کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6023349 | 1580874 | 2016 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Sample-poor estimation of order and common signal subspace with application to fusion of medical imaging data
ترجمه فارسی عنوان
تخمین نمونه ضعیف نظم و زیر فضای مشترک سیگنال با استفاده از تلفیق داده های تصویربرداری پزشکی
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کلمات کلیدی
موضوعات مرتبط
علوم زیستی و بیوفناوری
علم عصب شناسی
علوم اعصاب شناختی
چکیده انگلیسی
Due to their data-driven nature, multivariate methods such as canonical correlation analysis (CCA) have proven very useful for fusion of multimodal neurological data. However, being able to determine the degree of similarity between datasets and appropriate order selection are crucial to the success of such techniques. The standard methods for calculating the order of multimodal data focus only on sources with the greatest individual energy and ignore relations across datasets. Additionally, these techniques as well as the most widely-used methods for determining the degree of similarity between datasets assume sufficient sample support and are not effective in the sample-poor regime. In this paper, we propose to jointly estimate the degree of similarity between datasets and their order when few samples are present using principal component analysis and canonical correlation analysis (PCA-CCA). By considering these two problems simultaneously, we are able to minimize the assumptions placed on the data and achieve superior performance in the sample-poor regime compared to traditional techniques. We apply PCA-CCA to the pairwise combinations of functional magnetic resonance imaging (fMRI), structural magnetic resonance imaging (sMRI), and electroencephalogram (EEG) data drawn from patients with schizophrenia and healthy controls while performing an auditory oddball task. The PCA-CCA results indicate that the fMRI and sMRI datasets are the most similar, whereas the sMRI and EEG datasets share the least similarity. We also demonstrate that the degree of similarity obtained by PCA-CCA is highly predictive of the degree of significance found for components generated using CCA.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: NeuroImage - Volume 134, 1 July 2016, Pages 486-493
Journal: NeuroImage - Volume 134, 1 July 2016, Pages 486-493
نویسندگان
Yuri Levin-Schwartz, Yang Song, Peter J. Schreier, Vince D. Calhoun, Tülay Adalı,