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Signal processing approaches to MEG ...

Kucukaltun-Yildirim, Esen.

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Signal processing approaches to MEG data analysis: Comparison of localization methods and neural synchrony detection.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Signal processing approaches to MEG data analysis: Comparison of localization methods and neural synchrony detection./
Author:	Kucukaltun-Yildirim, Esen.
Description:	117 p.
Notes:	Source: Dissertation Abstracts International, Volume: 67-10, Section: B, page: 5947.
Contained By:	Dissertation Abstracts International67-10B.
Subject:	Engineering, Biomedical. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3237718
ISBN:	9780542924378

Signal processing approaches to MEG data analysis: Comparison of localization methods and neural synchrony detection.
Kucukaltun-Yildirim, Esen.

Signal processing approaches to MEG data analysis: Comparison of localization methods and neural synchrony detection. - 117 p.

Source: Dissertation Abstracts International, Volume: 67-10, Section: B, page: 5947.

Thesis (Ph.D.)--University of Southern California, 2006.

Functional neuroimaging modalities improve our knowledge about normal brain function and the cause of pathological diseases. Among these Magnetoencephalography (MEG) is a very promising modality since it provides unique insights into the spatio-temporal dynamics of neural activation in the human brain. Users of MEG are faced with a vast array of inverse methods that can be used to process their data, yet there are few guidelines available to decide which might be appropriate for a given problem. Receiver Operating Characteristics (ROC) has been the most widely used for assessing system performance for lesion detection in x-ray and nuclear medicine imaging. Here we describe an objective task-based framework for comparing inverse methods using the free response receiver operating characteristic (FROC), which is the most suitable approach among the other variations of ROC. The FROC framework is adapted to functional neuroimaging so that it favors localization methods that give clinically acceptable and meaningful results. We show the application of this objective evaluation criteria by comparing the performance of a variety of localization methods used in MEG studies for known cortical activation.

ISBN: 9780542924378Subjects--Topical Terms:

1017684
Engineering, Biomedical.

Signal processing approaches to MEG data analysis: Comparison of localization methods and neural synchrony detection.
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100 1 $a Kucukaltun-Yildirim, Esen. $3 1923464
245 1 0 $a Signal processing approaches to MEG data analysis: Comparison of localization methods and neural synchrony detection.
300 $a 117 p.
500 $a Source: Dissertation Abstracts International, Volume: 67-10, Section: B, page: 5947.
500 $a Adviser: Richard M. Leahy.
502 $a Thesis (Ph.D.)--University of Southern California, 2006.
520 $a Functional neuroimaging modalities improve our knowledge about normal brain function and the cause of pathological diseases. Among these Magnetoencephalography (MEG) is a very promising modality since it provides unique insights into the spatio-temporal dynamics of neural activation in the human brain. Users of MEG are faced with a vast array of inverse methods that can be used to process their data, yet there are few guidelines available to decide which might be appropriate for a given problem. Receiver Operating Characteristics (ROC) has been the most widely used for assessing system performance for lesion detection in x-ray and nuclear medicine imaging. Here we describe an objective task-based framework for comparing inverse methods using the free response receiver operating characteristic (FROC), which is the most suitable approach among the other variations of ROC. The FROC framework is adapted to functional neuroimaging so that it favors localization methods that give clinically acceptable and meaningful results. We show the application of this objective evaluation criteria by comparing the performance of a variety of localization methods used in MEG studies for known cortical activation.
520 $a One of the significant challenges for neuroscientific research is solving the mystery of the binding problem. Despite considerable efforts, the question of how the brain orchestrates the large scale integration of specialized brain regions into a functional whole remains unanswered. Over the years, linear methods have been extensively used to investigate the role of specialized brain regions in a variety of tasks. Synchronized activity of different brain regions is accepted to be an important mechanism for large scale integration and a variety of nonlinear methods have been used to investigate such interactions. Higher order statistics have found a place in EEG data analysis for detecting phase coupling between frequency bands. Although brain signals are known to be highly non-stationary, most of these studies have focused on nonparametric applications despite the penalty of low frequency resolution. This thesis introduces a novel method for parametric cross-bispectrum estimation for a higher frequency resolution in short time window EEG/MEG analysis.
590 $a School code: 0208.
650 4 $a Engineering, Biomedical. $3 1017684
650 4 $a Engineering, Electronics and Electrical. $3 626636
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710 2 0 $a University of Southern California. $3 700129
773 0 $t Dissertation Abstracts International $g 67-10B.
790 1 0 $a Leahy, Richard M., $e advisor
790 $a 0208
791 $a Ph.D.
792 $a 2006
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3237718