کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4329032 | 1614200 | 2008 | 11 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Group independent component analysis reveals consistent resting-state networks across multiple sessions
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کلمات کلیدی
موضوعات مرتبط
علوم زیستی و بیوفناوری
علم عصب شناسی
علوم اعصاب (عمومی)
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چکیده انگلیسی
Group independent component analysis (gICA) was performed on resting-state data from 14 healthy subjects scanned on 5 fMRI scan sessions across 16Â days. The data were reduced and aggregated in 3 steps using Principal Components Analysis (PCA, within scan, within session and across session) and subjected to gICA procedures. The amount of reduction was estimated by an improved method that utilizes a first-order autoregressive fitting technique to the PCA spectrum. Analyses were performed using all sessions in order to maximize sensitivity and alleviate the problem of component identification across session. Across-session consistency was examined by three methods, all using back-reconstruction of the single-session or single-subject/session maps from the grand (5-session) maps. The gICA analysis produced 55 spatially independent maps. Obvious artifactual maps were eliminated and the remainder were grouped based upon physiological recognizability. Biologically relevant component maps were found, including sensory, motor and a 'default-mode' map. All analysis methods showed that components were remarkably consistent across session. Critically, the components with the most obvious physiological relevance were the most consistent. The consistency of these maps suggests that, at least over a period of several weeks, these networks would be useful to follow longitudinal treatment-related manipulations.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Brain Research - Volume 1239, 6 November 2008, Pages 141-151
Journal: Brain Research - Volume 1239, 6 November 2008, Pages 141-151
نویسندگان
Sharon Chen, Thomas J. Ross, Wang Zhan, Carol S. Myers, Keh-Shih Chuang, Stephen J. Heishman, Elliot A. Stein, Yihong Yang,