A new approach to estimating the signal dimension of concatenated resting-state functional MRI data sets |
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Authors: | Sharon Chen Thomas J. Ross Keh-Shih Chuang Elliot A. Stein Yihong Yang Wang Zhan |
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Affiliation: | 1. Neuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD 21224, USA;2. National Tsing-Hua University, Hsin Chu 30013, Taiwan;3. Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan;4. Department of Radiology, University of California San Francisco, VA Medical Center, San Francisco, CA 94121, USA |
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Abstract: | Estimating the effective signal dimension of resting-state functional MRI (fMRI) data sets (i.e., selecting an appropriate number of signal components) is essential for data-driven analysis. However, current methods are prone to overestimate the dimensions, especially for concatenated group data sets. This work aims to develop improved dimension estimation methods for group fMRI data generated by data reduction and grouping procedure at multiple levels. We proposed a “noise-blurring” approach to suppress intragroup signal variations and to correct spectral alterations caused by the data reduction, which should be responsible for the group dimension overestimation. This technique was evaluated on both simulated group data sets and in vivo resting-state fMRI data sets acquired from 14 normal human subjects during five different scan sessions. Reduction and grouping procedures were repeated at three levels in either “scan–session–subject” or “scan–subject–session” order. Compared with traditional estimation methods, our approach exhibits a stronger immunity against intragroup signal variation, less sensitivity to group size and a better agreement on the dimensions at the third level between the two grouping orders. |
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Keywords: | Data reduction Data-driven analysis Dimension estimation Group fMRI Resting state Signal dimension |
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