Journal article
A high performance 3D cluster-based test of unsmoothed fMRI data
NeuroImage (Orlando, Fla.), Vol.98, pp.537-546
09/2014
DOI: 10.1016/j.neuroimage.2014.05.015
PMID: 24836011
Abstract
Cluster-size tests (CST) based on random field theory have been widely adopted in fMRI data analysis to detect brain activation. However, most existing approaches can be used appropriately only when the image is highly smoothed in the spatial domain. Unfortunately, spatial smoothing degrades spatial specificity. Recently, a threshold-free cluster enhancement technique was proposed which does not require spatial smoothing, but this method can be used only for group level analysis. Advances in imaging technology now yield high quality high spatial resolution imaging data in single subjects and an inference approach that retains the benefits of greater spatial resolution is called for. In this work, we present a new CST with a correction for voxelation to address this problem. The theoretical formulation of the new approach based on Gaussian random fields is developed to estimate statistical significance using 3D statistical parametric maps without assuming spatial smoothness. Simulated phantom and resting-state fMRI experimental data are then used to compare the voxelation-corrected procedure to the widely used standard random field theory. Unlike standard random field theory approaches, which require heavy spatial smoothing, the new approach has a higher sensitivity for localizing activation regions without the requirement of spatial smoothness.
•We proposed a voxelation-corrected cluster-size test for subject-level inference.•It is a robust approach within GRF theory that does not require spatial smoothing.•It estimates image smoothness taking into account the effect of voxel size.•It has superior sensitivity over standard random field theory under low smoothness.•It is ideally suited for analysis of unsmoothed high spatial resolution imaging data.
Details
- Title: Subtitle
- A high performance 3D cluster-based test of unsmoothed fMRI data
- Creators
- Huanjie Li - Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics,School of Physics, Peking University, Beijing, ChinaLisa D Nickerson - McLean Imaging Center, McLean Hospital/Harvard Medical School, Belmont, MA, USAJinhu Xiong - Department of Radiology, University of Iowa, Iowa City, IA, USAQihong Zou - Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics,School of Physics, Peking University, Beijing, ChinaYang Fan - Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics,School of Physics, Peking University, Beijing, ChinaYajun Ma - Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics,School of Physics, Peking University, Beijing, ChinaTingqi Shi - College of Medicine, University of Illinois, Chicago, IL, USAJianqiao Ge - Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics,School of Physics, Peking University, Beijing, ChinaJia-Hong Gao - Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics,School of Physics, Peking University, Beijing, China
- Resource Type
- Journal article
- Publication Details
- NeuroImage (Orlando, Fla.), Vol.98, pp.537-546
- Publisher
- Elsevier Inc
- DOI
- 10.1016/j.neuroimage.2014.05.015
- PMID
- 24836011
- ISSN
- 1053-8119
- eISSN
- 1095-9572
- Grant note
- name: National Strategic Basic Research Program, award: 2012CB720700; DOI: 10.13039/501100001809, name: National Natural Science Foundation of China, award: 81227003, 81201142, 31200761; DOI: 10.13039/100000002, name: NIH, award: 1U54MH091657; DOI: 10.13039/100001225, name: McDonnell Center for Systems Neuroscience
- Language
- English
- Date published
- 09/2014
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering
- Record Identifier
- 9984083271902771
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