Cortical activity predicts good variation in human motor output

Area of Science:

• Neuroscience
• Motor Control
• Computational Neuroscience

Background:

• Human movement exhibits variability when multiple muscle activity combinations achieve the same goal (goal-equivalence).
• The nervous system appears to intentionally vary motor output among goal-equivalent strategies, possibly to minimize fatigue or distribute load.
• The underlying neural mechanisms driving this adaptive variation remain largely unknown.

Purpose of the Study:

• To investigate if cortical signals can predict goal-equivalent variations in bimanual finger force output.
• To explore the neural basis of adaptive motor variability during goal-directed tasks.

Main Methods:

• Utilized a bimanual finger force task involving 18 healthy participants.
• Employed electroencephalography (EEG) to record brain activity.
• Applied machine learning algorithms to analyze EEG signals and predict force output variations.

Main Results:

• Observed significantly greater variability in muscle activity across repetitions when achieving the task goal compared to non-goal-achieving activity.
• EEG signals recorded 500 milliseconds prior to task execution significantly predicted whether participants would exert more force with the right or left finger.
• Cortical maps derived from predictive signals revealed involvement of both motor and pre-motor areas in the dominant hemisphere.

Conclusions:

• Goal-equivalent variation in motor output, a strategy potentially for fatigue reduction, may be implemented and controlled at the cortical level.
• Predictive cortical signals precede and potentially orchestrate the selection of specific muscle activation patterns for achieving a motor goal.
• This research provides insights into the neural control of motor variability and its adaptive functions.