A graph-theoretical analysis algorithm for quantifying the transition from sensory input to motor output by an...

Area of Science:

• Neuroscience
• Cognitive Science
• Network Science

Background:

• Graph-theoretical analysis is established for identifying brain subnetworks, particularly during the Default Mode State.
• Understanding sensory and motor subnetwork interactions is crucial for elucidating cognitive processes like decision-making.

Purpose of the Study:

• To expand graph-theoretical methods for analyzing sensory and motor subnetwork interactions during an approach-avoidance paradigm.
• To quantify the synchrony and interaction strengths between sensory (visual cortex) and motor (motor cortex) regions using fMRI data.

Main Methods:

• Functional magnetic resonance imaging (fMRI) data from 9 healthy volunteers.
• Calculation of correlation difference maps between primary visual cortex and motor cortex signal time courses.
• Graph network creation, visualization (2D/3D layouts), and cluster identification using the MCODE algorithm.

Main Results:

• Quantified interaction strengths between sensory and motor subnetworks.
• Visual cortex involved in 85%-18% of interactions; motor cortex in 40%-9%.
• High interaction percentages also observed in frontal cortex (19%), insula (17%), cuneus (16%), SMA (11%), and subcortical regions (11%).

Conclusions:

• Graph-theoretical analysis effectively visualizes and quantifies brain network dynamics during specific cognitive tasks.
• Demonstrated significant interactions between sensory and motor systems, providing a quantitative basis for their functional interplay.
• Findings support the integration of visual, motor, and association areas in approach-avoidance behaviors.