Understanding Neuromotor Strategy During Functional Upper Extremity Tasks Using Symbolic Dynamics

Document Type

Article

Language

eng

Format of Original

23 p.

Publication Date

2012

Publisher

Society for Chaos Theory in Psychology & Life Sciences

Source Publication

Nonlinear Dynamics, Psychology, and Life Sciences

Source ISSN

1090-0578

Abstract

The ability to model and quantify brain activation patterns that pertain to natural neuromotor strategy of the upper extremities during functional task performance is critical to the development of therapeutic interventions such as neuroprosthetic devices. The mechanisms of information flow, activation sequence and patterns, and the interaction between anatomical regions of the brain that are specific to movement planning, intention and execution of voluntary upper extremity motor tasks were investigated here. This paper presents a novel method using symbolic dynamics (orbital decomposition) and nonlinear dynamic tools of entropy, self-organization and chaos to describe the underlying structure of activation shifts in regions of the brain that are involved with the cognitive aspects of functional upper extremity task performance. Several questions were addressed: (a) How is it possible to distinguish deterministic or causal patterns of activity in brain fMRI from those that are really random or non-contributory to the neuromotor control process? (b) Can the complexity of activation patterns over time be quantified? (c) What are the optimal ways of organizing fMRI data to preserve patterns of activation, activation levels, and extract meaningful temporal patterns as they evolve over time? Analysis was performed using data from a custom developed time resolved fMRI paradigm involving human subjects (N=18) who performed functional upper extremity motor tasks with varying time delays between the onset of intention and onset of actual movements. The results indicate that there is structure in the data that can be quantified through entropy and dimensional complexity metrics and statistical inference, and furthermore, orbital decomposition is sensitive in capturing the transition of states that correlate with the cognitive aspects of functional task performance.

Comments

Nonlinear Dynamics, Psychology, and Life Sciences, Volume 16, Issue 1, pp 37-59 (January, 2012). Permalink: http://www.societyforchaostheory.org/ndpls/show_issues.cgi?vol=16

© 2012 Society for Chaos Theory in Psychology & Life Sciences

Link to Full Text

Share

COinS