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A GLM/ICA analysis using motion trac...

Hervey, Nathan.

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A GLM/ICA analysis using motion tracking and electromyography differentiates between brain activation due to intended and unintended motions in fNIRS images acquired during a finger tapping task performed by children with cerebral palsy.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	A GLM/ICA analysis using motion tracking and electromyography differentiates between brain activation due to intended and unintended motions in fNIRS images acquired during a finger tapping task performed by children with cerebral palsy./
Author:	Hervey, Nathan.
Description:	73 p.
Notes:	Source: Masters Abstracts International, Volume: 53-02.
Contained By:	Masters Abstracts International53-02(E).
Subject:	Engineering, Biomedical. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1559701
ISBN:	9781321006070

A GLM/ICA analysis using motion tracking and electromyography differentiates between brain activation due to intended and unintended motions in fNIRS images acquired during a finger tapping task performed by children with cerebral palsy.
Hervey, Nathan.

A GLM/ICA analysis using motion tracking and electromyography differentiates between brain activation due to intended and unintended motions in fNIRS images acquired during a finger tapping task performed by children with cerebral palsy. - 73 p.

Source: Masters Abstracts International, Volume: 53-02.

Thesis (M.S.)--The University of Texas at Arlington, 2014.

Functional neurological imaging has been shown to be valuable in evaluating brain plasticity in children with cerebral palsy (CP). In recent studies it has been demonstrated that functional near-infrared spectroscopy (fNIRS) is a viable and sensitive method for imaging motor cortex activities in children with CP. However, during unilateral finger tapping tasks children with CP often exhibit mirror motions (unintended motions in the non-tapping hand), and current fNIRS image formation techniques do not account for these motions. Therefore, the resulting fNIRS images contain activation from both intended and unintended motions. In this study, cortical activity was mapped with fNIRS on eight children with CP during a finger tapping task. Finger motion and arm muscle activation were concurrently measured using motion tracking cameras and electromyography (EMG). Subject-specific regressors were created from motion capture and EMG data, and independent component analysis (ICA) was applied to the DeltaHbO time series data to create a new method, called GLM/ICA, to analyze fNIRS data. The motion regressors and independent components were used in a general linear model (GLM) analysis in an attempt to create fNIRS images representative of different motions.

ISBN: 9781321006070Subjects--Topical Terms:

1017684
Engineering, Biomedical.

A GLM/ICA analysis using motion tracking and electromyography differentiates between brain activation due to intended and unintended motions in fNIRS images acquired during a finger tapping task performed by children with cerebral palsy.
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020 $a 9781321006070
035 $a (MiAaPQ)AAI1559701
035 $a AAI1559701
040 $a MiAaPQ $c MiAaPQ
100 1 $a Hervey, Nathan. $3 2105041
245 1 2 $a A GLM/ICA analysis using motion tracking and electromyography differentiates between brain activation due to intended and unintended motions in fNIRS images acquired during a finger tapping task performed by children with cerebral palsy.
300 $a 73 p.
500 $a Source: Masters Abstracts International, Volume: 53-02.
500 $a Adviser: George Alexandrakis.
502 $a Thesis (M.S.)--The University of Texas at Arlington, 2014.
520 $a Functional neurological imaging has been shown to be valuable in evaluating brain plasticity in children with cerebral palsy (CP). In recent studies it has been demonstrated that functional near-infrared spectroscopy (fNIRS) is a viable and sensitive method for imaging motor cortex activities in children with CP. However, during unilateral finger tapping tasks children with CP often exhibit mirror motions (unintended motions in the non-tapping hand), and current fNIRS image formation techniques do not account for these motions. Therefore, the resulting fNIRS images contain activation from both intended and unintended motions. In this study, cortical activity was mapped with fNIRS on eight children with CP during a finger tapping task. Finger motion and arm muscle activation were concurrently measured using motion tracking cameras and electromyography (EMG). Subject-specific regressors were created from motion capture and EMG data, and independent component analysis (ICA) was applied to the DeltaHbO time series data to create a new method, called GLM/ICA, to analyze fNIRS data. The motion regressors and independent components were used in a general linear model (GLM) analysis in an attempt to create fNIRS images representative of different motions.
520 $a The analysis provided an fNIRS image representing activation due to motion and muscle activity for each hand. In some cases the method removed mirror motion contaminations from activation images, and in other cases the image created for the mirror motions revealed interesting information not attainable with current analysis methods. Despite these positive results, the GLM/ICA method was only applicable to four out of eight subjects. Three of the recruited subjects did not have measurable mirror motions, one did not follow the protocol, and for one subject mirror motions were too correlated with the intended motions for the GLM/ICA method to be effective in some trials. Despite these limitations, this work demonstrates the feasibility and utility of the GLM/ICA method to help understand the contribution of mirror motions to fNIRS images from patients with motor deficits.
590 $a School code: 2502.
650 4 $a Engineering, Biomedical. $3 1017684
650 4 $a Health Sciences, Pathology. $3 1017854
690 $a 0541
690 $a 0571
710 2 $a The University of Texas at Arlington. $b Biomedical Engineering. $3 1264771
773 0 $t Masters Abstracts International $g 53-02(E).
790 $a 2502
791 $a M.S.
792 $a 2014
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1559701