کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
10712774 1025257 2011 12 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Neuronal event detection in fMRI time series using iterative deconvolution techniques
موضوعات مرتبط
مهندسی و علوم پایه فیزیک و نجوم فیزیک ماده چگال
پیش نمایش صفحه اول مقاله
Neuronal event detection in fMRI time series using iterative deconvolution techniques
چکیده انگلیسی
An iterative estimation algorithm for deconvolution of neuronal activity from Blood Oxygen Level Dependent (BOLD) time series data is presented. The algorithm requires knowledge of the hemodynamic impulse response function but does not require knowledge of the stimulation function. The method uses majorization-minimization of a cost function to find an optimal solution to the inverse problem. The cost function includes penalties for the l1 norm, total variation and negativity. The algorithm is able to identify the occurrence of neuronal activity bursts from BOLD time series accurately. The accuracy of the algorithm was tested in simulations and experimental fMRI data using blocked and event-related designs. The simulations revealed that the algorithm is most sensitive to contrast-to-noise ratio levels and to errors in the assumed hemodynamic model and least sensitive to autocorrelation in the noise. Within normal fMRI conditions, the method is effective for event detection.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Magnetic Resonance Imaging - Volume 29, Issue 3, April 2011, Pages 353-364
نویسندگان
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