroscience Letters ◽

10.1016/j.neulet.2021.136171 ◽

2021 ◽

pp. 136171

Author(s):

Andrew Eisen ◽

Roger Lemon

Keyword(s):

Muscle Strength ◽

Motor Deficit ◽

Muscle Synergies ◽

Motor Tasks ◽

Complex Motor

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A Reservoir Computing Model of Reward-Modulated Motor Learning and Automaticity

Neural Computation ◽

10.1162/neco_a_01198 ◽

2019 ◽

Vol 31 (7) ◽

pp. 1430-1461 ◽

Cited By ~ 2

Author(s):

Ryan Pyle ◽

Robert Rosenbaum

Keyword(s):

Motor Learning ◽

Learning Algorithm ◽

Motor Task ◽

Reservoir Computing ◽

Motor Tasks ◽

Biologically Inspired ◽

Target Time ◽

Learning Rules ◽

Complex Motor ◽

Experimental Findings

Reservoir computing is a biologically inspired class of learning algorithms in which the intrinsic dynamics of a recurrent neural network are mined to produce target time series. Most existing reservoir computing algorithms rely on fully supervised learning rules, which require access to an exact copy of the target response, greatly reducing the utility of the system. Reinforcement learning rules have been developed for reservoir computing, but we find that they fail to converge on complex motor tasks. Current theories of biological motor learning pose that early learning is controlled by dopamine-modulated plasticity in the basal ganglia that trains parallel cortical pathways through unsupervised plasticity as a motor task becomes well learned. We developed a novel learning algorithm for reservoir computing that models the interaction between reinforcement and unsupervised learning observed in experiments. This novel learning algorithm converges on simulated motor tasks on which previous reservoir computing algorithms fail and reproduces experimental findings that relate Parkinson's disease and its treatments to motor learning. Hence, incorporating biological theories of motor learning improves the effectiveness and biological relevance of reservoir computing models.

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Failure of Motor Learning for Large Initial Errors

Neural Computation ◽

10.1162/0899766041336431 ◽

2004 ◽

Vol 16 (9) ◽

pp. 1873-1886 ◽

Cited By ~ 41

Author(s):

Terence D. Sanger

Keyword(s):

Motor Learning ◽

Failure Modes ◽

Learning Rule ◽

Motor Tasks ◽

Mathematical Basis ◽

Initial Errors ◽

Repeated Practice ◽

Complex Motor ◽

Human Skills ◽

Training Examples

For certain complex motor tasks, humans may experience the frustration of a lack of improvement despite repeated practice. We investigate a computational basis for failure of motor learning when there is no prior information about the system to be controlled and when it is not practical to perform a thorough random exploration of the set of possible commands. In this case, if the desired movement has never yet been performed, then it may not be possible to learn the correct motor commands since there will be no appropriate training examples. We derive the mathematical basis for this phenomenon when the controller can be modeled as a linear combination of nonlinear basis functions trained using a gradient descent learning rule on the observed commands and their results. We show that there are two failure modes for which continued training examples will never lead to improvement in performance. We suggest that this may provide a model for the lack of improvement in human skills that can occur despite repeated practice of a complex task.

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