کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6036366 | 1188775 | 2019 | 14 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Adjusting the effect of nonstationarity in cluster-based and TFCE inference
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موضوعات مرتبط
علوم زیستی و بیوفناوری
علم عصب شناسی
علوم اعصاب شناختی
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چکیده انگلیسی
⺠In this paper adjusted cluster sizes are used in a permutation-testing framework for both cluster-based and threshold-free cluster enhancement (TFCE) inference and tested on both simulated and real data. ⺠We find TFCE inference is already fairly robust to nonstationarity in the data, while cluster-based inference requires an adjustment to ensure homogeneity. ⺠A group of possible multi-level adjustments are introduced and their results on simulated and real data are assessed using a new performance index. ⺠We also find that adjusting for local smoothness via a local estimation of a statistic's null distribution (from a first pass through the permutation testing) is more effective at removing nonstationarity than correction via local smoothness estimation.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: NeuroImage - Volume 54, Issue 3, 1 February 2011, Pages 2006-2019
Journal: NeuroImage - Volume 54, Issue 3, 1 February 2011, Pages 2006-2019
نویسندگان
Gholamreza Salimi-Khorshidi, Stephen M. Smith, Thomas E. Nichols,