A new approach to estimating the signal dimension of concatenated resting-state functional MRI data sets

Chen, Sharon; Ross, Thomas J.; Chuang, Keh-Shih; Stein, Elliot A.; Yang, Yihong; Zhan, Wang

doi:10.1016/j.mri.2010.04.002

« Previous Next »Magnetic Resonance Imaging
Volume 28, Issue 9 , Pages 1344-1352, November 2010

A new approach to estimating the signal dimension of concatenated resting-state functional MRI data sets

Sharon Chen
- Neuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD 21224, USA
- National Tsing-Hua University, Hsin Chu 30013, Taiwan
- Department of Medical Imaging and Radiological Sciences, Kaohsiung Medical University, Kaohsiung, Taiwan
Thomas J. Ross
- Neuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD 21224, USA
Keh-Shih Chuang
- National Tsing-Hua University, Hsin Chu 30013, Taiwan
Elliot A. Stein
- Neuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD 21224, USA
Yihong Yang
- Neuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD 21224, USA
Wang Zhan
- Neuroimaging Research Branch, Intramural Research Program, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD 21224, USA
- Department of Radiology, University of California San Francisco, VA Medical Center, San Francisco, CA 94121, USA
- Corresponding author. Department of Radiology, University of California San Francisco, VA Medical Center 114M, San Francisco, CA 94121, USA. Tel.: +1 415 221 4810x2454; fax: +1 415 668 2864.

Received 24 September 2009; received in revised form 29 March 2010; accepted 1 April 2010. published online 23 July 2010.

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.

Keywords: Data reduction, Data-driven analysis, Dimension estimation, Group fMRI, Resting state, Signal dimension