Imaging body-mind crosstalk in young adults

Area of Science:

• Neuroscience
• Cognitive Science
• Human Physiology

Background:

• Complex relationships between motor functions, brain structure, and cognition are known in aging populations.
• It remains unclear if these associations extend to younger adults.

Purpose of the Study:

• To investigate associations between motor functions, brain structure, and cognitive functions in young adults.
• To explore if machine learning can identify individuals with varying motor and cognitive abilities.

Main Methods:

• Analysis of data from 910 young adults (22-35 years) from the Human Connectome Project.
• Examination of interactions between motor functions (cardiorespiratory fitness, gait speed, hand dexterity, handgrip strength), brain structure (cortical thickness, surface area, subcortical volumes), and cognitive functions.
• Application of linear mixed-effects models, mediation analyses, and machine-learning classifiers.

Main Results:

• Cardiorespiratory fitness and hand dexterity positively correlated with fluid and crystallized intelligence.
• Gait speed and handgrip strength correlated with specific fluid intelligence measures.
• Brain structures (cortical/subcortical volumes) mediated the relationship between motor functions and cognition.
• Machine learning models showed potential for classifying individuals based on motor function levels.

Conclusions:

• Motor functions are positively related to cognitive functions in young adults, similar to older adults.
• Brain structures partially mediate these motor-cognitive relationships.
• Machine learning offers a promising tool for identifying individuals with suboptimal motor and cognitive performance.