کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
530287 869756 2012 6 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Clustered sampling improves random subspace brain mapping
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
Clustered sampling improves random subspace brain mapping
چکیده انگلیسی

Intuitive and efficient, the random subspace ensemble approach provides an appealing solution to the problem of the vast dimensionality of functional magnetic resonance imaging (fMRI) data for maximal-accuracy brain state decoding. Recently, efforts to generate biologically plausible and interpretable maps of brain regions which contribute information to the ensemble decoding task have been made and two approaches have been introduced: globally multivariate random subsampling and locally multivariate Monte Carlo mapping. Both types of maps reflect voxel-wise decoding accuracies averaged across repeatedly randomly sampled voxel subsets, highlighting voxels which consistently participate in high-classification subsets. We compare the mapping sensitivities of the approaches on realistic simulated data containing both locally and globally multivariate information and demonstrate that utilizing the inherent volumetric nature of fMRI through clustered Monte Carlo mapping yields dramatically improved performances in terms of voxel detection sensitivity and efficiency. These results suggest that, unless a priori information specifically dictates a global search, variants of clustered sampling should be the priority for random subspace brain mapping.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 45, Issue 6, June 2012, Pages 2035–2040
نویسندگان
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