The Impact of Cognitive Load on Interlimb Differences in Motor Control in Older Adults

Academic Background

In daily life, we often observe that the dominant hand (e.g., the right hand in right-handed individuals) performs better than the non-dominant hand in simple tasks. However, these interlimb differences may be influenced by task complexity and biomechanical demands. The Dynamic Dominance Hypothesis proposes that the left hemisphere (the dominant hemisphere in right-handed individuals) is primarily responsible for controlling movement trajectories, while the right hemisphere (the non-dominant hemisphere) is specialized for postural control. However, in real-world scenarios, cognitive challenges may modulate these specialized behaviors. Therefore, researchers hypothesized that as cognitive load increases, the lateralized processes of motor control would become more asymmetrical.

To test this hypothesis, researchers designed an experiment to explore the impact of cognitive load on interlimb differences in motor control in older adults. The older adult population was chosen as the focus of the study due to their limited neural resources (e.g., reduced working memory capacity). Some studies suggest that older adults may exhibit reduced motor and cognitive processing asymmetries due to decreased hemispheric lateralization. However, other research has shown that older adults may exhibit greater motor lateralization, possibly due to the reduced contribution of the right hemisphere to impedance control. Thus, studying how cognitive load affects motor control in older adults not only helps to understand the mechanisms of motor lateralization but may also provide important insights for developing motor training strategies for this population.

Source of the Paper

This paper was authored by S. A. L. Jayasinghe, affiliated with the Division of Physical Therapy and Rehabilitation Science, Department of Family Medicine and Community Health, University of Minnesota, United States. The paper was first published on December 3, 2024, in the Journal of Neurophysiology (J Neurophysiol), with the DOI 10.1152/jn.00167.2024.

Research Process

Participants and Experimental Design

The study recruited 16 right-handed older adults (11 females, 5 males, mean age 65.88 years) with no neurological diseases or other conditions affecting sensorimotor function. Participants performed a unilateral reaching task using the Kinereach virtual reality motion capture system. Each participant completed 170 trials with each hand, with task complexity gradually increasing.

Experimental Setup and Data Collection

The experimental setup included: - Kinereach System: Used to record the position and orientation of the hands and upper arms, with a sampling rate of 116 Hz. - Trigno Research+ System: Used to collect electromyography (EMG) data, with a sampling rate of 1,250 Hz. Sensors were placed on specific muscles in the shoulder and elbow, including the posterior deltoid, clavicular head of the pectoralis major, long head of the biceps brachii, and lateral head of the triceps brachii.

Task Design

Each trial consisted of two phases: 1. Memory Phase: Participants had 2 seconds to memorize pictorial instructions displayed on the screen (including shape, size, color, and direction). 2. Execution Phase: A nine-object array appeared on the screen, and participants had 3 seconds to identify the correct object based on their memory and quickly reach for it.

The cognitive load of the task increased from level 0 (only one target) to level 4 by increasing the number of memory items or the complexity of the object array.

Data Analysis

Researchers used IgorPro and MATLAB for data processing and analysis. Key metrics included: - Reaction Time: The time from the appearance of the object array to the onset of movement. - Deviation from Linearity: The ratio of the minor axis to the major axis of the hand path, reflecting movement coordination. - Joint Cocontraction: Calculated from EMG data to assess the level of cocontraction in shoulder and elbow muscles.

Key Findings

1. Reaction Time: The right hand had significantly longer reaction times than the left hand (p = 0.0004), and reaction times increased with higher cognitive load.
2. Movement Quality: As cognitive load increased, movement trajectories became more curved, and accuracy decreased, but there were no significant interlimb differences in movement quality.
3. Joint Cocontraction: Cocontraction levels were significantly higher in the right elbow and shoulder compared to the left (p < 0.05), but cognitive load did not significantly affect cocontraction levels.

Conclusion

The study found that under increased cognitive load, older adults exhibited significantly longer reaction times with their right hand compared to their left, but there were no significant interlimb differences in movement quality or joint coordination. This finding challenges the Dynamic Dominance Hypothesis, suggesting that cognitive load may have complex effects on the lateralization of motor control processes.

Research Highlights

1. Impact of Cognitive Load: This study is the first to systematically explore the impact of cognitive load on interlimb differences in motor control in older adults, revealing significant asymmetries in reaction time.
2. Discovery of Joint Cocontraction: Cocontraction levels were significantly higher in the right elbow and shoulder, indicating that the right hand may require more muscle coordination during movement preparation.
3. Innovative Task Design: By integrating cognitive challenges with motor tasks, the study provides new insights into the mechanisms of motor lateralization.

Research Significance

This study not only enhances our understanding of the mechanisms underlying motor lateralization but also provides important references for developing motor training strategies for older adults. Particularly under conditions of increased cognitive load, optimizing motor control strategies may be crucial for preventing motor dysfunction in older adults.

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Through innovative experimental design and detailed data analysis, this paper reveals the complex effects of cognitive load on interlimb differences in motor control in older adults, offering a solid theoretical foundation for future research and applications.