کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6040148 | 1188835 | 2007 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Learning effective brain connectivity with dynamic Bayesian networks
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کلمات کلیدی
Effective connectivity - اتصال موثرCausal effects - اثرات علمیFunctional MRI - افامآرآی یا تصویرسازی تشدید مغناطیسی کارکردیHemodynamic response function - تابع پاسخ همودینامیکMarkov chain Monte Carlo methods - روش مارکف مونت کارلو زنجیره ایMarkov chains - زنجیره مارکفdynamic Bayesian networks - شبکه های بیسیم پویاBayesian networks - شبکهٔ بیزی یا «شبکه باور» یا «شبکه باور بیزی»
موضوعات مرتبط
علوم زیستی و بیوفناوری
علم عصب شناسی
علوم اعصاب شناختی
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چکیده انگلیسی
We propose to use dynamic Bayesian networks (DBN) to learn the structure of effective brain connectivity from functional MRI data in an exploratory manner. In our previous work, we used Bayesian networks (BN) to learn the functional structure of the brain (Zheng, X., Rajapakse, J.C., 2006. Learning functional structure from fMR images. NeuroImage 31 (4), 1601-1613). However, BN provides a single snapshot of effective connectivity of the entire experiment and therefore is unable to accurately capture the temporal characteristics of connectivity. Dynamic Bayesian networks (DBN) use a Markov chain to model fMRI time-series and thereby determine temporal relationships of interactions among brain regions. Experiments on synthetic fMRI data demonstrate that the performance of DBN is comparable to Granger causality mapping (GCM) in determining the structure of linearly connected networks. Dynamic Bayesian networks render more accurate and informative brain connectivity than earlier methods as connectivity is described in complete statistical sense and temporal characteristics of time-series are explicitly taken into account. The functional structures inferred on two real fMRI datasets are consistent with the previous literature and more accurate than those discovered by BN. Furthermore, we study the effects of hemodynamic noise, scanner noise, inter-scan interval, and the variability of hemodynamic parameters on the derived connectivity.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: NeuroImage - Volume 37, Issue 3, 1 September 2007, Pages 749-760
Journal: NeuroImage - Volume 37, Issue 3, 1 September 2007, Pages 749-760
نویسندگان
Jagath C. Rajapakse, Juan Zhou,