Concurrent Changes of Brain Functional Connectivity and Motor Variability When Adapting to Task Constraints.
Grégoire Vergotte1, Stéphane Perrey1, Muthuraman Muthuraman2
1EuroMov, Université de Montpellier, Montpellier, France.
Frontiers in Physiology
|July 26, 2018
Summary
Human adaptability involves dynamic brain networks and fractal motor patterns. This study links brain network changes and motor variability during sensory feedback deprivation, revealing how the brain adapts without altering performance.
Area of Science:
- Behavioral Neuroscience
- Systems Neuroscience
- Cognitive Neuroscience
Background:
- Human adaptability is observed in dynamic brain networks and fractal properties of sensorimotor variables.
- A gap exists between understanding brain-level network dynamics and behavioral-level adaptability measures.
Purpose of the Study:
- To bridge the gap between brain network connectivity and motor variability in adaptability research.
- To investigate how sensorimotor cortex network modularity and motor variability multifractality change under sensory feedback deprivation.
Main Methods:
- Utilized modularity analysis for sensorimotor cortex network connectivity.
- Employed multifractal analysis for motor variability (inter-tap interval series).
- Assessed adaptation during a prolonged tapping task with varying sensory feedback deprivation (0, 1, 2, or 3 senses).
Main Results:
- Tapping performance remained consistent across all sensory feedback conditions.
- The number of involved brain networks and the multifractality of motor variability increased with sensory feedback deprivation.
- A significant positive correlation was found between brain modularity and multifractal properties.
Conclusions:
- Concomitant changes in brain modularity and multifractal properties characterize adaptations that maintain consistent performance.
- Findings support the concept of degeneracy in complex systems, where multiple network configurations can support the same function.
- Highlights the interplay between adaptability and effective adaptation under constraints.
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