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題名 Resting-State Functional Magnetic Resonance Imaging: The Impact of Regression Analysis
作者 蔡尚岳
Tsai, Shang-Yueh ; Wang, Woan-Chyi ; Lin, Yi-Ru
貢獻者 應物所
關鍵詞 rsfMRI;resting state;default mode network;functional connectivity;regression
日期 2014
上傳時間 24-Apr-2014 15:08:31 (UTC+8)
摘要 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.
關聯 JOURNAL OF NEUROIMAGING, Article first published online: 26 FEB 2014
資料類型 article
DOI http://dx.doi.org/10.1111/jon.12085
dc.contributor 應物所en_US
dc.creator (作者) 蔡尚岳zh_TW
dc.creator (作者) Tsai, Shang-Yueh ; Wang, Woan-Chyi ; Lin, Yi-Ruen_US
dc.date (日期) 2014en_US
dc.date.accessioned 24-Apr-2014 15:08:31 (UTC+8)-
dc.date.available 24-Apr-2014 15:08:31 (UTC+8)-
dc.date.issued (上傳時間) 24-Apr-2014 15:08:31 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/65616-
dc.description.abstract (摘要) 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.en_US
dc.format.extent 1098329 bytes-
dc.format.mimetype application/pdf-
dc.language.iso en_US-
dc.relation (關聯) JOURNAL OF NEUROIMAGING, Article first published online: 26 FEB 2014en_US
dc.subject (關鍵詞) rsfMRI;resting state;default mode network;functional connectivity;regressionen_US
dc.title (題名) Resting-State Functional Magnetic Resonance Imaging: The Impact of Regression Analysisen_US
dc.type (資料類型) articleen
dc.identifier.doi (DOI) 10.1111/jon.12085en_US
dc.doi.uri (DOI) http://dx.doi.org/10.1111/jon.12085 en_US