Data-Driven and Predefined ROI-Based Quantification of Long-Term Resting-State fMRI Reproducibility.

Published

Journal Article

Resting-state functional magnetic resonance imaging (fMRI) is a promising tool for neuroscience and clinical studies. However, there exist significant variations in strength and spatial extent of resting-state functional connectivity over repeated sessions in a single or multiple subjects with identical experimental conditions. Reproducibility studies have been conducted for resting-state fMRI where the reproducibility was usually evaluated in predefined regions-of-interest (ROIs). It was possible that reproducibility measures strongly depended on the ROI definition. In this work, this issue was investigated by comparing data-driven and predefined ROI-based quantification of reproducibility. In the data-driven analysis, the reproducibility was quantified using functionally connected voxels detected by a support vector machine (SVM)-based technique. In the predefined ROI-based analysis, all voxels in the predefined ROIs were included when estimating the reproducibility. Experimental results show that (1) a moderate to substantial within-subject reproducibility and a reasonable between-subject reproducibility can be obtained using functionally connected voxels identified by the SVM-based technique; (2) in the predefined ROI-based analysis, an increase in ROI size does not always result in higher reproducibility measures; (3) ROI pairs with high connectivity strength have a higher chance to exhibit high reproducibility; (4) ROI pairs with high reproducibility do not necessarily have high connectivity strength; (5) the reproducibility measured from the identified functionally connected voxels is generally higher than that measured from all voxels in predefined ROIs with typical sizes. The findings (2) and (5) suggest that conventional ROI-based analyses would underestimate the resting-state fMRI reproducibility.

Full Text

Duke Authors

Cited Authors

  • Song, X; Panych, LP; Chen, N-K

Published Date

  • March 2016

Published In

Volume / Issue

  • 6 / 2

Start / End Page

  • 136 - 151

PubMed ID

  • 26456172

Pubmed Central ID

  • 26456172

Electronic International Standard Serial Number (EISSN)

  • 2158-0022

Digital Object Identifier (DOI)

  • 10.1089/brain.2015.0349

Language

  • eng

Conference Location

  • United States