English  |  正體中文  |  简体中文  |  Post-Print筆數 : 20 |  Items with full text/Total items : 90029/119959 (75%)
Visitors : 24039672      Online Users : 173
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/65616


    Title: Resting-State Functional Magnetic Resonance Imaging: The Impact of Regression Analysis
    Authors: 蔡尚岳
    Tsai, Shang-Yueh;Wang, Woan-Chyi;Lin, Yi-Ru
    Contributors: 應物所
    Keywords: rsfMRI;resting state;default mode network;functional connectivity;regression
    Date: 2014
    Issue Date: 2014-04-24 15:08:31 (UTC+8)
    Abstract: PURPOSE: To investigate the impact of regression methods on resting-state functional magnetic resonance imaging (rsfMRI). During rsfMRI preprocessing, regression analysis is considered effective for reducing the interference of physiological noise on the signal time course. However, it is unclear whether the regression method benefits rsfMRI analysis. MATERIALS AND METHODS: Twenty volunteers (10 men and 10 women; aged 23.4 ± 1.5 years) participated in the experiments. We used node analysis and functional connectivity mapping to assess the brain default mode network by using five combinations of regression methods. RESULTS: The results show that regressing the global mean plays a major role in the preprocessing steps. When a global regression method is applied, the values of functional connectivity are significantly lower (P ≤ .01) than those calculated without a global regression. This step increases inter-subject variation and produces anticorrelated brain areas. CONCLUSION: rsfMRI data processed using regression should be interpreted carefully. The significance of the anticorrelated brain areas produced by global signal removal is unclear.
    Relation: JOURNAL OF NEUROIMAGING, Article first published online: 26 FEB 2014
    Data Type: article
    DOI 連結: http://dx.doi.org/10.1111/jon.12085
    DOI: 10.1111/jon.12085
    Appears in Collections:[應用物理研究所 ] 期刊論文

    Files in This Item:

    File Description SizeFormat
    jon12085.pdf1072KbAdobe PDF906View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告
    1.本網站之數位內容為國立政治大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,惟仍請適度,合理使用本網站之內容,以尊重著作權人之權益。商業上之利用,則請先取得著作權人之授權。
    2.本網站之製作,已盡力防止侵害著作權人之權益,如仍發現本網站之數位內容有侵害著作權人權益情事者,請權利人通知本網站維護人員(nccur@nccu.edu.tw),維護人員將立即採取移除該數位著作等補救措施。
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback