Date of Award

2009

Degree Type

Dissertation

Department

Chemical and Biomedical Engineering

First Advisor

Yue, Guang H.

Subject Headings

Brain -- Physiology, Magnetic resonance imaging, Brain -- Magnetic resonance imaging, Muscle Fatigue, Functional magnetic resonance imaging(fMRI), Brain connectivity

Abstract

Traditional brain activation studies using neuroimaging such as functional magnetic imaging (fMRI) have shown that muscle fatigue at submaximal intensity level is associated with increased brain activity in various cortical regions from low- to high-order motor centers. However, how these areas might interact remain unclear since previous activation studies related to motor control could not reveal information of between-area interaction. This issue can be addressed by evaluating brain activation data using the framework of connectivity analysis. Three types of brain connectivity, functional connectivity (FC), effective connectivity (EC) and structural connectivity (SC) have been examined to investigate the effect of voluntary muscle fatigue on the interaction within the cortical motor network. The aim of the study was to propose a new framework to reveal adaptive interactions among motor regions during progressive muscle fatigue. We hypothesized that the brain would exhibit fatigue-related alterations in the FC and EC. Ten healthy subjects performed repetitive handgrip contractions (3.5s ON/6.5s OFF) for 20 minutes at 50 maximal voluntary force (MVC) level using the right hand (fatigue task). Significant MVC reduction occurred at the end of the fatigue task, indicating muscle fatigue. Histogram and quantile analysis confirmed that FC of the brain increased in the severe fatigue stage (the last 100s of the fatigue task) compared with the minimal fatigue stage (the first 100s of the fatigue task). Structural equation modeling (SEM) was used to evaluate the EC of the brain during fatigue. We found the path from the prefrontal cortex (PFC) to the supplementary motor area (SMA) decreased during fatigue while the path from the premotor area (PMA) to the primary motor cortex (M1) increased. We also found supporting evidence from SC analysis using diffusion tensor image (DTI). The new framework of connectivity analysis, combining the work of SC, FC and EC, provides greater insights into the dynamic adaptations of inte

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