کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6023279 | 1580871 | 2016 | 13 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Reliability of dissimilarity measures for multi-voxel pattern analysis
ترجمه فارسی عنوان
قابلیت اطمینان اقدامات غیرمستقیم برای تجزیه و تحلیل الگوی چند واکسل
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کلمات کلیدی
Crossvalidation - اعتبار سنجی متقابلLinear discriminant - تبعیضی خطیMulti-voxel pattern analysis - تجزیه و تحلیل الگوی چند واکسلRepresentational similarity analysis - تجزیه و تحلیل شباهت نمایشیfMRI - تصویرسازی تشدید مغناطیسی کارکردیdecoding - رمزگشاییClassification - طبقه بندیMachine learning - یادگیری ماشین
موضوعات مرتبط
علوم زیستی و بیوفناوری
علم عصب شناسی
علوم اعصاب شناختی
چکیده انگلیسی
Representational similarity analysis of activation patterns has become an increasingly important tool for studying brain representations. The dissimilarity between two patterns is commonly quantified by the correlation distance or the accuracy of a linear classifier. However, there are many different ways to measure pattern dissimilarity and little is known about their relative reliability. Here, we compare the reliability of three classes of dissimilarity measure: classification accuracy, Euclidean/Mahalanobis distance, and Pearson correlation distance. Using simulations and four real functional magnetic resonance imaging (fMRI) datasets, we demonstrate that continuous dissimilarity measures are substantially more reliable than the classification accuracy. The difference in reliability can be explained by two characteristics of classifiers: discretization and susceptibility of the discriminant function to shifts of the pattern ensemble between imaging runs. Reliability can be further improved through multivariate noise normalization for all measures. Finally, unlike conventional distance measures, crossvalidated distances provide unbiased estimates of pattern dissimilarity on a ratio scale, thus providing an interpretable zero point. Overall, our results indicate that the crossvalidated Mahalanobis distance is preferable to both the classification accuracy and the correlation distance for characterizing representational geometries.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: NeuroImage - Volume 137, 15 August 2016, Pages 188-200
Journal: NeuroImage - Volume 137, 15 August 2016, Pages 188-200
نویسندگان
Alexander Walther, Hamed Nili, Naveed Ejaz, Arjen Alink, Nikolaus Kriegeskorte, Jörn Diedrichsen,